Reviewerly/exHarmony huggingface dataset:
Autotuned Classification Based on Knowledge Transferred from Self-supervised Models

Abstract With the recent advancements of deep learning-based methods in image classification, the requirement of a huge amount of training data is inevitable to avoid overfitting problems. Moreover, supervised deep learning models require labeled datasets. Preparing such a huge amount of labeled data takes a lot of human effort and time. In this scenario, self-supervised models are becoming popular because of their ability to learn even from unlabelled datasets. This paper presents a method to automatically tune the hyperparameters of a self-supervised model for image classification. The learned features are transferred from a self-supervised model to a set of Fully Connected (FC) layer, where the hyperparameters related to the architecture are tuned. A softmax layer is used following the FC layers for classification. The hyperparameters such as the number of layers, number of units in each layer, learning rate, dropout, and optimizer are automatically tuned in these FC layers, using a Bayesian optimization technique called the Tree-structured Parzen Estimator Approach (TPE) algorithm. To evaluate the performance of the proposed method, state-of-the-art self-supervised models such as Simclr and SWAV are used to extract the learned features. Experiments are carried out on CIFAR-10, CIFAR-100, and Tiny Imagenet datasets. The proposed method outperforms the state-of-the-art.
Geology, mineralogy, geochemistry and conditions of forming the Tukan gold-quartz deposit (Khudolaz trough, South Urals)

Research subject. Detailed mineralogical and geochemical studies of rocks of the Tukan gold-quartz deposit (quartz veins, dolerites, gabbro-diorites) were carried out. The deposit is one of numerous small gold-quartz occurrences in the Khudolaz trough of the West Magnitogorsk zone. Methods . The morphology and composition of native gold was studied using an Axios kop 40 A optical microscope and Tescan Vega 3 and JEOL-6390LV scanning electron microscopes. The chemical composition of rocks was determined by X-ray fluorescence (Carl Zeiss VRA-30) and atomic emission (Shimadzu ICPE-9000) analyzes. Fluid inclusions were analyzed using Linkam TMS-600 thermostage equipped with Link-System 32 DV-NC software and an Olympus BX51 optical microscope. The gas composition of fluid inclusions was examined by Raman spectroscopy (Horiba LabRam HR800 Evolution). Results. Gold-bearing quartz veins and dolerites intersect ing the gabbrodiorite intrusion of the Khudolaz complex are associated with submeridional strike-slip faults. Native gold has a various morphology and is confined to cracks in quartz veins and altered dolerites. The composition of gold is stable, with the average fineness of 871 ± 8.3‰. An analysis of fluid inclusions in a gold-bearing quartz vein showed that gold was formed at temperatures of no less than 186–230°С from a fluid with a salinity of 4–8 wt % NaCl-eq. The presence of gases СО 2 , N 2 and CH 4 was found in fluid inclusions. Dolerites and gabbrodiorites hosting gold-bearing veins contain sulfides (pyrite, pyrrhotite, chalcopyrite, pentlandite, violarite), sulfoarsenides (cobaltite and its analogs), as well as mine rals of Ag (hessite) and Pb (kuranakhite and phases similar in composition to minerals of the dugganite group and burckhardtite). Conclusions . The sustained composition of native gold and a narrow range of salinity and homogenization temperatures of fluid inclusions indicate the formation of gold during a single stage of mineralization. The main source of gold could have been the dolerite dikes of the Ulugurtau complex. The presence of CO 2 , N 2 and CH 4 gases in fluid inclusions from goldbearing quartz indicates that not only magmatic fluid, but also fluid from the host rocks took part in mineral formation. The low value of the ratio X(CO 2 )/X(CH 4 ) ≤ 0.7 indicates a moderately reduced ore-forming fluid. Low Au concentrations (0.1– 0.4 ppm) in quartz veins, dolerites, and gabbro-diorites, including those containing visible gold, indicate a weak commercial potential of the deposit
CHORAL PERFORMANCE IN A PANDEMIC: MODERN SOLUTIONS AND PROSPECTS

