Reviewerly/exHarmony huggingface dataset:
Human Action Recognition and Coding based on Skeleton Data for Visually Impaired and Blind People Aid System

This work aims to contribute to the development of a novel device, perceivable by the touch sense, that allows visually impaired and blind persons understand the actions of the people around them. Therefore, we propose an end-to-end system that takes skeleton data, captured by kinect sensor, as input data and recognizes the performed human actions in the surrounding scene using a state-of-the-art GCN model (MS-G3D). To detect transitions between two actions in untrimmed video streams, a new machine learning (SVM) based method is integrated. It helps achieve less computational cost. Finally, we propose a novel human action coding to label the recognized action that will be mapped on the tactile device.
Image Anomaly Detection With Semantic- Enhanced Augmentation and Distributional Kernel

In the field of image anomaly detection, self-supervised learning using only normal samples has become a hot research topic since it is difficult to acquire sufficient abnormal data. However, existing self-supervised methods usually consider the impact of the superficial information rather than the semantics of images, resulting in limited generalisation performance. Furthermore, the small amount of training data further limits the performance of anomaly detection. To overcome both issues, we propose a two-stage self-supervised learning framework based on CutPaste with a feature extraction stage followed by an anomaly detection stage. We first add a diminished semantics augmentation to generate harder abnormal samples, so that the feature extraction model pays more attention to semantic information. Furthermore, we increase the density of normal samples with normal sample augmentations and integrate a data-dependent distributional kernel to improve the detection of abnormal images in the second stage, based on the adaptive distributional similarity. Our extensive experiments on the MVTec anomaly detection dataset demonstrate the superior performance of the proposed method.
A One-Class Anomaly Detection Method for Drives based on Adversarial Auto-Encoder

In recent years, hard disk drives (HDDs) and solid-state drives (SSDs) are both widely deployed in data centers. As a proactive warning technology, drive anomaly detection can detect anomalies in advance so that it is of good practicability. The existing anomaly detection methods are generally based on supervised learning, which requires a large amount of abnormal data as the basis. However, in the early stage of establishment of a data center or the deployment of new devices, anomalies or faults rarely occur, resulting in serious data imbalance problem, which causes many difficulties in the use of classification algorithms. To tackle the above problems, we propose a one-class drive anomaly detection method-AAD, which combines adversarial auto-encoder (AAE) and long short-term memory (LSTM). AAD model is trained on normal data (healthy drives) only in an unsupervised way. Since the pattern of normal data and abnormal data is obviously different, AAD learns the distribution of normal data and calculates the anomaly score of input data by integrating the reconstruction error and discrimination error to determine whether the anomalies exist. The experimental results show that AAD outperforms multiple traditional one-class classifiers, and compared with the two baseline methods, the fault detection rate (FDR) and false alarm rate (FAR) of our method on HDD dataset are 32.44%/6.885% higher and 2.3%/0.06% higher, respectively. The results on SSD dataset are 22.54%/0.01% higher and 5%/-0.004% higher, respectively. AAD outperforms the baseline methods in most cases.
FuzzGAN: A Generation-Based Fuzzing Framework for Testing Deep Neural Networks

Deep neural networks (DNNs) are increasingly deployed in various fields. Despite their spectacular advances, DNNs are known to suffer from adversarial vulnerabilities. The robustness of DNNs is then threatened by leading them to misclassifications with unexpected inputs (adversarial examples). The fuzzing technique frequently used for testing traditional software has recently been adopted to evaluate the robustness of DNNs. Current DNN fuzzing techniques focus on image classification DNNs and generate test cases by mutations, e.g., image transformations and adversarial perturbations. However, mutation-based test cases usually lack diversity and have distribution deflection from the original DNN input space, which impacts the evaluation of DNNs. In this paper, we propose a generation-based fuzzing frame-work FuzzGAN to detect adversarial flaws existing in DNNs. We integrate the testing purpose and the guidance of the neuron coverage into the original objectives of auxiliary classifier generative adversarial networks. Hence, FuzzGAN learns the representation of a DNN's input space and generates test cases without the limitation of any concrete seed input. We evaluate the performance of FuzzGAN on two DNN models that have classical network structures and are trained on public datasets. The experiment results demonstrate that FuzzGAN can generate realistic, diverse and valid test cases and achieve high neuron coverage. Moreover, these test cases can be used to improve the performance of the target DNN through adversarial retraining.
Construction of Multimedia Integrated Management Information System for Smart Classroom Based on Internet of Things Technology

