Reviewerly/exHarmony huggingface dataset:
Management of Blockchain Based Hybrid P2P Energy Trading Market

In the local energy trade market peer to peer energy trading and generation of local energy can condense the cost of energy consumption and harmful gases emission ( results in to generate the energy at consumer’s end the renewable energy sources are used) and rise the resilience of smart grid. However, privacy issues and trust may available in the trading of local energy with peers. To manage the energy trading a centralized system can be utilized but it faces the several issues and also it promotes the overall system cost. In this tabloid, based on the blockchain system a hybrid P2P energy trading market is proposed. This system totally decentralized and this system allows that members in trading market can able to interact with each other member and can able to trade the energy without the third party involvement. In the blockchain based energy exchange market the smart agreements play a vital role. Smart agreements follows the all the essential rules in the energy trading. To develop the hybrid electricity exchange market the 3 smart agreements are proposed in this tabloid. The three smart agreements are Main Smart agreement, P2P smart agreement and Producer to Grid smart agreement. Initially the market member interacts with main smart agreement and for further process it follows the other 2 smart agreements. The implementation of an efficient hybrid energy exchange market is the main objective of this tabloid and also reduces the cost of energy and peak to average ratio of electricity.
Identification of Research Data References Based on Citation Contexts

In this paper, a method for the automatic identification of research data references in publications is proposed for automatically generating research data repositories. The International Conference on Language Resources and Evaluation (LREC) requires authors to list research data references separately from other publication references. The goal of our research is to automate the discrimination process. We investigated the reference lists in LREC papers and the citation contexts to find characteristic features that are useful for identifying research data references. We confirmed that key phrases appeared in the citation contexts and the bibliographical elements in the reference lists. Our proposed method uses the presence or absence of key phrases to identify research data references. Experiments on LREC proceedings papers proved the effectiveness of using key phrases in the citation context.
KNOWING OUR STUDENTS IN THE VIRTUAL ENVIRONMENT

The global pandemic has forced many of us to learn new skills and adapt to a new "normal". While we may desire the return to past practices, our current situation remains full of uncertainty. Many teachers and parents around the world are looking ahead and thinking what the new academic year might bring. Students are also wondering what the start of the new school year will look like. Using the reflections sent in by international school teachers, parents and students towards the end of the 2019-2020 academic year, common questions have come up. "If we have to start virtually, how will I get to know my students?" "How do I develop a positive class culture and identity in the virtual environment?" "How do I plan and engage my students, even my most reluctant students when not in a face-to-face situation?" We know that robust data on student learning has powerful effects on the multiple aspects of learning including the cognitive, emotional and the social (Campbell, Brownlee & Renton, 2016). The data collected becomes a tool for learning, not only for the students but also for those who facilitate learning. We also know that robust data on student learning involves a pattern of observations and a process of documentation (Axelsson, 2019). This requires co-construction between the adults and students so that student thinking and learning becomes visible to all. Data collection remains one of the most challenging aspects for teachers in distance learning (Global Online Academy, 2020). How might we gather data to come to know our students as learners when we do not have the benefit of face-to-face connections? This question frames the scope of this paper. COVID-19 placed distance learning as a key mode of education delivery rather than a complementary feature to face-to-face learning. Given this context, it is essential to rethink the way we collect data as directly transferring our practices from a face-to face setting will not necessarily work online. What is paramount is to keep the student at the forefront, thinking flexibly and asking ourselves: What will I relinquish? What will I restore? And what will I revolutionize? (Murdoch, 2020). Taking our experiences in responding to the pandemic thus far, we can use the lessons learnt as windows of opportunity. Distance learning requires extra considerations. It requires adults to think strategically and creatively. In gaining the competence and confidence in collecting data effectively to know our students, this paper will be structured around the following principles:-Relationships are critical-All behavior is communication-Maintain a growth mindset
i-DATAQUEST: A Proposal for a Manufacturing Data Query System Based on a Graph

During the manufacturing product life cycle, an increasing volume of data is generated and stored in distributed resources. These data are heterogeneous, explicitly and implicitly linked and they could be structured and unstructured. The rapid, exhaustive and relevant acquisition of information from this data is a major manufacturing industry issue. The key challenges, in this context, are to transform heterogeneous data into a common searchable data model, to allow semantic search, to detect implicit links between data and to rank results by relevance. To address this issue, the authors propose a query system based on a graph database. This graph is defined based on all the transformed manufacturing data. Besides, the graph is enriched by explicitly and implicitly data links. Finally, the enriched graph is queried thanks to an extended queries system defined by a knowledge graph. The authors depict a proof of concept to validate the proposal. After a partial implementation of this proof of concept, the authors obtain an acceptable result and a needed effort to improve the system response time. Finally, the authors open the topic on the subjects of right management, user profile/customization and data update.
Sliding prediction algorithm using polynomial approach for photovoltaic system energy management

