Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Animasi sebagai Media Pembelajaran Berbasis Teknologi untuk Meningkatkan Motivasi Belajar

This study examines the utilization of animation as a technology-based learning media to enhance student motivation. In the midst of rapid advancements in information and communication technology, education requires innovative transformation in learning methods. Animation has been recognized as an effective tool to enrich student learning experiences through clear and interactive visual representations. The aim of this research is to analyze literature related to the utilization of animation as a learning media, explore its benefits and challenges, and convey the contribution of this study to the development of innovative learning practices. The method employed in this research is literature review, where data is obtained through analysis of theoretical sources and relevant studies. The results of the literature analysis indicate that animation holds great potential in enhancing student motivation, engagement, creativity, and personalized learning. However, it is crucial for educators and researchers to pay attention to good animation design and the role of educators in facilitating effective animation utilization. This research contributes to the development of innovative learning practices and enriches the understanding of the utilization of animation as an effective learning media in enhancing student motivation.
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing

In the traditional vehicular network, computing tasks generated by the vehicles are usually uploaded to the cloud for processing. However, since task offloading toward the cloud will cause a large delay, vehicular edge computing (VEC) is introduced to avoid such a problem and improve the whole system performance, where a roadside unit (RSU) with certain computing capability is used to process the data of vehicles as an edge entity. Owing to the privacy and security issues, vehicles are reluctant to upload local data directly to the RSU, and thus federated learning (FL) becomes a promising technology for some machine learning tasks in VEC, where vehicles only need to upload the local model hyperparameters instead of transferring their local data to the nearby RSU. Furthermore, as vehicles have different local training time due to various sizes of local data and their different computing capabilities, asynchronous federated learning (AFL) is employed to facilitate the RSU to update the global model immediately after receiving a local model to reduce the aggregation delay. However, in AFL of VEC, different vehicles may have different impact on the global model updating because of their various local training delay, transmission delay and local data sizes. Also, if there are bad nodes among the vehicles, (that is, the amount of data and local computing resources are small, and the local model is polluted by random noise), it will affect the global aggregation quality at the RSU. To solve the above problem, we shall propose a deep reinforcement learning (DRL) based vehicle selection scheme to improve the accuracy of the global model in AFL of vehicular network. In the scheme, we present the model including the state, action and reward in the DRL based to the specific problem. Simulation results demonstrate our scheme can effectively remove the bad nodes and improve the aggregation accuracy of the global model.
Hybrid approach for text categorization: A case study with Bangla news article

The incredible expansion of online texts due to the Internet has intensified and revived the interest of sorting, managing and categorising the documents into their respective domains. This shows the pressing need for automatic text categorization system to assign a document into its appropriate domain. In this article, the focus is on showcasing the effectiveness of a hybrid approach that works elegantly by combining text-based and graph-based features. The hybrid approach was applied on 14,373 Bangla articles with 57,22,569 tokens collected from various online news corpora covering nine categories. This article also presents the individual application of both the features to explicate how they generally work. For classification purposes, the feature sets were passed through the Bayesian classification methods which yield satisfactory results with 98.73% accuracy for Naïve Bayes Multinomial (NBM). Also, to test the robustness and language independency of the system, the experiments were performed on two popular English datasets as well.
Skew cyclic codes over $$\mathbb {Z}_{4}+u\mathbb {Z}_{4}+v\mathbb {Z}_{4}$$

In this paper, we study the skew-cyclic codes (also called $$\varvec{\theta }$$ -cyclic codes) over the ring $$\varvec{S}=\varvec{\mathbb {Z}}_{{\textbf {4}}}+\varvec{u}\mathbb {Z}_{{\textbf {4}}}+v\mathbb {Z}_{{\textbf {4}}}$$ , where $$\varvec{u}^2=v^{{\textbf {2}}}=\varvec{u}\varvec{v}=\varvec{v}\varvec{u}={\textbf {0}}$$ . Some structural properties of the skew polynomial ring $$\varvec{S}[\varvec{x},\varvec{\theta }]$$ , where $$\varvec{\theta }$$ is an automorphism of $$\varvec{S}$$ are discussed and the elements of $$\varvec{S}^{\varvec{\theta }}$$ , the subring of $$\varvec{S}$$ fixed by $$\varvec{\theta }$$ , are determined. Skew cyclic codes over $$\varvec{S}$$ are viewed as left $$\varvec{S}[\varvec{x},\varvec{\theta }]$$ -submodules. Generator and parity-check matrices of a free $$\varvec{\theta }$$ -cyclic code of even length over $$\varvec{S}$$ are determined and a Gray map on $$\varvec{S}$$ is used to obtain the $$\mathbb {Z}_{{\textbf {4}}}$$ -images. We show that the Gray image of a free skew cyclic code over $$\varvec{S}$$ is a free linear code over $$\mathbb {Z}_{{\textbf {4}}}$$ . Furthermore, these codes are generalized to double skew-cyclic codes. We obtained new linear codes over $$\mathbb {Z}_{{\textbf {4}}}$$ from Gray images of double skew-cyclic codes over $$\varvec{S}$$ .
Local quantum uncertainty and non-commutativity measure discord in two-mode photon-added entangled coherent states