With the development of education reform and Internet of things technology, the renewal of classroom equipment has attracted more and more attention. In order to facilitate students' learning, the classroom currently implements open management, which also brings difficulties to the management of classroom lighting, air conditioning, multimedia and other equipment. The smart classroom described in this article is a modern teaching method using Internet of things management technology, which can not only realize the intelligent control of local teaching equipment and teaching environment in multimedia classroom, In the digital multimedia classroom solution, the system functions of demonstration course recording, broadcasting, security monitoring, access control and attendance, electronic class card and broadcasting are also realized, forming an overall intelligent teaching solution based on information construction. The integrated information management system of multimedia equipment in smart classroom based on RFID technology of Internet of things is designed to realize the functions of multimedia equipment asset management, real-time recording of equipment use and operation and maintenance information, and management of relevant personnel, so that the staff can clearly and quickly grasp and manage all kinds of information of multimedia equipment in smart classroom, So as to ensure the orderly and normal progress of teaching activities and management. By analyzing the shortcomings of the traditional multimedia classroom platform, this paper proposes to build a multimedia intelligent classroom based on the Internet of things, and analyzes its function of optimizing the traditional teaching environment on the basis of expounding its composition, in order to provide some reference significance for the relevant research in the future.
Research on Intelligent Optimization Algorithm of Arranging Based on Reinforcement Learning

Reinforcement learning is an important machine learning method, which has been widely used in the fields of intelligent robot, economics, industrial manufacturing, game and so on. Reinforcement learning is a kind of learning from environmental state to action mapping, and it is expected that action will get the maximum cumulative reward from the environment. Reinforcement learning can be divided into two basic processes: learning process and planning process. Learning refers to the process of directly interacting with the environment, and in this process, using the obtained direct experience to update the value function to improve the strategy. Planning refers to the process of learning in the environmental model, and in this process, the simulation experience generated by the model is used to update the value function to improve the strategy. The research of intelligent algorithm is not only conducive to all aspects of the national economy, but also brings convenience to people's activities. It has certain theoretical significance and great application prospects. This paper summarizes the research trends of optimization algorithms, reviews the development status of swarm intelligence algorithms, and provides a theoretical basis for the follow-up research of swarm intelligence algorithms. This paper summarizes and reviews the relevant theoretical methods and Application Research of using deep reinforcement learning method to solve combinatorial optimization problems in recent years, summarizes and summarizes its basic principles, relevant methods and application research, and points out some problems to be solved in this direction in the future.
An Empirical Evaluation of Deep Neural Networks in Federated Learning

With the fast development of artificial intelligence (e.g., deep learning), it brings the new opportunities and challenges for business management and service optimization of the business process management. However, there are few works to present the comprehensive analysis of neural network models in the new intelligent paradigm, especially of federated learning. In this paper, we attempt to give an empirical study of deep neural networks in federated learning, which is used for analyzing the possibility of applying federated learning to BPM. At first, we present an overview of the theoretical analysis of deep learning and its distributed extension, i.e., federated learning. Then, We introduce three classic deep neural network models, including multi-layer perception, convolutional neural network, and residual network, and the corresponding structure and the key components. Next, we give the detailed experimental setting for the common test dataset called MNIST, including the structure of neural networks and the setting of the hyperparameters. Finally, extensive experiments are performed to evaluate the performance of the neural networks in federated learning.
A Vision-Based Low-Cost Power Wheelchair Assistive Driving System for Smartphones

Power wheelchairs (PWC) are essential for people with mobility impairment, and many research studies have been reported to ease their operations. However, the existing approaches either rely on extra hardware components or demand complex software that incurs high costs. In this work, we propose a low-cost, computer-vision-based assistive driving system that runs on a smartphone with the objective of safely driving a PWC in a hands-free manner with reduced attention in an indoor environment to relieve the arduous operations of disabled users and reduce their stress. The system adopts a modified and pre-trained ResNet-50 model running on a smartphone to derive the driving instructions using the images captured in real-time with its built-in camera. The smartphone interacts with a control interface to send the driving instructions to the PWC. A prototype of the proposed driving assistive system is implemented on a Pixel-6 Android phone and evaluated on a mobile robot as the proof-of-concept design. The experiments show that the smartphone can process input at up to 3.4 images per second to generate driving instructions in time to safely navigate the mobile robot at reasonable speeds in the testing environment with minimal intervention from the user.
Personalized Review Recommendation without User Interactive Data

