Reviewerly/exHarmony huggingface dataset:
Regulation of Digital Behavior Models for Knowledge Transfer: Organizational Concerns of Remote Learning

Research on behavior regulation was carried out after several months of social isolation, provoked by the pandemic, between the months of February and March 2020. In spring 2020, many higher education institutions began to introduce digital tools of education, remote learning, and distance teaching. The reaction during the first weeks and months was negative, but the experience of this remote regime of work and learning continued into the autumn semester due to COVID-19. This experience included the perceptions of new organizational approaches that were needed to regulate digital behavior as a specific type of strategy and choices made in the virtual space. This need was expressed in an understanding of the improvements to be implemented in the organization of educational processes at traditional institutions to efficiently apply the remote learning regime. Between December 2020 and March 2021, six focus groups were conducted to investigate if the regulation of behavior for remote work and learning (work for university administrative staff and academic teachers; studying for students) differed, with informal interviews also conducted to check the validity of the opinions formulated. The hypotheses of the lack of responsibility, and of iterative accomplishment of shorter and simpler tasks, were supported with the data obtained. The hypothesis on an imbalanced vision of mutual interests, and of the assessments of gains and costs of the remote activity, was confirmed. The hypothesis of the externalization of motivation was neither confirmed nor rejected, contradictory opinions were obtained, and, thus, further quantitative study is required. The conclusions based on the obtained results included support for improving the regulation mechanisms required to organize knowledge transfer when digital tools are applied at traditional educational institutions. To enhance the remote regime of learning, redesign and reorganization is necessary when considering the assistance needed by teachers and learners. Specific organizational efforts need to be implemented to restructure the teaching to shorter sequences, to stimulate the creativity of both teachers and learners (due to the readiness to experiment and the lack of critics, and constant access to online bases), and to identify the borders of the “sandbox” to clearly define and articulate the common rules of behavior.
Mediating Effect of Organization Learning on the Relationship Between Strategic Change, Knowledge Management and Transformational Leadership ; Case of Indonesia Islamic Banks

This study aims to know influence the mediation of organization learning on strategic change, knowledge management and transformational leadership and performance of Indonesian Islamic Banks. Design / methodology / approach - This study uses quantitative research with a purposive sampling technique with a population of 14 Indonesian islamic banks. The targeted unit of analysis is the Islamic banks manager. There were 239 questionnaires returned and 11 of them did not meet the requirements, so only 228 were used in this study. This study uses SmartPLS 3 to test the hypothesis. This research shows that strategic change, knowledge management and transformational leadership affect organization learning. This study also shows that strategic change, transformational leadership knowledge management and organization learning have an effect on firm performance. Apart from that, the mediating role of the learning organizations also provides consistent results. Research limitation / implications - Islamic banks managers must be able to identify the importance of change for the company, because of the big risks. Supporting knowledge and leadership skills in companies makes Islamic banking better competitive. These three variables become better if the company uses a learning organization because the company will change more quickly and achieve better performance with previous experience. Originality / value - Similar research is usually carried out in manufacturing and service companies and is carried out in developed countries (Europe, America and Africa). The focus of this research is the development of strategic change supported by knowledge management and transformational leadership on performance mediated by learning organizations. This research was also conducted for the first time in Asia with the object of Islamic banks in Jakarta - Indonesia
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks

The field of adversarial machine learning has experienced a near exponential growth in the amount of papers being produced since 2018.This massive information output has yet to be properly processed and categorized.In this paper, we seek to help alleviate this problem by systematizing the recent advances in adversarial machine learning black-box attacks since 2019.Our survey summarizes and categorizes 20 recent black-box attacks.We also present a new analysis for understanding the attack success rate with respect to the adversarial model used in each paper.Overall, our paper surveys a wide body of literature to highlight recent attack developments and organizes them into four attack categories: score based attacks, decision based attacks, transfer attacks and non-traditional attacks.Further, we provide a new mathematical framework to show exactly how attack results can fairly be compared.
Efficient Blockchain-Empowered Data Sharing Incentive Scheme for Internet of Things

