Reviewerly/exHarmony huggingface dataset:
Deep neural networks classifying transfer efficiency in complex networks

Transport of particles in networks is a broad interdisciplinary scientific field. Efficient transport of particles is of practical importance for communication tasks. To improve the efficiency of the transport, the quantum phenomenon of interference is of relevance. A particle that is transferred through a network interferes with itself, allowing for transfer properties that are different from non-quantum, classical, dynamics. However, it is not known in which networks quantum properties provide an advantage. It was shown in Melnikov et al. [New J. Phys., Vol. 21, 125002, 2019] and [Adv. Quantum Technol., Vol. 3, 1900115, 2020] that convolutional neural networks are well-suited for classifying which complex networks can have a fast quantum transport. Here we discuss these results and applicability of the approach.
Adversarial Permutation Guided Node Representations for Link Prediction

After observing a snapshot of a social network, a link prediction (LP) algorithm identifies node pairs between which new edges will likely materialize in future. Most LP algorithms estimate a score for currently non-neighboring node pairs, and rank them by this score. Recent LP systems compute this score by comparing dense, low dimensional vector representations of nodes. Graph neural networks (GNNs), in particular graph convolutional networks (GCNs), are popular examples. For two nodes to be meaningfully compared, their embeddings should be indifferent to reordering of their neighbors. GNNs typically use simple, symmetric set aggregators to ensure this property, but this design decision has been shown to produce representations with limited expressive power. Sequence encoders are more expressive, but are permutation sensitive by design. Recent efforts to overcome this dilemma turn out to be unsatisfactory for LP tasks. In response, we propose PermGNN, which aggregates neighbor features using a recurrent, order-sensitive aggregator and directly minimizes an LP loss while it is `attacked' by adversarial generator of neighbor permutations. PermGNN has superior expressive power compared to earlier GNNs. Next, we devise an optimization framework to map PermGNN's node embeddings to a suitable locality-sensitive hash, which speeds up reporting the top-K most likely edges for the LP task. Our experiments on diverse datasets show that PermGNN outperforms several state-of-the-art link predictors by a significant margin, and can predict the most likely edges fast.
Perbandingan Kinerja Algoritma Support Vector Machine dan K-Nearest Neighbor Terhadap Analisis Sentimen Kebijakan New Normal

Twitter is one of the popular microblogging sites among internet users, so that many people use Twitter to convey their positive and negative sentiments towards the new normal policy. The pandemic period raises much public sentiment towards the policy of adapting to the new normal. This study aims to classify sentiment tweets into positive and negative classes. The classification algorithms used are k-NN and SVM. The test results show that the k-NN algorithm is better than SVM in solving this sentiment case with an accuracy of 72.96%.
Efektivitas Penerapan Metode Discovery Learning dalam Pembelajaran Imla’

This study aims at knowing the results of using discovery learning in Imla’ and its effectiveness. The research method to be used was quantitative method with experimental design. Data were collected through tests, observation and documentation. The data were analyzed using statistical data analysis t test to determine the level of the effectiveness of discovery learning method. The obtained results illustrated that the discovery learning method in Imla’ consists of four stages including exposure, observation and analysis, preparation or formulation of linguistic rules, and application of rules. The application of discovery learning method was very effective in imla’ course because it can improve students' understanding of the materials, so that students can write hamzah wasal in the form a word or sentence properly and correctly. This can be seen from the results of the t test carried out in the pre-test and post-test values that the t value was greater than the t table, namely 2.09 &lt;21.91&gt; 2.86. Thus, H˳ is rejected, which means that the method is effective for improving students' writing skill.
The Impact of Artificial Intelligence Technologies on Audit Evidence

Technologies of Artificial Intelligence (AI) are critical for future of the auditing profession. These technologies are actually vital tools that provide the auditing professionals with the means necessary for increasing the effectiveness and efficiency of their jobs. The aim of this study is to examine the effect of artificial intelligence technologies on audit evidence, from the point of view of certified auditors in IT companies in Jordan. Descriptive research design was adopted in the study among 314 auditors. Structured questionnaire was used to obtain the information needed for the study. The Findings of the study showed include that expert system has a significant effect on the audit evidence. Neural network technology has not significant effect on the audit evidence. The study recommended increased interest in artificial intelligence technologies by audit offices operating in Jordan because of its scientific importance in improving the collection of audit evidence.
Iterative Utterance Segmentation for Neural Semantic Parsing

Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To address this issue, we present a novel framework for boosting neural semantic parsers via iterative utterance segmentation. Given an input utterance, our framework iterates between two neural modules: a segmenter for segmenting a span from the utterance, and a parser for mapping the span into a partial meaning representation. Then, these intermediate parsing results are composed into the final meaning representation. One key advantage is that this framework does not require any handcraft templates or additional labeled data for utterance segmentation: we achieve this through proposing a novel training method, in which the parser provides pseudo supervision for the segmenter. Experiments on Geo, ComplexWebQuestions and Formulas show that our framework can consistently improve performances of neural semantic parsers in different domains. On data splits that require compositional generalization, our framework brings significant accuracy gains: Geo 63.1~81.2, Formulas 59.7~72.7, ComplexWebQuestions 27.1~56.3.
BLENDED LEARNING PERSPECTIVES FOR STUDENTS (UNIVERSITY EXPERIENCE UNDER CONSTRAINTS DUE TO COVID-19 PANDEMIC)

Today, measures to prevent the spread of the coronavirus COVID-19 have covered all spheres of life in Russian society and directly influenced the system of organizing the educational process, in particular in higher education. During the COVID-19 pandemic, the integration of technological advances and the educational process has been transformed from modern innovation to an urgent requirement of the time and has undergone intensive development. The study provides a theoretical analysis of the concept of blended learning. Based on an expert survey, the most preferred models of blended learning in the context of the COVID-19 pandemic have been identified, their capabilities and features have been considered, and foreign experience in the implementation of blended learning has been analyzed.
Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed

Filter pruning has drawn more attention since resource constrained platform requires more compact model for deployment. However, current pruning methods suffer either from the inferior performance of one-shot methods, or the expensive time cost of iterative training methods. In this paper, we propose a balanced filter pruning method for both performance and pruning speed. Based on the filter importance criteria, our method is able to prune a layer with approximate layer-wise optimal pruning rate at preset loss variation. The network is pruned in the layer-wise way without the time consuming prune-retrain iteration. If a pre-defined pruning rate for the entire network is given, we also introduce a method to find the corresponding loss variation threshold with fast converging speed. Moreover, we propose the layer group pruning and channel selection mechanism for channel alignment in network with short connections. The proposed pruning method is widely applicable to common architectures and does not involve any additional training except the final fine-tuning. Comprehensive experiments show that our method outperforms many state-of-the-art approaches.
[Quantitative assessment of surface hydrological connectivity in Momoge National Nature Reserve, Northeast China].

Quantitative assessment of hydrological connectivity is a hot but difficult issue in current research. Using the 30-m resolution global monthly surface water remote sensing dataset released by the EU Joint Research Center and three indicators of geostatistical connectivity, maximum distance of connection (MDC), and surface water extent of connectome (i.e., seasonally connected water bodies), we quantified the hydrological connectivity of surface water in Momoge National Nature Reserve in different months of a normal year (May to October 2016), and in September of different hydrological years (a wet year, namely 1998; a normal year, namely 2016; a drought year, namely 2002), and different directions (west-east and north-south). Our results showed that: 1) the geostatistical connectivity function (GCF) along the west-east direction was better than that along the north-south direction. The GCF in August and July was better than that in other months. The GCF along the west-east direction of each hydrological year was better than that in the drought year, whereas the GCF in the drought year was better than the corresponding value in the normal year. The GCF along the north-south direction in each hydrological year was better than that in the normal year, whereas the GCF in the normal year was better than that in the drought year. The MDC along the west-east direction in June, July, September, and October of the normal year was all concentrated at 25.26 km. MDC was more concentrated along the north-south direction, with 10 km for all months. The MDC in the normal and drought years was relatively close, but both were much smaller than that in the wet year. 2) frequent seasonal connections between the Yuelianghu Reservoir and the Nenjiang River, and between Etoupao and its neighboring lakes occurred in the study area during the normal year, while most of the other lakes remained isolated. The patterns of hydrological connection in the study area differed across different hydrological years: two giant connectomes were formed in the wet year, some lakes are periodically connected in the normal year, and all lakes remain isolated in the drought year. 3) As a drainage area for farmland receding water, the surface water extent of the Etoupao connectome increased visibly during the three water supplement seasons (spring, summer, and autumn). By quantifying the surface hydrological connectivity in Momoge National Nature Reserve with multiple water sources from different perspectives, our results provide a scientific basis for wetland protection and restoration and integrated management of watershed water resources.
THE IDEA OF THE EDUCATIONAL ECOSYSTEM IN THE MODERNIZATION OF CADET EDUCATION

