Reviewerly/exHarmony huggingface dataset:
A Review on Action Recognition and Action Prediction of Human(s) using Deep Learning Approaches

Human Action Recognition and Prediction are some of the hot topics in Computer Vision these days.It has its formidable contribution in the Anomaly detection.Many research scientists have been working in this field.Many new algorithms have been tried out in recent decades.In this paper, eight such approaches proposed in eight research papers have been reviewed.Compared to their counterparts for still images (the 2D CNNs for visual recognition), the 3D CNNs are considered to be comparatively less efficient, due to the limitations like high training complexity of spatio-temporal fusion and huge memory cost.So in the first referred paper the authors have proposed MiCT (Mixed Convolution Tubefor videos) with the right use of both 2D CNNs and 3D CNNs which reduces the training time.In the second research paper, the glimpse sequences in each frame correspond to interest points in the scene that are relevant to the classified activities.Unlike the last referred paper, the third referred paper presents a novel method to recognize human action as the evolution of pose estimation maps.The fourth referred paper presents a model for long term prediction of pedestrians from on-board observations.In the fifth research article referred, an attempt has been made to recognize the Human Rights Violation activities using the Deep Convolutional Neural Networks.In the sixth research article, Convolutional LSTM is used for the purpose of detecting violent videos.The seventh paper introduces a new Two-Stream Inflated 3D ConvNet (I3D) that is based on 2D ConvNet inflation.In the eighth research paper, a new temporal transition layer (TTL) that models variable temporal convolution kernel depths is embedded into 3D CNN to form T3D (Temporal 3D Convnets).Transferring knowledge from a pre-trained 2D CNN to a 3D CNN reduces the number of training samples required for 3D CNNs.
Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules

Today, patients generate a massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients’ pathological conditions or diagnoses is essential for the reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining is capable of discovering hidden interesting knowledge and relations among attributes in datasets, including medical datasets, yet is more likely to produce many anomalous rules (i.e., subsumption and circular redundancy) depends on the predefined threshold, which lead to logical errors and affects the reasoning process of rule-based systems. Therefore, the challenge is to develop a method to extract concise rule bases and improve the coverage of non-anomalous rule bases, i.e., one that not only reduces anomalous rules but also finds the most comprehensive rules from the dataset. In this study, we generated non-anomalous association rules (NAARs) from a cerebrovascular examination dataset through several steps: obtaining a frequent closed itemset, generating association rule bases, subsumption checking, and circularity checking, to fit production rules (PRs) in rule-based systems. Toward the end, the rule inferencing part was performed by PROLOG to obtain possible conclusions toward a specific query given by a user. The experiment shows that compared with the traditional method, the proposed method eliminated a significant number of anomalous rules while improving computational time.
Real-life clinical data mining: generating hypotheses for evidence-based medicine

Zhou et al . report in this journal a series of methodological reviews of clinical prediction model construction in 16 sections. They thoroughly explain the concept, current application and construction methods required to create such models with real-life data. Evidence-based medicine is based on randomized clinical trials designed to answer specific questions, like the efficacy or toxicity of a treatment.
Building a Project Culture through Research and Innovation Training of Future PhDs in Ukraine

Ever since the European model of training of PhD students has been introduced in Ukraine in 2016, the issue of finding effective forms and methods of doctoral studies has been raised in the national education system. The aim of this study is to present a strategy for creating a project culture in which PhD students develop and implement real projects and solve regional problems as a basis of their preparation for research and innovation activities. The main methods used in this study are the theoretical description of the program of research and innovation training of postgraduates using project-based learning and its application in practice. This program includes the following stages of project activities: preparatory, initiation, formation of the project team, analysis of the situation, development of the project concept, аs well as: fundraising, implementation and concluding of the project. The effectiveness of training and its results were tested through empirical and statistical processing methods. The results of the experiments show the positive impact of the developed training program in building of the project culture and developing transferable skills of the future PhDs (instrumental, systemic, and interpersonal), as well as gaining of the pre-professional experience in the process of working on real projects. The study also reiterates the role and importance training in bringing effectiveness of research and innovation of postgraduates.
Network Attack and Defense Game Theory Based on Bayes-Nash Equilibrium

