Reviewerly/exHarmony huggingface dataset:
Matching algorithm performance analysis for autocalibration method of stereo vision

Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
Perbandingan Algoritma Naive Bayes dan Naive Bayes Berbasis PSO untuk Analisis Kredit pada PT. BPR Syariah Paokmotong

According to Law No.21 of 2008, it is stated that Islamic banks are business entities that collect funds from the public in the form of deposits and channel to the public in the form of loans and / or other forms in order to improve people's lives. Credit is the provision of money or bills that can be equated with it, based on a loan and loan agreement between the bank and another party which requires the borrower to repay the debt after a certain period of time with interest (Banking Law No.10 of 1998). In analyzing a credit, sometimes an analysis performs an inaccurate analysis, so there are some customers who are less able to pay credit installments, resulting in substandard loans and even defaults. From the above problems, researchers conducted a credit analysis using computerized techniques by utilizing RapidMiner software in processing data. The right data processing technique to use is classification. One method of classification of data mining is the Naive Bayes algorithm. The researcher used weighting by applying Particle Swarm Optimization (PSO) for attribute selection to improve the accuracy of Naive Bayes. After testing with the Naive Bayes algorithm model of 71.00%, the Naive Bayes algorithm based on particle swarm optimization results in a higher accuracy value of 88.51% compared to the Naive Bayes algorithm model. From these results the difference between the two models is 17.51%. DOI : 10.29408/jit.v2i2.1460
Reimagining Relativity - Transitioning the physical body into a virtual inhabitant

This paper explores the ideas and mechanics through a case study which generated a reimagined means of inhabiting a speculative immersive environment. Currently, many users reside within virtual environments for their own leisure, work, or any other reason desired from short amounts of time to extreme lengths. This paper shows the generation directly relative to the inhabitant, where gravity, orientation, scale, and locomotion is completely dynamic. Details within this paper experiment with the laws and bounds of the virtual space within a real-time game engine where reimagining the way one inhabits space compared to current norms of real-world inhabitation is possible with creativity and applied knowledge. Escher's lithograph of Relativity is the driving concept explored within this paper beginning with creating gravitational pulls in multiple directions within the immersive virtual reality environment to accommodate various sources of gravity. The result of the case study demonstrated the generation of new virtual relativity laws reimagining how the virtual space is inhabited, in short, omnidirectional flying, gravitation defined by the inhabitant to geometry relationship, controlled local scaling, and populating space with multiple inhabitants in a unique manner.
От классических интертипных отношений к биполярным признакам типов и обратно

A comparative analysis of two didactic directions of representation of the mathematical part of socionic theory has been carried out. In one case, the classic model of intertype relations, based on the representation of types of information metabolism by their models A, is first considered, and then bipolar traits of types are introduced. In another case, an attempt to obtain a classic table of intertype relations based on four-position codes, in which the types are assigned to the poles of basic bipolar traits, is made. Herewith, the intertype relation operators are defined by 4?4 matrices. The study of the second direction is continued. New opportunities to choose a suitable basis in which types are represented are shown. The “semi-Jungian” basis of bipolar traits is analyzed in detail.
Universities Minister visits campus

Maths, artificial intelligence, robotics and fluid dynamics were on the agenda when Universities Minister Chris Skidmore visited the University.
Feature Selection using k-Medoid Algorithm for Categorization of Hadith Translation in English

The problem of document classification is the number of features that are very large. the number of features depends on the number of terms or vocabulary used. Obviously, for every document, it contains only a small number of words in a vocabulary. So that will cause the number of elements zero. Therefore, a method is proposed to select some features that can represent all features. the method used is to cluster the vocabulary. representatives of each cluster of clustered results are used as a feature for each document in the categorization process. the categorization process is done by the k-Neirest Neighbor (k-NN) and Nearest Centroid (NC) algorithms. The data used is the translation of English hadith. with this method, it is expected that computation time will be faster and categorization result will be better (accuracy more precise).
GPU paralleled transformation and quantization for wavelet-based bitplane coding of multiresolution meshes

