Reviewerly/exHarmony huggingface dataset:
Experiments about the Performance of GPU Oversubscription Strategy

GPU is gaining popularity in various applications due to its preeminent parallel computing power. However, GPU cloud is still far from mature, especially in resource supplying mode to guarantee possible highest yield. These deficiencies significantly impose restrictions on the potential markets of GPU. This paper introduces resource oversubscription and conducts experiments to evaluate the performance of oversubscribed GPU based on the cloud-style workloads with different features. The results of our experiments demonstrate that GPU oversubscription performs excellent improvement on economic benefits compared to original GPU supplying mode and a acceptable safety in QoS control. It is necessary and beneficial for datacenters to employ GPU oversubscription technology.
Logic of Behaviors and Somaesthetics Driven User Experience Research: Design and Innovation in Air Energy Products

Combining the theory of logic of behaviors and somaesthetics, this article explores their application in the field of user experience and the internal relationship between the two. A product user experience design process based on logic of behaviors and logic of things is proposed, and a study of the mechanism in user behaviors from three aspects of somaesthetics (the body as the aesthetic object, the body as the aesthetic subject, and the aesthetic embodiment) is presented, which brings up a design model of embodied user experience integrating the body, behaviors and aesthetics. Under the guidance of this theory, this article studies body’s bathing behaviors and its user experience. In order to verify effectiveness of the design model, the new generation of air energy bathing product is taken as an example to design and innovate the product user experience.
An Automatic Identification of Team Roles in Forums

The success of a collaborative learning experience and the coordination of the work depend on appropiate performance of team roles. This paper introduces an intelligent system able of recognizing team roles manifested by students who interact in a virtual forum. The system has two kind of software agents: a group agent and personal agents. Nine Bayesian networks were specially developed to detect automatically the team roles manifested during the collaboration dynamic. The system was validated through collaborative experiences in forums with university students. The stored interactions were processed and the roles played by each group member were identified. In order to evaluate the effectiveness in the automatic recognition of team roles, the roles identified by Bayesian networks were compared with roles allocated manually by an educational psychologist. This intelligent system will faciliate to teachers the monitoring of work dynamics in forums.
A Low-Cost Sun Tracking System Based on Image Processing and a Robot Arm

Concentrated photovoltaics (CPV) envisages the use of optical systems built of lenses and/or mirrors to focus sunrays in highly efficient cells, thus reducing the amount of active material while increasing efficiency and energy production. When high concentration is desired, precise tracking systems must be implemented, as only direct radiation will contribute to energy conversion. This requires the development of tracking mechanisms that be compatible with the low angular displacements allowed. The higher the desired concentration, the higher the complexity and cost of typical tracking systems. This paper proposes a low-cost tracking mechanism, built by a webcam on a robotic arm, with distinct tracking modes implemented. The architecture and hardware and software components of the system are described, and its performance is assessed in clear sky and partial cloudy days. The system performance is more affected by the precision of the stepper motors used in the drive of the robotic arm, but a viable tracker, that can control large tracking structures, is built and demonstrated.
Utilization of Resources for Effective Teaching and Learning of Science

This paper examined the resource materials for teaching and learning of science ­ .  The paper also looks at various types of resources available to teachers in schools to pact knowledge. Different definitions of resource material were mentioned for example resource materials are potent tools which can be used to effectively communicate science, while enhancing the learning experiences of learners and achieving the lesson objective. Some of these resources include material, human and physical resources among others. Example of resource materials include motion pictures, television, model, while human resources include resource person or resource individual and physical resources include an abandoned farm, a wood of forest field and Printed materials like Learning Activity Package (LAP) etc. The advantages of these resources include making learning more enjoyable, meaningful and permanent. In the absence of these resources, teachers are advised to improvise. It was recommended among others that teaching and learning of science cannot successfully be implemented without adequate science resources or facilities. There is need for the government, parents, and voluntary organizations to join hands in procuring necessary facilities or resources in schools.
Extrication of Apriori Algorithm using Association Rules on Medical Data sets

During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The APRIORI growth algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or APRIORI tree), and frequent item set is mining by using of APRIORI tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates item sets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (APRIORI-Growth, COFI-Tree, CT-PRO) based upon the APRIORI- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on MEDICAL data to generate rules and patterns using Frequent Pattern (APRIORI)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on MEDICAL data.
A Method of Mesh Simplification for Drone 3D Modeling with Architectural Feature Extraction

