Reviewerly/exHarmony huggingface dataset:
Enabling Object Detection and Distance Calculation in AI based Autonomous Driving System

Vehicle identification and distance estimation are perhaps some of the core problems in the Advanced Driver Assistance System (ADAS) developments worldwide. The paramount importance of the autonomous vehicle is safe driving in traffic, which is driven by fundamentals such as object identification and distance estimation to guarantee the protected separation between the vehicles and to ensure a safe ride on the roads. In view of the above problem, we propose a secure real-time vehicle tracking and distance estimation model for vehicles in motion in the light of Stereovision. The proposed model uses a 3D(stereoscopic) framework to capture the traffic participants and afterward recognize moving vehicles with the You Only Look Once (YOLO) V3 method. Subsequently, continuous video frame run-through calculation is done using optics-based estimation. Thus, we can determine the distance between the nearest car and object. YOLO is one of the best performing models for object discovery in a highly dynamic environment. In our case, it is used to locate the closest vehicle identification based on the location accuracy. YOLO processes every picture separately, even in a continuous video or frames. Because of its intricate inherent characteristics, even an identification of minor items will not be missed out. Thus, in this project, object recognition and distance estimation between two dynamic vehicles are carried out successfully.
EPCL:Encoder Perturbation can Replace the Data Augmentations in Contrastive Learning for Image Classification

Contrastive learning has been applied to various fields such as visual representation, graph representation, etc. due to its advantages of being able to extract self-supervised information from raw data without manual annotation. The contrastive view is the core of contrastive learning, and current contrastive learning methods are mainly constructed through data augmentation. Data augmentation forms pairs of samples that learn by attracting pairs of positive samples and repelling pairs of negative samples. However, current contrastive learning methods mainly perform instance-level data augmentation operations on the original input data, which may lead to differences in semantic information between positive sample pairs that degrade performance. At the same time, studies have shown that data augmentation does not play a large role in contrastive learning. Therefore, in order to effectively avoid the negative effects of data augmentation, in this paper, we propose encoder perturbation contrastive learning(EPCL), which abandons the image augmentation operation. Specifically, we take the original image as input and perform feature extraction on the image with a perturbed version of the convolutional neural network model to obtain two related views for contrast. Experiments show that the information of the image can still be well preserved and represented in the process of encoder perturbation. Compared with the current contrastive learning methods, the results show that our proposed method has strong competitiveness and performance.
A Low-Power Sparse Convolutional Neural Network Accelerator With Pre-Encoding Radix-4 Booth Multiplier

Working on edging device, convolutional neural network (CNN) inference application demands low-power consumption and high-performance computation. Therefore, exploiting energy-efficient multiply-and-accumulate (MAC) unit and high-throughput sparse CNN accelerator is of great importance. In this brief, we develop a sparse CNN accelerator achieving a high MAC-unit utilization ratio and great power efficiency. The accelerator includes a radix-4 Booth multiplier for pre-encoding weights to reduce the number of partial products (PPs) and the encoder power consumption. The proposed accelerator has the following three features. Firstly, we reduce the bit number of PPs exploiting the features of radix-4 Booth algorithm and offline weight pre-processing. Secondly, we extract eight encoders from relevant multipliers and merge them into one pre-encoding module to reduce area. Finally, after encoding non-zero weights offline, we design an activation selector module to select the activations corresponding to non-zero weights for subsequent multiple-add operations. The proposed work is designed by Verilog HDL language and implemented in a 28nm process. The proposed accelerator achieves 7.0325 TOPS/W with 50% sparsity and scales with sparsity up to 14.3720 TOPS/W at 87.5%.
Research on Moving Object Detection of Animated Characters

In recent years, excellent 3D animation works continue to appear, 3D animation copyright protection has begun to be paid attention to. At present, most of the copied animations copy the movements and movements of characters. In order to carry out copyright authentication and plagiarism detection for such plagiarism, it is necessary to carry out target detection for animation characters first. In this paper, Yolov5, a target detection method based on deep learning, is used for target detection of characters in 3D animation. CSPDarknet53 feature extraction network is used in Yolov5, Focus structure is used as the benchmark network, and FPN+PAN structure is added, which not only improves the accuracy and speed, but also greatly improves the detection of small targets. In this paper, SENET attention mechanism module is added in Yolov5, which reduces the speed of target detection and improves the accuracy of target detection, and is more suitable for the applicability of moving target detection of animated characters.
Machine Learning Inspired Fault Detection of Dynamical Networks