Fast mesh compression is becoming a requisite in several applications such as medical imaging and video games. Graphics Processing Units (GPUs) are recently becoming massively parallel devices for Single Instruction, Multiple Data (SIMD) computing, addressing hence greater implementation challenges. Transformation and Quantization (TQ) is considered the second highest workload part of the wavelet-based mesh coding. Therefore, its acceleration will further improve the overall processing speed of the coding. In this paper, an OpenCL (Open Computing Language) acceleration of TQ is proposed. The Butterfly Wavelet Transform (BWT) based on the unlifted scheme is adopted in the transformation method while the embedded deadzone quantization is employed for the wavelet quantization. A chunk rearrangement process is applied for the computation of the neighborhood information needed for the Butterfly subdivision stencils. Accordingly, every chunk proceeds independently the prediction of the wavelet coefficients and their quantization. The key insights behind the proposed TQ method on GPU are a smart memory management and an efficient memory data mapping. Extensive experimental assessments demonstrate the effectiveness of our GPU implementation in terms of memory and runtime costs while preserving the rate distortion performance of the state-ofthe-art Bitplane coder.
Wavelet domain residual proximity search for image detail enhancement

In this paper, a wavelet domain based algorithm is proposed for the image detail enhancement. The proposed algorithm works on the principle of residual proximity measure between the image and it's residual. The detail features of an image are enhanced by using a pixel and patch-based fast search mechanism. The unique properties of the stationary wavelet transform such as redundancy and approximate scale invariance are utilized along with the fast patchbased search mechanism. The proposed model works well for natural images. By incorporating the wavelet analysis along with the residual structure similarity, we are able to mine the fine detail features required for the image enhancement. The proposed algorithm is compared with benchmark algorithms. The experimental analysis is carried out to show the efficacy of the proposed algorithm. The visual, as well as quantitative results show that proposed algorithm is on par with the other detail enhancement algorithms.
THE USE OF YOUTUBE MEDIA THROUGH GROUP DISCUSSION IN TEACHING SPEAKING

Possessing adequate English speaking ability is required for senior high school students in Indonesia as shown with the minimum passing criteria of 75 in the National Curriculum (2013). However, the teaching techniques used by teachers are commonly unattractive and monotonous, which therefore affects students’ opportunity to enhance their speaking ability. This study aimed at finding out whether the use of YouTube videos through group discussion assisted students to improve their speaking skills (pronunciation, grammar, vocabulary, fluency and comprehension). The subject of this study was second grade students of two classes of SMA Inshafuddin Banda Aceh, who were chosen by using random sampling technique. The method used in this study was quantitative method with experimental design. The results of this study showed that using YouTube videos through discussion group assisted the students to improve their speaking skills (p<0.05), with the most significant improved skill was comprehension. It is, therefore, suggested that teachers consider using YouTube videos in their teaching techniques in an attempt to attract students’ motivation to improve their speaking ability.
IMPLEMENTATION OF EXTENSIVE READING THROUGH A DIGITAL INSTRUCTIONAL MEDIA

This study explored the implementation of extensive reading through a digital instructional media. In this case, it was Padlet. Moreover, the students’ difficulties in conducting the extensive reading through Padlet was investigated. The extensive reading implemented in this research referred to the ten principles of the extensive reading programme proposed by Day and Bamford (2002). This research was conducted at a private university in West Java by applying a case study and involving thirty college students. Moreover, observations and questionnaire were used as data collection techniques. The findings show that students are excited during the extensive reading processes by using their smart phones because they are allowed to select reading material freely. During the extensive reading process, they practiced recalling the text they had read by using Padlet application. Having implementing the extensive reading through Padlet, they feel that extensive reading has made them accustomed to reading everyday subconsciously. Thus, they are able to comprehend the reading material more easily. It can be seen from their developement in stating the generic structure of certain genre in the recall session. However, some students got troubles during the extensive reading processes, such as less capablity of digital literacy and low motivation.
The group of automorphisms of the set of self-dual bent functions.

