Reviewerly/exHarmony huggingface dataset:
Public Speaking serta Teknik Ice Breaking dan MC Sebagai Upaya Pengajaran yang Menarik

Methods of public speaking and ice breaking, master of ceremonies (MC), or "master of ceremonies" are very important to support activities that are often carried out in an institution or institutions, both governmental and non-governmental organizations in activity both formal or activity on formal at every activity event. Students often feel less touched by the teaching methods presented so there is a need for creativity in communication that has an impact on the effectiveness and is interesting in the teaching and learning process. Public speaking, can improve expertise in academics and careers, improve communication skills in general. Public Speaking will develop and improve communication skills, improve speaking skills in front of an audience. The speaker is not born but is created. Ice-breaking can create conditions that are equal or equal to fellow trainees, eliminating gaps or boundaries between participants, so there are no differences so that similarities and opportunities for progress arise. The Master of Ceremonies MC was introduced by the SAPA method (Greetings and Greetings, Appreciation, Introductions, Attention and Event Description). This method is easy to remember and easy to memorize. All the participants felt the benefits and said that the event was lacking in time. So it was felt that it was necessary to make similar activities aimed at providing soft skills to all audiences
Blockchains and Internet of Things for the Pooling of Warehouse Resources

Aims: The purpose of this research paper is the study of the applicability of Blockchain technology and Smart Contracts to manage, control and secure an industrial Internet of Things network (IoT) for pooled warehouses with multiple actors and activities (not only restricted to transport and storage as currently developed in enterprises).&#x0D; Problem: IoT technology enables things to connect and exchange data, resulting in efficiency improvements, economic benefits, reduced human intervention and enabled interactions (information exchange and analysis) between human/machine to machine. However, the main difficulties lie in the definition of a robust global mechanism to secure the IoT, the awareness of resources diversity (heterogeneity of devices), and physical management of IoT. This article will attempt to answer these questionings.&#x0D; Results: To respond to these questions, we propose a hybrid architecture based on blockchains and multi-agents system to secure an industrial IoT corresponding to a pooled warehouse system. The proposed hybrid architecture completely represents our system, with all actors and connected objects in the pooled warehouse strategy. It is composed by two parts: a centralized part based on the multi-agents system to represent dynamic evolutions of actors’ behavior and a decentralized part, which corresponds to our block chain and smart contracts network to dynamically connect actors’ agents to pooled resources. The multi-agent system is coupled with game theory (cooperative game) to have a good systemic representation of our pooled warehouse management problem.
A new multi-level key block cypher based on the Blowfish algorithm

Blowfish is a block cypher algorithm used in many applications to enhance security, but it includes several drawbacks. For example, the mix between the key and data is limited. This paper presents a new modification to the Blowfish algorithm to overcome such problems realised through a multi-state operation instead of an XOR. Our proposed algorithm uses three keys in the encryption and decryption processes instead of one for controlling the variable block bits sizes (1, 2, 4, and 8) bits and for determining the state table numbers. These tables are formed from the addition in a Galois field GF (2 n ) based on block bit size to increase the complexity of the proposed algorithm. Results are evaluated based on the criteria of complexity, time encryption, throughout, and histogram, and show that the original Blowfish, those modified by other scholars, and our proposed algorithm are similar in time computation. Our algorithm is demonstrated to be the most complex compared with other well-known and modified algorithms. This increased complexity score for our proposed Blowfish makes it more resistant against attempts to break the keys.
Research on the Innovation of E-commerce Talent Cultivation Model in Application-oriented Colleges

With the adjustment of the socio-economic structure and the acceleration of industrial transformation and upgrading, the contradiction between talent supply and demand has become prominent, and the problem of difficult employment and low employment quality for graduates has not been effectively alleviated. This article explores the innovative mode of talent cultivation for e-commerce majors in application-oriented colleges on the background of rapid development in the era of "Internet +".At the current stage, there are many problems of E-commerce talent cultivation in application-oriented colleges such as unclear talent cultivation goals, the lack of integration of disciplines in the curriculum, the lack of systematisms and comprehensiveness of practice teaching system, insufficient integration of teaching content and regional economic development and the lack of double qualified teachers, which resulting in low employment satisfaction, poor innovation consciousness and innovation ability, prominent contradiction between talent supply and demand of e-commerce graduates. In addition, the talent cultivation structure and cultivation quality that cannot meet the requirements of regional economic structural adjustment and industrial upgrading. Aiming at the above problem has carried on the research and exploration, this paper puts forward ideas and suggestions on the construction of e-commerce talents cultivation mode in application-oriented colleges from the aspects of orientation of cultivation objectives, the formulation of talent cultivation scheme and curriculum system, the construction of practical teaching system, the construction of double qualified teachers teaching team, as well as school-enterprise cooperation, which aims to explore a suitable and effective path of e-commerce talent cultivation in application-oriented colleges.
Care Label Recognition

