Reviewerly/exHarmony huggingface dataset:
Multi-domain Cross-dataset and Camera Style Transfer for Person Re-Identification

The major reason of performance drop in person reidentification (re-id) models is domain gap between various datasets.Each person re-id dataset is different from other dataset because of change in lightning conditions, camera angle, pose, illumination, background and resolution.This results in domain gap between these datasets and leads to performance drop in person re-identification models.The domain gap not only exits between distinct datasets but it is also present in a dataset which contains images taken from multiple cameras.Generative Adversarial Networks (GANs) have achieved successful results in various fields, especially for image-to-image translation.In this paper, we propose an unsupervised domain adaptation method based on ComboGAN and Xception to perform cross-dataset and camera style translation.Our framework generates images in different styles of a single person according to various domains.Experimental results show the effectiveness of the proposed model on two popular re-id datasets Market-1501 and PETA.
Processes of Internationalization in the University with the Participation of Foreign Students

This article presents the problems of the development of higher education in Russia, including the priorities of higher education institutions in the program for the development of competitiveness. Kazan Federal University is considered to be the leader in Russia in the number of foreign students studying in Russia, with more than 7,000 students from 98 countries studying there. Noting that internationalization has become a "universal" phrase that people use to best fit their purpose, we emphasize the need for a more focused definition that sets internationalization parameters if it is to be understood and treated them with importance (Knight & de Wit, 1995 ). The main focus of this study is on the study of internal processes of internationalization occurring in the university: 1) The study of factors contributing to and hindering the processes of internationalization; 2) What is internal internationalization in university substructures; its parameters; 3) the practice of social work at the university contributes to the maintenance and development of internal internationalization. The major method is a survey method conducted among 700 foreign students, aimed at studying the choice of a university by foreign students, studying the factors that contribute to adaptation and hinder adaptation including the internationalization of foreign students. The key result of this study describes the experience of social practice when working with foreign students and the impact of university activities on internationalization. The internationalization model proposed by the author Schoorman helped to make an analysis of the internationalization process using the example of studying the social life of foreign students.
Understanding and Measuring E-Book Packages: Print Purchasing Patterns and Book Usage

When libraries make the decision to transition from print to electronic books, it is often a zero-sum game; if the library is to license the electronic version of a title, it tends not to buy it in print.This study examines print purchase and usage patterns after a large e-book acquisition by the University of Toronto Libraries, when subject selectors had the option to continue to purchase the titles in print.Data on print purchasing patterns, print title circulation, and electronic book use was examined both at the aggregate and the subject level.The study adds evidence to the growing body of literature on the transition from print to e-books in academic libraries.
Japanese Vocabulary Acquisition Through Anime: A Case Study On Dwimeilinda

(Japanese Vocabulary Acquisition through Anime: A Case Study on Dwimeilinda) This study attempted to investigate the influence of watching anime on Japanese vocabulary acquisition from Dwimeilinda. The study used a mixed-method. There are three collection data in this study; the first data is the interview; the second is observation; the last is the aural test. There are two questions from this study: first, how did she acquire the vocabulary through anime? Second. How is the influence of watching anime on her Japanese vocabulary acquisition? The results showing there are some strategies that she uses in acquiring Japanese vocabulary. First repetition, second cognate and third is mind mapping. The study also found that watching anime can influence her phonological vocabulary knowledge and daily expression.
Musical instrument directivity measurements

Over the past several decades, researchers have developed various techniques to measure directional radiation patterns of musical instruments. After summarizing and categorizing several of them, this presentation will focus on recent developments made by the authors to measure directivities with high angular resolution. The frequency-dependent patterns of 16 played musical instruments were acquired using 2522 unique microphone positions over a sphere in an anechoic chamber. This led to associated frequency-dependent spherical-harmonic expansions with readily shareable coefficients. Several steps were implemented to improve and validate the measurements, and make their costs manageable. The presentation will discuss key results and future directions.
PENGEMBANGAN BAHAN AJAR MEMBACA PERMULAAN DALAM PEMBELAJARAN TEMATIK DENGAN MODEL VARK (VISUAL, AUDITORY, READ/WRITE AND KINESTHETIC) DI KELAS I SEKOLAH DASAR

