Miracle-dz/UniFAR-FTUs huggingface dataset:
“UTTAM”

In this article, we propose a system called “UTTAM,” for correcting spelling errors in Hindi language text using supervised learning. Unlike other languages, Hindi contains a large set of characters, words with inflections and complex characters, phonetically similar sets of characters, and so on. The complexity increases the possibility of confusion and occasionally leads to entering a wrong character in a word. The existence of spelling errors in text significantly decreases the accuracy of the available resources, like search engine, text editor, and so on. The proposed work is the first approach to correct non-word (Out of Vocabulary) errors as well as real-word errors simultaneously in a sentence of Hindi language. The proposed method investigates the human behavior, i.e., the type and frequency of spelling errors done by humans in Hindi text. Based on the type and frequency of spelling errors, the heterogeneous data is collected in matrices. This data in matrices is used to generate the suitable candidate words for an input word. After generating candidate words, the Viterbi algorithm is applied to perform the word correction. The Viterbi algorithm finds the best sequence of candidate words to correct the input sentence. For Hindi, this work is the first attempt for real-word error correction. For non-word errors, the experiments show that “UTTAM” performs better than the existing systems SpellGuru and Saksham.
Elementary Matrix-computational Proof of Quillen-Suslin Theorem for Ore Extensions

In this short note we present an elementary matrix-constructive proof of Quillen-Suslin theorem for Ore extensions: If $K$ is a division ring and $A:=K[x;\sigma,\delta]$ is an Ore extension, with $\sigma$ bijective, then every finitely generated projective $A$-module is free. We will show an algorithm that computes the basis of a given finitely generated projective module. The algorithm has been implemented in a computational package, and some illustrative examples are included.
Requirements elicitation and refinement in collaborative research projects

European Union (EU) projects are means of the European Commission for funding research activities. Such projects address challenging research objectives by involving both academic and industrial partners, from several countries. Information and communication technologies–related projects often undertake to deliver a software system prototype. In such a context, most of the typical issues of global requirements engineering may emerge. Partners can have different background and expertise, needs are not sharply defined, and communication is hampered by linguistic and cultural differences. If these issues are not carefully taken into account from the beginning, problems frequently emerge during project execution. This paper presents the experience of applying a customized elicitation and refinement approach in the context of the Learn PAd EU project, which involved about 50 people. The approach combines collaborative elicitation and wiki‐based refinement sessions to come to a set of consolidated requirements. Lessons learnt are discussed as a guidance for researchers dealing with analogous issues in similar contexts. Some of the major observations refer to the importance of initial face‐to‐face meetings when combined with asynchronous remote interactions; the role of moderators that have to encourage collaboration and foster a shared understanding; and the definition of guidelines to select wiki‐based platforms.
Persuasive technology in African context: deconstructing persuasive techniques in an African online marketplace

Research has shown the effectiveness of persuasive technologies in promoting desirable behaviors in many domains such as marketing and eCommerce. However, according to a recent meta-review, most of the existing studies of persuasive technologies have focused on people from the developed countries. There is still a dearth of research on persuasive technology (PT) application to eCommerce, in the African context. Therefore, the overall objective of this paper is to study an African online marketplace to identify persuasive technology (PT) techniques used to optimize customer experiences on Jumia (an African eCommerce site). It is essential to evaluate how PT techniques were used to motivate desirable behaviours in the African context. Consequently, eCommerce conducted an expert evaluation to deconstruct what persuasive techniques were used in Jumia using the Persuasive System Design (PSD) framework. We also analyzed how each of the PT techniques was operationalized on the Jumia eCommerce. As a secondary objective, we conducted a study of 112 users to determine their awareness of the use of PT techniques in Jumia. The results Jumia employed some persuasive techniques to attract and retain customers. It also shows that customers are aware of and appreciated the fact that certain PT techniques were operationalized on the Jumia site. Finally, we compared our findings with that of a similar study from the Western culture. We contribute to the AfriCHI, Persuasive Technology, and HCI community in general by establishing the applicability of persuasive techniques in African context and suggesting some design implications for eCommerce sites.
Cross-cultural perspectives on eHealth privacy in Africa

