Reviewerly/exHarmony huggingface dataset:
Sorting of Scrambled Video Frames Using Temporal Order Verification

The Sorting Problem is exceedingly popular in our contemporary world for its contribution in many of our daily activities. In this paper, we tackle the problem of sorting through providing a method that sorts a variable number of scrambled video frames. The sorting algorithm is accompanied by two Machine Learning models. A supervised learning approach using video frames with semantic labels is presented through these models. The learning method was formulated as a sequential verification task, that is, determine whether a sequence of frames from a video is in the correct temporal order. Using two different classes of artificial neural networks, Three Dimensional Convolutional Neural Network (3D-CNN), and Recurrent Neural Network (RNN), temporally shuffled frames (frames in non- chronological order) are taken as inputs, then, the neural network is trained to first determine whether that sequence is sorted or not Afterwards, these models are used as validators in the sorting algorithm, to sort shuffled frames in the input sequence, to end up with a sorted frame sequence. The experimental results show good accuracy as different tests ended up with accuracy above 82%. This accuracy is related to the performance of an individual neural network, which means that it could be higher if both models are combined.
Implementation on Partially Verifiable Multi-signature Scheme in Electronic Contract Systems

With the rapid development of mobile devices and information technology in recent years, electronic contract systems, by which mutually agreed contracts can be easily and conveniently signed and stored, have steadily advanced. In addition, as the concept of self-sovereign identity has emerged, it is recommended to select and submit only the data an individual wants to prove, out of all data related to the individual, when using data that contains personal information. Although several multi-signature schemes have been proposed, they have limitations such as sequential signing or disclosure of the original data. Thus, in this paper we developed and implemented a Multi-signature scheme, in which only the data to be verified are disclosed and verified. We analyzed the security of the scheme to demonstrate its usability in electronic contract system environments.
Vehicle Re-Identification Based on Multi-View and Convolutional Block Attention

research-article Vehicle Re-Identification Based on Multi-View and Convolutional Block Attention Share on Authors: Hao Zhang Hubei Key Laboratory of Intelligent Robot,School of Computer ScienceampEngineering, Wuhan Institute of Technology, China Hubei Key Laboratory of Intelligent Robot,School of Computer ScienceampEngineering, Wuhan Institute of Technology, ChinaSearch about this author , Tongwei Lu Hubei Key Laboratory of Intelligent Robot,School of Computer ScienceampEngineering, Wuhan Institute of Technology, China Hubei Key Laboratory of Intelligent Robot,School of Computer ScienceampEngineering, Wuhan Institute of Technology, ChinaSearch about this author , Shihai Jia Hubei Key Laboratory of Intelligent Robot,School of Computer ScienceampEngineering, Wuhan Institute of Technology, China Hubei Key Laboratory of Intelligent Robot,School of Computer ScienceampEngineering, Wuhan Institute of Technology, ChinaSearch about this author Authors Info & Claims AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern RecognitionSeptember 2021 Pages 225–231https://doi.org/10.1145/3488933.3489038Online:25 February 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
XML2NN: A Unified Modeling Method Accelerated by Distributed Training with an XML File

Deep learning community in the computer vision field shows a trend of significantly expanding the scale of models and data. To ensure training and deployment efficiency, fast and effective modeling and distributed parallel training have become essential needs. However, there are various existing deep learning frameworks such as TensorFlow, Pytorch, Caffe, etc., which have different characteristics and programming syntax. This status quo hinders the progress of efficient modeling and model sharing. Meanwhile, these frameworks do not provide easy-to-use distributed training functions. To solve the above problems, in this paper, we introduce XML2NN, a unified modeling method for CV field with distributed training functions. With just an XML file, complex neural network construction, hyper-parameters setting and distributed parameters setting can be completed by users. XML2NN is based on multiple deep learning frameworks, which enables easy model construction and quick cross-framework model sharing. Extensive experiments on two CV tasks object detection and image classification have verified the modeling effectiveness and parallel performance of XML2NN.
Research and Application of Distributed Stability Control Test System

