Reviewerly/exHarmony huggingface dataset:
ADTCD: An Adaptive Anomaly Detection Approach Toward Concept Drift in IoT

The data collected by sensors is streaming data in the Internet of Things (IoT). Although existing deep-learning-based anomaly detection methods generally perform well on static data, they struggle to respond timely to streaming data after distribution changes. However, streaming data suffers from conceptual drift due to the highly dynamic nature of IoT. In network security, concept drift-oriented anomaly detection is a crucial task, because it can adjust the model to adapt to the latest data, and detect attacks in time. Existing streaming anomaly detection methods are confronted with some challenges, including the latency of model updates, the uneven importance of new data, and the self-poisoning due to model self-updates. To tackle the above challenges, we propose a knowledge distillation-based adaptive anomaly detection model toward concept drift, ADTCD. ADTCD transfers the knowledge of the teacher model to the student model and only updates the student model to reduce the delay. We construct an algorithm of dynamically adjusting model parameters, which dynamically adjusts model weights through local inference on new samples, in order to improve the model’s responsiveness to new distribution data, meanwhile solving the problem of uneven importance of new data. In addition, we adopt a one-class support vector-based outlier removal method to tackle the self-poisoning problem. In comprehensive experiments on seven high-dimensional data sets, ADTCD achieves an AUC improvement of 12.46% compared to the state-of-the-art streaming anomaly detection methods. Our future direction will focus on exploring the concept-drift problem using methods beyond autoencoders.
Securing AES Designs Against Power Analysis Attacks: A Survey

With the advent of Internet of Things (IoT), the call for hardware security has been seriously demanding due to the risks of side-channel attacks from adversaries. Advanced encryption standard (AES) is the de facto security standard for such applications and needs to ensure a low power, low area, and moderate throughput design apart from providing high security to these devices. Substitution-box (S-box), being the core component of AES, has always drawn the attention of the cryptographic community. A chronological development of the S-box over a period of 20 years since the inception of AES is presented. This article provides the first comprehensive review of the state-of-the-art S-box design techniques, identifying current advancements and analyzing their impact on gate count, area, maximum frequency of operation, throughput, and power. The other goal of the survey is to study the countermeasures designed for AES to protect it against side-channel attacks. In particular, we consider the power analysis attacks (PAAs), and the countermeasures are investigated in terms of their security metrics and design overheads, such as area, power, and performance. The countermeasures are based on hiding or masking approaches depending on their design principle. Similar to the S-box survey, a chronological development of the countermeasures since the discovery of PAAs in 1999, is presented. Finally, we suggest some open research gaps and possible direction of research in terms of S-box and countermeasure designs.
Measuring Linkability of Protected Biometric Templates Using Maximal Leakage

As the applications of biometric recognition systems are increasing rapidly, there is a growing need to secure the sensitive data used within these systems. Considering privacy challenges in such systems, different biometric template protection (BTP) schemes were proposed in the literature, and the ISO/IEC 24745 standard defined a number of requirements for protecting biometric templates. While there are several studies on evaluating different requirements of the ISO/IEC 24745 standard, there have been few studies on how to measure the linkability of biometric templates. In this paper, we propose a new method for measuring linkability of protected biometric templates. The proposed method is based on maximal leakage, which is a well-studied measure in information-theoretic literature. We show that the resulting linkability measure has a number of important theoretical properties and an operational interpretation in terms of statistical hypothesis testing. We compare the proposed measure to two other linkability measures: one previously introduced in the literature, and a similar measure based on differential privacy. In our experiments, we use the proposed measure to evaluate the linkability of biometric templates from different biometric characteristics (face, voice, and finger vein), which are protected with different BTP schemes. The source codes of our proposed measure and all experiments are publicly available.
State of the Art of Mobile Learning in Jordanian Higher Education: An Empirical Study

