Reviewerly/exHarmony huggingface dataset:
Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain

To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).
Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.
The Effect of Recursive Feature Elimination with Cross-Validation (RFECV) Feature Selection Algorithm toward Classifier Performance on Credit Card Fraud Detection

Credit cards are one of the most popular non-cash payment methods used by the public. Credit cards are considered as one part of the lifestyle of modern society. However, transactions using credit cards are often fraudulent. For this reason, Fraud Detection classification is carried out on credit card transactions. The data mining process is used to overcome this. The data mining process is carried out by training a dataset to be able to classify fraud in credit card transactions. The use of high-dimensional datasets can cause problems with accuracy and training time in the classification process. There are several ways to overcome this, one of which is by doing feature selection. This study conducted to perform feature selection on the dataset to select the best attributes for classification that influence the classification results. The algorithm used is Recursive Feature Elimination with Cross-Validation (RFECV). Based on research that has been done with 3 different k values, namely k=5, k=10, and k=15, the RFECV algorithm can reduce the accuracy of the Decision Tree (DT) classification algorithm. Meanwhile, in the Naïve Bayes (NB) algorithm, the RFECV feature selection algorithm does not affect the evaluation results. The evaluation results on the NB algorithm before and after the application of the RFECV algorithm did not change. In the RFECV algorithm, the greater the value of k, the fewer attributes selected tend to be. RFECV with values of k=5 and k=10 displays the best 13 attributes. While the RFECV with a value of k=15 displays the 10 best attributes. The application of the RFECV algorithm can reduce computational time during the classification process using DT and NB. It is because, with the application of a larger k value, fewer attributes are used for the classification process, thus accelerating the classification process.
SSPNet: Scale Selection Pyramid Network for Tiny Person Detection From UAV Images

With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by unmanned aerial vehicles (UAVs), which is quite challenging due to extremely small scales of objects. Most existing methods employed a feature pyramid network (FPN) to enrich shallow layers&#x2019; features by combining deep layers&#x2019; contextual features. However, under the limitation of the inconsistency in gradient computation across different layers, the shallow layers in FPN are not fully exploited to detect tiny objects. In this article, we propose a scale selection pyramid network (SSPNet) for tiny person detection, which consists of three components: context attention module (CAM), scale enhancement module (SEM), and scale selection module (SSM). CAM takes account of context information to produce hierarchical attention heatmaps. SEM highlights features of specific scales at different layers, leading the detector to focus on objects of specific scales instead of vast backgrounds. SSM exploits adjacent layers&#x2019; relationships to fulfill suitable feature sharing between deep layers and shallow layers, thereby avoiding the inconsistency in gradient computation across different layers. Besides, we propose a weighted negative sampling (WNS) strategy to guide the detector to select more representative samples. Experiments on the TinyPerson benchmark show that our method outperforms other state-of-the-art (SOTA) detectors.
A big data framework for E-Government in Industry 4.0

Abstract The next generation of E-Government and healthcare has the potential to increase the more intelligent governance with improvements in transparency, accountability, efficiency, and effectiveness. It enables organizations to use the benefits of information via big data analysis to settle the difficulties effectively. Big Data has emerged which plays a significant role in many sectors around the world. Global trends in taking advantage of the benefits from big data are considered with an overview of the US, European Union, and several developing countries. To deeply understand the utilization of big data in several domains, this study has presented a brief survey of key concepts (such as IoT-enabled data, blockchain-enabled data, and intelligent systems data) to deeply understand the utilization of big data in several domains. Our analysis sets out also the similarities and differences in these concepts. We have also surveyed state-of-the-art technologies including cloud computing, multi-cloud, webservice, and microservice which are used to exploit potential benefits of big data analytics. Furthermore, some typical big data frameworks are surveyed and a big data framework for E-Government is also proposed. Open research questions and challenges are highlighted (for researchers and developers) following our review. Our goal in presenting the novel concepts presented in this article is to promote creative ideas in the research endeavor to perform efficaciously next-generation E-Government in the context of Industry 4.0.
Issues, Challenges, and Suggestions for Empowering Technical Vocational Education and Training Education during the COVID-19 Pandemic in Malaysia

