Reviewerly/exHarmony huggingface dataset:
An Extremely Pipelined FPGA-based accelerator of All Adder Neural Networks for On-board Remote Sensing Scene Classification

Directly completing remote sensing scene classification (RSSC) on space platforms can minimize latency and relieve data downlink burdens. The all adder neural network $( \mathrm{A}^{2}$NN) is a novel network for on-board RSSC with lower resource overhead than convolutional neural networks (CNNs). However, most of the existing FPGA-based accelerators are designed for CNNs and are not applicable to deploy $\mathrm{A}^{2}$NNs. In this paper, we propose an extremely pipelined FPGA-based accelerator of $\mathrm{A}^{2}$NNs and implement a VGGNet-11 backbone for on-board RSSC. In the proposed FPGA-based accelerator, an extremely pipelined processing engine (PE) suitable for accelerating the adder layer is designed. Each adder layer in the $\mathrm{A}^{2}$NN is mapped to a dedicated extremely pipelined PE, which achieves low-latency calculations. Besides, the entire parameters of the network are stored in block RAMs, and the intermediate data are cached on the FPGA chip, thereby eliminating external memory accesses and reducing power consumption. To evaluate the performance of the proposed extremely pipelined FPGA-based accelerator of $\mathrm{A}^{2}$NN, we implemented an $\mathrm{A}^{2}$NN-based RSCC model with the VGGNet-11 backbone on the Xilinx Virtex7 XC7VX690T FPGA by the proposed accelerator. The experimental results show that the proposed FPGA-based accelerator of $\mathrm{A}^{2}$NNs can achieve a throughput of 3.04 tera operations per second (TOPs) at 200 MHz while consuming 8.27 W.
Covert-channels in FPGA-enabled SmartSSDs

Cloud computing providers today offer access to a variety of devices, which users can rent and access remotely in a shared setting. Among these devices are SmartSSDs, which are solid-state disks (SSD) augmented with an FPGA, enabling users to instantiate custom circuits within the FPGA, including potentially malicious circuits for power and temperature measurement. Normally, cloud users have no remote access to power and temperature data, but with SmartSSDs they could abuse the FPGA component to instantiate circuits to learn this information. Additionally, custom power waster circuits can be instantiated within the FPGA. This paper shows for the first time that by leveraging ring oscillator sensors and power wasters, numerous covert-channels in FPGA-enabled SmartSSDs could be used to transmit information. This work presents two channels in single-tenant setting (SmartSSD is used by one user at a time) and two channels in multi-tenant setting (FPGA and SSD inside SmartSSD are shared by different users). The presented covert channels can reach close to 100% accuracy. Meanwhile, bandwidth of the channels can be easily scaled by cloud users renting more SmartSSDs as the bandwidth of the covert channels is proportional to the number of SmartSSD used.
Stochastic Implementation of Simulated Quantum Annealing on PYNQ

It is well-known that quantum annealing (QA) is an excellent algorithm for solving combinatorial optimization problems (COPs). However, its hardware implementation poses challenges, requiring quantum devices. Therefore, a quantum Monte Carlo (QMC) method-based annealing approach called Simulated Quantum Annealing (SQA), which aims to emulate quantum annealing using classical computers, has been proposed. In the demo-night session, we will demonstrate a SQA based on stochastic computing (SC) on a standard FPGA board, called “PYNQ”. The use of SC-based data representation, along with the realization of SC-based spin-gate circuits, makes it possible to drastically reduce hardware costs compared to QA. This enhances the suitability for extending large-scale COPs.
E<sup>2</sup>-ACE: An Energy-Efficient Reconfigurable Crypto-Accelerator with Agile End-to-End Toolchain

In today’s tech-driven society, the emphasis on data privacy and security has skyrocketed. With technological progress, the emergence of new encryption algorithms and advanced attack technologies compel the need for algorithm upgrades. With rising hardware costs and demand for flexible cryptographic platforms, single-algorithm accelerators are insufficient, making versatile accelerators supporting multiple encryption algorithms essential. Besides flexibility, energy and area efficiency are increasingly important for various encrypted application platforms like embedded devices. Currently, few cryptographic processing accelerators like Anole [1] prioritize energy efficiency and flexibility. However, their primary focus is on symmetric key algorithms and Hash algorithms, without including Fully Homomorphic Encryption over the Torus (TFHE) [2]. Solutions like MATCHA [3] focus on TFHE but compromise compatibility with other algorithms. Additionally, a user-friendly, end-to-end toolchain is lacking in existing solutions. To address these challenges, we propose $\mathrm{E}^{2}$-ACE, based on the TRAM [4], supporting symmetric key algorithms, Hash algorithms, and TFHE.
Traffic Flow Optimization using a Chaotic Boltzmann Machine Annealer on an FPGA

