Reviewerly/exHarmony huggingface dataset:
MeterSSD: Scale and rotate invariance lightweight detector

Since traditional CNNs do not have explicit rotation and scale invariance, it is difficult to adapt to the scale changes of objects in real scenes when dealing with object detection tasks. There have been many researches on rotating object detection, which have achieved outstanding performance. But most of these works focus on anchor design, such as different multi-scale anchors, rotating BBox and rotating ROI Pooling. Although these methods have solved some practical problems in reality, the methods increase the cost of box generation significantly. We are committed to designing a rotation and scale invariance detector. A lightweight and efficient detector MeterSSD we proposed is based on spatial attention, channel attention and linear transformation. We not only decrease the rotation transformation cost but also ensure the detector to be able to learn multi-scale features. We improve MobileNetV2 and use it as the backbone for SSDLite. Adaptive calibration of feature channels concentrates more on specific features. In order to compress MeterSSD, we replace partial convolution operations with linear transformation operations. We apply spatial attention to the detection head to obtain more orientation and scale invariance of feature map. MeterSSD significantly improve the deformable detection accuracy of SSD without speed sacrifice. It can be applied to general object detection problems, and reaches 65.53% mAP on Pascal VOC2012 and 22.74% mAP on COCO2017. Furthermore, we research the field of automatic meter reading and propose a large-scale annotated datasets GPT-AMR, which contains 78,091 water meter images from different manufacturers and different types. Our MeterSSD achieves 90.00% mAP at 52 fps on GPT-AMR datasets. The overall accuracy of digit recognition is 97.16%. Experiments show that MeterSSD enhance accuracy slightly compared to the current SOTA detector dose, but improves the detection accuracy of deformable objects prominently, and can adapt to any scene without modification.
Detection of Twitter Bots using DNA-based Entropy Technique

Twitter is an interactive microblogging platform where registered users share their thoughts using tweets. Currently, Twitter has reached almost 396.5 million users. The proportion of Twitter bots has grown with their popularity. It is estimated that about 52 million Twitter accounts are bots. Bot identification is significant to prevent false information, malware and protect the reliability of online discussions. Most techniques focus on Twitter's topological structure, neglecting the account heterogeneity. Further, they use supervised learning, which demands large training sets. In this paper, the user behaviors are modeled as DNA sequences. Information gain-based entropy is computed on fragments of DNA sequences throughterm frequency-inverse document frequency to determine DNA patterns that contribute to bots.
A Dynamic CGE Model for Consumer Trust Mechanism within an E-Commerce Market

This study aims to use the computable general equilibrium (CGE) model to evaluate the economic consequences of foreign direct online purchases on the chain economy and to forecast the future development of e-commerce. We expected that international online direct shopping reduced transaction costs in two ways: duty equivalent were reduced, and direct import prices were reduced. The development of e-commerce markets for products, services, and information is important for consumer trust. Customers are becoming more interested in these industries because companies increased in number and users. Rules and regulations that are customized to the demands of both businesses and consumers are important for the industries’ continued growth. This study proposes a CGE model and analyzed the dynamic trust mechanisms of individual customers in the financial inclusion market to achieve the entire equilibrium state of the rural financial sector and maintain the stable development of the financial inclusion consumer market. Through the initiation of the era of big data, e-commerce has become a key part of the development of the new economy. In order to ensure the reliability, security, and service quality of e-commerce transactions in different transaction environments, how to accurately and quantitatively express and predict the trust relationship enhance the trust of both parties. This paper uses artificial intelligence to check relevant materials, then passes the problem to experts for processing, analyzes the security issues in e-commerce, summarizes the e-commerce security system framework centered on trust management, and describes the contextual trust management model. Experimental outcomes show that the proposed technique can accurately assess the trust of individual consumers and plan the e-commerce market based on the trust mechanism.
Cloud Computing Predictive Resource Management Framework Using Hidden Markov Model

Volunteer and cloud computing are heterogeneous environments that aggregate the capabilities of their resources to solve large scale computationally-intensive problems and provide various services to users. Due to the dynamic nature of these environments, performance states of resources rapidly change, making elasticity characteristic and task allocation very challenging aspects. In order to implement a scalable elastic mechanism while utilizing the resources efficiently and maintaining the overall balance of these systems, real-time performance data need to be collected periodically. However, data collection may significantly increase the communication overhead in the cloud and volunteer network and consume from the limited processing power, energy and bandwidth of resources. Accordingly, this paper proposes solutions for balancing the load while reducing the communication overhead. A reactive and proactive resource auto-scaling task allocation algorithms are proposed. The proactive auto-scaling algorithm is based on the Hidden Markov Model (HMM). Performance evaluation using computer simulations show that the proposed algorithm achieves high prediction accuracy, enhances the overall system utilization and significantly decreases the communication overhead.
Parallel Frequent Subtrees Mining Method by an Effective Edge Division Strategy