Technical Vocational Education and Training (TVET) is one of the critical drivers in the transformation of Malaysia's education system and contributes towards economic growth.It works to develop and equip individuals with current technical skills based on industry demand.Many issues faced by the government in the way to strengthen the field of Technical and Vocational Education and became worse when the outbreak of COVID-19 pandemic changed the way of delivering education from conventional methods to e-learning.Hence, the main thrust of this review paper is to highlight the issues of TVET education and challenges TVET education faced during the COVID-19 pandemic.For the findings, articles related with issues and challenges in TVET education during pandemic outbreak are chosen from SCOPUS, WOS and ERIC databases.The results have reported that lack of student motivation and career counselling, stigma towards TVET education, lecturer's competency and inadequacies of infrastructure resources in TVET are issues that happen in TVET education throughout the years while internet connectivity problem, learning platform, content of curriculum and assessment, the readiness of trainers and trainees for e-learning have been discussed as challenges of TVET education during COVID-19 pandemic.
Reversible Data Hiding for JPEG Images Based on Multiple Two-Dimensional Histograms

Reversible data hiding (RDH) for JPEG images has attracted extensive attentions in recent years. However, since DCT is a de-correlation operation, it is difficult for JPEG images to employ the image redundancy for RDH design like uncompressed images. In this paper, considering better utilizing the special properties of quantized DCT coefficients, a new RDH scheme for JPEG images based on multiple two-dimensional (2D) histograms modification is proposed. Firstly, by combining every two nonzero alternating current (AC) coefficients of adjacent DCT blocks in each band as a pair, a new coefficients pairing strategy is proposed for a sharper 2D histogram. Then, with a classification process, multiple 1D and 2D histograms are generated, in which the 2D histograms are selected for data embedding and the bins in 1D histograms are shifted for reversibility. Finally, to further enhance the embedding performance, the 2D mappings are adaptively determined for different 2D histograms through a formulated rate-distortion model. Experimental results demonstrate the superiority of the proposed scheme both in visual quality and file size preservation.
Digital Certificate Authority with Blockchain Cybersecurity in Education

In the 21st century the development of internet technology has experienced explosive developments with complex threats in Higher Education. Cyber ​​security is a priority issue for all countries by using data and communication technology in various aspects of life. In order to advance and improve the digital economy in the implementation of a comfortable and reliable electronic system with methods of increasing competitiveness, cyber innovation also builds understanding and sensitivity to national security and resilience. Cybersecurity is constantly changing and learning providers often do not have the authority or facilities and capacity in various activities that connect students, in ensuring their knowledge and expertise. In carrying out the provision and validation of qualifications that are tried exclusively at the centralized management of learning institutions or employing institutions, these days have more ownership of learning experiences with results without risking safety, security and accessibility. This validation provision is not prolonged because learning is far more international than before and education continues to frequently use online platforms. Education providers offer degree internships that represent a new method of expertise and provide a route of advancement in increasing employability prospects. Blockchain is a digital and decentralized ledger, encompassing a set of interlocking technologies. Blockchain is designed to transform the centralized as well as validation model to a decentralized ledger from a secure database. These databases are shared, replicated, and synchronized for validation at universities, legal or regulatory agencies and industrial bodies across the internet.
WavBERT: Exploiting Semantic and Non-Semantic Speech Using Wav2vec and BERT for Dementia Detection

In this paper, we exploit semantic and non-semantic information from patient's speech data using Wav2vec and Bidirectional Encoder Representations from Transformers (BERT) for dementia detection. We first propose a basic WavBERT model by extracting semantic information from speech data using Wav2vec, and analyzing the semantic information using BERT for dementia detection. While the basic model discards the non-semantic information, we propose extended WavBERT models that convert the output of Wav2vec to the input to BERT for preserving the non-semantic information in dementia detection. Specifically, we determine the locations and lengths of inter-word pauses using the number of blank tokens from Wav2vec where the threshold for setting the pauses is automatically generated via BERT. We further design a pre-trained embedding conversion network that converts the output embedding of Wav2vec to the input embedding of BERT, enabling the fine-tuning of WavBERT with non-semantic information. Our evaluation results using the ADReSSo dataset showed that the WavBERT models achieved the highest accuracy of 83.1% in the classification task, the lowest Root-Mean-Square Error (RMSE) score of 4.44 in the regression task, and a mean F1 of 70.91% in the progression task. We confirmed the effectiveness of WavBERT models exploiting both semantic and non-semantic speech.
Arabic text classification: the need for multi-labeling systems

