Reviewerly/exHarmony huggingface dataset:
Dynamic Partitioning Method for Near-Memory Parallel Processing of Sparse Matrix-Vector Multiplication

Near-memory processing (NMP), which places lightweight processing units near the DRAM memory, has been actively studied to speed up the execution of memory-intensive applications by reducing the amount of data traffic between the DRAM and the CPU. Sparse matrix-vector multiplication (SpMV) is a representative memory-bound kernel used in various applications such as graph analytics, scientific computing, and machine learning. There are prior works to accelerate SpMV by NMP employing a fixed partitioning scheme that hides random access of SpMV using a parallel NMP core. However, due to the various distributions of the matrix, the fixed partitioning of prior works causes a load imbalance in which a sparse matrix is unevenly allocated to processing units for NMP. To resolve this, dynamic partitioning methods to distribute matrices and vectors to the NMP processing units can be effective. In this paper, we propose a dynamic partitioning algorithm (DPA) that analyzes the distribution of non-zero elements in a sparse matrix to classify it into three types (even distribution, skewed distribution, and power-law distribution) and partitions the matrix according to each distribution. Our proposed distribution scheme alleviates load imbalance by up to 73% when compared to static distribution schemes, and such improvement achieves an average speed-up 1.37x (up to 1.84x) over the NMP architecture with static distribution schemes.
Distributed Frank-Wolfe Algorithm for Stochastic Aggregative Optimization

This paper is concerned with the distributed stochastic aggregative optimization (DSAO) problem with constraint set, where the local expected-value cost function of each agent depends both on its own decisions and on the aggregation of other agents' decisions, i.e., the aggregation function. For this reason, a distributed aggregative stochastic Frank-Wolfe (DAS-FW) algorithm is designed by introducing the momentum-based variance reduction technique to reduce the variance due to stochastic gradients, introducing the Frank-Wolfe method to deal with constraint. Then, it is theoretically shown that the DAS-FW algorithm owns a sublinear convergence rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(k^{-\frac{1}{2}})$</tex> for the convex and smooth cost functions. Finally, simulations are presented to verify the validity of our theoretical results.
Application Research on Lightweight Vehicle Detection Based on YOLO

A lightweight object detection algorithm based on YOLOv5 is proposed to address the problem of deploying detection models for traffic targets. This method was proposed to reduce the number of channels in the backbone and introduced GSConv to improve performance. At the same time, GSConv was improved to cut the parameters. The C3 module was replaced in the Neck with C2f to obtain more comprehensive gradient flow information. Finally, the model parameters are only 1.41M. Experiment on BDD100K public traffic dataset shows that the lightweight model reduces the number of parameters while losing a small amount of Recalls, and its performance is better than mainstream lightweight networks.
Virtual 3D Viewpoint Control Based on Integration of Around-View and Back-View Monitors

This paper proposes a method of presenting intuitively easy-to-grasp images by merging the images of an around view monitor and a back-view monitor into a single image. In the proposed method, the viewpoint automatically changes as the vehicle moves, enabling the display of an overhead 3D image that always shows the vehicle. This video generation is composed of simple image deformation by projective transformation, which serves as a distortion correction as well as a viewpoint change. Experiments conducted in a parking situation showed that the system automatically switches the 3D viewpoint depending on the distance to the parking position to present an easy-to-understand image including the surrounding environment.
An Efficient End-to-End Lightweight Object Detection Method Based on YOLOv5 for Intelligent Sweeping Robots

This paper focuses on developing modern, efficient, lightweight object detection a method for sweeping robots while trading off parameters, FLOPs and performance. In order to comply with the real-time requirements of sweeping robots, a method combining layer pruning and channel pruning is used to compress the initial model before optimization. First, a C2f module based on shunt gradient is introduced to lighten the network. Second, a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{C}3_{-}\mathbf{DCN}$</tex> (Deformable convolutional networks) module is used to fit the shape and size of the object when sam-pling. Finally, a Convolutional Block Attention Module(CBAM) is added behind the backbone network to enhance the extraction of important features. In the experimental studies, we compare our method with state-of-the-art methods, and the results reveal that the new model accuracy can reach 88.5 % with only 2.27M parameters and 5.5G FLOPs. Furthermore, the experiment results show that our proposed method achieves a better balance between model size and accuracy.
A Data-Centric Approach for Object Recognition in Hemispherical Camera Images

