Reviewerly/exHarmony huggingface dataset:
Evaluating Recommender Systems

Replicating the results of the recommender system's evaluation is one of the main concerns in the area. This paper discusses this issue from different angles: 1) It investigates the uniformity of recommenders' evaluation designs presented in practice and their consistency with the theoretical side. 2) It highlights some of the issues and challenges that face recommenders' evaluators. 3) It provides stepwise guidelines for offline evaluation settings. A quantitative study of articles published in the last decade is studied. The search process is a manual search for a conference and a random search of journals. The results show a lack of uniformity and consistency in presenting the evaluation methods. Most of the articles miss at least one evaluation aspect (i.e., some aspects are not presented in the article). These discrepancies and the wide variety of evaluation settings lead to non-replicable experiments. To mitigate this issue, the paper proposes the recommender evaluation guidelines (REval), which presents a roadmap for recommender systems' evaluators.
Joint Effects of Neutralisation Techniques and the Dark Triad of Personality Traits on Gender : An Insider Threat Perspective

Addressing the problem of the insider threat is multifaceted as it involves trusted individuals who have legitimate authorisation to the information resources of an organisation. It has been reasoned that understanding the catalysts, that is, motivation, rationalisation and opportunity that converge in a crime scenario, may assist in managing the problem. However, not all individuals who are motivated and in the presence of an opportunity for a crime will be maleficent. It is the rationalisation that intercedes in the decision-making step as to whether to commit a crime or not. Criminals must neutralise their feelings towards conformity using rationalisations or more specifically apply "techniques of neutralisation" (i.e. a set of rationalisation techniques), to engage in criminal activities. As the thought patterns and behaviour associated with the successful application of neutralisation techniques, appear to be characteristic of maladaptive behaviour, it may be contended that personality is a mediating factor in the process. This notion underpinned the theoretical basis of this research study which is founded on the socially aversive traits of the Dark Triad (DT), which may enable a dissociation with conformity thereby enhancing an insiders' capacity to invoke techniques of neutralisation. However extant research shows these traits may be influenced by gender variations. Consequently, the goal of this research is to understand the joint effect of the traits of the DT and the techniques of neutralisation as moderated by gender from the perspective of an insider threat. The findings of this study may help in improving the management of insider threats.
Development of a low-cost immersive virtual reality solution for STEM classroom instruction: A case in Engineering Statics

Virtual reality (VR) technology has received great interest from STEM (Science, Technology, Engineering, Mathematics) educators in all levels of education due to its endless possibilities in enhancing student learning of complex and abstract concepts in the fields. Although the recent advances of VR technology have made many VR solutions more affordable than in the past, it is still more expensive to provide students fully immersive learning experience at the classroom level. This study describes the efforts to develop a low-cost 3D immersive application (or app) for classroom instruction of Engineering Statics. Students’ achievement in the course depends not only on their competencies in mathematics and physics but also on the ability to visualize 3D structures from 2D representations and retrieve correct spatial information for solving problems. Built for smartphones and cardboard headsets, the app provides students with an immersive environment to explore 3D structures from different viewing angles and resolve the ambiguity associated with many 2D graphics in Statics textbook problems. The app developed in this study and the preliminary evaluation results will be used as an intervention and inputs in an ongoing study that aims to evaluate the educational benefits of the app in Statics education.
Empowering Teachers Who Work in Inclusive Practices

Taking into account the effects of integration practices and the coronavirus (COVID-19) epidemic, e-coaching, which handles distance education practices and coaching approach together, came to the fore. With the global epidemic of COVID-19, new ones have been added to the professional competencies of teachers working in integration practices. In this respect, this chapter discusses the professional competencies of teachers working in integration practices and the competencies that they should have in order to use technology and emergency distance education applications. In addition, new approaches have been brought to the agenda with the effect of the epidemic in order to improve the emerging competencies of teachers and to strengthen teachers. Thus, in the new world, e-coaching, which handles distance education practices and coaching approach together, has become important. As a result, it is aimed to address the e-coaching approach, which offers a hybrid perspective to empower teachers working in integration environments in the context of their emerging new competencies.
Unsupervised Methods for the Study of Transformer Embeddings

Over the last decade neural word embeddings have become a cornerstone of many important text mining applications such as text classification, sentiment analysis, named entity recognition, question answering systems, etc. Particularly, Transformer-based contextual word embeddings have gained much attention with several works trying to understanding how such models work, through the use of supervised probing tasks, and usually emphasizing on BERT. In this paper, we propose a fully unsupervised manner to analyze Transformer-based embedding models in their bare state with no fine-tuning. We more precisely focus on characterizing and identifying groups of Transformer layers across 6 different Transformer models.
Scheduling mix-flow in SD-DCN based on Deep Reinforcement Learning with Private Link

