Reviewerly/exHarmony huggingface dataset:
What about the Latent Space? The Need for Latent Feature Saliency Detection in Deep Time Series Classification

Saliency methods are designed to provide explainability for deep image processing models by assigning feature-wise importance scores and thus detecting informative regions in the input images. Recently, these methods have been widely adapted to the time series domain, aiming to identify important temporal regions in a time series. This paper extends our former work on identifying the systematic failure of such methods in the time series domain to produce relevant results when informative patterns are based on underlying latent information rather than temporal regions. First, we both visually and quantitatively assess the quality of explanations provided by multiple state-of-the-art saliency methods, including Integrated Gradients, Deep-Lift, Kernel SHAP, and Lime using univariate simulated time series data with temporal or latent patterns. In addition, to emphasize the severity of the latent feature saliency detection problem, we also run experiments on a real-world predictive maintenance dataset with known latent patterns. We identify Integrated Gradients, Deep-Lift, and the input-cell attention mechanism as potential candidates for refinement to yield latent saliency scores. Finally, we provide recommendations on using saliency methods for time series classification and suggest a guideline for developing latent saliency methods for time series.
Counter-terrorism in cyber–physical spaces: Best practices and technologies from the state of the art

Context: The demand for protection and security of physical spaces and urban areas increased with the escalation of terroristic attacks in recent years. We envision with the proposed cyber-physical systems and spaces, a city that would indeed become a smarter urbanistic object, proactively providing alerts and being protective against any threat. Objectives: This survey intend to provide a systematic multivocal literature survey comprised of an updated, comprehensive and timely overview of state of the art in counter-terrorism cyber-physical systems, hence aimed at the protection of cyber-physical spaces. Hence, provide guidelines to law enforcement agencies and practitioners providing a description of technologies and best practices for the protection of public spaces. Methods: We analyzed 112 papers collected from different online sources, both from the academic field and from websites and blogs ranging from 2004 till mid-2022. Results: a) There is no one single bullet-proof solution available for the protection of public spaces. b) From our analysis we found three major active fields for the protection of public spaces: Information Technologies, Architectural approaches, Organizational field. c) While the academic suggest best practices and methodologies for the protection of urban areas, the market did not provide any type of implementation of such suggested approaches, which shows a lack of fertilization between academia and industry. Conclusion: The overall analysis has led us to state that there is no one single solution available, conversely, multiple methods and techniques can be put in place to guarantee safety and security in public spaces. The techniques range from architectural design to rethink the design of public spaces keeping security into account in continuity, to emerging technologies such as AI and predictive surveillance.
Modelling and optimization of nitrates adsorbed on iron plate electrodes by electrocoagulation process analyzed by response surface methodology

The goal of this study is to remove nitrate from aqueous solutions using iron plate electrode as anode and cathode by electrocoagulation process. Nitrate has a moderate capacity for precipitation or adsorption and is a stable, highly soluble ion. As a function of pH, Electrocoagulation time, NaCl concentration, temperature, and voltage, the tests are run. The findings indicate that the highest removal efficiency of 87.52% was attained at a pH of 11.0, Electrocoagulation time of 80 min, NaCl concentration of 2 g/L, voltage of 30 V and 303 K temperature. The monolayer coverage of the adsorbed molecules is suggested by the adsorption of NO3–, which ideally fits the Langmuir adsorption isotherm. First-order kinetics model that describes the adsorption process. The reaction is endothermic and spontaneous, according to thermodynamic studies. Using the experimental data, the predicted data is been analyzed by Response surface methodology.
HCURec: Hierarchical candidate-aware user modeling for news recommendation

Matching candidate news with user interests is critical for news recommendation. Current studies on news recommendation mainly model a single user interest embedding from the user’s clicked news. One of the major challenges that affects the recommendation is to match candidate news with the user’s multi-field and multi-grained interests. To confront this difficulty, we investigate the multi-grained correlation of user interests and candidate news to obtain their matching features. We propose a hierarchical candidate-aware user modeling framework for news recommendation that matches users’ multi-field and multi-grained interests with candidate news. The framework incorporates candidate news into the modeling of user interests at different levels, namely subcategory-level, category-level and global-level, which learn fine-grained, coarse-grained and overall user interests, respectively. The user interest is finally hierarchically matched at different levels with candidate news to achieve accurate targeting. A collection of experiments were carried out on four real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the existing state-of-the-art methods owing to its highly effective and efficient performance of news recommendation.
Dataset agnostic document object detection