In recent years, with many devices continuously joining the Internet of Things (IoT), data sharing as the main driver of the IoT market has become a research hotspot.However, the users are reluctant to participate in data sharing due to security concerns and lacking incentive mechanisms in the current IoT.In this context, blockchain is introduced into the data sharing of IoT to solve the trust problem of users and provide secure data storage.However, in the exploration of building a secure distributed data sharing system based on the blockchain, how to break the inherent performance bottleneck of blockchain is still a major challenge.For this reason, the efficient blockchain-based data sharing incentive scheme is studied for IoT.In the scheme, an efficient data sharing incentive framework based on blockchain is proposed, named ShareBC.Firstly, ShareBC uses sharding technology to build asynchronous consensus zones that can process data sharing transactions in parallel and deploy efficient consensus mechanisms on the cloud/edge servers and asynchronous consensus zones in sharding, thus improving the processing efficiency of data sharing transactions.Then, a sharing incentive mechanism based on a hierarchical data auction model implemented by a smart contract is presentedto encourage IoT users to participate in data sharing.The proposed mechanism can solve the problem of multi-layer data allocation involved in IoT data sharing and maximize the overall social welfare.Finally, the experimental results show that the proposed scheme is economically efficient, incentive-compatible, and real-time, with scalability, low cost, and good practicability.
Boosting the Quantum State of a Cavity with Floquet Driving

The striking nonlinear effects exhibited by cavity QED systems make them a powerful tool in modern condensed matter and atomic physics. A recently discovered example is the quantized pumping of energy into a cavity by a strongly coupled, periodically driven spin. We uncover a remarkable feature of these energy pumps: they coherently translate, or boost, a quantum state of the cavity in the Fock basis. Current optical cavity and circuit QED experiments can realize the required Hamiltonian in a rotating frame. Boosting thus enables the preparation of highly excited nonclassical cavity states in near-term experiments.
Towards a Blockchain-based Smart Certification System for Higher Education: An Empirical Study

Due to the growing interest in distributed ledger technologies such as blockchain, researchers in different areas, including the higher education sector, are investigating possibilities surrounding blockchain adoption.Despite this, no guiding framework has been developed for the application of blockchain and related technologies in the use of certificating systems that issue sharable and authentic student credentials.Existing certificating systems are marked by their lack of speed and low reliability and, in certain education systems, they produce social and cultural conflicts.Therefore, this paper offers an analysis of blockchain adoption in this field, focusing especially on the process of producing and sharing higher education certificates.The paper describes the second phase of an ongoing research project by presenting a certificate validation and sharing framework that guarantees authenticity through leveraging the privacy and security features of a blockchain network.The framework also covers the architectural design of a blockchain-based certificating system to solve problems and offer solutions in the context of higher education.Two groups of participants are also included in an empirical study focusing on an evaluation of the proposed framework in relation to its ability to describe and explain blockchain adoption in higher education.The validity and reliability of the framework is tested using a sequential mixed methods design involving the collection of quantitative survey data.It is expected that the proposed framework will have useful applications in different fields where it is necessary to determine whether a satisfactory level of blockchain technology has been maintained in accordance with relevant privacy and security standards.Finally, this study is about attitudes towards the technology and theory of acceptance by users, students and prospective employers, rather than a technological development study.
An Internet of Things (IoT) Reference Model for an Infectious Disease Active Digital Surveillance System

Internet of Things (IoT) technological assistance for infectious disease surveillance is urgently needed when outbreaks occur, especially during pandemics.The IoT has great potential as an active digital surveillance system, since it can provide meaningful time-critical data needed to design infectious disease surveillance.Many studies have developed the IoT for such surveillance; however, such designs have been developed based on authors' ideas or innovations, without consideration of a specific reference model.Therefore, it is essential to build such a model that could encompass end-to-end IoT-based surveillance system design.This paper proposes a reference model for the design of an active digital surveillance system of infectious diseases with IoT technology.It consists of 14 attributes with specific indicators to accommodate IoT characteristics and to meet the needs of infectious disease surveillance design.The proof of concept was conducted by adopting the reference model into an IoT system design for the active digital surveillance of the Covid-19 disease.The use-case of the design was a communitybased surveillance (CBS) system utilizing the IoT to detect initial symptoms and prevent closed contacts of Covid-19 in a nursing home.We then elaborated its compliance with the 14 attributes of the reference model, reflecting how the IoT design should meet the criteria mandated by the model.The study finds that the proposed reference model could eventually benefit engineers who develop the complete IoT design, as well as epidemiologists, the government or the relevant policy makers who work in preventing infectious diseases from worsening.
Infinite-dimensional gradient-based descent for alpha-divergence minimisation