The African continent is making considerable strides to develop and implement technology-driven health innovations. Policymakers are increasingly acknowledging the rising concerns for online personal privacy and data protection as advances in eHealth results in increased levels of data collection and surveillance. In this paper, we propose a research agenda to investigate the effect of cultural, constitutional, and societal factors on privacy concerns and preferences among the different African countries in the context of healthcare technologies. In addition to helping us understand policy and design implications for members of this region, this research will broaden our understanding of cultural factors influencing privacy worldwide.
DjangoChecker: Applying extended taint tracking and server side parsing for detection of context‐sensitive XSS flaws

Cross‐site scripting (XSS) flaws are a class of security flaws that permit the injection of malicious code into a web application. In simple situations, these flaws can be caused by missing input sanitizations. Sometimes, however, all application inputs are sanitized, but the sanitizations are not appropriate for the browser contexts of the sanitized values. Using an incorrect sanitizer can make the application look protected, when it is in fact vulnerable as if no sanitization was used, creating a context‐sensitive XSS flaw. To discover context‐sensitive XSS flaws, we introduce DjangoChecker. DjangoChecker combines extended dynamic taint tracking with a model browser for context analysis. We demonstrate the practical application of DjangoChecker on eight mature web applications based on Django, discovering previously unknown flaws in seven of the eight applications, including highly severe flaws that allow arbitrary JavaScript execution in the seven flawed applications.
The talking book: participatory design of an icon-based user interface for rural people with low literacy

The Talking Book is an audio technology for sharing knowledge about health and agriculture among oral cultures in rural settings. As a technology, without a display or mouse; navigation is through audio instructions and buttons labelled with icons. This paper presents the iterative Participatory Design (PD) approach employed in redesigning the iconography of the User Interface (UI). We found that the PD process created a feeling among users of ownership and acceptance of the technology as their own creation. For users, the most suitable icons are not those that denote any function, but those that are simply recognizable, which led to replacing international icons such as arrows, with icons representing local objects such as bowls, trees, and hands. An extensive evaluation showed that the new UI was more user-friendly and better liked. This work contributes a novel interface and highlights the value and the challenges of including users who are less familiar with technology in design.
Influence of internet experience on the judgment of mobile web design: a case study of West Africa

Human-computer interaction (HCI) systems are more likely to be successful if tailored to different user groups. However, in Africa, research on how internet experience (IE) and web design (WD) influence the perception of mobile websites is scarce. To bridge this gap, we conducted an empirical study among 191 participants from West Africa to investigate the influence of IE and WD on users' perception of mobile websites and preferences. We based our study on four different mobile website designs in the tourism domain as a case study. The results of our analysis show that IE negatively influences the perception of aesthetics and credibility, with high-experience (HE) users being more critical of mobile websites than low-experience (LE) users overall. More specifically, LE users prefer highly colorful website design to minimalist website design. In contrast, HE users prefer minimalist website design to highly colorful website design. Moreover, both user groups prefer grid-style mobile website design to list-style mobile website design. These findings can be leveraged by HCI designers to tailor mobile websites to both user groups in West Africa.
Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Over the past decade, there has been an unprecedented boom in the application of big data derived from location‐aware technology in GIScience. However, reflections on data quality lead to concerns about inaccurate or even erroneous spatial analysis results. This article contributes a meta‐analysis of big data‐enabled social sensing in spatial analysis. Big data‐enabled research has substantially enhanced our understanding of human population distribution and dynamics, land use, travel behavior, movement patterns, and spatial social networks from city to country to global levels. However, the effectiveness of big data‐enabled social sensing can be undermined by five critical issues related to research design: data volume, spatial coverage, locational information quality, and representativeness across socioeconomic and demographic groups. We suggest a framework for addressing the inherent bias in spatial big data at multiple stages of the research lifecycle and applying them to appropriate topics and scales of applications.
New sampling strategies to reduce the effect of autocorrelation on the synthetic T2 chart to monitor bivariate process

In this paper, synthetic T2 chart is developed to monitor bivariate process with correlated variables and autocorrelated observations. The proposed chart is a combination of the Hotelling's T2 chart and the conforming run length chart. The operation and design of the chart are described when observations are autocorrelated and cross correlated. The first‐order vector autoregressive process VAR (1) is used to model the bivariate data from an autocorrelated process of interest. Using an average run length as performance measure criterion in the VAR (1) model, it is observed that autocorrelation seriously impact the performance of the synthetic T2 chart. To reduce the effect of autocorrelation on the performance of the synthetic T2 chart, the skip and mixed sampling strategies are implemented to form rational subgroups in the construction of synthetic T2 chart. The average run length performance of the synthetic T2 chart implementing these strategies is compared with that of the standard strategy of formation of rational subgroups. It is observed that implementing skip and mixed sampling strategies within rational subgroup improves the performance of the synthetic T2 chart.
Bringing a virtual string quartet to life

