Reviewerly/exHarmony huggingface dataset:
Occluded Pedestrian-Attribute Recognition for Video Sensors Using Group Sparsity

Pedestrians are often obstructed by other objects or people in real-world vision sensors. These obstacles make pedestrian-attribute recognition (PAR) difficult; hence, occlusion processing for visual sensing is a key issue in PAR. To address this problem, we first formulate the identification of non-occluded frames as temporal attention based on the sparsity of a crowded video. In other words, a model for PAR is guided to prevent paying attention to the occluded frame. However, we deduced that this approach cannot include a correlation between attributes when occlusion occurs. For example, "boots" and "shoe color" cannot be recognized simultaneously when the foot is invisible. To address the uncorrelated attention issue, we propose a novel temporal-attention module based on group sparsity. Group sparsity is applied across attention weights in correlated attributes. Accordingly, physically-adjacent pedestrian attributes are grouped, and the attention weights of a group are forced to focus on the same frames. Experimental results indicate that the proposed method achieved 1.18% and 6.21% higher F1-scores than the advanced baseline method on the occlusion samples in DukeMTMC-VideoReID and MARS video-based PAR datasets, respectively.
Comparative Assessment of WBC Scattergram, Histogram and Platelet Indices in COVID-19 and Non COVID-19 Patients: A Cross-sectional Study

Introduction: Coronavirus Disease 2019 (COVID-19) is an extremely transmissible infectious disease. Detection of coronavirus by Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) requires expert skills and moreover, it is not widely available in all the laboratories. Aim: To evaluate Red Blood Cell (RBC), platelet histogram and White Blood Cell (WBC) scattergram graphic patterns and interpretation of corresponding parameters along with peripheral smear in 500 COVID-19 RT-PCR positive study cases (COVID-19) and to compare them with normal study controls (non COVID-19). Materials and Methods: This was a laboratory-based observational study, conducted at a fully accredited National Accreditation Board for testing and calibration Laboratories’s (NABL) Central Diagnostic Research Laboratory, RL Jalapa Hospital Tamaka, Kolar, Karnataka, India from September 2020 to April 2021. RBC, platelet histogram, WBC scattergram graphic patterns with corresponding parameters were noted in RT-PCR COVID-19 positive patients and compared with controls using (Automated Haematology Analyser XN11500). Cases were further subcategorised into moderate and severe categories. For statistical analysis, Chi-square test or Fisher’s exact test, Independent t test was used for assessing qualitative and quantitative data respectively. Mean and standard deviation were depicted in box plots. Receiver Operating Characteristic (ROC) curve was used for predicting severity. A p-value &lt;0.05 was considered statistically significant. Results: Among 500 subjects, males were 359 and females 141 with the mean age 50.5 years. Present study showed a characteristic finding of “sandglass” effect in WBC scattergram which is described as discontinuous cluster of plasmacytoid lymphocytes. RBC histogram and parameters did not show any significant changes. In severe COVID-19 cases among WBC parameters most common finding was neutrophilia. Platelet Lymphocyte Ratio (PLR), Platelet Monocyte Ratio (PMR), Platelet Neutrophil Ratio (PNR) were statistically significant in severe COVID-19 cases (p-value &lt;0.001) corresponding ROC curve for WBC and platelet showed WBC count and PLR as the significant parameter in severe COVID-19 positive cases. Conclusion: Current study reported a specific and unique sandglass effect in WBC scattergram in severe COVID-19 subjects which can help the physicians for predicting the severity of disease and to prevent further progression of disease.
Correlation Analysis of Interpersonal Trust among Grassroots Soldiers

Troops; Soldiers; Interpersonal Trust; Empirical Research
ICT assisted English learning in preschool education