Most data with a complicated structure can be represented by a tree structure. Parallel processing is essential to mining frequent subtrees from massive data in a timely manner. However, only a few algorithms could be transplanted to a parallel framework. A new parallel algorithm is proposed to mine frequent subtrees by grouping strategy (GS) and edge division strategy (EDS). The main idea of GS is dividing edges according to different intervals and then dividing subtrees consisting of the edges in different intervals to their corresponding groups. Besides, the compression stage in mining is optimized by avoiding all candidate subtrees of a compression tree, which reduces the mining time on the nodes. Load balancing can improve the performance of parallel computing. An effective EDS is proposed to achieve load balancing. EDS divides the edges with different frequencies into different intervals reasonably, which directly affects the task amount in each computing node. Experiments demonstrate that the proposed algorithm can implement parallel mining, and it outperforms other compared methods on load balancing and speedup.
Analysis of Requirements for Video Information Coding Technologies for UAV Information Resources

The article analyzes the requirements for information resources coding technologies for UAV and formulates requirements for the method of identifying areas for embedding additional information.
Matrix as an Alternative Solution for Evaluating Sentence Reordering Tasks

Although sentence reordering is a popular practice in educational contexts its scoring method has virtually remained ‘all-or-nothing’. The author proposed a more psychologically valid means of partial scoring called MRS (Maximal Relative Sequence) where a point is counted for each ascending run in the answer sequence allowing gaps and the final score reflects the length of the longest sequence of ascending elements. This scoring method, together with an additional consideration of recovery distances, was woven into an executable programme, and then transplanted to Excel without having to understand a programming language. However, the use of Excel was severely limited by the number of columns available. This chapter reviews the past practices of evaluating partial scoring of reordering tasks and proposes an alternative solution LM (Linearity Matrix), also executable on Excel, with far smaller consumption of columns and with the idea of calculating the recovery distances as well as MRS scores. Although LM and MRS are different scoring procedures, they both reflect psychological complexity of the task involved. Furthermore, LM is versatile as to the adjustability of adjacency weights as an extended model of Kendall’s tau. Some reflections on practical application are referred to as well as future directions of the study.
State-of-the-Art Architectures for Interoperability of Heterogeneous Clouds

Cloud computing provides convenient access to abstract computational resources over the Internet. In addition to common services like virtual machines, cloud providers are offering own proprietary domain-specific services and frameworks. The absence of common standards and interfaces makes it difficult to connect services of different clouds, migrate between them, or to distribute tasks across various providers. Extensive research in industry and academia has partially addressed the problem of vendor lock-in for cloud migration, however, no solutions are known to enable running applications in a dynamic multi-cloud infrastructure on a permanent basis. This paper describes state-of-the art of cloud infrastructure and considers the combined usage of multiple providers. It analyses approaches of multi-cloud usage that takes advantage of combining platform-specific features of different cloud providers.
Precomputed fast rejection ray-triangle intersection

We propose a ray-triangle intersection algorithm with fast-rejection strategies. We intersect the ray with the triangle plane, then transform the intersection problem into 2D by applying a transformation matrix to the ray-plane intersection point. For 2D transformation, we study two different approaches. The first approach uses a transformation matrix which transforms the triangle into a unit triangle. Then, simple 2D tests are performed. The second approach transforms the triangle into a 2D triangle while preserving similarity. This allows us to prune (i.e., to clip away) areas surrounding the triangle, determining whether the transformed intersection point lies within the triangle. We discuss several optimizations for this pruning approach. We implemented both approaches into the CPU-based ray-tracing framework PBRT, version 3, and we performed a time-based comparison against PBRT’s default intersection algorithm and Baldwin and Weber’s algorithm. The results show that our algorithms are faster than the default algorithm. They are comparable to or slightly slower than Baldwin and Weber’s algorithm, however, the pruning approach produces watertight results and may be further optimized. Moreover, additional CPU/GPU experiments outside of PBRT document promising speedup over the standard Möller–Trumbore algorithm in areas like ray-casting or collision detection.
Virtual Lab Assistant with Live-Monitoring System