In the process of constructing the traditional offensive and defensive game theory model, these are some shortages for considering the dynamic change of security risk problem.By analysing the critical indicators of the incomplete information game theory model, incomplete information attack and defense game theory model and the mathematical engineering method for solving Bayes-Nash equilibrium, the risk-averse income function for information assets is summarized as the problem of maximising the return of the equilibrium point.To obtain the functional relationship between the optimal strategy combination of the offense and defense and the information asset security probability and risk probability.At the same time, the offensive and defensive examples are used to visually analyse and demonstrate the incomplete information game and the Harsanyi conversion method.First, the incomplete information game and the Harsanyi conversion problem is discussed through the attack and defense examples and using the game tree.Then the strategy expression of incomplete information static game and the engineering mathematics method of Bayes-Nash equilibrium are given.After that, it focuses on the offensive and defensive game problem of unsafe information network based on risk aversion.The problem of attack and defense is obtained by the issue of maximizing utility, and then the Bayes-Nash equilibrium of offense and defense game is carried out around the security risk of assets.Finally, the application model in network security penetration and defense is analyzed by designing a simulation example of attack and defense penetration.The analysis results show that the constructed income function model is feasible and practical.
FORMATION OF THE RESEARCH SKILLS OF STUDENTS DURING STUDYING THE FOREIGN LANGUAGE (ENGLISH) AT THE UNIVERSITY

The purpose of the article is to research the influence of English lessons and students’ scientific conferences on the formation of the research skills of university students.Materials and methods: The second-year students of the Institute of Psychology and Education, Kazan Federal University took part in the study. In the process of study, the following research methods were used: theoretical analysis and synthesis of scientific literature; the study and synthesis of advanced educational experience; pedagogical observations of the process of foreign language education at the university, interviews, and questionnaires.Results of the research: There were identified the key research skills for university students. The initial and final level of the formation of the research skills during the academic year was measured. The results show that the lessons of the foreign language (English) along with the participation in student’s conference influence effectively on the research skills formation. Applications: This research can be used for the universities, teachers, and students. Novelty/Originality: In this research, the model of Formation of the research skills of students during studying the foreign language (English) at the university is presented in a comprehensive and complete manner.
Development of Omni-Directional Proximity Sensor for Drone

Unmanned aerial vehicles including drones are attracting attention in recent years. A drone is required to approach an object and obstacles when the drone uses for inspection and an indoor space. However, the collision risk is increased when the drone is close to the object. In this paper, we propose the omni-directional proximity sensor for a drone to avoid a collision. The sensor system consists of 30 units of time-of-flight (ToF) sensors that are able to detect the distance between the sensors and objects. The sensor system attaches the propeller guard and the drone, and detects the object at non-contact area. Thus, this sensor system can avoid unnecessary contact between the drone and objects. Therefore, the sensor system may be useful for drones.
Typomorphism of fine placer gold and potential primary sources of the Anabar mineral subprovince (North-eastern Siberian Platform)

Abstract Typomorphic features of placer gold of the Anabar and Bolshaya Kuonamka river basins are studied with the aim of determining its indicators and forecasting potential primary sources. All analytical work was carried out in the department of physico-chemical methods of analysis IGABM SB RAS. A detailed study of the morphology, surface microrelief, photographing was performed on a JSM - 6480 LV scanning (microscope) microscope of JEOL company with Oxford Instruments analytical prefixes (wave and energy dispersive spectrometers). Gold fineness is determined on Camebax-micro X-ray microanalyzer a «Cameca» firms. It is shown that the main sources for the gold are auriferous reservoir rocks of Neogene-Quaternary age. The indicator typomorphic features of the gold include a small size (-0.5 mm), scaly and platy forms, rough shagreen surface with casts of minerals pressing, and high fineness (900-999%o). The most highly informative feature is a strongly modified internal structure of the gold showing evidence of granulation and recrystallization. Autochthonous gold of ore habit is also found. Characteristic features of this type of gold are a very small size (-0,2 mm), angular-cloddy forms of individual particles, rough porous surface, widely ranging fineness (535 to 999% ), heterogeneous chemical composition, and a complex, multi-phase internal structure. This indicates the presence of neighboring primary sources. Hydrothermal-metasomatic formations with disseminated gold-sulfide mineralization, discovered at the places of tectonic jointing of carbonate rocks, developed in fault zones could be such sources. Considering epigenetic mineral associations, quartz-potassium feldspar and quartz hydrothermal-metasomatic formations are identified. Ore mineralization of disseminated type is represented by (in decreasing order) pyrite, gold, chalcopyrite, galena, sphalerite and silver. Gold is found in native form and in association with pyrite (quartz metasomatites) and potassium feldspar metasomatites). Thus, as a result of performed studies, indicator typomorphic features of autochthonous fine gold are identified. Near-fault hydrothermal-metasomatic formations with gold-sulfide disseminated mineralization are discovered in the studied area for the first time and examined.
Query Optimization : A Metaheuristics Approach Using Modified Memetics Algorithm (MMA)

