extfieldsofstudy:
Computer Science
Mathematics

text:
A Comparative Study on Remote Tracking of Parkinsons Disease Progression Using Data Mining Methods

In recent years, applications of data mining methods are become more popular in many fields of medical diagnosis and evaluations. The data mining methods are appropriate tools for discovering and extracting of available knowledge in medical databases. In this study, we divided 11 data mining algorithms into five groups which are applied to a data set of patients clinical variables data with Parkinsons Disease (PD) to study the disease progression. The data set includes 22 properties of 42 people that all of our algorithms are applied to this data set. The Decision Table with 0.9985 correlation coefficients has the best accuracy and Decision Stump with 0.7919 correlation coefficients has the lowest accuracy.

INTRODUCTION
The Parkinson's Disease (PD) is a chronic neurological disorder with an unknown etiology, which usually affects people over 50 years old [1]. It was reported that the Parkinson, beyond many unknown cases, has affected one million people [2]. It is expected that as the age of earth's population increases, the number of patients with PD also increases.
The number of Parkinson's disease (PD) patients is estimated to be 120-180 out of every 100,000 people, although the percentage (and hence the number of affected people) is increasing as the life expectancies increase. The causes of Parkinson's disease (PD) are unknown; however, researches have shown that the degradation of the dopaminergic neurons affects the dopamine production [3].The PD symptoms include limb tremor (especially at rest), muscles stiffness and movement slowness, difficulty in walking, balance and coordination (especially when beginning of motion), difficulty in eating and swallowing, and vocal disorder and mood disorders [4,5,6]. This disease causes voice disorder in 90% of patients [7]. Dysarthria is a motor speech disorder that indicates inability in expression properly.
It's observable in patients with PD to have hypokinetic dysarthria, and its classical symptoms include reduced vocal loudness (hypophonia), monopitch, disruption of voice quality, and abnormally fast rate of speech [8]. For decades, researchers have strived to understand more about this disease and therefore to find some methods for successfully limiting its symptoms which are most commonly periodic muscle tremor and/or rigidity. Other symptoms such as akinesia, bradykinesia, and dysarthria may occur in later stages of PD [9].
In recent years, development and innovation in fusion data equipment such as sensors in scientific and medicine areas has led to production of massive data values. The modern medicine produces massive amounts of data stored in databases whose use of these large values of data is timeconsuming and in some cases it is impossible. In this regard, we can use some data mining methods to perform this difficult task. Of course, all data mining methods have their own limitations and constraints and are our task to compare methods and algorithms to choose the best and most useful method.
The term used as "Data Mining", was introduced in the 1990s; however data mining is the progress and improvement of a field with a long history [10]. As datasets are growing in size, applications and complexity, Direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been achieved by other discoveries in computer science such as artificial neural networks (ANNs), classification and clustering algorithms plans. Data mining techniques and algorithms has been accomplished for genetic algorithm (GA) in the 1950s, and for decision trees (DTs) in the 1960s. Also it's a supported vector machine (SVM) in 1990s methods [11]. The first use of data mining techniques in health information systems (HIS) and clinical application was fulfilled with the expert systems developed since the 1970s [12].
Given to knowledge, experience and the sense of physicians, medicine decisions and reviews are done, and because of massive amount of medicine data which are kept in medical clinics and health centers, the data mining methods can be appropriate tools for discovering available knowledge in this database to help the physicians with diagnosis, treatment and tracking process.
The rest of this paper is organized as follows. First explained data mining techniques and algorithms used in this study in the next section; and data mining application in healthcare field are described in Section 3. Then the data mining algorithms are categorized and applied to a special dataset in Section 4. The results are analysed and discussed in Section 5. Finally, some concluding remarks and proofs outcome as well as future research lines are presented in Section 6.

DATA MINING
Database pertaining to trade, agriculture, cyberspace, details of phone calls, medical data and etc. are collected and stored very rapidly with the development of information technology, data collection and production methods. So, since three decades ago, the human was thinking about a new method to access hidden data in this huge and large database because the traditional systems were not able to do so. Competition in economic, scientific, political, military fields and the importance of access to useful information among a high amount of data without human intervention caused the data analysis science or data mining to be established.
The concept of data mining has been developed in the late 1980s, which went further during 10 years later [13]. Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting and investigating through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques (Gartner Group). As like as mining gold through huge rocks and large amount of soils, data mining is extracting beneficial knowledge from bulky data sets. But they aren't entirely the same; there is a fine point which is data mining but it isn't just searching and gaining knowledge, it is to obtain knowledge by analysing and reconsidering [14].
Many people consider data mining as equivalent of another commonly used term, Knowledge Discovery from Data, or the KDD. Alternatively, others view data mining as simply an indispensable step in the process of knowledge discovery [12].
Typically, the steps of knowledge discovery (KD) are divided into 7 phases. Four primary steps are pre-processing data and different forms of data preparation for data mining. As mentioned above, data mining as a fifth steps, is one of the necessary steps of this process. Finally, two last steps have the task of identifying useful patterns and displaying it to user. Below there is a brief description of these steps:  Data cleaning: clean incompatible data and noises.  Data integration: integrate multiple sources, if any.  Data selection: restore data related to analysis and evaluate database.  Data transformation: transform or modulate data into searchable forms by summarizing or aggregating.  Data mining: process of applying intelligent algorithms to exploiting data patterns.  Pattern evaluation: identify related patterns.  Knowledge presentation: present extracted knowledge to users using presentation techniques and visual modeling.
Therefore data mining is a step in the KDD process consisting of a particular enumeration of patterns over the data, subjected to some computational limitations. The term pattern goes beyond its traditional concept and notion to include structures or models in the data warehouses. Historical data are used to discover regularities and improve future decisions [15].
The goals of data mining are to divide it into two general types as predictive and descriptive [12]. By application of the predictive type, we understand that application of the model which attempts to 'predict' the value that a certain variable may take ,given what we know at present [16]. Descriptive data mining characterizes the generic attributes of the data in the database [12].

DATA MINING IN HEALTHCARE
Today, in medical areas, data collection about different diseases is very important. Medical centers with different purposes are to collect these data. To survey these data and to obtain useful results and patterns in relation to disease is one of the objectives of the use of these data. Collected data volume is very high and we must use data mining techniques to obtain desired patterns and results among massive volume of data.
Medical and health areas are one of the most important sections in industrial societies. The extraction of knowledge among massive volume of data related to diseases and people medical records using data mining process can be lead to identifying the laws governing the creation, development and epidemic diseases. We can allow expertise and health area staffs to access these valuable data according to the environmental factors in order to identify diseases causes, anticipate and treat the diseases. Finally, this means extending the life and comforting people in the community.
For example some medicine applications of data mining are:  Predicting health care costs.  Determination of disease treatment.  Analyzing and processing medical images.
 Analyzing health information system (HIS).  Effect of drugs on the disease and its side effects.  Predicting the success rate of medicine operations like surgeries.  Diagnosing and predicting of most kind of diseases such as cancer.
In a study published in 2011, Cheng-Ding Chang et al. used data mining techniques for predicting the incidental multi-disease (hypertension and hyperlipidemia). They presented a two-phase analysis procedure to simultaneously predict both hypertension and hyperlipidemia. Firstly they chose six data mining approaches, and then they used these algorithms to select the individual risk factors of these two diseases, afterward determined the common risk factors using the voting principle. Next, they used the Multivariate Adaptive Regression Splines (MARS) method to construct a multiple predictive model for hypertension and hyperlipidemia. This study used data from a physical examination center database in Taiwan that included 2048 subjects. The proposed multi-disease predictor method in this study has a classification accuracy rate of 93.07% [17].
Also Ömer Eskidere and two other colleagues studied the performance of Support Vector Machines (SVM), Least Square Support Vector Machines (LS-SVM), Multilayer Perceptron Neural Network (MLPNN), and General Regression Neural Network (GRNN) regression methods in application to remote tracking of Parkinson's disease progression. It is found that using LS-SVM regression with log-transformation, the estimated motor-UPDRS is within 4.87 points (out of 108) for log-FS-4 feature set and total-UPDRS is within 6.18 points (out of 176) for Log-FS-4 feature set of the clinicians' estimation [18].
In one of the last-mentioned ascertainments in current year (2013)

PROPOSED METHODS AND IMPLEMENTATION
Data mining has wide and common uses in classification and recognition of the problems of health systems. In this section, we describe the materials and methods which are used in current research. Depending on the situation and desired outcome, there are various types of data mining algorithms that you can use. All experiments described in this paper were performed using libraries from Weka 3.7.9 machine learning environment. Before building models, the data set were randomly split into two subsets: 75% (n=4406) of the data for training set and 25% (n=1469) of the data for test set and all of data mining methods apply to total-UPDRS property.