The article is devoted to the research of new forms of organization of remote work in choirs. With the introduction of quarantine restrictions, the rhythm of life has changed significantly, which has led to the search for new forms and methods of work. Various methods of introduction of the newest technologies in music education of different levels are considered. The experience of using new teaching methods in professional and educational choirs in schools and universities is analyzed. The influence of new forms of choral creativity on Ukrainian culture in general is determined. Eric Whitaker’s experience in organizing the world’s first virtual choir as an international art project has been studied. Professional, amateur and educational choirs in the pandemic were forced to look for new forms of work organization. This was the impetus for the introduction of new forms of work. A characteristic feature of remote choral rehearsal is the shift of emphasis from collective to individual performance. Awareness of oneself as an integral part of the whole characterizes the positive consequences of remote work. Among the negative consequences of remote work, we can identify low efficiency, depression as a result of loneliness, deterioration of the quality of the material. The unification of singers into virtual choirs has become a new wave in the modern cultural process of Ukraine and the world.
4D Temporally Coherent Multi-Person Semantic Reconstruction and Segmentation

Abstract We introduce the first approach to solve the challenging problem of automatic 4D visual scene understanding for complex dynamic scenes with multiple interacting people from multi-view video. Our approach simultaneously estimates a detailed model that includes a per-pixel semantically and temporally coherent reconstruction, together with instance-level segmentation exploiting photo-consistency, semantic and motion information. We further leverage recent advances in 3D pose estimation to constrain the joint semantic instance segmentation and 4D temporally coherent reconstruction. This enables per person semantic instance segmentation of multiple interacting people in complex dynamic scenes. Extensive evaluation of the joint visual scene understanding framework against state-of-the-art methods on challenging indoor and outdoor sequences demonstrates a significant ( $$\approx 40\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>≈</mml:mo> <mml:mn>40</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ) improvement in semantic segmentation, reconstruction and scene flow accuracy. In addition to the evaluation on several indoor and outdoor scenes, the proposed joint 4D scene understanding framework is applied to challenging outdoor sports scenes in the wild captured with manually operated wide-baseline broadcast cameras.
Community Service Monitoring Information System at The Level of Community Harmony of Citizens

A website-based community service information system can improve the performance of sub-district, village, community units and neighborhood units so that it can speed up report generation and can also improve the quality of better services because it is faster and without queuing. This research is to design and build an Information System for the Rukun Warga (RW) Service at Perumnas Antang Blok 10 by using the System Development Life Cycle (SDLC) method and the Multi-Objective Optimization On the Basis Of Ratio Analysis (MOORA) method that is able to provide effective services. and efficient to citizens and able to facilitate in supporting decision making acceptance of social assistance. The results of the study show that the Community Service Information System (RW) at Perumnas Antang Blok 10 can help provide effective and efficient services to residents and is able to facilitate decision making in receiving social assistance. Based on the results of the respondent's assessment through the distribution of questionnaires, the Information System for the Rukun Warga (RW) at Perumnas Antang Blok 10 is very good to use because it meets the final score criteria of 88%.
Customer knowledge management: a systematic literature review and agenda for future research

Purpose - Reviewing the literature from 2000 until 2020, this paper aims to analyse the nature of existing research on customer knowledge management (CKM) and outlines a future research agenda. &#x0D; Design/methodology/approach – Selected articles on CKM are examined using a structured literature review methodology. The paper analyses 75 articles published from 2000 to 2020 within journals specializing in the fields of management, information systems and marketing.&#x0D; Findings – CKM is a research area of growing importance especially in the marketing and IS domains. Findings show different definitions of the construct depending on the examined field. Additionally, qualitative research on customer knowledge management is scarce and there seems to be a lack of understanding concerning CK creation, collection, transfer, and dissemination within the firm.&#x0D; Originality/value – The paper integrates the literature on CKM within a framework that links the antecedents, consequences, and moderators. Finally, the review identifies 3 research streams to extend the literature and outline key practical implications: implementation, firm performance and, customer engagement.&#x0D; Paper type - Literature review&#x0D; Keywords - systematic literature review; customer knowledge management; antecedent; moderators; consequences; research agenda
Special Issue on Machine Learning Approaches in Big Data Visualization

The articles in this special section focus on analytic rendering and hardware-accelerated simulation for scientific applications. Data visualization is now one of the cornerstones of data science, turning the abundance of big data being produced through modern systems into actionable knowledge. Data visualization in the big data era raises the need to co-design and more closely align the underlying data management systems with the user-oriented techniques that state-of-the-art visualization systems now offer. In addition, the tight integration of suitable machine learning approaches with data visualization and their control by users-in-theloop promises to enhance scalability, effectiveness, and adaptivity of the interactive visual data analysis process. This special issue attracted and publishes research work on multidisciplinary research areas from the human–computer interaction, computer graphics, and data management communities.
Competitiveness of Universities in the Context of Sustainable Higher Education

