Improving search engine interfaces for blind users: a case study

This article describes a research project aimed at improving search engine usability for sightless persons who use assistive technology to navigate the web. At the beginning of this research, a preliminary study was performed concerning accessibility and usability of search tools, and eight guidelines were formulated for designing search engine user interfaces. Then, the derived guidelines were applied in modifying the source code of Google’s interface, while maintaining the same look and feel, in order to demonstrate that with very little effort it is possible to make interaction easier, more efficient, and less frustrating for sightless individuals. After providing a general overview of the project, the paper focuses on interface design and implementation.     

Accessible smartphones for blind users: A case study for a wayfinding system

While progress on assistive technologies have been made, some blind users still face several problems opening and using basic functionalities when interacting with touch interfaces. Sometimes, people with visual impairments may also have problems navigating autonomously, without personal assistance, especially in unknown environments. This paper presents a complete solution to manage the basic functions of a smartphone and to guide users using a wayfinding application. This way, a blind user could go to work from his home in an autonomous way using an adaptable wayfinding application on his smartphone. The wayfinding application combines text, map, auditory and tactile feedback for providing the information. Eighteen visually impaired users tested the application. Preliminary results from this study show that blind people and limited vision users can effectively use the wayfinding application without help. The evaluation also confirms the usefulness of extending the vibration feedback to convey distance information as well as directional information. The validation was successful for iOS and Android devices.     

Profiling information technology users: en route to dynamic personalization

This study describes the initial stages in the development of a Technology Profile Inventory (TPI) and its potential use in a variety of contexts, including dynamic personalization. The initial version of the TPI was constructed based on the responses of 318 participants. Factor analysis was used to establish the major components of individuals' attitudes toward information technology. The factors extracted demonstrate the utility of updating measures of computer attitudes to include items related to the Internet and to a broader range of attitudes. The factors of the TPI were correlated with a variety of demographic and usage variables. Gender differences were found for most TPI factors, though not for approval of information technology, and possible explanations of these differences are discussed. Correlations with usage variables provided both convergent and divergent validation, as both past experience and present use of computers and the Internet were associated with more positive TPI attitudes, but cellular phone use was unrelated to all TPI factors.     

Computational models for integrating linguistic and visual information: A survey

This paper surveys research in developing computational models for integrating linguistic and visual information. It begins with a discussion of systems which have been actually implemented and continues with computationally motivated theories of human cognition. Since existing research spans several disciplines (e.g., natural language understanding, computer vision, knowledge representation), as well as several application areas, an important contribution of this paper is to categorize existing research based on inputs and objectives. Finally, some key issues related to integrating information from two such diverse sources are outlined and related to existing research. Throughout, the key issue addressed is the correspondence problem, namely how to associate visual events with words and vice versa.     

An incremental algorithm for clustering spatial data streams: exploring temporal locality

Clustering sensor data discovers useful information hidden in sensor networks. In sensor networks, a sensor has two types of attributes: a geographic attribute (i.e, its spatial location) and non-geographic attributes (e.g., sensed readings). Sensor data are periodically collected and viewed as spatial data streams, where a spatial data stream consists of a sequence of data points exhibiting attributes in both the geographic and non-geographic domains. Previous studies have developed a dual clustering problem for spatial data by considering similarity-connected relationships in both geographic and non-geographic domains. However, the clustering processes in stream environments are time-sensitive because of frequently updated sensor data. For sensor data, the readings from one sensor are similar for a period, and the readings refer to temporal locality features. Using the temporal locality features of the sensor data, this study proposes an incremental clustering (IC) algorithm to discover clusters efficiently. The IC algorithm comprises two phases: cluster prediction and cluster refinement. The first phase estimates the probability of two sensors belonging to a cluster from the previous clustering results. According to the estimation, a coarse clustering result is derived. The cluster refinement phase then refines the coarse result. This study evaluates the performance of the IC algorithm using synthetic and real datasets. Experimental results show that the IC algorithm outperforms exiting approaches confirming the scalability of the IC algorithm. In addition, the effect of temporal locality features on the IC algorithm is analyzed and thoroughly examined in the experiments.     

DMAKit: A user-friendly web platform for bringing state-of-the-art data analysis techniques to non-specific users