The process of tagging a given text or document with suitable labels is known as text categorization or classification. The aim of this work is to automatically tag a news article based on its vocabulary features. To accomplish this objective, 2 large datasets have been constructed from various Arabic news portals. The first dataset contains of 90k single-labeled articles from 4 domains (Business, Middle East, Technology and Sports). The second dataset has over 290 k multi-tagged articles. To examine the single-label dataset, we employed an array of ten shallow learning classifiers. Furthermore, we added an ensemble model that adopts the majority-voting technique of all studied classifiers. The performance of the classifiers on the first dataset ranged between 87.7% (AdaBoost) and 97.9% (SVM). Analyzing some of the misclassified articles confirmed the need for a multi-label opposed to single-label categorization for better classification results. For the second dataset, we tested both shallow learning and deep learning multi-labeling approaches. A custom accuracy metric, designed for the multi-labeling task, has been developed for performance evaluation along with hamming loss metric. Firstly, we used classifiers that were compatible with multi-labeling tasks such as Logistic Regression and XGBoost, by wrapping each in a OneVsRest classifier. XGBoost gave the higher accuracy, scoring 84.7%, while Logistic Regression scored 81.3%. Secondly, ten neural networks were constructed (CNN, CLSTM, LSTM, BILSTM, GRU, CGRU, BIGRU, HANGRU, CRF-BILSTM and HANLSTM). CGRU proved to be the best multi-labeling classifier scoring an accuracy of 94.85%, higher than the rest of the classifies.
Digital Literacy of EFL Students: An Empirical Study in Vietnamese Universities

Abstract The current research investigates the digital literacy levels of 1661 English as a foreign language (EFL) learner at Vietnamese universities. We used an adapted questionnaire to assess students’ digital knowledge and their perceived skills, their attitudes toward the use of digital technologies, and the frequency of use of technology applications in English learning. The findings reveal that most Vietnamese students can access digital technologies at home and in their institutions. Furthermore, students achieve an adequate level of knowledge regarding digital literacy, and their technological skills range from low to average. Students’ attitudes toward technologies are positive, but they do not use technologies extensively when learning English. Comparisons show that males have better digital knowledge and skills than their female peers. Although female students are more aware of the digital integration benefits of learning than their male peers, males tend to use technologies more extensively than females. There are also discrepancies among different year groups. Seniors have the best digital knowledge, while freshmen possess the highest technical skill levels. Junior and senior students’ attitudes toward technology applications in English learning are more positive than those of freshmen and sophomores.
Learning object-uncertainty policy for visual tracking

In research, we found that the purpose of most trackers is to obtain an accurate and robust score map, neglecting how to further examine the confidence of the results. Inspired by the Siamese trackers, which merely use the template from the first frame to locate the target, we propose a novel object-uncertainty policy. Firstly, we propose a dynamic design of the target template set for the tracked target, considering the initial target template and the reliable target template of the subsequent frames concurrently. Secondly, we adopt the multi-layer fusion to represent the target while analyzing the fusion of various feature layers. Moreover, we use a more effective cosine similarity function to calculate the similarity instead of the correlation operation. Finally, we propose a novel voting mechanism in accordance with the similarity between the target tracked in subsequent frames and the target template set. More importantly, this method can be embedded into DCF-like methods to improve tracking performance, which is embedded into the recent DiMP and PrDiMP trackers separately for comparison. Extensive experiments demonstrate that the discriminative ability of the model can be enhanced effectively by using our proposed method, capable of preventing the model from learning the background information. The code and raw tracking results are available at https://github.com/hexdjx/OUPT.
IoT Solution for AI-Enabled PRIVACY-PREServing with Big Data Transferring: An Application for Healthcare Using Blockchain