At the end of the 20th century and the beginning of the 21stcentury, the humanity has entered a new stage of development when changes in various industries and spheres of human activity are not episodic, but obtain a lasting and continuous nature, so organisations, each individual of the society and the society as a whole must live with constant changes and be able to develop themselves in the interacting and constantly changing environment. Thus, the university teachers’ competitiveness in an ever-changing environment is becoming topical in the context of sustainable higher education. The aim of the research: on the basis of the results of theoretical research, to develop a methodological base for studying the competitiveness of universities in the contextof university sustainability. Theoretical research methods: study, analysis and evaluation of the content of scientific literature, and empirical research methods: reflection of the authors’ experience, are used in the current research. There are variousmanifestation forms of competitiveness in the activities of universities. Aspirations of universities to ensure their viability, including competitiveness in the current changing conditions, as well as sustainability in the future perspective, greatly motivates university teachers to constantly improve professionally, thereby developing their competitiveness. There is a correlation between the competitiveness of the university and its teachers: the more competitive the university is, the more competitive its teachers are, and vice versa, the more competitive the university teachers are, the more competitive the university is as a whole.
Using Android Platform for Conducting Ideological and Political Education in Colleges and Universities

Due to advancements in information technology, handheld computers and smartphones have progressively supplanted desktop computers and older mobile phones. Due to the development and use of a series of mobile device operating systems such as Android, IOS, Harmony, Windows OS, and Symbian, mobile education comes into being. For this purpose, this article develops an ideological and political online classroom mobile learning system based on an Android application. The study created a set of mobile teaching systems based on open-source Android technology and C/S (Client-Server) architecture. Given its typical teaching time, the system is dedicated to the teaching of courses, with registration and login, notice and announcement, course information release, questions and answers, teaching resources display and projection, and other functions. It can also serve as a platform for pupils to learn independently. Following the practical application in ideological and political teaching, the system runs well, functions normally, can meet the needs of students to learn in a personalized and dispersed manner, and effectively speeds up the teaching network and intelligence process. It also has a high application value. It combines network teaching and traditional classroom to create a new education model. The purpose is to effectively assist the development of ideological and political mobile education that can be used as a reference point. Finally, this study examines the system architecture and core modules and presents an Android-based solution for a mobile client of a teaching resource library. It uses the teaching resource library of the school as an example to implement the system, which has some reference value for the study of mobile campus applications and mobile learning systems.
Learning to Navigate Through Reinforcement Across the Sim2Real Gap

&lt;p&gt;Amid the recent advances in robotics and machine learning, unmanned aerial vehicles (UAVs) have shown evident proliferation across various applications. Consequently, the involvement of UAVs in populated environments has progressively become inevitable, putting forward stringent safety and security measures. In this work, we develop a deep reinforcement learning-based UAV-navigation approach that blends decision making with behavioral intelligence. In particular, a reinforcement learning (RL) agent is trained to instruct the UAV on how to accomplish a goal-oriented task, while assuring the safety of the UAV and its surroundings. Upon arriving at the goal position, the RL agent slows the UAV down preparing it for landing. The safety of the UAV and the environment are attained through a robust collision avoidance capability, embedded into the RL-based navigation system and considers both static and dynamic obstacles in the environment. Training is exclusively carried out in simulation, where a high fidelity UAV controller model is used to perform the simulated maneuver. The proposed approach was tested in simulation and then shown to directly transfer to reality without explicit sim2real gap transfer techniques. Experimental results demonstrated the agent's capability to achieve the navigation task with 90% success rate.&lt;/p&gt;
Dataset Quality Assessment with Permutation Testing Showcased on Network Traffic Datasets

&lt;p&gt;Intelligent and autonomous networks require precise and fast mechanisms that ensure error-free and efficient operation. Modern solutions are increasingly based on artificial intelligence, in particular on machine learning, to reliably process huge amounts of data. Therefore, high-quality datasets are essential to train machine learning models. Unfortunately, the problem of assessing the quality of datasets is very challenging and often overlooked. This paper proposes a method for assessing the dataset quality in the context of binary classification. It is based on permutation testing and examines the strength of the relationship between observations and labels. Experiments carried out on simulated and real network datasets show that the method is sensitive to detect errors/mislabels in the labeled dataset. We also present theoretical considerations justifying our results.&lt;/p&gt;
Interpreting Arabic sign alphabet by utilizing a glove with sensors