We demonstrate a chaotic Boltzmann machine annealer, which is a field-programmable gate array (FPGA)-based high-performance annealing machine, and a control system powered by two open-source software packages. The annealer is employed in solving a large-scale real-world optimization problem, the traffic flow optimization. This involves distributing 500 car routes between the Haneda Airport area to the Pacifico Yokohama area, the venue of the International Conference on Field Programmable Technology 2023, while minimizing the total driving distance and preventing traffic jams. We obtain solutions that are comparable in accuracy to solutions of simulated annealing running on a graphics processing unit (GPU-SA) and a central processing unit (CPU-SA). The annealing machine on an FPGA is approximately $\mathbf{5 7 1}$ and $\mathbf{9 7 6 0 0}$ times as fast as the GPUSA and the CPU-SA, respectively. We will demonstrate solving traffic flow optimization in the towns familiar to the visitors.
BayesLSTM-Based Algorithm for Predicting Water, Electricity and Gas Usage in Hospital Logistics

OBJECTIVE: To explore the effect of machine learning models on time series data prediction of hospital logistics water, electricity and gas usage in the era of big data, with a view to obtaining the optimal prediction model to better improve the resource management and resource allocation capacity of hospital logistics. METHODS: Monthly data of water consumption, electricity consumption and gas usage of logistics of a tertiary general hospital in Tianjin from January 2016 to July 2023 were selected to predict resource usage by constructing a Bayesian algorithm-based optimized long and short-term memory network algorithm, and multiple indicators were used to evaluate the prediction effect of the model under different training set occupancy ratios. RESULTS: For the prediction of water and electricity usage, the model's prediction effect is optimal in all indicators when the training set is 80 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ; for the prediction of gas usage, the model's prediction effect reaches the optimal value at 70% of the occupancy ratio, and the MSE, RMSE, NRMSE, and MAE are respectively 100385880, 10019.2754, 0.097924 and 7480.2344.CONCLUSION: The Bayesian optimization-based long and short-term memory network is able to improve the prediction performance of the model by automatically tuning the parameters and optimizing the hyperparameters, and is able to provide predictive data support for decision-making.
Research on Enterprise Credit Risk Assessment Based on Big Data of Electricity

Credit risk assessment is a very important part of safeguarding the healthy operation of banks. However, existing theories and methods are too dependent on financial data and complex methods. Based on this, this paper proposes the application of electricity data for enterprise credit risk assessment. By analysing the relationship between electricity data and corporate credit, this paper puts forward two theoretical hypotheses for electricity data to reflect corporate credit status. Firstly, from the perspective that abnormal fluctuations in electricity consumption reflect abnormal fluctuations in production, abnormal fluctuations in electricity consumption relative to the industry average tend to reflect abnormal fluctuations in the enterprise's production and operation conditions. Secondly, in terms of electricity payment behaviour reflecting cash flow conditions, late payment of electricity bills by a company is likely to indicate that its cash flow is under pressure, and therefore companies that are assumed to be unable to pay their electricity bills on time are at greater credit risk. Finally, this paper argues that the application of electricity data for credit default risk prediction of enterprises has broad prospects for practical application.
Recurrent Dendritic Neural Network

In the realm of deep learning, the prevalent use of the simplistic McCulloch-Pitts Neuron as a foundational building block lacks the interpretability required for complex models. In contrast, the dendritic neuron model (DNM) closely emulates biological neural networks and has demonstrated its effectiveness in diverse classification and prediction tasks. This paper introduces the Recurrent Dendritic Neural Network (RDNN), a novel artificial neural network based on the DNM framework. RDNN offers substantial advantages over traditional neural networks, particularly in financial time series forecasting. We propose that the incorporation of DNM as the fundamental unit in artificial neural networks enhances computational capabilities, positioning it at the forefront of the next generation of deep learning technologies.
Multilingual Text Classification Based On Deep Learning Models

In an era characterized by global interconnectedness, the imperative for seamless communication across diverse languages is more pronounced than ever. The present research introduces a sophisticated pre-trained model, denoted as Multilingual BERT (mBERT), meticulously engineered to execute text classification with efficacy across 21 European languages. This model is pivotal in augmenting performance uniformity amongst a myriad of European language families. Significantly, mBERT showcases enhanced parallelization and a notable reduction in processing time for extensive documents, standing out in comparison to its contemporaries. In addition, a distilled model is cultivated under the tutelage of mBERT, aiming to delve into the intricacies of knowledge transfer between the two entities. The empirical results corroborate the superior proficiency of mBERT in the realm of language type classification, thereby furnishing a robust methodology for language categorization. Concurrently, the distilled model adeptly assimilates knowledge from its precursor, mBERT, thereby validating the efficacy of the proposed methodology.
RGA-Reid: Neural Architecture Search Based on Relation-Aware Global Attention for Person Re-Identification