The pandemic situation has greatly affected normal working in various sectors. One of the affected sectors is education. Due to online meeting platforms, there was not much difficulty in conducting theory classes. But the real challenge was conducting the lab classes and exams. We take up the problem of conducting computer programming assessment in online mode. One may argue that computer systems and handheld devices are common nowadays, and there are not many hurdles in handling lab sessions. But conducting a fair assessment is a challenging task. We propose a novel virtual platform with monitoring features to facilitate a smooth assessment. Our platform detects the student&#x0027;s face and verifies if there is any proxy. Further, the platform sends the student&#x0027;s image to the examiner for verification. Our platform detects the number of faces within the camera frame. We include modules to detect if the student uses a mobile phone during the examination. The trained model will be deployed in tensorflow workspace which can be easily integrated into the platform as an API and we have verified the efficiency of the model. We observe a 1.47 loss value, which shows the efficiency and feasibility of our proposed model.
Toward Generalized Psychovisual Preprocessing For Video Encoding

Deep perceptual preprocessing has recently emerged as a new way to enable further bitrate savings across several generations of video encoders without breaking standards or requiring any changes in client devices. In this article, we lay the foundation for a generalized psychovisual preprocessing framework for video encoding and describe one of its promising instantiations that is practically deployable for video-on-demand, live, gaming, and user-generated content (UGC). Results using state-of-the-art advanced video coding (AVC), high efficiency video coding (HEVC), and versatile video coding (VVC) encoders show that average bitrate [Bjontegaard delta-rate (BD-rate)] gains of 11%–17% are obtained over three state-of-the-art reference-based quality metrics [Netflix video multi-method assessment fusion (VMAF), structural similarity index (SSIM), and Apple advanced video quality tool (AVQT)], as well as the recently proposed nonreference International Telecommunication Union-Telecommunication?(ITU-T) P.1204 metric. The proposed framework on CPU is shown to be twice faster than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 264$ </tex-math></inline-formula> medium-preset encoding. On GPU hardware, our approach achieves 714 frames/sec for 1080p video (below 2 ms/frame), thereby enabling its use in very-low-latency live video or game streaming applications.
RETRACTED: Analysis of developments and hotspots of international research on sports AI

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219328.
Crystal Dilithium Algorithm For Post Quantum Cryptography:Experimentation and Usecase for eSign

Cryptography has been useful in many applications such as transactions, computer or mobile passwords, internet connectivity, e-commerce transactions etc. Most of the applications use RSA algorithm for digital signature (dongle based or eSign) or digital certificate (issued by a certified authority). RSA strength is based on the fact that it is difficult to factorize a large integer. With the improvements in the quantum computers, classical cryptography is under the threat of quantum attacks. Quantum computers can factorize the large modulus with the help of Shor's algorithm in much shorter time. This paper provides literature survey of Crystals Dilithium Algorithm which is one of the post quantum digital signature schemes that has reached the final round of NIST competition for post quantum cryptography and its use case in online digital signing facility, eSign.
Sports Action Recognition and Analysis Relying on Inertial Sensors

The process of sports action recognition involves not only the change of motion speed but also the change of motion attitude, so it is necessary to carry out coordinate system transformation and attitude behavior recognition in combination with the actual situation, and inertial sensors play an important role in this process. Moreover, this paper combines inertial sensors to construct a sports action recognition and analysis system. In addition, based on the STA/LTA-AIC vibration wave picking method, an improved STA/LTA-AIC method based on wavelet packet decomposition is proposed to automatically identify the first arrival of vibration waves. Through the experimental research, it can be seen that the sports action recognition system based on an inertial sensor proposed in this paper has a good performance in sports action feature recognition.
Feature Descriptors based on Circular Forms of Local Patterns for Texture Classification

Five novel Local Binary Pattern (LBP) based descriptors are presented in this paper for performing texture analysis of an image. The performance of any texture classification method depends upon its dimensionality. As existing lo-cal based extractor methods generate descriptors with huge dimensions, the classifiers using them will suffer. In order to deal with this problem Uniform Weighted LBP (UWLBP), Strong and Uniform Weighted -LBP (SUWLBP), Uni-form Circular and Elliptical Weighted Texture Matrix (UCEWTM), Strong and Uniform CEWTM (SUCEWTM) and Strong and Uniform Twofold Spatial Weighted Complex Patterns (SUTSWCP) are proposed in this paper. The out-puts of these descriptors are fed into machine learning algorithms like NavieBayes (NB), Multilayer-perceptron (MLP), Ibk and J48. Image datasets like Brodtaz, UIUC, Outex-TC-12, KTH-TIPS, ALOT were used to train and test the models. The five descriptors were combined with MLP and compared. The CEWSTM coupled with MLP attained has attained a classification rate of 93.11 % which is the best out of the proposed five descriptors.
Group Face Recognition Smart Attendance System Using Convolution Neural Network