Internet of Things (IoT) performs a vital role in providing connectivity between computing devices, processes, and things. It significantly increases the communication facilities and giving up-to-date information to distributed networks. On the other hand, the techniques of artificial intelligence offer numerous and valuable services in emerging fields. An IoT-based healthcare solution facilitates patients, hospitals, and professionals to observe real-time and critical data. In the literature, most of the solution suffers from data intermission, high ethical standards, and trustworthiness communication. Moreover, network interruption with recurrent expose of sensitive and personal health data decreases the reliance on network systems. Therefore, this paper intends to propose an IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain. Firstly, the proposed algorithm uses a graph-modeling to develop a scalable and reliable system for gathering and transmitting data. In addition, it extracts the subset of nodes using the artificial intelligence approach and achieves efficient services for the healthcare system. Secondly, symmetric-based digital certificates are utilized to offer authentic and confidential transmission with communication resources using blockchain. The proposed algorithm is explored with existing solutions through multiple simulations and proved improvement in terms of realistic parameters.
Privacy-preserving COVID-19 contact tracing using blockchain

The outbreak of the COVID-19 virus has caused widespread panic and global initiatives are geared towards treatment and limiting its spread. With technological advancements, several mechanisms and mobile applications have been developed that attempt to trace the physical contact made by a person with someone who has been tested COVID-19 positive. While designing these apps, user's privacy has been an afterthought and has resulted in mass violations of privacy of the public and the patients. A total of 32 countries have designed apps and rely on them as a strategy to flatten the pandemic curve. Along with lack of privacy, these methodologies are centralized, where they are fully controlled by the government and the healthcare providers. Owing to these and many other concerns, people are hesitant in the adoption of these technologies. This paper presents a detailed analysis of user tracking apps belonging to 32 countries, thus demonstrating that they collect personal data and are a gross violation of user privacy. This paper presents a novel architecture for the efficient, effective and privacy-preserving contact tracing of COVID-19 patients using blockchain. The proposed architecture preserves the privacy of individuals and their contact history by encrypting all the data specific to an individual using a privacy-preserving Homomorphic encryption scheme and storing it on a permissioned blockchain network. The contacts made with a COVID-19 positive patient are identified by performing search queries directly over the Homomorphic encrypted data stored in the blocks. Therefore, only those contacts that are suspected to be COVID-19 positive may be decrypted by the healthcare professional or government for further contact tracing/diagnosis and COVID-19 testing; thereby leading to enhanced privacy.
Integrated non-volatile plasmonic switches based on phase-change-materials and their application to plasmonic logic circuits

Integrated photonic devices or circuits that can execute both optical computation and optical data storage are considered as the building blocks for photonic computations beyond the von Neumann architecture. Here, we present non-volatile hybrid electro-optic plasmonic switches as well as novel architectures of non-volatile combinational and sequential logic circuits. The electro-optic switches consist of a plasmonic waveguide having a thin layer of a phase-change-material (PCM). The optical losses in the waveguide are controlled by changing the phase of the PCM from amorphous to crystalline and vice versa. The phase transition process in the PCM can be realized by electrical threshold switching or thermal conduction heating via external electrical heaters or the plasmonic waveguide metal itself as an integrated heater. We have demonstrated that all logic gates, a half adder circuit, as well as sequential circuits can be implemented using the plasmonic switches as the active elements. Moreover, the designs of the plasmonic switches and the logic operations show minimum extinction ratios greater than 20 dB, compact designs, low operating power, and high-speed operations. We combine photonics, plasmonics and electronics on the same platform to design an effective architecture for logic operations.
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution

Multi-label text classification is a challenging task because it requires capturing label dependencies.It becomes even more challenging when class distribution is long-tailed.Resampling and re-weighting are common approaches used for addressing the class imbalance problem, however, they are not effective when there is label dependency besides class imbalance because they result in oversampling of common labels.Here, we introduce the application of balancing loss functions for multilabel text classification.We perform experiments on a general domain dataset with 90 labels (Reuters-21578) and a domain-specific dataset from PubMed with 18211 labels.We find that a distribution-balanced loss function, which inherently addresses both the class imbalance and label linkage problems, outperforms commonly used loss functions.Distribution balancing methods have been successfully used in the image recognition field.Here, we show their effectiveness in natural language processing.