Reviewerly/exHarmony huggingface dataset:
Classification of Assembly Operations Using Recurrent Neural Networks

Abstract Visual sensor data of manual assembly operations offers rich information that can be extracted in order to analyze and digitalize the assembly. The worker’s interaction with tools and objects, as well as the spatial–temporal nature of assembly operations, makes the recognition and classification of assembly operations a complex task. Therefore, classical methods of computer vision do not provide a sufficient solution. This paper presents a recurrent neural network for the classification of manual assembly operations using visual sensor data and addresses the question as to what extent such a solution is feasible in terms of robustness and reliability. Since complex assembly operations are a combination of basic movements, four main assembly operations of the Methods Time-Measurement base operations are classified using a machine learning approach. A dataset of these four assembly operations, reach, grasp, move and release, containing RGB-, infrared-, and depth-data is used. A Convolutional Neural Network—Long Short Term Memory architecture is investigated regarding its applicability due to the spatial–temporal nature of the data.
A Blockchain-based Protocol for Power Data Preservation in Ping Dingshan vs. Wang Case

power companies, the data collected by smart meters on electricity consumption is of vital interest. In 2019, Wang had a dispute with Ping Dingshan. They came into disagreement regarding the amount of electricity consumed, which was a difference of 1334 kWh. It was not settled until 2021. To avoid the above problem, smart meters could upload power information to the blockchain. In this paper, we improved the authentication protocol key proposed by Zhang and proposed a new scheme for uploading the power information from smart electricity meters, which ensures the integrity, confidentiality and authentication of the power information. At the same time, the proposed scheme is unbalanced for the computing capability of both communication parties, and can effectively reduce the burden of the server.
Design and Simulation Transfer Learning on Image Processing for Determining Condition of Robot Based on Neural Network

Neural Networks or Artificial Neural Networks (ANN) are an interesting topic in the last decade. This is due to the ability of ANN to mimic the nature of the input system. ANN is an information processing system that has similar characteristics to biological neural networks. This research will discuss the application of the Neural Network method for image processing which aims to classify the condition of the robot. The input for dataset are RSTRAIGHT, RTURNRIGHT, and RTURNLEFT, with a total dataset of 300 images. The image was taken using a camera with various conditions of the robot in various positions. The output of simulation is a learning rate precision which will use as a reference for whether the system can recognize the robot’s condition or not. In this study, the learning rate precision output was 98% which was obtained in the 12th iteration with an epoch of 50 and a batch size of 10 using the Adam optimizer.
Surface reconstruction algorithm of 3D scene image based on Virtual Reality Technology

Aiming at the practical problems of traditional 3D scene image surface reconstruction, such as low matching accuracy and complex calculation, a 3D scene image surface reconstruction algorithm based on virtual reality technology is proposed. Wavelet decomposition is used to process the image texture information, quantify the encoded image, take the distance between visual points and image as the measurement value, select the appropriate critical value to complete the image compression, and eliminate the redundant data of 3D scene image; Calculate the heuristic information in the image, update the pheromone matrix, use the critical value of the pheromone matrix to determine whether the pixel is the image edge point, and extract the edge information of the 3D scene image; The virtual reality equipment is used to calculate the minimum recognition distance of 3D scene, calculate the deviation between the theoretical image projection value and the actual projection value, calibrate the image surface reconstruction pixel value through the deviation value, and complete the high-precision 3D scene image surface reconstruction target. Simulation results show that the proposed method can effectively capture the key features in 3D scene images, and the reconstructed image resolution is significantly improved.
Afmf:Adaptive Fusion of Multi-Scale Features for Pixel-Level Object Detection

Small targets have the characteristics of low resolution, less feature information, leading to the weak expression ability of extracted features, which will greatly hinder the improvement of object detection accuracy. In this paper, the pixel-level prediction and regression method based on Fully Convolutional Networks (FCN) is adopted to establish the one-stage anchor-free object detection model. In this model, Adaptive Spatial Pyramid Pooling (ASPP) module and Adaptive Spatial Fusion Pyramid Network (ASFPN) module are proposed. ASPP module, attached to the backbone network, can obtain more fine-grained features by enlarging the receptive field of original features, and adaptively aggregate the features of different receptive fields to enrich the context information of local areas. ASFPN module adaptively fuses multi-scale features to build feature pyramid with rich multi-scale context information to establish the connection among pixels. Meanwhile, residual connection is added to obtain spatial context information with ratio-invariance to reduce the loss of location information of original features. In the single-model and single-scale test, the detector AFMF proposed in this paper uses ResNext-64x4d-101 to achieve 44.3% AP on the MS COCO dataset, which surpasses that of the previous anchor-free one-stage detector based on FCN and maintains real-time detection.
A Modified Key Generation Scheme of Vigenère Cipher Algorithm using Pseudo-Random Number and Alphabet Extension