Projections of education development in the information society actively use the idea of an educational ecosystem. The educational ecosystem as a theoretical construction is a compilation of the generally accepted tendencies that have preserved their potential methodological approaches of pedagogy with the concepts of a new technological order, projected onto the relations of the information society. The ecosystem, as a construction, quality and requirement for the organizational and technological modernization of cadet education, sets its targets, paths and limitations related to three focuses: educational space; the movement of the subject in the educational space; interaction of subjects of the educational space. The article reveals the fundamental positions in the organizational and technological modernization of cadet education based on the idea of an educational ecosystem.
A Memetic Algorithm for Curvature-Constrained Path Planning of Messenger UAV in Air-Ground Coordination

This paper addresses a UAV path planning problem for a team of cooperating heterogeneous vehicles composed of one unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs). The UGVs are used as mobile actuators and scattered in a large area. To achieve multi-UGV communication and collaboration, the UAV serves as a messenger to fly all UGVs to transmit information. The path planning of messenger UAV is formulated as a Dynamic Dubins Traveling Salesman Problem with Neighborhood (DDTSPN). A novel memetic algorithm is proposed to find the shortest route enabling the UAV to fly over all requested UGVs. In the memetic algorithm, the combination of genetic algorithm and local search is employed to find a high-quality solution in a reasonable time, and a gradient-based repair strategy is used to repair the individuals violating dynamic constraints. The calculation results on both small and large instances show that the proposed method can generate high-quality solutions as compared with the state-of-the-art algorithms.
The University digital educational environment potential in the future teacher professional and personal development

Abstract The transition to digital education is associated with economic, political changes in society, as well as with changes in infrastructural, communicative, and cultural nature. The University digital educational environment role in a professional and personal development system is being updated. The "professional and personal development a future teacher" scientific and theoretical analysis concept made it possible to consider professional and personal development as a becoming a teacher dynamic process, which entails the subject inner essence transformation through the interaction of self-improvement motivation, professional orientation and competence, creativity, personality traits and activities. This study purpose was to study different approaches to understanding this category, disclosing its essence and structure, establishing the basic requirements for its organization. The main research question was in which case the digital educational environment provides professional and personal development, competitiveness formation, communicative equality, the future teacher social experience interactivity. The leading methods in the research were: theoretical and methodological analysis, scientific research interdisciplinary analysis, system analysis, pedagogical interpretation method, comparison, generalization, concretization; experience in organizing multi-level education study, questionnaires, testing. It is shown that a teacher at a university professional and personal development effectiveness largely depends on the students, teachers, the digital educational environment and various conditions’ optimal interaction.
Information And Communication Technologies In The Educational Process Within The Digitalization

The paper studies the information and communication technologies, their varieties and characteristics. The relevance of this topic is dictated by the fact that information and communication technologies are being introduced into the field of education. This process is a natural step in global informatization. The main aspects and advantages of using information and communication technologies in the educational process are described in the article. The paper determines and substantiates possibilities of using informatization means in the educational process for students studying in the direction of training bachelors "Advertising and Public Relations" of the Humanitarian faculty of the St. Petersburg State Economic University. The article provides material reflecting the forms, methods and techniques of using information and communication technologies which are contributing to increase the efficiency of mastering disciplines relating the communication cycle. The results of the research show the importance of maintaining didactic continuity during the transition to the new learning technologies, which required systematic development of scientifically based methodological approaches and models for the educational process using information and communication technology. The following conclusions are drawn the fact that the educational process in a new technological environment should preserve a humanitarian orientation and be focused on the cooperation between a student and a lecturer.
Operational Capabilities during Crisis: The Chilean Seismographic Network

Abstract The severe mobility restrictions imposed countrywide by authorities of Chile in response to the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have impacted all areas of activities. Major difficulties began in March 2020, with partial quarantines in the capital city, which later extended to other cities in the country, and it soon transformed in total confinement. We examine the evolution of the pandemic and its consequences on the field and headquarters operations of the National Seismological Centre (CSN), as remote stations could not be visited to carry out maintenance work. Several indicators, as a function of time, such as station operability percentage, timely reports, and ability to deliver requested information, reveal some negative impact on the uptime of stations but not in the capability of the CSN to fulfill its mission, which is to deliver timely seismic information to emergency services. The largest event in the country within this period took place on 3 June 2020, activating in a timely manner, the new tools on finite-fault modeling being developed within the center.
The impact of customer assisted knowledge production capacity on customer capital in a knowledge-based center