Tremendous advances in different areas of knowledge are producing vast volumes of data, a quantity so large that it has made necessary the development of new computational algorithms. Among the algorithms developed, we find Machine Learning models and specific data mining techniques that might be useful for all areas of knowledge. The use of computational tools for data analysis is increasingly required, given the need to extract meaningful information from such large volumes of data. However, there are no free access libraries, modules, or web services that comprise a vast array of analytical techniques in a user-friendly environment for non-specific users. Those that exist raise high usability barriers for those untrained in the field as they usually have specific installation requirements and require in-depth programming knowledge, or may result expensive. As an alternative, we have developed DMAKit, a user-friendly web platform powered by DMAKit-lib, a new library implemented in Python, which facilitates the analysis of data of different kind and origins. Our tool implements a wide array of state-of-the-art data mining and pattern recognition techniques, allowing the user to quickly implement classification, prediction or clustering models, statistical evaluation, and feature analysis of different attributes in diverse datasets without requiring any specific programming knowledge. DMAKit is especially useful for users who have large volumes of data to be analyzed but do not have the informatics, mathematical, or statistical knowledge to implement models. We expect this platform to provide a way to extract information and analyze patterns through data mining techniques for anyone interested in applying them with no specific knowledge required. Particularly, we present several cases of study in the areas of biology, biotechnology, and biomedicine, where we highlight the applicability of our tool to ease the labor of non-specialist users to apply data analysis and pattern recognition techniques. DMAKit is available for non-commercial use as an open-access library, licensed under the GNU General Public License, version GPL 3.0. The web platform is publicly available at https://pesb2.cl/dmakitWeb. Demonstrative and tutorial videos for the web platform are available in https://pesb2.cl/dmakittutorials/. Complete urls for relevant content are listed in the Data Availability section.     

Robot-assisted shopping for the blind: issues in spatial cognition and product selection

Research on spatial cognition and blind navigation suggests that a device aimed at helping blind people to shop independently should provide the shopper with effective interfaces to the locomotor and haptic spaces of the supermarket. In this article, we argue that robots can act as effective interfaces to haptic and locomotor spaces in modern supermarkets. We also present the design and evaluation of three product selection modalities—browsing, typing and speech, which allow the blind shopper to select the desired product from a repository of thousands of products.     

Towards a framework to select techniques for error prediction: Supporting novice users in the healthcare sector

Whilst healthcare has increased its awareness of the retrospective safety assessment techniques, such as root cause analysis, adoption of the corresponding predictive safety assessment techniques has been slow and sporadic. Reasons for this may include lack of support in technique choice and practical knowledge in the published literature. Whilst there have been many publications on these techniques, few have aimed to support the novice user in selecting a technique from the broad array of choice to facilitate targeting of education in techniques for specific purposes.This paper aims to address this through collecting an evidence base towards developing a bottom-up (resources and constraints) and top-down (requirements) approach to technique selection.Conclusions indicate there is a lack of practical experiences described in the literature to conclusively define a technique for selection and a need for a dedicated research in this area to make it accessible for healthcare and other novice users.     

Municipal web sites accessibility and usability for blind users: preliminary results from a pilot study

Despite the existing regulations and standards at national and international level, web content is still difficult to use, if not completely unusable, for visually impaired people. This paper presents the evaluation results for three municipal web sites. A combined method, based on conformance review and expert review, was employed. Overall, the results reveal a low conformance to WCAG2 and many accessibility and usability problems. The content is not properly structured, and this reduces usability even for sighted user. The analysis of the evaluation data suggests that many accessibility barriers could be avoided by adopting a user-centered approach during web design.     

Beyond web content accessibility guidelines: Design of enhanced text user interfaces for blind internet users

Websites do not become usable just because their content is accessible. For people who are blind, the application of the W3C's Web Content Accessibility Guidelines (WCAG) often might not even make a significant difference in terms of efficiency, errors or satisfaction in website usage. This paper documents the development of nine guidelines to construct an enhanced text user interface (ETI) as an alternative to the graphical user interface (GUI). An experimental design with 39 blind participants executing a search and a navigation task on a website showed that with the ETI, blind users executed the search task significantly faster, committing fewer mistakes, rating it significantly better on subjective scales as well as when compared to the GUIs from other websites they had visited. However, performance did not improve with the ETI on the navigation task, the main reason presumed to be labeling problems. We conclude that the ETI is an improvement over the GUI, but that it cannot help in overcoming one major weakness of most websites: If users do not understand navigation labels, even the best user interface cannot help them navigate.     

Auditory navigation in hyperspace: design and evaluation of a non-visual hypermedia system for blind users

This paper presents the design and evaluation of a hypermedia system for blind users, making use of a nonvisual interface, non-speech sounds, three input devices, and a 37 node hypermedia module. The important components of an effective auditory interface are discussed, together with the design of the auditory interface to hypermedia material. The evaluation, which was conducted over several weeks and used a range of complementary objective and subjective measures to assess users' performance and preferences, is described. The findings from the evaluation with nine visually impaired student participants are presented. The results from this research can be applied to the design and evaluation of other non-visual hypermedia systems, such as auditory World Wide Web (WWW) browsers and digital talking books.     

GraphLMI: A data driven system for exploring labor market information through graph databases

Multimedia Tools and Applications - Labor Market Intelligence (LMI) is an emerging field of study that has been gaining interest as it allows employing Artificial Intelligence (AI) algorithms on...     