Data Set
Parkinson's telemonitoring dataset was created by Athanasios Tsanas and Max Little of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation which developed the AHTD telemonitoring device to record the speech signals [6]. trial periods, but the voice recordings were obtained at weekly intervals. Hence both Motor-UPDRS and total-UPDRS linearly interpolated [6]. Table.1 represents baseline, after three and six months, UPDRS scores with the feature labels and short explanations for the measurement along with some basic statistics of the dataset.

Simple Linear Regression (SLR)
The simple linear regression model y = α + βx + e, with measurement error (errors-in-variables model) has been subject to extensive research in the statistical literature for over a century. It is well known that the simple linear regression model with error in the predictor x is not identifiable without any extra information in the normal case. In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible [20].
So in the simple linear regression model, the observations y i are generated according to: where α is the intercept parameter, β the slope parameter, x i the explanatory variables and finally e i the error terms. If α=0 we obtain the simple linear regression model without intercept.
You can see a simple linear regression model in Figure.1 that shows this model's parameters.

Multi-Layer Perceptron (MLP)
An MLP is based on single perceptron that was introduced by Rosenblatt [21].The network contains an input layer, a hidden layer and an output layer. Each neuron at one layer receives a weighted sum from all neurons in the previous layer and provides an input to all neurons of the later layer [22]. However there is merely a single neuron in the output layer. The architecture of the Neural Network, used in this research, is the multilayer perceptron network architecture with 22input nodes, 13 hidden nodes in our approach. We showed the MLP structure used in this study in.
The number of input nodes is determined by the ultimate data; the number of hidden nodes is determined through trial and error; and the number of output nodes is represented as a rage, demonstrating the disease classification. Each neuron at one layer receives a weighted sum from all neurons in the previous layer and provides an input to all neurons of the later layer.

SMOreg
The Sequential Minimal Optimization (SMO) algorithm on classification tasks defined on sparse data sets has been shown to be an effective method for training SVM. SMOreg implements a sequential minimal optimization algorithm for training a support vector regression model. This implementation globally replaces all missing values and transforms nominal attributes into binary ones [23]. It also normalizes all attributes by default. We 'normalized' training dataset used the 'polynomial' kernel: K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1) ^p for kernel and used 'RegSMO' to optimizing.

M5Rules
Generate a decision list for regression problems used to divide and to conquer. It builds a model tree using M5 and makes the "best" leaf into a rule in each iteration of progress. The method for generating rules from model trees, called M5-Rules, is straightforward and works as follows; a tree learner is applied to the full training dataset and a pruned tree is learned. Then, the best branch is made into a rule and the tree is discarded.
All instances covered by the rule are removed from the dataset. The process is applied recursively to the remaining instances and terminates when all instances are covered by one or more rules. This is a basic divide-and-conquer strategy for learning rules; however, instead of building a single rule, as it is usually done, we built a full model tree at each stage, and make its "best" branch into a rule. In contrast to PART (partial decision trees), which employs the same strategy for categorical prediction, M5-Rules builds full trees instead of partially explored trees. Building partial trees leads to a greater computational efficiency, and does not affect the size and accuracy of the resulting rules [24, 25, 26, 27 and 28]. In simulating this mode we set the minimum number of instances to allow at a leaf node to 4.0. Table   The Decision Table (DT) is a symbolic way to express the test experts or design experts knowledge in a compact form [29].A decision table includes a hierarchical table. These tables consider that each entry in a higher level table gets broken down by the values of a pair of additional attributes to form another table. The structure is similar to dimensional stacking [30]. For DT implementation we used "Best First" method to find good attribute combinations for the decision table. For example in Table.2, the final row tells us to classify the applicant as Heart Failure (HF) if gender is Female, age is more than 42 and blood pressure isn't important.

M5P
This is an algorithm for generating M5 model trees [31]. M5 builds a tree for a given instance, to predict numeric values. When the input attributes can be either discrete or continuous the algorithm requires the output attribute to be numeric. For a given instance the tree is traversed from top to bottom until a leaf node is reached. At each node of the tree a decision is made to follow a particular branch based on a test condition on the attribute associated with that node. Each leaf node has a linear regression model associated with the form:  in the instance and whose respective weights ,..., , 1 0 are calculated using standard regression. The tree is called a model tree as the leaf nodes contain a linear regression model to generate the predicted output [32]. We start with a set of training instances to build a model tree, using the M5 algorithm. The tree is built using a divide-and-conquer method. The M5P Model Tree algorithm in WEKA is available in the Java class ''weka.classifiers.trees.M5P". The minimum number of instances to allow at a leaf node was 2.0 and after simulation number of rules was 170.

REPTree
This algorithm is a fast decision tree learner. It is also based on C4.5 algorithm and can cause classification (discrete outcome) or regression trees (continuous outcome). It uses information gain/variance to building a regression/decision tree using and prunes it using reduced-error pruning (with back-fitting). Only sorts values for numeric attributes once. Missing values are dividing by splitting the corresponding instances into pieces [33].
In implement of REPTree we set the maximum tree depth to -1, it means there was no restriction. The minimum proportion of the variance on all the data that needs to be presented at a node was 0.001, which is used for splitting, to be performed in regression trees. After implementation we have a tree whose size is 675.

Decision Stump
The decision stump is the simplest special case of a decision tree. Each decision stump consists of a single decision node and two prediction leaves. Each decision node is a rule that checks the presence or absence of specified n-gram of command sequence. The boosted decision stumps are created using a weighted voting of the decision stumps [34]. It's usually used in conjunction with a boosting algorithm, does regression or classification. Missing is treated as a separate value.

IBk
This is the K-nearest neighbour classifier. We used IB1 which is the simplest instance-based learning algorithm. It gives a test instance, and to find the training instance closest to the given test instance uses a simple distance measure, and predicts the same class as this training instance. If more than one instance is the same smallest distance (multiple) to the test instance, the first one found is used.
for Boolean and symbolic-valued attributes. The difference of IBI and nearest neighbour algorithm is that IBI normalizes its attributes ranges, processes instances incrementally, and unlike nearest neighbour algorithm has a simple policy for tolerating missing values [35]. In this model we used "Linear NN Search" algorithm for searching nearest neighbour which is implementing the brute force to search algorithm for nearest neighbour search in 1 nearest neighbour(s) for classification.

LWL
Lazy learning methods defer training data process until a query needs to be answered. This algorithm to assign instance weights uses an instance-based. A good choice for classification is Naive Bayes. This implementation surveys a form of lazy learning, locally weighted learning, that uses locally weighted training to average, interpolate between, extrapolate from, or otherwise combine training data [36]. In implementation of LWL model we used all of the neighbours in weighting function which is linear function and "Linear NN Search" algorithm for searching nearest neighbour which is implementing the brute force to search algorithm with Euclidean distance (or similarity) function.

Regression by Discretization
A class for a regression scheme that utilizes any distribution classifier on a version of the data that has the class attribute discretized. The predicted value based on the predicted probabilities for each interval is the expected value of the mean class value for each discretized interval. This class now also supports conditional density estimation by building a univariate density estimator from the target values in the training data, weighted by the class probabilities. To implement this model, J48 with C4.5 algorithm was adopted due to it abilities to apply negotiation strategies; and set Number of bins for discretization to 10 and estimator type for density estimating was histogram estimator. Our created J48 pruned tree size is 315 and it has 158 leaves node.