A new approach to learning is mobile learning (m-learning), which makes use of special features of mobile devices in the education sector. M-learning is becoming increasingly common in higher education institutions all around the world. The use of mobile devices for education and learning has also gained popularity in Jordan. Unlike studies about Jordan, there are many studies that thoroughly analyze the situation of m-learning in other countries. Thus, it is important to understand the current situation of m-learning at Jordanian universities, especially in light of the COVID-19 pandemic. While there have been some studies conducted prior to COVID-19 and a few studies after COVID-19, there is a need for a comprehensive study that provides an in-depth exploration of the current situation, student adoption, benefits, disadvantages, and challenges, particularly following COVID-19. Therefore, this study utilizes a sequential exploratory mixed research method to investigate the current state of the art of m-learning in Jordanian higher education with a particular focus on student adoption, benefits, disadvantages, and challenges. Firstly, the study explores the existing literature on m-learning and conducts 15 interviews with educators and learners in three Jordanian universities to gain insights into their experiences with m-learning. The study then distributes a survey to students at four Jordanian universities, representing both public and private universities, to generalize the results from the qualitative study. Additionally, the study investigates the relationship between student enrollment in public/private universities and the adoption of m-learning. The study came to the conclusion that students have a positive opinion of m-learning and are also willing to use it. However, there are a number of disadvantages and challenges to its adoption. Additionally, there is a relationship between student enrolment in public/private universities and the adoption of m-learning. These findings have important implications for institutions that want to incorporate m-learning into their undergraduate and graduate degree programs, as they aid decision-makers in these universities in creating frameworks that may be able to meet the needs of m-learning.
Real-Time Instance Segmentation Models for Identification of Vehicle Parts

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="normal">A</mi> <msup> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi mathvariant="normal">I</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">U</mi> <mo>=</mo> <mo>.</mo> <mn>50</mn> </mrow> </msup> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="normal">A</mi> <msup> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi mathvariant="normal">I</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">U</mi> <mo>=</mo> <mo>.</mo> <mn>75</mn> </mrow> </msup> </math> reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi mathvariant="normal">A</mi> <msup> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msup> </math> with 44.0 and 2.6 for object detection and segmentation categories, respectively.
A novel abnormal data detection method based on dynamic adaptive local outlier factor for the vibration signals of rotating parts

Abstract Abnormal signals are inevitable in big data acquired from harsh industrial environments. Abnormal data detection is a crucial component of condition monitoring for rotating parts and is also the premise of data cleaning, compensation, and mining. To detect abnormal data segments of rolling bearings, this paper proposes a dynamic adaptive local outlier factor (DALOF) anomaly detection method. First, a data dynamic segmentation method based on sliding windows is designed to determine samples with variable lengths. Then, a time-domain feature extraction and fusion method based on principal component analysis is exploited to reduce the feature space discrepancy. To improve the accuracy of abnormal data detection, a data quality evaluation model is established to assess each data segment using DALOF. The validity of the proposed method is also verified by analyzing signals including missing data, random interference data, and drift data. Several other methods are respectively applied to identify these abnormal data to further demonstrate the benefits of the developed methodology.
Modern trends of professional training of specialists in the conditions of digital transformation of science and education

The article examines the current trends of professional training of specialists in the conditions of digital transformation of science and education. Integration of information technologies into almost all spheres of life support of citizens, digitalization of various types of professional activities conceptually changes not only the previously existing formats of relations, but also directly affects the culture and forms of professional and interpersonal interaction between people, and also provides a certain level of information socialization. Taking into account the uniqueness of the possibilities of information technologies in science and education allows us to talk about a qualitative change in the socio-economic life of society, rethinking scientific and educational activities, effective communication between subjects of digital transformation at various levels. Priority and significant today are such trends in the context of professional training of specialists as: continuing education of specialists studying, taking into account the use of various information resources; collective form of interaction in teaching, with a focus on solving certain applied tasks, focusing on obtaining specific practical knowledge, skills and abilities; implementation of a differentiated approach to building pedagogical interaction between the teacher and students, individualization of learning; use of distance and mixed learning formats; improvement of educational platforms in educational practice; the use of artificial intelligence technologies in online education, virtual and augmented reality, gamification of the educational process; the construction and use of digital twins in education; the acquisition of knowledge, skills, skills, competencies in the framework of mentoring, with the participation of a professional (teacher-mentor, teacher-practitioner, practicing consultant, expert); the development of educational programs, corresponding to the priority directions of development of society and economy. The use of new educational practices in the framework of professional training, based both on the use of information technologies and on traditional approaches of pedagogical interaction, allows not only to realize learning goals, but also to be involved in active social life and professional activities.
VR-Enhanced Cognitive Learning: Method, Framework, and Application