The more complex business process of a system, the greater data that is stored. Increase of data transactions has an impact on a system performance. Therefore it is needed to optimize the query processing on data storage to maintain and improve the system performance. A memetics algorithm (MA) is a population-based metaheuristics approach which is the development of traditional genetics algorithms (GA) combined with local search (LS) technique. By using tabu search (TS) technique on the crossover operation in GA, this research proposes the modified memetics algorithm (MMA) for query optimization. The result shows that the processing time of the optimized (MMA) query is faster than the unoptimized query.
Thyroid image classification algorithm using DT CWT

The following paper presents an algorithm for thyroid image classification. To design it we used the Dual-Tree Complex Wavelet Transform for image feature extraction and Hausdorff distance to compute similarity distance between the feature vector of the query-image and all the image feature vectors stored in the image database. Then, we performed experiments to estimate the effectiveness of the proposed algorithm which showed high values of precision.
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations

In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance. Most of the current systems incorporate visual features and textual concepts as a sketch of an image. However, plainly inferred representations are usually undesirable in that they are composed of separate components, the relations of which are elusive. In this work, we aim at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics. To this end, we build the Mutual Iterative Attention (MIA) module, which integrates correlated visual features and textual concepts, respectively, by aligning the two modalities. We evaluate the proposed approach on two representative vision-and-language grounding tasks, i.e., image captioning and visual question answering. In both tasks, the semantic-grounded image representations consistently boost the performance of the baseline models under all metrics across the board. The results demonstrate that our approach is effective and generalizes well to a wide range of models for image-related applications. (The code is available at https://github.com/fenglinliu98/MIA)
Yamarna Geology: Foundations for Further Discovery

SummaryWorld class gold discoveries are being made in the Yamarna Terrane, one of the least understood terranes of the Yilgarn Craton. Despite many and varied challenges, Gold Road are in the unique position of compiling and constructing the stratigraphic map for this immature exploration terrane allowing continual improvement to ongoing targeting and exploration programmes.The key to successful exploration is the development of a regional stratigraphic and structural geological model through acquisition of new data, application of modern science, and integration of fundamental geological observation. Lithogeochemical classifications, geochronology, and development of mineralisation models are the three areas of focus Gold Road considers paramount to combine with the fundamental structure of the Yamarna Terrane. New geochronological data has implications for the understanding of the geodynamic evolution of the Yilgarn Craton, identifying some of the oldest rocks so far found in Eastern Goldfields Superterrane.A case study is presented of the development in the understanding of the tectonostratigraphic evolution of the Yamarna Terrane to target for gold efficiently and effectively.
HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide Assessment, Intervention and Performance Optimization

Heart rate variability (HRV) is increasingly recognized as a central variable of interest in health maintenance, disease prevention and performance optimization. It is also a sensitive biomarker of health status, disease presence and functional abilities, acquiring and processing high fidelity inter beat interval data, along with other psychophysiological parameters that can assist in clinical assessment and intervention, population health studies/digital epidemiology and positive performance optimization. We describe a system using high-throughput artificial intelligence based on the KUBIOS platform to combine time, frequency and nonlinear data domains acquired by wearable or implanted biosensors to guide in clinical assessment, decision support and intervention, population health monitoring and individual self-regulation and performance enhancement, including the use of HRV biofeedback. This approach follows the iP4 health model which emphasizes an integral, personalized, predictive, preventive and participatory approach to human health and well-being. It therefore includes psychological, biological, genomic, sociocultural, evolutionary and spiritual variables as mutually interactive elements in embodying complex systems adaptation.
Explain the physics concepts with flood phenomena using augmented reality technology

This study focuses on developing learning media to explanation of physics concepts for students’ elementary schools. The design of learning media in the form of flood simulation integrated with Augmented Reality. This simulation explains how capillarity and Archimedes Laws in flood. This media uses a unique card as a medium that raises a flood simulation. Flood simulation designed using Unity 3D software. The resulting water effect a more real by utilizing an asset from Obi Fluid. The output of this simulation is an application to be used on smartphones with the Android Operating System.
‘Let There Be Light!’ Dark Patterns Under the Lens of the EU Legal Framework

