Reviewerly/exHarmony huggingface dataset:
New Advanced Technology Methods for Energy Efficiency of Buildings

Energy consumed by buildings represents a large part of the world’s total energy consumption with a total share of 40%. This is the reason why energy efficiency of buildings has become a very important scientific field. For the purpose of this paper a critical review of old and new methods of controlling the parts of a building’s automation and thus achieving energy savings are compared, analyzed and presented. The method of Fuzzy Cognitive Maps (FCM) and its significant impact on the improvement of the management of a building is being presented. FCMs is a new soft computing method which combine neural networks and Fuzzy Logic. They have been used with very promising results in many fields such as medicine, transportation, manufacturing agriculture, food industry and energy. In this paper the use of FCMs is exploited and specifically used in issues of energy efficiency of buildings. Obtained results, simulation and experimental, for case studies where FCMs were used in buildings, of residential and commercial use, in Southern Greece will be presented. Software tools based on the aforementioned applications will be briefly presented. In the near future these tools are going to be integrated in even more buildings thus giving us real data which can and will be used in future research for moving from high energy consumption to Net-Zero Energy Buildings (NZEB).
Modality and the plurality of logics

Modality, the realm of the possible and the necessary, has played a significant role in the study of logical consequence. This chapter discusses the role of modality in current debates in the foundations of logic. In the background, two families of views regarding the metaphysics of modality are relevant. The chapter examines the role of modality and of modalism in the examination of logical consequence. It considers logical pluralism, the view according to which there is a plurality of logical consequence relations (a plurality of logics); different logics are adequate to different domains, and, in some cases, several logics are adequate to the same domain. One of the most salient features of logic from the twentieth century on is the plurality of non-classical logics, such as constructive, paraconsistent, and quantum logics, among others. There are different ways of conceptualizing a logical space. The chapter concludes that the combination of both modalism and logical pluralism provides an attractive, integrated view.
LDA Based on Real-time Classification of CCTV Systems Using Codeblocks Information

Today, CCTV systems play a preponderant role in public security. While we are witnessing many tools for processing these video sequences, the potential to identify abnormal events in such sequences in real time is a difficult problem in computer vision. Using the Latent Dirichlet Allocation (LDA) has brought significant results. In this article, we propose an optimization of the LDA method by relying on information codeblocks to provide a better classification of abnormal events in real-time. The implementation is tested on the normalized PETS datasets. The results obtained make it possible to identify the ‘Running’ event considered to be abnormal with an accuracy ranging from 86.55%, informed by the assessment of the proposed method with the ROC curves.
Fuzzy based iterative matting technique for underwater images

The paper presents an iterative matting technique for extraction of underwater objects from images. The technique adopts histogram division and stretching to obtain multiple images of different contrast levels that exhibit all image details. For each contrast image, alpha matte is produced and is further refined with every iteration. In the end, fuzzy weights are assigned to the alpha mattes obtained at different contrast levels that are combined using weighted average. The resultant alpha matte thus includes more accurate pixels from multiple alpha mattes and generates much refined matte image. The proposed technique is tested, visually and quantitatively, on a manual dataset containing 50 images. The less MSE shows that the proposed technique achieves noticeably higher accuracy as compared with contemporary image matting techniques.
Blockchain Based Public Cloud Security for E-Voting System on IoT Environment

Abstract Digital Technology is inevitable for better life of the people. Voting by people plays the vital role in democracy as the leaders will be elected by it. IoT helps many edge devices to interact with a Cloud for storage and computation of the Voting results. The major concern for the Voter is whether the vote casted by him is tamperproof or not. Though Cloud offerings are started a decade ago still information security in the cloud is always a concern. This paper focuses on creating a tamperproof framework to ensure the data Integrity. It proposes a novel approach where the user can cast the vote from any of the electronic devices with a unique ID allotted to him, a two-step authentication can be applied like OTP to ensure the security. The votes from these edge devices will be sent to the untrusted third party public cloud for computation, the votes will be saved in the encrypted format. The computation will be done on encrypted data. The Block chain based workflow ensures that the votes which are shared in the encrypted format will not be tampered to ensure trust and Integrity to the proposed model. The model also can be extended to many public shared e-models like E-Auction and healthcare systems.
Feature Selection using Machine Learning Techniques Based on Search Engine Parameters

