Reviewerly/exHarmony huggingface dataset:
Capacity value estimation of random solar power through associative searching in a k‐d tree

The estimation of capacity values of unconventional sources like solar and wind is quite difficult due to their intermittent nature. Mainly reliability-based methods are generally followed to calculate the capacity values. Intermittent solar power cannot be clubbed directly in the process of reliability calculation. Solar power is generally taken as negative load in adequacy assessment and subtracted from the load on hourly basis. Random solar radiation creates redundant solar power generation. Bypassing multiple estimations of capacity values for the redundant solar power, an approach is made to construct a database considering the incremental added capacities with their resulting loss of load expectation. The database is portrayed in a multidimensional k-d tree to locate queried expected loss of load of solar power effectively using nearest neighbor search algorithm. The algorithm is providing the best match record as well as the associated information like the capacity values stored along with that record. A case study of 75 MW of conventional with 15 MW solar powers is considered for the validation of the proposed methodology. Capacity values corresponding to each reliability-based methods resulted from the database seem to be satisfactory in evaluating the capacity values of solar power. The applicability of the proposed method is extended through implementing on the IEEE RTS system.
A Brief Survey on Exploratory Search Systems

The definition of exploratory search is complex and multifarious. It is a process of seeking information to help the users who are unfamiliar to the domain of their search goal or have no idea how to achieve their goal. There exists several search systems which support exploratory search. In this rapidly developing field, there is a need to look at the achievements and tendencies in order to give a better angle to the future research works. This paper provides an overview of exploratory search systems and discusses the state of the art. We analyze these available systems based on features such as algorithms, result ranking, explanations, etc.
Novel Automatic Bank Check Recognition Based on Deep Learning

In spite of speed evolution of digital techniques, a huge systems continue to rely on the use of paper as the dominant medium. Bank checks are part too and still widely used all over the world for financial transactions. In this paper, we propose a new architecture for automatic bank check recognition based on deep learning. The proposed architecture involves four stages: segment regions of interest into individual digits, normalization, recognition of each character using a convolutional neural network classifier and syntactic verification. Experimental results are presented using multiple checks from Moroccan banks to show the importance of each module by analyzing the effect over the recognition accuracy. All the modules proposed are highly configurable and can be adapted to suit the characteristics of checks from different countries.
Windowed Fourier Transform

This chapter presents the time–frequencyTime–frequency analysis theory by introducing two variants of Fourier transformFourier transform , i.e., short-time Fourier transformShort-time Fourier transform and Gabor filtersGabor filters . The chapter first analyzes the limitation of FT in capturing time and spatial info from a signal or image. It then introduces a naïve windowed FT or STFT to overcome the limitation. A more powerful Gaussian windowed FT or Gabor transformGabor transform is then introduced to further improve FT’s capability. Difference between FT and windowed FT is discussed with illustrations. By understanding how STFT and Gabor filtersGabor filters work, readers are prepared for the more advanced and powerful contemporary wavelet theories.
Information Culture in the Structure of the Activity and the Individual of the Educator

Pedagogical activity is considered in the context of its cultural signifi cance, provides the author’s interpretation of pedagogical activities. Three main approaches to the study of structural and substantive organization of pedagogical activity-system-functional, cultural and competence are given. On the basis of systemic-functional approach comparative-comparative analysis of the structure and content of educational activities was carried out presented in the writings of some domestic authors. The article shows the relevance of studying professionally important qualities of a teacher, highlights the personality system, composable around professional-pedagogical orientation. Special attention is given to the quality of the article of the teacher as information culture. On the basis of the cultural approach the composition of professional-pedagogical culture and the place in the information culture was considered. The intrinsic features of the concept of “information culture of the teacher” are given, an attempt was made to designate its complex composition. The factors are highlighted contributing to the need for raising and resolving problems of formation of information culture of the teacher in higher educational practice.
Principle of PWC method application in ZEMAX

The mathematical forms of Seidel coefficients are different in ZEMAX and PWC method. They are bridged by the wavefront aberration coefficients. The relationship between the wavefront aberration coefficients and Seidel coefficients in PWC method is derived in detail. The basis of PWC method application in ZEMAX is established.
Cyber-Physical Network Mapping Attack Topology