This paper introduces the (α,Γ)-descent, an iterative algorithm which operates on measures and performs α-divergence minimisation in a Bayesian framework. This gradient-based procedure extends the commonly-used variational approximation by adding a prior on the variational parameters in the form of a measure. We prove that for a rich family of functions Γ, this algorithm leads at each step to a systematic decrease in the α-divergence and derive convergence results. Our framework recovers the Entropic Mirror Descent algorithm and provides an alternative algorithm that we call the Power Descent. Moreover, in its stochastic formulation, the (α,Γ)-descent allows to optimise the mixture weights of any given mixture model without any information on the underlying distribution of the variational parameters. This renders our method compatible with many choices of parameters updates and applicable to a wide range of Machine Learning tasks. We demonstrate empirically on both toy and real-world examples the benefit of using the Power Descent and going beyond the Entropic Mirror Descent framework, which fails as the dimension grows.
Asymmetric steerability of quantum equilibrium and nonequilibrium steady states through entanglement detection

Einstein-Podolsky-Rosen steering describes a quantum correlation in addition to entanglement and Bell nonlocality. However, conceptually different from entanglement and Bell nonlocality, quantum steering has an asymmetric definition. Motivated by the asymmetric definition of quantum steering, we study the steerability of two interacting qubits, which have asymmetric energy levels, coupled with asymmetric environments. The asymmetric (nonequilibrium) environments are two environments with different temperatures or chemical potentials. The Bloch-Redfield equation is applied to study the dynamics of two qubits and its long-time behavior. In our study, the steady-state steerability is determined by an experimentally friendly steering criteria, which demonstrates steering through the entanglement detection. Our results show that the steady states of two asymmetric qubits have the advantage for one direction of steering, compared to the symmetric setup. We also provide analytical results on the minimal coupling strength between the two qubits in order to be steerable. The asymmetric steerability is collectively determined by the nature of the two qubits and the influence from equilibrium or nonequilibrium environments. Nonequilibrium environments with the cost of nonzero entropy production can enhance the steerability in one direction. We also show the strict hierarchy of entanglement, steering, and Bell nonlocality of the nonequilibrium steady states, which shows a richer structure of steering than entanglement and Bell nonlocality.
Modelling of Crime Record Management System Using Unified Modeling Language

Crime records management is a system that helps police keep records of citizens’ complaints files, investigation evidence and processes. In addition, it helps police keep records of the criminals who have been arrested or who are to be arrested. This paper aims to model the Crime Record Management System (CRMS) using various Unified Modeling Language (UML) diagrams, to demonstrate an explicit visualization of the system. Also, showing the communication among different actors, and the sequence of activities and interactions. The perspective of modeling the CRMS is by covering four stages, which are Counter (Station Diary Constable), Crime Investigation, Prosecution, and Adjudication. It is concluded that applying UML diagrams facilitates understanding and perceiving the processes of recording crime files, as it illustrates detailed activities and interactions.
Data-Efficient Sensor Upgrade Path Using Knowledge Distillation

Deep neural networks have achieved state-of-the-art performance in image classification. Due to this success, deep learning is now also being applied to other data modalities such as multispectral images, lidar and radar data. However, successfully training a deep neural network requires a large reddataset. Therefore, transitioning to a new sensor modality (e.g., from regular camera images to multispectral camera images) might result in a drop in performance, due to the limited availability of data in the new modality. This might hinder the adoption rate and time to market for new sensor technologies. In this paper, we present an approach to leverage the knowledge of a teacher network, that was trained using the original data modality, to improve the performance of a student network on a new data modality: a technique known in literature as knowledge distillation. By applying knowledge distillation to the problem of sensor transition, we can greatly speed up this process. We validate this approach using a multimodal version of the MNIST dataset. Especially when little data is available in the new modality (i.e., 10 images), training with additional teacher supervision results in increased performance, with the student network scoring a test set accuracy of 0.77, compared to an accuracy of 0.37 for the baseline. We also explore two extensions to the default method of knowledge distillation, which we evaluate on a multimodal version of the CIFAR-10 dataset: an annealing scheme for the hyperparameter α and selective knowledge distillation. Of these two, the first yields the best results. Choosing the optimal annealing scheme results in an increase in test set accuracy of 6%. Finally, we apply our method to the real-world use case of skin lesion classification.
Implementasi Penilaian Hasil Belajar Siswa Berbasis Aplikasi Raport Digital (ARD) Di MTs Negeri Kota Kupang