Object recognition is a machine learning problem that involves the correct classification and localization of objects in an image. Object recognition has found wide applications in Industry 4.0, surveillance, and autonomous driving. Prominent datasets such as Pascal VOC, ImageNet, and MS COCO have further spurred advances in object recognition. However, the images in these datasets were captured using ordinary perspective lenses. Unlike ordinary cameras, fisheye or hemispherical cameras have a wide FoV reaching 180°. Due to this property, they can acquire wide panoramic images, thus, capturing more objects in a scene as compared to ordinary perspective lenses. In this work, we provide the first benchmark testing dataset of real fisheye images containing commonly-occurring objects in the human environment obtained with hemispherical cameras. The dataset comprises 1,000 images containing 39 classes with a total number of 14,218 object instances. We trained the YOLOv7 model on the original MS COCO and transformed FisheyeCOCO datasets and tested the obtained models with our dataset. The results indicate that training the model on the combination of original and transformed datasets improves the performance by 2.5% compared to using these datasets separately.
Knock-to-Enter Authentication: A Rhythm-Based Smartphone Authentication Mechanism

With 2-factor authentication practices becoming ever more popular, the need for developing new authentication procedures is gaining ever more traction. Rhythm-based gesture authentication appears to be a promising area of research as it encompasses two of the three main authenticator factors: something you know(the chosen rhythm) and something you are(how you enter that rhythm). Rhythm-based authentication approaches have mostly been achieved using touchscreen functions. But accelerometers and gyroscope sensors have the unique ability to capture the small differences in how users enter a set rhythm and how they hold their phone. Additionally, these sensors do not limit our approach to mobile devices with mobile screens but can be expanded to headsets, smart glasses, and screen-less fitness trackers. It also circumvents issues like wearing face masks and gloves. All 12 of our participants were asked to input the same tapping rhythm consisting of 7 taps, 50 times, totaling 600 samples to be used for trial and testing. If our system is able to perform well under these conditions it proves that even an attacker who knows your rhythm, wouldn't be able to access your device. This could be equated to you telling someone your password, but them still not being able to gain access to your phone. Our models were able to achieve an authentication accuracy of 99.65% only using 10 valid samples for training and an identification accuracy of 99.17 %.
YOLOv5 with Combination of Coordinate Attention and CBAM for Object Detection on Drone

Object detection is an important study in computer vision to discriminate the position and class of an object in an image. Object detection in drone images is a technology that automatically detects and classifies objects using deep learning algorithms in flight images taken by drones. Object detection using drone images can rescue human life in disaster situations, grasp the situation at the disaster site, and identify the growth status of crops or pests in agriculture. In addition, it can be used in various fields such as infrastructure management, roads and railways, and city planning. A quick calculation is required. Although rapid computation is possible due to recent hardware development, there are many difficulties in using GPUs in industrial settings. In order to utilize drones in industrial sites, an object detection algorithm capable of real-time operation in a low-cost device is required. In this paper, we propose YOLOv5 with the combination of Coordinate Attention and CBAM for Object Detection on Drone for an algorithm capable of real-time operation in a low-cost device. The proposed architecture makes the model lighter by reducing the number of parameters and improves the object detection rate of the model through Coordinate Attention and CBAM. The model is trained using the VisDrone dataset, and the object detection rate, mAP, increased by about 10% to 22.2mAP, and the number of parameters decreased by about 70% to 2,147,589.
Improved Stochastic Recurrent Networks for Nonlinear State Space System Identification

This paper presents an improved version of the stochastic recurrent networks (STORN) for identification of non-linear state space systems with more complex model structures, including long short-term memory network (LSTM) and bidirectional gated recurrent unit (BiGRU). Both LSTM and BiGRU are recurrent neural networks with multiple state variables, which can store different information in the modeling of sequential data. Applying such a priori information into the prediction of data distribution can lead to better model performance. In this paper, several information fusion techniques are compared, and the effectiveness of the method is verified on three benchmark identification datasets. Our model outperforms the state-of-the-art baseline by 0.3 percent with 8 times fewer parameters.
Industrial Chip Positioning Method in Surface Mount Technology