Reviews from other raters play an important role in the user's decision making (e.g., purchasing a product). However, it is difficult for users to accurately find useful information from a large number of user reviews. The process of browsing these reviews is often very boring and time-consuming. Therefore, this paper aims to propose a personalized review recommendation method to help users solve this problem. Meanwhile, in most service communities, such as e-commerce services, the review reply is not available, which makes most of the current studies losing the efficacy. Therefore, realizing an accurate personalized review recommendation based on non-interactive user review data is easier to be promoted, but more challenging. In addition, most of the personalized recommendation related algorithms introduce complex large models, which require greater computational power and bandwidth. Our method proposed in this paper is an unsupervised lightweight neural network model, which can be trained only with ratings and non-interactive reviews. This method can be deployed hierarchically in the cloud and devices with less computational consuming. This method has been tested on five different mobile devices with four datasets from Amazon. The experiments illustrate that our approach can achieve a good personalized review recommendation result. Besides that, the resulting device memory usage and energy consumption are within the acceptable range.
Efficient Electronic Transaction Query Model for Light Nodes Based On Optimized Blockchain Structure

Various lightweight devices, such as mobile banks and IoT devices used for wireless payments and others, are increasingly incorporating blockchain-based electronic transaction systems. However, querying transactions requires synchronizing all current blocks, which wastes a great deal of time and storage space and is unsuitable for the timeliness requirements of electronic transaction scenarios and light node's low storage capacity environment. Therefore, studying the electronic transaction query methods appropriate for light nodes is urgent. In this paper, we optimize the block structure by introducing a block storage structure that can dynamically increase the number of transactions; and we also designed an electronic transaction model for an automotive manufacturer that can query data from both blockchain and non-blockchain systems. Simulation results reveal that the electronic transaction query model presented in this work reduces the quantity of data that must be downloaded for querying and boosts query speed by about fourfold compared to the SPV query model of blockchain, it also greatly improves the performance of the original system in the car manufacturer. Our architecture allows light nodes to adapt to the query needs of electronic transaction situations and may be adapted to other blockchain application scenarios.
Research on Path Planning of Welding Robot Based on Particle Swarm Optimization Algorithm

Welding robots are widely used in the field of automobile manufacturing. They can not only improve welding accuracy and efficiency, but also reduce labor costs. In the process of automobile body production, there are as many as 4000 ~ 6000 solder joints. Reasonable welding sequence is of great help to improve production efficiency. Therefore, it is extremely important to study the path planning problem of welding robot. A reasonable path helps to optimize the process of the production line, improve the production efficiency of the workstation, and improve the efficiency of engineering design. At the same time, it provides an important theoretical reference for robot trajectory debugging and simplifies the complicated and cumbersome teaching work. In this paper, particle swarm optimization (PSO) algorithm is used to solve the path planning problem of single robot. The corresponding trajectory is generated by establishing mathematical model and calling algorithm. Finally, the effectiveness of the algorithm is verified by a series of experiments and simulations.
A Fine-grained Multi-label Privacy Detection Model for Unstructured Data Based on BERT Pre-training

At present, a major bottleneck in the development of artificial intelligence is the difficulty of training data collection. Data holders are reluctant to share data because of concerns that sharing data may reveal private information. In order to solve this problem, this paper proposes a fine-grained multi-tag privacy detection model for unstructured data based on BERT pre-training. The model extracts the feature representation at the sentence level through BERT, and then allocates the weight to each sentence through the attention mechanism at the document level to obtain the feature representation of the document. Finally, combined with the calculation results of tag relevance based on GCN network, the result of multi-tag classification of unstructured text is obtained. This model achieves good results in the data set used in this paper.
Cryptanalysis of the Modified Hill Cipher Algorithm using Myszkowski Transposition

Barrieta et al., authored a modified Hill Cipher algorithm to address the vulnerability of Classic Hill Cipher against Known-Plaintext attack. The said authors claimed that the modified algorithm removed the vulnerabilities of the original Hill Cipher and is resistant to Known-Plaintext attack. In this paper, the researchers intend to perform a more comprehensive analysis by utilizing Cryptanalysis Techniques and using several attacks aside from the initial tested vulnerability to further prove the strength and efficiency of the modified algorithm as well as to show that it is non-linear. Two major techniques which are Chosen-Plaintext attack and Chosen-Ciphertext attack were conducted. Analysis showed that the produced Ciphertext are completely different to each other and is non-linear in nature. In addition to the Cryptanalysis conducted, Strict Avalanche Criterion and Randomness testing were performed. Results show a 52.78 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> yield for the Strict Avalanche Criterion followed by a Randomness Test which shows P-values greater than 0.01 for all subsequent tests, proving that the Ciphertext produced by the Modified Hill Cipher Algorithm has a high randomness result.
Optimal strategy selection for attack graph games using deep reinforcement learning

