Reviewerly/exHarmony huggingface dataset:
A Smith-Waterman Hardware Accelerator Design using Sliding Window for Genomic Sequence Alignment

This paper presents a hardware accelerator based on the Smith-Waterman algorithm, a well-known sequence alignment technique in bioinformatics. The proposed accelerator enhances its hardware efficiency significantly by employing a sliding window technique under a linear systolic array structure. The multiple processing element (PE) in the array allows parallel computations of the alignment, improving hardware resource utilization. When implemented in a 32-nm CMOS technology, the proposed Smith-Waterman hardware design enhances the area, delay, power, and energy by 80.6%, 52.9%, 67.4%, and 84.6%, respectively, versus the traditional counterpart without the sliding window scheme.
Time Series Analysis with Three Types of Noise-Mixing Effects by Neural Network

One-dimensional convolutional neural networks are often used in time series analysis. However, when noise is mixed into the data, incorrect recognition is performed and discrimination is affected. Therefore, in this study, three types of color noise are mixed into the chaotic data and compared with the case where an autocorrelation function is used.
Computation Exactness Exploration of Exact Quantum Adders in NISQ

With the rise of Noisy Intermediate-Scale Quantum (NISQ) computing, the selection of suitable adders becomes more crucial for optimizing quantum computations. The ripple carry adder (RCA) and carry lookahead adder (CLA) are fundamental quantum adder architectures, yet, their performance under NISQ environments remains to be largely unexplored. In this paper, we extensively compare the RCA and CLA designs through a systematic analysis in NISQ computing. Our results reveal that the proposed quantum RCA has a 29.7% lower error rate than the existing quantum CLA, highlighting the importance of simple exact quantum adder designs tailored for specific noisy quantum hardware constraints. These findings offer the crucial insights for designing efficient quantum algorithms and optimizing quantum circuits for practical NISQ systems.
Design of Wafer Vision Alignment System using Hardware Simulator

This paper presents the results of designing a wafer vision alignment system using hardware simulator. As semiconductor processes become finer, a high-speed template matching system is required to process high-resolution images repeatedly and quickly. Current software vision alignment systems have limitations in processing high-resolution images at high speed. To improve these problems, we propose a low-latency, high-speed wafer vision alignment system optimized for FPGA-based image markers. As a preliminary work for the design of an FPGA-integrated system, we change an OpenCV-based software with an algorithm optimized on a hardware simulator and presents verification results.
Quantum Biometric Fingerprint Encryption based on Twofish Algorithm

The use of large-scale biometric fingerprint systems in both commercial and governmental systems has increased the interest and acceptance of users worldwide. Unfortunately, someone can copy and use fingerprints to assume another identity. Fingerprint encryption is the first and most effective way to keep it confidential. This paper presents a quantum-based method to encrypt fingerprints using the Twofish algorithm. The quantum representation used to store fingerprints is an adapted version of the Novel Enhanced Quantum (NEQR) model. The quantum circuits have been implemented and tested both on the IBMQ quantum computer and locally, on the QASM quantum simulator.
Continuous Convolution Accelerator with Data Reuse based on Systolic Architecture

Convolution operation is a crucial technique in the field of artificial intelligence (AI), particularly in image processing-based applications. However, a significant amount of computation time is required to perform an operation on a vast amount of data. The conventional operation processing method of the systolic array architecture tends to accelerate the speed of convolution operations by reusing only the weight data. To minimize data movement time, we propose a systolic array architecture that partially reuses the input feature map. Compared to the conventional systolic array accelerator, the proposed architecture demonstrated a throughput improvement of ×6.
High-Performance Rejection Sampling Hardware Circuit Design for Kyber

CRYSTALS-Kyber is a powerful Post-Quantum Cryptography(PQC) with high resistance to quantum computer attacks. In order to optimize the core sampling structure of Kyber so as to improve the hardware execution efficiency, this paper proposes a high-performance rejection sampling hardware circuit design scheme for Kyber. The scheme first divides the input random numbers by width converter, and then combines four parallel sampling cores to shorten the sampling time period; then on the basis of the high parallelization of sampling cores, this work use the reorganization of random numbers and the bit-weighted value characteristics of binary numbers to design high-efficiency comparator to reduce the rejection rate, and reduce the sampling time and overhead; and finally, splice the valid inputs and set up a buffer to store the generated samples. The scheme is verified for its performance by testing under Taiwan Semiconductor Manufacturing Company(TSMC) 65nm process and FPGA implementation under Vivado platform. The experimental results demonstrate that the optimized sampling circuit reduces the rejection rate from 18.73% to 11.33% and shortens the sampling clock period by 80.84% compared to the basic implementation; comparing with the related work, it reduces the area by 67.77% while controlling the sampling time, and the consumption of hardware resources is greatly reduced, which improves the efficiency of the hardware execution of the Kyber rejection sampling.
An Automated Toolchain for QUBO-based Optimization with Quantum-inspired Annealers

