Reviewerly/exHarmony huggingface dataset:
A Pilot Study on Human Pose Estimation for Sports Analysis

Human pose estimation is the identification and detection of different poses of a human through the information collected from body part movements where the body parts refer to the joints and the bones. By referencing a video, it can calculate accurate poses and body movements for athletes so that they can accomplish optimum results in their performance. Pose Estimation can also be further used to identify the health condition of a particular player. We have developed a model which identifies various anatomical key points of a person in a given image or a video (frames) for Pose Estimation. We further attempt to extract insights on the body movement of an athlete to carry out analysis of their running behavior. The model accurately extracts 18 anatomical key points (like Hip Joint, Knee joint, Ankle Joint, etc.) without the need of any laboratory settings and special sensors, which makes it easy for anyone to use and implement the model. The model used is based on the MobileNet CNN architecture. For analysis we use various gait parameters such as Cadence, Knee angle, and Velocity. We further attempt to compare the results of the striding patterns with the running patterns shown by an athlete. The model is able to track the body movements of an athlete and then output the various gait parameters associated with these body movements. The implementation of the model has been made easy to assist the athletes in achieving optimal performance without the need of personal trainers and equipment, which can be quite costly.
Structure Level Pruning of Efficient Convolutional Neural Networks with Sparse Group LASSO

The rapid progress of convolutional neural networks (CNNs) in multiple applications of practical implementation is generally hindered by an upsurge in network size and computational complexity.Currently, engineers focus on reducing these problems through compressing the CNNs by pruning filters and their weights.In this paper, we present a fresh and easy-to-use pruning approach that reduces the model size by eliminating complete filters and filter weights based on the sparse group LASSO (Least Absolute Shrinkage and Selection Operator) method across the convolutional layers.More precisely, it regulates the sparsity at the feature level and the group level.During the process of pruning, the unnecessary filters with their weights eliminate directly without sacrificing accuracy in the test, resulting in much compact and slimmer architectures.We experimentally compute the effectiveness of our methodology with various state-of-art CNN models on various benchmark data sets.Mainly, CIFAR-10 data sets applied on VGG-16 model and reduce the parameters approx.96.1% and saved approx.83.55% float-point-operations (FLOPs) without sacrificing accuracy and have obtained development in state-of-art.
TRAINING OF SCHOOL HEADS IN TERMS OF PROFESSIONAL STANDARDS: COMPARATIVE ANALYSIS

Рассматривается тематика программ повышения квалификации руководителей образовательных учреждений России, Великобритании, Австралии, Швеции, Нидерландов и других стран в контексте требований, поставленных профессиональными стандартами. Выявлено, что функциональный состав этих требований в разных странах во многом совпадает, однако отечественные институты развития образования пока не предлагают полного спектра тематики программ повышения квалификации руководителей, направленных на формирование полного функционала руководителя, отраженного в профессиональных стандартах. The article discusses the topic of advanced training programs for heads of educational institutions in Russia, Great Britain, Australia, Sweden, the Netherlands and other countries in the context of requirements set by professional standards. It has been revealed that the functional composition of these requirements in different countries largely coincides, but Russian institutions do not yet offer a full range of topics of programs for the professional development of managers, aimed at the formation of a full functional manager, reflected in professional standards. In comparison with many foreign countries in the Russian Federation higher requirements to the education of the head of the school, which in our country should have higher education in the pedagogical and administrative sphere, and requires at least five years’ experience in teaching and/or management positions in educational institutions. The analysis of requirements to the director of the school from the point of view of the work functions of the professional standard of the school leader was conducted. Especially the «not covered» from the point of view of the topic of the proposed training programs are the areas related to functions, skills, knowledge in the field of designing the development of the organization, identification of factors and risks of development, motivation of the team, defining the tasks and prospects of personal and professional development of the manager and some others.
Proposal and Experimental Verification of an Implicit Control Based Navigation Scheme in Unknown Environment for a Centipede Type Robot