In order to improve the test efficiency and test accuracy of the engineering stability control system, a automated test system solution for the stability control system based on distributed collaborative testing and virtual device communication is proposed, which can adapt to different test situations including multiple stability control stations and single stability control station. The paper introduces the principles and implementation methods of the four key technologies which used in the stability control system test system in detail: wireless collaborative testing technology, virtual simulation of stability control device technology, test case generation technology and test report generation technology. Then a detailed explanation about the realization process of the key technology is introduced through the stability control engineering examples.
A Recursive Local Search Method of Separators for Bayesian Network Decomposition Structure Learning Algorithm

The decomposition structure learning algorithm is the most effective for the structure learning of large Bayesian networks. Maximal prime subgraph decomposition is one of the main methods of Bayesian network structure decomposition learning. The key to maximal prime subgraph decomposition is the search for separators. However, existing methods of searching separators are inefficient, because they all search for separators globally. To improve the search efficiency of separators, we propose a recursive local search method in this paper. Each iteration includes two steps. The first step is to determine a target node according to the minimum node degree. The second step is to perform a local search for the separator in the adjacent node set of the target node. The separator splits the network into two subgraphs, where the small subgraph contains the target node and the larger one does not. The iterative local search is only performed in the large subgraph. Finally, the proposed method is embedded in the structural decomposition learning algorithm, and experiments show that our method has better performance than state-of-the-art methods.
Ever Change a Running System: Structured Software Reengineering Using Automatically Proven-Correct Transformation Rules

Abstract Legacy systems are business-critical software systems whose failure can have a significant impact on the business. Yet, their maintenance and adaption to changed requirements consume a considerable amount of the total software development costs. Frequently, domain experts and developers involved in the original development are not available anymore, making it difficult to adapt a legacy system without introducing bugs or unwanted behavior. This results in a dilemma: businesses are reluctant to change a working system, while at the same time struggling with its high maintenance costs. We propose the concept of Structured Software Reengineering replacing the ad hoc forward engineering part of a reengineering process with the application of behavior-preserving, proven-correct transformations improving nonfunctional program properties. Such transformations preserve valuable business logic while improving properties such as maintainability, performance, or portability to new platforms. Manually encoding and proving such transformations for industrial programming languages, for example, in interactive proof assistants, is a major challenge requiring deep expert knowledge. Existing frameworks for automatically proving transformation rules have limited expressiveness and are restricted to particular target applications such as compilation or peep-hole optimizations. We present Abstract Execution , a specification and verification framework for statement-based program transformation rules on JAVA programs building on symbolic execution. Abstract Execution supports universal quantification over statements or expressions and addresses properties about the (big-step) behavior of programs. Since this class of properties is useful for a plethora of applications, Abstract Execution bridges the gap between expressiveness and automation. In many cases, fully automatic proofs are in possible. We explain REFINITY, a workbench for modeling and proving statement-level JAVA transformation rules, and discuss our applications of Abstract Execution to code refactoring, cost analysis of program transformations, and transformations reshaping programs for the application of parallel design patterns.
Energy-efficient distributed password hash computation on heterogeneous embedded system

This paper presents the improved version of our cool Cracker cluster (cCc), a heterogeneous distributed system for parallel and energy-efficient bcrypt password hash computation. The cluster consists of up to 8 computational units (nodes) with different performances measured in bcrypt hash computations per second [H/s]. In the cluster, nodes are low-power heterogeneous embedded systems with programmable logic containing specialized hash computation accelerators. In the experiments, we used a combination of Xilinx Zynq-series SoC boards and ZTEX 1.15y board which was initially used as a bitcoin miner. Zynq based nodes use the improved version of our custom bcrypt accelerator, which executes the most costly parts of the bcrypt hash computation in programmable logic. The cluster was formed around the famous open-source password cracking software package John the Ripper (abbr. JtR). On the communication layer, we used Message Passing Interface (MPI)library with a standard Ethernet network connecting the nodes. To mitigate the different performances among the cluster nodes and to balance the load, we developed and implemented password candidate distribution scheme based on the passwords' probability distribution, i.e. the order of appearance in the dictionary. We tested individual nodes and the cluster as a whole, trying different combinations of nodes and evaluating our distribution scheme for password candidates. We also compared our cluster with various GPU implementations in terms of performance, energy-efficiency, and price-efficiency. We show that our solution outperforms other platforms such as high-end GPUs, by a factor of at least 3 in terms of energy-efficiency and thus producing less overall cost of password attack than other platforms. In terms of the total operational costs, our cluster pays off after 4500 cracked passwords for a bcrypt hash with cost parameter 12, which makes it more appealing for real-world password-based system attacks. We also demonstrate the scalability of our cCc cluster.
Automatic Physiognomy System using Active Appearance Model and Convolutional Neural Network