In the last two decades, Internet visibility became mandatory for any companies wishing to get exposure and get revenues. Among many ways to be visible on the Internet, one of the most important is to be on top of search engines' results for keywords relative to companies' business. It is the art of Search Engine Optimization (SEO), which is a collection of techniques to get more traffic from a search engine. More a website is SEO optimized, thus more search engines give it a high ranking on results' pages for a maximal exposure. So, Google, with 90% market share worldwide, is the main search engine outside of China (Baidu) and Russia (Yandex), and its algorithm is like a black box all marketers want to discover. Google claims to have more than 200 features in his algorithm made to rank results for queries among billions of pages. This article tries different machine learning methods to determine the most important parameters using a selection of 30 features in a dataset made with around 28,000 observations. A binary classification approach was done to detect if a keyword can be found or not in Top10 search engine result. During the simulation, the importance of features was determined to find the most important parameters used for building related search results. According to the research result, it leads that there are three kinds of parameters which influence the process of ranking the results on search engine Google for web pages: editorial features, notoriety features and technical features. Moreover, few features with minimum importance were found, for example, the low importance of using "https" protocol in a web resource.
A Novel Method of Chinese Electronic Medical Records Entity Labeling Based on BIC model

In the field of bio-medicine, mass data are generated every day, such as Chinese electronic medical record (EMR), containing massive medical terminology and specific categories of entities. The way to analyze and obtain effective information from these sparse data is a difficulty in research. As the foundation of analyzing huge amount of biomedical text data, Named Entity Recognition (NER) is essential in Natural Language Processing (NLP) complementing with effective labeling data. One of the two basic sequence labeling methods is rule-based bulk corpus tagging, requiring domain experts to establish targeted recognition rule base. However, in the application field, this method is single, and the portability does not make the expectation, bringing great limitations; The other is complete manual labeling, but it is time-consuming and laborious. Based on Bidirectional Long Short-Term Memory network (BiLSTM), Iterated Dilated Convolution Neural Network (IDCNN) and Conditional Random Field (CRF), we proposed the BIC model. This paper proposes a method for EMR entity labeling based on BIC model, realizing automatic annotation of Chinese EMR data. Machine labeling data can be used after manual review, which will reduce the workload of manual labeling bestially. Compared with other models, F1 value of BIC model reached 91.90% in CCKS2017 dataset, and 78% in PACS report data. Experiments show that our method is superior to the others.
University Education Resource Sharing Based on Blockchain and IPFS

A large number of educational resources are stored in major university databases, resources are not fully shared and resulting in waste of resources. In order to solve the above problem, a method of university education resource sharing based on blockchain and IPFS is proposed in this paper. Using the technology of smart contract and digital signature to authenticate the identity of universities and establish the alliance chain of universities. The design of IPFS storage combined with blockchain technology and university education resources are stored in IPFS distributed storage and the hash addresses and basic information are stored in the blockchain to achieve the protection and sharing of university education resources. The scheme proposed in this paper can not only improve the storage security of information resources, but also enhance the collaborative processing of educational resources among universities, which is conducive to the sharing of university educational resources.
Multi-scale temporal feature-based dense convolutional network for action recognition

We propose a network structure for action recognition that is capable of extracting multi-scale temporal representations of actions. The key of the network is to combine a multi-scale temporal pooling module with a dense connection module, called multi-scale temporal pooling dense convolutional network (MTPDNet). The multi-scale temporal pooling module consists of multiple temporal scale levels. At each scale level, video frames are divided into several segments and a pooling operation is then performed on each segment to get temporal pooling information. The number of segments is set differently at different time scale levels, aiming to obtain multi-scale temporal pooling information. In addition, at each scale level, we adopt a redesigned dense connection module to learn motion representations from temporal pooling information. Finally, predictions are independently made at each scale level and the class scores of each scale level are fused to get the final prediction scores. Experimental results on two standard datasets, UCF101 and HMDB51, demonstrate that MTPDNet gets comparable or even better results among leading methods, which proves the effectiveness of the strategy combining multi-scale temporal pooling and dense connection.
QUANTIFYING SEMANTIC SHIFT VISUALLY ON A MALAY DOMAIN SPECIFIC CORPUS USING TEMPORAL WORD EMBEDDING APPROACH

In this study, we propose an alternative approach to analyzing a domain-specific time series corpus for detecting word evolution. The method trains a target corpus in time series into a temporal word embedding (TWE) model. The advantage of TWE is that one can see how the meaning of a word changes over time. We have chosen the TWEC approach to model a Malay domain-specific time-series corpus, the Malaysian Hansard Corpus (MHC), to a TWE model and called the model as MHC-TWEC. Two primary analyses, i.e., self-similarity analysis and user-defined method analysis, were performed to validate the effectiveness of the MHC-TWEC model in quantifying semantic shift on MHC visually. From those analyses, we visually find out that the TWE model can capture the semantic shift in the temporal corpus (the MHC).
EXTRA EDUCATIONAL WORK WITH STUDENTS AT HIGHER SCHOOL: PROBLEMS AND OPPORTUNITIES