Classical music is a very traditional art and not often associated with modern technology. This paper proposes an approach to combine both classical music and modern technology in order to create an interactive Augmented Reality (AR) experience. The developed application brings the string quartet of the Konzerthaus in Berlin to life using the 3D engine Unity and the AR library Vuforia. The AR string quartet lets users freely interact with the four individual musicians of a string quartet and listen to as many musicians at the same time as one likes.
SoundPlay: an interactive sound installation for playful intergenerational encounters in public areas

Intergenerational playful interactions are seen as a way to foster intergenerational social interaction. These types of interactions have been linked with both cognitive and physical benefits. While previous works have focused on enhancing intergenerational social needs between relatives, non-familial intergenerational needs have scarcely been explored. Especially in urban areas where large non-familial communities are likely to exist, both young and senior residents have been reported to be at risk of social isolation. We present an interactive playful system (SoundPlay) that was developed based on guidelines elicited from preliminary motivational studies for non-familial intergenerational interaction. Through developing moments of prosocial play, we establish a formative step in improving intergenerational relations. Mixed-aged cohorts can have brief encounters that potentially develop into prolonged social relationships. We draw inspiration from socio-psychology, playful persuasive systems and participatory co-design.
Cloud based Dynamic Course Selection Framework using Network Graphs with Term Difficulty Estimation

The system developed in this paper uses a cloud based technology to implement and design a software as a service (SAAS) application for adaptive course selection and term difficulty estimation for a networked curriculum. The choice of courses in every term is completely in the hands of the students who enroll for a particular program in Universities. The order of courses taken in every term is ad hoc due to different factors like student interests, uncertainty about the student pass rates, frequent changes in admission policies and curriculum requirements. However, this choice of course plays a vital role in students graduating in time from the university. In this paper, we analyze student success ratios in terms of time to graduation. To illustrate the designed models, data from different colleges of the Public Authority of Applied Education and Training (PAAET), Kuwait, is used. Graph-based complex networks are used for analyzing the courses and how crucial they are. The difficulty levels of courses are estimated based on the institutional data from spring 2013 to fall 2016 and term difficulties are estimated based on the courses chosen. This work presents a robust framework which is adaptable to the courses chosen by the students and the ease of flow of students through the curriculum with the aim of improving the university’s graduation rate.
Human-based fraudulent attempts on gait based profiles

Recent research involves biometrics in authentication as they establish a natural way of communication. Accordingly, in this paper, we describe the implementation of an Android-based authentication application and we focus on human factor impostor attacks on gait based systems. We used the smartphone's built-in accelerometer to record gait cycles while walking. Results proved that imitating other's gait cycles is possible and can be a huge threat.
An Approximative Bayes-Optimal Kernel Classifier Based on Version Space Reduction

The Bayes-optimal classifier is defined as a classifier that induces an hypothesis able to minimize the prediction error for any given sample in binary classification problems. Finding the Bayes-optimal classifier is an intractable problem. It is known that it is approximately equivalent to the center of mass of the version space, which is given by the set of all classifiers consistent with the training set. Previously solutions to find the center of mass are not feasible, as they present a high computational cost, and do not work properly in non-linear separable problems. Aiming to solve these problems, this paper presents the Dual Version Space Reduction Machine (Dual VSRM), an effective kernel method to approximate the center of mass of the version space. The Dual VSRM algorithm employs successive reductions of the version space based on an oracle's decision. As an oracle, we propose the Ensemble of Dissimilar Balanced Kernel Perceptrons (EBPK). EBPK enhances the accuracy of each individual classifier by balancing the final hyperplane solution while maximizing the diversity of its components by applying a dissimilarity measure. In order to evaluate the proposed methods, we conduct an experimental evaluation on 7 datasets. We compare the performance of our proposed methods against several baselines. Our results for EBKP indicate the strategies for improving individual accuracy and diversity of the ensemble components work properly. Also, the Dual VSRM consistently outperforms the baselines, indicating that the proposed method generates a better approximation to the center of mass.
Boundary spanning leadership, resource mobilisation, and performance of university-industry R&D projects: a study in a Japanese university