In recent years, many modifications have been done to combat the weaknesses of the Vigenère Cipher Algorithm. Several studies have been carried out to rectify the flaw of the algorithm’s repeating key nature by increasing the key length equal to that of the plain text. However, some characters cannot be encrypted due to the limited set of characters in the key. This paper modified the algorithm’s key generation process using a Pseudo-Random Number Generator to improve the algorithm’s security and expanded the table of characters to up to 190 characters. The results show that based on Monobit examination and frequency analysis, the repeating nature of the key is non-existent, and the generated key can be used to encrypt a larger set of characters. The ciphertext has a low IC value of 0.030, which is similar to a random string and polyalphabetic cipher with an IC value of 0.038 but not equal to a monoalphabetic cipher with an IC value of 0.065. Results show that the modified version of the algorithm performs better than some of the recent studies conducted on it
Circuit-based PSI for Covid-19 Risk Scoring

Many solutions have been proposed to improve manual contact tracing for infectious diseases through automation. Privacy is crucial for the deployment of such a system as it greatly influences adoption. Approaches for digital contact tracing like Google Apple Exposure Notification (GAEN) protect the privacy of users by decentralizing risk scoring. But GAEN leaks information about diagnosed users as ephemeral pseudonyms are broadcast to everyone. To combat deanonymisation based on the time of encounter while providing extensive risk scoring functionality we propose to use a private set intersection (PSI) protocol based on garbled circuits. Using oblivious programmable pseudo random functions PSI (OPPRF-PSI) , we implement our solution CERTAIN which leaks no information to querying users other than one risk score for each of the last 14 days representing their risk of infection. We implement payload inclusion for OPPRF-PSI and evaluate the efficiency and performance of different risk scoring mechanisms on an Android device.
Expert System to Detect Social Anxiety in Student using the Rule-based Reasoning Method

Social anxiety is a condition where a person experiences fear or anxiety about a social situation that allows a person to be watched and to be the object of attention. Fear or anxiety can be experienced by anyone, including students. The symptoms that are felt by many students are still reluctant to consult and know the extent of social anxiety, then it needs alternative media to measure the level of social anxiety and recommendations for handling it. The development of an expert system technology can be used for handling recommendations for social anxiety syndrome. Therefore, this research aims to develop an expert system which can detect and give recommendation based on Liebowitz Social Anxiety Scale (LSAS) measurement tool to determine the results of social anxiety levels. Then, the method that used in this research is Rule-based Reasoning to obtain treatment recommendations with 41 rules. The experiment result showed that from 105 testing data, the system can detect all of conditions and give the correct recommendation based on social anxiety level which detected.
Study on Academic Stress and Athletes’ Stress-related Health Status

The purpose of this study is to identify the effects of lifestyle, study stress and training stress on health status, and provide fundamental data for health management of university students majoring in physical education. In this study, 149 students participated and they were surveyed demographic characteristics, lifestyle, Maslach burnout inventory (student stress inventory), training stress inventory, and Mibyeong index. Height and weight were measured for calculating BMI. For statistical analysis, Student t-test, ANOVA test, chi-square test, correlation analysis, and multinominal logistic regression test has been used. There were differences between Mibyeong groups according to digestion status, smoking for female students and quality of sleeping for both male and female students. Study stress and training stress also affected to health status. Cynicism among study stress categories and all categories among training stress showed differences between sex. Correlation analysis and logistics regression analysis was used to estimate related factors of health status after adjusting for sex and age. Based on logistics regression analysis, quality of sleeping affected to Mibyeong 1 group and quality of sleeping, smoking and digestion status affected to Mibyeong 2 group. Among training stress category, dissatisfaction with game result and skills and lack of leisure time were affected to both Mibyeong 1 and 2 group. This study suggests that lifestyle, study stress and training stress might be significantly associated with university students majoring in physical education. Through managing those influence factors, health status of students could be improved.
Multi-level Spatio-temporal Matching Network for Multi-turn Response Selection in Retrieval-based Dialogue Systems

