Reviewerly/exHarmony huggingface dataset:
Improving the Effectiveness in Research and Developments Department from Automotive Industry

Abstract The scope of this paper is to propose a solution for decreasing the rework that has to be made inside a team, in a new offshore Research and Development department, mainly because of assumptions and lack of experience. This is based on learning and applying the lessons learned. First, it will be presented an order assignment in an offshore branch in the field of automotive software companies. From the concept, further on, the usual process that takes place is presented and the potential flaws are analyzed. The major problems identified are random allocation of the orders and assumptions that are made in the branch offices. After inspecting the steps of the process, a model of process is proposed in order to avoid the problems identified at the usual process. This proposal has the principles from Advanced Product Quality Planning and the Scrum framework. The goal is to reduce the rework using the learning process presented. If no assumptions are made, if everything is clear from the beginning, if all the necessary documentation is done, if the proper mentoring is made and if the process is continuously improved, the probability of the identified problems to occur again is highly decreased. The most important points in the proposed process for improvement are the following: weekly meeting for status update, assigning the orders according with the needs, assign a mentor for new entries, eliminating the assumptions, discuss the problems as fast as possible and obtain feedback fast from the client to check the direction of the work.
BOOMER: A Tool for Blending Visual P-Homomorphic Queries on Large Networks

The paradigm of interleaving (i.e. blending) visual subgraph query formulation and processing by exploiting the latency offered by the GUI brings in several potential benefits such as superior system response time (SRT) and opportunities to enhance usability of graph databases. Recent efforts at implementing this paradigm are focused on subgraph isomorphism-based queries, which are often restrictive in many real-world graph applications. In this demonstration, we present a novel system called BOOMER to realize this paradigm on more generic but complex bounded 1-1 p-homomorphic(BPH) queries on large networks. Intuitively, a BPH query maps an edge of the query to bounded paths in the data graph. We demonstrate various innovative features of BOOMER, its flexibility, and its promising performance.
Modeling and research on power supply of distributed sensor networks with DC-DC boost converter

In the marine seismic exploration streamer system (MSESS), the underwater power supply network transmits high voltage direct current (DC) to all acquisition and transfer nodes in the streamer. As oil exploration develops from the shallow offshore to deep sea, the depth of the exploration target layer is deeper and deeper. The exploration instrument requires a large-scale sensor array, and the corresponding length of the streamer is greatly increased. The increase of streamer length often results in insufficient power supply at the end-node, which makes MSESS unable to work. This paper proposes a power supply model of distributed sensor networks with boost converter, and designs a high-efficiency DC-DC boost converter for marine seismic exploration. Based on genetic algorithm, the optimal position of applying the boost converter is calculated. To some extent, it can solve the problem of power supply for large-scale sensor networks for marine seismic exploration. The research results of the model also have important reference value in applications such as large-scale sensor networks in land exploration.
Agrupamento de dados como instrumento de apoio a estratégias de negócio

ABSTRACTWith the high volume of information that is stored in corporate databases on products,customers and suppliers, alied to the increasingly competitive market and the most demandingconsumers, the application of knowledge discovery techniques in corporate databases hasbecome a powerful weapon for business strategy. This work presents the technique of dataclustering, which discovers groups of similar objects in a data set from, only, informationcontained in the data itself. Three clustering techniques are discussed: the agglomerativehierarchical clustering method, the K-means Algorithm and Genetic Algorithm. Next, threecase studies are presented in the context of business with application of the techniques studiedwith the objective of tracing customer profiles and classifying suppliers to find usefulknowledge to support business strategies. Finally, and a discussion is made on thecharacteristics of each problem that motivated the choice of each technique adopted andresults achieved.
Gibbsite as low-temperature hydrothermal overprint on lateritised Cenozoic sediments, Quadrilátero Ferrífero of Minas Gerais, Brazil

