Reviewerly/exHarmony huggingface dataset:
Editorial Deep Learning for Anomaly Detection

A nomaly detection aims at identifying data points which are rare or significantly different from the majority of data points. Many techniques are explored to build highly efficient and effective anomaly detection systems, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions or extract good feature representations. Deep-learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, including anomaly detection. To address this new trend, we organized this Special Issue on Deep Learning for Anomaly Detection to cover the latest advancements of developing deep-learning techniques specially designed for anomaly detection. This editorial note provides an overview of the paper submissions to the Special Issue, and briefly introduces each of the accepted articles.
Ensuring determinism in blockchain software with GoLiSA: an industrial experience report

Ensuring determinism is mandatory when writing blockchain software. When determinism is not met it can lead to serious implications in the blockchain network while compromising the software development, release, and patching processes. In the industrial context, it is widespread to adopt general-purpose languages, such as Go, for developing blockchain solutions. However, it is not surprising that non-deterministic behaviors may arise, being these programming languages not originally designed for blockchain purposes. In this paper, we present an experience report on ensuring determinism in blockchain software with GoLiSA, a static analyzer based on abstract interpretation for Go applications, in an industrial context. In particular, we ran GoLiSA on Commercio.network, a blockchain-based solution for exchanging electronic documents in a legally binding way. Thanks to GoLiSA, non-trivial bugs got detected and the analysis performed made it possible to identify the critical points where to apply the fixes.
An Application of English Reading Mobile Teaching Model Based on K-Means Algorithm

The rapid development and maturity of emerging technologies such as mobile Internet and artificial intelligence have had a subversive impact on all aspects of teaching, and mobile learning has become the normal learning of students. Educational technology has developed from traditional technology and ordinary media technology to multimedia information network technology. Personal computers and wired networks have been more and more widely used. Wireless communication technology and mobile Internet technology of mobile computing devices will be popularized, and the trend from digital learning to mobility will be more and more obvious. This study uses the k-means algorithm to analyze the English reading performance, finds out the factors affecting the test performance, provides guidance for improving the pass rate of CET-4, and focuses on the rise of mobile learning under this background and its specific application in English teaching. The experimental results show that 39 students (55%) believe that mobile reading learning provides rich and real reading materials, can obtain real and authentic language input, and increase their interest in English reading. 31 students (44%) believed that the learning model was helpful to enhance autonomous learning ability. Therefore, one of the biggest advantages of this clustering algorithm is that it has high scalability and efficiency in the processing of large data sets. The mobile teaching model of English reading based on the k-means algorithm can more effectively promote the interaction of English reading teaching and improve students’ English text reading comprehension ability than the traditional teaching model. In addition, it can promote students’ active learning and cultivate students’ awareness of autonomous learning and cooperation.
Kernel Matrix-Based Heuristic Multiple Kernel Learning

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods.
Residual Language Model for End-to-end Speech Recognition

End-to-end automatic speech recognition suffers from adaptation to unknown target domain speech despite being trained with a large amount of paired audio--text data. Recent studies estimate a linguistic bias of the model as the internal language model (LM). To effectively adapt to the target domain, the internal LM is subtracted from the posterior during inference and fused with an external target-domain LM. However, this fusion complicates the inference and the estimation of the internal LM may not always be accurate. In this paper, we propose a simple external LM fusion method for domain adaptation, which considers the internal LM estimation in its training. We directly model the residual factor of the external and internal LMs, namely the residual LM. To stably train the residual LM, we propose smoothing the estimated internal LM and optimizing it with a combination of cross-entropy and mean-squared-error losses, which consider the statistical behaviors of the internal LM in the target domain data. We experimentally confirmed that the proposed residual LM performs better than the internal LM estimation in most of the cross-domain and intra-domain scenarios.
Multi-frame Scheduling for Federated Learning over Energy-Efficient 6G Wireless Networks