The paper introduces the problem of care label recognition and presents a method addressing it. A care label, also called a care tag, is a small piece of cloth or paper attached to a garment providing instructions for its maintenance and information about e.g. the material and size. The informationand instructions are written as symbols or plain text. Care label recognition is a challenging text and pictogram recognition problem - the often sewn text is small, looking as if printed using a non-standard font; the contrast of the text gradually fades, making OCR progressively more difficult. On the other hand, the information provided is typically redundant and thus it facilitates semi-supervised learning. The presented care label recognition method is based on the recently published End-to-End Method for Multi-LanguageScene Text, E2E-MLT, Busta et al. 2018, exploiting specific constraints, e.g. a care label vocabulary with multi-language equivalences. Experiments conducted on a newly-created dataset of 63 care label images show that even when exploiting problem-specific constraints, a state-of-the-art scene text detection and recognition method achieve precision and recall slightly above 0.6, confirming the challenging nature of the problem.
Penerapan Algoritma ELECTRE pada Pemilihan Cream Pelembab Berdasarkan Konsumen

The aim of the study is to recommend the selection of a moisturizing cream using a decision support system ranking technique seen from consumer ratings. This research was conducted in the city of Pematangsiantar. Data obtained by observation to several stores to ensure the availability of moisturizing cream, interviews and observations to 250 consumers who were randomly conducted when they made a transaction to purchase a moisturizing cream. In this case, this research needs to be done considering that moisturizer is a drug used to make facial skin feel moist because with skin that feels moist will make women avoid various problems such as blackheads and acne. Keeping facial skin moist and oil free is not an easy thing for users to do. In addition, the number of moisturizing cream products that are currently making many women confused in choosing a moisturizing cream. Therefore researchers used a Decision Support System (SPK) with the ELECTRE algorithm in recommending the selection of a moisturizing cream based on consumer ratings. In this case the researchers used several assessment criteria including: product price (C1), side effects of usage (C2), product quality (C3), customer commitment (C4), customer trust (C5) and usage reaction (C6). While the alternatives used include: Citra Hazeline (A1), Fair &amp; Lovely (A2), Garnier (A3), Olay (A4), Sariayu (A5) and Wardah (A6). The results of the assessment using the ELECTRE method are Fair &amp; Lovely (A2) and Wardah (A6) as the best moisturizing cream recommendations based on consumer choice
Towards Discourse Parsing-inspired Semantic Storytelling.

Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term goal is the development of an approach for Semantic Storytelling that has broad coverage and that is, furthermore, robust. We provide first results on experiments that involve discourse parsing, applied to a concrete use case, Explore the Neighbourhood!, which is based on a semi-automatically collected data set with documents about noteworthy people in one of Berlin's districts. Though automatically obtaining annotations for coherence relations from plain text is a non-trivial challenge, our preliminary results are promising. We envision our approach to be combined with additional features (NER, coreference resolution, knowledge graphs
Selection of features into the object's own space based on the measure of its compactness

Н.А.Игнатьев, А.И
Dynamically updated diversified ensemble-based approach for handling concept drift

Concept drift is the phenomenon where underlying data distribution changes over time unexpectedly. Examining such drifts and getting insight into the executing processes at that instance of time is a big challenge. Prediction models should be capable of handling drifts in scenarios where statistical properties show abrupt changes. Various strategies exist in the literature to deal with such challenging scenarios but the majority of them are limited to the identification of a particular kind of drift pattern. The proposed approach uses online drift detection in a diversified adaptive setting with pruning techniques to formulate a concept drift handling approach, named ensemble-based online diversified drift detection (En-ODDD), with an aim to identify the majority of drifts including abrupt, gradual, recurring, mixed, etc. in a single model. En-ODDD is equipped with a dynamically updated ensemble to speed up the adaptability to changing distributions. Unlike prevalent approaches, which do not consider correlations between experts, En-ODDD entails experts using varying randomized subsets of input data. Different levels of sampling having been applied for diversity generation to promote generalization. Prediction accuracy has been used to evaluate the effectiveness of the proposed approach using Massive Online Analysis software and compared with ten state-of-the-art algorithms. Experimental results on fifteen benchmark datasets (artificial and real-world) having up to one million instances depict that En-ODDD outperforms the existing approaches irrespective of nature of drift.
Rancang Bangun Sistem Informasi Retensi Rekam Medis dengan Metode Imaging Berbasis Web