With the continuous development of deep learning, reid has been continually improved. However, the network backbones of reid are mostly manually designed. This paper proposes RGA-Reid based on auto-reid. NASBR-Reid obtains the network backbone through neural architecture search. It replaces the part-base module in the auto-reid search algorithm with the RGA module, which effectively integrates global relationships to enhance attention training between different parts. Experimental results demonstrate that RGA-Reid can retrieve superior network backbones.
Identification of Market Power Abuse by Power Generation Enterprises Based on SVM-ICC-DPRP Algorithm

With the advancement of the spot market, market power has become an existence that cannot be ignored, and there is currently no real-time monitoring method that abuses market power on the market. To this end, this article combines an indicator system with China's economic characteristics and a support vector machine that can realize automatic identification to obtain a list of companies suspected of abusing market power in real time, minimizing the workload of expert judgment. First, the article divides the constructed violation identification method into the construction stage and the use stage, and describes in detail how this method fully combines expert judgment and intelligent identification methods to achieve real-time identification; secondly, in terms of identification algorithm, it uses the network Based on the lattice algorithm, the SVM-ICC-DPRP algorithm is constructed. Finally, using simulation data as an experiment, the identification method of this article can quickly obtain the suspect list, with an accuracy of 99.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , which illustrates the effectiveness of this method.
Application of SSA-Fuzzy-PID in Liquid Level Control Systems

In industrial production, multi-capacity water tank level system as a typical control object, many controlled objects can be treated as a whole or locally as the mathematical model of multi-capacity water tank, which is very representative. The paper takes the double-capacity water tank as the control object, and by studying the working principle and dynamic characteristics of the automatic liquid level control system, the traditional PID control, though simple and easy to realize, performs poorly in dealing with uncertainty. In order to better find the optimal PID parameters from the complex nonlinear relationship, a PID controller combining SSA and mode Fuzzy-PID controller is adopted, namely SSA-Fuzzy-PID controller. Simulation modeling is built based on Simulink, and this controller is applied to the liquid level control system and compared with the traditional PID controller in the liquid level control system for simulation experiments. The simulation results show that the SSA-Fuzzy-PID controller has faster response speed, better robustness, can effectively suppress external disturbances, and realize faster and more accurate liquid level control.
Analysis of documents issued during two phases responding to COVID-19 in Dong Thap through matching against the set of essential functions of public health in Viet Nam.

The Dong Thap province study in 2022-2023 on "Analysis of Documents issued during the COVID-19 Pandemic Through Matching against the Essential Functions of Public Health" is one of a series of basic studies that the Public Health Association (VPHA) has conducted right during the COVID-19 pandemic in Vietnam. The main objective of these studies is to understand the gaps in the practical application of the functions and subfunctions of the EFPHs toolkit and to find effective complementaries to have a good preparedness for future health emergency response. The research series was divided into two phases: In phase one, the research object is documents issued during the pandemic, but only from agencies at the central level, including the government, the National Steering Committee, and the Ministry of Health. those default places are the most important decision-making bodies directly related to the pandemic. This first study was conducted by the Office of the Vietnam Public Health Association. The second phase is conducted in three provinces of the Public Health Association in three regions of Vietnam, including Yen Bai, Hue, and Dong Thap. The main objective of this second phase study is to describe the epidemic pattern occurring at the provincial level and decisions made at the local levels through documents issued: the textual data from the Province, District, and Commune were also matched against the EFPHs toolkit to draw specific lessons at the grassroots level in responding to a real pandemic. These studies were conducted by Yen Bai, Hue, and Dong Thap Public Health Associations with technical support from the Vietnam Public Health Association. Readers are invited to follow the specific research results of each province. Keywords: public health functions, directive documents, Covid-19, Dong Thap
Proses Pembentukan Efikasi Diri dalam Pembelajaran Tari bagi Siswa Kelas XI SMAN 1 Soreang