It is envisioned that data-driven distributed learning approaches such as federated learning (FL) will be a key enabler for 6G wireless networks. However, the deployment of FL over wireless networks suffers from considerable energy consumption on communications and computation, which is challenging to meet stringent energy-efficiency goals of future sustainable 6G networks. In this paper, we investigate the energy consumption of transmitting scheduling decisions for FL deployed over a wireless network where mobile devices upload their local model to a coordinator (6G base station) for computing a global machine learning (ML) model iteratively. We consider that the coordinators have stringent energy efficiency goals. Therefore, to reduce the energy consumption due to the deployment of FL, we propose a novel multi-frame framework for FL that enables the coordinator to schedule wireless devices in one global round by only sending scheduling decisions at the beginning of each global round and setting the coordinator’s transmission module to sleep mode to save power. In particular, we formulate a mixed-integer non-linear problem (MINLP) to minimize the average collection time of all device’s local models by considering transmission errors. Then, we provide a novel method to solve the MINLP approximately and schedule wireless devices and allocate network resources. We demonstrate that our framework can save about 15 to 20 percent in some specific settings. Simulation results also show that our proposed algorithm outperforms traditional resource allocation methods and saves about 10% battery life per hundred global rounds in mobile device coordinators under certain scenarios.
Predicting Phishing Victimization

Phishing is a common vector for cybercrime and hacking. This research examines participants' personality styles (e.g. decision-making styles, self-control) and the likelihood of falling victim to phishing attacks. Over 300 participants completed an online survey assessing protective and vulnerable strategies, personality styles, trust in people, prior victimization from catphishing or identity theft, and demographics information. Unbeknownst to the participants, 2 to 4 weeks after completing the survey they received a phishing e-mail asking them to click on a link. Individuals with a stronger systematic decision-making style were more likely to have a greater number of protective strategies, and those with greater protective strategies were less likely to be a victim of catphishing and identity theft. Individuals with low avoidant decision-making styles and prior vulnerable strategies were more likely to be phished. These findings suggest that learning protective strategies and not using vulnerable strategies are insufficient to lower substantially the risk of being phished. Training might be improved through considering the match between decision-making styles and the content of the training.
Deploying Serious Games for Cognitive Rehabilitation

The telerehabilitation of patients with neurological lesions has recently assumed significant importance due to the COVID-19 pandemic, which has reduced the possibility of access to healthcare facilities by patients. Therefore, the possibility of exercise for these patients safely in their own homes has emerged as an essential need. Our efforts aim to provide an easy-to-implement and open-source methodology that provides doctors with a set of simple, low-cost tools to create and manage patient-adapted virtual reality telerehabilitation batteries of exercises. This is particularly important because many studies show that immediate action and appropriate, specific rehabilitation can guarantee satisfactory results. Appropriate therapy is based on crucial factors, such as the frequency, intensity, and specificity of the exercises. Our work’s most evident result is the definition of a methodology that allows the development of rehabilitation exercises with a limited effect in both economic and implementation terms, using software tools accessible to all.
Quality as a drive-up digital teaching: Analysis of virtual classes in Colombian business schools

The quarantines caused by the Covid-19, generated that thousands of university students had to abruptly abandon their classroom classes, and receive them in a virtual way. This sudden change caused a break in the daily life of thousands of university students, who were forced to use a teaching method that, although not new, was unusual at that time. This, initially generated a situation of rejection, stress and a perception of low quality classes that were received in a virtual way. However, with the passage of time, there was a need to know the perception of students about the quality of their virtual classes, but once the mandatory quarantine ended, because the literature has been focused only during the quarantine period. On the other hand, most studies on this topic have focused on developed countries, so there is little literature on the perception of virtual classes, taken by students from South America, whose countries have economic, social and technological characteristics, very different from developed countries. Based on the above, this study analyzes the perceptions of 867 students from 12 private business schools in the city of Bogota, Colombia. The data processed through PLS, allows us to observe that students have changed their negative perception towards virtual classes, and that, at present, these classes are considered of quality. Similarly, we can see that satisfaction, the willingness to continue using this methodology in the future, and the good performance of the university brand, are direct results of the quality of virtual classes.
Evaluation of clustering techniques on Urdu News head-lines: a case of short length text