This research discusses the design and development of an automatic physiognomy system to determine a person’s tendencies based on the features of its face. Physiognomy itself is a method of predicting a person’s characteristics based on their facial features. Each facial feature has its uniqueness and characteristics, such as variations in distance, overall shape, and size. The facial image as input data is processed in every system step. Finally, the system displays the personality of that person. Simulations show that each algorithm can perform its respective functions well. The simulation results show that the combination of extracting facial features using the Active Appearance Model and Convolutional Neural Network for solving classification problems produces a very good number of personality traits predictions with each model accuracy value between 0.8 to 1, or 0.8797 on average. In addition, the model made proved to produce a good performance for the classification process with a true positive rate between 0.8834 to 1, or 0.9417 on average. This method can also detect many personality traits, with 28 personality traits that can be detected.
Performance Ratio Test and Uncertainty Evaluation of Photovoltaic Power Generation System based on Linear Regression

With the development of photovoltaic industry technology, the installed capacity of photovoltaic power generation is increasing, and the evaluation of performance ratio (PR) of photovoltaic power generation system is extremely important. The PR test of photovoltaic power generation system has been studied and defined in domestic and foreign standards early, and test methods have also been given, but these test methods have a strong dependence on the weather conditions during testing, and the uncertainty evaluation is extremely difficult. In this paper, a testing and evaluation method including uncertainty evaluation for PR of photovoltaic system based on linear regression is proposed. This method reduces the dependence on the weather conditions during the whole testing period, and also makes the uncertainty evaluation feasible. Finally, a numerical example proves the rationality of using this method to test the PR of photovoltaic power generation system. Compared with the traditional method, it is shown that this method reduces the sensitivity to the weather conditions in the test, it can truly reflect the performance of the photovoltaic power generation system, and has a higher engineering practice and application value.
Data security and privacy in cloud computing focused on transportation sector with the aid of block chain approach

Despite the exponential growth of cloud infrastructure over several years, data protection and reliable computing are still major obstacles for modern cloud computing applications. In order to address this issue, several researchers have done a lot of work on this and have suggested a variety of models, including data integrity checking and stable multi-party estimates. Vehicle ad-hoc networks (VANETs) have been diagnosed with several security issues, such as confidentiality, safe authorization/authentication, and system stability. Nevertheless, almost all these alternatives face challenges like over-computational complexity or absence of scalability. This paper explores the usage of blockchain technologies in order to strengthen this scenario. Blockchain is collaborative modern framework for distributed computation. Applying blockchain technologies to cloud infrastructure, leveraging the former encryption framework to boost the efficiency of data storage and decentralized computation, is an exciting research area. A system of message verification for the privacy and decentralization of knowledge utilizing blockchain technologies is proposed in this paper. This is where we add message authentication code (MAC) and public-private key for stable authentication. In this document, we follow consensus algorithms for computing blockchain structures such as proof of work (PoW) and Practical Byzantine Fault Tolerance (PBFT) in the proposed authorization method. Finally, it is shown that the suggested approach is secure from threats that require the impersonation of the internal intruder as well as the usual threats.
Binocular Visual Pendulum Angle Detection and Anti-Swing Control of Quadrotor Suspension System

This paper presents a pendulum angle vision measurement scheme based on the model of the quadrotor suspension system, and applied to the quadrotor anti-swing control scheme with feedforward controller. In order to measure the pendulum angle, a spherical symbol is fixed at the end of the cable right above the hanging payload. The theoretical expression of the coordinate relationship between the pendulum angle and the spherical symbol is obtained by establishing the mathematical model of the quadrotor suspension system. Then, a binocular camera is used to detect the location of the center of the spherical symbol. According to the theoretical expression, the pendulum angle of the payload is calculated. Simulations and experiments show that the proposed method can accurately and effectively measure the pendulum angle of the quadrotor suspension system, and the anti-swing control scheme with feedforward controller can effectively restrain the payload’s swing.
Score-adjusted methods for estimation of shape parameters in Gamma-Poisson and Beta-Binomial distributions