With the increasing miniaturization of electronic devices, the demand for smaller and more precise components has led to the increasing popularity of surface mount technology (SMT) in the electronics manufacturing industry. This paper proposes an industrial chip positioning method in surface mount technology, which has good robustness to illumination changes. Firstly, the image processing algorithm is used to extract the edge of the chip, and the discrete Fourier transform method is used to obtain the frequency domain features of the image. Based on the frequency domain features, the angle of the chip is detected using the radial projection method. Then, the threshold image is obtained based on OTSU and the chip region is segmented using a projection method to achieve chip positioning. Experimental results conducted under different lighting conditions demonstrate that the proposed method exhibits high accuracy and robustness, making it effective for industrial applications.
A Proposal for Expansion of Conversation Variations in an English Conversation Learning System for Elementary School Students

In this research, we are developing an English Conversation Learning System for elementary school students in Japan that can be used in elementary school classes for elementary school students who are beginning students of English. Our research team faced two challenges: one related to conversation control and the other to speech recognition. In this study, we will focus on the issue of conversation control. It is that if a child responds to the same question with the same answer, the response pattern is fixed, so the breadth of the conversation depends on the child's answer. In addition, since the dialog history was only used to keep in the database, it is not possible to recognize the likes and interests of the children. In other words, each time the unit button is pressed, the same questions are repeated for every child, and personalized questions cannot be asked for each child. In this study, we propose an English Conversation Learning System that uses a graph database called Neo4j as a method of dynamically generating questions based on the dialog history of the children's answers.
The Impact of Virtual Reality Experiences on the Quality of Life and Psychological Well-Being of the Elderly

As aging societies emerge, the holistic well-being and quality of life of the elderly have come into focus on a global scale. In recent times, the progress of technology has led to the widespread application of Virtual Reality (VR) technology across diverse sectors. This study adopted a rehabilitation interactive game as its experiential content, involving a total of 13 participants. The gaming experience was facilitated through the utilization of the Cardboard VR device. For usability assessment, the study employed the System Usability Scale (SUS) questionnaire, with an average total score of 77.8. Impressively, 11 participants scored above 70. In summation, the Cardboard VR system received affirmative evaluations in terms of user experience within the elderly demographic.In addition to positive feedback, this study utilized semi-structured interviews to understand the challenges encountered during the experience. The results of the interviews revealed that some elderly participants felt confused or uncomfortable while using the device. A minority of participants experienced dizziness in the virtual environment or had difficulty adjusting the focal length for optimal viewing. Suggestions were provided regarding interface color combinations and motion feedback mechanisms. This study employed the user-friendly Cardboard VR as the experimental device. Future research could further explore the impact of immersive VR experiences on the quality of life and mental well-being of the elderly population.
Development of a hybrid information recommendation system considering serendipity

In conventional recommendation systems, once an item is recommended, only similar items are recommended. The user becomes bored with the recommendation results. To resolve this shortcoming, we specifically examine "serendipity", an indicator of unexpectedness. This study was conducted to improve serendipity using the popularity rankings of items to find rare items and to assign priority to recommendation of those items using User-based Collaborative Filtering. Furthermore, to make recommendations suitable for user preferences, Item-based Collaborative Filtering or SlopeOne were introduced to create hybrid recommendations. The results underscore the effectiveness of our proposed system.
Integrating Spatial Prior Adapter for Enhancing SAM Performance in Medical Image Segmentation

The Segment Anything Model (SAM) has emerged as a focal point of interest within the domain of image segmentation. Capitalizing on extensive pretraining with vast datasets and an interactive prompting mechanism, SAM stands out as a formidable base model for natural image segmentation tasks. However, a number of empirical studies indicate that SAM's performance diminishes substantially when applied to standard medical segmentation tasks. This is largely attributed to the modal and feature disparities between natural and medical images. In response to this challenge, this study introduces a novel, parameter-efficient fine-tuning approach termed the Spatial Prior Adapter (SPA). SPA leverages convolutional neural networks (CNNs) to capture multi-scale features, which are seamlessly amalgamated with the features harnessed from the SAM image encoder. Notably, during the model's fine-tuning phase, the SAM image encoder is frozen, enabling exclusive refinement of the SPA for optimal parameter calibration. Experimental evaluations on two medical segmentation datasets validate the effectiveness of our proposed method.
Improvement of Face Detection Accuracy for Mask Wearers

In face detection of mask wearers, the detection rate of profile tends to be low. In this study, the object detection method for YOLOv7 is used. Model performance is improved by adjusting the dataset and training more mask wearers' profile. Experimental results show that higher results in mAP and increased the number of profile detections are obtained.
A Study on Recording Conditions of Self-Video for Relaxed Talking and Giving Better Impression

