Reviewerly/exHarmony huggingface dataset:
Exploring the mobile learning needs amongst performing arts students

Purpose This paper aims to study the information needs and online information-seeking behaviors on mobile platforms of performing arts students at a college level. Design/methodology/approach Survey instruments were used to collect data from performing arts students at the Hong Kong Academy of Performing Arts (HKAPA), a metropolitan’s major performing arts tertiary institution. Data collected were analyzed through descriptive statistics and other statistical methods, and the music-related students were compared with the production-related students. Findings The result reveals that performing arts students all owned their mobile devices and often used mobile apps for non-academic purposes, but they did not often use mobile library services or read online academic contents with their mobile devices. The participants considered inadequate signal coverage, slow loading time, difficulty in reading on a mobile device and the lack of specialized mobile apps as more significant barriers affecting their usage. There are some significant differences between the music-related and production-related student groups in that music-related students watched lectures on the library websites and used electronic music scores more often than the production-related students. Practical implications This study contributes to the input for enhancements and policies to future mobile services and facilities of performing art libraries. Originality/value There have been scant studies on the mobile learning needs of performing arts students, especially in Asia.
Digitalisation of Professional Education: Prospects and Invisible Barriers

Introduction . Nowadays, digital education is one of the priorities of state policy in modern Russia. The Federal Institute for the Development of Education has developed a draft didactic concept of digital education to maximise the potential of digital technology (DT) in vocational training. Nevertheless, while policymakers and education managers are aware of the need to move to a new level of digital technology implementation, there are challenges to this process. The technological resources of educational environment have been increasing intensively in the past decade, and their intensive scientific and pedagogical development is observed. However, little attention is given to the willingness of teachers to work with continuously evolving technological tools. The aim of the research is to identify the barriers, which hinder the professional development of teachers in mastering information and communications technologies (ICT) and digital technologies, as well as some mental attitudes circulating in the society and slowing down the process of digitalisation of vocational education. Methodology and research methods . The present research is based on personal-context and personal-development approaches. In the course of experimental research and two pilot studies, teachers and specialists of professional educational organisations were interviewed (n = 187 people). The methods of questionnaire-based survey, sociological analysis, statistical data-processing tools and Google Forms tools were used. Results and scientific novelty . On the basis of the outcomes of the conducted surveys, the following aspects were analysed: the degree of teachers’ skills formation declared in the professional standard; willingness to implement e-learning, expand the range of ICT and GT included in teaching practice; respondents’ attitudes towards the digitalisation of education and their self-recognition in this process. Clear and invisible obstacles to the process under discussion are identified and sistematised. The found barriers are classified into risk, image and didactic barriers. The recommendations to overcome the barriers are proposed. According to the authors of the present article, it is advisable to introduce a new specialty “digital engineer-teacher” due to the obvious integration of pedagogical and engineering functions into the activities of teachers. Working in one team consisted of a specialist, who designs an effective teaching environment, and a teacher-didactist, will reduce a part of unjustified functional workload of the latter. Practical significance . The materials of the undertaken research have significant implications for further development of perceptions of the essence and content of digital didactics; justification and formation of optimal conditions for digitalisation of vocational education, including psychological and pedagogical adjustment of the system of training and retraining of pedagogical and managerial personnel for the digital economy.
Still a long way to go

Drawing on 178 video-recorded lessons from 47 lower-secondary classrooms with high technological infrastructure in Norway, the present study sheds light on how and for what purposes teachers use technology in their everyday instruction, providing important insights into what kind of digital literacy practices students experience in a classroom context. Key findings are that teachers' implementation and uptake of technology in everyday instruction was narrow and limited. Despite good access and high national ambitions for the development of students' digital competence, teachers' uptake of the available digital technology was very often limited to supporting traditional teacher-centered practices, with low student participation, suggesting that information and communications technology (ICT) was used for traditional transmissive pedagogy. Teachers' discourse around ICT in class was practical and technical, rather than conceptual. Further, students' use of technology mainly revolved around writing digital texts, individually, not the promising pedagogical practices associated with ICT in previous research. These findings show that the implementation of digital technology and the development of digital competence in schools require far more than an ambitious curriculum and a basic digital infrastructure. Structures at a national level are not enough, and there is an urgent need for professional development at the local level to increase the instructional repertoire and the didactical motivation of teachers in relation to digital technology.
Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network

Malicious social bots generate fake tweets and automate their social relationships either by pretending like a follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, a learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes' theorem, and the indirect trust is derived from the Dempster-Shafer theory (DST) to determine the trustworthiness of each participant accurately. Experimentation has been performed on two Twitter data sets, and the results illustrate that the proposed algorithm achieves improvement in precision, recall, F-measure, and accuracy compared with existing approaches for MSBD.
On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising

We study the nonparametric maximum likelihood estimator (NPMLE) for estimating Gaussian location mixture densities in d-dimensions from independent observations.Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization.We prove finite sample results on the Hellinger accuracy of every NPMLE.Our results imply, in particular, that every NPMLE achieves near parametric risk (up to logarithmic multiplicative factors) when the true density is a discrete Gaussian mixture without any prior information on the number of mixture components.NPM-LEs can naturally be used to yield empirical Bayes estimates of the oracle Bayes estimator in the Gaussian denoising problem.We prove bounds for the accuracy of the empirical Bayes estimate as an approximation to the oracle Bayes estimator.Here our results imply that the empirical Bayes estimator performs at nearly the optimal level (up to logarithmic factors) for denoising in clustering situations without any prior knowledge of the number of clusters.
Security Analysis and Preserving Block-Level Data DE-duplication in Cloud Storage Services

Secure information deduplication can altogether decrease the correspondence and capacity overheads in distributed storage benefits and has potential applications in our large information-driven society. Existing information deduplication plans are commonly intended to either oppose savage power assaults or guarantee the effectiveness and information accessibility, yet not the two conditions. We are additionally not mindful of any current plan that accomplishes responsibility, in the feeling of lessening copy data divulgence (e.g., to decide if plain-writings of two scrambled messages are indistinguishable). Right now, examine the three-level cross-space design and propose an effective and protection safeguarding huge information deduplication in distributed storage (from now on alluded to as EPCDD). EPCDD accomplishes both protection safeguarding and information accessibility and opposes beast power assaults. Plus, we consider the responsibility to offer preferable protection affirmations over existing plans. We at that point show that EPCDD beats existing contending plans, as far as calculation, correspondence, and capacity overheads. Additionally, the time unpredictability of copy search in EPCDD is logarithmic.
General approach for constructing Hamiltonians for nonadiabatic holonomic quantum computation

The main challenges in achieving high-fidelity quantum gates are to reduce the influence of control errors caused by imperfect Hamiltonians and the influence of decoherence caused by environment noise. To overcome control errors, a promising proposal is nonadiabatic holonomic quantum computation, which has attracted much attention in both theories and experiments. While the merit of holonomic operations resisting control errors has been well exploited, an important issue following is how to shorten the evolution time needed for realizing a holonomic gate so as to avoid the influence of environment noise as much as possible. In this paper, we put forward a general approach of constructing Hamiltonians for nonadiabatic holonomic quantum computation, which makes it possible to minimize the evolution time and might open a new horizon for the realistic implementation of nonadiabatic holonomic quantum computation.
Digitalization of education: problems and prospects

Introduction . The article analyzes the process of digitalization of modern education. The relevance of the study is determined by the special role of education in the life of modern society, based on the principles and values of the post-industrial era. The quality of human potential and people's readiness to face natural and social challenges largely depend on the effectiveness of the education system. In the process of improving national education, modern technologies are becoming more and more important every year, the introduction of which contributes to the modernization and development of education, as well as improving the quality of training of future specialists and bringing education closer to science. At the same time, such technologies require a review of existing approaches to educational activities, as well as an analysis of their impact on society and individual social groups. In this regard, the study of the digitalization of education and its social consequences is a very relevant area of research. The purpose of this study is to identify the main problems and determine the prospects for digitalization in education. Materials and methods . To achieve this goal, we used methods of dialectics of social cognition, analysis of philosophical, sociological, and pedagogical literature, and synthesis and modeling of socio-cultural processes. Results . The conclusions identify the main problems of digitalization of education: the dehumanization of educational, and then all other social relations, the possible deepening of the crisis of people's intellectual culture, their ability to create, the growth of pragmatism and individualism based on the values of personal comfort and selfish consumption. Prospects for the development of digitalization in the education system are outlined: instrumental - equipping educational institutions with high-quality software, information systems that provide access to educational resources, the introduction of information (distance) technologies, online learning, changing requirements for teachers and students, the formation of new organizational educational structures, inevitable changes in the usual forms and methods of training and value-semantic – finding a harmonious unity with the best traditions of domestic education. Discussion and Conclusions . These conclusions are aimed at solving practical issues related to the implementation of the state policy of digitalization of various spheres of public relations. They can help coordinate the efforts of scientists, politicians, and civil society institutions to promote the socio-economic and cultural development of our society. The results obtained allow us to approach the fundamental problems of managing the cultural policy of modern society more objectively and meaningfully. They are designed to stimulate the creation of new forms of culture based on the priorities of harmonizing national cultural characteristics and the specifics of the world's techno-scientific processes.
DukeNet

Today's conversational agents often generate responses that not sufficiently informative. One way of making them more informative is through the use of of external knowledge sources with so-called Knowledge-Grounded Conversations (KGCs). In this paper, we target the Knowledge Selection (KS) task, a key ingredient in KGC, that is aimed at selecting the appropriate knowledge to be used in the next response. Existing approaches to Knowledge Selection (KS) based on learned representations of the conversation context, that is previous conversation turns, and use Maximum Likelihood Estimation (MLE) to optimize KS. Such approaches have two main limitations. First, they do not explicitly track what knowledge has been used in the conversation nor how topics have shifted during the conversation. Second, MLE often relies on a limited set of example conversations for training, from which it is hard to infer that facts retrieved from the knowledge source can be re-used in multiple conversation contexts, and vice versa.
Heterogeneous Graph Transformer for Graph-to-Sequence Learning