In this paper, we investigate quantum correlations in entangled coherent states with photon added, which are produced by the creation operator acting on Bell-like entangled coherent states. The non-classical characteristics of the quantum state created by combining two-mode coherent states with any number of photons added are also taken into account. We provide explicit expressions for the pairwise quantum correlations found in superpositions of coherent states with $$\left( m,n\right)$$ -photon addition. A comparison study between a non-commutativity discord-like measure and the evaluation of the local quantum uncertainty to measure quantum correlations is also produced. We consider some special superpositions of Bell-like entangled coherent photon-added states to be illustrative of our analytical findings. The amount of entangled even/odd coherent states of two modes coherent states with a number of photon added by non-commutativity discord is found that to be more entangled than the local quantum uncertainty. Finally, the non-commutativity discord and local quantum uncertainty under a dephasing channel is discussed.
Metaverse in the Virtual Workplace

The emergence of digital technologies has significantly transformed the workplace by enhancing productivity and improving employee wellbeing. One of the latest technological advancements in the workplace is the metaverse, which has tremendous potential to facilitate collaboration in virtual teams. In this study, we conducted a comprehensive bibliometric analysis to identify core research themes on applying metaverse in the workplace. Our findings reveal core themes, including an avatar, computer graphics, immersion, virtual world and virtual reality. We also developed a theoretical and conceptual framework to define a metaverse in the workplace, which includes individual, team and organizational factors as well as contextual moderators that influence its implementation. Finally, we propose a definition of a metaverse in the workplace as a 3D virtual immersive environment where employees interact with each other using their avatar identities, perform work tasks, and have autonomy and opportunities for creativity. Our study contributes to the growing body of research on the practical applications of a metaverse in the workplace. It highlights the importance of considering various factors for its successful implementation.
Inquiry Practice Capability and Students’ Learning Effectiveness Evaluation in Strategies of Integrating Virtual Reality into Vehicle Body Electrical System Comprehensive Maintenance and Repair Services Practice: A Case Study

VR has shown positive growth in the world in recent years, which is mainly due to projects such as learning, games, entertainment and experiential activities. VR has changed the way of life of users, providing users with more interesting interactions and immersive experiences. This study aims to investigate students’ practical capabilities and learning effectiveness under the instruction strategy of integrating virtual reality into simulation games into the Vehicle Body Electrical System Comprehensive Maintenance and Repair Services Practice curriculum for students of the Dept. of Auto Mechanics in a skills-based senior high school. Two student classes of the Dept. of Auto Mechanics major in Electrical Engineering featuring practical subjects in one skills-based senior high school in central Taiwan were chosen as the participants for this study. By way of pretest–post-test research design and heterogeneous grouping, an 8-week instruction experiment was conducted in which ZPD (zone of proximal development) instruction strategies were used in the experimental group (with 43 persons), while traditional didactic instruction strategies were used in the control group (with 36 persons). ZPD instructional strategies analyze and collect quantitative and qualitative data to investigate the instructional effectiveness and feasibility in developing ZPD as the research material in the practical curriculum for the study area of the Power Machinery in Vehicle Body Electrical System Comprehensive Maintenance and Repair Services practice. According to the research objective, the results are concluded as follows. (1) Students achieved the best learning effectiveness when adopting ZPD (zone of proximal development) strategies in which virtual reality was integrated into the vehicle charging and starting system to investigate students’ automotive diagnostic troubleshooting and fault-clearing capabilities. (2) Students attained the highest acceptance in learning phenomenon when adopting ZPD (zone of proximal development) strategies in which virtual reality was integrated into students’ familiar practice factory environment and the tools and equipment operation process. (3) Students had a higher acceptance of learning effectiveness when using virtual reality simulation games in the disassembly and functional detection of vehicle charging and starting systems. (4) There is a positive effect when integrating virtual reality simulation games into ZPD instruction strategies in the knowledge, skills and attitude on students’ overall inquiry practical capabilities and their learning effectiveness.
Why Does Science Need Losers?