Online video meetings and interviews have become popular. Recording message and self-introduction videos alone has also become common. In these backgrounds, it is important to record the self-image better and to give a better impression through the video. The authors experimented to find the condition that the speaker feels relaxed in recording and that the viewer receives a good impression. Participants recorded a self-introduction video in several conditions and ranked the condition in terms of easiness of speaking. Then participants ranked their videos in terms of impression. The result of the experiments is that taking with seeing the self or the receiver’s image gives a better impression in general. However, when the speaker doesn’t recognize him/herself as a good speaker, seeing a black image is the second choice for relaxed speaking.
Research on Video Action Detection Based on Attention Mechanism

The self-attentional Transformer model has achieved good performance in video action detection. Among the current popular models, most of them use the anchor-free method and no longer use the predefined anchor window for action positioning. In this paper, based on the ActionFormer, a new model named ASEFormer is proposed, which is mainly improved by feature fusion and attention. Meanwhile, under the same experimental conditions, the average mAP in the THUMOS14 dataset reaches 67.6%, which is 1.50% higher than the Baseline. The average mAP reached 36.0% on the ActivityNet1.3 dataset, an improvement of 0.28% over the Baseline.
Research on Naive Bayes Integration Method based on Kmeans++ digital teaching clustering

Naive Bayes method is simple, efficient, accurate, and has a solid theoretical basis, has been widely used. In view of the difference is the key condition of integrated learning in digital teaching, this paper proposes a method to improve the integration difference of naive Bayes classifier based on Kmeans++ clustering technology, so as to improve the generalization performance of naive Bayes. Firstly, several naive Bayesky classifier models are developed through training of training samples. Then, in order to increase the difference between base classifiers, Kmemeans ++ algorithm is used to cluster the prediction results of base classifiers on the verification set. Finally, the base classifier with the best generalization performance is selected from each cluster for ensemble learning, and the final result is obtained by simple voting method. The method is verified by UCI standard data set, and the results show that the generalization performance of the method is greatly improved.
Multimodal Link Prediction Method for Commodity Knowledge Graph

Knowledge graph link prediction is to predict whether there is a relation between two entities according to the existing knowledge graph data, which is of great significance for improving the existing knowledge graph. Based on the application of knowledge graph in electronic commerce, this paper proposes a new multimodal link prediction method for commodity knowledge graph. This method uses the visual features extracted from commodity images by the pre-trained visual model, then integrates entity embedding to enrich entity semantics, and finally learns entity and relation representation by knowledge graph embedding method. The experimental results show that the proposed method achieves better results than the existing methods in the task of commodity knowledge graph link prediction, which proves the effectiveness of the multimodal fusion method in the knowledge graph link prediction.
PEPC: Parallel and Extensible PC Implementation for Causal Structure Learning

Learning the causal relationships among variables from observational data has been a significant problem in statistics and data mining, and one such algorithm is the PC algorithm. However, the existing CPU-based implementations are difficult to support Causal Structure Learning(CSL) on complex observational data due to inefficiency. In this paper, we propose an efficient CPU-based parallel implementation of the PC algorithm, called PEPC. It has an extensible framework, and some optimizations are adopted to reduce the CSL computation of the PC algorithm. Moreover, our PEPC supports efficient multi-thread parallelism. The experimental results demonstrate that PEPC outperforms existing methods in serial efficiency and multi-thread parallelism. In serial tests, our method achieves the maximum speedup ratios of 577.26, 116.74, and 134.68 over three existing approaches, Stable, Stable.fast, and Tetrad. In parallel experiments, it achieves 10.66X speedup when running the PC algorithm with 12 threads.
A Survey of Complex Network Representation Learning Methods

Network representation learning is a technique that embeds the nodes in a network into a low-dimensional vector space, providing strong support for network analysis tasks such as node classification, link prediction, and community detection. This paper provides a summary of the research in the field of network representation learning in recent years. Firstly, the definition of network representation learning is introduced. Then, traditional network representation learning methods are presented, including matrix factorization-based methods, random walk-based methods, and neural network-based methods. However, traditional network representation learning methods can only be applicable to the traditional networks abstracted as the graphs and not applicable to the hypernetworks abstracted as the hypergraphs. Therefore, this paper also introduces hypernetwork representation learning methods, including homogeneous hypernetwork representation learning and heterogeneous hypernetwork representation learning. Finally, this paper summarizes future research directions in network representation learning.