The article justifies that alongside with the key destination of changes in the modern higher education system - refers to quality improvement in professional training, the important aspect in formation of future specialists at the university is the problem of extra educational process organization. The authors expand the concept of pedagogical potential in extra educational work at higher school by considering the future specialists formation. The extra educational work organization is presented as the important direction in the higher school activity. The extra educational process organization at the student's hostel is considered as a important condition for the formation and development of the future professional personality.
UDante: First Steps Towards the Universal Dependencies Treebank of Dante’s Latin Works

This paper1 presents the early stages of the development of a new treebank containing all of Dante Alighieri’s Latin works. In particular, it describes the conversion of the original tei-xml files to CoNLL-U, the creation of a gold standard, the process of training four annotators and the evaluation of the syntactic annotation in terms of inter-annotator agreement and la, uas and las. The aim is to release a new resource, in view of the celebrations for the 700th anniversary of Dante’s death, which can support the development of the Vocabolario Dantesco.
Harnessing the Potentials of Mobile Phone for Adoption and Promotion of Organic Farming Practices in Nigeria

The goal of organic agriculture is to show the interrelationship between farm biota, its production, and the overall environment. In cooperation with various Organic Farming unions, a system called the organic farming mobile agricultural extension services (OrgFarMob) was developed to provide farmers with instant information to problems with organic crop cultivation. Hence, this project provides organic farmers with crop lifecycle support system, leading to effective management and sustainable economic development in organic agriculture. The system powered by Azure cloud platform, was developed using Model-View-Architecture and programmed with AngularJS and Ionic. The design followed the computer research framework with incorporate users’ requirement elucidation and validation. The system was designed and evaluated using data obtained through the collection of materials from professionals in the field of organic agriculture. The results showed that the system can be used in real-time and foster organic agriculture cultivation.
Sectional category and the fixed point property

For a Hausdorff space $X$, we exhibit an unexpected connection between the sectional number of the Fadell-Neuwirth fibration $\pi_{2,1}^X\colon F(X,2)\to X$, and the fixed point property (FPP) for self-maps on $X$. Explicitly, we demonstrate that a space $X$ has the FPP if and only if 2 is the minimal cardinality of open covers $\{U_i\}$ of $X$ such that each $U_i$ admits a continuous local section for $\pi_{2,1}^X$. This characterization connects a standard problem in fixed point theory to current research trends in topological robotics.
An Incremental Learning approach using Long Short-Term Memory Neural Networks

Due to Big Data and the Internet of Things, Machine Learning algorithms targeted specifically to model evolving data streams had gained attention from both academia and industry. Many Incremental Learning models had been successful in doing so, but most of them have one thing in common: they are complex variants of batch learning algorithms, which is a problem since, in a streaming setting, less complexity and more performance is desired. This paper proposes the Incremental LSTM model, which is a variant of the original LSTM with minor changes, that can tackle evolving data streams problems such as concept drift and the elasticity-plasticity dilemma without neither needing a dedicated drift detector nor a memory management system. It obtained great results that show it reacts fast to concept drifts and that is also robust to noise data.
Philosophy Of Interpenetration Of Traditions And Innovations In Primary School Teacher Training

The article theoretically and empirically proves the possibility and necessity of interpenetration of traditional and innovative teaching technologies in primary school teacher training. Through experimental work the authors justify the efficiency of the higher school lectures, developed on the basis of the authors’ technology of intellectual-developmental education, which requires the presentation of educational information in the form of the sum-total of developmental tasks aimed at developing students’ analytical and synthetic perception, thinking, imagination, semantic memory.“Acceptance” and the accomplishment of developmental tasks are achieved due to the anticipation of the desired result using hidden clues.Anticipation sin spire students to have a positive emotional reaction to the tasks and a sense of confidence in their intellectual capabilities, thereby actualizing their internal inducements to action.The case method at such lectures serves as a separate instructional technique: students solve specific professional pedagogical problems, relying on the lecturer’s assistance.The article also presents an innovative experience of lecturing on basic subjects for future primary school teachers using such learning strategies that will prepare them for methodological activities in primary classes on an unconscious, intuitive level, through “insight”. The authors suggest a didactic model of students’ “immersion” into innovative professional activities, which will form their universal instrumental learning actions.The authors substantiate the conclusion that the balance between traditions and innovations ensures the manifestation of intellectual freedom for all educational process participants in higher education.
Application of Lightweight Convolution Neural Network in Cancer Diagnosis