Dark patterns can be found both offline and online in various shapes and sizes. They can take the form of aesthetically coloured ‘Accept’ buttons capable of cajoling users into accepting terms and conditions, trick questions and default settings which lure users into sharing more personal data, or even the manner in which supermarkets choose to stack packs of water in their back shelves while displaying sweets in the front at the checkout counter. These practices go by the name of dark patterns, as they systematically and covertly aim to influence individuals into making choices by inhibiting the control of their decision-making.Dark patterns can be considered unethical since they do not take into account the interests of individuals, who, following the deployment of dark patterns become a means to an end. Relying only on ethics or social norms to prevent the use of dark patterns, however, is not effective given the difficulty in enforcing them. Thus, this paper will explore the underlying legal aspects of dark patterns.Chapter 1 sets the scene by introducing the central research question of determining whether the EU legal framework needs a specific law to prevent and sanction the use of dark patterns. The first half of Chapter 2 reveals a taxonomy of dark patterns and further defines this concept before the second half delves into a legal analysis of the laws applicable to dark patterns. From privacy and data protection to consumer protection, media law, competition law or contract law, this legal analysis unveils the existence of a broad legal framework which can protect individuals’ decision-making process against deceptive, manipulative, coercive or excessively persuasive dark patterns.Finally, Chapter 3 reveals the conclusions and recommendations of this paper. While the EU legal framework does not require a specific law to prevent and sanction the use of dark patterns, there is still a lack of awareness and enforcement. The solution, therefore, lies in a three-fold approach (individuals, companies, and authorities), which would leverage the best results in increasing awareness and enforcement in relation to dark patterns.
Computer-aided Analysis of Mammograms

Consultant radiologists are generally the prime experts for mammogram reading and interpretation.
A brief description of the Andromeda Zn-Cu prospect in the Albany-Fraser Orogenic belt: A geophysical discovery using HT SQUID

SummaryThe Andromeda Zn-Cu-Au-Ag prospect is a geophysical discovery that represents the first VMS prospect of significance in the Albany-Fraser Orogenic belt, Western Australia. The prospect was first identified following a ground Transient Moving Loop Electromagnetic survey over a structural ellipse that is recognised in aeromagnetic data and has similarities to the Nova “eye”. A conductive plate, 450 m from surface was drilled, but it was the subsequent Down Hole Transient Electromagnetic survey that ultimately led to the intersection of 29.9 m @ 2.5% Zn, 1.4% Cu from 548 m and confirmed the discovery.Andromeda is classified as a strongly deformed Besshistyle, VMS deposit that has been metamorphosed at low granulite grade conditions. Ore lenses reach ~20 m true width and comprise coarse‐grained pyrrhotite, pyrite, sphalerite and chalcopyrite. This paper outlines the geophysical methods used to identify and discover Andromeda and describes our current understanding of the geology, mineralogy and representative geochemistry.
Learning to Order Graph Elements with Application to Multilingual Surface Realization

Recent advances in deep learning have shown promises in solving complex combinatorial optimization problems, such as sorting variable-sized sequences.In this work, we take a step further and tackle the problem of ordering the elements of sequences that come with graph structures.Our solution adopts an encoder-decoder framework, in which the encoder is a graph neural network that learns the representation for each element, and the decoder predicts the ordering of each local neighborhood of the graph in turn.We apply our framework to multilingual surface realization, which is the task of ordering and completing sentences with their dependency parses given but without the ordering of words.Experiments show that our approach is much better for this task than prior works that do not consider graph structures.We participated in 2019 Surface Realization Shared Task (SR'19) , and we ranked second out of 14 teams while outperforming those teams below by a large margin.
Efficient Pattern Recognition Algorithm Including a Fast Retina Keypoint FPGA Implementation

The field of computer vision is continuously increasing and becoming more complex and power demanding. Using feature detection and description allows a fast object detection without needing big databases. FPGAs are predestined for different requirements, like real-time and power constraints, which are important in many application areas. This work proposes a new pattern recognition algorithm, based on an improved Accelerated KAZE (AKAZE) detector and Fast Retina Keypoint (FREAK) descriptor. Our software implementation increased the repeatability in comparison to the original algorithm using optimized configurations. The percentage of correct matching features between two images (repeatability) increased from 85.7% to 91.4%, while the computation time decreases from 70.3ms to 24.9ms. Furthermore, we present an efficient FPGA implementation of the FREAK descriptor. The accelerator processes 2048 features at 73.4 frames per second; achieving a repeatability of 90.9%, while being optimized for resource utilization and memory bandwidth consumption. Additionally, we show an efficient Integral Image implementation that processes four image pixels per clock cycle at a high frequency (204 MHz on xc7z020clg484-1) consuming minimum resources.