Reviewerly/exHarmony huggingface dataset:
Multistage Feature Complimentary Network for Single-Image Deraining

Rain will cause the occlusion and blur of background and target objects and affect the image visual effect and subsequent image analysis. Aiming at the problem of insufficient rain removal in the current rain removal algorithm, in order to improve the accuracy of computer vision algorithm in the process of rain removal, this paper proposes a multistage framework based on progressive restoration combined with recurrent neural network and feature complementarity technology to remove rain streak from single images. Firstly, the encoder-decoder subnetwork is adapted to learn multiscale information and extract richer rain features. Secondly, the original resolution image restored by decoder is used to preserve refined image details. Finally, we use the effective information of the previous stage to guide the rain removal of the next stage by the recurrent neural network. The final experimental results show that a multistage feature complementarity network performs well on both synthetic rainy data sets and real-world rainy data sets can remove rain more completely, preserve more background details, and achieve better visual effects compared with some popular single-image deraining methods.
A Novel Moore-Penrose-inverse-matrix-based Data Compression Method

Data is growing explosively in digital world, which requires efficient technique to store and transmit data. Because of limited resources, we need data compression (DC) techniques to minimize the size of data being stored or communicated. A Moore-Penrose-inverse-matrix-based compression method is proposed in order to compress data easily and simply. We use a non-square matrix with more columns than rows to compress data in matrix. Taking random binary data as an example, we use random matrix as compression matrix. Simulation results show that the error rate get lower as the compression ratio gets close to 1. Moreover, the error rate is neither relevant with the size of data nor with that of compress matrix. Most importantly, this method is more suitable to the situation with data and compression matrix subjected to uniform distribution.
Model for Verification of Agent Commutations of Distributed Multiagent Systems

The development of multi-agent technologies and their widespread implementation in distributed computer systems is due to the properties of autonomy, mobility, intelligence and cooperativity, which provide power and flexibility in solving distributed, dynamic, fuzzy problems. At the same time, it is precisely these characteristic properties of multi-agent systems, that require special measures to ensure their operability and reliability of functioning, especially in the case of critical use. The proposed model for verifying the properties of dynamic communication is based on extended Petri nets and automata behavioral check and recognition experiments. Verification of two-tier and multi-tier communications of arbitrary spatial and temporal topology includes behavioral check of the mechanisms of conditions, preparation, transmission, buffering, broadcasting, taking into account probabilistic-temporal, group and concurrent characteristics. The verification model of agent commutation is designed to determine the conditions for analyze, check and recognizing of agent interactions in diagnostic methods for distributed multi-agent systems and allows to reduce the computational complexity of analysis, check and recognition of behavior of multi-agent systems.
Space Robot Arm Deployment Path Planning Based on FMT* Algorithm

Space operation and control is an effective means to deal with space environment problems. The space robot motion and control are the core of space operation and control. In this paper, the robot arm deployment problem of a certain type of space platform is analyzed in detail from the aspects of problem description, constraint analysis and algorithm implementation. The algorithm of robot arm deployment path planning based on FMT*algorithm is proposed, and the effectiveness of the algorithm is verified by simulation.
Analysis of Use of Figures and Tables in Computer Vision Papers Using Image Recognition Technique

In scientific publications, information is conveyed in the form of figure and table as well as text. Among the many fields of research, computer vision focuses on visual information like image and video. Therefore, figure and table are useful to convey information in computer vision paper such as image used in the experiment or output of the proposed method. In the field of computer vision, conference papers are important, which is different from journal publications considered important in other fields. In this work, we study the use of figures and tables in computer vision papers in conference proceedings. We utilize object detection and image recognition techniques to extract and label figures in papers. We conducted the experiments from five aspects including (1) comparison with other field, (2) comparison among different conferences in computer vision, (3) comparison with workshop papers, (4) temporal change, and (5) comparison among different research topics in computer vision. Thorough the experiments, we observed that the use of figure and table has been changing between 2013 and 2020. We also revealed that the tendency in the use of figure is different among topics even in computer vision papers.
An accelerated and robust algorithm for ant colony optimization in continuous functions