Recently, quantum computers have drawn attention to their potential to solve problems faster than classical computers. However, quantum hardware’s limited practicality and scalability have led to an interest in alternative approaches to solving optimization problems. One such approach is classical quantum-inspired annealers, which provide efficient and scalable solutions for combinatorial optimization problems (COPs) using classical hardware. To use annealers, COPs must be formulated as quadratic unconstrained binary optimization (QUBO) forms. Current tools require coding expertise and manual parameter tuning, posing barriers to entry. To address these challenges, we developed a user-friendly software toolchain that offers several advantages. Our toolchain features a friendly input and output interface, an automated parameter tuner, and a library of commonly encountered COPs. Our software toolchain’s accessibility promotes the use of quantum-inspired annealers and accelerates the development of practical solutions for real-world problems.
General Vector Instruction Extension for GF(2<sup>m</sup>) Polynomial Operation in Post-quantum Cryptography

We present a general vector instruction extension applicable for both ARM NEON and RISC-V Vector Extension. The extension targets efficient bit-manipulation and can provide considerable speedup for applications in GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) such as code-based post-quantum cryptography schemes. The effectiveness of the extension is evaluated by using the custom instructions to optimize the kernel operations in BIKE key-encapsulation schemes. We first innovate vectorized versions of bit-polynomial multiplication and inversion algorithms in GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) and propose vector instruction extension. Furthermore, a configurable hardware unit has been proposed to support custom operations of different bandwidths at little cost and constant latency. Both experiments on Xilinx UltraScale+ ZCU104 for ARM and simulations on gem5 for RISC-V have been carried out. Compared to portable C implementation, the result shows a speedup for bit-polynomial multiplication and inversion of up to 13x and 16x in ARM, 13x and 22x in RISC-V respectively.
Memory-Efficient Compression Based on Least-Squares Fitting in Convolutional Neural Network Accelerators

Convolutional Neural Networks (CNNs) generate massive interlayer feature data during network inference. To improve the throughput and energy efficiency in embedded systems, the feature map compression has been discussed for reducing the data movement. In this paper, we present a hardware compression scheme leveraging least-squares fitting (LSF), which substantially reduces the amount of interlayer data generated in the CNN inference process. The mean-square error (MSE) threshold for LSF fitting is optimized through a derivative-free algorithm. This work can be incorporated with Huffman coding method to further compress the interlayer data. A prototype accelerator equipped with compression scheme is implemented in TSMC 28-nm CMOS technology. We design an approximate calculation circuit to optimize the area consumption of the heavily used divider in compression method, which facilitates a remarkable reduction in area, which is less than 10% of systolic array. The compression scheme achieves 1.43x~1.70x interlayer feature map reduction by adding light hardware area overhead.
Back to the Analog Neural Network and Linear Circuit Theory

This paper describes two research results inspired by classical analog neural networks composed of resistor networks. The first one is the dynamics of active resistor network motivated by vision chip; this is curiosity-driven theoretical research. The second one is an asynchronous SAR ADC architecture with parallel comparators and switched capacitor configuration, based on asymmetric Hopfield network; this is targeted for practical application.
A Low-Complexity Algorithm for JPEG-LS-Based RAW Domain Compression

In this paper, a novel lossless and near-lossless RAW image compression algorithm based on JPEG-LS is proposed. To achieve high performance and low complexity, the algorithm reduce the context module, merge the run mode with the regular mode and propose an adaptive Golomb-Rice coding technique. The algorithm mainly consists of four parts : pixel structure conversion, pixel prediction, run mode module and adaptive Golomb-Rice coding module. Compared to JPEG-LS, this algorithm achieves comparable performance to JPEG-LS while reducing complexity. Additionally, it allows users to adjust parameters to achieve lossless and near-lossless compression supporting random access and non-random access.
Aircraft aerodynamic parameter correction technology based on Tent-PSO