This study aims to produce the process of developing teaching reading materials and produce teaching reading materials with Vark (Visual, Auditory, Read / write, and Kinesthetic) models for class 1 valid and practical educators and students. The development is based on the Plomp model (Preliminary Research, Prototyping Phase and Assessment Stage) which emphasizes the context of needs (educators and students) so that the initial reading skills learning material is produced based on the Vark model. Based on the results of the Kevalitan trial, it can be seen from the validation of content, language, presentation, graphics and lesson plans, overall 93.10% with valid valid categories. Praticality of teaching materials is seen from the educator's response, 98.92% of the students' responses are 96.15% while the effectiveness of Reading is a percentage of 100%. It can be concluded that the preliminary reading material using the Vark model that was developed is suitable for use in reading the beginning with the Vark model in grade 1 elementary school.
Sistem Pakar Mendiagnosa Penyakit Kulit pada Anak Menggunakan Metode Backward Chaining

The purpose of writing this thesis is to build an expert system to diagnose skin diseases. in children and can make it easier for specialist doctors to diagnose skin diseases and provide solutions and information about skin diseases based on symptoms. If something happens to the child, the organ that is first affected is the skin. If there is a skin disease disorder in children, the parents trust the doctor more. But not all doctors can help overcome these problems, so they cannot know the type of disease directly. The method used is backward chaining, i.e. the inference process that starts the search from the premise or input data in the form of symptoms leading to conclusions, namely conclusions of types of diseases and solutions based on symptoms suffered by patients. The results of this study were to determine the patient's illness and its web use.
A New Algorithm for High Average-utility Itemset Mining

High utility itemset mining (HUIM) is a new emerging field in data mining which has gained growing interest due to its various applications. The goal of this problem is to discover all itemsets whose utility exceeds minimum threshold. The basic HUIM problem does not consider length of itemsets in its utility measurement and utility values tend to become higher for itemsets containing more items. Hence, HUIM algorithms discover a huge enormous number of long patterns. High average-utility itemset mining (HAUIM) is a variation of HUIM that selects patterns by considering both their utilities and lengths. In the last decades, several algorithms have been introduced to mine high average-utility itemsets. To speed up the HAUIM process, here a new algorithm is proposed which uses a new list structure and pruning strategy. Several experiments performed on real and synthetic datasets show that the proposed algorithm outperforms the state-of-the-art HAUIM algorithms in terms of runtime and memory consumption.
Cross-Category Cross-Semantic Regularization for Fine-Grained Image Recognition

Fine-grained image recognition (FGIR) is challenging due to the local and subtle differences between subordinate categories. Existing methods adopt a two-step strategy by first detecting local parts from images, and then extracting features from them for classification. Although steady progress has been achieved, these methods localize object parts separately while neglecting the relationships between them. In this paper, we propose cross-category cross-semantic ( $$C^{3}S$$ ), a regularization module that exploits the relationships between object parts from different images to regularize the fine-grained feature learning for FGIR. $$C^{3}S$$ encourages the features of the same object part from different images to have strong correlations while decorrelating the features from different object parts as much as possible. $$C^{3}S$$ can be incorporated into networks without introducing any extra parameters. Experiments on five benchmark datasets (CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC-Aircraft and NABirds) validate the effectiveness of $$C^{3}S$$ and demonstrate its comparable performance to existing methods.
From Poster to Mobile Calendar: An Event Reminder using Mobile OCR

Technological innovations are the foundation of new services today.Successful services address real-life issues that help people manage life more conveniently using relevant technologies.Currently, images are a part of daily life.People are often taking pictures of different posters for different events as exhibitions, workshops, conferences, etc., with their mobile.Unfortunately, sometimes these pictures are forgotten and events' dates expire.As consequence, people miss events they were interested in.Hence, with the vision to provide technologypowered services, affordable and turnkey applications, this paper presents Event-Reminder, a fully automated lightweight reminder system builds upon a mobile offline OCR (Optical Character Recognition) with touch interaction making some daily tasks easier.Event-Reminder is a mobile application that would recognize the images' text content, extract event's date and venue and upload this event information automatically to mobile calendar in order to remind the user about the event at proper time.A prototype system is introduced in this paper.
EXPLORING OLDER ADULTS’ PERCEPTIONS OF PRESENCE AND IMMERSION IN DIVERSE VIRTUAL ENVIRONMENTS