In software-defined datacenter networks, there are bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline. They compete with each other for limited network resources, and how to effectively schedule such mix-flow is a huge challenge. We propose DRL-PLink (deep reinforcement learning with private link) that combines software-defined network and deep reinforcement learning (DRL) to schedule mix-flow. It divides the link bandwidth and establishes some corresponding private links for different types of flows respectively to isolate them. DRL is used to adaptively allocate bandwidth resources for these private links. Furthermore, DRL-PLink introduces Clipped Double Q-learning and parameter exploration NoisyNet technology to improve the scheduling policy for overestimated value estimates and action exploration problems in DRL. The simulation results show that DRL-PLink can effectively schedule mix-flow. Compared with ECMP and pFabric, the average flow completion time of DRL-PLink has decreased by 68.87% and 52.18% respectively. At the same time, it maintains a high deadline meet rate (>96.6%) close to pFabric and Karuna very much.
Toward digital twins' workload allocation on clouds with low-cost microservices streaming interaction

A Digital Twin (DT) is a set of computational models representing real-time physical objects and processes in a digital world. The increasing adoption of this paradigm by the major industrial equipment vendors to simulate real-time working conditions and perform smart decision-making, established the Smart Factory Digital Twins architectures, where a set of DTs published as microservices interact with each other exchanging information by streaming technology. In this sense, a fundamental problem consists of selecting adequate computational resources to simulate the physical objects. In an Infrastructure as a Service (IaaS) cloud for DTs, the allocation focuses on distributing the jobs of the set into the virtual machine instances in a way that computational resources demand is satisfied and the cost is minimized. In this paper, we propose a set of algorithms based on heuristics, metaheuristics, and Mixed Integer Programming to find low-cost solutions. The performance of algorithms is evaluated using Amazon EC2 instances and DT jobs with randomly generated bandwidth, memory, and processor requirements. The experimental results show that the proposed approaches based on bin packing, genetic algorithms, partition, filtering, set coverage, and branch and bound strategies present a competitive performance in the workload allocation of the computational set of jobs of a DT into an IaaS cloud environment. Our allocation heuristic-based techniques allow considerable cost savings in medium and large periods concerning standard approaches such as local search.
Speaker Identification Approach for the Post-pandemic Era of Internet of Things

With the rapid aggravation of COVID-19 pandemic, the organizations, industries, institutions, etc., are forced to rapidly adapt to social distancing by limiting physical contact and thereby limit the person to person contamination. The scenario of Internet of things in the post COVID era will be interesting indeed. Inorder to ensure public health, the social distancing and semi lockdown will continue in the foreseeable future, and therefore, the need of secure remote person authentication methods is being more and more critical especially in the Internet of things which is a multitude of networks consisting of a huge number of uniquely identifiable devices. As far as human_device authentication strategies are concerned, the one which needs less human involvement will be preferable in a post COVID-19 IoT scenario. Moreover, since the range of IoT devices may span from tiny sensors to complex machines, an authentication method which will be adaptable to each and every type of device will be more welcomed. Considering these facts, voice biometric authentication seems to be the most suitable one which can provide a balanced mix of security, adaptivity and convenience to such an advanced world of connectivity. Here, we introduce a lightweight text independent voice biometric method for IoT using extreme learning machines, and we perform a comparative analysis with a deep learning-based method of speaker identification using 3D convolutional neural networks. We have performed experimental study using different datasets and concluded that the extreme learning-based method is more suitable for IoT, considering the trade-off between the recognition accuracy and the training time requirements.
Android game: Education Javanese vocabulary

Abstract The research aim to develop android games for use in learning Javanese vocabulary for children age 5 (five) to 6 (six) years. Methods used in android game development use models research and development (R&amp;D) with ADDIE development model. Use 5 stages: analysis (analysis), Design (Design), Development (production), Implementation (Implementation) and Evaluation (Evaluation).The result of this development is an android game named “Sinau Basa Jawa”. Android Game contains interesting features in the form of agency or control, interactivity, feedback, curiosity challenge, identity, immersion, fantasy. With 2D graphic evolution. The results of this study were tested toward media experts, early childhood education specialists, Javanese language expert, kindergarten teacher. The quality of this media product is seen from the aspect of material and media feasibility. The result with media outcomes that are declared very feasible. Android game “Sinau Basa Jawa” very suitable applied for education Javanese language. android game can help teacher and parents teaching Javanese vocabulary to children.
Semantic Query Labeling Through Synthetic Query Generation

