Reviewerly/exHarmony huggingface dataset:
METACOMPETENCY STATUS IN COMPETENCY HIERARCHY

Statement of the problem and purpose of the article. The article deals with the problem of developing a competency approach in education in the context of expanding types of competences when considering super subject meta-competency as a methodological approach of search activity regardless of the scope of its implementation. The topicality of the metacognitive methods of activity formation in the subject is determined by new living conditions of a modern person, characterized by high dynamics, leading to situations of uncertainty, unpredictability, making continuous productive self-education necessary. The research was based on the methodology of the polyparadigmal approach, which allows solving certain pedagogical problems with adequate use of systemic, competent and activity approaches. Published articles, monographs and normative documents on the problem of research were used as materials. There was carried out a content analysis, synthesis and research generalization revealing the doctrines of metacognitive psychology, as well as research related to the problem of self-education of a person and self-educational competence development. The research results were the essence identification of the meta-competence concept of the activity subject, which is specified by the separated structure of its components with disclosure of their content. Meta-competency is defined as the integrative dynamic quality of an individual, oriented towards self-development in the context of productive continuous education and manifested in the ability and willingness to consciously use appropriate strategies of targeting, self-organization, self-control and self-regulation based on reflection results and intellectual activity boundaries. The competency classification variant is obtained; the status of meta-competency is defined as top-level super subject competency. The research results enrich the theory of competence approach in terms of expansion of the conceptual field of competences and raise the problem of pedagogical conditions identification of meta-competence formation in the educational process and its diagnosis.
Function System and Application Scenario Design of Energy Big Data Application Center

Giant potential value lies in the application of energy big data on energy resource distribution and energy structure transformation. On the basis of the investigation of the national energy information system and big data platform for energy enterprises, development goals are specified, the overall structural design of the "three platforms and two systems" and the scheme of data management are proposed. Aiming at government, enterprises, and the public, design and blueprint of the multi-dimensional application scenario system are presented, and the function scheme of typical application scenarios serving the government and the public are discussed.
Lexical Ambiguity in Arabic Information Retrieval: The Case of Six Web-Based Search Engines

In recent years, both research and industry have shown an increasing interest in developing reliable information retrieval (IR) systems that can effectively address the growing demands of users worldwide. In spite of the relative success of IR systems in addressing the needs of users and even adapting to their environments, many problems remain unresolved. One main problem is lexical ambiguity which has negative impacts on the performance and reliability of IR systems. To date, lexical ambiguity has been one of the most frequently reported problems in the Arabic IR systems despite the development of different word sense disambiguation (WSD) techniques. This is largely attributed to the limitations of such techniques in addressing the issue of linguistic peculiarities. Hence, this study addresses these limitations by exploring the reasons for lexical ambiguity in IR applications in Arabic as one step towards reliable and practical solutions. For this purpose, the performances of six search engines Google, Bing, Baidu, Yahoo, Yandex, and Ask are evaluated. Results indicate that lexical ambiguities in Arabic IR applications are mainly due to the unique morphological and orthographic system of the Arabic language, in addition to its diglossia and the multiple colloquial dialects where sometimes mutual intelligibility is not achieved. For better disambiguation and IR performances in Arabic, this study proposes that clustering models based on supervised machine learning theory should be trained to address the morphological diversity of Arabic and its unique orthographic system. Search engines should also be adapted to the geographic location of the users in order to address the issue of vernacular dialects of Arabic. They should also be trained to automatically identify the different dialects. Finally, search engines should consider all varieties of Arabic and be able to interpret the queries regardless of the particular language adopted by the user.
Conditional Generative Adversarial Network on Semi-supervised Learning Task

Semi-supervised learning has recently gained increasingly attention because it can combine abundant unlabeled data with carefully labeled data to train deep neural networks. However, common semi-supervised methods deeply rely on the quality of pseudo labels. In this paper, we proposed a new semi-supervised learning method based on Generative Adversarial Network (GAN), by using discriminator to learn the feature of both labeled and unlabeled data, instead of generating pseudo labels that cannot all be correct. Our approach, semi-supervised conditional GAN (SCGAN), builds upon the conditional GAN model, extending it to semi-supervised learning by changing the discriminator's output to a classification output and a real or false output. We evaluate our approach with basic semi-supervised model on MNIST dataset. It shows that our approach achieves the classification accuracy with 84.15%, outperforming the basic semi-supervised model with 72.94%, when labeled data are 1/600 of all data.
Providing Anonymous Communication, Privacy-Preserving Data Aggregation and Dynamic Billing System in Smart Grid Using Permissioned Blockchain