Abstract Virtual Reality (VR) holds great promise for enhancing the health, well-being, and skills of older adults. However, VR solutions must consider the age-related “digital divide;”many older adults have less experience and proficiency with a number of newer technologies, which may serve as a barrier. Older adults especially have less experience with virtual environments, an experience many younger adults have acquired through video gaming. This study compared younger and older adults’ perceptions of immersion and presence in a series of diverse virtual environments using the HTC Vive. Participants experienced a VR meditation task, “indoor" and “outdoor” navigation tasks, and a fast-paced action game. Importantly, younger and older adults reported similarly high experiences of immersion and presence within virtual environments, and contrary to expectations, older adults reported fewer symptoms of cybersickness. Results suggest VR as a promising tool to promote the health and well-being of older adults.
Implementation of hyyrö’s bit-vector algorithm using advanced vector extensions 2

The Advanced Vector Extensions 2 (AVX2) instruction set architecture was introduced by Intel’s Haswell microarchitecture that features improved processing power, wider vector registers, and a rich instruction set. This study presents an implementation of the Hyyrö’s bit-vector algorithm for pairwise Deoxyribonucleic Acid (DNA) sequence alignment that takes advantage of Single-Instruction-Multiple-Data (SIMD) computing capabilities of AVX2 on modern processors. It investigated the effects of the length of the query and reference sequences to the I/O load time, computation time, and memory consumption. The result reveals that the experiment has achieved an I/O load time of ϴ(n), computation time of ϴ(n*⌈m/64⌉), and memory consumption of ϴ(n). The implementation computed more extended time complexity than the expected ϴ(n) due to instructional and architectural limitations. Nonetheless, it was par with other experiments, in terms of computation time complexity and memory consumption.
Effects of Content-Associated Applied Measurement and Evaluation Course

The objective of the present study is to examine the effects of content-associated applied measurement and evaluation course on pre-service teachers' assessment literacy, their attitudes towards the measurement and evaluation course, and their field-specific content knowledge. Furthermore, the study aims to reveal the pre-service teachers' views on content-associated applied measurement practices. The mixed method research approach, which combines qualitative and quantitative research methods, was used in the study. The quantitative data collection was conducted with 90 science teacher candidates. The qualitative data collection was conducted with six teacher candidates from the experimental group. The Classroom Assessment Literacy Inventory (CALI), Measurement and Evaluation Course Attitude Scale (MECAS), Academic Achievement Test (AAT), and Focus Group Interview Form were used as the data collection tools in the study. It was found that the content-associated applied measurement and evaluation course improves the measurement and evaluation literacy of the candidate teachers, fosters their positive attitudes towards the course of measurement and evaluation, and increases their content knowledge. Furthermore, the focus group interviews with the pre-service teachers both helped explain the reasons behind the quantitative results, and provided detailed information about the degree of effectiveness of the method applied in the teaching process.
On the Impact of Machine Learning: Architecture without Architects?

Architecture has always followed and adopted technological breakthroughs of other areas. As a case in point, in the last decades, the field of computation changed the face of architectural practice. Considering the recent breakthroughs of Machine Learning (ML), it is expectable to see architecture adopting ML-based approaches. However, it is not yet clear how much this adoption will change the architectural practice and in order to forecast this change it is necessary to understand the foundations of ML and its impact in other fields of human activity. This paper discusses important ML techniques and areas where they were successfully applied. Based on those examples, this paper forecast hypothetical uses of ML in the realm of building design. In particular, we examine ML approaches in conceptualization, algorithmization, modeling, and optimization tasks. In the end, we conjecture potential applications of such approaches, suggest future lines of research, and speculate on the future face of the architectural profession.
Automatic speech emotion recognition using deep learning for analysis of collaborative group meetings

Emotion is a central component of verbal communication between humans. Due to advances in machine learning and the development of affective computing, automatic emotion recognition is both increasingly possible and increasingly sought-after. In this project, deep learning architectures, such as long short-term memory networks, are used to classify emotion from the prosody of human speech. Emotional groupings are considered both categorically (i.e., happiness, anger, etc.) and with respect to a three-dimensional emotion space divided into axes of valence, activation, and dominance. The network is trained on publicly available emotional speech corpuses and tested on a new dataset as part of a larger project to understand the dynamics of collaborative groupwork. Specifically, small groups were asked to complete a lunar survival task while recorded in a smart meeting room equipped with multimodal sensors. The results of the emotion classification, as well as other non-verbal speech metrics, are correlated with user-reported rankings including emergent group leader and major group contributor, in the hopes of better understanding the complex dynamics of team meetings. [Work supported by NSF IIP-1631674, a Northeastern University Tier 1 Seed Grant, and the Cognitive and Immersive Systems Laboratory (CISL).]
Novel applications of re-tooled open-source acoustic simulation algorithms