Abstract There is a wide variety of computational methods used for solving optimization problems. Among these, there are various strategies that are derived from the concept of ant colony optimization (ACO). However, the great majority of these methods are limited-range-search algorithms, that is, they find the optimal solution, as long as the domain provided contains this solution. This becomes a limitation, due to the fact that it does not allow these algorithms to be applied successfully to real-world problems, as in the real world, it is not always possible to determine with certainty the correct domain. The article proposes the use of a broad-range search algorithm, that is, that seeks the optimal solution, with success most of the time, even if the initial domain provided does not contain this solution, as the initial domain provided will be adjusted until it finds a domain that contains the solution. This algorithm called ARACO, derived from RACO, makes for the obtaining of better results possible, through strategies that accelerate the parameters responsible for adjusting the supplied domain at opportune moments and, in case there is a stagnation of the algorithm, expansion of the domain around the best solution found to prevent the algorithm becoming trapped in a local minimum. Through these strategies, ARACO obtains better results than its predecessors, in relation to the number of function evaluations necessary to find the optimal solution, in addition to its 100% success rate in practically all the tested functions, thus demonstrating itself as being a high performance and reliable algorithm. The algorithm has been tested on some classic benchmark functions and also on the benchmark functions of the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC 2019 100-Digit Challenge).
THE SYSTEM OF TRAINING HIGHER PEDAGOGICAL STAFF AT THE TURN OF THE XIX–XX CENTURIES

Preparation of undergraduates and privat-docents for professorship in the late XIX – early XX century is considered on the example of Moscow University through the prism of the biography of a historian and a jurist Sergei Andreevich Kotlyarevsky. The uniqueness of his example lies in the fact that he defended four dissertations: master’s and doctoral dissertations on foreign history and master’s and doctoral dissertations on public law. This demonstrates some individualism of the trajectory showing his professional development and formation as a scientist and a teacher.&#x0D; The research perspective includes the process of young scientists’ formation from the moment of continuing working at the university’s profile department to prepare a dissertation up to obtaining the degree. Attention is paid to socio-political circumstances and conditions affecting the possibility of becoming a professional scientist and teacher. Both formal and special features of the training scientific and teaching staff inherent in a particular scientific community are noted.&#x0D; The professional development of future scientists and teachers of higher education was associated with active preparation for the defense of the master’s exam, the first teaching experience, scientific and research work. The best graduates were left at the departments with the support of leading university professors who saw new forces and future professionals in them. However, the personal contribution of the applicants themselves was important, since they were required to reveal their intellectual potential, pedagogical and research skills. At this, an important aspect was the opportunity to go on foreign business trips, in which not only the material of scientific research was collected, but knowledge was enriched as well, including getting to know the peculiarities of teaching in European countries and their socio-political life. The preparation process was completed with the public defense of the master’s dissertation and awarding a master’s degree. At the same time, the path to the teaching environment began, which required further disclosure of scientific potential and the defense of a doctoral dissertation in order to obtain a professorship.
The Kaon’CPT Collective: Building a musical culture of not-in-real-life performance through conducted live comprovisation

In this article we describe a Networked Music Collective for online live performance events. Four characteristics of live performance (bodies, space and time, musical culture and social process) are identified as the conceptual and technological basis of our approach. Our recent distributed comprovisation, Perripplayear , is used to illustrate these concepts and to describe the technology stack we employed. The Kaon’CPT collective uses diverse instrumentation including acoustic and electronic instruments, voice and digital musical instruments (DMIs). Its members span 12 time zones and their comprovisation is conducted via a custom distributed score.
The summary lesson on the theme "number systems" in the web quest format

The theme "Number systems" is one of the basic in the school informatics course. The material of this theme is present in the tasks of the Main State Examination and the Unified State Examination, therefore it is very important to timely and efficiently consolidate the basic algorithms to convert numbers from one number system to another. The article provides a variant of such a summary lesson on the theme "Number systems" for the 8th grade in the format of web quest. The quest was developed in the Thinglink environment and contains several locations, each of which is associated with the convertion of numbers between different number systems. The tools for solving the quest tasks are Google Sheets and the LearningApps service, in which the author of the article has developed his own educational resources, as well as the Geogebra environment for performing constructions in one of the tasks. The presented quest can also be carried out in a computer-less form
THE FORMS AND METHODS OF ART DISCIPLINES' TEACHING UNDER THE CONDITIONS OF DISTANCE LEARNING