This paper proposes an efficient data aggregation and dynamic billing system that it uses anonymous communication for exchanging information between smart meters and CC.Moreover, the given scheme consists of a permissioned blockchain.This blockchain contains the ledger that keeps users' anonymized identities and electricity consumption for predefined time ranges.Using consumption data of users, a billing mechanism can bill the users accordingly.In the construction, since all the parties in the system have the ledger, every party has the aggregated usage of the electricity without using very heavy cryptographic operations such as homomorphic encryption, bilinear pairing, etc.Using the ledger in our model, the aggregation of the users' electricity consumption can be computed by anyone in the system.Moreover, users can verify their bills and check any data using a signature scheme.This results in that the integrity of all data is going to be preserved.The proposed approach mainly uses hash functions to provide the same functionality (aggregation of the users' data consumption, data integrity check, and dynamic pricing and billing) with preserving data privacy of the users.
Data Security Methods in Cloud Computing

Digital technology has become an important part of organizations due to the global spread of the internet and the increase in the number of smart devices, offering multiple benefits to users.The need for adapting to current requirements, has led to emergence of a new technology, cloud computing, developed for modern people needs, addicted to information and on the run.Information security is one of the main challenges for cloud developers, data loss causing damage in various fields and this is one of the reasons why some organizations do not adopt cloud migration.This paper analyzes the security of cloud computer technology, as well as the issues to ensure the security of the data in the cloud, according to the current technological methods and the specific architecture.The final part of the paper proposes a customized method for data security according to their importance to a particular organization, using current security methods.
C1 triangular Coons surface construction and image interpolation based on new Side-Side and Side-Vertex interpolation operators

In this paper, a new class of rational quadratic/linear trigonometric Hermite functions with two shape parameters is proposed. Based on these Hermite functions, new improved first class of Side-Side (FCSS), second class of Side-Side (SCSS), first class of Side-Vertex (FCSV) and second class of Side-Vertex (SCSV) interpolation operators are proposed respectively, which can be used to construct C1 Coons surfaces over triangular domain. By altering the values of two shape parameters, the shape of the Coons surface patch can be adjusted flexibly, but without affecting the function values and partial derivatives of the boundaries. For constructing the triangular surface patches with the center of mass passing through a fixed point, we also give a center of mass function value control method, by which we can solve the corresponding shape parameter values. Moreover, we also apply these four improved interpolation operators to image interpolation. Compared with some widely used image interpolation methods, our methods achieve competitive performance.
Promoting Digital Literacy Skills: An Action Research to People of Kampung Literasi

Indonesian people's reading index was considered poor.In this fast-changing information era, efforts to address this issue have to be in accordance with the underlying princip les of 21 st century that are regarded as digital literacy.The current technology advancement has led to abundant information, but with lack critical ability in choosing informat ion sources.In response, the mastery of digital literacy skill is required to address this issue.As response, this study seeks to enhance the digital literacy skills of the people in the Kampung Literasi by focusing on the implementation and media targeting digital literacy skill imp rovement.Th is study exemp lified the work of collaborative teams to produce change in improving digital literacy skills by using Creswell's action research model modified into ICARE model (Identificat ion, Consolidation, Action, Rev iew, and Evaluation).A co mmunity focusing on literacy empowerment program named Kampung Literasi located in Su medang Regency, West Java, Indonesia, was selected to be the participant considering its concern toward literacy develop ment in their area.The action results revealed that the established programs done in Kampung Literasi in Sumedang Regency have improved community's digital literacy skills in utilizing e-library and managing e-resources.In specific, this ICA RE model could serve as an alternative solution to address literacy issues in the Kampung Literasi to make its program succeed.Considering this positive outcome, this study contributes to the development of literacy movement in Indonesia.Therefore, this ICA RE model is reco mmended to be imp lemented by more Kampung Literasi in other villages in Indonesia, or by other literacy improvement programs.
SIoT Framework: Towards an Approach for Early Identification of Security Requirements for Internet-of-things Applications

Background: Security has become more of a concern with the wide deployment of Internet-of-things (IoT) devices.The importance of addressing security risks early in the development lifecycle before pushing to market cannot be over emphasized.Aim: To this end, we propose a conceptual framework to help with identifying security concerns early in the product development lifecycle for Internet-of-things, that we refer to as SIoT (Security for Internet-of-Things). Method: The framework adopts well known security engineering approaches and best practices, and systematically builds on existing research work on IoT architecture.Results: Practitioners at a Norwegian start-up company evaluated the framework and found it useful as a foundation for addressing critical security concerns for IoT applications early in the development lifecycle.The output from using the framework can be a checklist that can be used as input during security requirements engineering activities for IoT applications.Conclusions: However, security is a multi-faced concept; therefore, users of the SIoT framework should not view the framework as a panacea to all security threats.The framework may need to be refined in the future, particularly to improve its completeness to cover various IoT contexts.
Structure–texture image decomposition using a new non‐local TV‐Hilbert model

