Reviewerly/exHarmony huggingface dataset:
A Study on the Application of Zero Trust Security Model in the Financial Sector

The changed hybrid work environment such as the expansion of cloud computing technology and the increase in telecommuting due to the coronavirus infection (COVID-19), has brought about new security problems. The traditional perimeter security model has limitations in protecting various devices and environments because it is difficult to clearly distinguish network boundaries. The Zero Trust model, which has been proposed as an alternative security strategy to overcome this, is based on the premise that there is no trust in the assets and actors that are connected components when configuring corporate infrastructure and workflow. The financial sector is actively seeking to introduce the zero trust security model as a means of strengthening security. Demand for responding to advanced cyber-security threats, strengthening security policies, and applicability and necessity of a zero trust security model in the financial sector has expanded. This study proposed the components of a zero trust security model to be applied to the financial world based on financial governance and zero trust principles, components of the maturity model, and the results of expert interviews. The zero trust security model in the financial sector consists of four layers and eight detailed steps: perimeter security layer, authority definition layer, behavior detection layer, and internal control layer. This zero trust security model for the financial sector is expected to provide guidelines for key elements and construction steps to be considered when introducing zero trust in the financial sector.
Analysis and Suggestion of Adjective Vocabulary List in Elementary Chinese Textbooks

This paper examined what adjectives are introduced for beginner Chinese textbooks “Chinese Master” step 1 to 3 and “New Strategy Chinese” 1 to 3, and how much the list of adjectives in these textbooks meets the “Chinese Grading Standards(Chinese Proficiency Grading Standards for International Chinese Language Education)”, a new standard for international Chinese education.94 adjective vocabularies were presented in step 1 to 3 of “Chinese Master” and 104 adjective vocabularies in “New Strategy Chinese” 1 to 3. The adjective vocabularies in the two textbooks were mostly in the beginner level, but the intermediate level adjectives also accounted for a considerable proportion of about 20% in both textbooks. As it is a beginner Chinese textbook, it is necessary to reduce the proportion of intermediate and advanced words.There are 259 elementary adjective vocabularies in “Chinese Grading Standards”, and among them, 63 and 75 adjective vocabularies presented as new words in the two textbooks, respectively, account for 24.3% and 29% of elementary adjective vocabulary in “Chinese Grading Standards”. Based on “Chinese Grading Standards”, it can be seen that it does not contain many adjective vocabulary that should be learned at the beginner level. In particular, since the reflection rate of adjective vocabulary in the second and third grades is low, it seems necessary to supplement the adjective vocabulary in the second and third grades in a large amount.Since the “Chinese Grading Standards” has a significant change in the number of vocabulary compared to existing standards, Chinese textbooks need to be designed based on the vocabulary list of the “Chinese Grading Standards” in the future. In the case of adjectives, it is possible to increase the ratio of vocabulary reflection in the corresponding level by introducing related vocabularies such as antonyms and synonyms together.Based on the list of adjectives in beginner Korean vocabulary, this paper also looked at adjective vocabulary that is especially useful for beginner Chinese learners with Korean as their mother tongue. Since both textbooks are mainly used for Korean learners in Korea, beginner Chinese adjectives that may be frequently used by native Korean learners can be included first. In addition, in the case of intermediate and advanced adjectives corresponding to beginner Korean adjectives, it was suggested that vocabulary that is highly utilized by Korean learners can be selectively reflected in the textbook.
Research on the production, application and management of virtual reality in the National Palace Museum

Purpose Virtual reality (VR) can be used as an alternative mean for viewing collections at home when it is not possible to visit museums due to COVID-19. This study took the development process of VR at Taiwan's National Palace Museum (NPM) as a case to discuss the characteristics of VR developed there in different periods and how NPM transforms the contents of its collections into VR. Design/methodology/approach This study used a case study to analyze the development process of VR at NPM from 2014 to 2019 and summarized the characteristics of the development and application of VR. Findings The authors find that the history of VR application in NPM is a process from exploring the technology to gradually getting familiar with the potential of its application. Its development can be divided into the exploration and experiment stage from 2014 to 2015, the single collection interpretation stage in 2016 and the multipurpose application stage from 2017 to 2019. It is suggested that museums should adopt a long-term strategy to introduce VR, make plans carefully and pay attention to the limitations of VR application. Research limitations/implications The results of this study are suitable for art and history museums. Originality/value Many research studies on the application of VR in museums mostly focused on the benefits and technologies of adopting VR in museums as well as specific museum VR projects. There is still scant literature on the development process of museum VR from the perspective of museum organizations.
SLR : SPI Dan Kecurangan Akuntansi Terhadap Keamanan Data Dalam SIA Di Era BIG DATA