Dynamical networks are a framework commonly used to model large networks of interacting time-varying components such as power grids and epidemic disease networks. The connectivity structure of dynamical networks play a key role in enabling many interesting behaviours such as synchronisation and chimeras. However, dynamical networks can also be vulnerable to network attack, where the connectivity structure is externally altered. This can cause sudden failure and loss of stability in the network. The ability to detect these network attacks is useful in troubleshooting and preventing system failure. Recently, a backpropagation regression method inspired by RNN training algorithms was proposed to infer both local node dynamics and connectivity structure from measured node signals. This paper explores the application of backpropagation regression for fault detection in dynamical networks. We construct separate models for local dynamics and coupling structure to perform short-term freerun predictions. Due to the separation of models, abnormal increases in prediction error can be attributed to changes in the network structure. Automatic detection is achieved by comparing prediction error statistics across two windows that span a period before and after a network attack. This method is tested on a simulated dynamical network of chaotic Lorenz oscillators undergoing gradual edge corruption via three different processes: edge swapping, moving and deletion. We demonstrate that the correlation between increased prediction error and the occurrence of edge corruption can be used to reliably detect both the onset and approximate location of the attack within the network.
LET: Vision Transformer based Refinement Network for Light Field Editing

The LF editing through propagation enables temporal change of the photographed virtual space. Existing LF propagation schemes are largely divided into two types. One is based on image warping. It moves the pixels of the updated area to other images in the LF. Although there is little change in the pixel value itself, artifacts such as speckling and distortion often occur. The other approach synthesizes images based on a convolutional neural network (CNN). However, this method can only partially observe the characteristics of the image due to the local receptive field of CNN, and the output result is easily blurred while creating an image with down-sampled features. To overcome the limitations of conventional techniques, this paper proposes a vision transformer based LET model which consists of two steps. First, an initial edited LF with minimal change in pixel values is generated by propagating the updated region to other images using the traditional forward warping technique. Second, the visual quality is consistently improved through the refinement network which is based on the dense prediction transformer (DPT). In the warping process of the first step, approximate propagation is performed minimizing the loss of pixel values. Then, the angular consistency of the LF is maintained based on the global information in the refinement network of the second step. Experimental results show that the proposed LF editing scheme achieves significant improvement both quantitatively and subjectively.
Efficient Subhypergraph Containment Queries on Hypergraph Databases

In the real world, many complex systems consist of a large number of interacting groups of entities. A hypergraph consists of vertices and hyperedges that can connect multiple vertices. Since hypergraphs can effectively simulate complex intergroup relationships among entities, they have a wide range of applications such as computer vision and bioinformatics. In this paper, we study the subhypergraph containment query problem which is one of the most basic problems in the processing of hypergraphs. Existing methods on the subgraph query are designed for ordinary graphs and do not consider hypergraph features. If they are directly applied to subhypergraph containment query, they will suffer from hyperedge semantic incompleteness and label diversity sensitivity issues, resulting in inefficient algorithm performance. This motivates us to improve the performance by exploiting hyperedge features. In our work, we propose a novel framework for subhypergraph containment query called hyperedge filtering vertex testing. Based on the features of hypergraph, we propose an efficient filtering algorithm that can reduce the cost of the traditional filtering stage. In addition, we further propose efficient isomorphism testing techniques based on hyperedge vertex candidates to improve the performance. Extensive experiments on real datasets validate the superiority of our algorithm compared to existing methods.
Personalized Recommendation Using Extreme Individual Guided and Adaptive Strategies

In the era of information explosion, recommender systems have been widely used to reduce information load nowadays. However, mainly traditional recommendation techniques only paid attention on improving recommendation accuracy without considering additional criteria such as diversity, novelty. Moreover, such traditional recommendation algorithms were also struggled with matthew effect, that is, the gap between the popularity of popular and non-popular items grows. Therefore, a multi-objective recommendation model with extreme individual guided and mutation adaptation based on multi-objective evolutionary algorithms (MOEA-EIMA) is proposed in this paper. It maximizes two conflicting performance metrics termed as precision and novelty. In MOEA-EIMA, the iteration of population is guided by extreme individuals, and the adaptive mutation operator is designed for saving the better individuals. The algorithm is tested in several sparse datasets. The experiment results demonstrate the proposed algorithm can achieve a good trade-off between accuracy and novelty.
PROFESSIONAL MERCENARY CRIME AS A THREAT TO ECONOMIC SECURITY