Penelitian ini dilatarbelakangi oleh suatu proses pembentukan efikasi diri yang diterapkan dalam pembelajaran tari. Istilah efikasi diri bagi peneliti merupakan sebuah usaha untuk berproses dalam kegiatan belajar dan untuk menentukan hasil evaluasi belajar siswa dari segi kompetensi dan kognitif dengan tujuan meningkatkan kualitas belajar siswa. Tahapan proses pembentukan efikasi diri yaitu proses kognitif, proses motivasi, proses afeksi, dan proses seleksi. Selain itu, efikasi diri juga dapat berperan sebagai pengontrol emosi, kemampuan, dan kecemasan dalam diri individu siswa ketika menghadapi atau menyelesaikan pekerjaan dalam sebuah pembelajaran. Penelitian ini menggunakan metode deskriptif dengan pendekatan kualitatif. Informasi diperoleh dengan teknik pengumpulan data secara observasi langsung, wawancara, kuesioner, dan studi dokumentasi. Berdasarkan temuan di lapangan terlihat bahwa dengan adanya proses efikasi diri ini mampu mendorong perubahan positif bagi siswa dalam mengembangkan diri dan kreativitas melalui pembelajaran tari, sehingga siswa mampu dan berani mengalahkan keraguan akan kemampuannya pada pembelajaran tari. Hasil penelitian ini diketahui dan dipahami seperti apa dan bagaimana proses serta hasil efikasi diri yang diperoleh dalam pembelajaran tari dan menghasilkan perubahan tingkatan kemampuan individu pada siswa secara maksimal. The Formation Process of Self-Efficacy in Dance Learning for SMAN 1 Soreang 11th-grade Students ABSTRACT This research is motivated by a self-efficacy formation process applied in dance education. The term self-efficacy, for the researcher, signifies an effort to engage in the learning process and determine students' learning evaluation results in terms of competence and cognition with the goal of enhancing the quality of student learning. The stages of the self-efficacy formation process include cognitive processes, motivation processes, affective processes, and selection processes. Additionally, self-efficacy can also function as a control mechanism for emotions, abilities, and anxiety within individual students when they encounter or complete tasks in a learning environment. This research employs a descriptive method with a qualitative approach. Information is gathered through techniques such as direct observation, interviews, questionnaires, and document analysis. Based on field findings, it is apparent that the presence of this self-efficacy process can stimulate positive changes in students who are developing themselves and their creativity in dance education, thus enabling students to overcome doubts about their abilities in dance education. Therefore, this research aims for the researcher to understand the nature and process of self-efficacy and its outcomes in dance education, with the ultimate goal of achieving maximum improvement in individual students' skills.
A Study on the Perception of the Principals Regarding the Difficulties in the Implementation of Online Education in Rural Small Schools

This study examines the challenges faced by principals in rural small schools when implementing online education in response to the COVID-19 pandemic. The Main objective was to understand the nature of the online education in these schools and identify specific difficulties encountered by the principals. The study used a mixed-method, sequential explanatory research design. Forty eight school principals were selected randomly out of 130 schools according to the research criteria (n=48). Data was collected through online questionnaires and qualitative interviews. Findings indicated that although principals have a basic understanding of online education, further development is needed. It was found that practical problems and difficulties hindered the effective implementation of online education in rural small schools. Recommendations included implementing a formal awareness program for all school stakeholders and mobilizing necessary resources.
Intellectual Property Rights in the Era of Artificial Intelligence: Navigating the Challenges and Expanding the Boundaries

This article delves into the tangled web of artificial intelligence (AI) and intellectual property (IP) rights. It investigates the issues raised by AI generated works such as machine-generated art, music, and literature, as well as the issues of ownership and authorship in these cases. The essay also examines the influence of AI on conventional intellectual property domains such as patents, copyrights, and trademarks, as well as the legal and ethical consequences of AI-driven innovation. This article provides ways for balancing IP protection and supporting innovation in the AI age by studying case law, international treaties, and developing policy.
An Analytical Study of the Subject of Education Practice in National Colleges of Education

National Colleges of Education, which contribute greatly to the quality development of Sri Lankan education, are engaged in the role of training teachers and provide necessary human resources for the school system. Currently, 19 National Colleges of Education have been established across the island and are the main institutions involved in pre-service teacher training. The learning teaching process of the teacher directly affects the development of the students in the school system. Therefore, teacher preparation programs greatly affect students’ learning, and as a result the quality and performance of educational practice programs implemented in National Colleges of education should be investigated. Thus, this research has been done to study how education practice subject in the National Colleges of Education contributes to the development of teaching professional skills. The main problem of this research is the analytical study of the content of the education practice subject in the National Colleges of Education and how it works. The study also aims to examine the contribution of education practice subject to the development of the professional skills of the National Colleges of Education trainees. In relation to the main research problem, the nature of the education practice subject implemented in the National Colleges of Education and its implementation status in the institutional training as well as the student perception of the subject have been investigated. The convergent parallel design has been used for this research under mixed research methodology. The teacher trainees of five national colleges of education were selected under the simple random sampling method, the lecturers were selected under the purposive sampling method and a sample size of three hundred and thirty-five has been used for this research. In this research, strategies for modernizing the educational practice subject have been discussed. This research which examines the subject of educational practice in the National Colleges of Education will be useful for the professional development of the student teachers studying in the colleges and for future research.
FEATURES OF DEVELOPING THE CREATIVITY OF PROSPECTIVE PRIMARY SCHOOL TEACHERS IN A CONTINUOUS EDUCATION SYSTEM

Creativity in a continuous education system is a process that involves direct innovation. This article discusses the specifics of developing the creativity of prospective primary school teachers.