RESULTS AND DISCUSSION
We report the results from eleven methods used to study the usefulness of the data mining algorithms for remote tracking of Parkinson's disease progression. The data set, used in the detection of PD progression with eleven data mining methods, was divided into two subsets as Training and Test data. There were a total of 4406 pieces of data, exclusively used for training and 1469 pieces of data exclusively used for testing. The classification accuracies obtained by this study for PD dataset are presented in Table.3 You can compare and assimilate results of the algorithms in correlation coefficient, Mean absolute error, Root mean squared error, relative absolute error (in percentage) and root relative squared error (in percentage). In statistics, the Mean Absolute Error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes and the Root Mean Square Error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. Table 3. Experiential results of classification models. Figure.3 summarized correlation coefficient of each algorithm in categories. We notice Decision Table gave higher accuracy of correlation coefficient on our dataset (99.85%) in Rules category and M5Rules algorithm has comparatively a good accuracy equivalent to 99.67%. The relative absolute error is very similar to the relative squared error in the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor and the root relative squared error is relative to what it would have been if a simple predictor had been used. More specifically, this simple predictor is just the average of the actual values. Thus, the relative squared error takes the total squared error and normalizes it by dividing by the total squared error of the simple predictor. By taking the square root of the relative squared error one reduces the error to the same dimensions as the quantity being predicted. In Figure.

CONCLUSION AND FUTURE WORKS
This experimental study compares classification performance of different eleven data mining algorithms via using of a Parkinson's Telemonitoring dataset. This dataset comprises of 22 attributes with various ranges of values. Early detection of any kind of disease is an important factor and for PD, remote tracking of UPDRS using voice measurements would facilitate clinical monitoring of elderly people and increase the chances of its early diagnosis.


extfieldsofstudy:
Computer Science
Engineering

text:
CarbonCulture at DECC: Digital Engagement for Sustainability at Work

This paper reports on CarbonCulture at DECC, an employee engagement platform for sustainable behaviour, which took place at the Department of Energy & Climate Change in London. Through a participatory design process, we developed simple web apps enabling staff to log actions around late working, food choices and commuting, and see colleagues’ actions, providing a digital interface linking physical and spatial behaviour. We achieved a high level of engagement (16% of staff used the apps) and identified patterns of user engagement to inform future development.

INTRODUCTION
Sustainable HCI is a rapidly growing applied subdiscipline (DiSalvo et al, 2010) addressing, as considered by Mankoff et al (2007), both sustainability in design and sustainability through design. Sustainability through design often takes the form of interventions aiming to change users' behaviour-making lifestyle changes and influencing interaction with products and systems. This can be about adopting new systems, or using existing ones more efficiently (Lockton et al, 2009), individually or in groups.
Given the importance of physical products and the built environment in CO 2 emissions, the broader scope of the Internet of Things has much potential, offering meaningful connections between physical and digital interaction, and new opportunities for generating and interpreting both quantitative and qualitative data. Sustainable HCI and design for sustainable behaviour have become the focus of a range of projects which can broadly be considered in this category; most centre on reduced CO 2 emissions through influencing energy use and transport choices (e.g. Cowan et al, 2013;Foster et al, 2011, Froehlich et al, 2009).
Reducing household CO 2 emissions through behaviour change (e.g. Lockton et al, 2013a) is considered a major priority, including at government level (Cabinet Office Behavioural Insights Team, 2011;Dietz et al, 2009), but nondomestic emissions also offer opportunities. For example, CO 2 emissions arising from non-domestic buildings-primarily workplaces-make up 18% of the UK's footprint (Technology Strategy Board, 2009); while infrastructure and operating changes can lead to major efficiency gains, staff behaviour is also "a key determinant of energy consumption" (Hadi & Halfhide, 2009). Thus, interventions to address staff behaviour are increasingly commonoften communication-led, drawing on social marketing, though some approaches use persuasive technology and allied HCI perspectives, e.g. via feedback and visualisation (Pierce & Paulos, 2012). Organisational motives include cutting energy bills (and 'carbon tax' in some countries) and wider benefits such as increasing overall employee engagement.

THE CARBONCULTURE AT DECC PROJECT
The UK's Department of Energy and Climate Change (DECC) aims to lead by example in its own building in central London. Technical efficiency improvements led to a 21.3% reduction in CO 2 emissions in 2010-11, but DECC has recognised that further improvements will come in part through behaviour change. CarbonCulture at DECC, introduced in this paper, comprises a range of lowcost HCI-led interventions developed through a pilot study at DECC's offices (c.1,000 staff), by an external team (CarbonCulture team & DECC, 2012).
Our aim was to develop effective methods of engaging employees in understanding and caring about the part their behaviour plays in CO 2 emissions, both as a precursor to behaviour change, and to increase 'buy-in' to relevant building operation changes. Many techniques for influencing behaviour through design have been recognised (Lockton et al, 2010) and applied in domains such as health as well as sustainability; gamification (Deterding et al, 2011), behavioural economics and the quantified self (Munson et al, 2012) are approaches currently receiving significant attention. With CarbonCulture we adopted elements of these as part of a participatory design methodology, involving DECC employees in development through workshops and iterative prototyping. DiSalvo et al's (2010) distinction between seeing "users as the problem" and "solving users' problems" is useful here; solving users' problems through more sustainable behaviour seems preferable for engagement. (This does assume that engagement is the answer to more sustainable behaviour, which is, however, not necessarily the whole story (Stern, 2000)). Could we engage large numbers of people, in one building, in voluntary carbon-saving behaviours by making them useful, fun and rewarding? Workplaces and households offer some different challenges for behaviour change; at DECC, there were also political considerations such as avoiding the perception of 'bonus' incentives using taxpayers' money. On the other hand, some DECC staff were already betterinformed on environmental issues than the general population.

THE ELEMENTS OF THE PROJECT
The project started with user research via interviews, workshops and the use of simple physical touchpoints, to identify both behaviours which it would be possible to address, and the kinds of tools which would be useful.

Physical touchpoints
Activities included asking employees to 'annotate' their working environment with 'arrow' comment cards ( Figure 1) to raise issues around CO 2 impact and encourage participation.
Comments on the arrows highlighted details of everyday interactions: for example, that the building's lifts are controlled by two separate circuits with two buttons on each floor; people habitually press both to try to get a lift quickly, meaning that both lifts respond each time.
One of the aims here was to help make visible some of the 'invisible' links between systemsincluding the positions of sensors which provided input to the building management system-and provide a tangible starting point for exploring staff's mental models of the systems around them. For example, the prevalence of staff interest in (and confusion over) the heating and air conditioning systems, revealed through the arrows exercise, led to exploring behavioural heuristics around heating and cooling (Lockton et al, 2013b).

The digital platform
The main focus of the project was a digital platform, supporting a number of web apps, user dashboards, an internal blog, and near-realtime electricity and gas use visualisations. The apps were developed via workshops with staff and with quantification of the CO 2 reductions possible through different behaviour changes, andapplying a relatively simple game mechanicoffered users points for logging particular actions, which could be collected and redeemed for a limited number of 'sustainable' prizes (so the 'most engaged' people were closest to winning when prizes were announced). The DECC building has seven floors, with a majority of staff hot-desking. Keeping lights on outside office hours in case people are working late is expensive and wasteful. The Scrunch app ( Figure 2) aimed to reduce the 'size of the office' to the (quite small) size of the workforce outside of normal work hours, by encouraging staff to move to a single 'Scrunch' floor. Staff received points for logging 'heading home' or checking in on the Scrunch floor, and a 'social proof' ticker showed colleagues' actions. The 'head home' option was rewarded equally, since taking part is fundamentally what mattered; the focus was on user engagement to encourage moving floor, or going home. Barriers identified through the workshops included statements such as "I'll only be here another 15 minutes, I promise" and "I have to shut down my laptop and restart it again, which takes ages." To address the first barrier, we differentiated between staff finishing work and leaving soon, and those who would be working late. The process of undocking and re-docking laptops was made easier through creating simple (laptop-shaped) postcards explaining very simply how to do it without requiring a full shutdown-a conversation-starter initially aimed at Scrunchers, but which became a useful tool for all DECC staff. The designated 'Scrunch' floor was made appealing through free cakes and tea, with printers also set up to make the move easier; the aim was to make the environment better than staying put, with the app providing support and allowing tracking as well as rewards.