Technological advances in the big era have brought accounting to balance its existence as a discipline that continues to grow. Apart from the many conveniences experienced by users of the accounting information system (AIS), data security and the threat of cybercrime are new challenges for accountants to effectively strengthen the internal control system (SPI). Therefore this study aims to examine the role of SPI and accounting fraud on data security in AIS in the big data era. The method used is Systematic Literature Review with PRISMA. The results of the study showed that of the 200 articles downloaded, there were 34 articles that discussed indicators in this study, and only 25 articles met the inclusion and exclusion criteria. Based on the 25 articles, it explains that: (1) SPI can reduce accounting fraud that occurs in an organization; (2) technological advances increase data security threats in AIS; and (3) cyber crime cases can threaten data security in accounting information systems.
An adaptive output-feature knowledge distillation of YOLOv5 for identifying magnetic tiles

Identifying magnetic tiles is vital in the sorting and transfer process of magnetic tile manufacturing. Target detection algorithms empowered by deep learning facilitate accurate and fast identification for magnetic tiles. However, many existing deep learning algorithms are limited by the computing power of edge devices to be deployed easily in real production. In this paper, we propose a lightweight target detection method based on the output-feature knowledge distillation of the compressed YOLOv5 network to identify magnetic tiles. Adaptive weighting is used for the output feature distillation to transfer the helpful feature information from the teacher network to the student network in improving the efficiency of the model. To verify the effectiveness of the proposed method, the accuracy, speed, and size of the compressed model in identifying magnetic tiles are tested and evaluated, respectively. The experimental results show that the lightweight model obtained by our method reaches 98.1% in mAP, which is 1.1% higher than the original model. The average inference speed of a single image is 11.1ms faster than that of the teacher network, but only 16.2% of the teacher's model in terms of the number of model parameters.
UAV Trajectory Planning via Adaptive Potential Field Ant Colony Algorithm

An innovative approach to UAV trajectory planning is introduced through the utilization of an adaptive potential field ant colony algorithm. This method enhances initial-stage search efficiency by refining adaptive state transition rules. By integrating the initial trajectory from the artificial potential field method alongside distance data for subsequent nodes, heuristic information optimization is employed to circumvent local extrema. Concurrently, the introduction of a heuristic information attenuation coefficient serves to diminish the influence of heuristic data. Dynamically modify pheromone update rules to enhance initial global search, expedite mid to late-stage convergence, and maintain trajectory diversity. Simulation results confirm the algorithm's feasibility and efficacy.
Privacy preserving Double Auction using Secure Multiparty Computation for Local Electricity Markets

A major challenge for the implementation of local electricity markets (LEM) with small-scale prosumers is to ensure the security and privacy of data. The prosumers will need to reveal their future energy usage to the market operator (MO), which is a huge privacy concern. To address this issue, this paper presents a double auction model for local electricity markets, where the MO does not have access to the individual prosumer’s bids, ensuring complete privacy for prosumers. The scheme uses Pallier encryption cryptosystem which allows aggregation of prosumer bids without the need for decryption. The MO receives only the aggregated prosumer bids, for implementing the double auction algorithm. The proposed scheme is illustrated through a case study with 15 prosumers in a LEM.
Blockchain-Based Mechanism for Smart Record Monitoring During and After the COVID-19 Pandemic

The COVID-19 pandemic has highlighted the significance and importance of introducing intelligent devices to improve living standards. Technological advancements and inventions have proven particularly beneficial during times when face-to-face contact is limited. Machine vision, as a cutting-edge paradigm, has emerged as a significant tool in handling various COVID-19 and post-COVID-19 situations. Intelligent devices aim to automate processes and enhance society’s quality of life by reducing reliance on human intervention. However, security concerns and technological advancements necessitate organizations and businesses to adopt robust security and privacy mechanisms instead of solely relying on intelligent and smart measurement methods. This article aims to present a comprehensive overview of security and privacy mechanisms for facilitating efficient communication, decision making, planning, information recording, and management using smart devices in postpandemic scenarios. Additionally, the article offers a summary of potential techniques and basic solutions for secure communication in the future.
Transformer-based Point Cloud Denoising Network