Both constructivist learning and situation-cognitive learning believe that learning outcomes are significantly affected by the context or learning environments. However, since 2019, the world has been ravaged by COVID-19. Under the threat of the virus, many offline activities, such as some practical or engineering courses, have been subjected to certain restrictions. Virtual Reality (VR) is an emerging, promising, and rapidly developing technology that enables users to obtain a near-real immersion experience by combining technologies such as computer science, communication, vision, etc. In the context of COVID-19, the advantages of VR immersive experiences are highlighted. By constructing a virtual learning environment, VR technology can greatly compensate for the shortage of traditional teaching conditions and help learners to carry out cognitive learning better. However, currently, VR-enhanced cognitive learning is still in its infancy, along with numerous problems and limitations. Therefore, this paper first conducted an in-depth study of some related concepts, such as constructivist learning and situated cognition learning. Then it proposes a general VR-enhanced cognitive learning framework and designs the general steps for constructing learning situations with VR technology. Based on the proposed model and framework, it developed a campus knowledge-learning APP using VR tools. Through a case study, it verified the validity and performance of the model and strategies. Questionnaire survey and experimental results show that the new model achieves a good learning effect and improves the efficiency of learning by at least 20% compared to the traditional learning methods.
Weather Radar Super-Resolution Reconstruction Based on Residual Attention Back-Projection Network

Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead to the loss of some valid information by using image processing methods for super-resolution reconstruction. To solve this problem, a weather radar that echoes the super-resolution reconstruction algorithm—based on residual attention back-projection network (RABPN)—is proposed to improve the the radar base data resolution. RABPN consists of multiple Residual Attention Groups (RAGs) connected with long skip connections to form a deep network; each RAG is composed of some residual attention blocks (RABs) connected with short skip connections. The residual attention block mined the mutual relationship between low-resolution radar echoes and high-resolution radar echoes by adding a channel attention mechanism to the deep back-projection network (DBPN). Experimental results demonstrate that RABPN outperforms the algorithms compared in this paper in visual evaluation aspects and quantitative analysis, allowing a more refined radar echo structure, especially in terms of echo details and edge structure features.
Multi-Label Learning in Computer Networks

An important problem of intelligent data processing of system logs is the existence of data sets containing records with multiple associations of class labels. Research objective: To formalize the problem of multi-label classification of experimental data (binary or multi-class) on the example of computer networks (CN) log records. Novelty: consists in illustrating the presence of multi-label class labels in the analysis of system log records generated by the CN. Results: It is shown that the problem of multivaluedness of system log class labels is relevant for the analysis of accessibility and integrity of information circulating in the CN.
Following the Leader in Multiplayer Tabletop Games

In a two-player zero-sum game, players classically want to maximise their chance of winning. When a game has more than two players, using the binary win rate as an objective is no longer such an obvious choice. A player might instead have the objective of doing as well as possible in terms of ranked order, or in maximising their score. We investigate the impact of different game-agnostic objectives in several popular tabletop games, and whether it can be better to use the game score as a proxy for winning. We find that the games considered largely fall into two groups. In one it is helpful to focus just on one's own score during the game, and then shift to beating opponents only in the end-game. In the other, larger, group it is better to 'Follow the Leader' and constantly track one's relative position to the opponents throughout the game.
“That Darned Sandstorm”: A Study of Procedural Generation through Archaeological Storytelling