People who are deaf or dumb in Arab communities face several challenges. The most important challenge is to communicate with people. In this study, a new approach for identifying the alphabet in the Iraqi Sign Language (IrSL) is proposed, which makes use of a suggested deep neural network called the Deep Recurrent Alphabet Sign Language (DRASL). It utilizes the Long Short-Term Memory (LSTM) technique for classifying the outputs and recognizing the alphabet in the SL. The dataset is constructed with the use of a glove that is coupled to flex sensors on each finger; each sensor gives a variable value based on the curvature ratio of the fingers. The sensors were connected to an Arduino which was then linked to a computer to transfer the data we collected. The data were divided into three groups, which had 29 different movements. All of these groups had a remarkably high accuracy equal to 100%.deep learning
Digital currency in China: pilot implementations, legal challenges and prospects

Digital currency is supported by blockchain as the underlying technology, and possesses the characteristics of decentralization, programmability, and security verification based on cryptographic principles. In fact, it can be divided into legal digital currency and non-legal digital currency depending to whether the digital currency is issued by the competent authority. China’s legal digital currency (DC/EP) has implemented early and already has a certain scale of development. This article examines the legal issues that arise from the implementation of a legal digital currency in China, issues that Chinese lawmakers inevitably face. These legal issues may arise at both domestic and international levels, such as personal privacy protection, currency sovereignty conflicts, potential cross-border crimes risks. This article emphasizes that on the one hand, the development of legal digital currency must be carried out within a reasonable legal framework to avoid new systemic risks caused by the development of technology. On the other hand, it is also necessary for China to put in place as soon as possible an appropriate legal framework, explore the international cooperation of diversified supervision as well to ensure the healthy development of legal digital currency.
Inspecting Cloud Storage System for Secure Data Forwarding Using Advanced Encryption Standard

Abstract: Data storage and sharing services within the cloud, users can easily modify and share data as a bunch. to make sure share data integrity are often verified publicly, users within the group must compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users thanks to data modifications performed by different users. Encryption schemes available for data privacy but it limits the number of functions exhausted storage system. Building a secure storage system that supports multiple functions is hard when the storage system is distributed and has no central authority. a brand-new idea is proposed proxy re-encryption scheme for decentralizes erasure code for defending the distributed system. The easy method, which allows an existing user to download the corresponding a part of shared data, is inefficient because of the big size of shared data within the cloud. We propose a unique public auditing mechanism. Moreover, our mechanism is in a position to support batch auditing by verifying multiple auditing tasks simultaneously. Key Words: Cloud computing, Cloud storage, AES, Cryptography Upload, Auditing
Multimodal Across Domains Gaze Target Detection

This paper addresses the gaze target detection problem in single images captured from the third-person perspective. We present a multimodal deep architecture to infer where a person in a scene is looking. This spatial model is trained on the head images of the person-of-interest, scene and depth maps representing rich context information. Our model, unlike several prior art, do not require supervision of the gaze angles, do not rely on head orientation information and/or location of the eyes of person-of-interest. Extensive experiments demonstrate the stronger performance of our method on multiple benchmark datasets. We also investigated several variations of our method by altering joint-learning of multimodal data. Some variations outperform a few prior art as well. First time in this paper, we inspect domain adaptation for gaze target detection, and we empower our multimodal network to effectively handle the domain gap across datasets. The code of the proposed method is available at https://github.com/francescotonini/multimodal-across-domains-gaze-target-detection.
Facilitating Cyber Security Threat Modelling: A Social Capital Perspective

To identify and manage their cyber security risks, organisations need to form a thorough understanding of various factors that may expose them to these risks. While cyber security professionals and scholars have developed a plethora of practical methodologies and frameworks to support cyber security risk identification and mitigation, the theoretical foundations on what promotes effective knowledge creation when using these methodologies and frameworks are nascent. Yet, theories developed in the field of knowledge management and intellectual capital may provide valuable insight on how to enhance cyber security risk related knowledge creation in organisations. For example, social capital is considered as an important prerequisite for knowledge exchange and combination when creating new intellectual capital (Nahapiet &amp; Ghoshal, 1998). However, more focused research is required to understand how social capital affects knowledge creation in the context of organisational cyber security risk related activities.&#x0D; &#x0D; Using qualitative data gathered from three cyber security threat modelling workshops, this paper examines how social capital enables conditions for exchanging and combining knowledge on cyber security threats. By comparing the empirical observations with Nahapiet and Ghoshal’s (1998) model, this study identifies practical approaches that are used by threat modelling workshop facilitators to create conditions for effective knowledge exchange and combination. This study provides both cyber security scholars and professionals with an example on how to use knowledge creation related academic theories to analyse and further enhance cyber security risk management approaches by creating a connection between Nahapiet and Ghoshal’s (1998) social capital model and cyber security threat modelling.