This Cyber-Physical Network Mapping Attack Topology paradigm provides cyber analysts with appropriate background information to underpin efforts to provide accurate and comprehensive assessments in the development of cyber analytic products. In addition, this framework will assist in providing information regarding cyber threats, vulnerability and consequence analysis for the network assets that are being attacked by an adversary. The Cyber-Physical Mapping Network Topology will dramatically enhance the vulnerability and consequence analysis of cyber threats by improving the monitoring, detection, analysis, and mitigation capabilities in responding to cyber incidents in the United States. Network systems that control the critical infrastructure in most case operate constantly and the impact of downtime from a cyber exploit of the control systems that potentially could endanger public health and safety can range from inconvenient to catastrophic.
A pixel-based color transfer system to recolor nighttime imagery

Most state-of-the-art techniques focus primarily on daytime surveillance, and limited research has been performed on nighttime monitoring. Surveillance is often more important in darker environments since many activities of interest often occur at night. But, nighttime imagery presents its challenges: they are mostly monochrome and very noisy. Night vision systems are also affected in the presence of a bright light or glare off a shiny surface. Color imagery has several benefits over grayscale imagery. The human eye can discriminate broad spectrum of colors but is limited to about 100 shades of gray. Also, color drives visual attention and aids in better understanding of the scene. Moreover, context information of the scene affects the way humans distinguish and recognize things. The essential step of a coloring process is the choice of an appropriate color image model and color mapping scheme. To enhance relevant information of nighttime images, a color mapping or color transfer technique is employed. The paper proposes a robust pixel-based color transfer architecture that maps the color characteristics of the daytime images to the nighttime images. The architecture is also capable of compensating for image registration issues encountered during acquisition. A visual analysis of the results demonstrate that the proposed method performs better in comparison to the state-of-the-art methods and is robust to different imaging sensors.
The Importance of Bell States in Quantum Computing

The rise of Quantum Computing in the industry begets a closer analysis of the topic and its methods. Quantum computers are not limited to the two states 0 and 1; rather, they encode information as quantum bits, or qubits, which can exist in superposition and can be entangled. Understanding the entanglement of qubits requires an understanding of the Bell states. The first Bell state(Φ +), has been widely studied and is prominent in quantum computing literature. However, the other three (Φ −, Ψ+, Ψ −), are relatively untouched by researchers since Quantum Computers have only recently been available for research that could test them. Years of theoretical research has not been backed by experiments due to the lack of available technology. Now with its availability from companies like IBM we have the resources to test and work with all four Bell states, further developing Quantum Computing as a strong pillar in computing.
A Development Research of Teachers in Private Higher Vocational Colleges

Private colleges and universities have made great contributions to solving the problem of insufficient educational resources. However, private colleges and universities have encountered many problems in the process of development, and the development of teaching staff is a prominent problem. Based on the author’s work experience, the paper analyzes the problems existing in the development of the teaching staff of the private higher vocational colleges, and puts forward corresponding countermeasures and suggestions, which will play a guiding role in the development of the teachers of the private higher vocational colleges.
Single-shot 3D shape reconstruction using multi-wavelength pattern projection

This paper presents an approach for single-shot 3D shape reconstruction using a multi-wavelength array projector and a stereo-vision setup of two multispectral snapshot cameras. Thus, a sequence of six to eight aperiodic fringe patterns can be simultaneously projected at different wavelengths by the developed array projector and captured by the multispectral snapshot cameras. For the calculation of 3D point clouds, a computational procedure for pattern extraction from single multispectral images, denoising of multispectral image data, and stereo matching is developed. In addition, a proof-of-concept is provided with experimental measurement results, showing the validity and potential of the proposed approach.
The Pattern of Crime Occurrence in South Korea Using Watson Analytics

Crime has become a major concern in South Korea. OECD Better Life Index indicates that South Korea ranked 26th among 38 Organization for Economic Cooperation and Development (OECD) countries. This paper explores crime patterns in South Korea by using IBM's Watson Analytics. Identifying patterns can contribute to provide more efficient solutions to reducing crimes. Moreover, this information can be provided to citizens to help them avoid dangerous situations.
MMOU-AR: Multimodal Obtrusive and Unobtrusive Activity Recognition Through Supervised Ontology-Based Reasoning