Localizing document objects such as tables, figures, and equations is a primary step for extracting information from document images. We propose a novel end-to-end trainable deep network, termed Document Object Localization Network (dolnet), for detecting various objects present in the document images. The proposed network is a multi-stage extension of Mask r-cnn with a dual backbone having deformable convolution for detecting document objects with high detection accuracy at a higher IoU threshold. We also empirically evaluate the proposed dolnet on the publicly available benchmark datasets. The proposed DOLNet achieves state-of-the-art performance for most of the bench-mark datasets under various existing experimental environments. Our solution has three important properties: (i) a single trained model dolnet‡ that performs well across all the popular benchmark datasets, (ii) reports excellent performances across multiple, including with higher IoU thresholds, and (iii) consistently demonstrate the superior quantitative performance by following the same protocol of the recent works for each of the benchmarks.
Quality evaluation of preschool video games based on RNN

With the rapid development of video multimedia technology, video games have started to be applied in the field of education. A video game quality evaluation method needs to be designed to help teaching staff select video materials. In view of this, a Recurrent Neural Network (RNN)-based video game quality evaluation method for preschool education is proposed. Firstly, an improved attention mechanism null domain weight assignment method is proposed for the video quality evaluation problem. Then a video game quality evaluation model based on RNN is proposed. Finally, performance testing is conducted on the proposed quality evaluation method. The results show that in the comparison of fitness values, when the number of iterations is 4 or 16, the maximum fitness value and the average fitness value of the research method are 97 and 96 respectively. In the prediction of different video datasets, the research methods can achieve prediction accuracy of 0.958 and 0.947 for LIVE and CSIQ videos respectively. In the analysis of practical application effects, all students show significant improvement in language reading, writing, understanding, and spelling. The above results indicate that the research method has a faster convergence speed and prediction accuracy. It can effectively improve students' learning efficiency and provide new references for the education and teaching evaluation of preschool children.
Constructing a Meta-Learner for Unsupervised Anomaly Detection

Unsupervised anomaly detection (AD) is critical for a wide range of practical applications, from network security to health and medical tools. Due to the diversity of problems, no single algorithm has been found to be superior for all AD tasks. Choosing an algorithm, otherwise known as the Algorithm Selection Problem (ASP), has been extensively examined in supervised classification problems, through the use of meta-learning and AutoML, however, it has received little attention in unsupervised AD tasks. This research proposes a new meta-learning approach that identifies an appropriate unsupervised AD algorithm given a set of meta-features generated from the unlabelled input dataset. The performance of the proposed meta-learner is superior to the current state of the art solution. In addition, a mixed model statistical analysis has been conducted to examine the impact of the meta-learner components: the meta-model, meta-features, and the base set of AD algorithms, on the overall performance of the meta-learner. The analysis was conducted using more than 10,000 datasets, which is significantly larger than previous studies. Results indicate that a relatively small number of meta-features can be used to identify an appropriate AD algorithm, but the choice of a meta-model in the meta-learner has a considerable impact.
Character recognition competition for street view shop signs

This paper presents the inaugural character recognition competition for street view shop signs, including the associated tasks, datasets, participating teams, the winning team's solution, and justification for the award.
A scoping review and a taxonomy of the use of motion-based technology centered on the end user. A special focus on elderly health

Abstract Motion-based technology (MBT) has been applied in the last decades with enormous success in a high number of applications. Its use continues growing and is specially interesting in the health area. Nowadays, its employment is being more and more specialised with respect to the profile of the end user (i.e., child, adolescent/teenager, adult or elderly). This paper first reviews the use of MBT centered in the end user from a global perspective. It also proposes a taxonomy that allows cataloguing the MBT employment directed to the end user. Then, from these results, the paper centers the review on the MBT application aiming to improve the health of elderly. The results highlighted in this paper can help to a better understanding of MBT, especially when it is applied thinking in elderly as the end users.
R for Data Analysis: An open-source resource for teaching and learning analytics with R

existing educational program.Each lesson contains a conceptual overview, practical examples, resources for further study, and exercises designed to reinforce understanding.
A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents

Named Entity Recognition (NER) is a key step in the creation of structured data from digitised historical documents. Traditional NER approaches deal with flat named entities, whereas entities are often nested. For example, a postal address might contain a street name and a number. This work compares three nested NER approaches, including two state-of-the-art approaches using Transformer-based architectures. We introduce a new Transformer-based approach based on joint labelling and semantic weighting of errors, evaluated on a collection of 19th-century Paris trade directories. We evaluate approaches regarding the impact of supervised fine-tuning, unsupervised pre-training with noisy texts, and variation of IOB tagging formats. Our results show that while nested NER approaches enable extracting structured data directly, they do not benefit from the extra knowledge provided during training and reach a performance similar to the base approach on flat entities. Even though all 3 approaches perform well in terms of F1-scores, joint labelling is most suitable for hierarchically structured data. Finally, our experiments reveal the superiority of the IO tagging format on such data.
Communities of Practice of the Future in Russian Universities: Fablabs, Centers for Youth Innovative Creativity, Kruzhoks

The purpose of this article is to determine the potential for constructive social change that can reach the communities of technology enthusiasts in Russian universities, and to identify possible directions for such changes. University communities of technology enthusiasts and the sites where they work are chosen as the object of research: fab labs, CMITs, circles. The basic conceptual framework of the research is the theory of fields, which asserts that society is a system of embedded social fields. Social changes in this theory are referred to as the transformation of existing social fields or the emergence of new ones. The term “practice of the future” is first introduced, and understood as a group of people united by common interests in advanced technological and social solutions sharing common activities and knowledge (sharing), but not included in a sustainable social field. The hypothesis of the present study is that communities of practice of the future have the capacity for social change and the directions of such changes can be identified through the analysis of strategies that are consistently applied in these communities. The semi-structured in-depth interviews of leaders and participants of university student technological centers are used as the material for the study. The article concludes that the two most obvious social fields in which communities of technology enthusiasts of Russian universities are capable of producing changes are the field of technological entrepreneurship and the field of educational innovation. In the first of these fields, the strategies of technology enthusiasts (initiative, project orientation, diversity and meritocracy) give advantages over players who do not employ these strategies. In the second field (educational innovation) these strategies can give the direction of changes, bringing together enthusiasts of relevant educational technologies. Social changes of the techno-optimistic type generated by the university communities of technology enthusiasts is not yet directed to a specific social field. However, examples of large-scale country-wide projects show that such a field is beginning to form. It is quite possible that modern university communities of technology enthusiasts are able to generate a social field no less powerful than their predecessors – the movement of technological kruzhoks – at the begining of the 20th century.
Do Educational Paradigms Influence Students’ Motivation to Study History?

The article highlights the features of three paradigms of education: competency-based, digital and modernization. The purpose of the study is to analyze the influence of factors of educational paradigms on the motivation of students to study history at a university. The empirical base of the study includes materials from an Internet survey of first- and second-year students of universities in Samara region (N=389) conducted in 2022. Depending on the attitude of students to the study of history at the university, three groups of respondents were identified: “supporters”, “moderate supporters” and “indifferent to the study of history at the university”. The grouping of students made it possible to analyze the differentiated influence of the factors of three types of educational paradigms on the motivation of students. The modernization paradigm has the greatest positive potential in shaping the motivation of students to study history. It, to a greater extent than other paradigms, takes into account the specifics of history as a humanitarian discipline. Within the framework of three paradigms, factors that have a negative impact on the motivation of students to study history are identified. Among them, insufficient development of independent work skills is of great importance. The practical significance of the study is to use the information obtained to improve the methods of teaching history at the university, as well as other social and humanitarian disciplines, taking into account the factors of student motivation.
Federated data health networks hold potential for accelerating emergency research