The accuracy of aerodynamic parameters is a key factor affecting the guidance and control system of aircraft, but most of the current theoretical calculation methods have certain errors. To address this problem, this paper uses the flight test data of aircraft to correct the theoretically calculated aerodynamic parameters. In order to solve the problem of slow manual correction of aerodynamic parameters, firstly, a particle swarm optimization (PSO) algorithm is proposed to intelligently correct the aerodynamic parameters, and on this basis, a chaos (Tent) initialization strategy and a dynamic inertia weight strategy are introduced to obtain the Tent-PSO algorithm. Secondly, the optimization performance of Tent-PSO and PSO algorithms on standard functions is compared, and the results show that Tent-PSO has better performance than PSO. Finally, Tent-PSO and PSO are used for aerodynamic parameter correction to obtain the corrected data. A digital simulation is performed using the corrected aerodynamic parameters. The simulation results are consistent with the actual flight data, indicating that the aerodynamic parameter correction technology based on Tent-PSO has advantages and certain engineering significance.
Design of human-computer interaction system based on virtual reality and its application in the dissemination of study lodge culture

Virtual reality (VR) is an emerging computer environment that utilizes virtual environments to achieve virtual assembly. It has broad application prospects and important value in multiple fields. Interactivity is one of the main characteristics of virtual environments. In order to improve the intelligence of the cultural experience process in the "study lodge" and meet the dissemination of traditional Chinese culture among a wider audience, VR technology is introduced to design and apply an intelligent human-computer interaction system for the "study lodge" from both hardware and software aspects. Firstly, the hardware structure of the system was designed and MEMS measurement and communication equipment was determined. Secondly, with the support of hardware devices, VR technology is matched with PC terminals to establish an indoor 3D environment on the terminal devices. Communication between different servers and terminals is established to achieve the web embedding and "study lodge" communication control in 3D scenes. Using touch screen interaction as the interactive end of the human-computer interaction system, we adopt the method of developing human-computer interaction interfaces to design good interaction between multiple terminals. The user interface interaction function and virtual handwriting function were implemented in the Unity engine. Finally, the features of the collected data were extracted through human-computer interaction method, and the extracted features were input into the classifier for feature recognition, achieving human-computer interaction.
The Application Path of AIGC in Assisting Game Character Design - A Case Study of the Black Bear Monster from Journey to the West

With the continuous development of artificial intelligence technology, AIGC (AI Generated Content) technology has been widely applied. However, there is still a lack of an effective AI-assisted design pathway in the field of game character design. This paper takes a perspective from game character design and is based on the cultural connotations and aesthetic features of the Black Bear Monster from "Journey to the West". It constructs an application pathway that introduces AIGC to promote innovative thinking activities and enhance the creative abilities of designers, thereby achieving AIGC-assisted game character design. The aim of this paper is to optimize the game character production process, provide insights for the creation of monster characters in "Journey to the West", and promote Chinese monster culture and the spirit of the journey.
A Study on the Impact of Deviation in VR Natural Pointing

Virtual reality technology supports more immersive, interactive, and imaginative interactive scenarios, and it has demonstrated unique advantages in various application domains. Selection is the basic task in 3D interaction, and its efficiency influences the usability of interactive systems. Different factors in both the input and output spaces of the interactive environment can affect selection tasks. Accordingly, this study examines the magnitude of deviation between eye-hand ray and index finger ray during natural pointing behavior towards targets. It also investigates whether the deviation of these two raycasting methods is affected by changes in target location factors, and the patterns of this influence. The research findings indicate that target location factors, particularly target distance and target azimuth, have a more significant impact on the deviation, and there is a positive correlation between the deviation of the two raycasting methods. These results provide valuable insights for optimizing pointing techniques in virtual reality interactive environments.
Financial News Sentiment Analysis Method Based on WMSA-Bi-LSTM

In order to analyze and judge the sentiment of financial market more accurately, a financial news sentiment analysis model WMSA-Bi-LSTM is proposed. First of all, the financial market sentiment lexicon is established to assist Word2Vec to convert the input text data into word vector representation in line with the financial direction. Then, Bi-LSTM is used to extract the context information and important features of financial news text data. Finally, Multi-head Self-Attention mechanism is added to enhance the ability of the model to extract key features. In the SAFN dataset and the SACFN dataset, the Acc of WMSA-Bi-LSTM is 82.19%, 74.93%, and F1 is 82.22%, 75.05%, respectively, superior to RNN, LSTM and GRU series models.
Analysis of Network Access Behaviour Preferences and its Impact on Campus Networks QoS in Chinese Vocational Colleges