This paper proposes a method of mesh simplification for 3D terrain or city models generated photogrammetrically from drone captured images, enabled by the ability of extracting the architectural features. Compare to traditional geometric computational method, the proposed method recognizes and processes the features from the architectural perspectives. In addition, the workflow also allows exporting the simplified models and geometric features to open platforms, e.g. OpenStreetMap, for practical usages in site analysis, city generation, and contributing to the open data communities.
Compositional Techniques for Sound Design

This chapter explores compositional theory, mining it for techniques that can be applied to the process of conceptual sound design. The lines often blur between composer and sound designer, and examples of music composition exist on a spectrum. The spectrum is wide and can include “note-for-note” notation for live musicians, sequencing for virtual or electronic instruments, plundering and reconstructive sampling, musique concrete, aleatory and algorithmic music or any combination of these. Whatever the technique for the creation of organized sound—they all share certain fundamental principles that can be applied to the sound design process, whether the process of design is for film, theater, installation work or games.
Classifying the Level of Economic of Citizen Using Fuzzy Multiple Attribute Decision Making

Economics is the first supply for the survival of a country, especially in the development, development and progress. The more developed a country is the better economic growth. Indonesia including the user databases on the economy, especially in the banking sector and the government. Government as the manager of the country's economy in order to make extra efforts of the people and citizens can get considerable economic assistance through various operations conducted by the government such as the division of poor rice and BPJS card. BPJS stands for Badan Penyelenggara Jaminan Sosial (Social Insurance Administration Organization). By doing classifying economic levels using Fuzzy Multiple Attribute Decision Making (FMADM) methods (Simple Additive Weighting) meant that applications created can be used as a tool to suppress errors and improve accuracy by minimizing the possibility of such a wrong target or targets. The application uses the input in the form of data that has a high level of security to be forged such as: proof of payment of electricity bills, vehicle tax, and property tax. Data from the family card to input the number of people staying. The results are sorted according emerged from the lowest to the highest. The calculations were already system and by calculation that has been designed is expected to work as expected.
On the Optimal Number of Computational Resources in MapReduce

Big data computing in the cloud needs faster processing and better resource provisioning. MapReduce is the framework for computing large scale datasets in cloud environments. Optimization of resource requirement for each job to satisfy a specific objective in MapReduce is an open problem. Many factors, e.g., system side information and requirements of each client must be considered to estimate the appropriate amount of resources. This paper presents a mathematical model for the optimal number of map tasks in MapReduce resource provisioning. This model is to estimate the optimal number of the mappers based on the resource specification and the size of the dataset.
Hardware Designs for Histogram of Oriented Gradients in Pedestrian Detection: A Survey

Feature detection, narrowed down to pedestrian detection, is an imperative domain where automated applications such as Automatic Driver Support Systems, Robotics and similar image vision and machine vision technologies. Histogram of Oriented Gradients (HOG) is a robust, scalable and efficient feature extraction method that works on luminance gradients among neighboring pixels. The extracted feature is normalized and classified through support vector machines (SVM). Improvements in the design through approximate computations, parallelism and pipelining applied to SVM classification and histogram generation, Parallel implementation of entire HOG and exploration of possible applications of the algorithm. This paper cites the software improvements of HOG and hardware implementations targeted on FPGA for variations of HOG.
Horizontal and Vertical Scalability of Machine Learning Methods

The main stages of Machine Learning Pipelines are considered in the paper, such as: train data collection and storage, training and scoring. The effect of the Big Data phenomenon on each of the stages is discussed. Different approaches to efficient organization of computation are on each of the stage are evaluated. In the first part of the paper we introduce the notion of horizontal and vertical scalability together with corresponding cons and pros. We consider some limitations of scaling, such as Amdahl's law. In the second part of the paper we consider scalability of data storage routines. First we discuss relational databases and scalability limitations related to ACID guarantees, which such database satisfy. Then we consider horizontally scalable non-relational databases, so called NoSQL databases. We formulate CAP-theorem as a fundamental limitation of horizontally scalable databases. The third part of the paper is dedicated to scalability of computation based on the MapReduce programming model. We discuss some implementations of this programming model, such as Hadoop and Spark together with some basic principles which they are based on. In the fourth part of the article we consider various approaches towards scaling of Machine Learning methods. We give the general statement of Machine Learning problem. Then we show how MapReduce programming model can be applied for horizontal scaling of Machine Learning methods on the example of Bayessian pattern recognition procedure. On the example of Deep Neural Networks we discuss Machine Learning methods which are not horizontally scalable. Then we consider some approaches towards vertical scaling of such methods based on GPU’s and the TensorFlow programming model.
Multi-keyword Ranked Fuzzy Keyword Search Over Encrypted Cloud Data