Medical record is a file that contains notes and documents about the patient's identity, examination, treatment, actions and other services that have been given to the patient. Based on Minister of Health Regulation No.269 / Menkes / Per / III / 2008 the medical record file can be destroyed (retention) after being stored for a period of 5 (five) years from the last date the patient was treated or returned to avoid the buildup of non-active medical record documents. As one of the private hospitals in the city of Padang that has been operational for 14 years, Siti Rahmah Islamic Hospital has carried out the process of destroying medical record documents with imaging methods and storing them to an external hard drive before the medical record file is destroyed to anticipate various needs. relating to medical records, for example, a medical record that is still valuable to be needed can be searched and reprinted. However, the method used is not optimal because of the potential for data loss and the difficulty of the data search process and has not been managed using an information system in the form of a truly computerized program. Optimizing data processing is needed so that integrity, access rights and data availability can be maintained. The application system that will be proposed later uses PHP and Mariadb programming and web-based. The results of the image will be processed in an application and stored in the database. The diverse data will be managed well, easily and safely in a well-structured system.&#x0D; Keywords: Information Systems, Retention, Imaging, Medical Records, Web&#x0D;
Hermeneutical approach as a methodological basis of soft skills development of technical students

Increased requirements of employers forced researchers to pay attention to the lack of academic knowledge and professional competence, so-called hard skills, when applying for a job. Multitasking has become a defining trend in all professional fields, which confronts professionals with problems that do not have a solution algorithm. We need special mechanisms for adapting the employees to constant challenges and additional knowledge and skills, the function of which is performed by soft skills. This article focuses on the development of creative and critical thinking as one of the categories of soft skills, as it is the style of thinking that determines the level of success in the professional sphere. Today, with the development of the information society, the problem of understanding a large amount of information, its processing, correct interpretation by students and further adaptation for application in professional field is a crucial one. Communication in the field of science and technology is realized in the form of discourse, which actualizes the development of future graduates creative thinking through the ability formation to explicate meaningful content, decode and deverbalize in the process of perceiving, understanding and interpreting professionally relevant information when working with scientific and technical discourse. The discipline of "Foreign Language" provides the necessary environment for developing soft skills working with the discourse in question. In this case reading is a tool for obtaining information by comparison, comparing data, analyzing the information received, highlighting the main and secondary information, establishing causality, assessing. The hermeneutic approach to the development of soft skills ensures the optimal conditions to activate mental operations responsible for effective perception and further adaptation of the information received for professional purposes. Practical part of the research is to compile a set of tasks for working with text material, which will ensure the information understanding and interpretation through certain reading strategies stimulating mental activity.
Message Passing for Query Answering over Knowledge Graphs

Recent works on representation learning for Knowledge Graphs have moved beyond the problem of link prediction, to answering queries of an arbitrary structure. Existing methods are based on ad-hoc mechanisms that require training with a diverse set of query structures. We propose a more general architecture that employs a graph neural network to encode a graph representation of the query, where nodes correspond to entities and variables. The generality of our method allows it to encode a more diverse set of query types in comparison to previous work. Our method shows competitive performance against previous models for complex queries, and in contrast with these models, it can answer complex queries when trained for link prediction only. We show that the model learns entity embeddings that capture the notion of entity type without explicit supervision.
Green Energy-aware task scheduling using the DVFS technique in Cloud Computing

Nowdays, energy consumption as a critical issue in distributed computing systems with high performance has become so green computing tries to energy consumption, carbon footprint and CO2 emissions in high performance computing systems (HPCs) such as clusters, Grid and Cloud that a large number of parallel. Reducing energy consumption for high end computing can bring various benefits such as reducing costs, increasing system reliability & availability, and environmental respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cloud data center with the Dynamic Voltage Frequency Scaling (DVFS) technique and task duplication. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS-enabled processors, and energy consumption. In this paper, we develop a new scheduling algorithm called Energy Aware Scheduling Algorithm based on DVFS technique and task duplication strategy, called EADUPDVFS. Models and scheduling heuristics are examined with a simulation study. Using simulations we show our algorithm not only maintains good performance, but also has a good improvement on energy efficiency for parallel applications.
Competences in English