The important task of multi-turn response selection in conversation systems must consider sufficient semantic information and spatio-temporal information when building retrieval-based chatbots. However, existing studies do not pay enough attention to both factors. In this study, a scheme of multi-turn response selection that combines a primary temporal matching module, an advanced temporal matching module, and a spatial matching module is proposed to extract matching information from context and response. The temporal matching modules progressively construct representations of the context and candidate responses at different granularities. Similarity matrices of the context and candidate responses are calculated and stacked using the spatial matching module. Convolutional neural network is then utilized to extract the spatial matching information. Finally, matching vectors of the three modules are fused to calculate the final matching score. Experimental results on two public datasets verify that our model can outperform state-of-the-art methods.
Generative Adversarial Networks at Development of Automatic Document Processing System

The work is aimed to solving the applied problem of developing an automatic system for processing scanned / photo documents, based on machine learning approaches. The difficulty in developing systems of this type is the lack of unique examples to train the selected model. Therefore, to solve this problem, it is proposed to use generative adversarial neural networks. At this work was studied possibility of applying approach for artificial expansion of the training sample for further training of the selected model. The paper presents the classical architecture of the generator and discriminator, gives the number of parameters of the selected models and shows the time required to train these networks. Was making a study of the effects of the main parameters of generative adversarial networks on the quality of new examples generated by the generator. The results of this work show the possibility of applying the proposed approach in the development of systems of this type, for artificial, software expansion of the training sample by new, unique representatives.
Distilling Reinforcement Learning Tricks for Video Games

Reinforcement learning (RL) research focuses on general solutions that can be applied across different domains. This results in methods that RL practitioners can use in almost any domain. However, recent studies often lack the engineering steps (“tricks”) which may be needed to effectively use RL, such as reward shaping, curriculum learning, and splitting a large task into smaller chunks. Such tricks are common, if not necessary, to achieve state-of-the-art results and win RL competitions. To ease the engineering efforts, we distill descriptions of tricks from state-of-the-art results and study how well these tricks can improve a standard deep Q-learning agent. The long-term goal of this work is to enable combining proven RL methods with domain-specific tricks by providing a unified software framework and accompanying insights in multiple domains.
Critical Cybersecurity Threats

One common reason for cybercrime is the goal of damaging a business by hacking or destroying important information. Another such reason is the criminal's goal of gaining financially from the hack. This chapter analyzes Bahraini organizations' vulnerability to digital security threats. It has used qualitative research to analyze industry performance. Moreover, with the support of secondary research, it has also explored cybersecurity threats faced by such organizations. The discussion based on secondary data analysis has explored two major aspects of Bahraini organizations and the cybersecurity threats they face. Firstly, the data and finances of both sectors are at huge risk in Bahraini organizations. Secondly, one important aspect of exploration has been to identify the most frequently encountered forms of cybercrime. Its analysis reveals that the kind of cybersecurity threat that a business is most likely to face is cyberwarfare. This may affect two rival businesses while they are competing with each other. Competitors' data may be destroyed or hacked—leading to long-term losses.
Virtual Dressing Room: Smart Approach to Select and Buy Clothes

The clothing industry portrays a major part of a respective country`s economy. Due to the predilection for clothing items of the people have led to the increasing of physical and online clothing stores in all around the world. Most of the people are used to go to the physical shopping and purchase their desired clothing items. But, as a consequence of the current pandemic situation, most of the people are unable to step out from their homes. This application is intended to cater an opportunity to the customers, who are not able to reach the physical clothing stores due to a pandemic situation and mobility difficulties. In addition, this application diminishes the time wastage, clothing size mismatches and the lesser user satisfaction ratio inside a physical clothing store. A customized 3D model has featured in the application to cater the virtual fitting experience to the customer. And the AI chatbot assistant in the application interacts with the user while catering virtual assistance for a better cloth selection process. In addition to that, this application has concentrated on the clothing shop by providing a future sales prediction component utilizing the K-Nearest Neighbors algorithm to provide an aid to their business commitments.
The Alert Collector: Scams and Flimflam: The Other True Crime