Gibbsite, typically a clay-sized mineral in lateritic profiles, occurs as crystals between 50 and 150 µm across in druse-like cavities that characterise veins hosted in lateritised Cenozoic sediments, exposed along a road cut in the Quadrilátero Ferrífero of Minas Gerais, Brazil. Both the veins and the host rock, a red diamictite, contain gibbsite, but the drusy gibbsite in the former is remarkably larger than the matrix gibbsite in the latter. It is suggested that the coarser gibbsite is a low-temperature hydrothermal mineral. The coarse grain size of gibbsite and its occurrence in veins comprise important criteria to recognise hydrothermal overprint of low temperature on lateritic profiles.
Gravitational K-Means Algorithm

The technologies of the present era produce large amount of spatiotemporal data related to various fields. Clustering is a tool to extract useful information from the large repository of data and helps in data analytics. We propose a Gravitational K-Means clustering algorithm as an extension of K-Means algorithm to exploit the idea of relative importance of one object to other objects in a phenomenon. The notion of relative importance can be realized by the concept of mass and gravity. Therefore, we employ the concepts of gravitational laws in the formation of clusters. We propose a function to map an intrinsic attribute of the object to mass of the object. The mass function is used to calculate center of mass of a cluster in place of geometry-based centroid. We further propose to use gravitational force as a measure of dissimilarity in place of Euclidean distance. Silhouette index is used to select the value of input parameter and also for measurement of quality of obtained clusters. The proposed algorithm is applied on a real fire dataset. The result reveals that the proposed algorithm produces more natural clusters. We also perform the comparative study with K-Means algorithm and found that the proposed algorithm outperforms K-Means algorithm in terms of convergence and semantics.
The Republic of Letters and the Origins of Scientific Knowledge Commons

The knowledge commons framework, deployed here in a review of the early network of scientific communication known as the Republic of Letters, combines a historical sensibility regarding the character of scientific research and communications with a modern approach to analyzing institutions for knowledge governance. Distinctions and intersections between public purposes and privacy interests are highlighted. Lessons from revisiting the Republic of Letters as knowledge commons may be useful in advancing contemporary discussions of Open Science.
Quantum Dots Tracking with N-Memory Cluster Indexed SMC-PHD filter

A Cluster-Indexed SMC-PHD filter was presented to track quantum dots for certain biological purpose. It committed track association by labeling targets with track numbers. One problem of it is the labels of targets shift often when dealing with unsteady targets. The label shifting made it hard to obtain longterm tracks. A novel N-memory Cluster-Indexed SMC-PHD filter was proposed to control the sensitivity to targets birth and death. It checked the tracks back N time intervals to find out if any one of them could be associated with the currently emerging targets, and thereby, rejected false detection alarm resulting from the go-and-back of unsteady targets. Results show the method successfully track quantum dots of high dynamics.
Design and Implementation of Intelligent Garbage Classification System

An intelligent garbage classification system was designed which mainly used for front-end processing of garbage sorting. The system established a speech corpus of garbage vocabulary at first, and then extracted Mel Frequency Cepstral Coefficients (MFCC) from each garbage word in the speech corpus, moreover, set up characteristic parameters pattern base. Finally, it can correctly classify the garbage word of specific person and non-specific person separately speaking based on Dynamic Time Warping(DTW) matching reference characteristic parameters pattern with test pattern. Test results show that the system's speech recognition rate for specific person and non-specific person is up to 100% and 98.75% respectively.
The Promotion Mechanism Development for User's Information Need Understanding Based on Knowledge Structure Precise Matching

Understanding user's information needs is the premise and basis to improve the quality of information services and systems. When engaging in various activities, people have run into problems that are followed by the realization of insufficient knowledge structure to resolve these problems, and the need for information has arisen. After information needs are generated, they have experienced an expression process from the internal to the external and from the abstract to the concrete. The process of information need expression is influenced by the users themselves and their surrounding environment. Ambiguities will appear during this process. The current systems mainly promote understanding through (1) information extraction and knowledge acquisition, (2) ontology, semantic web, and knowledge graphs, (3) semantic disambiguation, and (4) topic modeling. This research proposes a new promotion mechanism for the understanding of user information needs. Based on collaborative filtering and folksonomy, the mechanism can precisely match the user with other most related users and get the user's knowledge structure.
The Development of Budgeting Module on Android based on Backpacker BackInd Applications: Sorting and Filtering Features