The cloud servers help in sharing data among the people within an organization or outside it. The outsourcing of one’s data over the Internet has become much easier with the help of the newer technology, the cloud servers. The cloud servers help in sharing data among the people within an organization or outside it. As security over the cloud has become a major challenge and also a very important aspect, providing security for the data that is being stored has become crucial. Hence, encryption of data that would be outsourced has become a very prominent talk recently. Similarly, fetching of the documents over the cloud should give faster and better results which would eventually give a better performance result. The number of keywords defined can be numerous, and hence the term multi-keyword.
Matrix Morphology with Extremum Principle

The fundamental operations of mathematical morphology are dilation and erosion. In previous works, these operations have been generalised in a discrete setting to work with fields of symmetric matrices, and also corresponding methods based on partial differential equations have been constructed. However, the existing methods for dilation and erosion in the matrix-valued setting are not overall satisfying. By construction they may violate a discrete extremum principle, which means that results may leave the convex hull of the matrices that participate in the computation. This may not be desirable from the theoretical point of view, as the corresponding property is fundamental for discrete and continuous-scale formulations of dilation and erosion in the scalar setting. Moreover, if such a principle could be established in the matrix-valued framework, this would help to make computed solutions more interpretable. In our paper we address this issue. We show how to construct a method for matrix-valued morphological dilation and erosion that satisfies a discrete extremum principle. We validate the construction by showing experimental results on synthetic data as well as colour images, as the latter can be cast as fields of symmetric matrices.
Causes of Energy Wastage in Cloud Data Centre Servers : A Survey

Datacenters are becoming the indispensable infrastructure for supporting the services offered by cloud computing. Unfortunately, datacenters consume a lot of energy, which currently stands at 3% of global electrical energy consumption. Consequently, cloud service providers (CSP) experience high operating costs (in terms of electricity bills), which is, in turn, passed to the cloud users. In addition, there is an increased emission of carbon dioxide to the environment. Before one embarks on addressing the problem of energy wastage in a datacenter, it is important to understand the causes of energy wastage in datacenter servers. In this paper, we elaborate on the concept of cloud computing and virtualization. Later, we present a survey of the main causes of energy wastage in datacenter servers as well as proposed solutions to address the problem.
Maritime Event Recognition

In present times, where the shipping industry has an important role in the global trade, there is an increased need for safer and more secure shipping. Maritime Monitoring systems allow the real-time detection of dangerous, suspicious and illegal vessel activities. We demonstrate such a system, that uses the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. All the activities we demonstrate were detected using real-world maritime data and contextual geographical information.
Team Activities Measurement Method for Open Source Software Development Using the Gini Coefficient

In recent years, open source software (OSS) has made remarkable contributions as social infrastructure. The model of the OSS development (OSSD) team, as seen in the case of GitHub, is becoming the de facto standard. The OSSD team develops source codes for realizing requirements and, further, test codes for testing the source codes. The present study measures the team activity in OSSD, and it includes aspects of software testing activity. Our previous research revealed the characteristic of the OSSD team, which was that the team consists of a small number of enthusiastic contributors (core members) and a large number of small contributors. This characteristic has some inequalities. The Gini coefficient is a well-known economic index used to measure inequality. This study proposes a method to estimate and use an Effort Person-Months metric as an object of the Gini coefficient for software development. In this method, the difference in team activity can be detected more precisely than by the Gini coefficient obtained from the number of commits and the modification amount. We believe that this method covers detecting software testing activities precisely.
Lessons Learned from Challenging Data Science Case Studies