ABSTRACT Acquisition of university knowledge has grown significantly for firms to complement an internal knowledge base of technological innovation. Recognising the significance, an increasing body of literature has analysed the performance of university-industry R&D projects by adopting managerial and social approaches. Despite the important role played by project leaders, existing research paid scant attention to the critical ability and quality of successful project leaders in acquiring university knowledge. To address these limitations, this study performed a survey of firms that collaborated with the Ritsumeikan University in Japan and investigates the influence of boundary spanning leadership of project leaders in firms on both knowledge and commercial performance of R&D projects. Furthermore, it also evaluates the mediation effect of university resource mobilisation that links boundary spanning leadership and project performance.
Using Communities of Words Derived from Multilingual Word Vectors for Cross-Language Information Retrieval in Indian Languages

We investigate the use of word embeddings for query translation to improve precision in cross-language information retrieval (CLIR). Word vectors represent words in a distributional space such that syntactically or semantically similar words are close to each other in this space. Multilingual word embeddings are constructed in such a way that similar words across languages have similar vector representations. We explore the effective use of bilingual and multilingual word embeddings learned from comparable corpora of Indic languages to the task of CLIR. We propose a clustering method based on the multilingual word vectors to group similar words across languages. For this we construct a graph with words from multiple languages as nodes and with edges connecting words with similar vectors. We use the Louvain method for community detection to find communities in this graph. We show that choosing target language words as query translations from the clusters or communities containing the query terms helps in improving CLIR. We also find that better-quality query translations are obtained when words from more languages are used to do the clustering even when the additional languages are neither the source nor the target languages. This is probably because having more similar words across multiple languages helps define well-defined dense subclusters that help us obtain precise query translations. In this article, we demonstrate the use of multilingual word embeddings and word clusters for CLIR involving Indic languages. We also make available a tool for obtaining related words and the visualizations of the multilingual word vectors for English, Hindi, Bengali, Marathi, Gujarati, and Tamil.
The state‐of‐the‐art in software development effort estimation

The software developers and researchers have been facing difficulties regarding software development effort estimation (SDEE) since 1960s. Both overestimation and underestimation are problematic for future software development. The software engineering field is continuously adapting new technologies and development methodologies, so there is always a requirement to have an accurate SDEE method that can cater the needs of continually growing software industry. The major purpose of this state‐of‐the‐art review is to provide an additional insight of existing SDEE studies while considering five points of reference: techniques used to construct models, strengths and weaknesses of different models, availability of benchmark data sets, data set characteristics, generalization ability of models. We have performed a comprehensive review of SDEE studies published in the period 1981‐2016. We have defined a new scheme of categorizing existing SDEE models. We have found that a majority of available data sets do not include complete information of projects, which misleads the direction of research. To compare SDEE models, we recommend to use same data sets while focusing on specific aspects of accuracy as none of SDEE studies has yet been able to compare all the existing models over same data sets while considering same aspects of accuracy.
Application of data mining methods for effort estimation of software projects

Information technology companies currently use data mining techniques in different areas with the goal of increasing the quality of decision‐making and to improve their business performance. The study described in this paper uses a data mining approach to produce an effort estimation of a software development process. It is based on data collected in a Croatian information technology company. The study examined 27 software projects with a total effort exceeding 42 000 work hours. The presented model employs a modified Cross‐Industry Standard Process for Data Mining, where prior to model creation, additional clustering of projects is performed. The results generated by the proposed approach generally had a smaller effort estimation error than the results of human experts. The applied approach has proved that sound results can be gained through the use of data mining within the studied area. As a result, it would be wise to use such estimates as additional input in the decision‐making process.
Co-designing with mothers and neonatal unit staff: use of technology to support mothers of preterm infants

There are several digital technologies which have been designed and successfully used to support mothers of preterm infants. However, none have been designed for application in the developing world context. For the existing interventions, none have involved mothers (who are the intended beneficiaries of these technologies) in the design process. This paper reports on a process that involved Neonatal Intensive Care Unit (NICU) staff and mothers in the design of technological interventions that focus on enhancing communication between mothers and staff in the NICU context. We used the co-design approach, focusing on identifying methods that ensure participants fully participate in the design process despite facing co-design dynamics such as power imbalances and conflict. Our results demonstrate the benefits of choosing an approach that focuses on building trust with stakeholders before delving into co-design process and empowering participants thus enabling them to fully participate in a design process. We argue that while working with multiple stakeholders, co-design readiness is dependent on methodological choice, stakeholders' relationship with the researcher and stakeholders' cohesion.