This article describes the development of forms and methods of art disciplines’ teaching under the conditions of distance learning. The main aim of the research is to develop recommendations for the artistic disciplines' teaching by educational institutions under the conditions of distance learning. To achieve this goal during the research, the following tasks will be carried out: - substantiation of the relevance of the study under the conditions of quarantine restrictions; - critical analysis of the literature on the development of the form and methods of teaching art disciplines; - the features of online work, and the features of work under the conditions of distance learning in higher education institutions. To conduct empirical research aimed at confirming the effectiveness of the developed teaching forms, we surveyed a group of students. It allowed proving the hypothesis that distance learning can be effective. The article contains recommendations for lecture classes, the organization of independent and group work, and other types of distance learning. The main direction of the development of teaching artistic disciplines is the development of educational technologies within the virtual environment. The developed forms and methods of distance learning are expedient to use both in the work with full-time students and the work with extramural students, attracting the possibilities of web-conferencing and communication using Skype, which forms the practical significance of the research.
A Pilot Study of Requirement Prioritization Techniques in Agile Software Development.

Requirement prioritization (RP) is a method for identifying the most critical requirements that should be immediately addressed. The results show how much all parties truly understand the requirements of the project. A trustworthy requirements prioritization process can ensure that the most important requirements are in line with business needs and should be developed first. The process results in the prioritized list of requirements that support decision making in release planning and subsequent activities to maximize software quality and customer satisfaction. This paper identified strengths and weaknesses of existing RP techniques. That leads to identification of factors in selecting the appropriate RP techniques for software projects. The recommendation can lead to improvement of the existing RP techniques. In addition, this paper presents the survey results of RP techniques used in participating software companies which developed software in Agile Software Development (ASD) context. The results reveal the challenges faced in their RP processes.
Social Media Analytics, Learning Analytics and Healthcare Industry: Risky Drinking

Much is written about social media analytics, learning analytics and health care industry research projects. However, it is rare to find all three domains in the same study, as each discipline is usually kept separate from another. The concern with this isolation approach is the probability of missing essential synergies, leading to a combined effect more significant than the sum of their particular products. Social media analytics may uncover key social media search terms that reflect the language used by groups of people engaging in risky drinking behaviour. At the same time, the view of behaviour through a psychometric testing lens may reveal other things. This chapter describes both social media analytics and learning analytics from the perspective of the health care industry. The chapter explains the intersection of social media analytics and learning analytics; in the first instance, the Rasch measurement model is used to discover the probability of agreement relating to human behaviour issues; a case study amplifies health services provision outcomes. The results from the social media analytics revealed that groups’ risky drinking behaviour data collected by Talkwalker showed linguistic variations with the double meanings of words used, which affected results. The learning analytics of alcohol consumption included a post-hoc comparison using a Bonferroni correction t-test which revealed the mean of never-married people was significantly higher than married people. The significance of these findings demonstrates that data analysis should be open to using a mixture of data analytics tools to reach a finer-grained interpretation to model other complex problems.
Finding Runtime Usable Gadgets: On the Security of Return Address Authentication

Return address authentication mechanisms protect return addresses by calculating and checking their message authentication codes (MACs) at runtime. However, these works only provide empirical analysis on their security, and it is still unclear whether the attacker can bypass these defenses by launching reuse attacks.In this paper, we present a solution to quantitatively analysis the security of return address authentication mechanisms against reuse attacks. Our solution utilizes some libc functions that could leakage data from memory. First, we perform reaching definition analysis to identify the source of parameters of these functions. Then we infer how many MACs could be observed at runtime by modifying these parameters. Afterward, we select the gadgets that could be exploited by reusing these observed MACs. Finally, we stitch desired gadget to craft attacks. We evaluated our solution on 5 real-word applications and successfully crafted reuse attacks on 3 of them. We find that the larger an application is, the more libc functions and gadgets can be found and reused, and furthermore, the more likely the attack is successfully crafted.
Occlusion Handling Based on Motion Estimation for Multi-Object Tracking

