Reviewerly/exHarmony huggingface dataset:
Language Learning Strategies Used by Art School ESL Learners

Language Learning Strategies (LLSs) are important in learning a language especially in learning second and foreign language.Many studies on LLSs have been conducted by scholars around the world that prove the significant impact of employing LLSs in acquiring a second language.In Malaysia, English is regarded as a second language and learnt formally in school as an academic subject from 7 to 17 years old.This paper aims to investigate LLSs used by ESL learners who are artistically talented in an art school.This study is a survey and the data were gathered by administering the Strategy Inventory Language Learning (SILL) questionnaire by Oxford (1990).The questionnaire was given to 77 pupils who are 16 and 17 years old.They study in an art school in Malaysia and they have different talents in arts.They are considered as artistically talented to be enrolled in art school.The data were analysed descriptively.The finding shows that the most employed LLSs by art school ESL learners are Metacognitive Strategy and the least employed LLSs are Compensation Strategy.Most ESL learners in Malaysia are very dependent on teachers and have difficulty in achieving a certain level of fluency.They are not aware of their self-ability to govern self-learning.That is why LLS is very important as it effectively improves learners' proficiency in acquiring the second language.Both teachers and pupils need to be aware of the learners' ability and preference in choosing suitable LLSs.Since most of the previous researches focus on successful and good language learner, more studies need to be carried out to help and guide less successful learners to be at the same par as a good language learner.
Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.
Using extended siamese networks to provide decision support in aquaculture operations

Abstract Aquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.
Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications.
DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing

Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.
Quantum forgery attacks on COPA, AES-COPA and marble authenticated encryption algorithms

The classic forgery attacks on COPA, AES-COPA and Marble authenticated encryption algorithms need to query about 2^(n/2) times, and their success probability is not high. To solve this problem, the corresponding quantum forgery attacks on COPA, AES-COPA and Marble authenticated encryption algorithms are presented. In the quantum forgery attacks on COPA and AES-COPA, we use Simon's algorithm to find the period of the tag generation function in COPA and AES-COPA by querying in superposition, and then generate a forged tag for a new message. In the quantum forgery attack on Marble, Simon's algorithm is used to recover the secret parameter L, and the forged tag can be computed with L. Compared with classic forgery attacks on COPA, AES-COPA and Marble, our attack can reduce the number of queries from O(2^(n/2)) to O(n) and improve success probability close to 100%.
Faculty Publication Activity and Resources for Its Increase

The article discusses the current situation in the Russian higher school system related to the publication activity of faculty, including publications in journals indexed in international citation databases Scopus and Web of Science. The author argues that the vast majority of scientific and pedagogical workers of Russian universities, especially those teaching social and economic disciplines, are unable to provide the necessary number of such publications required by the administration, due to both organizational and financial reasons. In order to ensure at least a relative balance between the interests of the faculty and their employer, it is proposed to create new structural entities in large public and private universities – centers for support of the publication activity of teachers. The author has developed a project of standard organizational structure of management for this Center, as well as an algorithm of its work and the main functions of its divisions.
Fine-Grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement

This paper presents a novel design of neural network system for fine-grained style modeling, transfer and prediction in expressive text-to-speech (TTS) synthesis. Fine-grained modeling is realized by extracting style embeddings from the mel-spectrograms of phone-level speech segments. Collaborative learning and adversarial learning strategies are applied in order to achieve effective disentanglement of content and style factors in speech and alleviate the "content leakage" problem in style modeling. The proposed system can be used for varying-content speech style transfer in the single-speaker scenario. The results of objective and subjective evaluation show that our system performs better than other fine-grained speech style transfer models, especially in the aspect of content preservation. By incorporating a style predictor, the proposed system can also be used for text-to-speech synthesis. Audio samples are provided for system demonstration https://daxintan-cuhk.github.io/pl-csd-speech .
IoT-CCAC: a blockchain-based consortium capability access control approach for IoT