While various defense mechanisms have been proposed in cybersecurity, it is still unclear how these defense mechanisms should be deployed in practice to mitigate the damage of cyber attacks. In this work, we propose a Stackelberg game model to simulate the interaction between cyber attackers and defenders. We develop a reinforcement learning (RL) based approach to seek the optimal defense strategy. We further design a policy iteration method to accelerate the convergence speed of training. We conduct experiments with real network data and various game settings to evaluate the performance of our approach. Experiment results show that our RL-based approach outperform baselines, and the approach is robust to the uncertainty security environment.
Evaluating BERT on cloud-edge time series forecasting and sentiment analysis via prompt learning

Existing pre-trained language models (PTLMs), like BERT, have shown their powerful ca-pabilities in many natural language processing tasks. In sequence analysis, such as time series forecasting, anomaly detection, and sentiment analysis, PTLMs have also achieved new state-of-the-art results. However, does this mean that PTLMs know sequence analysis? This paper explores whether BERT pre-trained on a large amount of data contains knowledge of sequence analysis. Specifically, we adopt prompt learning to see whether BERT will achieve good results on cloud-edge time series forecasting and sentiment analysis tasks. For the cloud-edge time series forecasting task, we give BERT some regular cloud-edge data and let it predict the features of the next time step; For the sentiment analysis task, we give BERT some sentence with sentiment and ask it what sentiment these sen-tences carry. Our experimental results reveal that: (1) BERT performs not well on the cloud-edge time series forecasting task, which means the logical reasoning of BERT is not good; (2) for sentiment analysis task, BERT with the prompt template performs poorly on both English and Chinese datasets; and (3) for sentiment analysis task, BERT appears to be more likely to perceive the text as carrying positive sentiment.
An Adaptive RPC Mechanism for Performance and Node Fault Tolerance Optimization in HDFS

With the rapid development and evolution of information technologies, the big data industry has been experiencing exponential explosive data growth. Since the huge business and research value behind the large-scale data, big data technology has become one of the hotest research fields in academia and industry. To the big data, storing and processing the data is core function of the file systems. Hadoop Distributed File System (HDFS) is the most typical distributed big data architecture, which has the characteristics of high reliability, high fault tolerance, and low hardware cost. HDFS provides the efficient task implementation and fault tolerance among nodes by Remote Procedure Call (RPC) mechanism. However, the traditional RPC mechanism has remarkable defects with the fixed timing method, and cannot quickly establish the relationship between node downtime and network congestion. In this paper, we propose an improved adaptive RPC mechanism for node fault tolerance and performance. The proposed method classifies nodes by the data block access statistic, and dynamically adjusts the RPC interval time. This method can be used to reduce the networking traffic and processing pressure of NameNode nodes, and improve the I/O and task performance. Through the node classification, an inrack method is used to achieve better performance on fault tolerance. Eventually, we design the extensive experiments to evaluate the proposed method, and experimental results show it improves the performance compared with state-of-the-art method.
Cross-Model Interpretation of Machine Learning-Based Automatic Baybayin Script Recognition Systems

In the past years different image recognition models have been used and while there are models that are more dominant in all image recognition task, remaining models are still as important, and some perform even better than the current generation models. This study compares four models with the assigned task to recognize Baybayin script characters. Results show that CNN outperformed the other models with a result of 89% accuracy compared to the new Vision transformer that has 52% accuracy. After some investigation the researchers found that transformer-based models for computer vision demands a high requirement in term of processing power and requires a large amount of data in order to reach its peak performance.
GDMD: A Transmission Control Scheme with Block Information-aware for Delay Sensitive Multimedia

Live or interactive Multimedia applications have taken the whole world by storm, such as Tiktok. These applications expect high throughput within end-to-end latency, and they also want to meet the deadline requirements and send highpriority data blocks to improve the quality of experience (QoE). In this paper, we propose GDMD, a transmission control scheme with block information-aware for delay-sensitive multimedia. Considering block scheduling and congestion control, we design a novel scheduling algorithm to select the block that has minimal impact on other blocks to send first. The new congestion control algorithm based on reinforcement learning observes the network condition and controls the sending rate. Our evaluation shows that our solution can improve the QoE for delay-sensitive multimedia and significantly outperforms the baseline methods, which indicates the great potential of the combined scheduling scheme.