Pachyderm Acoustic Simulation is an open-source acoustics simulation tool that is freely available to the public through the General Public License 3.0. In the 11 years, that Pachyderm Acoustic Simulation has been available, its open and conventional acoustics simulation algorithms (including geometrical, finite volume, transfer matrix, etc.) have been applied in ways that are less conventional by certain creative individuals. Users leverage access to the algorithms through its native interface, grasshopper (for visual generation of algorithms), IronPython, and the software’s native source language C#. Examples of these creative applications include augmented geometrical visualizations, customized finite volume method simulations for proof of concept, visualization of material data, and form-finding using genetic algorithms. This talk will explore several exemplary cases in which a user successfully applied the simulation and calculation algorithms in a way that was not originally intended.
Analysis of the Commutative Method Approach on English Thesaurus for Developing Synonym Sets

WordNet is a lexical database for languages, the difference between WordNet and dictionaries in general is that WordNet focuses on the synonyms. The main unit of WordNet is synonym set (synset), synset is a set of one or more words that have the same meaning and certainly can be replaced in certain contexts. Synset is a very important element in implementing WordNet. In this paper, an analysis of the synonym extraction process is carried out by using commutative approach, the data test obtained from the Oxford Paperback Thesaurus by taking 51 word entries. Commutative method has similar characters with synonym set, synonym set can replace each other in certain contexts. The data test extraction process is carried out until the performance measurement evaluation process using F1Score. The system generates synonym sets that matched with the manual extraction, the result of F1Score between the program and Princeton synonym sets are worth 10%.
Adaptive hybrid threshold shrinkage using singular value decomposition

We present a method for image denoising based on singular value shrinkage that fuses soft and hard thresholds. The technique simply groups similar patches from a noisy image as low-rank matrices and shrinks the singular values by the combination of soft and hard thresholds. On one hand, a hard threshold approximation method based on nonlocal self-similarity and low-rank approximation is used for fast selection of hard threshold; on the other hand, a soft threshold selection method based on random matrix and asymptotic matrix reconstruction theory is designed. In addition, we also propose an adaptive backward projection algorithm based on image phase congruency and gradient calculation so that the input images participating in the iteration are adaptive. This method improves the traditional fixed coefficient backward projection method and makes the robustness of the algorithm better. The experimental results of denoising and enhancement for a number of natural images show that the proposed algorithms have significant improvement in both subjective visual effect and objective quantization index by comparing with some related state-of-the-art denoising algorithms.
Modelling emotion expression through agent oriented methodology

&lt;span&gt;This paper presents Modelling Emotion Expression through Agent Oriented Methodology. Considering emotions of the intended users in the software engineering can uncover new requirements to improve and more accepted the system. While emotion is paying much attention nowadays, there is lacking systematic way to model the emotion based system. Without the systematic approach, it is hard to debug, design and develop an emotion based system. Since the emotional requirement of people has not being fully investigated, the research outcome propose the emotion modelling as part of the complete set of agent-oriented modelling for virtual character in eLearning system, The contribution of this paper is to introduce agent oriented modelling to systematic model an emotion based solution for an eLearning system and instructional video design. With the emotion model, it can serve as a guide to design, redesign, and discuss the emotion elements among the software development team. This is important for better debugging and project management especially for emotion led system.&lt;/span&gt;
Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment

&lt;p align="justify"&gt;In a context of constant evolution of technologies for scientific, economic and social purposes, Artificial Intelligence (AI) and Internet of Things (IoT) have seen significant progress over the past few years. As much as Human-Machine interactions are needed and tasks automation is undeniable, it is important that electronic devices (computers, cars, sensors…) could also communicate with humans just as well as they communicate together. The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines, illustrated with chat-bots. Nonetheless, using this technology is not sufficient, as they often give inappropriate or unrelated answers, usually when the subject changes. To improve this technology, the problem of defining a communication language constructed from scratch is addressed, in the intention to give machines the possibility to create a new and adapted exchange channel between them. Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment, the convergence toward a common ‘’language’’ is analyzed, exactly as it is supposed to have happened for humans in the past. By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality, rapidly converging evolution of syntactic communication is obtained, opening the way of a meaningful language between machines.&lt;/p&gt;