Access control is a critical aspect for improving the privacy and security of IoT systems. A consortium is a public or private association or a group of two or more institutes, businesses, and companies that collaborate to achieve common goals or form a resource pool to enable the sharing economy aspect. However, most access control methods are based on centralized solutions, which may lead to problems like data leakage and single-point failure. Blockchain technology has its intrinsic feature of distribution, which can be used to tackle the centralized problem of traditional access control schemes. Nevertheless, blockchain itself comes with certain limitations like the lack of scalability and poor performance. To bridge the gap of these problems, here we present a decentralized capability-based access control architecture designed for IoT consortium networks named IoT-CCAC. A blockchain-based database is utilized in our solution for better performance since it exhibits favorable features of both blockchain and conventional databases. The performance of IoT-CCAC is evaluated to demonstrate the superiority of our proposed architecture. IoT-CCAC is a secure, salable, effective solution that meets the enterprise and business’s needs and adaptable for different IoT interoperability scenarios.
Outage and asset damage triggered by malicious manipulation of the control system in process plants

Intentional acts consisting in remote (cyber) or physical manipulations of the BPCS (Basic Process Control System) and the SIS (Safety Instrumented System) of a process plant may result in severe consequences for the affected industrial facilities. Interruption of productivity, with or without asset damages, generally results in huge economic losses and, at times, in damages to reputation, people and the environment. Despite the existence of several international standards aimed at the assessment and management of cybersecurity of IT (Information Technology) and OT (Operational Technology) systems of a facility, only few contributions are present in the literature addressing the concrete connection between malicious manipulations of the BPCS and SIS systems and the impacts on the physical process system that can be initiated. In this panorama, the present work fills this gap by developing a systematic qualitative methodology supporting the identification of possible security events affecting the operability and/or system integrity of a process plant, of the malicious manipulations by which they may be initiated, and of the existing safeguards in place. The results can be used within the standard procedure for cyber risk management of the IT-OT system (e.g. ISA/IEC 62443), to support the identification of protection requirements and countermeasures. The methodology is complementary to current safety and security assessments and is intended for application to front-end design phase as well as to the security review of operating plants. The methodology was applied to a case study (an offshore Oil&Gas compression plant) to demonstrate the potential of the methodology and the results obtained.
SSD-EMB: An Improved SSD Using Enhanced Feature Map Block for Object Detection

The development of deep learning has achieved great success in object detection, but small object detection is still a difficult and challenging task in computer vision. To address the problem, we propose an improved single-shot multibox detector (SSD) using enhanced feature map blocks (SSD-EMB). The enhanced feature map block (EMB) consists of attention stream and feature map concatenation stream. The attention stream allows the proposed model to focus on the object regions rather than background owing to channel averaging and the effectiveness of the normalization. The feature map concatenation stream provides additional semantic information to the model without degrading the detection speed. By combining the output of these two streams, the enhanced feature map, which improves the detection of a small object, is generated. Experimental results show that the proposed model has high accuracy in small object detection. The proposed model not only achieves good detection accuracy, but also has a good detection speed. The SSD-EMB achieved a mean average precision (mAP) of 80.4% on the PASCAL VOC 2007 dataset at 30 frames per second on an RTX 2080Ti graphics processing unit, an mAP of 79.9% on the VOC 2012 dataset, and an mAP of 26.6% on the MS COCO dataset.
SDG: A Simplified and Dynamic Graph Neural Network

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many high-impact applications such as fraud detection, information retrieval, and recommender systems due to their powerful representation learning capabilities. Some nascent efforts have been concentrated on simplifying the structures of GNN models, in order to reduce the computational complexity. However, the dynamic nature of these applications requires GNN structures to be evolving over time, which has been largely overlooked so far. To bridge this gap, in this paper, we propose a simplified and dynamic graph neural network model, called SDG. It is efficient, effective, and provides interpretable predictions. In particular, in SDG, we replace the traditional message-passing mechanism of GNNs with the designed dynamic propagation scheme based on the personalized PageRank tracking process. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our proposed SDG. We also design a case study on fake news detection to show the interpretability of SDG.
Switches for HIRE: resource scheduling for data center in-network computing

The recent trend towards more programmable switching hardware in data centers opens up new possibilities for distributed applications to leverage in-network computing (INC). Literature so far has largely focused on individual application scenarios of INC, leaving aside the problem of coordinating usage of potentially scarce and heterogeneous switch resources among multiple INC scenarios, applications, and users. The traditional model of resource pools of isolated compute containers does not fit an INC-enabled data center.
Semantic Correlation Rules as a Logic Layer between Content Management and Content Delivery