The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major healthcare challenges. To address these unmet healthcare challenges, monitoring and Activity Recognition (AR) are considered as a subtask in pervasive computing and context-aware systems. Innumerable interdisciplinary applications exist, underpinning the obtrusive sensory data using the revolutionary digital technologies for the acquisition, transformation, and fusion of recognized activities. However, little importance is given by the research community to make the use of non-wearables i.e. unobtrusive sensing technologies. The physical state of human pervasively in daily living for AR can be seamlessly presented by acquiring health-related information by using unobtrusive sensing technologies to enable long-term health monitoring without violating an individual’s privacy. This paper aims to propose and provide supervised recognition of Activities of Daily Livings (ADLs) by observing unobtrusive sensor events using statistical reasoning. Furthermore, it also investigates their semantic correlations by defining semantic constraints with the support of ontological reasoning. Extensive experiments were performed with real-world dataset shared by the University of Jaén Ambient Intelligence (UJAmI) Smart Lab in order to recognize the human activities in the smart environment. The evaluations show that the accuracy of the supervised method (87%) is comparable to the one, state of the art semantic approach (91%).
Constructive Cascade Learning Algorithm for Fully Connected Networks

The Fully Connected Cascade Networks (FCCN) were originally proposed along with the Cascade Correlation (CasCor) learning algorithm that having three main advantages over the Multilayer Perceptron (MLP): the structure of the network could be determined dynamically; they were more powerful for complex feature representation; the training was efficient by optimizing newly added neuron only in every stage. However, at the same time, they were criticized that the freezing strategy usually resulted in an overlarge network with the architecture much deeper than necessary. To overcome the disadvantage, in this paper, a new hybrid constructive learning (HCL) algorithm is proposed to build a FCCN as compact as possible. The proposed HCL algorithm is compared with the CasCor algorithm and some other algorithms on several popular regression benchmarks.
Research on the Competency Models of the Foreign Language Teachers in Colleges and Universities

The teaching competence of the foreign language teachers in colleges and universities refers to the trait groups of the foreign language teachers in colleges to accomplish the teaching goals effectively, including knowledge, abilities, motivations, attitudes, values and personal characteristics. The researches on the teaching competency of the foreign language teachers in colleges and universities in China are still in its infancy. In recent years, there have been many achievements in the studies of the competence of the foreign language teachers in colleges and universities, but they are basically the common or general competence of the foreign language teachers in colleges and universities, and the achievements in the study of the teaching competence of the foreign language teachers in colleges and universities are seldom seen.
A Study on the Performance Evaluation of Machine Learning Models for Phoneme Classification

This paper provides a comparative performance analysis of both shallow and deep machine learning classifiers for speech recognition task using frame-level phoneme classification. Phoneme recognition is still a fundamental and equally crucial initial step toward automatic speech recognition (ASR) systems. Often conventional classifiers perform exceptionally well on domain-specific ASR systems having a limited set of vocabulary and training data in contrast to deep learning approaches. It is thus imperative to evaluate performance of a system using deep artificial networks in terms of correctly recognizing atomic speech units, i.e., phonemes in this case with conventional state-of-the-art machine learning classifiers. Two deep learning models - DNN and LSTM with multiple configuration architectures by varying the number of layers and the number of neurons in each layer on the OLLO speech corpora along with six shallow machine learning classifiers for Filterbank acoustic features are thoroughly studied.
Weighted Feature Pooling Network in Template-Based Recognition

Many computer vision tasks are template-based learning tasks in which multiple instances of a specific concept (e.g. multiple images of a subject’s face) are available at once to the learning algorithm. The template structure of the input data provides an opportunity for generating a robust and discriminative unified template-level representation that effectively exploits the inherent diversity of feature-level information across instances within a template. In contrast to other feature aggregation methods, we propose a new technique to dynamically predict weights that consider factors such as noise and redundancy in assessing the importance of image-level features and use those weights to appropriately aggregate the features into a single template-level representation. We present extensive experimental results on the MNIST, CIFAR10, UCF101, IJB-A, IJB-B, and Janus CS4 datasets to show that the new technique outperforms statistical feature pooling methods as well as other neural-network-based aggregation mechanisms on a broad set of tasks.
M2U2: Multifactor Mobile Based Unique User Authentication Mechanism

Identifying and validating the user is a major concern in a heterogeneous network domain. Conventional algorithms and mechanisms could authenticate the users/messages, but with certain vulnerabilities. This paper analyzes the vulnerabilities and downsides of some present-day authentication mechanisms that are being used and proposes an authentication mechanism that cannot be bypassed easily. The proposed mechanism incorporates multi-factor authentication and validates the user based on a username, a password, a security question which is sent to the user via Short-Message-Service (SMS), and a security pin (if necessary). List of security questions posed to the end-user makes the proposed mechanism stronger and protect the system from security breaches by an unknown user to guess the answer.
Greedy Partition Distance Under Stochastic Models - Analytic Results