Foodprints: Healthier lunches, lower carbon
In the UK, food can represent 20% of an individual's carbon footprint (Sustainable Development Commission, 2009); this can be cut by eating less meat and dairy, which can also be healthier. The Foodprints app enabled staff to log their lunchtime choices in the DECC canteen (Figure 3), providing both personal 'diet' and 'sustainability' tracking, together with printed 'pledge cards' which canteen staff would stamp with each meal.
Equal points were awarded for the action of logging-not for the meal choice-but some game mechanics were used to maintain continued engagement, such as 'Hidden Treasure' (where extra points were hidden somewhere in the lunch recording tool for that week) and '5 in a row' (extra points for completing the recording for a whole week). The cards provide the opportunity for verification by canteen staff, enabling different point levels to be introduced at a later date if desired.

OK Commuter: How was your journey today?
Transport choices are a significant factor in CO 2 emissions. With DECC's central London location, few staff live nearby and most commute using train, tube or bus. Many cycle or walk; driving is difficult and parking expensive. Hence, transport choices are already mostly lower-carbon than at many other workplaces. However, there are still benefits of a recording tool for the commute, and behaviours may still be changed-for example, encouraging more walking and cycling, with attendant health benefits. The action of logging each day's journeys, and visualisation of the past month's travel, can maintain engagement, and this function was built into the OK Commuter app (Figure 4), with points awarded for logging. The app included daily visualisations of the transport modes used as well as a live 'social proof' activity feed.
We found from early workshops that many staff who cycle, in particular, are often proud of their commitment, and showing this to colleagues via the feed capitalised on this. Equally, though, most people use the same form of transport every day, reducing the personal interest in logging it. We thus introduced a 'creative description' field encouraging users to 'tag' each journey with a short (often witty) comment-for example, "Maria A's bus ride in was: Smelly" or "Brian F's cycle in was: Blissful". This maintained engagement as well as providing a playful talking point among colleagues.

Other apps
Two other apps were prototyped: 'GO2', dealing with business travel, and 'Thermopeople', dealing with thermal comfort, but the audience and potential CO 2 impact at DECC was small due to contextual factors of the organisation and building management. The impact could be larger elsewhere.

Energy visualisation
Visualisations of the DECC building's electricity and gas use were also made available under the CarbonCulture banner ( Figure 5), both 5-second data and daily/weekly/monthly, with kWh, costs and CO 2 given. This ongoing component demonstrated a link between the behavioural aspects of the apps, and the overall impact of the building, as well as enabling seasonal trends to be discussed more clearly through the internal blog. It also enabled DECC's facilities managers to identify-and implement-savings in gas use of around 10% through changing water heating schedules, not through staff behaviour change, but useful nonetheless. As well as via users' CarbonCulture dashboards, the visualisations were (and are) publicly accessible via the open web at http://carbonculture.net/orgs/decc

RESULTS AND DISCUSSION
We ran the pilot study at DECC in the last quarter of 2011, with apps introduced over time. Scrunch was the first to be launched, following the earlier workshops, arrows and blog posts. Of 1,000 staff at DECC, 412 voluntarily signed up to CarbonCulture, establishing a total membership for the programme of around 40%. While figures for comparable internal programmes are difficult to obtain, these compare very favourably with, for example, 13% of eBay employees signed up to the 'Green Team' and 15% of US employees signed up to BT's solar education programme (Bin, 2011). Of the CarbonCulture membership, 166 used at least one app, representing again 40% 'engagement' of members. Overall, then, 16% of all staff used the apps.
DECC staff 'Scrunched' 81 times, and used the 'Head Home' button 1,561 times. With Foodprints, 2,747 lunches were logged and 110 cards registered. OK Commuter users logged 2,229 journeys. Aggregate figures hide the detail, of course, but by analyzing individual users' behaviour (and users' comments) we identified some patterns of engagement and retention, as users were attracted or incentivised by different elements-for example, some competed or collaborated with immediate colleagues to earn points (and prizes), while others used the logging features primarily for their own interest. Some collected sufficient points but never claimed prizes. Although 123 users of the 166 used all three apps at least once, many users stuck with only one or two of them.
Adding additional apps over time led to uplifts in user numbers, and we believe that this can provide a sustainable mechanism to attract and retain users by keeping them interested in fresh new tools, as well as attracting new staff to the platform as colleagues sign up. However, it makes statistical comparisons more difficult.
Scrunch has a high initial barrier, but also a high reward of carbon reduction to match, if used at capacity. Foodprints offers a medium barrier to engagement (everyone has to eat), and a high potential of carbon savings, although these don't produce direct energy savings in the building.
Finally, OK Commuter has a low initial barrier, but acted mainly as a baselining tool: the substantial carbon savings available would have to be accessed in a future development through specifically enabling and encouraging changes to transport choice.
As well as quantitative data on user actions, users provided qualitative feedback on their experience. This was continued with in-depth user interviews as the pilot came to a close.
It is likely, of course, that DECC staff were already generally interested in energy and environmental issues, and so perhaps easier to engage. Running CarbonCulture at other organisations will need to address different issues, both in terms of CO 2 impact and staff culture, but we believe the basic platform is flexible enough to achieve this, in conjunction with a participatory design process.
One question worth considering is around the longer-term sustainability of this kind of intervention. Was the degree of engagement, and staff's motivation, dependent on the points? Did attitudes and awareness change permanently? A few quotes from DECC staff as the pilot came to a close suggest some effect, e.g. "It has changed my behaviour: I am car-pooling to work with a colleague", but a longitudinal study would be needed to assess the effects in the longer term.

CONCLUSIONS
In the Sustainable HCI field, burgeoning with projects addressing sustainable behaviour, our contribution is a demonstration of engaging employees around behavioural sustainability, via a digital platform. The CarbonCulture platform employs similar features (logging actions, awarding points, showing other users' actions in an activity feed) across three apps targeting different behaviours, all linked to elements of the physical environment.
We achieved a high level of engagement, and while the project ran for only three months, we found that while the aspects which engaged users differed, together they addressed multiple motivations. Adding and improving apps over time was an important component in maintaining engagement.
As the pilot's focus was understanding how to drive engagement rather than behaviour change itself, we cannot say that the pilot led to reducing DECC's emissions, but what was learned will enable future versions of CarbonCulture to be deployed with behaviour change more specifically the goal, translated into measurable impacts on carbon. Commercially, a similar CarbonCulture programme is in development for universities and a property management and construction group. The platform will also be used for a behaviour change project in the UK Parliament itself.
Aside from the apps, the energy visualisation component facilitated communication between building users and building management teams, and helped staff gain some more understanding of the systems around them-which may be a potential benefit of Internet of Things-type approaches more broadly. The displays we developed and used at DECC have now been deployed across eight UK government departments (including 10 Downing Street), Tate Modern, and Cardiff Council's estate.

ACKNOWLEDGEMENTS
Thanks to DECC participants, and the communications and estates teams. The project was part-funded by the Technology Strategy Board under the 'EMPOWER' title.


Learning Input and Recurrent Weight Matrices in Echo State Networks

Echo State Networks (ESNs) are a special type of the temporally deep network model, the Recurrent Neural Network (RNN), where the recurrent matrix is carefully designed and both the recurrent and input matrices are fixed. An ESN uses the linearity of the activation function of the output units to simplify the learning of the output matrix. In this paper, we devise a special technique that take advantage of this linearity in the output units of an ESN, to learn the input and recurrent matrices. This has not been done in earlier ESNs due to their well known difficulty in learning those matrices. Compared to the technique of BackPropagation Through Time (BPTT) in learning general RNNs, our proposed method exploits linearity of activation function in the output units to formulate the relationships amongst the various matrices in an RNN. These relationships results in the gradient of the cost function having an analytical form and being more accurate. This would enable us to compute the gradients instead of obtaining them by recursion as in BPTT. Experimental results on phone state classification show that learning one or both the input and recurrent matrices in an ESN yields superior results compared to traditional ESNs that do not learn these matrices, especially when longer time steps are used.