The article raises the problem of functionality and rational explanation of large arrays of communicative and unclaimed scientific knowledge. To solve the problem and explain this phenomenon, the resources of the system-communicative theory of scientific communication and social-evolutionist approaches are involved. The ability of the system-communicative theory itself to explain this phenomenon is considered as a possibility of its verification. In conclusion, a working hypothesis is proposed linking the existence of a class of unclaimed research and researchers with the function of meta-observation: through their online network reactions on appropriate electronic platforms (downloads, citations, readings, reviews, recommendations, etc.), the distribution of scientific reputations of science leaders and selection of the best scientific knowledge is ensured. This function, according to the author, compensates for the lack of an external audience or public capable of understanding and adequately evaluating scientific achievements in the scientific communication system. It is concluded that the past “collegial and deliberative” assessment of scientific achievements, the appropriate distribution of reputations and support for research projects is incompatible with the dynamic conditions of the “publication market”.
Distributed Public Key Certificate-Issuing Infrastructure for Consortium Certificate Authority Using Distributed Ledger Technology

With the development of cloud services and the Internet of Things, the integration of heterogeneous systems is becoming increasingly complex. Identity management is important in the coordination of various systems, and public key infrastructure (PKI) is widely known as an identity management methods. In PKI, a certificate authority (CA) acts as a trust point to guarantee the identity of entities such as users, devices, and services. However, traditional CAs that delegate the operations to a specific organization are not always suitable for heterogeneous services, and a new methodology is required to enable multiple stakeholders to securely and cooperatively operate a CA. In this study, we introduce the concept of a consortium CA and propose a distributed public key certificate-issuing infrastructure that realizes a consortium CA. The proposed infrastructure enables multiple organizations to cooperatively operate a CA suitable for services involving multiple stakeholders. We identify four requirements for the cooperative operation of a consortium CA and design the proposed infrastructure with distributed ledger technology. Furthermore, we present the implementation of smart contracts with Hyperledger Fabric and prove that the proposed infrastructure satisfies the four requirements. Finally, we confirm that certificate issuance and verification are stable at approximately 4 and 3 ms, respectively.
A dynamic method to protect user privacy against traffic-based attacks on smart home

The Internet of Things (IoT) has revolutionized how people interact with their living spaces. However, attackers can perform traffic-based attacks to reveal the behavior of legitimate users, seriously compromising their privacy. Studies have proposed to obfuscate network traffic to avoid these attacks. However, there is still the challenge of ensuring a trade-off between privacy and network overhead. This work introduces a Method for IoT Network Traffic Obfuscation (MITRA) that protects user privacy in smart homes while keeping IoT network overhead low. The method relies on the dummy traffic injection following different levels of obfuscation. The different levels mask network traffic, improving privacy without harming network performance unnecessarily. Results show that the obfuscation of IoT device traffic reduces traffic identification accuracy by up to 42%.
Report Generation on Chest X-rays using Deep Learning

Nowadays the chest X-ray is the most frequently used diagnostic procedure. A radiology specialist can understand the detailed information contained in an X-ray of a chest regarding the human body’s heart and lungs. Specialists in radiology are typically tasked with reviewing chest X-rays so that patients can get the right treatment. Because a doctor may see more than 100 X-rays each day in larger cities and thorough inspection necessitates skilled doctors, obtaining a thorough medical diagnosis from such Xrays is frequently laborious and time-consuming. This project’s goal is to demonstrate deep learning techniques for autonomously extracting clinical data from X-ray images. Deep learning approaches have been combined with algorithms to tackle this difficult challenge, with promising performance. If such reports can be generated automatically by a trained model, a lot of time and effort can be saved. In this project, some deep learning techniques such as an encoder and decoder and a pre-trained chexnet model have been used. The task of obtaining visual features from an x-ray is performed by the chexnet model which is passed to an encoder which then sends its result to a decoder these types of techniques are mainly used in image captioning which aims to produce text from an image Here, LSTM is employed as the encoder while GRU and Bi GRU is used as the decoder. Both of these are recurrent neural networks. The generated text report is evaluated by the BLEU score.
How to react to hacker types and asset types in security decision-making