One of the major issues in the modern environment of complex data systems is authentication; several strategies are used to address this issue. Face recognition is regarded as one of the most dependable solutions. This research proposes a convolution neural network (CNN) for face detection and recognition that outperforms existing methods. To extract acceptable features from images, machine learning approaches necessitate expert knowledge and experience. To categorize images in an automated manner, a proposed deep learning-based strategy can be employed, which uses channel wise separable CNN to extract image features and also uses Support Vector Machine (SVM) and Softmax classifiers to classify the images. Face recognition was used to verify the accuracy of the proposed system by tracking student attendance. The public of the market tagged faces in the wild (LFW) dataset is used to train the face recognition system. On the testing data, the proposed system had a 98.11 percent accuracy rate. Furthermore, the data created by the smart classroom is processed and transferred through the use of an IoT-based edge computing approach.
Ethical Demands of the Youth as the Foundation of Brand Promotion Strategy in Digital Society

The article, using cultural and values-based approach, considers top brand promotion trends in digital space, which are determined by the increased moral and ethical demands for business set by the current generation of young adults. The article is theoretically based on the works by N. Howe, W. Strauss, E. Shamis, E. Nikonov, V. P. Rudnev, R. Inglehart, etc.Expected results: to determine the nature of formal and substantive changes in the mechanisms of brand loyalty formation determined by the ethical sensitivity of the youth of digital generation.
Embedding graphs on Grassmann manifold

Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural property of graphs, preserving the original graph data's similarity relationship in the embedded space needs specific tools and a similarity metric. This paper develops a new graph representation learning scheme, namely Egg, which embeds approximated second-order graph characteristics into a Grassmann manifold. The proposed strategy leverages graph convolutions to learn hidden representations of the corresponding subspace of the graph, which is then mapped to a Grassmann point of a low dimensional manifold through truncated singular value decomposition (SVD). The established graph embedding approximates denoised correlationship of node attributes, as implemented in the form of a symmetric matrix space for Euclidean calculation. The effectiveness of Egg is demonstrated using both clustering and classification tasks at the node level and graph level. It outperforms baseline models on various benchmarks.
Data Storage Security Service in Cloud Computing

Cloud computing is an emerging computing paradigm that is rapidly gaining attention as an alternative to other traditional hosted application models. The cloud environment provides on-demand, elastic and scalable services, moreover, it can provide these services at lower costs. However, this new paradigm poses new security issues and threats because cloud service providers are not in the same trust domain of cloud customers. Furthermore, data owners cannot control the underlying cloud environment. Therefore, new security practices are required to guarantee the availability, integrity, privacy and confidentiality of the outsourced data. This paper highlights the main security challenges of the cloud storage service and introduces some solutions to address those challenges. The proposed solutions present a way to protect the data integrity, privacy and confidentiality by integrating data auditing and access control methods.
Contrast Modification Forensics Algorithm Based on Merged Weight Histogram of Run Length

A novel image forensic algorithm against contrast modification based on merged weight histogram of run length is proposed. In the proposed algorithm, the run length histogram features were firstly extracted, and then those of different orientation were subsequently merged; after normalization of the prior features, the authors calculated leaps in the histogram numerically; lastly, the generated features of authentic and tampered images were trained by a SVM classifier. Large amounts of experiments show that, the proposed algorithm has low cost of computation complexity, compared with some existing scheme, and it has better performance with many test databases, furthermore, the proposed algorithm can effectively detect local contrast modification of image.
The Use of Mathematical Tasks for Development by Younger Schoolchildren Skills of Research Activities

The article deals with the formation of research competence of primary schoolchildren through the use of heterogeneous mathematical tasks. The author analyses the types of classifications of mathematical tasks of different types that contribute to the activation and development of thinking in younger schoolchildren: creative tasks and tasks, the solution of which leads to the emergence of a new idea. It justifies the conclusion that&#x0D; the work on the development of logical thinking and the formation of a non-standard approach to solving problems in the course of teaching mathematics can and should start from the first grade.
Binary Search Trees

Knuth in [4] studied the problem of average depth minimization for binary search trees with two kinds of keys related to successful and unsuccessful search with their probabilities.
Introduction

How can we select the best performing data-driven model and quantify its generalization error? This question has received a solid answer from the field of statistical inference since the last century and before [, ].