Reviewerly/exHarmony huggingface dataset:
Integration of the Latest Information and Communication Technologies into Pedagogical Practice: Impact Analysis, Effectiveness and Challenges on the way to Sustainable Implementation in Higher Education

The present era of societal advancement is distinguished by a substantial proliferation and intensification of pedagogical research and development, spanning various domains and levels of society. Consequently, both established and emerging fields of knowledge, as well as cutting-edge technologies, are being cultivated. This pursuit has led to the formation of sophisticated and efficient tools for intellectual activities, yielding enhanced productivity. Furthermore, novel materials and alternative sources for the advancement of goods and services are being explored. This necessitates the effective utilization of capital, investments, innovations, material and technical resources, energy, information resources, and labor. In light of the aforementioned considerations, enhancing the management systems governing socio-economic, technical, and technological processes becomes imperative. Drawing upon advancements in education and science, and harnessing knowledge-intensive technologies, sophisticated devices, and tools, contemporary high-tech platforms are being developed and employed. These systems play a pivotal role in organizing and regulating activities, profoundly influencing the nature and outcomes of innovation endeavors. Moreover, they serve as a testament to the scientific and technological progress witnessed by civilization. The objective of this article is to ascertain the distinctive characteristics of the influence exerted by information and communication technologies on the educational process within higher education institutions. Additionally, it aims to evaluate the effectiveness of their implementation and address the challenges encountered by the educational system. The study employs a range of research methods, including general scientific methods such as analysis, synthesis, deduction, induction, and generalization. These methods facilitated the identification of the object, subject, purpose, and objectives of the investigation, as well as the analytical processing of scientific sources. Additionally, specific scientific methods, namely historical and structural approaches, were utilized to organize the existing pedagogical literature concerning the integration of the latest information and communication technologies into the educational process within higher education. The method of extrapolation was employed to extend the conclusions derived from the analysis. The study has successfully identified the fundamental theoretical aspects associated with the integration of the latest information and communication technologies for the enhancement of English-language communicative competence in higher education. Additionally, the study has addressed the pivotal practical considerations on this matter.
Classifying Internet of Things (IoT) devices and non-IoT devices in data center edge network using statistical and network flow features by one dimensional convolutional neural network

The development of edge computing has led to the aggregation of traffic from IoT and non-IoT devices at edge data center networks located closer to cloud and computing resources. To provide application-specific Quality of Service (QoS) for these devices, classification of IoT and non-IoT devices at edge data centers is crucial. Our research suggests using a simple, compact, and computationally less expensive shallow one-dimensional convolutional neural network (1D-CNN) for real-time processing needs. The model was trained on a dataset consisting of 22 IoT devices and 7 non-IoT devices. The network flow and statistical features extracted from the network traces of these devices were fed into the model. The research demonstrated that with just 15 features, the model can achieve around 94% accuracy, and with 55 features, nearly 97% accuracy in classifying IoT and non-IoT devices.
A Graph Partitioning Algorithm Based on Graph Structure and Label Propagation for Citation Network Prediction

With the development of deep learning and artificial intelligence technologies, Graph Neural Network (GNN) has become a popular research topic in recent years. As the scale of graph data continues to increase, training massive graph data with GNN is still challenging. Distributed GNN training has become an ideal solution. However, most real-world large-scale graph data exhibit power-law distribution characteristics. Existing graph partitioning algorithms are unable to partition graphs with computation load balancing for distributed GNN training yet. This paper proposes a graph partitioning algorithm based on multi-level partitioning that considers the degree information of the graph structure, label propagation, and migration. We verified the applicability of the proposed algorithm using citation network datasets. The experimental results demonstrate that the algorithm improves the load balancing level of subgraph partitioning. Furthermore, we verified in distributed GNN training that the algorithm has a better effect on graph partitioning than that of traditional graph partitioning algorithms. Compared to non-distributed GNN training, distributed GNN training based on the graph partitioning algorithm presented in this paper can achieve better acceleration while maintaining prediction accuracy.
Community-Enhanced Contrastive Siamese Networks for Graph Representation Learning