The gamma-Poisson and beta-binomial mixture distributions are used for analyzing count-valued data, and the estimation of the hyper-parameters including the shape and/or scale parameters is important in the empirical Bayes inference. The maximum likelihood method requires the nested loops for solving the non-linear equations at each step of iteration in the EM algorithm. To avoid the extra loops, we derive the closed-form updating procedures at each step of iteration by using the score-adjusted method. The performance is compared by simulation with the maximum likelihood estimators.
Using Image Segmentation Neural Network Model for Motion Representation in Sport Analytics

The article describes the relevance of understanding the structure of motion in sports. An important component of understanding movement is the visualization of actions over time, which can be done by video analytics. The paper highlights the advantages and disadvantages of the background subtraction and the optical flow approaches, which try to get a mask of the moving objects, and highlights methods and algorithms for determining the motion in sports. The paper proposes an inverse solution to the motion representation problem. First, develop an approach to obtain masks using a neural network, and then use it to describe the change of the object’s location per frame. An understanding of the structure of motion was compiled and investigated based on the “COCO” dataset, from which 2693 annotated images were used. The solution to the problem was implemented using deep learning methods and Python programming language as a basis for the software product. The paper presents the results of training on such metrics like the total loss function for segmentation, loss function for masks only, learning rate, false negative, false positive, and accuracy. Examples of the developed algorithm work and its visualization are given. Results for several sports, such as tennis, gymnastics, and football, are presented. The algorithm output images of these examples clearly show the change in the position of objects over time. The prospect for further research is the development of an information system that can be integrated into the software environment of sports analytics projects.
Design of Visual Platform for Complex Medical Information Retrieval Considering Complex Information Structure

Design of visual platform for complex medical information retrieval considering complex information structure is studied in the paper. Before the software performs all operations, there must be a data structure that stores and describes the information transferred between the modules. The data transferred between modules is mainly the function information in the database. The function information mentioned here includes the function name, the return value of the function, the exit and entry parameter information of the function, and the description of the function in the database. From an application perspective, the semantic retrieval converts user query requirements into semantic concepts through semantic understanding and calculations, thereby retrieving the information related to this concept and the users really want, with this integration, the complex information analysis model is then designed. Through the testing on the different data sets, the performance of the model is then verified.
Revisiting Applications of Blockchain Technology in Business Ecosystems

In this paper, the authors provide a comprehensive analysis of blockchain technology and its emerging and state-of-the-art applications in business ecosystems. The authors define types of blockchain-enabled applications across various domains and research areas. Additionally, the authors investigate open issues of blockchain technology, identify challenges to be addressed, propose potential solutions, and mark future research directions that are of high relevance and value for both academics and practitioners.
Virtualization Scene Mining and Streaming Media Integration for the Information Technology Guiders' Comprehensive Development

Following the advancement of the basic information and the deepening of a new round of accurate revolution, the status of information and technology guiding in primary and secondary schools has become increasingly prominent. As one of the main bodies of information and technology teaching practice, the promotion of the information-science teachers' professional quality is currently an urgent problem to be solved. This paper studies the virtual scene mining and streaming media integration of the career development of information-science teachers, and analyzes the different application scenarios of the development path of information-science teachers in the era of big data. The novel technologies are combined for reviewing the latest methods of the IT and the combination for the comprehensive development modelling are both studied. The simulations also show the performance.
Multi-Head Attention with Disagreement Regularization for Multimodal Sentiment Analysis

Multimodal sentiment analysis refers to the use of computers to analyze and recognize the emotions that people want to express through the extracted multimodal emotional features. It plays a major role in human-computer interaction and financial market forecasting. Most of the existing multimodal sentiment analysis method use contextual information for modeling, but the feature information is not abundant enough. there is no problem of mining useful sentiment feature information. This paper proposes a multimodal sentiment analysis method based on the multi-head attention mechanism. The method uses Bi-GRU to capture contextual relationships, and adopts the constrained multi-head attention mechanism to focus on learning different modalities the feature representation of the subspace. We evaluated our proposed method on two multimodal sentiment analysis benchmark datasets, CMU-MOSI and CMU-MOSEI, and achieved accuracy of 82.63% and 81.82% respectively, proving the effectiveness of our method.