Point clouds are widely used for 3D data representation in various applications, but they often suffer from different types of noise during acquisition and processing. Point cloud denoising is the process of effectively removing noise while preserving geometric features. Traditional methods rely on handcrafted filters and may struggle with unknown noise types, while learning-based approaches may focus primarily on local information, leading to potential loss of fine details. In this paper, we introduce TPCDN, a denoising network for point cloud based on the transformer architecture, which combines local and global information to improve denoising performance. TPCDN leverages three essential components to optimize denoising performance: (1) a feature extraction module captures local features adaptively for each point, providing a foundation for denoising; (2) a point cloud Transformer module captures long-range dependencies to enhance denoising; (3) a feature fusion module seamlessly integrates local and global features, aiming to provide precise information for denoising. Through rigorous experimentation with both synthetic and real-scanned datasets, it has been demonstrated that TPCDN surpasses existing methods in quantitative accuracy and preservation of structure.
Teaching Experience on The Concept of Sensory Disability

The development of teaching skills from educational innovation seen from inclusive education recognizes the parameter of alignment from the role of how to relate new alternatives, the use of virtual reality in didactic situations device called SISI that accounts for the role of the relational aspects of teachers when faced with social problems, and collects through a Likert-type survey the perceptions of 166 teachers about their knowledge and teaching skills to work with deaf, blind, deafblind and low vision students. This is a descriptive, non-experimental, quantitative study. This type of alternative as a result of research in the field facilitates the coupling of new work elements.
Pengembangan Bahan Ajar Keterampilan Menulis Bahasa Inggris Berbasis Creative, Active, Systematic, dan Effective (CASE) Bagi Siswa Sekolah Dasar di Kota Tangerang

The research aim is to develop teaching materials for English writing skills based on Creative, Active, Systematic, and Effective (CASE) for Elementary School Students in Tangerang City. Creative, Active, Systematic, and Effective (CASE) based teaching materials are learning resources that can direct students to learn creatively, actively, systematically, and effectively so they can gain maximum knowledge and understanding of the material. The research method used in this study uses Research and Development (R&amp;D) using the Dick &amp; Carey approach. The subjects of this study were fourth grade students from SDN Cikokol 4 Tangerang. The instrument in this study was a product assessment questionnaire given to students, teachers, and media expert, material expert as well as English language experts. The result indicated that the product of material development gained average of 82,4% or in category of very sufficient, and the pretest posttest result was t-count 2,319 &gt; t-table 2,048. In other words, it can be concluded that there was a significant different score between pretest and posttest in terms of writing skills. Thus, it can be stated that the product of materials development can be used in the teaching learning English and can develop students writing skills effectively.
Effectiveness Of Utilization Of Gutter Fund-Based Bos Funds At Public Elementary School 2 Budiasih

Finances in an educational unit must be properly managed. It is important to participate in contributing to school funds so that the funds received function effectively and efficiently. The use of BOS or funds needs to be based on agreements and conclusions with the managers of these BOS funds, teachers and school institutions. Based on the results of the agreement, it is submitted in the form of written minutes of the meeting and signed by all meeting participants. This research is a type of qualitative method which requires the design of field case study research with descriptive-analytic characteristics. In collecting data using techniques for collecting this data will use, by way of interviews, documentation and observation. This can be shown by the results of this study by showing that the frequent delays in the allocation of funds at SD Negeri 2 Budiasih have an impact on learning activities at the school. If there is a delay in the distribution of school operational funds, the school's needs will become more difficult. Evaluation must be carried out so that school operational assistance funds are distributed on time with the right calculation in achieving all school activities and needs
Special Issue on Multimodal processing and robotics for dialogue systems (Part 1)

"Special Issue on Multimodal processing and robotics for dialogue systems (Part 1)." Advanced Robotics, 37(21), pp. 1347–1348
NASA-F: FPGA-Oriented Search and Acceleration for Multiplication-Reduced Hybrid Networks