Graph representation learning is the encoding of graph nodes into a low-dimensional representation space, which can effectively improve graph information representation while reducing the information dimensionality. To overcome the heavy reliance on label information of previous graph representation learning, the graph contrastive learning method has received attention from researchers as a self-supervised learning method, but it introduces the problem of sample selection dependence. To address this issue, inspired by deep clustering methods and image contrastive learning methods, we propose a novel Siamese network method, namely Community-enhanced Contrastive Siamese networks for Graph Representation Learning (MEDC). Specifically, we employ a Siamese network architecture to contrast two augmented views of the original graph and guide the network training by minimizing the similarity of positive sample nodes and negative sample nodes. Meanwhile, to take full advantage of the potential community structure of graph, we add a deep clustering layer in the network architecture, and the perceived community structure information is used to guide the selection of positive and negative samples. To demonstrate the effectiveness of the proposed method, we conducted a series of comparative experiments on three real datasets to validate the performance of our method.
KSRL: Knowledge Selection Based Reinforcement Learning for Knowledge-Grounded Dialogue

In the domain of multi-turn knowledge-grounded dialogues, the sequential coherence among knowledge elements chosen across various conversational turns presents potential cues for knowledge selection. However, this aspect has been largely overlooked in preceding studies. To tackle this issue, the present study introduces an innovative methodology that employs reinforcement learning to enhance knowledge selection in open-domain dialogue systems. By recasting the knowledge selection challenge as a sequential decision-making task and implementing reinforcement learning, the dialogue system is capable of discerning which knowledge to choose based on the conversational context and preceding dialogue turns, thereby generating high-quality responses. The system acquires a reward signal contingent upon the quality of the generated responses and subsequently updates its policy to maximize the expected reward over time. Harnessing the capabilities of reinforcement learning, our proposed method effectively learns to identify the most pertinent knowledge, thereby generating superior-quality responses. The study assesses the proposed approach using multiple open-domain dialogue datasets, demonstrating that it surpasses the performance of prior methodologies.
Contextual Information Augmented Few-Shot Relation Extraction

Few-Shot Relation Extraction is a challenging task that involves extracting relations from a limited number of annotated data. While some researchers have proposed using sentence-level information to improve performance on this task with Prototype Network, most of these methods do not adequately leverage this valuable source of sentence-level information. To address this issue, we propose a novel sentence augmentation method that utilizes abundant relation information to generate additional training data for few-shot relation extraction. In addition, we add a new “None of Above” class for each task, thereby enhancing the model’s classification ability for similar relations and improving its overall performance. Experimental results on the FewRel demonstrate that our method outperforms existing methods on three different few-shot relation extraction tasks. Moreover, our method also provides a new idea for both few-shot learning and data augmentation research.
Multi-display Graph Attention Network for Text Classification

Text classification, a fundamental task in natural language processing, has been extensively studied by researchers worldwide. The primary focus of text classification is on extracting effective features from text, as accurate information extraction is crucial for the task. However, the current utilization of text information in text classification is not optimal, and thus, effective extraction of text information remains an important research topic. Graph attention networks (GATs) have gained popularity among researchers due to their excellent performance in various tasks, including text classification. Additionally, previous graph neural network-derived models only address the differences in importance of edges within nodes during the information aggregation process, but ignore the importance differences between different nodes. But, the correlation between nodes also needs to be exploited for a more comprehensive understanding of the text. In this paper, we propose a novel multi-display graph attention network (MDGAT)-based model to address the challenges of inadequate text information capture and higher-order interactions between words. Our approach involves fusing multiple display graphs to capture diverse features of the text, for comprehensive text information representation. Additionally, we introduce a new information aggregation method called multi-step information aggregation, which considers the importance within nodes and the correlation between nodes, leading to improved text representation learning. We validate the performance of our proposed method through extensive experiments on various benchmark datasets. The results demonstrate the superior performance of our approach in text classification tasks.
A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplet of an aspect term, an opinion term, and their corresponding sentiment polarity from the review texts. Due to the complexity of language and the existence of multiple aspect terms and opinion terms in a single sentence, current models often confuse the connections between an aspect term and the opinion term describing it. To address this issue, we propose a pairing enhancement approach for ASTE, which incorporates contrastive learning during the training stage to inject aspect-opinion pairing knowledge into the triplet extraction model. Experimental results demonstrate that our approach performs well on four ASTE datasets (i.e., 14lap, 14res, 15res and 16res) compared to several related classical and state-of-the-art triplet extraction methods. Moreover, ablation studies conduct an analysis and verify the advantage of contrastive learning over other pairing enhancement approaches.
Chapter 7 Quantum processors/networks/sensors