Society development and globalization, nowadays, imposes the need to master foreign languages. An increasing number of parents insist for their children in kindergarten to have the opportunity to learn English. It is well known that preschool children mostly learn and adopt new concepts by playing. Playing is an integral part of a child’s upbringing and for that reason it is used in kindergartens, i.e. in preschool education. Computers and information and communication technology (ICT) have got a dominant role in many aspects of our lives, and the most numerous population of new technology’s users are children and young people. Using of ICT can greatly facilitate the achievement of teaching goals and learning outcomes in the process of implementing the preschool program. ICT is recognized as suitable for use in the learning process, so they can be used effectively for the purpose of learning English in kindergarten. Lecturers and educators can easily and simply use or create content that will be interesting to children and that will encourage their desire to learn. The successful application of ICT in the realization of the educational process with preschool children requires the appropriate IT expertise of preschool teachers.
THE EFFECT OF USING AUDIO-VISUAL MEDIA ON THE ABILITY TO WRITE SHORT STORIES

This study aims to determine the effect of using audio visual media on the ability to write short stories for seventh grade students of SMP Muhammadiyah 1 Tangerang City. The method used is a quasi-experimental experimental method using two classes, namely the experimental class which is given treatment in the form of audio visual media and the control class which is treated with audiovisual media. not given treatment as a comparison class. The data collection technique uses a short story writing essay test and a questionnaire to determine student responses to audio-visual mediaFrom the pretest data, students in the control class obtained an average score of 52.1 with a variance of 52.04, while the average score of students in the experimental class obtained an average score of 52.3 with a variance of 53.96. Then H0 is accepted because tcount = 0.1069 &lt; 2.0021 = ttable, it can be stated that there is no difference in giving the pretest to the control class and the experimental class. From the posttest data of students in the control class using conventional learning, an average score of 59.8 with a variance of 86.89 was obtained, while the average score of students in the experimental class using audio-visual media obtained an average score of 74.2 with a variance of 103, 22. Then HI is accepted because tcount = 5.7491 &gt; 2.0021 = ttable, it can be stated that giving posttest to the control class that uses conventional learning and the experimental class that uses audio-visual media, there are differences from the results of this research, it can be concluded that there is an effect of using audio-visual media on the ability to write short stories for seventh grade students of SMP Muhammadiyah 1 Tangerang City
“Seeing” the Invisible: Under Vehicle Reconstruction (UVR) for Surround View Visualization

Abstract Providing blind-spot-free vehicle surround view to the driver is important for many driving maneuvers such as parking. Existing vehicle Surround View System (SVS) can only visualize front, left, rear and right side of the vehicle but leaves the under vehicle area unknown. However, perceiving the under vehicle area is critical for many tasks such as passing through speed bumps, avoiding potholes, driving on narrow roads with high curbs or the unpaved terrain. In this paper, we propose a novel Under Vehicle Reconstruction (UVR) algorithm which utilizes what the vehicle sees in the past and vehicle egomotion to “see” through the original invisible under vehicle area. First, front or back fisheye cameras, are utilized to build a local textured map for future usage. Second, vehicle’s precise location and orientation within the local map is estimated using the vehicle egomotion. Finally, correspondent under vehicle area texture is retrieved from the map using vehicle’s pose and stitched together with traditional Surround View System to provide a new blind-spot-free visualization. As far as we know, our work is the first solution that can provide full under vehicle area reconstruction which empowers many Advanced Driving Assistant System (ADAS) functionalities such as transparent hood or transparent vehicle. Experiments on both simulated and real data are presented to show the effectiveness and robustness of the proposed algorithm.
LIBRARY AND INFORMATION NETWORKS -AN OVERVIEW (MAJOR NATIONAL AND INTERNATIONAL NETWORKS)

This article describes the basic concepts of library networks, objectives of library networks, need of library networking and coverage of networks, major Library networks in India, such as ADINET, DELNET, INFLIBNET, MANLIBNET, MALIBNET and other countries, such as AARLIN, CALIS, JANET, JISC, OCLC, and RLG. The reason of this article is to encourage librarian to rethink library networking and sharing, cooperation, and overall execution.
Development of Deep Learning-based Self-adaptive Harmony Search