Combining the advantages of the non-local total variation (TV) and the Gabor function, a new Gabor function based non-local TV-Hilbert model is presented to separate the structure and texture components of the image. Computationally, by introducing the dual form of the non-local TV, the authors reformulate the non-local TV-Hilbert minimisation problem into a convex–concave saddle-point problem. In the aspect of solving algorithm, by transforming the Chambolle–Pock's first-order primal–dual algorithm into a different equivalent form. The authors propose a proximal-based primal–dual algorithm to solve the convex–concave saddle-point problem. At last, experimental results demonstrate that the proposed new model outperforms several existing state-of-the-art variational models.
A Fundamental Upper Bound for Signal to Noise Ratio of Quantum Detectors

Quantum fluctuations yield inevitable noises in quantum detection. We derive an upper bound of signal to noise ratio for arbitrary quantum detection described by trace-class operators with discrete spectra. The bound is independent of observables to be detected and is computed by quantum fidelity of two initial quantum states. We provide applications of the upper bound.
A Study on the Correlation between Motivation and Students’ Speaking Performance at an Indonesian Vocational High school Context

This study aims to find out vocational high school students’ speaking performance in correlation with their motivation to learning English. This study employs a quantitative research method using questionnaire to gather the data. The whole population in this study was the first grade of SMK Negeri 6 Makassar which consists of three classes numbering 86 students. The samples were students from class b which consists of 25 students. The sample was taken by using purposive sampling technique. This study was conducted by distributing questionnaires to students who were respondents and test, after which they were input and analyzed through the SPSS program. The findings revealed that the correlation between motivation and speaking performance for vocational high school students indicated that it was strong enough or categorized as medium correlation. Furthermore, the findings showed that the motivated students usually feel more comfortable in their learning providing the students to speak up confidently wherever they are even although English is considered as a foreign language.
The Fixed Point Property of the Infinite M-Sphere

The present paper is concerned with the Alexandroff one point compactification of the Marcus-Wyse (M-, for brevity) topological space ( Z 2 , γ ) . This compactification is called the infinite M-topological sphere and denoted by ( ( Z 2 ) ∗ , γ ∗ ) , where ( Z 2 ) ∗ : = Z 2 ∪ { ∗ } , ∗ ∉ Z 2 and γ ∗ is the topology for ( Z 2 ) ∗ induced by the topology γ on Z 2 . With the topological space ( ( Z 2 ) ∗ , γ ∗ ) , since any open set containing the point “ ∗ ” has the cardinality ℵ 0 , we call ( ( Z 2 ) ∗ , γ ∗ ) the infinite M-topological sphere. Indeed, in the fields of digital or computational topology or applied analysis, there is an unsolved problem as follows: Under what category does ( ( Z 2 ) ∗ , γ ∗ ) have the fixed point property (FPP, for short)? The present paper proves that ( ( Z 2 ) ∗ , γ ∗ ) has the FPP in the category M o p ( γ ∗ ) whose object is the only ( ( Z 2 ) ∗ , γ ∗ ) and morphisms are all continuous self-maps g of ( ( Z 2 ) ∗ , γ ∗ ) such that | g ( ( Z 2 ) ∗ ) | = ℵ 0 with ∗ ∈ g ( ( Z 2 ) ∗ ) or g ( ( Z 2 ) ∗ ) is a singleton. Since ( ( Z 2 ) ∗ , γ ∗ ) can be a model for a digital sphere derived from the M-topological space ( Z 2 , γ ) , it can play a crucial role in topology, digital geometry and applied sciences.
Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent

In prior works, stochastic dual coordinate ascent (SDCA) has been parallelized in a multi-core environment where the cores communicate through shared memory, or in a multi-processor distributed memory environment where the processors communicate through message passing. In this paper, we propose a hybrid SDCA framework for multi-core clusters, the most common high performance computing environment that consists of multiple nodes each having multiple cores and its own shared memory. We distribute data across nodes where each node solves a local problem in an asynchronous parallel fashion on its cores, and then the local updates are aggregated via an asynchronous across-node update scheme. The proposed double asynchronous method converges to a global solution for L-Lipschitz continuous loss functions, and at a linear convergence rate if a smooth convex loss function is used. Extensive empirical comparison has shown that our algorithm scales better than the best known shared-memory methods and runs faster than previous distributed-memory methods. Big datasets, such as one of 280 GB from the LIBSVM repository, cannot be accommodated on a single node and hence cannot be solved by a parallel algorithm. For such a dataset, our hybrid algorithm takes less than 30 s to achieve a duality gap of 10−5 on 16 nodes each using 12 cores, which is significantly faster than the best known distributed algorithms, such as CoCoA+, that take more than 160 s on 16 nodes.
Pseudoscience and information security in smart-society