The concept of quantum computing was introduced to make processing faster so that processes can run in parallel. It harnesses the law of quantum mechanics that aids in solving highly complex problems. The law incorporates the concept of superposition and entanglement, which are the primary properties of quantum. The qubits are those involved in quantum, with the feasibility that a bit can represent any of the two states, unlike classical computers. With the advent of the new technology, it is important to enhance the other units that support in building up the technology of the future. This chapter proposes the different aspects involved in quantum computing, such as quantum sensors, the processing of quantum, the networks established, etc. It also emphasizes on the importance of moving from the classical computers to quantum, with comparisons. The focus is on knowing that there is the possibility of quantum computing in solving problems that cannot be solved in polynomial time. All these aid in uplifting the classic computing power to quantum. As it is understood that this technology is still in the research stage, the chapter provides information on what it is to actually build a quantum system. Having a brief idea on the different units, it also identifies the different applications of quantum with a few case studies.
A Novel Decoupled Feature Pyramid Networks for Multi-Target Ship Detection

The efficiency and accuracy of ship detection is of great significance to ship safety, harbor management, and ocean surveillance in coastal harbors. The main limitations of current ship detection methods lie in the complexity of application scenarios, the difficulty in diverse scales object detection, and the low efficiency of network training. In order to solve these problems, a novel multi-target ship detection method based on a decoupled feature pyramid algorithm (DFPN) is proposed in this paper. First, a feature decoupling module is introduced to separate ship contour features and position features from the multi-scale fused features, to overcome the problem of similar features in multi-target ships. Second, a feature pyramid structure combined with a gating attention module is constructed to improve the feature resolution of small ships by enhancing contour features and spatial semantic information. Finally, a feature pyramid-based multi-feature fusion algorithm is proposed to improve the adaptability of the network to changes in ship scale according to the contextual relationship of ship features. Experiments on the multi-target ship detection dataset showed that the proposed method increased by 6.3% mAP and 20 FPS higher than YOLOv4, 7.6% mAP and 36 FPS higher than Faster-R-CNN, 5% mAP and 36 FPS higher than Mask-R-CNN, and 4.1% mAP and 35 FPS higher than DetectoRS. The results demonstrate that the DFPN can detect multi-target ships in different scenes with high accuracy and a fast detection speed.
VSSB-Raft:A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm(VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm’s complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency.
Pelatihan pembuatan sensor medis berbasi IoT sebagai pengenalan smart medical devices