Abstract Decision making problems often occur in tourists to determine the activity within the tourist destinations or location. For tourists, information about attractions also is needed to allow their planning to be precise and meaningful. The BackInd (Backpacker Management System) have been developed as an application to help and support tourists by providing comprehensive information about tourist attractions through filtering and sorting mechanism. In addition to provide those type of information, tourists also allowed to buy tickets directly without much more burden to wait physically in that attractions counter. The development of this application is expected to help tourists get recommendations based on their preferences that are in accordance with the budget of the travellers. Thus, this study uses the Rapid Application Development (RAD) method, which the results focused on the featured added to the application in term of sorting, filtering and budgeting features so the homestay that tourist want will match accordingly with their plan.
Hyper AT-ideal on AT-algebra

Abstract In this paper is to introduce the concept of hyper AT-algebras is a generalization of AT-algebras and study a hyper structure AT-algebra and investigate some of its properties. “Also, hyper AT-subalgebras and hyper AT-ideal of hyper AT-algebras are studied. We study on the fuzzy theory of hyper AT-ideal of hyper AT-algebras hyper AT-algebra”. “We study homomorphism of hyper AT-algebras which are a common generalization of AT-algebras.
Disinformation Detection using Passive Aggressive Algorithms

Disinformation, also as known as fake news, is overwhelming. Intentionally false information is widespread. However, the detection of fake news is remaining to be a challenge due to the nature of the complexity of languages. Linear regression algorithms are proven to be effective in many practices. In this paper, the Passive-Aggressive and the Multinomial Naive Bayes are studied for fake news detection that involves term frequency and inverse document frequency to vectorize news content. Our results show that the Passive-Aggressive is more efficient than Multinomial Naive Bayes by combing with term frequency and inverse document frequency and could be applied as a primary screen for complex disinformation detection practically.
A Compact Sequence Encoding Scheme for Online Human Activity Recognition in HRI Applications

Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.
Establishing the Syntactic Rules of the Kankana-ey Dialect using TensorFlow

Abstract Kankana-ey is a widely used dialect in the northern region of the Philippines. Unfortunately, there are documented studies on the syntactic rules of this dialect. This study explored the development of a corpus for the Kankana-ey dialect. Further, the corpus was then used to establish the syntactic rules of Kankana-ey. A Kankana-ey version of the bible, dictionaries, news articles, songs and various online resources were used to collect words for the corpus of the Kankana-ey dialect. These identified words were also tagged using the parts of speech tags of the Penn TreeBank. Using the corpus and TensorFlow, 320 Kankana-ey sentences were analysed to determine the syntactic rules. In addition, 80 sentences were used to test the accuracy of the identified rules. At the end of the study, the created corpus has 3,412 tagged Kankana-ey words, while the analysis of the syntactic rules resulted to 1,722 rules. Testing also showed a 60% accuracy of the syntactic rules. In conclusion, the high number of identified rules from the 320 sentences was due to multiple Kankana-ey words having different possible tags. This also resulted to the low accuracy of the syntactic rules.
On development of models and algorithms for automated metallographic measurement of visible metal slice grain sizes

Abstract The article proposes an approach to metallographic research based on solving the problem of semantic segmentation using a trained neural network classifier. To solve the problem of isolating grains in micrographs of longitudinal and transverse slices of metal, the neural network model U-Net was adapted. In order to obtain closed image contours, a post-processing algorithm was developed using the OpenCV open source computer vision library. The article describes the training of a neural network and the conversion of its results, as well as the comparative analysis of the histograms between the reference grain area distribution and the distribution obtained using the proposed algorithm.
Classification of elderly based on attitude for health promotive in Deli Serdang using machine learning approach

Abstract Health promotion is the process of enabling people to increase control over and to improve their health. Health promotion is a global issue. In the most city in Indonesia, health is improving. In this case, we collected the elderly data from Deli Serdang, North Sumatera, Indonesia. The aim of this study to classify the elderly dataset using Support Vector Machine and Logistic Regression as a machine learning approach. The result of this study indicated the SVM and Logistic Regression is available to train and test the dataset. The dominant sample in this study is female. The evaluation of the elderly dataset using two models of machine learning indicated there is a positive attitude with the age for health promotion.
Privacy-Preserving DBSCAN Clustering Algorithm Based on Negative Database