Introduction
Recurrent Neural Networks (RNNs) belong to a general type of deep neural networks which are used to model time sequences and dynamical systems [1,2,3,4,5,6]. Echo State Networks (ESNs) belong to the general class of the RNNs [7,8,9,10]. The following properties of an ESN make it distinct from other types of RNNs. First, both the recurrent and input matrices in an ESN are fixed and not learned (this is largely due to the difficulty in learning RNN [11,12]. Second, the number of hidden neurons in an ESN is typically much larger than those in regular RNNs. The main challenges of training RNNs described in [11,12] is avoided in ESN by not training most of the very large number of difficult network parameters. This also leads to avoiding potential overfitting problems. Third, the output units, also called readout units, in an ESN are linear. This is unlike the typically nonlinear output units in regular RNNs. Given the very large number of hidden neurons of an ESN, the output or readout weight matrix is very large as well. The use of linear output units allows the output weight matrix to be learned very efficiently and with a simple regularization mechanism based on ridge regression. Fourth, the learning of the ESN parameters (i.e. output matrix) is much simpler than that for regular RNNs. The former uses linear learning with convex optimization, and the latter, is based typically on Back Propagation Through Time (BPTT) and is highly nonlinear and non-convex. As a result, learning the ESN parameters can be effectively carried out via batch training. This greatly facilitates parallel implementation. Learning the general RNN parameters on the other hand, typically requires stochastic gradient descent, and is more difficult for parallelization.
The simplicity in ESN learning comes at the cost of not learning some important parameters (including the input and the recurrent weight matrices) and of using linear output units. While the special design of the recurrent matrices (see [13]) and the use of a large number of hidden neurons help in reducing the weakness of using fixed parameters, it is desirable to make these parameters adapt to the data. This is as long as the required learning remains simpler and more parallelizable than the common BPTT learning method applied to regular RNNs. This paper presents this type of learning for the input and recurrent matrices of ESNs. We propose a technique that makes full use of the linearity in the output units when constructing constraints on all three input, output, and recurrent matrices in an ESN. The constraints enable us to compute the gradients as the learning signal in an analytical form, and this makes the gradient estimate more accurate than when computed by recursion as in BPTT. Our preliminary experimental results on phone classification are highly positive. It is demonstrated that learning one or both of the input and recurrent matrices in an ESN gives better phone classification accuracy than that obtained by a traditional ESN without learning them. Furthermore, when longer time steps are used in analytically computing the gradients, the better classification results are obtained.

General Recurrent Networks and Specific Echo State Networks
A general RNN has temporal connections as well as input-to-hidden layer, hidden layer-to-output connections. These connections are mathematically represented by the recurrent weight matrix W rec , the input weight matrix W, and the output weight matrix U, respectively. The RNN architecture is illustrated in Fig. 1. It also includes input-to-output and output-to-hidden (feedback) connections, with the latter denoted by W f b . The sequential sections of Fig. 1(a), 1(b), 1(c), . . . , denote the RNN as it unfolds in time. Note that all the weight matrices are constrained to be the same (i.e. they are tied) at any discrete point in time.

Wfb
Wfb Wfb Wfb  In Figure 1, x i , h i and y i represent the input, hidden and output vectors at discrete time t = i. Again, the connections between the input (x i ) and hidden (h i ) layers, the hidden and output (y i ) layers, and the output and hidden layers are represented by W, U and W f b respectively. The temporal connection between h i and h i+1 is represented by the matrix W rec . Note that the direct connections from the input to the output layer form a part of the matrix U; i.e., it is equivalent to concatenating the input layer with the hidden layer.
There are two standard methods to train RNNs, BPTT and the method based on Extended Kalman Filtering (EKF) [13]. BPTT is a first order method, which extends error backpropagation for feedforward networks by treating each time step as a new hidden layer ( but ties all the weight matrices across time). Generally, BPTT has slow convergence. Its difficulty in capturing long-term memory due to vanishing gradient and exploding gradient problems has been well known for many years [11]. It is often non-trivial to obtain good results with BPTT; see the tremendous amount of engineering required to make BPTT work [2,14,15]. The EKF-based method, on the other hand, has fast convergence properties and belongs to a second-order method. However, the computational requirements of the EKF method are high and its implementation is non-trivial [13], especially for large-scale problems.
One prominent approach that has been proposed to overcome the difficulty in training RNNs is the ESN [9], [13]. As explained above, an ESN is a special type of RNNs whose recurrent weights (W rec ) and input to hidden layer weights (W) are fixed and only U ( that represents the hidden layer to output and the input to output weights ) is trained. The recurrent connections in W rec are sparse and their values are carefully fixed in a way that the echo-state property is preserved. ESNs can be trained very fast because the only connections that are trained are the output connections. With good initialization, ESNs have been shown to yield good performance for one dimensional sequences; but for high dimensional data such as speech, the studies have been relatively limited; please see [10].
Since the output units in an ESN have a linear activation function and assuming the hidden units have a sigmoid activation function, the formulation of an ESN as a special type of RNNs ( as shown in Fig. 1 ) can be succinctly described by 1+e −x . As discussed earlier, an ESN has a special designed recurrent matrix, which is endowed with the echo state property in most existing versions [13,9,10]. The echo state property implies that the state, or the hidden units' activities, of the network can be determined uniquely based on the current and previous inputs and outputs provided the network has been running for a sufficiently long time. The formal definition of echo states is described in [13]. Assume that the maximum eigenvalue of W rec is λ max , the activation function of the hidden units is sigmoid and | λ max |> 4, then the network does not have echo states. This is a sufficient condition for the echo states to not exist 1 [13]. However, as emphasized in [13], in practice, when | λ max |< 4 the network has echo states. There is a similar sufficient condition under which the exploding gradient problem for recurrent weights would not happen [16].
In ESN training, only the output weight matrix, U, is trained. The input and recurrent weight matrices should be carefully fixed. There are three main steps in training an ESN: constructing a network with the echo state property, computing the network states, and estimating the output weights.
To construct a network with the echo state property, the input weight matrix W and the sparse recurrent weight matrix W rec are randomly generated. Then, the maximum eigenvalue of W rec is calculated and all entries of W rec are renormalized as follows: where λ < 4 for sigmoid activation function. λ is also an important parameter which affects the memory length of the network and should be determined based on the required memory size for the specific task. As emphasized in [13], the entries of the input weight matrix are also of great importance; large entries cause most hidden units to saturate while small entries lead hidden units to stay in the linear region of the sigmoid function. The value of λ should be predetermined and fixed ( based on the desired task ) before the second step (that calculates the network states).
To find the outputs of the hidden layer units, the hidden states are initialized to zero or another initial state. Then the network runs freely for i trans time steps where the hidden states of each time step are calculated using (1)  To calculate the output weights U, we stack the desired outputs or targets corresponding to input signal x i as a matrix T; i.e., Since H is computed using all known quantities (incluidng the fixed input and recurrent matrices) using (1) and hence it is known also, U can be obtained by minimizing the following mean-squareerror cost function: where F stands for the Frobenius norm of a matrix, tr(.) is the trace of a matrix and Minimizing (6), we have the globally optimal estimate of U determined by setting the gradient of the above cost function to zero and solving it; i.e., In practical implementation, to prevent inaccurate results when HH T is singular or close to singular, the following solution of "ridge regression" is used for estimating U: where I is the identity matrix and µ is a fixed positive number.

Learning the Input Weight Matrix in ESN
Assuming the memory of the network extends back to m time steps, we use the following notation to facilitate the development of the learning method for the input weight matrix W: Therefore, equations (1) and (2) can be written as: To find the gradient of the cost function E with respect to W and learn input weights W we consider two cases in the remainder of this section. In Case 1, we assume that U does not depend on W and in Case 2 we take into account the dependency between U and W. Note that in both cases we take into account the time dependency among the hidden state vectors in H, i.e., the dependency of h i+1 on h i at every time step i 2 . Since Case 2 is a more realistic formulation of the gradient, it is used for learning the input weight matrix in our experimental results presented in Section 5. We derive the gradient for one time step dependency and then generalize it to n time step dependency, i.e., H i depends on H i−1 , H i−2 and so on up to n time steps.