A bent function is a Boolean function in even number of variables which is on the maximal Hamming distance from the set of affine Boolean functions. It is called self-dual if it coincides with its dual. It is called anti-self-dual if it is equal to the negation of its dual. A mapping of the set of all Boolean functions in n variables to itself is said to be isometric if it preserves the Hamming distance. In this paper we study isometric mappings which preserve self-duality and anti-self-duality of a Boolean bent function. The complete characterization of these mappings is obtained for $n\geqslant 4$ . Based on this result, the set of isometric mappings which preserve the Rayleigh quotient of the Sylvester Hadamard matrix, is characterized. The Rayleigh quotient measures the Hamming distance between bent function and its dual, so as a corollary, all isometric mappings which preserve bentness and the Hamming distance between bent function and its dual are described.
MSA-Net: Multiscale Spatial Attention Network for the Classification of Breast Histology Images

Breast histology images classification is a time- and labor-intensive task due to the complicated structural and textural information contained. Recent deep learning-based methods are less accurate due to the ignorance of the interfering multiscale contextual information in histology images. In this paper, we propose the multiscale spatial attention network (MSA-Net) to deal with these challenges. We first perform adaptive spatial transformation on histology microscopy images at multiple scales using a spatial attention (SA) module to make the model focus on discriminative content. Then we employ a classification network to categorize the transformed images and use the ensemble of the predictions obtained at multiple scales as the classification result. We evaluated our MSA-Net against four state-of-the-art methods on the BACH challenge dataset. Our results show that the proposed MSA-Net achieves a higher accuracy than the rest methods in the five-fold cross validation on training data, and reaches the 2nd place in the online verification.
Spoken Dialogue Systems for Medication Management.

The interest towards spoken dialogue systems has been rapidly growing in the last few years, including the field of health care. There is a growing need for automated systems that can do more than order airline and movie tickets, find restaurants and hotels, or find information on the internet. Eliciting information from patients about their current health and medications using natural language at the point of care is a task currently performed by skilled nurses during the intake interview in both inpatient and outpatient settings. This routine task lends itself well to automation and a well-crafted dialogue system with state management can enable standardized yet individually tailored interactions with the patient using natural language. The need for extensive domain knowledge (e.g. medications, dosages, disorders, symptoms, etc.) in order to achieve broad coverage makes this task particularly challenging. In this project, we explore the use of the PyDial framework and a medication-oriented knowledge base containing information from RxNorm to create a dialogue system capable of eliciting medication history information from patients.
Person Re-identification from Videos Using Facial Features

To precisely re-identify a person is a daunting task due to various conditions such as pose variation, illumination variation, and uncontrolled environment. The methods addressed in related work were insufficient for correctly identifying the targeted person. There has been a lot of exploration in the domain of deep learning, convolutional neural network (CNN) and computer vision for extracting features. In this paper, FaceNet network is used to detect face and extract facial features and these features are used for re-identifying person. Accuracy of FaceNet is compared with Histogram of Oriented Gradients (HOG) method. Euclidean distance is used for checking similarity between faces.
OFFER Referees Suggester for the Journal Editors

Assigning appropriate referees to a journal or conference paper is a vital task for many reasons, including enhancing the journal venue quality and reliance, fair judgement of the papers, and among many others. While assigning the referees to the papers, the editors of a journal venue need to find suitable referees who are both related to field of the given paper and have no conflict of interest with the authors of the paper. Editorial-wise this referee assignment process is implemented in a hand-crafted manner, i.e., the editor needs to find the most suitable referees to the paper via a search engine and manually refines the all unrelated and having conflict of interest authors to the paper authors. Clearly, such a manual referee searching process is tedious and time consuming for the editors.In this paper, we present an alternate automated approach for assigning referees problem using intrinsic random walk with restart proximity measure. In our experiments based on a comprehensive DBLP networks, we show that our approach, called OFFER, significantly outperforms state-of-the-art the random walk with restart based method.
Offline Witness Encryption with Semi-Adaptive Security.