Purpose: The purpose of this research is to investigate the impact of customer-assisted knowledge production capacity on customer capital. Research methodology: The statistical population of the study consisted of 70 managers, researchers and experts of a scientific center and the statistical sample was counted and estimated 70 persons. The research instrument of the researcher-made questionnaire consists of six dimensions: ability to identify customer needs, create and manage a customer information system, customer loyalty, market share, customer service capability and target customer identification. The validity of the questionnaire was estimated 0.94%. Results: The results show that the impact of customer knowledge production capacity on all dimensions of customer capital is significant. Also, most influential among customer capital factors is customer service capability. Finally, by confirming the research hypotheses, suggestions were made based on the results of the research to improve the client's capital of the statistical society. Limitation: This research only described Knowledge Based Center. Contribution: Organizations realized that relying on existing knowledge alone was not enough to compete in a competitive environment, and went beyond the boundaries of their organization to acquire knowledge. Organizations considered customers as a very useful and knowledgeable resource, and activities should be done to interact with customers in leading organizations. Keywords: Customer assisted knowledge production capacity, Customer capital, Intellectual capital, Knowledge management
Towards a Minimal Stabilizer ZX-calculus

The stabilizer ZX-calculus is a rigorous graphical language for reasoning about quantum mechanics. The language is sound and complete: one can transform a stabilizer ZX-diagram into another one using the graphical rewrite rules if and only if these two diagrams represent the same quantum evolution or quantum state. We previously showed that the stabilizer ZX-calculus can be simplified by reducing the number of rewrite rules, without losing the property of completeness [Backens, Perdrix & Wang, EPTCS 236:1--20, 2017]. Here, we show that most of the remaining rules of the language are indeed necessary. We do however leave as an open question the necessity of two rules. These include, surprisingly, the bialgebra rule, which is an axiomatisation of complementarity, the cornerstone of the ZX-calculus. Furthermore, we show that a weaker ambient category -- a braided autonomous category instead of the usual compact closed category -- is sufficient to recover the meta rule 'only connectivity matters', even without assuming any symmetries of the generators.
The Chinese robot industry

Purpose This paper aims to provide an insight into China’s rapidly developing robot industry. Design/methodology/approach Following a short introduction, this paper first provides a market perspective and then identifies the key user industries and gives examples of applications. The robot supply companies are considered and details are provided for some of the more important. The research effort is then discussed, together with some examples of recent developments. Finally, conclusions are drawn. Findings The Chinese robot market is the largest in the world although the robot density remains far lower than that of many other industrialised nations. Major users include the electrical and electronics sector and the automotive industry, together with growing applications in the burgeoning hospitality and logistics sectors. Markets are presently dominated by foreign companies, but the government has ambitious plans to boost domestic production, and with the aid of financial incentives, the number of Chinese robot manufactures has recently increased dramatically. The country’s robot research effort is diverse and wide ranging and benefits from significant levels of government funding. China is ultimately expected to become a major force within the global robot business. Originality/value This illustrates the growing importance of robots in China, both in its industries and as a robot manufacturer.
Role of Communication and Interaction in Enhancing Organisational Performance

In circumstances of constantly changing concepts related to the organisation as well as the progress of its performance and behaviour, communication has become a focus of many researchers who have consistently analyzed it in relation to organisations of various forms. This study aims to assess the role of communication and interaction in enhancing organisational performance. This study involved 607 respondents employed in banks and insurance companies. The methodology used for this study was the integration of quantitative methods. We have used two instruments in this study, one questionnaire to measure the role of communication and interaction and the other one organisational performance.
Quantum Computing in the NISQ era and beyond

Noisy Intermediate-Scale Quantum (NISQ) technology will be available in thenear future. Quantum computers with 50-100 qubits may be able to perform taskswhich surpass the capabilities of today's classical digital computers, butnoise in quantum gates will limit the size of quantum circuits that can beexecuted reliably. NISQ devices will be useful tools for exploring many-bodyquantum physics, and may have other useful applications, but the 100-qubitquantum computer will not change the world right away --- we should regard itas a significant step toward the more powerful quantum technologies of thefuture. Quantum technologists should continue to strive for more accuratequantum gates and, eventually, fully fault-tolerant quantum computing.