The costly multiplications challenge the deployment of modern deep neural networks (DNNs) on resource-constrained devices. To promote hardware efficiency, prior works have built multiplication-free models. However, they are generally inferior to their multiplication-based counterparts in accuracy, calling for multiplication-reduced hybrid models to marry the benefits of both approaches. To achieve this goal, recent works, i.e., NASA and NASA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> , have developed Neural Architecture Search (NAS) and Acceleration frameworks to search for and accelerate such hybrid models via a tailored differentiable NAS (DNAS) engine and dedicated ASIC-based accelerators. In this paper, we delve deeper into the inherent advantages of FPGAs and present an enhanced approach called NASA-F, which focuses on FPGA-oriented search and acceleration for hybrid models. Specifically, on the algorithm level, we develop a tailored one-shot supernet-based NAS engine to streamline the search for hybrid models, eliminating the need for executing NAS for each deployment as well as additional training/finetuning steps. On the hardware level, we develop a chunk-based accelerator to fully leverage the diverse hardware resources available on FPGAs for the acceleration of heterogeneous layers in hybrid models, aiming to enhance both hardware utilization and throughput. Extensive experimental results consistently validate the superiority of our NASA-F framework, e.g., we can gain <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <inline-formula> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula>0.67<inline-formula> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> </b> top-1 accuracy over the prior work NASA on CIFAR100 even without additional training steps for searched models. Additionally, we can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 1.86 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> throughout and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 2.16 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> FPS with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 0.39% top-1 accuracy over the state-of-the-art multiplication-based system on Tiny-ImageNet. Codes are available at https://github.com/shihuihong214/NASA-F.
Secure Access Service Edge Convergence: Recent Progress and Open Issues

Secure Access Service Edge is a digital security technique attracting enterprise network and academic interest. Digital transformation is a fundamental goal for organizations, but maintaining and safeguarding remote and sophisticated IT systems is difficult.
EPM-Net: Efficient Feature Extraction, Point-pair Feature Matching for Robust 6-D Pose Estimation

Estimating the 6-D poses of objects from RGB-D images holds great potential for several applications. However, given that the 6-D pose estimation accuracy is significantly affected by occlusion and noise between the objects in an image, this paper proposes a novel 6-D pose estimation method based on Efficient feature extraction and Point-pair feature matching. Specifically, we develop the Efficient channel attention Convolutional Neural Network (ECNN) and SO(3)-Encoder modules to extract 2-D features from the RGB image and SO(3)-equivariant features from the depth image, respectively. These features are fused in the DenseFusion module to obtain 3-D features in the camera space. Meanwhile, we exploit CAD model priors to obtain 3-D features in the model space through the model feature encoder, and then we globally regress the 3-D features in the camera and model space. According to these features, we generate oriented point clouds in each space, and then conduct point-pair feature matching to obtain pose information. Finally, we perform direct pose regression on the 3-D features in the camera and model space, and then resulting point-pair feature matching pose information is combined with the direct point-wise pose regression information to enhance pose prediction accuracy. Experimental results on three widely used benchmarking datasets demonstrate that our method achieves state-of-the-art performance, particularly for severe occluded scenes. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Karthus0930/EPM-Net</uri>
CoLive: Edge-Assisted Clustered Learning Framework for Viewport Prediction in 360$^\circ$ Live Streaming

The exceptionally high bandwidth requirement for delivering high-quality live 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video poses a significant challenge to current network capacity. Mitigating such bandwidth starvation necessitates accurate field-of-view (FoV) prediction to focus limited resources on the viewer's area of interest. However, FoV prediction for live 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> streaming can be complex due to the time-sensitive nature of live content and the limited knowledge available for model training. Our paper introduces a novel framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoLive</i> , for predicting the FoV in 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> live streaming. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoLive</i> accelerates FoV prediction by offloading model training from viewers to the edge and migrating saliency feature detection to the server side. Observations on user clustering of viewing behaviors further motivate us to propose a novel dynamic clustered learning algorithm. The algorithm dynamically groups users according to their model update gradients and enables them to train a shared model that better suits their viewing preferences. We conduct extensive experiments on the public 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video datasets and demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoLive</i> outperforms state-of-the-art solutions in terms of prediction performance and bandwidth savings.