This article discusses kinds of English competences. In this article also discusses about the English language skills and components of English language use as the indicator of the student’s competence. The competences of language learning is in the means of communicative competence. To reach those competences, the students must be taught and trained English language in written and oral language skill. Acquiring English as international language is the main indicator of English learning success for students. This article uses qualitative research with a literature study. The data sources are taken from textbooks, journals, articles, research finding, internet articles, newspapers, magazines, etc. The objective of this studies that the result can develop the learner’s understanding about the competences in English language as the basic for the learner in learning and studying English language as the second or the foreign language in Indonesia. The finding shows that the communicative competence (communicative competences relates to the learner’s ability in using language established by language knowledge, skills, and attitude including grammatical, sociolinguistic, discourse, and strategic competence) and the components of English competences (listening, speaking, reading, writing, grammar, vocabulary, and pronunciation) are must be learnt and studied by the students integrative.
UBAL

Artificial neural networks, in particular the deep end-to-end architectures trained by error backpropagation (BP), are currently the topmost used learning systems. However, learning in such systems is only loosely inspired by the actual neural mechanisms. Algorithms based on local activation differences were designed as a biologically plausible alternative to BP. We propose Universal Bidirectional Activation-based Learning, a novel neural model derived from contrastive Hebbian learning. Similarly to what is assumed about learning in the brain, our model defines a single learning rule that can perform multiple ways of learning via special hyperparameters. Unlike others, our model consists of mutually dependent, yet separate weight matrices for different directions of activation propagation. We show that UBAL can learn different tasks (such as pattern retrieval, denoising, or classification) with different setups of the learning hyperparameters. We also demonstrate the performance of our algorithm on a machine learning benchmark (MNIST). The experimental results presented in this paper confirm that UBAL is comparable with a basic version BP-trained multilayer network and the related biologically-motivated models.
Blockchains and Transaction Costs

This paper examines current business applications of blockchain technology and discusses blockchain implications for transaction costs. Blockchains are a relatively new set of technologies that can be used for various business purposes, primarily activities related to contracting. Transaction costs comprise the operational costs of contacting (searching and communicating) as well as the costs of contracting (writing and enforcing contracts), and blockchains can be used to lower, first and foremost, the costs of writing and enforcing contracts. Other technology applications that have been investigated to a larger extent, such as multi-sided platforms, primarily help in lowering the costs of searching and communicating, while blockchains can contribute to lowering the costs of contracting.
Adaptive Target Detection Algorithm Based on Correlation Filtering

Correlation filtering is a fast and robust signal detection and processing algorithm. However, in the field of images processing, scale and rotation variation are important issues for correlation filtering algorithms. This paper proposes a detection method based on correlation filtering, which uses axis of symmetry and circumscribed rectangles to estimate the rotation and scale of the target. Firstly, using the HSV model to separate the colors to be found in the original image. Then, the axisymmetric shell intersection method is proposed to solve the symmetry axis and the circumscribed rectangle of the object. Finally, a correlation filter is used to solve all the areas that are likely to be objects. The response value, the position with the highest response value is the object position. The algorithm performs experiments on a set of artificially calibrated image sequences. Experiments show that this method can achieve better detection results when the number of training samples is small.
Efficient Block Coordinate Methods for Blind Cauchy Denoising

This paper deals with the problem of image blind deconvolution in presence of Cauchy noise, a type of non-Gaussian, impulsive degradation which frequently appears in engineering and biomedical applications. We consider a regularized version of the corresponding data fidelity function, by adding the total variation regularizer on the image and a Tikhonov term on the point spread function (PSF). The resulting objective function is nonconvex with respect to both the image and PSF block, which leads to the presence of several uninteresting local minima. We propose to tackle such challenging problem by means of a block coordinate linesearch based forward backward algorithm suited for nonsmooth nonconvex optimization. The proposed method allows performing multiple forward-backward steps on each block of variables, as well as adopting variable steplengths and scaling matrices to accelerate the progress towards a stationary point. The convergence of the scheme is guaranteed by imposing a linesearch procedure at each inner step of the algorithm. We provide some practical sound rules to adaptively choose both the variable metric parameters and the number of inner iterations on each block. Numerical experiments show how the proposed approach delivers better performances in terms of efficiency and accuracy if compared to a more standard block coordinate strategy.