Assessment of learning outcomes is an integral component in the implementation of education. The necessity to conduct an assessment of learning outcomes is emphasized in the Regulation of the Minister of Education and Culture Number 66 of 2013 concerning Educational Assessment Standards that these standards aim to ensure: (1) Planning of students is in accordance with the competencies achieved based on assessment principles, (2) Implementation of professional assessment of participants , open, educative, effective, efficient, and in accordance with the socio-cultural context, and (3) Reporting the results of participant assessments in an objective, accountable, and informative manner. manners. The approach used is qualitative. The type of research is case study. Data collection techniques using the method of observation, interviews, and documentation. The results obtained from this study are; 1) The Digital Report Card or ARD application is used to make it easier for teachers to process learning outcomes that have been achieved by students in the learning process. 2). In the process of inputting the assessment of student learning outcomes, 90% of teachers at MTs Negeri Kupang have been able to use the Digital Report Card (ARD) Application and 10% are still in the process of assisting ARD operators. The input of grades is done online and is connected directly to the server at the school.
Searching Silk Fabrics by Images Leveraging on Knowledge Graph and Domain Expert Rules

The production of European silk textile is an endangered intangible cultural heritage. Digital tools can nowadays be developed to help preserving it, or even to make it more accessible for the public and the fashion industry. In this paper, we propose an image-based retrieval tool that leverages on a knowledge graph describing the silk textile production as well as rules formulated by experts of this domain. Out of several possible similarity scenarios, two have proven to work best and have been integrated into an exploratory search engine.
The Application of Artificial Intelligence Technology in Cloud Computing Environment Resources

With the continuous innovation and development of modern computer science and mobile Internet and other information technologies, artificial intelligence (AI) is not a new thing. It has been widely studied and applied in many fields, and it is very important for people in modern society. The research fields of artificial intelligence mainly include: deep learning, natural language processing, computer vision, intelligent robot, automatic programming, data mining and so on. All kinds of industrial production and daily life will bring a very important practical significance and far-reaching influence. The rapid development and improvement of AI have effectively changed the daily life of modern people and improved work efficiency, and promoted the vigorous and healthy development of human economic and social civilization and the progress of information technology. When widely used, traditional network information and big data processing technologies are difficult to adapt to its development needs. Only by closely combining cloud computing technology with other technologies can it play a better role and give full play to AI technology and its development. The enthusiasm and promotion of related application technologies have promoted the smooth progress of AI technology and related undertakings. With the development and improvement of cloud computing technology, more and more users tend to use the cloud to work. However, a large number of cloud service failures occurred, causing huge losses for enterprises and individuals. In order to prevent damage to the interests of enterprises and individuals, cloud service providers will provide high-quality services as much as possible. This paper aims to study the application of AI technology in cloud computing environment resources, research on the indicator of reliability, and propose a cloud service reliability verification method for the infrastructure-as-a-service layer. Experimental research shows that through the reliability detection method in this paper, users can easily and quickly obtain the reliability of the purchased cloud service, and can intuitively feel whether the performance of each server meets the promised situation in the cloud service provider’s SLA.
The modern paradigm and components for the modernization of higher education

Global trends that require the use of information and communication, digital and innovative technologies and approaches to learning require us in turn, to re-examine the current educational paradigm. This article examines the modern paradigm and define the components for the modernization of higher education. The study used a comparative analysis, methods of synthesis, generalization and systematization of various scientific approaches, and a graphical method. It considers models of higher education in foreign countries in the context of their adaptation in Russia and examines characteristic features of traditional and modern higher education. Special attention was paid to the role of higher education institutions in training specialists. These meet the current requirements of the labor market, through their independence in strategy, courses, programs, as well as the introduction of progressive forms of teaching methods and educational technologies. The authors show that digitalization is deeply embedded into the administrative and managerial environment of higher education. They identify problems and prospects for the introduction of modern e-learning tools, individual educational trajectories, and distance learning technologies in Russia. This provides the justification for updating the modern paradigm and reorienting it to the components for the modernization of higher education (managerial, methodological, knowledge, innovative, and institutional). The authors develop key foci and define strategic objectives for the development of higher education which can inform educational practice in a modern university.