Procedural content generation has been applied to many domains, especially level design, but the narrative affordances of generated game environments are comparatively understudied. In this paper we present our first attempt to study these effects through the lens of what we call a generative archaeology game that prompts the player to archaeologically interpret the generated content of the game world. We report on a survey that gathered qualitative and quantitative data on the experiences of 187 participants playing the game Nothing Beside Remains. We provide some preliminary analysis of our intentional attempt to prompt player interpretation, and the unintentional effects of a glitch on the player experience of the game.
Automatic modulation classification using bimodal parallel multichannel deep learning framework for spatial multiplexing MIMO system

In recent years, automatic modulation classification (AMC) has proved its importance for the military as well as civil applications and deep learning (DL) based AMC has attracted wide attention. But existing methods neglect to consider the advantages of both multimodality and complementarities simultaneously in a single DL framework for multiple-input multiple-output (MIMO) system. To mitigate this, bimodal multichannel configurable DL-based AMC has been presented for the MIMO system under perfect channel state information with zero forcing equalizer. The proposed DL framework consists of two parallel structures of multichannel convolutional layers in which one multichannel structure is fed with in-phase/quadrature (I/Q) as first modal information while another multichannel structure accepts amplitude/phase as second modal information. Features extracted from this parallel structure then pass through long short-term memory (LSTM) layers for further extracting temporal information effectively. Finally, classification is accomplished through fully connected layers. Simulation results manifest the robustness of the proposed framework that achieves an average accuracy of about 0.6% to 12% higher compared to the state-of-the-art DL models. Simulations also illustrate the impact of antenna diversities with spatial multiplexing on the classification.
A taxonomy of assets for the development of software-intensive products and services

Developing software-intensive products or services usually involves a plethora of software artefacts. Assets are artefacts intended to be used more than once and have value for organisations; examples include test cases, code, requirements, and documentation. During the development process, assets might degrade, affecting the effectiveness and efficiency of the development process. Therefore, assets are an investment that requires continuous management. Identifying assets is the first step for their effective management. However, there is a lack of awareness of what assets and types of assets are common in software-developing organisations. Most types of assets are understudied, and their state of quality and how they degrade over time have not been well-understood. We performed an analysis of secondary literature and a field study at five companies to investigate and identify assets to fill the gap in research. The results were analysed qualitatively and summarised in a taxonomy. We present the first comprehensive, structured, yet extendable taxonomy of assets, containing 57 types of assets. The taxonomy serves as a foundation for identifying assets that are relevant for an organisation and enables the study of asset management and asset degradation concepts.
A Logical Modeling of Severe Ignorance

Abstract In the logical context, ignorance is traditionally defined recurring to epistemic logic. In particular, ignorance is essentially interpreted as “lack of knowledge”. This received view has - as we point out - some problems, in particular we will highlight how it does not allow to express a type of content-theoretic ignorance, i.e. an ignorance of φ that stems from an unfamiliarity with its meaning. Contrarily to this trend, in this paper, we introduce and investigate a modal logic having a primitive epistemic operator I , modeling ignorance. Our modal logic is essentially constructed on the modal logics based on weak Kleene three-valued logic introduced by Segerberg ( Theoria, 33 (1):53–71, 1997). Such non-classical propositional basis allows to define a Kripke-style semantics with the following, very intuitive, interpretation: a formula φ is ignored by an agent if φ is neither true nor false in every world accessible to the agent. As a consequence of this choice, we obtain a type of content-theoretic notion of ignorance, which is essentially different from the traditional approach. We dub it severe ignorance . We axiomatize, prove completeness and decidability for the logic of reflexive (three-valued) Kripke frames, which we find the most suitable candidate for our novel proposal and, finally, compare our approach with the most traditional one.