Document clustering and topic modelling methods are useful in determining groups of semantically similar documents. In literature, various studies have been conducted to evaluate the performance of lexical and semantic features on a clustering task. However, the focus of such studies was on the English language which is considered a high resource language. This paper analyses the effect of various feature extraction techniques combined with clustering and topic modelling algorithms on grouping news headlines written in Urdu, a low-resource language. Various features were first extracted using different approaches such as term-frequency inverse document frequency (TF-IDF), word embeddings learned using Word2vec and fastText models, deep contextualised word embeddings from BERT, and document embeddings extracted from the Doc2Vec model. Three different clustering algorithms: K-Means, Affinity Propagation and Density-based spatial clustering of applications with noise (DBSCAN) were then used to produce clusters for a given set of news headlines. The Latent Dirichlet Allocation topic model was also assessed for comparison with clustering algorithms. The evaluation based on extrinsic measures revealed that higher-quality clusters were produced when the K-Means algorithm was applied on a frequent unigrams-based TF-IDF feature matrix. In addition, several word embedding models have been trained on the domain of Urdu News headlines using state-of-the-art techniques such as Word2Vec, Doc2Vec, and fastText.
65‐4: Foveated Super Resolution Network for Virtual Reality Head‐Mounted Displays

In this paper, we propose a foveated super resolution (SR) algorithm to create high resolution images from low resolution inputs for virtual reality head mounted displays. The proposed SR algorithm integrated in the foveation‐based driving technology covers the small area around the foveation point that requires high rendering quality, substantially reducing the overall computational complexity. We employ cropping as well as progressive and recursive framework and reduce the number of neurons by placing the deconvolutional layer after convolutional layers.
vMFNet: Compositionality Meets Domain-Generalised Segmentation

Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from medical (e.g. MRI and CT) images with some limited guidance. Such recognition ability can easily generalise to new images from different clinical centres. This rapid and generalisable learning ability is mostly due to the compositional structure of image patterns in the human brain, which is less incorporated in medical image segmentation. In this paper, we model the compositional components (i.e. patterns) of human anatomy as learnable von-Mises-Fisher (vMF) kernels, which are robust to images collected from different domains (e.g. clinical centres). The image features can be decomposed to (or composed by) the components with the composing operations, i.e. the vMF likelihoods. The vMF likelihoods tell how likely each anatomical part is at each position of the image. Hence, the segmentation mask can be predicted based on the vMF likelihoods. Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image. Extensive experiments show that the proposed vMFNet achieves improved generalisation performance on two benchmarks, especially when annotations are limited. Code is publicly available at: https://github.com/vios-s/vMFNet .
Assistive Robotic Technologies for Next-Generation Smart Wheelchairs: Codesign and Modularity to Improve Users’ Quality of Life

This article describes the robotic assistive technologies developed for users of electrically powered wheelchairs, within the framework of the European Union’s Interreg ADAPT (Assistive Devices for Empowering Disabled People Through Robotic Technologies) project. In particular, special attention is devoted to the integration of advanced sensing modalities and the design of new shared control algorithms. In response to the clinical needs identified by our medical partners, two novel smart wheelchairs with complementary capabilities and a virtual reality (VR)-based wheelchair simulator have been developed. These systems have been validated via extensive experimental campaigns in France and the United Kingdom.
A systematic literature review of attribute based encryption in health services

The widespread adoption of cloud technology in the healthcare industry has achieved effective outcomes in sharing health records and sensitive data. In recent years, many organizations have prioritized E-Health as their primary goal for advancement in health services. As a result, Attribute Based Encryption (ABE) has emerged as a reliable model for health information exchange across cloud settings. Eventually, it leads to provide acceptable solutions for challenging scenarios like fine-grained access control. Despite ABE significance and its breadth of applications, no systematic and comprehensive survey exists in the literature that covers every variation of ABE in healthcare, highlighting its past and present status. This paper presents a systematic and comprehensive study of ABE works concerning E-Health as the authors rigorously investigate healthcare-focused ABE frameworks and examine them based on various descriptive criteria, along with categorizing them systematically in 10 distinct domains and sub-domains, ultimately offering observations and potential recommendations. The descriptive research design, significant findings along with the suggested future works will help future research in ABE to secure the existing E-Health data sharing more effectively. The present study will also facilitate researchers and practitioners to comprehend the past trends and current state of ABE architectures in secure health data-sharing scenarios, as well as the prospect of ABE deployment in most recent technological evolutions.
Analysis of the three-year work of a digital pathomorphological laboratory built from the ground