The graph-to-sequence (Graph2Seq) learning aims to transduce graph-structured representations to word sequences for text generation.Recent studies propose various models to encode graph structure.However, most previous works ignore the indirect relations between distance nodes, or treat indirect relations and direct relations in the same way.In this paper, we propose the Heterogeneous Graph Transformer to independently model the different relations in the individual subgraphs of the original graph, including direct relations, indirect relations and multiple possible relations between nodes.Experimental results show that our model strongly outperforms the state of the art on all four standard benchmarks of AMRto-text generation and syntax-based neural machine translation.
“Smittestopp”: If you want your freedom back, download now

The intervention attempts to engage critically with the Smittestopp app as a specifically Norwegian technofix. Culturally and politically, much of the Covid-19 response and the success of social distancing rules have been organized around the widespread trust in the government and public health authorities, and a focus on the citizens’ duty to contribute to the dugnaðr. The intervention argues that Smittestopp has been co-created by the mobilization of trust and dugnaðr, resulting in the launch of an incomplete and poorly defined data-hoarding product with significant vulnerabilities.
Herding behaviour in digital currency markets: An integrated survey and empirical estimation

This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.
The relationship between organizational changes and job satisfaction through the mediating role of job stress in the Jordanian telecommunication sector

The main purpose of this research is to investigate the mediating role of job stress in the relationship between organizational change and job satisfaction in telecommunication companies in Jordan.The research population is employees working at three communication companies in Jordan; as a convenience sample of 422 was chosen randomly.Descriptive analysis was used to provide general information on the employees of telecommunication companies.Furthermore, validity and reliability tests were used, and regression analysis was applied for the possibility of hypotheses rejection.The findings indicate a significant positive relationship between organizational change and job satisfaction, as well as a significant positive relationship between organizational change and job stress.Additionally, the results indicate a significant negative relationship between job stress and job satisfaction.The findings also revealed a partial mediating effect of job stress on the relationship between organizational change and job satisfaction.According to the findings of this research, to increase employees' job satisfaction, their level of job stress during organization change operations in telecommunication companies must be decreased.For future research, the present study recommends the investigation of the impact of organizational change on job satisfaction, along with the mediating role of job stress, using different dimensions and sectors.
Efficient sampling and noisy decisions

Human decisions are based on finite information, which makes them inherently imprecise. But what determines the degree of such imprecision? Here, we develop an efficient coding framework for higher-level cognitive processes in which information is represented by a finite number of discrete samples. We characterize the sampling process that maximizes perceptual accuracy or fitness under the often-adopted assumption that full adaptation to an environmental distribution is possible, and show how the optimal process differs when detailed information about the current contextual distribution is costly. We tested this theory on a numerosity discrimination task, and found that humans efficiently adapt to contextual distributions, but in the way predicted by the model in which people must economize on environmental information. Thus, understanding decision behavior requires that we account for biological restrictions on information coding, challenging the often-adopted assumption of precise prior knowledge in higher-level decision systems.
Semi-supervised Semantic Segmentation via Strong-Weak Dual-Branch Network

While existing works have explored a variety of techniques to push the envelop of weakly-supervised semantic segmentation, there is still a significant gap compared to the supervised methods. In real-world application, besides massive amount of weakly-supervised data there are usually a few available pixel-level annotations, based on which semi-supervised track becomes a promising way for semantic segmentation. Current methods simply bundle these two different sets of annotations together to train a segmentation network. However, we discover that such treatment is problematic and achieves even worse results than just using strong labels, which indicates the misuse of the weak ones. To fully explore the potential of the weak labels, we propose to impose separate treatments of strong and weak annotations via a strong-weak dual-branch network, which discriminates the massive inaccurate weak supervisions from those strong ones. We design a shared network component to exploit the joint discrimination of strong and weak annotations; meanwhile, the proposed dual branches separately handle full and weak supervised learning and effectively eliminate their mutual interference. This simple architecture requires only slight additional computational costs during training yet brings significant improvements over the previous methods. Experiments on two standard benchmark datasets show the effectiveness of the proposed method.
T-Count Optimization and Reed–Muller Codes

In this paper, we study the close relationship between Reed-Muller codes and single-qubit phase gates from the perspective of T -count optimization.We prove that minimizing the number of T gates in an n-qubit quantum circuit over CNOT and T , together with the Clifford group powers of T , corresponds to finding a minimum distance decoding of a length 2 n -1 binary vector in the order n-4 punctured Reed-Muller code.Moreover, we show that the problems are polynomially equivalent in the length of the code.As a consequence, we derive an algorithm for the optimization of T -count in quantum circuits based on Reed-Muller decoders, along with a new upper bound of O(n 2 ) on the number of T gates required to implement an n-qubit unitary over CNOT and T gates.We further generalize this result to show that minimizing small angle rotations corresponds to decoding lower order binary Reed-Muller codes.In particular, we show that minimizing the number of Rz(2π/d) gates for any integer d is equivalent to minimum distance decoding in RM(n -k -1, n) * , where k is the highest power of 2 dividing d.