In the past decades, robot navigation in an unknown environment has attracted extensive interest due to its tremendous application potential. However, most existing schemes rely on complex sensing systems and control systems to perceive and process the geometric and appearance information of the surrounding environment to avoid the collision, while making less use of the mechanical characteristics of the environment. In this research, in order to explore how to make a robot navigate in an unknown environment with minimal active control and minimal sensing by taking full advantage of the mechanical interactions from the environment, which is called implicit control in this study, we propose a centipede robot and its corresponding navigation scheme for navigating a 2D unknown environment without sensing information about the surrounding environment. In this scheme, the only observation input of this system is the goal direction information relative to the robot direction. Based on this scheme, we built a prototype robot and conducted navigation experiments in three environments with different levels of complexity. As a result, we obtained the navigation route map and navigation time distribution of each environment and analyzed the characteristics and applicability scenarios of the proposed navigation scheme compared to the traditional ones.
Security Challenges and Students’ Academic Achievement in English Language in the Senior Secondary Schools in Ebonyi State

The study examined security challenges and students' academic achievement in English language in the senior secondary schools in Ebonyi state.The study was a descriptive survey study.The population of the study consisted one thousand and thirty-seven language teachers out of which a sample study of 205 English language teachers with the school administrators were randomly selected in Eighteen (18) secondary schools in the three Education Zones.In each zone 6 schools were selected making it a total of 18 secondary schools.Three research questions and a null hypothesis were formulated to guide the study.The research instrument was security challenges and students' academic achievement English language questionnaire (SCSALQ).The instrument was validated by experts in the Arts and Social Science Education and Measurement and Evaluation units in the Ebonyi State University.The instrument was further subjected to a reliability test in a test-retest and an index of .082was realized and this certified the use of the instrument.Data collected were analyzed using percentages, rank order and t-test statistics respectively.Findings revealed that different security challenges existed in secondary schools in teaching English language in the State and that there were many causes of these security challenges.These security challenges had adversely affected students' academic achievement in the State.The implications were that brain drain and students' poor academic achievement were experienced in schools and the State as a whole.It was recommended that government should seriously and in all honesty put to a stop insecurity in the State.School administrators and individual students should be at alert and instill discipline in the students.More teachers should be recruited, trained and retrained to guard students' irrational behaviours and ensure effective teaching and learning.
SISTEM INFORMASI GEOGRAFIS PEMETAAAN LOKASI WISATA DI SUMATERA BARAT BERBASIS WEBGIS MENGGUNAKAN QGIS

The tourist attraction is something that everyone likes, because it offers beauty and happiness for anyone who will visit it, a tourist attraction that can be developed properly will certainly increase income very high. West Sumatra is one of the provinces that has various tourist attractions that can be visited, but due to a lack of information and the absence of instructions that can explain in detail where the position of the tourist attractions are located, therefore tourist attractions in West Sumatra are only partially visited by visitors. Therefore, a study was conducted to address this problem. That is by making a lokasi map to clearly determine the position of tourist objects in West Sumatra, namely the GIS web system for the position of tourist objects in the West Sumatra region by utilizing the QGIS application
Smart energy scheduling optimization model based on energy blockchain technology

Smart energy service is a new energy service model under the background of energy internet. There are many production and sales entities in the smart energy service system, and energy production and consumption are diversified. Aiming at the problems of information security and loss in the dispatching process of smart energy system, an energy-based Blockchain-based smart energy service scheduling optimization model. Using the characteristics of decentralization, trustworthiness and transaction transparency of blockchain technology, build a smart energy service network architecture based on blockchain; establish a scheduling optimization model with the goal of maximizing system economic benefits and environmental protection benefits, effectively reducing system costs. Operating costs have greatly improved economic and environmental benefits. The case analysis shows that the energy blockchain technology can improve the security of the smart energy system and ensure that the relevant data cannot be tampered with; at the same time, the constructed smart energy service scheduling optimization model can provide decision support and theory for the operation management and scheduling optimization problems of the smart energy system support.
IA-ICGCN: Integrating Prior Knowledge via Intra-event Association and Inter-event Causality for Chinese Causal Event Extraction