Although current computer vision-based object tracking has achieved excellent results in various rankings, the completeness and accuracy of the tracking trajectory are unsatisfactory when the objects are occluded. Moreover, object tracking in computer vision associates the objects before and after the occlusion by their appearance features yet lacks an accurate representation of the object's motion when the targets are occluded. We believe that Multi-Object Tracking (MOT) in autonomous driving needs to provide the vehicle with the motion state of the occluded object, including the position and velocity. Target's movement is a critical issue in autonomous driving, and we think it is more important to know how the target moves than what the target is. Therefore, this paper proposes a MOT algorithm for occlusion handling based on the tracking-by-detection strategy for the motion estimation of the occluded targets in autonomous vehicles. We first classify the occluded targets into two groups and track them with different strategies. The effectiveness of the proposed algorithm is validated on the MOTChallenge dataset by quantitative, qualitative, and ablation studies. Experimental results demonstrate that the algorithm can estimate the target motion accurately during occlusion and generate complete and higher quality motion trajectories.
ARS-Det: an axis-based anchor-free rotation detector for ship in remote sensing images

Ship detection in remote sensing images is a challenging and important task. Many methods of deep learning have been widely used in this field in recent years and most of them rely on a lot of predefined rotated anchor boxes, which not only lead to inaccurate angle predictions but also introduce excessive hyper parameters and high computational cost. In this paper, we propose an anchor-free rotation ship detector (ARS-Det) using axis-based representation scheme to address these issues. Our ARS-Det regards the detection of arbitrary-oriented objects as predicting the points at both ends of the longest axis and width of the objects. Attention FPN model is designed to capture multi-scale and key features of arbitrary-oriented target. Then, points assignment and orientation center-ness computation are proposed to screen of positive samples accurately. Experiments on public ship dataset HRSC2016 show that our method achieves state-of-art performance on ship detection in remote sensing images.
TM‐HOL: Topic memory model for detection of hate speech and offensive language

Abstract In the era of the explosion of digital content of large‐scale self‐media, user‐friendly social platforms such as Twitter and Facebook, provide opportunities for people to express their ideas and opinions freely. Due to lack of restrictions, hateful speech and its exposure can have profound psychological impacts on society. Current social networking platform is over‐reliant on the manual check, and it is labor‐intensive and time‐consuming. Although there are many machines learning methods for the detection of hate speech, short text with character limit on social platforms is more challenging for the detection of hate speech and offensive language. To address the problem of data sparsity, we have proposed a topic memory model for hate speech and offensive language detection (abbreviated as TM‐HOL). Potential topics are generated with our encoder and decoder to enrich short text features. Two memory matrices correspond to the topic words and the text, and the hate feature matrix is used to learn the syntactic features. It is demonstrated that our proposed method is effective on three datasets, performing better weighted‐F1.
Mutli-level Regression Anchor-free Object Detection

Recently, in the field of object detection, the anchor-free model has received widespread attention. It is based on keypoints to perform prediction and regression. Compared with the anchor-based model, it reduces the computational complexity and no additional hyperparameters are introduced. But the anchor-free approach faces the challenge of a large number of incorrect object regression boxes and the imbalance of the number of positive samples among objects in different sizes. To solve this, we present an efficient object detection algorithm named MRAF. We build our framework upon a representative anchor-free detector named FCOS. MRAF consists of two levels of detection network. The first network filters out most of the sample negative samples and regresses a two-dimensional vector for each sample point to roughly adjust the location. The second network performs classification and regression tasks based on the results of the first network to obtain the final detection results. Our approach greatly improves the accuracy of detection and the quality of the regression box.
Integration of Big Data Analytics and the Cloud Environment in Harnessing Valuable Business Insights

The integration of big data analytics and the cloud computing environment has become the focus area of many businesses and organizations. The growth that is being observed in big data requires more resources to perform analytics. In the business environment, it is important to be able to generate useful insights from the data that is collected. When integrating the cloud environment, the selection made for the chosen cloud service providers as well as the cloud services would highly impact the success of a business in bringing value to the data that is collected. The medical sector, development of smart cities as well as the entertainment industries are all examples of sectors with huge generation of big data. These are also some fields that have moved into the big data cloud computing environment. The main objective of businesses and organizations is to take the data that is being generated in its final form which can bring value to their decision-making capabilities. Being able to understand the impact of migrating to cloud services, the security challenges, and the wide spectrum of cloud services to choose from would be crucial for businesses. In this paper, the integration of big data technology in the cloud computing environment is discussed critically where different cloud services and tools suitable for generating business insights and the security and privacy challenges are evaluated.