Acute lymphoblastic leukemia is a major problem in people's lives. At present, the most widespread detection method is mainly manual, which is time-consuming and laborious. With the development of deep learning, specifically targeting the complexity of traditional convolutional neural network models, substantial resource costs, and non-conducive deployment. This paper proposes a simple and effective classification method. The use of lightweight convolutional neural network classified microscopic images of acute leukemia can more effectively assist doctors in the diagnosis of diseases. The method is evaluated in C-NMC 2019 data set and achieves an accuracy rate is 86.29%, F1 coefficient is 89.98%, and a recall rate of an astonishing 95.73% on the test set. Meanwhile, the model parameters are one ninth of those in the traditional convolution neural network. On the premise of ensuring high accuracy, this paper discuss that reduced complexity of the model, the facilitation of deployment and application of the model in the mobile terminal and the potential to help doctors to carry out auxiliary diagnosis of diseases with the help of portable instruments or modern equipment.
New initialization for algorithms to solve Median String Problem

The median string problem is NP-hard under several formulations, being the most competitive heuristics those using perturbation-based iterative algorithms. The initial string and the policy to order possible edit operations are key to the efficiency of such approaches. In this work, we tackle both sub-problems. We hypothesized that a better starting point for the algorithm can reduce the number of edit distances computed to obtain the median string, improving time performance without degrading the quality of the solution. Regarding the starting point, we use the median of a few strings of the input, that is selected as the Half Space Proximal (HSP) neighbors of the median of the set. The HSP neighbors are simultaneously close to the center but also diverse among them. To validate these results, we present comparative experiments, attending mainly to the quality of the median obtained, the time to compute such median, and the number of edit distances computed.
Design of Knowledge Graph of Traditional Chinese Medicine Prescription and Knowledge Analysis of Implicit Relationship

Purpose-As of March 2020, this paper collected 41 prescriptions for Covid-19 in official reports, and used machine learning and knowledge graph technology to discover the rules of common prescription medication for Covid-19 and the Chinese medicinal herb groups applicable to different groups of people to help the TCM prescriptions for the clinical treatment of Covid-19. Methods-The FP-Growth algorithm was used to analyze the properties, tastes, and meridian tropism of Chinese medicinal herb groups in prescription data, constructing a knowledge graph for Covid-19 description and mining the prescription medication rules.
PENGARUH PENGGUNAAN GADGET TERHADAP HASIL BELAJAR SISWA PADA MATA PELAJARAN EKONOMI SISWA KELAS XI IPS DI SMA NEGERI 3 KOTA JAMBI

This study aims to determine (1) an overview of the use of the MYOB application in computer accounting subjects at SMK Negeri 1 Bayung Lencir. (2) An overview of student learning outcomes in computer accounting subjects at SMK Negeri 1 Bayung Lencir. (3) the influence of the use of MYOB applications in computer accounting subjects on student learning outcomes at SMK Negeri 1 Bayung Lencir. The results showed that (1) for the use of MYOB applications obtained a TCR of 65.15% in the Fairly Good category. (2) The results of the calculation show that the learning outcomes of students in computer accounting subjects who score less than 7.5 are 40 students, while students who score above 7.5 are 41 students. (3) from the results of the calculation of simple regression analysis, there is an effect of the use of the MYOB (X) application on learning outcomes (Y) of 71.1%, as evidenced by the results of the t-test, which is 8.994, is greater than the t table, which is 1.644. Yang stated that Ha's knowledge was accepted and Ho was rejected
Predicting Prime Path Coverage Using Regression Analysis

Test coverage criteria help the tester to analyze the quality of the test suite, especially in an evolving system where it can be used to guide the prioritization of regression tests as well as the testing effort of new code. However, coverage analysis of more powerful criteria such as path coverage is still a challenging task due to the lack of supporting tools. In this paper, we evaluate the opportunity of using machine learning algorithms to estimate the prime-path coverage of a test suite at method level. We use a knowledge discovery in database framework and a dataset built from real-world projects to devise a regression model for prime-path prediction. Experimental results demonstrate that a suitable predictive model can be defined with RMSE 0.118 on cross-validation and an average error rate of 26% in a cross-project scenario. We also show how the tester can use the proposed strategy in a practical cross-project scenario.
Implementation and Evaluation of Obstacle Recognition System for the Blind

The visually impaired are exposed to many dangers while walking because of the lack of information on the various obstacles. In this paper, we proposed an obstacle recognition system that provides voice guidance in real-time for the visually impaired. The system informs on obstacles and finds locations of the obstacles such as traffic lights, bollards, and braille blocks. We developed an obstacle recognition system using Raspberry Pi and evaluated its performance by applying the system to a programmable remote control car. The performance evaluation proves that the system successfully avoids the obstacles and guide safely.