Nature-inspired optimization algorithms are widely used in various mathematical and engineering problems because of their usability and applicability. However, these optimization algorithms show different performances depending on the characteristics of the problem applied. There have been various effort to solve this problem by developing a new algorithm, applying other heuristics, changing parameters, etc. The deep learning-based self-adaptive harmony search (DLSaHS) developed in this study is another effort to tackle the problem by controlling the probability of heuristics by using recurrent neural network (RNN) and the parameter called checkpoint (CP). DLSaHS contains the heuristics obtained from harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), and copycat harmony search (CcHS). DLSaHS was applied to the ten mathematical benchmark problems obtained from IEEE CEC 2021. The performance of DLSaHS is compared to the HS which showed better performance. Also, the optimal CP value obtained when applied to low-dimensional problems and the probability of heuristics according to the CP were derived to efficiently apply the DLSaHS, and it is confirmed that the error and standard deviation of the result, and computation time can be considerably reduced by applying them to high-dimensional problems.
Recommending Best Quality Image Using Tracking and Re-identification Approaches

The society we live in has several issues as apart as catching thieves and capturing the best moments of a marathon or any special occasion. In this paper, we aim to tackle the problems, which involve person re-identification, tracking, detection, and image quality evaluation. These domains find their efficacy in a variety of situations such as crime investigations, wildlife photography, sports events, and many more. Our paper endeavors to provide a solution, which will be able to track, detect, re-identify the person under perception along with suggesting the best quality frame possible in the given input, which encompasses the target. The proposed architecture is composed of four modules. The video input received goes through detection module frame by frame. The target selected by the user goes tracked throughout, until it stays in the frame using the tracking module. The person re-id module kicks in when the target is out of the frame and starts scanning every person detected for a precise match. Finally, the image quality module is run over the frames, which holds the target and returns with the suggestion for the best quality images. We have used YOLOv5 for detection, IoU for tracking, deep learning algorithm for person re-id module and BRISQUE algorithm for image quality module.
Text and Multimedia Mining through Machine Learning

The process of mining in a certain field of study was taken into consideration of deep analysis in the field of multimedia, which comprises audio, video, text, images, and so on. This chapter helps to understand the concepts of text mining, video, and audio mining with the perspectives of the working principles and its extension in the work relation with machine learning. Many works have been done in the form of research by the industries in order of providing products to the society. We have listed the core concern of the algorithm of machine learning to be implemented in this field of mining.
[Optimization of urban ecological spatial structure based on network analysis: A case study of Changzhou City, China].