The negative database (NDB) is a new type of privacy protection and data security technology, which enhances the security of the data by storing the compressed form of the original data complement, thereby achieving the protection of privacy data. In practical applications, each record in the original database (DB) is usually transformed into a negative database to obtain negative database NDB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> to achieve the protection of privacy data. Then, use the classification and clustering methods on the negative database are used to mine and analyze the privacy data. The DBSCAN clustering algorithm is a classical density-based clustering algorithm, and the Euclidean distance formula is one of the most commonly used distance measurement formulas in the clustering algorithm, and the DBSCAN algorithm is of no exception. However, the current Euclidean distance measurement of DBSCAN clustering algorithm is based on the distance measurement of plaintext data, so it is impossible to cluster the privacy data transformed into negative database. In this paper, we introduce a DBSCAN clustering algorithm based on the Euclidean distance formula on a negative database, which is used to complete clustering research while protecting privacy data. The experimental result showed that our algorithm achieved high clustering accuracy and effectively protected the security of privacy data by using irreversible negative database. Therefore, the algorithm we designed is very effective and feasible.
Developing the information literacy of school students with the use of the technology of blended learning in biology classes

The article considers the ways of organizing lessons with the use of the technology of blended learning. Methods, forms and instruments needed for holding different classes with the 9th grade schoolchildren which ensure the development of information literacy of students within the framework of this technology are included. Lessons held according to the blended learning technology are described: “inverted class”, “laboratory rotation”, “station rotation”, “flexible model”. The advantages of each form of the lesson concerning the goal achievement are highlighted – the development of information literacy of students. Conclusions are confirmed by the experiment carried out.
Sound – [object] – dance

In this chapter, the author writes about the creative process behind her dance and sound collaborations that are mediated by interactive technology. The author argues that gesture-driven computer music often misses the aesthetic aspects of creating choreography in collaboration with dancers by lacking investigation into the process of contemporary dance composition. For this reason the author investigates fundamental principles of choreography on the basis of Rudolf Laban’s choreutics theory, and also the choreographic methods of contemporary choreographers such as William Forsythe and Wayne McGregor. In the initial experiment Locus, motion-sensing device Gametrak controllers were used as objects primarily to challenge the dancers to move beyond their habit during improvisations by restricting the size and shape of the kinesphere. The movements that are created result in a sound composition. In the later work The Music Room, the total condition of the piece – Gametrak controllers, the performance space and the sound triggered by the dancer – challenges the dancer as a physical enforcement of the restriction. Ultimately, the author draws out not only the technological development, but also the holistic collaborative compositional cycle with dancers as an interdisciplinary composition.
An Analysis of One-Roof Application Services Quality at the Faculty of Social and Political Sciences, University of North Sumatra

Abstract The University of North Sumatra is a government agency that organizes education, research and community service. It also includes efforts to enforce effective and efficient administrative governance. Integrated administration services in the world of collegeto serve student administrative affairs have been carried out by the Faculty of Social and Political Sciences, The University of North Sumatra through online services. The online-based service is called the One-Roof Application or abbreviated as ASA to facilitate the process of correspondence between students and the administration of the faculty in the orderly administration of the academic world which is provided under one roof of service. This study uses a questionnaire as one of the data collection tools, then the study used research respondents who used the one-roof application. Based on information received from the One-Stop Application Service Unit of the Faculty of Social and Political Sciences, that every day in the customer hall serves approximately 20 people using one roof with a range of service days in 1 month, Monday - Friday totaling 20 x 20 days = 400 population with a sample size of 196 people. This is based on the formulation of the creative and organic tables set out in the Regulation of the Minister of Administrative Reform and Bureaucratic Reform of the Republic of Indonesia Number 14 of 2017 Concerning Guidelines for the Preparation of Public Satisfaction Survey Public Service Provider Units. The results of the service satisfaction study are categorized GOOD.