Reviewerly/exHarmony huggingface dataset:
WEB-BASED EVENT SEMINAR REGISTRATION INFORMATION SYSTEM APPLICATION

Information on the list of seminar events and seminar registration which is still done manually results in limited information access being given to the seminar participants, besides the data processing process carried out by the committee becomes less effective and efficient. To solve these problems, an online computerized information system is needed that can provide information related to seminar participant registration, data management, and seminar events. With the implementation of this system, it is expected to facilitate access to information about the seminar event schedule and reduce the risk of recording errors in seminar participant registration. This system is also expected to further accelerate the process of making reports which in turn can help the performance of the seminar committee. The research method used is a survey method involving certain people, especially students. Whereas the system development method (SDLC) is carried out using the waterfall method
Digital Differentiator and Its Application to Edge Detection

We propose digital approximations of differentiators and integrators in this research work. Digital filters are preferred to the analog ones due to well-known advantages of the former. They can be realized either as FIR or IIR systems. The starting point of the suggested design is the classical backward integrator. Differentiator is obtained by transposing the system function of the integrator. The magnitude plot of the differentiator thus obtained is made better by cascading another filter which is designed using cftool of MATLAB. The differentiator so found has magnitude response more or less same as that of the ideal over entire frequency range. Points of transitions or discontinuities in an image are called edges. The aim of edge detection is to find the points at which the image brightness changes abruptly. Mathematics describes such discontinuities using derivatives. A differentiator proposed in this design is applied to images for edge detection.
PEDAGOGIC SUPPORT FOR PROFESSIONAL ADAPTATION OF YOUNG OFFICERS: DIRECTIONS AND CONDITIONS OF ORGANISATION

The article deals with issues related to the study of the problem space of the phenomenon of «professional adaptation of young offi cers», the study of the possibilities of pedagogic support for military specialists in the adaptation process in the conditions of its organisation in the military unit. Topicality of this direction is determined by the high social signifi cance of military work, the special nature of its organisation and implementation, which requires increasing the effi ciency of the process and the result of professional adaptation of young military specialists who are able to quickly «enter» a new professional environment, updating their personal and professional competences and qualities necessary to ensure the success of professional activities in the military environment in the framework of the implementation of functions for offi cial purpose in accordance with the requirements. Theoretical analysis of scientifi c works devoted to the problem of professional adaptation of the individual is carried out, the content and procedural characteristics of this phenomenon are determined, and its signifi cance for increasing the effi ciency of professional activity of military specialists is updated. On the basis of the conducted research, the spectrum of diffi culties that arise in young offi cers during the adaptation period, located within the boundaries of the problem fi elds of service-organisational, psychophysiological, communicative, social and household components of professional work, is revealed. It is established that increasing the effectiveness of professional adaptation of young offi cers and minimising the diffi culties that arise is possible with the use of mechanisms of pedagogic support of the individual in the adaptation process, implemented at various levels of management of the military unit with the involvement of its resource potential. A set of conditions aimed at providing pedagogic support to young offi cers during the adaptation period, which contribute to improving its effectiveness, is identifi ed. The results of the study established that the organisation of systematic work on the pedagogic support of professional adaptation of young military specialists, implemented at various levels of military control in the framework of the indicated conditions is an important factor in achieving personal and professional success of young offi cers, their development in space military environment.
Semantic Video Mining for Accident Detection

This paper depicts the efficient use of CCTV for traffic monitoring and accident detection. The system which is designed has the capability to classify the accident and can give alerts when necessary. Nowadays we have CCTVs on most of the roads, but its capabilities are being underused. There also doesn’t exist an efficient system to detect and classify accidents in real time. So many deaths occur because of undetected accidents. It is difficult to detect accidents in remote places and at night. The proposed system can identify and classify accidents as major and minor. It can automatically alert the authorities if it deals with a major accident. Using this system the response time on accident can be decreased by processing the visuals of CCTV. In this system different image processing and machine learning techniques are used. The dataset for training is extracted from the visuals of already occurred accidents. Accidents mainly occur because of careless driving, alcohol consumption and over speeding. Another main cause of death due to accidents are the delay in reporting accidents since there doesn’t exist any automated systems. Accidents are mainly reported by the public or by traffic authorities. We can save many lives by detecting and reporting the accident quickly. In this system live video is captured from the CCTV’s and it is processed to detect accidents. In this system the YOLOV3 algorithm is used for object detection. Nowadays traffic monitoring has a greater significance. CCTV’s can be used to detect accidents since it is present in most of the roads. It is only used for traffic monitoring. Normally accidents can be classified as two classes major and minor. The proposed system is able to classify the accident as major or minor by object detection and tracking methodologies. Every accident doesn’t need emergency support. Only major accidents must be handled quickly. The proposed system captures the video and undergo object detection algorithms to identify the different objects like vehicles and people. After the detection phase
A Genetic Algorithm-Based Solver for Small-Scale Jigsaw Puzzles