The rapid development of economy and society leads to the rapid expansion of cities, resulting in the atrophy of urban ecological space and the decline of ecological function, as well as a serious threat to urban ecological security. It is of great significance for the sustainable development of a city to systematically analyze the structure of urban ecological space and put forward targeted protection and optimization measures. Taking Changzhou City as the research area and considering the natural ecological function and social service function of urban ecological space, we constructed two ecological networks, the "source-corridor" ecological network based on natural ecology and the "supply-demand" ecological network based on human ecology. For the "source-corridor" ecological network, quantitative analysis was mainly carried out from the importance of nodes, network connectivity and stability. For the "supply-demand" ecological network, quantitative analysis was mainly carried out from the importance of nodes, supply-demand equilibrium and stability. The results showed that the levels of connectivity and stability of the "source-corridor" ecological network in the main urban area of Changzhou were not high, the stability level of the "supply-demand" ecological network was general, and there was spatial mismatch between service supply and demands. From the perspective of connectivity and stability improvement, an optimization scheme of "source-corridor" ecological network with 12 additional source nodes and 57 corridors was proposed. From the perspective of supply-demand balance and stability improvement, an optimization scheme of "supply-demand" ecological network with 22 new supply nodes was proposed. Compared with the original "source-corridor" ecological network, the connectivity level of the optimized network was improved by 10%, and the network stability was improved by 0.05. Compared with the initial "supply-demand" ecological network, the service level of the optimized network was improved by 4%, and the network stability was improved by 0.10. Finally, we integrated the two ecological networks, and formulated the implementation plan of protection and management for both the current protected patches and the new ecological nodes.经济社会的高速发展带来城市的快速扩张,造成城市生态空间的萎缩和生态功能的下降,城市生态安全受到严重威胁。系统研究城市生态空间结构,提出针对性保护和优化措施,对于城市的可持续发展具有重大意义。本研究以常州市为研究区,考虑城市生态空间的自然生态功能和社会服务功能两方面,构建基于自然生态的“源地-廊道”与基于人文生态的“供给-需求”两类生态网络,对于源地廊道生态网络,主要从节点重要性、网络连通性与稳定性进行定量分析,对于供给需求生态网络,主要从节点重要性、供需均衡与稳定性进行定量分析。结果表明: 常州市主城区源地廊道生态网络的连通性与稳定性水平不高,供给需求生态网络的稳定性水平一般且存在服务供给与需求的空间错位。从连通性与稳定性提升角度,提出新增12个源地节点与57条廊道的源地廊道生态网络优化方案;从供需均衡与稳定性提升角度,提出新增22个供给节点的供给需求生态网络优化方案。对比初始源地廊道生态网络,优化网络连通性水平提升10%,稳定性提升0.05;对比初始供给需求生态网络,优化网络的服务水平提升4%,网络稳定性提升0.10。最后,综合两类生态网络,分别针对现状保护斑块与新增节点两类对象,提出了保护与管理实施方案。.
Institutional Logic, Dilemma and Suggestions of Open Source Innovation: A Case Study of Blockchain

Open-source software has made a breakthrough in the traditional intellectual property theory from the aspects of Copyright, patent right, and trademark right, and it has created a new property rights form in the form of license. Taking blockchain as an example, this paper analyzes bitcoin and Ethereum and their open-source licensing strategies. At the same time, it explores the problems encountered in the property rights of open-source blockchain and three possible solutions to this dilemma: The industry-standard licensing plan, blockchain open-source licensing scheme, and open patent scheme. This research will be significant for expanding and enriching the theoretical and practical analysis of blockchain open source in the field of intellectual property.
A Novel Deep Learning ANN Supported on Langevin-Neelakanta Machine

In the contexts of deep learning (DL) considered in artificial intelligence (AI) efforts, relevant machine learning (ML) algorithms adopted refer to using a class of deep artificial neural network (ANN) that supports a learning process exercised with an enormous set of input data (labeled and/or unlabeled) so to predict at the output details on accurate features of labeled data present in the input data set.&#x0D; In the present study, a deep ANN is proposed thereof conceived with certain novel considerations: The proposed deep architecture consists of a large number of consequently placed structures of paired-layers. Each layer hosts identical number of neuronal units for computation and the neuronal units are massively interconnected across the entire network. Further, each paired-layer is independently subjected to unsupervised learning (USL). Hence, commencing from the input layer-pair, the excitatory (input) data supplied flows across the interconnected neurons of paired layers, terminating eventually at the final pair of layers, where the output is recovered. That is, the converged neuronal states at any given pair is iteratively passed on to the next pair and so on. The USL suite involves collectively gathering the details of neural information across a pair of the layers constituting the network. This summed data is then limited with a specific choice of a squashing (sigmoidal) function; and, the resulting scaled value is used to adjust the coefficients of interconnection weights seeking a convergence criterion. The associated learning rate on weight adjustment is uniquely designed to facilitate fast learning towards convergence. The unique aspects of deep learning proposed here refer to: (i) Deducing the learning coefficient with a compatible algorithm so as to realize a fast convergence; and, (ii) the adopted sigmoidal function in the USL loop conforms to the heuristics of the so-called Langevin-Neelakanta machine. The paper describes the proposed deep ANN architecture with necessary details on structural considerations, sigmoidal selection, prescribing required learning rate and operational (training and predictive phase) routines. Results are furnished to demonstrate the performance efficacy of the test ANN.
The Cyber Forensics and The Ipc Laws For Cyber Criminal’s