Fluctuating photovoltaic (PV) power creates a challenge for grid energy management as energy penetration increases rapidly. As a result, the forecasting of PV energy has become essential to ensure grid stability and promote optimal use of energy resources and more economical distribution. The objective of this work is to forecast PV power for a 10 min horizon, based on a set of data. This approach is used to ensure efficient operation of the fluctuating output of a PV station charging a station for electric vehicles. Simulation results show the reliability and efficiency of the technique.
Multiresolution Knowledge Distillation for Anomaly Detection

Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often large enough to learn a rich generalizable representation through conventional techniques. Secondly, while only normal samples are available at training, the learned features should be discriminative of normal and anomalous samples. Here, we propose to use the of features at various layers of an expert network, pre-trained on ImageNet, into a simpler cloner network to tackle both issues. We detect and localize anomalies using the discrepancy between the expert and cloner networks' intermediate activation values given the input data. We show that considering multiple intermediate hints in distillation leads to better exploiting the expert's knowledge and more distinctive discrepancy compared to solely utilizing the last layer activation values. Notably, previous methods either fail in precise anomaly localization or need expensive region-based training. In contrast, with no need for any special or intensive training procedure, we incorporate interpretability algorithms in our novel framework for the localization of anomalous regions. Despite the striking contrast between some test datasets and ImageNet, we achieve competitive or significantly superior results compared to the SOTA methods on MNIST, F-MNIST, CIFAR-10, MVTecAD, Retinal-OCT, and two Medical datasets on both anomaly detection and localization.
Font as a category of aesthetics and a means of communication in design, art, architecture

Abstract The article briefly describes the history of Old Russian writing in the context of the development of European type culture. The article analyzes the influence of material, technology, features of type design and the level of socio-economic development on the evolution of Cyrillic writing. There is an overview of the development of styles of handwritten writing in Russia from the XI-XVIII centuries till the reform of Peter I. The problem of coexistence and mutual influence of different types of arts within the same style is touched upon on the example of architecture, literature and book graphics. The conclusion is made about the influence of calligraphy on the formation of aesthetic taste, on the ability to solve educational and upbringing tasks, its role, as an ethno-cultural marker.
Influence of Information System Factors to Operational Performance Sustainability of UAE Manufacturing Sector

This paper presents an assessment of information system factors that influence the operational performance sustainability of UAE manufacturing sector. The study was conducted using quantitative approach where data was collected through the questionnaire survey and analysed statistically. A total of 41 factors of information system extracted from literature review and clustered into 4 domains namely transaction processing system; operation information system; decision support system and financial manager's workbench. The survey distributed 250 questionnaires among operations staff of UAE manufacturing industries. However, only 200 datasets were considered valid for further analysis. Ranking of factors were conducted using mean score and standard deviation. The results found that the most influential domain is decision support system having mean value of 4.19. The findings from this study give an indicator on the importance of information system toward the sustainable operational performance of the UAE manufacturing sector
A Fast and Safe Motion Planning Algorithm in Cluttered Environment using Maximally Occupying Convex Space

This paper presents a new planning method in cluttered environment using a newly defined concept called a maximally occupying convex space (MOCS). Motion planning methods that use safe flight corridor (SFC) generally construct SFC in every new trial based on each initial path. We define a MOCS in a three-dimensional rectangular world to be prebuilt as SFC, prior to the initial path computation that leads to optimization process without running SFC construction phase. In the rectangular world, creating every possible MOCS covers all the free space overlapping with others if there exists a feasible path among those spaces. Thus, expressing MOCSs as nodes and their overlappings as edges, we can formulate a simplified graph representing the free space. Dijkstra algorithm is applied to a simplified graph in this work. This approach enables to omit constructing SFC every new trial, and to use path planning algorithm with small amount of nodes thereby, achieve efficient computation. We provide a completeness of a planning algorithm in a rectangular space, and show computation efficiency compared with a state-of-the-art algorithm based on SFC.
Transfer Learning for Multilingual Offensive Language Detection with BERT

The popularity of social media platforms has led to an increase in user-generated content being posted on the Internet. Users, masked behind what they perceive as anonymity, can express offensive a ...
SASTRA ANAK SEBAGAI SARANA PENDIDIKAN KARAKTER DI SEKOLAH