Optimal group route query: Finding itinerary for group of users in spatial databases

The increasing popularity of location-based applications creates new opportunities for users to travel together. In this paper, we study a novel spatio-social optimization problem , i.e., Optimal Group Route, for multi-user itinerary planning. With our problem formulation, users can individually specify sources and destinations, preferences on the Point-of-interest (POI) categories, as well as the distance constraints. The goal is to find a itinerary that can be traversed by all the users while maximizing the group’s preference of POI categories in the itinerary. Our work advances existing group trip planning studies by maximizing the group’s social experience. To this end, individual preferences of POI categories are aggregated by considering the agreement and disagreement among group members. Furthermore, planning a multi-user itinerary on large road networks is computationally challenging. We propose two efficient greedy algorithms with bounded approximation ratio, one exact solution which computes the optimal itinerary by exploring a limited number of paths in the road network, and a scaled approximation algorithm to speed up the dynamic programming employed by the exact solution. We conduct extensive empirical evaluations on two real-world road network/POI datasets and our results confirm the effectiveness and efficiency of our solutions.     

Systems approach to clothing for disabled users. Why is it difficult for disabled users to find suitable clothing

Today's clothing market is not adapted for people with unusual body dimensions and/or different kinds of functional impairments. They will be refered to as disabled users. The method of user-oriented product development combined with the soft systems methodology has been applied in order to understand and possibly solve their problem. Sixty five disabled users were interviewed. The results show that the disabled users must be divided into subgroups. Their requirements are very much dependent on their impairment not only regarding the fit and function of the clothing, but also regarding shopping and service in the stores. The symbolic values of clothing are just as important as the functional values. However, it is essential not to focus on the end product only, but to look at the problem as a system consisting of: manufacturing, marketing and end use. A new method of taking three dimensional measurements from range images of the human body has been developed. In combination with other developments in information technology it will be possible to synthesise a new system where the manufacturers and retailers of clothing can co-operate in order to give service to disabled users.     

Natural language access to information systems. An evaluation study of its acceptance by end users

The question is asked whether it is feasible to use subsets of natural languages as query languages for data bases in actual applications using the question answering system “USER SPECIALTY LANGUAGES” (USL). Methods of evaluating a natural language based information system will be discussed. The results (error and language structure evaluation) suggest how to form the general architecture of application systems which use a subset of German as query language.     

Tracing requirements to defect reports: an application of information retrieval techniques

To support debugging, maintenance, verification and validation (V&V) and/or independent V&V (IV&V), it is necessary to understand the relationship between defect reports and their related artifacts. For example, one cannot correct a code-related defect report without being able to find the code that is affected. Information retrieval (IR) techniques have been used effectively to trace textual artifacts to each other. This has generally been applied to the problem of dynamically generating a trace between artifacts in the software document hierarchy after the fact (after development has proceeded to at least the next lifecycle phase). The same techniques can also be used to trace textual artifacts of the software engineering lifecycle to defect reports. We have applied the term frequency–inverse document frequency (TF-IDF) technique with relevance feedback, as implemented in our requirements tracing on-target (RETRO) tool, to the problem of tracing textual requirement elements to related textual defect reports. We have evaluated the technique using a dataset for a NASA scientific instrument. We found that recall of over 85% and precision of 69%, and recall of 70% and precision of 99% could be achieved, respectively, on two subsets of the dataset.     

On the use of information fusion techniques to improve information quality: Taxonomy,opportunities and challenges

The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of computer systems, and especially decision support systems, is to assure that the quality of the information they process is high. There are many different approaches for this purpose, including information fusion. Information fusion is currently one of the most promising methods. It is particularly useful under circumstances where quality might be compromised, for example, either intrinsically due to imperfect information (vagueness, uncertainty, …) or because of limited resources (energy, time, …). In response to this goal, a wide range of research has been undertaken over recent years. To date, the literature reviews in this field have focused on problem-specific issues and have been circumscribed to certain system types. Therefore, there is no holistic and systematic knowledge of the state of the art to help establish the steps to be taken in the future. In particular, aspects like what impact different information fusion methods have on information quality, how information quality is characterised, measured and evaluated in different application domains depending on the problem data type or whether fusion is designed as a flexible process capable of adapting to changing system circumstances and their intrinsically limited resources have not been addressed. This paper aims precisely to review the literature on research into the use of information fusion techniques specifically to improve information quality, analysing the above issues in order to identify a series of challenges and research directions, which are presented in this paper.     

Clustering-based method for large group decision making with hesitant fuzzy linguistic information: Integrating correlation and consensus

Aiming at the large-scale experts and the lower consensus in large group decision making, a novel clustering-based method integrating correlation and consensus of hesitant fuzzy linguistic information is proposed. Firstly, develop a new hesitant degree function for hesitant fuzzy linguistic element considering its scale. Secondly, put forward the correlation measure and consensus measure models combining the hesitant degree. And then present a clustering method integrating the correlation and consensus to divide the large-scale experts into several clusters. The clustering method simultaneously ensures the cohesion of clusters and the gradual increasing of the collective consensus level. After clustering, activate the selection process to update the weights of clusters combining the number of experts in clusters and the consensus level of clusters and use the score function considering the hesitant degree to rank the alternatives. Finally, a case and some comparisons are studied and analyzed to verify the rationality and effectiveness of the method.