In this paper, we present a genetic algorithm-based puzzle solver, which is mainly used to solve small-scale puzzle problems. We introduce a new measurement function that improves its accuracy by normalizing the Mahalanobis distance and the Euclidean distance between two puzzle pieces. By calculating the difference between edges of two puzzle pieces and using the genetic algorithm to assemble pieces correctly, two “parent” solutions are merged into one improved “child” solution. Using the idea of local search, it avoids the problem of local optimum solutions brought by the genetic algorithm, which greatly improves the accuracy of the puzzle.
A Dataset to Support Sexist Content Detection in Arabic Text

Social media have become a viral source of information. This huge amount of data offers an opportunity to study the feelings and opinions of the crowds toward any subject using Sentiment Analysis, which is a struggling area for Arabic Language. In this research, we present our approach to build a thematic training set by combining manual and automatic annotation of Arabic texts addressing Discrimination and Violence Against Women.
A Parallel Evolutionary Algorithm with Value Decomposition for Multi-agent Problems

Many real-world problems involve cooperation and/or competition among multiple agents. These problems often can be formulated as multi-agent problems. Recently, Reinforcement Learning (RL) has made significant progress on single-agent problems. However, multi-agent problems still cannot be easily solved by traditional RL algorithms. First, the multi-agent environment is considered as a non-stationary system. Second, most multi-agent environments only provide a shared team reward as feedback. As a result, agents may not be able to learn proper cooperative or competitive behaviors by traditional RL. Our algorithm adopts Evolution Strategies (ES) for optimizing policy which is used to control agents and a value decomposition method for estimating proper fitness for each policy. Evolutionary Algorithm is considered as a promising alternative for signal-agent problems. Owing to its simplicity, scalability, and efficiency on zeroth-order optimization, EAs can even outperform RLs on some tasks. In order to solve multi-agent problems by EA, a value decomposition method is used to decompose the team reward. Our method is parallel on multiple cores, which can speed up our algorithm significantly. We test our algorithm on two benchmarking environments, and the experiment results show that our algorithm is better than traditional RL and other representative gradient-free methods.
A Method for Localization and Classification of Breast Ultrasound Tumors

Ultrasound instruments are suitable for large-scale examination of breast tumors, especially for women from Asian whose glands are dense. However, ultrasound images have the low contrast and resolution, blurred boundary and artifacts, which bring great difficulties to the interpretation of the junior doctor. However, traditional methods of breast ultrasound tumor recognition often use manually extracted features to gradually realize ROI region location and tumor classification with low accuracy, poor robustness and weak universality. Deep learning is limited to the location of tumor ROI region or the classification of a given tumor ROI region. In this paper, YOLOV3 algorithm is used for breast ultrasound tumor recognition, which could realize ROI localization and tumor classification at the same time. In addition, K-Means is optimized by K-Means++ and K-Mediods algorithm to generate anchor boxes of YOLOV3, and based on the Darknet-53 network structure of YOLOV3, ResNet and DenseNet are combined to design ResNet-DenseNet_Darknet-53. The proposed method is tested on the breast ultrasound tumor data set. Experiments show that the improved YOLOV3 algorithm shows better detection results on multiple evaluation indicators.
A Comparative Study on Different Parameter Factors and Velocity Clamping for Particle Swarm Optimisation

Abstract Optimisation is an interesting topic because various optimum results can be obtained using limited resources. This is the reason that researchers are interested in further investigating the possibility of optimisation by testing numerous optimisation methods. As the current research trends are preferred to solve the problems using Artificial Intelligence (AI) methods, hence more nature-inspired optimisation methods are developed. Particles Swarm Optimisation (PSO) is one of the well-known optimisation methods in Swarm Intelligence (SI). It can be used to solve many problems. However, PSO is easily trapped in local optimum and encountered premature convergence problem, which indirectly affects its performance. Besides that, its performance with different parameter factors and velocity clamping is always not considered by researchers. Therefore, the objective of this research is to compare the performance of PSO with different parameter factors and velocity clamping using average fitness values. 10 different types of PSO models are applied and tested using 10 benchmark functions. Based on the experimental results, PSO10 with constriction factor and velocity clamping is proved to be the best PSO model because it can obtain 10 out of 10 minimum average fitness values. The outcomes can assist the researchers while applying the method to their research.
Personal and Communicative Contexts of the Creative Leadership Potential in Students of Humanities and Science