Literature is an expression of one's feelings as outlined in an article or story that is interestingly packaged by the reader. Not much different from literature, children's literature is an expression of the feelings of a child which is poured into written form and enjoyed by children. The development of some children's fiction in the world will affect the development of children's fiction. in Indonesia. Children's fiction is divided into five types, namely (1) science fiction, (2) historical fiction, (3) historical fiction, (4) formula fiction, (5) fantasy stories. Sastra merupakan ungkapan perasaan seseorang yang dituangkan dalam sebuah tulisan maupun cerita yang dikemas secara menarik pembacanya. Tidak jauh berbeda dengan sastra, sastra anak merupakan ungkapan perasaan seorang anak yang dituangkan kedalam bentuk tulisan dan dinikmati oleh anak-anak. Berkembangnya beberapa fiksi anak di dunia akan mempengaruhi perkembangan fiksi anak. di Indonesia. Cerita fiksi anak terbagi kedalam lima jenis, yaitu (1) fiksi ilmiah, (2) fiksi sejarah, (3) fiksi sejarah, (4) fiksi formula, (5) cerita fantasi. Keywords: Children, Literature, Character
Weakly Extractable One-Way Functions

A family of one-way functions is extractable if given a random function in the family, an efficient adversary can only output an element in the image of the function if it knows a corresponding preimage. This knowledge extraction guarantee is particularly powerful since it does not require interaction. However, extractable one-way functions (EFs) are subject to a strong barrier: assuming indistinguishability obfuscation, no EF can have a knowledge extractor that works against all polynomial-size non-uniform adversaries. This holds even for non-black-box extractors that use the adversary’s code. Accordingly, the literature considers either EFs based on non-falsifiable knowledge assumptions, where the extractor is not explicitly given, but it is only assumed to exist, or EFs against a restricted class of adversaries with a bounded non-uniform advice. This falls short of cryptography’s gold standard of security that requires an explicit reduction against non-uniform adversaries of arbitrary polynomial size. Motivated by this gap, we put forward a new notion of weakly extractable one-way functions (WEFs) that circumvents the known barrier. We then prove that WEFs are inextricably connected to the long standing question of three-message zero knowledge protocols. We show that different flavors of WEFs are sufficient and necessary for three-message zero knowledge to exist. The exact flavor depends on whether the protocol is computational or statistical zero knowledge and whether it is publicly or privately verifiable. Combined with recent progress on constructing three message zero-knowledge, we derive a new connection between keyless multi-collision resistance and the notion of incompressibility and the feasibility of non-interactive knowledge extraction. Another interesting corollary of our result is that in order to construct three-message zero knowledge arguments, it suffices to construct such arguments where the honest prover strategy is unbounded.
STATISTICAL ANALYSIS OF CREATIVE SELF-EFFICACY OF STUDENTS DEPENDING ON THEIR DEPARTMENT, GRADE AND GENDER

The primary objective of this research is to determine the possibility of measuring students’ creative self-efficacy on a linear scale within the framework of the theory of latent variables based on the Rasch model. The results obtained are used for statistical analysis of students' creative self-efficacy depending on classifying factors such as department, grade, and gender. The respondents were students of three departments of Kuban State University. The study was conducted within the framework of the theory of latent variables based on the Rasch model because traditionally used methods (indexes, weighting methods) for estimating latent variables have significant disadvantages. First, the scale is non-linear, which makes it difficult to use many statistical analysis procedures. In addition, the latent variable theory evaluates the quality of the questionnaire as a measuring tool. Thus, the indicators (questionnaire items) that differentiate students with low and high creative self-efficacy better than others are highlighted. The analysis of variance demonstrated the following: there is no statistically significant relationship between departments based on students' creative self-efficacy; the creative self-efficacy of males is statistically significantly higher than that of females; the creative self-efficacy of first and second-year students is higher than that of third and fourth-year students. Considering the relevance of creativity in the modern world, as well as the fact that education focuses on evaluating learning outcomes and evaluating competencies, such studies help to objectively assess the level of development of students' personal qualities.
HOW DOES A CHILD FEEL, UNDERSTAND AND DISCERN QUALITIES OF MUSIC SOUNDS: THE PATTERN OF CONTRAST OF VARIATION THEORY