Introduction. The subject of the study was the phenomenon of professional mercenary crime, which occupies a key place in the crime of Russia from ancient times to the present day. Crime is a complex mass historically stable phenomenon consisting in a set of socially dangerous illegal and criminally punishable acts committed during a certain period in a specific territory. As an extremely internally complex phenomenon, crime is differentiated into separate types and varieties in accordance with the characteristic features of its manifestation. Its types can be distinguished by the purpose, method, time of commission, characteristics of the criminal's personality, criminal motivation, etc. Traditionally, the leading sector of the crime structure is mercenary crime, which mostly intersects with the professional crime sector. The purpose of the work was a comprehensive criminological examination of the phenomenon of professional mercenary crime and the development of recommendations for government agencies, law enforcement agencies, organizations and members of civil society on the prevention of this type of crime. Methodology. The complex of research methods were the methods used in criminology, which form the scientific basis for the study of criminological phenomena. Results. According to the results of the study, conclusions of a criminological nature were obtained, describing such aspects of the phenomenon under consideration as the causes and conditions of the considered type of crime, its structure, social and psychological characteristics of the criminal's personality. Conclusions. As conclusions, the author formulated proposals of a preventive nature regarding the prevention of the capital type of crime, which can be used in practice in the processes of criminological forecasting and planning.
Application of CSGE-PSPnet Remote Sensing Image Semantic Segmentation Technology in Transmission and Transformation Engineering Design

With the breakthrough development of remote sensing technology and deep learning, remote sensing technology also has a broad application prospect in the field of transmission and transformation engineering design. Semantic segmentation, as a key link in remote sensing image processing, is the basis for improving the intelligence of transmission and substation engineering, for siting, construction, maintenance, and monitoring of transmission and substation engineering by accurately identifying important facilities, sensitive locations and environmental changes. Therefore, this paper proposes a remote sensing image semantic segmentation model(CSGE-PSPnet), which is based on PSPNet(Pyramid Scene Parsing Network) and SGE(Spatial Group-Wise Enhance Attention). CSGE-PSPnet could enhance local significance features while extracting global features, realizes the refined extraction of key information, and designs CSGE Block(Spatial-Channel Group-Wise Enhance Attention) to extract channel and spatial local information at the same time to enhance local significance features. Multi-level features are fused through dense connection structures to retain feature information of small-scale targets. Subsequently, the deep global features are mined by PSPNet on the feature map after fusion, and the contextual information of different regions is fused to obtain the segmentation results. In addition, one auxiliary loss layer and one major loss layer further improve the model's generalization ability. The experimental results show that CSGE-PSPnet is superior to other methods and can accurately identify small targets, and CSGE Block significantly enhances the feature representation.
On the Niho Type Locally-APN Power Functions and Their Boomerang Spectrum

This article focuses on the so-called locally-APN power functions introduced by Blondeau, Canteaut and Charpin, which generalize the well-known notion of APN functions and possibly more suitable candidates against differential attacks. Specifically, given two coprime positive integers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> such that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gcd (2^{m}+1,2^{k}+1)=1$ </tex-math></inline-formula> , we investigate the locally-APN-ness property of the Niho type power function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F(x)=x^{s(2^{m}-1)+1}$ </tex-math></inline-formula> over the finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{2^{2m}}$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s=(2^{k}+1)^{-1}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(2^{k}+1)^{-1}$ </tex-math></inline-formula> denotes the multiplicative inverse modulo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{m}+1$ </tex-math></inline-formula> . By employing finer studies of the number of solutions of certain equations over finite fields, we prove that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F(x)$ </tex-math></inline-formula> is locally-APN and determine its differential spectrum. We emphasize that computer experiments show that this class of locally-APN power functions covers all Niho type locally-APN power functions for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\leq m\leq 10$ </tex-math></inline-formula> . In addition, we also determine the boomerang spectrum of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F(x)$ </tex-math></inline-formula> by using its differential spectrum, which particularly generalizes a recent result by Yan, Zhang and Li.
Human-Computer Interaction Interface Design in Intelligent Medical System under the Background of Artificial Intelligence