Our lives have become increasingly dependent on computers. We do things differently because of them. Cybercrime is steadily rising as a result. Criminal activity has become a global phenomenon, and criminals have realized that, to continue their criminal activity, they must keep up with the times. The need to determine precisely what happened is therefore critical. A technique known as "cyber forensics" can aid this process. The global phenomenon has been impacted by Cybercriminals that have adapted more advanced tools and tactics. Anti-forensic techniques are also being used by these attackers to disguise evidence of cybercrime. . Cybercrime refers to the crimes/offenses committed online. Cyber law was introduced to stop or punish cyber criminals. As a branch of law, cyber-law focuses on cyberspace, the Internet, and legal issues.
Peningkatan Minat Baca Bagi Siswa MI Menggunakan Metode “ABACALAH” Berbasis Komunitas

The purpose of this community based is to increase the interest in reading by MI students using the community-based "ABACALAH" method. The method in community based is partnership-based. The location of this community service is carried out at MI Hayatul Ulum Pangean Maduran lamongan East Java. This form of community service activity is in the form of mentoring, providing motivation, lectures, and discussions. The result of this community service is that students are motivated and increasingly active in reading because this service also provides books collectively, as well as adding books in the school library. During the service activity, students were very enthusiastic in solving several questions provided. Based on the community service activities that have been carried out, it can be concluded that this service program runs in accordance with the purpose of the activity, namely students are motivated when they are guided to read both fiction and nonfiction books
Design of an XR Collab. Arch. for Mixed Immersive and MS Interaction

EXtended Reality (XR) is a rapidly developing paradigm for computer entertainment, and is also increasingly used for simulation, training, data analysis, and other non-entertainment purposes, often employing head-worn XR devices like the Microsoft HoloLens. In XR, integration with the physical world should also include integration with commonly used digital devices. This paper proposes an architecture to integrate head-worn displays with touchscreen devices, such as phones, tablets, or large tabletop or wall displays. The architecture emerged through the iterative development of a prototype for collaborative analysis and decision-making for the maritime domain. However, our architecture can flexibly support a range of domains and purposes. XR designers, XR entertainment researchers, and game developers can benefit from our architecture to propose new ways of gaming, considering multiple devices as user interfaces in an immersive collaborative environment.
The Effect of Online Audio Visual Assisted Story Methods on the Listening Skills of Grade IV Students Elementary School

This study aims to determine the effect of the story method on listening skills of elementary school students. This research is motivated by the students' listening skills which are quite low in the classroom. This happens because the media is less attractive so that it makes students' listening skills lacking. This audio-visual- assisted storytelling method is used to have a positive impact on students' listening skills. This research was conducted at SDN Kalitengah 1 Sidoarjo. Researchers use quantitative research with the type of research Quasi Experimental Design. With the design form Noneequivalent Control Group. The effect of the audio- visual-assisted storytelling method on elementary students was analyzed using the t-test. The research subjects were 46 students, with 23 students in the control class and 23 students in the experimental class. The data were analyzed through 2 stages, namely pretest and posttest. Based on the results of the hypothesis test count with a significant level of a = 0.05 or 5%, it means 0.001 &lt; 0.05 so that H0 is rejected and Ha is accepted. It can be concluded that there is an effect of the audio-visual-assisted storytelling method on the listening skills of elementary school students. From the eta squared analysis, it was obtained that 0.37 &gt; 0.14 showed that the audio-visual-assisted storytelling method had a major effect on the listening skills of elementary school students.