Searching in a domain-specific corpus of structured documents (e.g., e-commerce, media streaming services, job-seeking platforms) is often managed as a traditional retrieval task or through faceted search. Semantic Query Labeling --- the task of locating the constituent parts of a query and assigning domain-specific predefined semantic labels to each of them --- allows leveraging the structure of documents during retrieval while leaving unaltered the keyword-based query formulation. Due to both the lack of a publicly available dataset and the high cost of producing one, there have been few published works in this regard. In this paper, basing on the assumption that a corpus already contains the information the users search, we propose a method for the automatic generation of semantically labeled queries and show that a semantic tagger --- based on BERT, gazetteers-based features, and Conditional Random Fields --- trained on our synthetic queries achieves results comparable to those obtained by the same model trained on real-world data. We also provide a large dataset of manually annotated queries in the movie domain suitable for studying Semantic Query Labeling. We hope that the public availability of this dataset will stimulate future research in this area.
On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier

In classification, the use of 0-1 loss is preferable since the minimizer of 0-1 risk leads to the Bayes optimal classifier. However, due to the nonconvexity of 0-1 loss, this optimization problem is NP-hard. Therefore, many convex surrogate loss functions have been adopted. Previous works have shown that if a Bayes-risk consistent loss function is used as a surrogate, the minimizer of the empirical surrogate risk can achieve the Bayes optimal classifier as the sample size tends to infinity. Nevertheless, the comparison of convergence rates of minimizers of different empirical surrogate risks to the Bayes optimal classifier has rarely been studied. Which characterization of the surrogate loss determines its convergence rate to the Bayes optimal classifier? Can we modify the loss function to achieve a faster convergence rate? In this article, we study the convergence rates of empirical surrogate minimizers to the Bayes optimal classifier. Specifically, we introduce the notions of consistency intensity and conductivity to characterize a surrogate loss function and exploit this notion to obtain the rate of convergence from an empirical surrogate risk minimizer to the Bayes optimal classifier, enabling fair comparisons of the excess risks of different surrogate risk minimizers. The main result of this article has practical implications including: 1) showing that hinge loss (SVM) is superior to logistic loss (Logistic regression) and exponential loss (Adaboost) in the sense that its empirical minimizer converges faster to the Bayes optimal classifier and 2) guiding the design of new loss functions to speed up the convergence rate to the Bayes optimal classifier with a data-dependent loss correction method inspired by our theorems.
The Impact of India’s Cyber Security Law and Cyber Forensic On Building Techno-Centric Smartcity IoT Environment

Cyber Security and Cyber forensic infrastructure has always been a dependent component to achieve the goal of smart city, techno-centric ecosystem development. The non-existence of a Technology-Driven collaborated ecosystem of Cyber defense, cyber forensic, and cyber law has led to a situation of fragmenting the objective of techno-centric smart city evolvement. Trends of connecting humans and things in smart cities have merged everything in a single digital network and cybercriminals are getting integrated platforms to conduct cybercrimes. This indicates that there is a need for collaboration of Cybersecurity and cyber forensic infrastructure with the PPP model at the national and international levels for securing cyberspace. Different countries have adopted different approaches to deal with the exponential growth of cyber attacks depending upon the country's specific cyber law. Due to lack of Integration between India's Smart City Development Policy and Cyber Security Policy leading to a major roadblock in smart city development. Achievement of major Smart city objective of India become critical due to non-existence of collaborative approach between different cyber defense ecosystem pillars and country-specific regional cyber law. India's Cybersecurity policy "National Cyber Security Policy" 2013 is significant approach but not enough to create a robust techno-centric smart city. In this paper, we have proposed collaborative PPP model cyber law and cybersecurity technology ecosystem to enable secured cyber confident citizenship in the techno-centric smart city environment of India. This paper addressed the need for a dynamic technology-driven cybersecurity law ecosystem , "Call the police" by technology in smart city to build India's techno-centric stable smart city ecosystem.
Financial Access and Productivity Dynamics in Sub-Saharan Africa