Causal event extraction (CEE) is a joint extraction task of event detection and event causality discrimination, which is of great value on dialogue system, event prediction and so on. However, the ambiguity of Chinese event description and the implicit causal relationship makes the poor performance with existing models. To deal with this problem, we propose a model to incorporate domain knowledge by taking Intra-event Association and Inter-event Causality into account. We use causal indicators to obtain sentences containing causal relationships from domain texts, and use these sentences to construct event association networks and causality transition networks. To obtain complete event expressions and accurate event causality, we use graph convolutional neural networks (GCN) to encode the information of the two networks separately. Finally, all the information are fed into the Bidirectional Long Short-Term Memory networks. Experimental results on two datasets show that our method outperforms the state-of-the-art baselines.
Research on the generalization of social bot detection from two dimensions: feature extraction and detection approaches

Purpose The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots. Design/methodology/approach This paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection. Findings The experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score. Originality/value Based on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.
Reviewing the Case of Online Interpersonal Trust

The purpose of the study is to better qualify the problem of online trust. The problem of online trust is that of evaluating whether online environments have the proper design to enable trust. This paper tries to better qualify this problem by showing that there is no unique answer, but only conditional considerations that depend on the conception of trust assumed and the features that are included in the environments themselves. In fact, the major issue concerning traditional debates surrounding online trust is that those debates focus on specifc defnitions of trust and specifc online environments. Ordinarily, a defnition of trust is assumed and then environmental conditions necessary for trust are evaluated with respect to such specifc defnition. However, this modus operandi fails to appreciate that trust is a rich concept, with a multitude of meanings and that there is still no strict consensus on which meaning shall be taken as the proper one. Moreover, the fact that online environments are constantly evolving and that new design features might be implemented in them is completely ignored. In this paper, the richness of the philosophical discussions about trust is brought into the analysis of online trust. I frst provide a set of conditions that depend on the defnition of trust that can be assumed and then discuss those conditions with respect to the design of online environments in order to determine whether they can enable (and under which circumstances) trust. Tagliaferri, M. Reviewing the Case of Online Interpersonal Trust. Found Sci (2022).
Time-Bin Superposition Methods for DPS-QKD

Key generation efficiency, and security, in DPS-QKD improve with an increase in the number of path delays or time-bin superpositions. We demonstrate the implementation of super-position states using time-bins, and establish an equivalence with path-based superposition, thus yielding a simpler implementation of higher-order superposition states for differential phase-shift quantum key distribution (DPS-QKD). We set up DPS-QKD, over 105 km of single mode optical fiber, with a quantum bit error rate of less than 15% at a secure key rate of 2 kbps. With temporal guard bands, the QBER reduced to less than 10%, but with a 20% reduction in the key rate.
Application Analysis of Data Mining Technology in Marketing

In the increasingly innovative social environment, enterprises have been exploring ways to increase product sales. In this paper, based on the systematic understanding of data warehouse, data mining, multiway analysis, and other contents, the shortest way to extract data from the traditional database is defined, so as to complete the extraction and construction of data warehouse, and in the multiway analysis Angle, to truly realize the scientific processing of data analysis in marketing. At the same time, the mining of association rules in data mining is also deeply explored, and the Apriori TB algorithm and TID algorithm are selected for comparative analysis. Finally, the application value of data mining technology in marketing is clarified in the data mining mode of classification and intensity.
Curriculum learning for early Alzheimer's Disease diagnosis

The early and asymptomatic stages of Alzheimer's Disease (AD), such as mild cognitive impairment (MCI), are hard to classify, even by experienced physicians. Deep learning approaches, such as convolutional neural networks (CNNs), have been shown to help, achieving similar or even better results. Although these methods have the advantage that features are automatically extracted from images rather than handcrafted, they do not allow for incorporating medical knowledge. In this paper we propose curriculum learning (CL) strategies for CNNs designed to diagnose healthy subjects, MCI and AD, as a way to incorporate medical knowledge to boost the performance of the networks for early AD diagnosis. CL is a training strategy of the networks that tries to mimic the way humans, in this case doctors, learn. Several CL strategies were implemented and compared to commonly used baseline methods. The results show that they improve the performance, particularly that of MCI. Clinical relevance- This work shows that the use of CL strategies improve the diagnosis of AD, particularly at an early stage.
Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations

Purpose The purpose of this study is to solve the problems caused by the growing volumes of pre-annotated literature and variety-oriented annotations, including teamwork, quality control and time effort. Design/methodology/approach An annotation collaboration workbench is developed, which is named as Bureau for Rapid Annotation Tool (Brat). Main functionalities include an enhanced semantic constraint system, Vim-like shortcut keys, an annotation filter and a graph-visualizing annotation browser. With these functionalities, the annotators are encouraged to question their initial mindset, inspect conflicts and gain agreement from their peers. Findings The collaborative patterns can indeed be leveraged to structure properly every annotator’s behaviors. The Brat workbench can actually be seen as an experienced-based annotation tool by harnessing collective intelligence. Compared to previous counterparts, about one-third of time can be saved on Xinhuanet military news and patent corpora with the workbench. Originality/value The various annotations are very popular in real-world annotation tasks with multiple annotators. Though, it is still under-discussed on variety-oriented annotations. The findings of this study provide the practitioners valuable insight into how to govern annotation projects. In addition, the Brat workbench takes the first step for future research on annotating large-scale text resources.
Intrinsic Evolution of Analog Circuits Using Field Programmable Gate Arrays

Abstract Evolvable hardware is a field of study exploring the application of evolutionary algorithms to hardware systems during design, operation, or both. The work presented here focuses on the use of field programmable gate arrays (FPGAs), a type of dynamically reconfigurable hardware device typically used for electronic prototyping in conjunction with a newly created open-source platform for performing intrinsic analog evolvable hardware experiments. This work targets the reproduction of seminal field experiments that generated complex analog dynamics of unclocked FPGAs evolved through genetic manipulation of their binary circuit representation: the bitstream. Further, it demonstrates the intrinsic evolution of two nontrivial analog circuits with intriguing properties, amplitude maximization and pulse oscillation, as well as the robustness of evolved circuits to temperature variation and across-chip circuit translation.
Adaptive VR Video Data Transmission Method Using Mobile Edge Computing Based on AIoT Cloud VR

Aiming at the high requirements of cloud service-based virtual reality in AIoT for data transmission rate and delay sensitivity, a cloud VR system scheme based on MEC (Mobile Edge Computing) is proposed, which mainly incorporates viewpoint-based VR video data processing and hybrid digital-to-analog (HDA) transmission optimization and can be served for AIoT transmission filed. Firstly, a learning-driven multiaccess MEC offloading strategy is designed, in which the VR terminal automatically selects the optimal MEC server for task offloading, thereby effectively improving network efficiency and reducing service delay. Secondly, the progressive transmission of the VR data is realized through viewpoint-aware dynamic streaming based on RoI (region of interest) and the priorities of different objects. The transmission priority of each object in the scene is determined through the ROI layering, which effectively solves the contradiction between the large data volume in the VR scenes and the network bandwidth limitation when applied in AIoT domain, and further improves the real-time performance of the system. Then, the HDA (hybrid digital-analog) technique is introduced to optimize the transmission. Finally, the base station protocol stack is modified on the basis of the LTE (Long-Term Evolution) system, and the MEC technology is integrated to realize a complete cloud VR system in AIoT. The experimental results show that compared with other advanced schemes, the proposed scheme can achieve more robust and efficient data transmission performance and provide better VR user experience.
A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences

With the rapid increase in the amount of video data, efficient object recognition is mandatory for a system capable of automatically performing question and answering. In particular, real-world video environments with numerous types of objects and complex relationships require extensive knowledge representation and inference algorithms with the properties and relations of objects. In this paper, we propose a hybrid neuro-symbolic AI system that handles scene-graph of real-world video data. The method combines neural networks that generate scene graphs in consideration of the relationship between objects on real roads and symbol-based inference algorithms for responding to questions. We define object properties, relationships, and question coverage to cover the real-world objects in pedestrian video and traverse a scene-graph to perform complex visual question-answering. We have demonstrated the superiority of the proposed method by confirming that it answered with 99.71% accuracy to 5-types of questions in a pedestrian video environment.