Digital pathology is a new stage in the development of pathomorphological diagnostics. This topic was most widespread during the COVID-19 pandemic. The advantages of digitization of diagnostics include the possibility of remote work of a pathologist, remote asynchronous consultation, and automation of business processes. They provide an increase in diagnostic quality and speed up the diagnosis process. These benefits are only a small part of what digital cancer diagnostics can provide. This article is written on our own experience of Russia's first fully digital pathomorphological laboratory UNIM. All advantages and disadvantages of digitization, peculiarities of using technology, differences from the conventional approach to diagnostics, the economics of the process, the importance of integration with LIS (laboratory information system) and MIS (medical information system), errors and principles of their solution, payback will be discussed, and every stage of laboratory work will be considered in detail: from logistics and registration to diagnosis and archiving. Due to the fact that all data has been digitized over several years, we will present a comprehensive analysis of statistics and observations on how to organize processes in a fully digital laboratory. A key feature of our experience is the high cost-effectiveness of the platform and approach, which allowed us to win the competition in the market. The result of the survey of doctors' attitudes towards digital pathology will also be presented.
Speaking on the record: Combining interviews with search log analysis in user research

The use of mixed methods in library and information science research is more effective in generating insightful results when the methods are fully integrated with each other. A novel sequential mixed methods approach was developed to more holistically study user search behavior within a library discovery system. Customized semi-structured interview protocols, using the critical incident technique , were based on each participant's search logs. Findings from the interviews informed statistical log analysis, which identified features of search sessions that made accessing resources more likely. Individual interviews provide more precise data when protocols are created using the participants' logs. Similarly, statistical log analyses are enhanced with users' descriptions of their behaviors in library discovery systems. While prior studies have employed both interviews and log analysis, using the methods to inform one another reduces the limitations and enhances the benefits of each. • A novel sequential mixed methods approach provides a more holistic view of user behavior in library discovery systems. • Individual interviews provide more precise data when protocols are created using participants' logs. • Statistical log analyses are enhanced with users' descriptions of their behaviors in library discovery systems.
Computer Science Education in Ireland

Our world is both physical and digital. Students would benefit from understanding how this digital world works, and how algorithms drive it [2]. Students would also benefit from learning computational thinking, and becoming creators and designers of computer systems and applications. These are invaluable skills; not just a means to ensure a skilled workforce [1]. However, diversity, gender balance, and equality are recognized globally as challenges in this field. Looking through a lens filtered on four components: diversity, inclusion, teacher education and professional development, this study will evaluate current Computer Science (CS) learning opportunities in the Irish primary and post-primary curriculum. It aims to identify the opportunities and key factors for the growth and development of CS in Ireland. This research is divided into three phases, influenced by the three components of the CAPE model [3] that underpins this study: Capacity for, Access to, and Participation in CS education. Data will be gathered using various means: focus groups (students, teachers, principals and policymakers), and student questionnaires. The analytic approach is mixed, it involves document, thematic and content analysis. Findings from this study will provide a detailed view of the current landscape relating to CS education in Ireland, particularly its diversity and inclusion. Recommendations on the equitable integration of Computational Thinking/Coding/CS education across the formal education system in Ireland will be provided. This will have implications for educational policy, initial teacher education, and second-level teaching practice in Ireland and beyond.
An Edge Detection Method Based on Local Gradient Estimation: Application to High-Temperature Metallic Droplet Images

Edge detection is a fundamental step in many computer vision systems, particularly in image segmentation and feature detection. There are a lot of algorithms for detecting edges of objects in images. This paper proposes a method based on local gradient estimation to detect metallic droplet image edges and compare the results to a contour line obtained from the active contour model of the same images, and to results from crowdsourcing to identify droplet edges at specific points. The studied images were taken at high temperatures, which makes the segmentation process particularly difficult. The comparison between the three methods shows that the proposed method is more accurate than the active contour method, especially at the point of contact between the droplet and the base. It is also shown that the reliability of the data from the crowdsourcing is as good as the edge points obtained from the local gradient estimation method.