Semantic technologies have recently gained considerable influence and attention in the field of technical communication and information management. While metadata management is already a well-known field of content management technologies, its semantic extension addresses more recently, for example, problems of model-based product development and related content engineering processes. On the other hand, dynamic search technology and content delivery can benefit from semantic modelling by enhancing search functionalities or by integrating various data sources utilizing semantic mapping. In this evolving environment, we propose a logical layer of content correlations as so-called semantic correlation rules (SCR). This layer can be understood as an interface between content management systems, semantic modelling systems and content delivery portals. Semantic correlation rules serve as a light-weight ontology consisting primarily of untyped semantic relations between metadata classes. In doing so, class-to-class linking mechanisms can be implemented in content delivery and search environments while serving as a basis for the previously introduced microDoc concept
A new DNA coding and hyperchaotic system based asymmetric image encryption algorithm

<abstract> In this paper, an asymmetric image encryption algorithm based on DNA coding and hyperchaotic system is designed. Unlike other image encryption schemes, for example, sharing of same private keys between sender and receiver, and fixed rules with simple operation, three stages are studied as follows to deal with these problems. Firstly, to eliminate the possible risk of key transmission and management, the initial values of the hyperchaotic system are generated for ahead by the RSA (Rivest-Shamir-Adleman) algorithm and the plain image, in which the sum of odd rows, even rows, odd columns, and even columns are computed respectively to extra the plain message from the plain image as input of RSA algorithm. Then, a mathematical map is established to transform all of them into initial values of the hyperchaotic system. Secondly, the pixel level permutation is performed to confuse the image according to the chaotic sequences generated. Finally, to solve the problem of fixed rules with simple operations in current DNA based image encryption algorithms, dynamical DNA encryption is designed to diffuse the permuted image. The process of DNA encryption includes DNA coding, DNA operation and DNA decoding. In particular, DNA rules are selected according to chaotic sequences dynamically, rather than fixed rules with simple operation. Theoretical analysis and numerical simulations show that the proposed algorithm is secure and reliable for image encryption. </abstract>
End-to-End Speech Recognition from Federated Acoustic Models

Training Automatic Speech Recognition (ASR) models under federated learning (FL) settings has attracted a lot of attention recently. However, the FL scenarios often presented in the literature are artificial and fail to capture the complexity of real FL systems. In this paper, we construct a challenging and realistic ASR federated experimental setup consisting of clients with heterogeneous data distributions using the French and Italian sets of the CommonVoice dataset, a large heterogeneous dataset containing thousands of different speakers, acoustic environments and noises. We present the first empirical study on an attention-based sequence-to-sequence End-to-End (E2E) ASR model with three aggregation weighting strategies – standard FedAvg, loss-based aggregation and a novel word error rate (WER)-based aggregation, compared in two realistic FL scenarios: cross-silo with 10 clients and cross-device with 2K and 4K clients. This 4K cross-device ASR experiment is the largest ever performed. Our first-of-its-kind analysis on E2E ASR from heterogeneous and realistic federated acoustic models provides the foundations for future research and development of realistic FL ASR applications.
Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collision-avoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collision-avoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.
The Pandemic and Changes in the Self-Perception of Teacher Digital Competences of Infant Grade Students: A Cross Sectional Study

Without having a reaction time, the pandemic has caused an unprecedented transformation in universities around the world, leading to a revolution from structured models anchored in the conception of transmission of training towards a teaching approach-learning saved thanks to the incorporation of technology. This study aims to verify whether the pandemic situation has influenced the digital competence self-perception of students. Comparing two groups during the academic years 2019/2020 and 2020/2021, the instrument used is the questionnaire for digital competence “DigCompEdu Check-In” for future teachers. After the educational intervention, group A (before COVID-19) presented higher self-perceptions of competence than group B (during COVID-19); the pandemic situation caused by COVID-19 has negatively influenced students’ self-perception of their digital skills in the pretest in the different dimensions under study. Before receiving the training, the group that did not experience the pandemic enjoyed a higher self-perception of their competencies than the group that experienced the pandemic. The data obtained indicate that the difference exists, and that it is statistically significant, and may be a consequence of the clear relationship between self-perception and the way in which students face reality through their personal and subjective vision.