In China, many colleges are lack of adequate network capacity despite the rising Internet user demand. The QoS of campus networks is severely hampered by the volume of users using the same class of Internet apps simultaneously. This paper will review the outcomes of network access behavior preferences and how they affect the university network's quality of service. In this study, Internet access time was split into Peak Hours and Non-peak Hours, each included with three time periods. The study also categorized users' Internet access behavior into nine types namely the large-scale online games, news or web novels, watching videos, watching live broadcasts, casual online games, instant messaging, online shopping, downloading software and others. Users of the Anhui Institute of Technology and Business's surveys and Internet access logs were used for the analysis. The findings demonstrated that many factors, including application type, access time, location, and dormitory type, significantly influenced the college's campus Internet service quality. The results support user analysis techniques that optimize campus network QoS based on time and application type characteristics.
Schematic Edge Detection of Power Distribution Networks Using the Canny, Sobel, Robert, and Prewitt Algorithms

Electrical power distribution network schemes are often seen in electrical network learning manuals. Understanding these schemes is crucial for learning. However, schematic drawings of these networks can be unclear or difficult to interpret, leading to misunderstandings among learners. To address this issue, a detection system for power distribution network schemes using four algorithms, i.e., Canny, Sobel, Robert, and Prewitt, is proposed. From testing the 5 resulting schematic image data, it is found that Robert's Algorithm had the highest true positive rate (TPR) value of 0.823, indicating its effectiveness in edge detection. Meanwhile, the Canny algorithm had the lowest false positive rate and highest accuracy of 0.941, making it a suitable choice for both edge and non-edge detection. Therefore, the Canny algorithm can be recommended for learners to understand the electric power distribution network scheme better.
Investigation of Graph Modeling for Data Virtualization of SQL and NoSQL Databases

With the rise of data-centric applications, the diversity of data models has posed challenges in combining, interpreting, and retrieving data from disparate sources. As a response, data integration solutions have emerged, streamlining these intricate processes by automating the identification of entities spanning both structured and unstructured data sources. Among these endeavors, data virtualization, a prominent facet of data integration technology, emerges as a promising avenue. However, the successful implementation of data virtualization solutions mandates meticulous consideration of various aspects, most notably the intricate data heterogeneity stemming from syntactic, semantic, and structural disparities inherent in data sources. This paper is centered on tackling challenges related to syntax and structure, with a particular emphasis on investigating how flexible and feasible it is to use graph databases to enhance data virtualization. By examining findings from data integration efforts and identifying gaps in existing research, this study delves into the potential of using graph modeling to bridge these gaps and enhance data virtualization.
Intangible Cultural Dance Preservation Using Virtual Reality 360-Degree Video

Virtual Reality provides users with an unparalleled sense of presence, making them feel as if they are physically present in the virtual environment. It enables the digital preservation of cultural artifacts and artworks beyond conventional methods. Cultural artworks including cultural dance that descended from ancestors are increasingly forgotten. The younger generation is unaware of the value of carrying on the predecessors' legacies. VR experiences through 360-degree video may encourage future generations to explore and learn about their cultural heritage. Thus, introducing a VR 360-degree video for cultural preservation is proposed in this study. This paper presents the usage of virtual reality, the development of 360-degree videos, and an evaluation study to determine how well young people might use the videos. The 360-degree VR video followed stepwise approach in video production processes from pre-production, production, and to post-production. A software usability scale (SUS) was adopted to assess how usable this video is among youngsters. Fifty participants were involved in the testing. The result showed that the 360-degree VR video has an acceptable level of usability. This study has generally demonstrated that virtual reality has a positive outcome that could result in a larger and more extensive deployment for cultural heritage preservation activities.
Digital Asset Valuation Method for Online Games Based on Machine Learning Algorithms

Data has become a key production factor in the era of the digital economy, and promoting data assetization is a need for market development. With the rapid development of the mobile game market in recent years, the demand for game account transactions has increased. A reasonable valuation and pricing for the game account can facilitate the transaction between buyers and sellers on the one hand and save the time of price gaming between the two sides. Therefore, this paper takes an online game account as the research object. Proposes a machine learning algorithm-based online game account valuation scheme. The experiments show that the most critical evaluation indicator of the model, the decision coefficient reaches 0.8811, which can meet the requirements of an actual valuation scenario.
Spam Sorting Based on Naive Bayes Algorithm

The primary objective of this research is to classify spam, with the aim of mitigating the potential infringement on personal privacy resulting from spam. The categorization of spam messages involves the utilization of the Naive Bayes algorithm, which employs the principles of Bayes' Theorem and conditional independence assumptions to calculate and compare probabilities for classification purposes. The accuracy of spam categorization in the test is 98.3%, achieved by the implementation of Laplace smoothing and logarithmic accumulation techniques. The application of Naive Bayes for spam classification has several advantages, including the reduction of complexity associated with Bayes' Theorem, simplification of the procedure, decreased input requirements, and consistent classification accuracy and efficiency.