The first construction of Witness Encryption (WE) by Garg et al. (STOC 2013) has led to many exciting avenues of research in the past years. A particularly interesting variant is Offline WE (OWE) by Abusalah et al. (ACNS 2016), as the encryption algorithm uses neither obfuscation nor multilinear maps.
Classically Time-Controlled Quantum Automata: Definition and Properties

In this paper we introduce classically time-controlled quantum automata or CTQA, which is a reasonable modification of Moore-Crutchfield quantum finite automata that uses time-dependent evolution and a defining how long each Hamiltonian will run. Surprisingly enough, time-dependent evolution provides a significant change in the computational power of quantum automata with respect to a discrete quantum model. Indeed, we show that if a scheduler is not computationally restricted, then a CTQA can decide the Halting problem. In order to unearth the computational capabilities of CTQAs we study the case of a computationally restricted scheduler. In particular we showed that depending on the type of restriction imposed on the scheduler, a CTQA can (i) recognize non-regular languages with cut-point, even in the presence of Karp-Lipton advice, and (ii) recognize non-regular languages with bounded-error. Furthermore, we study the closure of concatenation and union of languages by introducing a new model of Moore-Crutchfield quantum finite automata with a rotating tape head. CTQA presents itself as a new model of computation that provides a different approach to a formal study of classical control, quantum data schemes in quantum computing.
Adult online hate, harassment and abuse: a rapid evidence assessment

The development of email and social media platforms has changed the way in which people interact with each other. The open sharing of personal data in public forums has resulted in online harassment in its many forms becoming increasingly problematic. The number of people having negative online experiences is increasing, with close to half of adult internet users reporting having seen hateful content online in the past year. This report presents findings from a collaborative study undertaken by the University of East London (UEL) and the London School of Economics and Political Science (LSE). It describes the findings from a Rapid Evidence Assessment (REA) of the evidence base in relation to adult online safety undertaken on behalf of the Department for Digital, Culture, Media & Sport (DCMS). The research was undertaken on behalf of the UK Council for Internet Safety Evidence Group. This REA focuses on exploring internet safety issues amongst adults, given the expansion of the remit of the UK Council for Child Internet Safety (UKCCIS) to include adults in the context of the new Internet Safety Strategy (2018) and Online Harms White Paper (2019).
Pedestrian Activity Recognition Using 2-D Pose Estimation for Autonomous Vehicles

Human activity recognition is the task of recognizing activities of any given subject in a scene, from a set of observation over time, taking into consideration the environmental and behavioral factors. It has application in a lot of fields including surveillance, assistance system, threat identification. Human activity recognition plays a vital role in human computer interaction, as it is very important that a computer correctly identifies human activity to really understand the human behavior and learn what the human is trying to convey through their action as more than 50% of communication humans do is through body language. In this paper, we propose a system for identifying activities of pedestrians on road using pose estimation to give the autonomous vehicles a better understanding of the humans’ actions and get better at driving and also provide a safer environment for the humans.
Specifics, problems and opportunities of development of business activity in the market of educational literature for higher education institutions

The article analyzes the reasons for the collapse of the market of educational literature for universities, the decline in entrepreneurial activity in this market segment. The issues of quality of published textbooks and manuals are touched upon by both the teaching staff and publishing houses that mass-produce textbooks in the so-called author's edition, i.e. without editing and proofreading. Recommendations are given for overcoming the crisis situation.
Design of Distributed Natural Gas Compression Process Control Systems

A new approach to the design of distributed automation systems for controlling the technological processes of natural gas compression is discussed. The concept of embedding unified computing and communication devices interacting through an interactive network in the technological blocks of the managed objects is described. An example of the implementation of distributed control systems for gas-compressor units is described.
Lexical Collocation Analysis: Advances and Applications

This book belongs to the Springer series on Quantitative Methods in the Humanities and Social Sciences, and it consists of six chapters presenting papers by known experts in the language-related an...
Modular Exponentiation, Inversion, and Division

Modular exponentiation is the computation of x e mod m, and multiplicative modular inversion is the computation of y such that x ∗ y mod m = 1. This chapter consists of two sections, one each on the two operations. Modular division is included implicitly in the second, as in practice it is effected as multiplication by an inverse.
Effect of top-down connections in Hierarchical Sparse Coding

Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layer-wise subproblems. However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers. In this study, a new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to assess the impact of this inter-layer feedback connection. In particular, the 2L-SPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of independent Lasso layers. The 2L-SPC and the 2-layers Hi-La networks are trained on 4 different databases and with different sparsity parameters on each layer. First, we show that the overall prediction error generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers prediction error between layers. Second, we demonstrate that the inference stage of the 2L-SPC is faster to converge than for the Hi-La model. Third, we show that the 2L-SPC also accelerates the learning process. Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the 2L-SPC features are more generic and informative.