The purpose of this study is to investigate whether enhancing financial access influences productivity in Sub-Saharan Africa. The research focuses on 25 countries in the region with data for the period 1980-2014. The adopted empirical strategy is the Generalised Method of Moments. The credit channel of financial access is considered and proxied by private domestic credit while four main total factor productivity (TFP) dynamics are adopted for the study, namely: TFP, real TFP, welfare TFP and real welfare TFP. It is apparent from the findings that enhancing financial access positively affects welfare TFP whereas the effect is not significant on TFP, real TFP and welfare TFP. Policy implications are discussed. The study complements the extant literature by engaging hitherto unemployed dynamics of TFP in Sub-Saharan Africa.
WebMythBusters: An In-depth Study of Mobile Web Experience

The quality of experience (QoE) is an important issue for users when accessing the web. Although many metrics have been designed to estimate the QoE in the desktop environment, few studies have confirmed whether the QoE metrics are valid in the mobile environment. In this paper, we ask questions regarding the validity of using desktop-based QoE metrics for the mobile web and find answers. We first classify the existing QoE metrics into several groups according to three criteria and then identify the differences between the mobile and desktop environments. Based on the analysis, we ask three research questions and develop a system, called WebMythBusters, for collecting and analyzing mobile web experiences. Through an extensive analysis of the collected user data, we find that (1) the metrics focusing on fast completion or fast initiation of the page loading process cannot estimate the actual QoE, (2) the conventional scheme of calculating visual progress is not appropriate, and (3) focusing only on the above-the-fold area is not sufficient in the mobile environment. The findings indicate that QoE metrics designed for the desktop environment are not necessarily adequate for the mobile environment, and appropriate metrics should be devised to reflect the mobile web experience.
Predicting User Demography and Device from News Comments

Demographics of online users such as age and gender play an important role in personalized web applications, particularly in the News domain. However, it is difficult to directly obtain the demographic information of online users. Past works have attempted to predict user demography based on reading patterns obtained from news browsing data. However, such data can be very limited. Luckily, in recent years, posts and comments have become much prevalent among online users, and the comments from users of different demographics exhibit differences in contents and writing styles. Thus, comments can provide additional clues for demographic prediction. In this paper, we study predicting users' demographics based on both news browsing data and the associated user generated comments. To this end, we make a novel use of a recently introduced BERT-based model to embed each comment in the context of its associated article. We experiment on real-world datasets, and explore the contribution of both browsing data and user generated data in the task of predicting three different user attributes: gender, location type (e.g., rural vs. urban), and mobile device. Finally we show that our approach can effectively improve the performance of such predictions and outperforms baseline methods.
Web News Data Extraction Technology Based on Text Keywords

In order to shorten the time for users to query news on the Internet, this paper studies and designs a network news data extraction technology, which can obtain the main news information through the extraction of news text keywords. Firstly, the TF-IDF keyword extraction algorithm, TextRank keyword extraction algorithm, and LDA keyword extraction algorithm are analyzed to understand the keyword extraction process, and the TF-IDF algorithm is optimized by Zipf’s law. By introducing the idea of model fusion, five schemes based on waterfall fusion and parallel combination fusion are designed, and the effects of the five schemes are verified by experiments. It is found that the designed extraction technology has a good effect on network news data extraction. News keyword extraction has a great application prospect, which can provide the basis for the research fields of news key phrases, news abstracts, and so on.
Complex Problem Solving Discussion and its Performance Indicators

A complex problem has no clear definition of goals or solutions. Many social challenges fall within the range of complex problems. This research aims to formulate cognition-oriented guidelines for conducting productive CPS discussions. In our previous work, we developed a method to extract the cognitive process of complex problem-solving discussions. In this study, we proposed two indicators, “source-outcome retrieval ratio” and “change in the count of overlapped solution utterances” for the performance evaluation of CPS discussions.
Tiga Dihaji Dam Access Route Plan Alternative using Geographic Information System (Cost Distance Method)

Abstract The Tiga Dihaji dam access route function is destined for heavy equipment during the dam construction period. It is located on Tiga Dihaji sub-district, Ogan Komering Ulu Selatan district, South Sumatera, Indonesia. The Tiga Dihaji access route supervisor stated that the planning justification was done using expert judgement based on contour map and local land cover. This method is considered less measurable. Therefore, the Geographic Information System method is offered in this paper to determine the most suitable Tiga Dihaji dam access route. The parameters considered are landslide vulnerability, land cover and construction factors. These parameters are overlaid based on weights determined by the Public Works Department then validated by expert judgement. The new route is proposed using the cost distance method. Based on the analysis, it can be concluded that the cost distance method is more measurable than the planned method, but this method produces a less suitable route in terms of civil engineering.