This research is part of a large-scale project aimed at creating a factor model of creative leadership potential. The present article features the effect of major and gender of students on their creative leadership potential. The authors identified groups of variables that characterize creative leadership potential. The study was based on the holodynamic and trans-communicative paradigm. The following range of scientific methods made it possible to determine the characteristics of creative leadership potential: the method of modeling communicative worlds, the method of psychosemantic graph, Myers-Briggs Type Indicator, sociability scale profile, and social network analysis. The study involved 189 students of humanities (101) and science (88). The results showed the specificity of personal creative characteristics and parameters of the communicative world of the participants. Gender proved insignificant for future research. Students that majored in humanities appeared both more original and more conformal. They tended to find unusual negative points in positive topics and problems. Science students worked better with specific information and facts. They were good at evaluating alternatives and planning.
MeDaS: An open-source platform as service to help break the walls between medicine and informatics

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields including computer vision, natural language processing, and healthcare. In particular, DL is experiencing an increasing development in applications for advanced medical image analysis in terms of analysis, segmentation, classification, and furthermore. On the one hand, tremendous needs that leverage the power of DL for medical image analysis are arising from the research community of a medical, clinical, and informatics background to jointly share their expertise, knowledge, skills, and experience. On the other hand, barriers between disciplines are on the road for them often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i.e., MeDaS -- the MeDical open-source platform as Service. To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side. Based on a series of toolkits and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed MeDaS platform can implement pre-processing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks including the subjects of lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realisable by using MeDaS.
Application of MBR Technology in Schoolyard Domestic Sewage Treatment

Abstract This paper discusses the special features of campus sewage, and chooses more suitable sewage treatment for campus sewage. At the same time, it expounds the advantages and disadvantages of it. Compared with city sewage from industry and agriculture, campus sewage has much more characteristic content of pollutants with higher COD, SS and tiny toxic pollutants[5]. Therefore rather than using the traditional activated sludge process, MBR is more appropriate for schoolyard domestic sewage treatment for small area, stable effluent quality, good removal efficiency and little residual sludge production. Additionally, new campuses of universities are always built far away from the city centre, and the water transportation cost can be saved with a sewage treatment system in the campus. The treated water has the quality to be used as gray water for campus watering, cleaning and so on, which can also help the universities to save money. Undeniably, it also has the disadvantages of high energy consumption and cost, for it needs to update the active membrane regularly. If it is used in cities, it will be a huge financial burden for governments. But when it is used in campuses, less sewage will need less membranes, and the polluted membranes can also be used for students’ research.
A Pyrenean-like model for the Variscan belt in NW Africa : insights from thermometry-based Raman spectroscopy study in the Khenifra Basin

&amp;lt;p&amp;gt;The Variscan belt in NW Africa is an intracontinental belt, resulting from far-field compressional stress during the closure of the Rheic Ocean between the Late Carboniferous and the Early Permian. In the classical view, this orogen building was preceded by a pre-orogenic stage, namely the Eo-variscan stage, suggested to have occurred at the Late Devonian-Early Carboniferous transition.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This view is now questioned, for multiple reasons. A first structural reason aims at re-interpreting the so-called Eovariscan features as extensional ones. Indeed, although many structures have been described, their integration into a compressional setting is not straightforward. A second reason is geodynamical, since this peculiar stage is bracketed between two general extensional phases recorded at the scale of NW Africa, and this leaves a very short time interval to proceed to a compressional phase that is geodynamically not integrated until today. At last, a third reason stems from early findings from metamorphic works in the Western Meseta that demonstrated the occurrences of previously unnoticed high geothermal gradients inside numerous Early Carboniferous basins (Chopin et al., 2014&amp;amp;#160;; Wernert et al., 2016&amp;amp;#160;; Delchini et al., 2018&amp;amp;#160;; Lahfid et al., 2019).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work, we sampled the Khenifra Basin within the easternmost part of the Western Meseta, where the Eovariscan deformation was defined (Allary et al., 1972). We carried on structural observations into the basement and sampled both the Ordovician basement and the Middle(?)-Late Visean series of the basin, which is thought the be extensional. Maximum temperatures reached by the 77 sampled rocks were obtained from the analysis of organic matter with the use of the Raman spectroscopy. The examination of this new dataset demonstrates that the Ordovician series acquired temperatures through a single event, consistently with their common record of the Eovariscan deformation. Instead, the unconformable Visean series on top of the basement show a pronounced basinal asymmetry, from low temperatures (&amp;lt; 160&amp;amp;#176;C) to temperatures equivalent to the Ordovician ones (&amp;gt; 250&amp;amp;#176;C). The Visean series do not record the Eovariscan deformation, and their thermal structure was acquired before the Variscan event, regarding their repartition within the basin. The examination of the different hypotheses for the timing of the maximal temperature acquisition (Variscan, compressional Eovariscan and extensional Eovariscan) leads to a single option only compatible with an extensional Eovariscan context.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The renewal on the knowledge about the early stages of the Variscan orogeny in NW Africa allows us to consider a Pyrenean-like model for the formation of this intraplate belt, resulting from the inversion of hot Early Carboniferous rifted basins.&amp;lt;/p&amp;gt;
PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision

Current state of the art methods for generating semantic segmentation rely heavily on a large set of images that have each pixel labeled with a class of interest label or background. Coming up with such labels, especially in domains that require an expert to do annotations, comes at a heavy cost in time and money. Several methods have shown that we can learn semantic segmentation from less expensive image-level labels, but the effectiveness of point level labels, a healthy compromise between all pixels labelled and none, still remains largely unexplored. This paper presents a novel procedure for producing semantic segmentation from images given some point level annotations. This method includes point annotations in the training of a convolutional neural network (CNN) for producing improved localization and class activation maps. Then, we use another CNN for predicting semantic affinities in order to propagate rough class labels and create pseudo semantic segmentation labels. Finally, we propose training a CNN that is normally fully supervised using our pseudo labels in place of ground truth labels, which further improves performance and simplifies the inference process by requiring just one CNN during inference rather than two. Our method achieves state of the art results for point supervised semantic segmentation on the PASCAL VOC 2012 dataset \cite{everingham2010pascal}, even outperforming state of the art methods for stronger bounding box and squiggle supervision.
Investigating Mobile Device-Based Interaction Techniques for Collocated Merging

In mixed-focus collaboration, group members create content both individually as a kind of groundwork for discussion and further processing as well as directly together in group work sessions. In case of individual creation, separate documents and contents need to be merged to receive an overall solution. In our work, we focus on mixed-focus collaboration using mobile devices, especially smartphones, to create and merge content. Instead of using emails or messenger services to share content within a group, we describe three different mobile device-based interaction techniques for merging that use built-in sensors to enable ad-hoc collaboration and that are easy and eyes-free to perform. We conducted a user study to investigate these merging interactions. Overall, 21 participants tested the interactions and evaluated task load and User Experience (UX) of the proposed device-based interactions. Furthermore, they compared the interactions with a common way to share content, namely writing an email to send attached content. Participants gave valuable user feedback and stated that our merging interaction techniques were much easier to perform. Furthermore, we found that they were much faster, less demanding, and had a greater UX than email.
SIMULATION VIRTUAL EDUCATIONAL ENVIRONMENT AS SPECIAL EDUCATIONAL SPACE: DUALITY OF NATURE

Based on the integrative interpretation of the Federal Law «On Education in the Russian Federation» and a number of positions of foreign and Russian scientists, this article comprehensively examines the essence of a simulated virtual educational environment. In the framework of the presented analysis, being a lecturer at a military university, the author actualises the problem from the standpoint of ensuring the qualitative growth of the potential of higher military education, where the formation of professional competency of future army offi cers via innovative technologies is deemed the key factor. The article emphasises that out of the existing educational media only the technosphere of e-learning is enshrined in the federal Law «On Education in the Russian Federation « (Article 16, Clause 1), i.e. the information presented in the databases and the infrastructure for its accumulation, storage and transmission, while the content and application methods along with the forms of e-learning are overlooked. In this regard, the article is focused on understanding the essence of a simulated virtual educational environment and learning tools in it. As a result of the analysis, the author comes to the conclusion that such environment is created not only with the help of information and communication technologies, but also with the intellect and imagination of a person, highlighting the duality of the essence of the virtual environment, its external and internal material and ideal manifestations.