There is no doubt that science-intensive technologies are crucial in the modern world, the lag in which leads to a lag in economic and military development.It is difficult to overestimate the role played by scientific rationality in the life of modern society.It is the basis of modern information technologies.Without it, achievements in space exploration, medicine, genetics, Cybernetics would be impossible.In the modern world there is no sphere of activity in which science has not penetrated.But not only does science have an impact on society, but society also has an impact on science as a social institution and, in particular, on scientists who are members of society.In the postmodern situation, the cultural function of science is changing.Her identity is blurred.Science, from this point of view, is one of the spheres of professional activity, has only applied value -in creating opportunities for the design of new technical systems-but claims to possess the truth have no grounds.With this understanding, both science and pseudoscience are equal in culture, the game attitude to life triumphs.Pseudoscientific games do not require the use of cumbersome and expensive equipment, and are more financially efficient than science classes.In these conditions, the boundary between science and pseudoscience is blurred, which inhibits technological development.The main principle of postmodernism is the new for the sake of the new.And this principle has fundamentally influenced the development of modern science.In today's modern society, the amount of information is growing in arithmetic progression, textbooks are overloaded with mathematical calculations.The Genesis of science cannot be considered separately from the events taking place in the cultural and political life of society.It is convenient to declare the representatives of the competing paradigm pseudoscientific and the paradigm itself pseudoscientific.Pseudoscience is a specific type of research and a form of ideology.Pseudoscience arises in the period of rapid development of science.This applies especially to those areas of natural science based on the unification of ideas and methods of different Sciences.
Optimizing FPGA-based Convolutional Encoder-Decoder Architecture for Semantic Segmentation

Convolutional neural networks (CNNs) for visual semantic segmentation have been attracting considerable attention recently because of their superior support for many significant tasks, such as autonomous driving, semantic SLAM (simultaneous localization and mapping) and remote sensing surveying and mapping. These kinds of applications generally need to be implemented on the smart terminals, which means that a kind of hardware platform with high energy efficiency and real-time performance is required. However, CNNs for semantic segmentation usually contain some symmetrical encoders and decoders, corresponding to the down-sampling process (e.g., pooling, convolution) and the up-sampling process (e.g., unpooling, deconvolution). All of these processes are computing and storage intensive, which limits their applicability in the resource constrained embedded systems. In this paper, an FPGA-based accelerator programed by OpenCL is proposed. We evaluate its performance on the CamVid dataset. The global accuracy only drops by 2.04% with 8-bit quantization. Additionally, the system shows 48.89 GOPS and 2.4x real-time performance against CPU when running on an Arria-10 GX1150 device.
Hybrid Nearest Neighbors Ant Colony Optimization for Clustering Social Media Comments

Ant colony optimization (ACO) is one of robust algorithms for solving optimization problems, including clustering. However, high and redundant computation is needed to select the proper cluster for each object, especially when the data dimensionality is high, such as social media comments. Reducing the redundant computation may cut the execution time, but it can potentially decrease the quality of clustering. With the basic idea that nearby objects tend to be in the same cluster, the nearest neighbors method can be used to choose the appropriate cluster for some objects efficiently by considering their neighbor’s cluster. Therefore, this paper proposes the combination of nearest neighbors and ant colony optimization for clustering (NNACOC) which can reduce the computation time but is still able to retain the quality of clustering. To evaluate its performance, NNACOC was tested using some benchmark datasets and twitter comments. Most of the experiments show that NNACOC outperformed the original ant colony optimization for clustering (ACOC) in quality and execution time. NNACOC also yielded a better result than k-means when clustering the twitter comments.
Privacy preservation with unequal data exchange strategy in participatory sensing*

Abstract In participatory sensing, participants contribute sensing data to task center. However, the task center is not always trustworthy. It may try to profile the participants by data mining, which is a great threat to the privacy of participants. To solve this problem, three collaborative data exchange strategies are studied. Participants exchange sensing data before upload to the task center. The mixed data protect the participants’ privacy from data mining by the task center. The simulations show that the unequal data exchange strategy is more efficient than the full exchange strategy because it considers both the tradeoff between privacy preservation and the cost of data exchange.