The article studies the problem of early music didactics applying the pattern of contrast of variation theory approaching it as a new phenomenon that emerges in the context of interpretative paradigm ideas. The article analyses the child’s learning as a teacher-child interaction, their communicative encounters, which create conditions for a child to engage in educational activities, to act and think together with the teacher and other children as well as to build own knowledge. Communication becomes one of the most significant mechanisms of child’s self-directed learning (Magnusson and Pramling, 2016; Veraksa et al., 2016). With the help of targeted communication, a teacher introduces a child into a domain of certain phenomena and categories (concepts), encourages children to explore and get an in-depth understanding of them. All this allows transforming those phenomena and concepts into child’s knowledge (Pramling et al., 2019).One of the most innovative approaches of modelling the learning process is grounded on variation theory (Marton, 2018, 2015), which enables a child to build own knowledge and develop abilities with the help of the pattern of contrast and other models. Using this approach of modelling the process of education, child’s learning is directed to the object of learning and its critical aspects and the pattern of contrast is applied helping a child to see, feel, understand, perceive, discern, discover and get a deep understanding of the differences in the qualities of the critical aspects of the chosen learning object (Ljung-Djärf, Wennas Brante and Holmqvist Olander, 2013; Marton, 2015; Zitkeviciene, 2018; Wallerstedt, 2011). While conducting the empirical research, attempts were made to identify the ways of teacher communication with the child, which are grounded on the pattern of contrast of variation theory and target at facilitating the child’s penetration into the qualities of music sounds. The research disclosed two groups of ways of teacher communication that encourage children to listen to music: ways of teacher communication that promote children to feel, understand and discern the critical aspects of the learning object, i.e. pitch, duration, timbre of music sound, while listening to music (group one) and after it (group two). The revealed ways of teacher communication with children applying the pattern of contrast of variation theory are new and enrich didactics of early music education.
Design Participation Theories

The level of the designer’s and user’s engagement in design processes has always been one of the most significant challenges. With respect to the wide scopes of design, the designer’s role can have diverse domains. On the other hand, participatory design is an approach that tries to involve citizens and users in the design process. Historically, in primitive local design or architecture without architects, settlers expanded their eye-catching architecture with local materials and prevalent local construction methods. In his famous book titled ‘Architecture without Architects’, Bernard Rudofsky introduces a collection of this kind of architecture.
Safe Systems Programming in Rust: The Promise and the Challenge

• Rust is the first industry-supported language to overcome the longstanding tradeoff between the safety guarantees of higher-level languages (like Java) and the control over resource management provided by lower-level programming languages (like C and C++).• It tackles this challenge using a strong type system based on the ideas of ownership and borrowing, which statically prohibits the mutation of shared state. This approach enables many common systems pitfalls to be detected at compile time.• There are a number of data types whose implementations fundamentally depend on shared mutable state and thus cannot be typechecked according to Rust’s strict ownership discipline. To support such data types, Rust embraces the judicious use of unsafe code encapsulated within safe APIs.• The proof technique of semantic type soundness, together with advances in separation logic and machine-checked proof, has enabled us to begin building rigorous formal foundations for Rust as part of the RustBelt project.
Using Features Specifically: An Efficient Network for Scene Segmentation Based on Dedicated Attention Mechanisms

Semantic segmentation is a challenging task in computer vision, which requires both context information and rich spatial detail. To this end, most methods introduce low-level features for spatial detail. However, low-level features lack global information. Too much low-level features will disturb the segmentation result. In this paper, we extract low-level features guided by abstract semantic features to improve segmentation results. Specifically, we propose a Pixel-wise Attention Module (PAM) to select low-level features adaptively and a Dual Channel-wise Attention Fusion Module (DCAFM) to fuse the context information further. These two modules use the attention mechanism from a more macro perspective, which is not limited to the inter-layer feature adjustments. There are not complicated and redundant processing modules in our architecture. By using features efficiently, the complexity of the network was significantly reduced. We evaluate our approach on Cityscapes, PASCAL VOC 2012, and PASCAL Context datasets, and we achieve 82.3% Mean IoU on PASCAL VOC 2012 test dataset without pre-training on the MS-COCO dataset.
Analyze the Cooperative Skills of Leadership and Followership using Machine Learning

This paper focused on cooperative skills in order to investigate physical human interaction key factors using a cooperative model. Human interaction is made up of both verbal and nonverbal communication. Especially, force communication has drawn a lot of attention as a means of nonverbal communication. Several studies focus on force communication, however, these studies have not considered human skills. In particular, cooperative skill is important for Human-Human interaction (HHI) or Human-Robot interaction (HRI). This paper proposes the neural networks that learn the CFO. CFO is estimated by comparison with learning the input command during the solo task using neural networks and the input command during the cooperative task. Also, this paper analyzes physical human interaction using the data which is compressed information by neural networks. In the experiment, the proposed neural networks can predict CFO precisely. In addition, the results show that followership on cooperative skills is induced by other players.