Case 1
The gradient of the cost function with respect to W can be written as Assuming that H 1 depends on W, i.e., H 1 = σ(W rec H 0 + W T X 1 ), and denoting the term independent of W by: then using chain rule of calculus we have: and therefore where • is element-wise multiplication.

Case 2
The gradient calculated in Case 1 is not accurate because the dependency between U and W is ignored. To take this dependency into consideration, the first line of equation (13) is rewritten as: Since tr(AB) = tr(BA) and tr(A) = tr(A T ) we have Using the chain rule presented in equation (126) of [20], the gradient can be written as: Below we first calculate matrix A and then matrix B.
For constant values of hidden states H 2 we define F, M and S as follows: Then, using the chain rule again, we obtain considering the fact that tr(AB) = tr(BA), A 1 can be written as From equation (107) of [20] we have Since F T = F and M T = M, A 1 can be written as Considering equation (114) of [20], A 2 will be as follows substituting S and using M = M T results in Now substituting M and F in (23) results in the final formulation for A as follows To calculate B, we assume that there is just one time step dependency, i.e., H 2 depends on H 1 and W and H 1 does not depend on H 0 but depends on W. The generalization to an arbitrary number of time steps is straightforward and is presented at the end of this section. From (22) and (11) we write B as: which is similar to the term calculated in (16) and therefore By substituting (30) and (32) in (22) we get the gradient formulation for one time step dependency as follows: This gradient formulation can be generalized for an arbitrary number of time steps as follows: where n is the number of time steps and To calculate A using (30), H n and T n are used.
After calculating the gradient of the cost function w.r.t W, the input weights W are updated using the following update equation where α is the step size and and where the initial value for m old and m new is 1. The third term in (36) helps the algorithm to converge faster and is based on the FISTA algorithm proposed in [21] and used in [19].

Learning the Recurrent Weight Matrix (W rec ) in the ESN
To learn the recurrent weights, the gradient of the cost function w.r.t W rec should be calculated. We first derive the formulation for the two time steps dependency, i.e., H 2 depends on H 1 and H 1 depends on H 0 but no more time dependencies. Then it will be generalized to the arbitrary number of time steps. The same method that is used in section 3 can be used to get the following formulation for the gradient: A will be the same as (30). To calculate B we have and therefore the gradient will be: It can be generalized for arbitrary number of time steps as follows: where H 0 includes the initial hidden states and and A is calculated using (30) based on H n and T n .
Only the non-zero entries of the sparse matrix W rec are updated using (36) and the gradient calculated in (41). To make sure that the network has the echo state property after each epoch, the entries of W rec are renormalized such that the maximum eigenvalue of W rec is λ that is predetermined in (3). This renormalization also prevents the gradient explosion problem for recurrent weights from happening.
A summary of the learning method is as follows: • The echo state network with predetermined maximum eigenvalue of W rec is constructed based on the explanations presented in section 2. • Input weights matrix W is updated based on (34), (35), (36) and (37).
• Non-zero entries of the sparse recurrent weights matrix W rec are updated based on (41), (42), (36) for W rec and (37). • Updated W rec is renormalized to have the predetermined maximum eigenvalue λ.
• The forward pass is repeated with the updated input and recurrent weights to find the hidden states. The network runs freely for i trans time steps and then the hidden states are recorded as matrix H. • Taking into account the direct connections from the input to output in the network, the output weight matrix U is calculated using (9).
To prevent the value of the gradient w.r.t W from exploding, we use a similar approach proposed in [16] where the gradient value is renormalized when it is greater than a threshold.

Experiments
We carried out the experiments for frame-level classification of phone states on the TIMIT dataset using an ESN with all parameters learned as discussed so far. The training data includes 1,124,589 frames. The validation set has 122,488 frames from 50 speakers. The results are reported using the core test set consisting of 192 sentences and 57,920 frames. The speech is analysed using the standard Mel Frequency Cepstral Coefficients (MFCC). Each feature vector has 39 entries, including first and second derivatives. We have used 3 states for each of 61 phones resulting in a target class vector with 183 entries. Phone state labels are extracted using a GMM-HMM system which aligns the frames with their corresponding states.
We have used a context window of 3 frames for all experiments resulting in the input vectors with 3 × 39 = 117 entries. The regularization parameter µ used in (9) is set to 10 −8 . The maximum eigenvalue of W rec is set to 3.9. We have used a step size (α in (36)) of 0.07. The task is to classify each frame in the TIMIT core test set into one of 183 phone states. The results are presented in Table 1 for different hidden layer sizes in the ESN, one for each row in the table. The results are also arranged by four different ways of learning the input and recurrent weigh matrices W and W rec , where m is the number of time steps in incorporating matrices' dependencies in the learning. The column of "ESN" refers to the traditional ESN as in [7,8,9,10] where W and W rec are not learned. The preliminary experimental results shown in Table verify that learning input and recurrent weight matrices in the ESN is superior to the ESN with the same structure but without learning the two matrices. Furthermore, the longer the time steps are incorporated in the learning, the lower error rates are. On the column of "ESN", we also observe that the traditional ESN improves its performance as the number of hidden units increases, consistent with the findings reported in [10]. Finally, we would like to remark that the results obtained so far are very preliminary, and the task is on framelevel classification of 183 phone states. Our first step of research is focused on this easiest task since it is a pure and simple machine learning problem and it requires no expertise in speech recognition. The next steps are to move 1) from 183-state classification to 39-phone classification; 2) from frame level to segment level (which requires dynamic programming over three states of each phone); and 3) from classification (with no phone insertion and deletion errors) to recognition (with phone insertion and deletion errors). Then we can meaningfully compare the results with other approaches in the literature on the TIMIT phone recognition task.

Discussion and Conclusion
The main idea of this paper is straightforward: the traditional ESN learns only one of three important sets of weight matrices, and we want to learn them all. The key property that characterizes the ESN is the use of linear output units so that the learning is simple, convex, forms a least-square ridge regression problem with a global optimum (in learning the output weights). In extending the learning of the output weights to only learning input and recurrent weights, we make use of the same property of linear output units to develop and formulate constraints among various sets of ESN weight matrices. Such constraints are then used to derive analytic forms of the error gradients w.r.t the input and recurrent weights to be learned. The standard learning method of BPTT for the general RNN (with typically nonlinear output units) does not admit analytical forms of gradient computation. BPTT requires recursively propagating the error signal backward through time, a very different style of computation and learning than what we have developed in this work for ESNs.
In this paper we focus on ESNs with one layer and with no feedback connection, i.e., W f b = 0, without loss of generality. As our future work, to build more layers of ESN, we can simply stack the output of one-layer ESN on top of another, or we can combine the output with the original data input and/or with hidden units. Also, in the current work with one-layer ESN, we take into account the dependency between U and W, h i and W, h i and h i−1 , etc. When more layers of ESN are built, a richer dependency becomes available to exploit but the same principle used in this work in deriving the analytic forms of the gradient computation would apply to the multiple-layer ESN in our future work.


Implications for Improving Accessibility to E-Commerce Websites in Developing Countries: A Subjective Study of Sri Lankan Hotel Websites

This research explores the accessibility issues with regard to the e-commerce websites in developing countries, through a subjective study of Sri Lankan hotel websites. A web survey and a web content analysis were conducted as the methods to elicit data on web accessibility. Factors preventing accessibility were hypothesized as an initial experiment. Hazardous design elements are identified through web content analysis, the results of which are utilized to develop specific implications for improving web accessibility. The hypothesis tests show that there is no significant correlation between accessibility and geographical or economic factors. However, physical impairments of users have a considerable influence on the accessibility. Especially, visual and mobility impaired users experience poor accessibility. Poor readability and less navigable page designs are two observable issues, which pose threats to accessibility. The lack of conformance to W3C accessibility guidelines and the poor design process are the specific shortcomings which reduce the overall accessibility. Guidelines aim to improve the accessibility of sites with a strategic focus. Further enhancements are suggested with adherence to principles and user centered design and developing customizable web portals compatible for connections with differing speeds. A need for developing new design models for differencing user groups and implementing web accessibility strategy are emphasized as vital steps towards effective information dissemination via e-commerce websites in the developing countries.