Gene partitioning is a very common task in genomics, based on several criteria such as gene function, homology, interactions, and more. Given two such partitions, a metric to compare them is called for. One such metric is based on multi symmetric difference and elements are removed from both partitions until identity is reached. While such a task can be done accurately by a maximum weight bipartite matching, in common settings in comparative genomics, the standard algorithm to solve this problem might become impractical. In previous works we have studied the universal pacemaker (UPM) where genes are clustered according to mutation rate correlation, and suggested a very fast and greedy procedure for comparing partitions. This procedure is guaranteed to provide a poor approximation ratio of 1/2 under arbitrary inputs. In this work we give a probabilistic analysis of this procedure under a common and natural stochastic environment. We show that under mild size requirements, and a sound model assumption, this procedure returns the correct result with high probability. Furthermore, we show that in the context of the UPM, this natural requirement holds automatically, rendering statistical consistency of this fast greedy procedure. We also discuss the application of this procedure in the comparative genomics rudimentary task of gene orthology where such a solution is imperative.
Building a web-based application for language resources in Indonesia

This paper presents our work on building a web-based application for language resources development and management for Indonesian languages. This web-based application is developed using PHP, following the model-view-controller (MVC) architectural pattern. The features of our web-based application are designed to enable users in utilizing language resources and to facilitate team members in developing and managing language resources collaboratively. Some features are accessible to all types of users, but some others are accessible only to a specific type of users. The application is also intended to cover all Indonesian languages. As a result, corpus files should be encoded in UTF-8 to enable the application analyze a wide range of language characters. At the moment, the implemented corpora are Indonesian, Javanese and Sundanese. The application is designed to bear future improvements.
Securing IoT Data with Pervasive Encryption

Securing the volumes of data created by the Internet of Things is essential as IoT devices are increasingly used in health care, smarter cities, transportation systems, and more. The privacy of personal data, the safety of users, and the security of our infrastructure depend on it. There are already billions of IoT devices in use, and the numbers will continue to grow. Of the 13 billion data records breached across IT systems from 2013 through 2018, only 4% were encrypted ( https://breachlevelindex.com/ ). Pervasive encryption can protect data in transit and data at rest at multiple layers of the computing environment. It is a crucial strategy to enhance privacy and security in our increasingly connected world.
Development of a Rigidity and Volume Control Module Using a Balloon Filled with Dilatant Fluid

In this paper, a rigidity and volume-changing device is proposed for presenting the shape and rigidity of a virtual object in the real world. This device comprises a balloon filled with a dilatant fluid and two syringes connected to electric linear actuators. The electric linear actuators enables controlling the volume of the balloon by allowing the user to change the amount of dilatant fluid in the balloon. Moreover, when the amount of fluid (water) contained in the balloon changes, the rigidity of the balloon changes in proportion to the composition of the fluid (ratio of the amount of water and starch). In this study, a prototype system was developed, and the performance of the system was evaluated by measuring the rigidity of the balloon at different volumes.
How to Create a Pedagogic Conversational Agent for Teaching Computer Science

Pedagogic conversational agents are computer applications that can interact with students in natural language. They have been used with satisfactory results on the instruction of several domains. The authors believe that they could also be useful for the instruction of computer science programming. Therefore, in this chapter, the MEDIE methodology is described to explain how to create an agent to teach programming to primary education children and develop their computational thinking. The main steps are to communicate with the teacher team, to validate the interface, and to validate the functionality, practical sessions, and evaluation. The first two steps are covered in this chapter.
Monitoring the Formation of Competences of Students of Economic Special Field As a Result of Work Placement

The article summarizes the experience of organizing, conducting and evaluating the results of educational and teaching practice. Practice is a tool for intermediate verifi cation of students’ readiness for independent work, allows the student to assess his level in achieving certain competencies and set himself the task of working on shortcomings and further progress in the educational process. The authors of the article conducted a sociological study, the purpose of which was to monitor students’ assessment of the degree of competence development as a result of undergoing educational practice at the undergraduate and pedagogical practice in the magistracy. The results of a study conducted among students on the eff ectiveness of educational and pedagogical practices in the acquisition of competences and skills of research and teaching work are presented in the form of comparative tables and diagrams. The article analyzes the problems faced by students during practical training. Such monitoring allowed to reveal the bottlenecks of the organization of the practice, the directions of the methodical support of students, to improve the tasks for the practice.