This list covers nonfiction on specific famous hoaxes and scams as well as overviews of the topic. Many of the books gathered here began as long-form news articles that were developed into a book. A short section of media items rounds out the column along with great podcast suggestions for gathering background information on what is undoubtedly a scintillating topic for users of all types of libraries.—Editor
Content and Structure in Recommender Systems

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
GCSR: Gray Code Super-Resolution 3D Scanning

Digital pattern projectors used in structured light 3D scanners cannot project infinitesimally thin columns of light – the projector pixel columns have a finite thickness. Current methods incorrectly model these columns of light as planes, which contributes to reconstruction errors. We propose a method to increase the resolution of structured light 3D scanners based on time-multiplexed discrete patterns by taking multiple acquisitions of the scene with different projector poses, and by modeling the light projected by each pixel column as a volume in space instead of as a plane, which results in upper and lower bounds on the resulting depth map. Furthermore, by analyzing multiple acquisitions in different projector poses, these upper and lower depth bounds can be tightened. We refer to this tightening as super-resolution, because it corresponds to an increase in the confidence in the location of the object’s surface. We describe our first implementation of such a system, and demonstrate its performance on a variety of objects.
A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the &#x201C;Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm&#x201D;. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot.
A Multi-level Feature Enhancement Network for Image Splicing Localization

Most current neural network-based splicing localization methods are based on subtle telltales from inter-pixel differences. But for recompressed and downsampled data, these artifacts are weakened. In this paper, we propose a novel multi-level feature enhancement network (MFENet) to enhance the features. Tampering with an image not only destroys the consistency of the inherent high-frequency noise in host images, but also is performed post-processing operations to weaken this discrepancy. Therefore, based on the high-pass filtered image residuals, we combine the detection evidence of post-processing operations to complete splicing forensic task. For the purpose of enhancing the distinguishability of features in the residual domain, we use bilinear pooling to fuse low-level manipulation features and residuals. In order to improve the consistency between the ground truth and the splicing localization result, we integrate global attention modules to minimize the intra-class variance by measuring the similarity of features. Finally, we propose a multi-scale training generation strategy to train our network, which provides local and global information for the input and pays more attention to the overall localization during gradient feedback. The experimental results show that our method achieves better performance than other state-of-the-art methods.
Intelligent Knowledge Modeling and Distributed Database Constructing for Intelligent Radar Countermeasure

Intelligent radar countermeasure are the current development trend in the field of radar countermeasure. Knowledge modeling can support the whole process of radar countermeasure. It can not only provide prior knowledge for the countermeasure system but also dynamically update the generated new knowledge. To deal with the rapidly growing mass of knowledge in the knowledge base system, the use of a distributed architecture can ensure the stability of the knowledge base. At present, there are two difficulties in the construction of a knowledge base system in radar countermeasure. First, it is difficult to construct a comprehensive representation for multiple data sources and complex counter-targets. Second, the massive data and knowledge gives huge pressure for storage. To deal with the above problems, we systematically design a hierarchical dynamic knowledge base as well as several sub-databases based on the specific application of intelligent radar countermeasure. Furthermore, the distributed database based on the architecture of Mycat is constructed, which relieves the pressure on storage, reading and writing, as well as the crash risk of a single machine. We have demonstrated the performance of the distributed knowledge base.
Urban Cable Field Inspection Technology Based on MR Technology

By introducing the real scene information of cable inspection into the simulation scene, hybrid reality technology (MR) brings wonderful experience to the inspection operator, and establishes the information exchange between the inspection operator and the real scene and virtual scene, which is the further development of cable intelligent inspection technology. This paper first describes the advantages and characteristics of hybrid reality technology and the development status at home and abroad, and then designs a virtual scene image segmentation algorithm of hybrid reality technology for cable inspection. The purpose is to obtain better visual effect of cable inspection scene, and realize the effective construction of virtual environment for cable inspection, the recovery of real environment space structure, and the realization of image segmentation And the natural integration of virtual and real environment. Finally, the MR effect of cable field inspection is given. The practical application shows that the technology can help the field staff to realize manual inspection more intelligently and efficiently, and make the field operation more standardized.