Multi-center research networks often supported by centralized data centers are integral in generating high-quality evidence needed to address the gaps in emergency care. However, there are substantial costs to maintain high-functioning data centers. A novel distributed or federated data health networks (FDHN) approach has been used recently to overcome the shortcomings of centralized data approaches. A FDHN in emergency care is comprised of a series of decentralized, interconnected emergency departments (EDs) where each site's data is structured according to a common data model that allows data to be queried and/or analyzed without the data leaving the site's institutional firewall. To best leverage FDHNs for emergency care research networks, we propose a stepwise, 2-level development and deployment process-creating a lower resource requiring Level I FDHN capable of basic analyses, or a more resource-intense Level II FDHN capable of sophisticated analyses such as distributed machine learning. Importantly, existing electronic health records-based analytical tools can be leveraged without substantial cost implications for research networks to implement a Level 1 FDHN. Fewer regulatory barriers associated with FDHN have a potential for diverse, non-network EDs to contribute to research, foster faculty development, and improve patient outcomes in emergency care.
Conversational Agents and Language Models that Learn from Human Dialogues to Support Design Thinking

Design is a highly creative and challenging task and research has already explored possible ways for using conversational agents (CAs) to support humans participating in co-design sessions. However, research reports that a) humans in these sessions expect more essential support from CAs, and b) it is important to develop CAs that continually learn from communication -like humans do- and not simply from labeled datasets. Addressing the above needs, this paper explores the specific question of how to extract useful knowledge from human dialogues observed during co-design sessions and make this knowledge available through a CA supporting humans in similar design activities. In our approach we explore the potential of the GPT-3 Large Language Model (LLM) to provide useful output extracted from unstructured data such as free dialogues. We provide evidence that by implementing an appropriate "extraction task" on the LLM it is possible to efficiently (and without human-in-the-loop) extract knowledge that can then be embedded in the cognitive base of a CA. We identify at least four major steps/assumptions in this process that need to be further researched, namely: A1) Knowledge modeling, A2) Extraction task, A3) LLM-based facilitation, and A4) Humans' benefit. We provide demonstrations of the extraction and facilitation steps using the GPT-3 model and we also identify and comment on various worth exploring open research questions.
Distraction Detection and Monitoring Using Eye Tracking in Virtual Reality

Effective learning is highly affected by attention levels. Hence, Intelligent Tutoring Systems and other technologies for learning should be able to monitor the attention levels of learners and detect distractions in real-time to improve the learning process. We study the feasibility of detecting and monitoring visual distraction of participants, while they complete cognitive tasks, using Eye Tracking in a Virtual Reality environment. We also investigate the possibility of improving the attention of participants using relaxation in Virtual Reality. The Eye Tracking distraction model we developed correctly predicts the distraction state of participants with an F1-score of 86%. We also found that the most appropriate window size to detect distraction ranges from three to six seconds. Furthermore, results suggest that our relaxation method significantly decreased the visual distraction of the participants.
Learning by Building Chatbot: A System Usability Study and Teachers’ Views About the Educational Uses of Chatbots

This article describes an experiment to investigate the usability of the “Learning by Building Chatbot” environment and the views of in-service teachers about the potential educational uses of chatbots. Chatbots are text or voice-based conversational interfaces that use natural language to simulate human conversations serving as virtual assistants to users. They have been used in a variety of areas such as e-commerce and healthcare while their use in education is relatively new. Chatbots have the potential to offer new dynamic forms of interactions, but there is often a burden to users to deploy and maintain their own chatbots. Our research group has proposed a web-based environment, “Learning by Building Chatbot”, that alleviates this burden by offering educators and students without any programming knowledge the opportunity to deploy and maintain their own chatbots for learning and training. The environment is a block-based, visual editing environment of creating RiveScript-powered chatbots for learning and training, without needing to know any RiveScript. Twelve in-service teachers registered in a postgraduate program in technology-enhanced learning used this environment to create their own chatbots. Afterwards, they completed the System Usability Scale (SUS) questionnaire. Results have shown that the visualized editing environment for building chatbot is marginally acceptable and fair to use with the SUS score 57.50. Participants found the system ease to use and felt quite confident to use it. Its functions are well integrated while there are a few inconsistencies in the current version. Moreover, participants self-reported their views about the educational uses of chatbots. They agreed that chatbots, if used appropriately, can be a valuable educational tool because they can automate administrative and teaching tasks and can be supportive and engaging for students. However, since chatbot development is challenging, teacher should be well supported to integrate chatbots in the educational practice if they intend to do so.