Introduction
Accessibility is a critical factor for e-commerce success. Particularly, the accessibility of e-commerce websites for users with impairments is an evolving research area in the contemporary academia. Apart, the accessibility of e-commerce websites in developing countries could be influenced by several other factors such as economic background and geographical location of users. Therefore, it is a vital requirement for web concept designers in the developing countries to consider the above factors in order to ensure the successful dissemination of business information through e-commerce sites. However, the means of designing accessible web interfaces is still remaining as an open question with regard to developing countries.
This study aims to formulate implications for improving accessibility to e-commerce websites in developing countries through a subjective study of Sri Lankan hotel websites. Specially, formulating implications for web designers in developing countries would be a vital contribution to the body of knowledge. In addition to this, the specificity of this research has been focused to Sri Lankan hotel websites considering the contextual relevance. Arguably, Sri Lankan hotel websites have a greater need for accessibility due to the reservations made by tourists from different physical, economic and geographic backgrounds. The current economic boom, opened by the post war scenario of the island further signifies the emerging need for designing accessible e-commerce sites, for effective web based promotion.

Literature Review
According to Good (2008, p. 16) "A website is said to be accessible when anyone, regardless of economic, geographic or physical circumstances, is able to access it". Hence, for this particular study, accessibility could be defined as the ease in which people with disabilities, people from different geographic regions and people having different internet connections could access the websites'.
The economical aspect of accessibility could be looked in two ways, (1) the category of internet connection -which depends on the economic background and usage frequency of customer, and (2) the time spent on information search which mostly depends on the time availability of consumer. Both these factors have an interrelationship and will have a direct effect on accessibility. The type of internet connection determines the speed of browsing, which influences the time a consumer spends on browsing. The less speedy the connection, the more the consumer will get discouraged and vice-versa. On the other hand consumer's average browsing time per day determines the category of connection they hold in the context of a developing country. However, search engine optimizationan evolving area in tourism research deals with this issue to a certain extent. The degree of filtration and the sequence of listings done by search engines will influence the accessibility of tourism sites. Law & Haung (2006) indicates that 47.4% users only check the first three pages of the search results listing. The above finding has not specified the number of results listed per page. However a recent study of Xiang et al (2008) moderates the previous finding, stating that travelers are only retrieving a tiny fraction of the huge list of search results. Evidently, search result listing is a dominant factor which could influence the decision behavior of consumers. But, it could be argued whether the curious consumer will not spend more time to get the best out of his search. Also, the above rule might be inapplicable for frequent customers as they would directly go to the hotel web page to check the information. However the information search behavior of consumers will trigger to have a look for 'innovation' in each hotel's website. On the other hand, hotels will not be able to survive in today's knowledge economy with their 'obsolete' ways of promoting products. Consequently, being in the forefront of search results listing and using a customer centric approach in wording websites are vital for getting the customers to stay with the site.
The geographic aspect is another major factor which determines accessibility. Accessing the website from rural areas with poor bandwidth is another challenge. Utilization of mobile commerce is another evolving area in tourism to solve this issue. According to Hyun et al (2009, p. 158) it is critical for hotels to utilize mobile commerce in tourism promotion to survive in an era of increasing mobile adaptation. In addition Buhalis (2008, p. 612) claims mobile technology increases the accessibility of digitally excluded communities. However, investing on mobile technology could pose a number of challenges to hotels. Pizam (2009, p. 301) indicates that the current global financial crisis has adversely affected the hospitality industry and tends the income of hotels to be reduced over time. On the other hand, Buick (2003, p. 244) identified customer demand as a pulling factor in IS/IT investment. Will it be advisable to invest on mobile technology in the light of the above contradicting dilemma? But the competitiveness gained through new technology could be powerful than any of the above factors. Supportively Kozak et al (2005, p. 7) ascertain that the ability to gain IT access is the deciding factor of first and second class tourist destinations, organizations and customer groups.
Finally, the influence of physical circumstances has a major role in accessibility. Eichhorn et al (2008) argues more sophisticated understandings of differential needs and appropriate sources as the most crucial step in enabling disable tourists to access tourism information. But this statement could be discarded, as there are almost enough specific needs identified by disability scholars, and the labor is entirely on the hands of technology researchers to contextualize and develop design frameworks for users with impairments. On the contrary, the doctoral research of Good (2008) investigated a method to improve the web accessibility of users with impairments. However; the profundity of Good's (2008) work could be criticized for its limitation to cover the whole population of disable consumers. Also the sample selected by Good (2008) is very small and could be claimed as inadequate to generalize the findings. However; Good (2008, p. 134) defended her research claiming that excellent results could be achieved using small groups. It is acceptable, as all the visually impaired users will face difficulties in reading the letters with small font size. Anyhow, further research is needed to extend the findings of Good (2008) and to contextualize which for the tourism domain. Concluding from the above findings, interface usability is the dominant factor deciding the web accessibility of people with impairments.

Methodology
A user centric approach was adapted for this research. The data collected through a web survey and web content analysis. Initial hypothesis testing was conducted based on the data collected through web survey. The hazardous design features which prevent accessibility were triggered through the web content analysis. The feasibility of collecting data from geographically dispersed samples was the main reason for selecting a web based survey for data collection. The web content analysis selected for specific identification of design shortcomings.
A website was created to provide the preliminary information to survey respondents. The web based survey was piloted with 7 participants representing all three sample groups to improve the usability of it. The improvement of language, inclusion of navigational links and the incorporation of rationale for questions were the notable outcomes of pilot study. The hotels falling under western region of Sri Lanka (22) were selected and the hotels with e-commerce websites were shortlisted based on a consequent Google search.
The number of questionnaires to be promoted was processed as 120. In fact the maximum number of dependent variables was determined as 4. Altogether 80 responses were estimated deciding 20 responses per variable. To reach 80 responses it was decided to gather 120 responses considering 40 invalid responses. Considering the above outcome the survey was planned for one full week. Links of all 22 hotel sites were given to the respondents and they were asked to browse one website on their own choice and to record their experience.
The elements rated by users analyzed through a quantitative approach. The analysis of statistical variance was undertaken to check the validity of each sub hypotheses. ANOVA test of user ratings was undertaken using online analysis software. The web content of selected five hotels was analyzed against a checklist prepared based on a randomly selected criterion from W3C web accessibility guidelines. Apart from this, short interviews were conducted with hotel managers to explore the organizational initiatives with regard to improving accessibility.

Sample Groups
The study was conducted in the natural environment of user. 120 participants from 14 countries participated in the survey. The sample was classified into different groups check the accessibility of websites for users from varied economic, physical and geographic backgrounds. User samples included connection types [ADSL broadband and any other], impairments categories [visual, mobility and cognitive / language impairments] and geographic regions [Sri Lanka and foreign users].
The connection types were classified into three main categories namely ADSL, broadband and any other connections. Any other connection category includes dial up connection as well. The first two connections represent people having good bandwidth and speedy internet accesses, while the other category denoted people with below average bandwidth.
The geographic destinations have divided into two categories namely, Sri Lankan and foreign users. Rationale for which was to assess the accessibility for both within and outside Sri Lanka. Justifiably, the research not intends to assess the country wise accessibility of sites, instead accessibility for different geographic destinations.
Special user needs have classified into four main categories namely, visual, auditory, cognitive/language and mobility. The categorization of visual and auditory needs includes all the impairments related to vision and hearing. The cognitive/language needs include impairments related to language, speech and cognition. The mobility includes dexterity and motor impairments. Good (2008, pp. 28) classified impairments into the above categories to demonstrate the relationship between design features and accessibility.
The above classification was adopted as it gives a comprehensive method for classifying disabilities with regard to internet accessibility. Arguably, one of the objectives of this study is to measure the accessibility of sites for users with different impairments, but not to measure the internet accessibility for individual impairments. However, the auditory impairment was eliminated from the list in later stages as there were not enough responses recorded from participants representing that group. A brief description of the above categorization was given in the research website and linked with survey to enable respondents understand the method followed for classification.
While there may be some criticism on selecting a sample of generic users instead of tourists, it is defendable in this instance. The purpose of study is to measure the accessibility of sites. Accessibility of a site is largely depending on aspects such as users' geographic, economic and physical backgrounds and site design (Good 2008). In fact, the accessibility of a site would be same for a tourist and non tourist user.