In this chapter, we revisit the conclusions and lessons learned of the chapters presented in Part II of this book and analyze them systematically. The goal of the chapter is threefold: firstly, it serves as a directory to the individual chapters, allowing readers to identify which chapters to focus on when they are interested either in a certain stage of the knowledge discovery process or in a certain data science method or application area. Secondly, the chapter serves as a digested, systematic summary of data science lessons that are relevant for data science practitioners. And lastly, we reflect on the perceptions of a broader public toward the methods and tools that we covered in this book and dare to give an outlook toward the future developments that will be influenced by them.
Nonparametric geometric outlier detection

Abstract Outlier detection is a major topic in robust statistics due to the high practical significance of anomalous observations. Many existing methods, however, either are parametric or cease to perform well when the data are far from linearly structured. In this paper, we propose a quantity, Delaunay outlyingness, that is a nonparametric outlyingness score applicable to data with complicated structure. The approach is based on a well‐known triangulation of the sample, which seems to reflect the sparsity of the pointset to different directions in a useful way. We derive results on the asymptotic behavior of Delaunay outlyingness in case of a sufficiently simple set of observations. Simulations and an application to empirical data are also discussed.
An Integrated UAV Platform for Real-Time and Efficient Environmental Monitoring

An important part of environmental monitoring is the collection of meteorological data. In this paper, we develop an integrated data acquisition and transmission platform utilizing unmanned aerial vehicles (UAVs) with an intelligent path planning algorithm to achieve efficient and accurate meteorological data collection in real-time. We adopt the improved traveling salesman problem model to represent the path planning problem. Based on the model, we propose an improved simulated annealing genetic algorithm (ISAGA) to solve the path planning problem. Our proposed ISAGA is able to overcome the deficiencies of the traditional genetic algorithm and simulated annealing algorithm. In addition, we design and implement a mobile application integrated with the path planning algorithm to control UAVs and conduct data exchange to the cloud. Our evaluation results demonstrate that data can be collected and transmitted more efficiently via selecting better paths.
Characterizing Organizational Micro-climates in Structural Groups

We use text to characterize micro-climates in a specific organizational context. We use Louvain clustering to create trees, and identify communities and smaller groups, which we call leaves. In comparing these structural groups, we see that most structural groups within this organization share locations and organizational functions. Our analyses show that location, gender, and minority status dominance tend to increase the strength of the textual distinction of the found structural groups.
Mathematical Expression Extraction from Unstructured Plain Text

Mathematical expressions are often found embedded inline with unstructured plain text in the web and documents. They can vary from numbers and variable names to average-level mathematical expressions. Traditional rule-based techniques for mathematical expression extraction do not scale well across a wide range of expression types, and are less robust for expressions with slight typos and lexical ambiguities. This research employs sequential, as well as deep learning classifiers to identify mathematical expressions in a given unstructured text. We compare CRF, LSTM, Bi-LSTM with word embeddings, and Bi-LSTM with word and character embeddings. These were trained with a dataset containing 102K tokens and 9K mathematical expressions. Given the relatively small dataset, the CRF model out-performed RNN models.
The Competence of Bachelor Graduates of the High Technical School as the Basis of Professional and Moral Stability of a Future Specialist

Abstract The article analyzes the General cultural and professional competences of future specialists of the higher technical school, considered as the basis of professional and moral stability of students. The essence of professional and moral stability is revealed as an integrative quality and internal characteristic of professionalism, aimed at self-realization of the personality of the future specialist in professional activity. Professional culture is studied from the point of view of axiological component as a set of stable values in the sphere of future professional activity with their student’s personally significant objectification. Professional and moral stability is analyzed as a state, productive process and the result of mastering and creating professional and moral values. The main common professionally significant qualities of personality and competence of future specialists, which form the basis of their professional and moral stability, are defined.
A Lightweight 1.16 pJ/bit Processor for the Authenticated Encryption Scheme KetjeSR

This paper presents the results of the first ASIC implementation of the authenticated encryption scheme KetjeSR. The design covers the encryption and decryption operation in combination with a handshake protocol for the data transfer. The chip implementation was done in a TSMC 90nm GUTM process. The encryption/decryption module has an area footprint of 12.2kGE. The processor reaches an end-to-end throughput of 2.08 Gbps when running at a clock frequency of 130 MHz. The design was further optimized for low power and consumes 2.421 mW. The optimization is based on the reuse of the permutation function in combination with extensive pipelining. In terms of energy, the encryption operation costs 1.16 pJ/bit.