Cardiovascular disease (CVD) is a leading cause of death globally, resulting in approximately 17.9 million deaths each year (WHO, 2017), with estimates projecting a rise to 23.3 million deaths by 2030 (Pusdatin Kemenkes RI, 2014). Early detection of heart disease plays a crucial role in CVD prevention, with heart rate (bpm) being a key indicator to assess heart function, ranging from 60 to 100 beats per minute. To address the need for early detection, a practical heart rate monitoring device utilizing the Internet of Things (IoT) and Smart Medical Devices (SMDs) was developed. This research aimed to provide training on IoT-based heart rate detection to high school students in Trenggalek. The training encompassed lectures and hands-on practice, successfully enhancing participants' knowledge of IoT, as demonstrated by improved test scores. Moreover, the training resulted in a prototype of an IoT-based heart rate monitoring system that utilizes Arduino and a heart rate sensor. Post-training evaluations showed the majority of participants were satisfied with the quality of materials and organization, indicating the positive impact of this engagement on the partners. The results support the potential of this IoT training to equip high school students with essential skills, fostering self-reliance in medical device production and reducing dependence on imports in the face of ASEAN Economic Community challenges. Ultimately, this initiative contributes to building a competent healthcare workforce in Indonesia
Pelatian penggunaan Zoom Meeting untuk pendidikan dan pelatihan di BKPSDM kabupaten Wakatobi

The use of information technology in training has posed a challenge to the sustainability of traditional training methods, be it at a slow or rapid pace. In the post-Covid-19 era, often referred to as the "new normal," the training landscape has demanded a paradigm shift from conventional to technology-based training. The transformation of training through the adoption of technology applications and e-learning has emerged as a viable solution. This study examines the strategic issues faced by the Government of Wakatobi Regency concerning Education and Training, primarily limited personnel/officials in the Civil Service Agency and Human Resources Development Agency of Wakatobi Regency, with limited proficiency in information technology and inadequate infrastructure to meet information technology standards. The collaborative community engagement initiative with the Civil Service Agency and Human Resources Development Agency of Wakatobi Regency through Zoom Meeting-based training has yielded highly positive outcomes, contributing to the implementation of sustainable training programs and enhancing the institution's readiness to conduct online technical and functional training or Diklat. Furthermore, this engagement has significantly increased the capacities of personnel in the field of information technology for educational and training purposes. As part of a continuous effort, this community engagement activity has also supported the institution in planning and budgeting for the provision of appropriate infrastructure for online Education and Training, earmarked for the fiscal year 2024
Application of short video recommendation technology in classical music teaching

Abstract Throughout the evolution of the global teaching model, from the traditional face-to-face teaching to the later Web 2.0, the integration of Weibo, blogs, and other media enriched the teaching model. However, with the emergence of Web 3.0, the mobile Internet makes music teaching further upgraded and become more intelligent, personalized, and adaptive, which provides more powerful technical support for the majority of music educators in the actual teaching process. This paper discusses the application of short video recommendation technology in classical music teaching and establishes a personalized short video recommendation method, which can realize the personalized and targeted push of classical music. The results show that the recommendation technology established in this paper has better superiority than the traditional method. Combined with the short video recommendation technology established in this paper, the hot word retrieval, push platform, audience age, and education effect of short video recommendation technology are studied. The research results show that the short video recommendation technology established in this paper can analyze the hot word retrieval volume of each platform, the main push platform, and the age of the audience. Based on this, further analysis is carried out, and the educational effect of classical music combined with short video recommendation technology is obtained. The research results have promoted the way of classical music education.
Performance Analysis of Load Balancing Mechanism in Cloud Computing

The advancement of Information Technology has made cloud computing technology an innovative model for offering its consumers services on a rental basis at any time or location. Numerous firms converted to cloud technology by establishing new data centers because of the flexibility of cloud services. However, it has become necessary to ensure successful job execution and effective resource usage. Load balancing (LB) in cloud computing remains a complex challenge, specifically in the Infrastructure as a Service (IaaS) cloud architecture. A server being overloaded or underloaded is a problem that mustn’t happen in the process of cloud access because it would slow down processing or causes a system crash. Hence, to ignore these problems, a suitable resource schedule must be taken, so that system can load balance tasks over all accessible assists. This research suggests effective load-balancing approaches by analyzing the advantages, applications, and disadvantages of conventional LB techniques. The conclusions show that this research provides an exceptional path for researchers to overcome major drawbacks of existing LB techniques and achieves greater efficiency based on makespan, execution and response time, resource usage, efficiency, load balancing and throughput.