Results
This section presents the accessibility ratings of users. Participants asked to rate the accessibility of sites. The correlation between accessibility and living zone, type of connection and physical impairments were measured accordingly. The, user ratings were aggregated and presented as a  Country wise accessibility scores show most of the responses are above 'neither agree nor disagree' category. There are no users with auditory impairments. Most of the users with cognitive/language and mobility impairments have rated above average score for accessibility. However a visually impaired user disagrees with the accessibility. Most of the responses for connection wise accessibility show above average category. Thus, generally the websites could be said to be accessible for users from varied geographic, economic and physical backgrounds, except the tiny fraction of negativity recorded from users with visual and mobility impairments. However, the statistical mean and median for each rating were calculated for further analysis.  The probability of this result assuming null hypothesis is 0.68. Therefore the null hypothesis is valid and the research hypothesis cannot be accepted. Hence, the type of connection does not have any significant influence over the accessibility of sample websites. Therefore, the economic background of the user does not need to be a deciding factor of accessibility according to the above hypotheses.  Especially, the regions have been divided into two main groups as Sri Lanka and foreign countries, and a hypothesis was tested to scrutinize whether the geographic diversity has any significant effect on the accessibility of websites. The results indicate that the probability of these results assuming the null hypothesis is 0.47, which is lesser but much closer to the middle point. However, the mean and median of the accessibility scores shows a more than average score for accessibility from differing regions. Especially, the raw data reveals that almost 90% users rated 'above average' for accessibility. Hence, it is concluded that the null hypothesis is valid and there is no significant influence on accessibility by the regional diversity.  The probability of these results assuming the null hypothesis is 0.77. Therefore the null hypothesis is valid and the research hypothesis cannot be accepted. Thus, the website accessibility cannot be proved to be influenced by the individual impairment. However, the raw data depicted in table 1 reveals, one each individual users from visual / sight and mobility impairment groups disagree with the accessibility of sites. A site inaccessible for a user with sight disorder, due to the lack of readable fonts or alter text features, should be inaccessible for all users with the same level of sight disorder. Hence, the sites are inaccessible for users with visual and mobility impairments. Abanumy et al (2005) reports the same phenomenon among the e-government websites of Saudi Arabia and Oman. Therefore this could be generalized as a general phenomenon in website design. The following section formulates the accessibility guidelines for hotel industry website designers. Thus, these results should be subjected to further analysis to identify the exact design elements which prevent the accessibility of sites for users with visual and mobility impairments. Consequently, a web content analysis of five sample sites was conducted based on randomly selected criterions from W3C accessibility guidelines. The shortcomings identified with the web content have been used extending the findings of preliminary survey and discussed in the proceeding section.

Design Elements
One each guideline from all four design principles, were randomly selected from the four main categories of W3C guidelines for analysis. The selected criterions are: (1) Text alternatives, (2) Navigable, (3) Readable, (4) Compatible. More than 60% of the sites reviewed do not have alt-text facility. This will have a significant effect for users with visual impairments. The navigation through pages is observed to be taking more time and this will definitely de-motivate the user. Especially, the location specification of the web pages has not been indicated during the course of navigating through pages. A very poor readability is observed in more than 60% of the sites analyzed. Especially, the sites do not have fonts which are visible to the users with sight impairments, which is mostly because of the inappropriate usage of color combination in the font selection. One hotel uses photo enlarging option to support visually impaired users. Another hotel holds two options in their homepage. One is for customers with slow connections and other one is for speed connections. Basic product information and company information are presented in both sites. Although, the site compatible with slow connections have designed with less multimedia objects, which lacks to incorporate readable fonts in web design.

Implications
Adherence to principles: The web content analysis showed a general weakness of websites with regard to lack of adherence to W3C accessibility guidelines. Disregarding W3C guidelines had led to poor design, which reduces the readability of websites to people with visual impairments. Also, the mismatch with W3C guidelines has prevented sites from being accessible to mobility users. This could be synthesized as a common weakness in most of the commercial websites from developing regions.
User-Centered Design: There are certain arguments on the danger of solely depending on the accessibility guidelines (Milne et al, 2005). On the other hand, the exact need of the user could be triggered solely by including user in design process. Although, this practice is well expounded in academic researches, it is identified as a common weakness in hotel websites. Therefore, it is recommended to adapt a user -centered approach in web design. Especially, it is vital to include users with visual and mobility impairments as well some elderly users in the design process, to ensure the responsiveness of websites to the needs diverse user groups.
Design Frameworks: Specific design models could be developed to support web designers in the developing countries. The development of design models and testing the design with end users would immensely increase the accessibility of websites. Although, the above best practices are well practiced by most of the web designers in the developed world, a major setback on accessibility concerns has been observed among the organizations located in the developing countries.
Portals with different speeds: Although the hypothesis tests shows there is no co-relation between the accessibility and the type of internet connections, the results of this study has been obtained from a comparatively small sample for connection wise users groups. Especially, the sample for users with other connection types was comparatively small. On the other hand, there is a high probability for greater number of users from developing countries holding low speed connections. Hence, it is recommended to design websites with customized portals to match with the differential speed categories of users.
Accessibility Strategy: A lack of accessibility strategy among the hoteliers has been clearly scrutinized through the above research. Especially, the websites are not having any proper standard with regards to the accessibility for users with differing needs. This could be scrutinized through improper color combination and lack of clarity of fonts. Particularly, the sites are designed on the convenience of designers than of users. The lack of mutual contribution between academia and industry and poor awareness level on accessibility issues could be the main reasons for the above phenomenon in developing countries. Christian (2009) reported a similar phenomenon with Australian small hotels and suggested to adapt a web strategy to avoid lapse. However, an accessibility strategy would be a sensible solution for the above issue in the context of this research. As part of accessibility strategy, the organizations could organize knowledge transfer sessions for web designers and walkthroughs with users. This will help the designers to grasp a high level understanding of the accessibility issues as well as to elicit the exact user requirements with regard to accessibility.

Evaluation & Conclusions
The study investigated on implications for improving the accessibility of e-commerce websites in developing countries. The study compiled with a case study of Sri Lankan hotel websites. The results indicate poor accessibility for users with visual and mobility impairments. Poor readability and less navigable pages in the websites were scrutinized as the principle reasons which prevent accessibility of sites for users with visual and mobility impairments. Also, the sites have been observed not adhering to W3C accessibility principles. The implications includes adherence to principles, user centered design, developing design frameworks, portals with different speeds and an accessibility strategy.
The study lays a foundation for web accessibility for websites research in developing countries. Especially, this is an explorative study for Sri Lankan hotel industry and the results will shed light on future research avenues towards developing specific design models for accessible web design. Also, the outcome could be extended further by developing testable prototypes to address differencing user needs, testing which with different user groups would reveal specific contextual needs of tourists with special needs.
On the other hand, this study could be criticized for a number of limitations. Firstly, the research hypothesis was developed solely based on a predisposition that the economic diversity of the sample could be exactly maintained through including users with different connection types. It could be argued that there could be other different parameters which could form a clear foundation for accessibility research of users from different economic backgrounds. Secondly, the selection of sample consists of generic users instead of tourists. However, it is defendable as the accessibility is same for a tourist and non-tourist. In addition to this, the non equal distribution of samples among the sample groups could be argued as another inherent weakness of this research. Although, accessibility for elderly people is an evolving area (Good et al, 2007), they have not been included in the study. In addition to this, the web content analysis is an unstructured one and could be improved by doing a detail analysis of the web content against W3C guidelines. That would results in developing new design frameworks for e-commerce sites in the developing countries.
In conclusion this research is just a beginning for Sri Lankan hotel industry. Further research needs to be undertaken to extend the findings on factors influencing the accessibility of sites for users from different economic, geographic and physical backgrounds. However, this particular study remains as beginning for Sri Lankan hotel industry, while adding some facts to the existing knowledge pool of accessibility research literature for the context of developing countries.