With the widespread application and rapid development of digital media technology, the interaction between humans and computers has increasingly become an important part of people’s daily lives. This article mainly studies the application of human-computer interaction interface design in intelligent medical system under the background of artificial intelligence. The system front end uses ASP.NET framework. This article adopts the form of pop-up page expansion to ensure that users are in a single-page operation state and reduce the adverse effects of page jumps. The user uses the dynamic simulation system to perform tasks, making the user’s interaction feel similar to the real system. According to the button function selection, the hardware interface self-check. If the hardware interface is abnormal, it will give the alarm indicator display function. After passing the self-check, data acquisition, analysis, display and storage of blood pressure, ECG, and pulse wave signals are carried out. The collected physiological data and calculation results are sent to the APP of the smartphone through the WIFI module. The data shows that each test takes less time, and the average task time is reduced by 24.60%. The results show that the system designed in this paper has good performance and high accuracy.
Stronger Together: On Combining Relationships in Architectural Recovery Approaches

Architecture recovery is the process of obtaining the intended architecture of a software system by analyzing its implementation. Most existing architectural recovery approaches rely on extracting information about relationships between code entities and then use the extracted information to group closely related entities together. The approaches differ by the type of relationships they consider, e.g., method calls, data dependencies, and class name similarity. Prior work shows that combining multiple types of relationships during the recovery process is often beneficial as it leads to a better result than the one obtained by using the relationships individually. Yet, most, if not all, academic and industrial architecture recovery approaches simply unify the combined relationships to produce a more complete representation of the analyzed systems. In this paper, we propose and evaluate an alternative approach to combining information derived from multiple relationships, which is based on identifying agreements/disagreements between relationship types. We discuss advantages and disadvantages of both approaches and provide suggestions for future research in this area.
Big Data analytics for sustainable fisheries in Sultanate of Oman

Sultanate of Oman is rich in natural resources and one of the significant natural resources is marine fisheries. Oman has a huge coastal area of 3165 KM which is large in proposition to the population of Oman. Marine fisheries have a significant contribution to the world economy however it is not yielding benefits to the extent as is expected in the Sultanate of Oman. An oil-dependent economy needs to explore other areas where there is a scope for development. The Ministry of Fisheries in the Sultanate of Oman has taken drastic measures to enhance the fisheries sector in the Sultanate of Oman and benefit local fishermen as much as possible. Technology also is playing a vital role in boosting this sector in the Sultanate of Oman. There are several high-end systems in place for monitoring, surveillance, assistance, weather forecasting, and identification of zones. This research study is related to the implementation of Big Data analytics in the fisheries sector. This research study provides comprehensive details about the current infrastructure of the Ministry of Fisheries and proposes a big data analytics system that can assist different stakeholders in numerous ways to reduce running costs, increase production, streamline the value chain, and provide predictive and preemptive capabilities at various levels. This research is an effort to improve the fisheries sector in the Sultanate of Oman by providing critical information access to different stakeholders that are very beneficial and effective for the overall improvement and enhancement of fisheries in the Sultanate of Oman. In addition to providing a mechanism for the implementation of big data analytics in the sultanate of Oman, this research also provides certain recommendations for the adaption of technological aids at various levels for the improvement of fisheries in the Sultanate of Oman.
PENINGKATAN HASIL BELAJAR IPS MENGGUNAKAN MEDIA RODA BERPUTAR DI KELAS IV SDN 60/II MUARA BUNGO

This research is motivated by student learning outcomes that do not meet the criteria for mastery learning, the application of varied, creative, innovative, and fun learning media in social studies learning, students in the class are still passive in the learning process, lack of mastery of social studies subject matter and average grades. for social studies subjects in class is still low. The solution to overcome these problems is to use a rotating wheel media. The location is in class IVBSDN 60/II Muara Bungo, Rimbo Tengah District, Bungo Regency, the method used in this research is CAR (Classroom Action Research).The results of the study showed that the learning process in the first cycle of implementation learning outcomes in the first cycle had a percentage of completeness at the first meeting 55.55% increased at the second meeting 61.11% increased, in the second cycle implementation at the first meeting 83.33% increased at the second meetingII 88.89%.The conclusion is that using the rotating wheel media for social studies learning can improve learning outcomes in IVB SDN 60/II.