To efficiently manage information security, firms typically allocate limited budgets to protect their information assets based on asset types and hacker types. This paper builds a game-theoretical model to discuss the strategic interaction between a firm that tries to protect its core and non-core assets and a strategic hacker that can launch a targeted attack or indiscriminate attack. The analysis results show that irrelevant of the hacker types, when the firm’s core assets become more important or more vulnerable, both the firm and the hacker increase their efforts on core assets. Considering hacker types, the hacker who launches a targeted attack will always exert the highest efforts and lowest efforts on the firm’s two types of assets, while exerting moderate efforts when launching an indiscriminate attack. While considering asset types, when the gap between the value of core and non-core assets is large, the hacker exerts more effort on the core asset than that on the non-core asset, thus the firm has to exert more effort on the core asset than that on the non-core asset. With an increasing budget, the firm will allocate more security efforts for core assets than for non-core assets, while the hacker always decreases its efforts irrelevant to hacker types. Moreover, we find that the hacker can get higher payoffs from a targeted attack than an indiscriminate attack when considering a budget while getting equal payoffs from the two hacker types without a budget, however, the firm always needs to pay more to respond to the targeted attack no matter considering the budget or not. These findings highlight the importance of distinguishing asset type and hacker type and give some management insights that can guide a firm that faces a security budget to determine the optimum security effort. Future research directions are discussed based on the limitations and possible extensions of this study.
Voice Encryption using Chaotic Signal

The history of voice encryption dates back to the world war II when secure communication was used. During that period, noise was added to the voice signal in order to prevent data breach. Due to advancements in the field of internet, the risk of data breach is ever increasing. This in turn increases the need for secure data transmission. In recent decades, cyber security has been used to shield Internet data from adversaries. This has hence made randomness in data processing an essential part of the security. For a number of years, researchers and scientists have been interested in the field of randomness and chaos. The randomness of the data can be implemented using chaos theory. For voice encryption and decryption, this report uses chaotic circuits. Bhalekar-Gejji system has been used here as the master for encryption and Chen system has been used as the slave. The slave follows the master when the controller is turned on and hence the decrypted voice signal is obtained back. This model ensures secure voice transmission.
Hierarchical Two-stage modal fusion for Triple-modality salient object detection

Salient object detection (SOD) is essential for home service robot grasping technology. Recently, studies have shown that using triple-modality (RGB-Depth-Thermal infrared, RGB-D-T) information can significantly improve the detection effect of salient object detection. The location accuracy and completeness of the salient object play a crucial role in the subsequent grasping process. This seriously affects the robot’s judgment of the object position and grasping position. Therefore, the critical problem of triple-modality SOD technology in robot grasping is locating the salient object accurately and detecting the salient object completely. Consequently, we propose a triple-modality salient object detection method based on hierarchical two-stage modal fusion. In the first fusion stage, we use the triple-modal information rationally to locate the salient object accurately. Considering the properties of the different modal information, we use the depth information to supplement and improve the visible light and thermal infrared information through an accurate selection fusion module (ASFM). Then in the second stage, we use the feature correlation enhancement module (FCEM) to realize the correlation of the different modal salient features. FCEM can perfectly combine the different modal information and make the salient object more complete. Comparative and challenging experiments demonstrate that the proposed method outperforms 14 state-of-the-art methods on the VDT2048 dataset. The code is available at: https://github.com/VDT-2048/HTMF.
Service Decomposition method for multimodal data exchange scenarios

Influenced by the complexity of multimodal data exchange scenarios and their differences in data flow graph construction, the objective basis of existing service decomposition methods is not sufficient, and the granularity of the results is too large and difficult to be decomposed. To address this problem, this paper presents a service decomposition method for multimodal data exchange scenarios. This method reconstructs the differentially constructed data flow diagram by unified rules at the stage of requirement analysis and design, and then completes the fine-grained division of microservices around documents based on service-decomposed rules. The case study shows that the method is more effective than the existing method in the multimodal data exchange scenario.
Differentially Private Algorithms for Synthetic Power System Datasets

While power systems research relies on the availability of real-world network datasets, data owners (e.g., system operators) are hesitant to share data due to privacy risks. To control these risks, we develop privacy-preserving algorithms for synthetic generation of datasets for optimization and machine learning. Taking a real-world dataset as input, the algorithms output its noisy, synthetic version, which preserves the accuracy of the real data on a specific downstream model or even a large population of those. We control the privacy loss using Laplace and Exponential mechanisms of differential privacy and preserve data accuracy using a post-processing convex (or mixed-integer) optimization. We apply the algorithms to generate synthetic power network parameters and wind power data.