Reviewerly/exHarmony huggingface dataset:
Evidential Value of Microobjects in Criminal Proceedings and Forensic Rules for Handling Them

The use of micro-objects in the process of investigating crimes has been considered for many decades an independent area of forensic expertise. Among physical evidence subject to microscopic examination, a special place has always been occupied by various material small bodies (objects) in the form of dust and soil compounds, various fibres (hairs) of human, animal and plant origin, paint and sawdust particles, stains, various types of contamination, pollen and plant particles as well as substances of various origin, which are as a rule associated with the crime.
548: ICU Risk Prediction From Commonly Measured Clinical Data Using Artificial Neural Network Tools

Szakmany, Tamas; Killick, Charlotte; Richards, Owen; Cheema, Yusuf; King, Charles; Al Hassan, Haamed; Kiss, Annamaria; Ball, Graham Author Information
TWITTER LOCATION-BASED DATA: EVALUATING THE METHODS OF DATA COLLECTION PROVIDED BY TWITTER API

Twitter data analysis is an emerging field of research that utilizes data collected from Twitter to address many issues such as disaster response, sentiment analysis, and demographic studies. The success of data analysis relies on collecting accurate and representative data of the studied group or phenomena to get the best results. Various twitter analysis applications rely on collecting the locations of the users sending the tweets, but this information is not always available. There are several attempts at estimating location based aspects of a tweet. However, there is a lack of attempts on investigating the data collection methods that are focused on location. In this paper, we investigate the two methods for obtaining location-based data provided by Twitter API, Twitter places and Geocode parameters. We studied these methods to determine their accuracy and their suitability for research. The study concludes that the places method is the more accurate, but it excludes a lot of the data, while the geocode method provides us with more data, but special attention needs to be paid to outliers.
RECURRENT NEURAL NETWORK (RNN) DENGAN LONG SHORT TERM MEMORY (LSTM) UNTUK ANALISIS SENTIMEN DATA INSTAGRAM

Social media is a new public space for channeling opinions and ideas. Social media such as Instagram has been used by STMIK AKAKOM Yogyakarta to provide information related to these high school institutions. This information can later become a topic of conversation and interesting things for the community to discuss. The response or public response to the Instagram content of STMIK AKAKOM Yogyakarta is of course very diverse. Therefore, the researcher tries to analyze the comments that talk about the content of the Instagram STMIK AKAKOM Yogyakarta. Sentiment analysis was performed using the Recurrent Neural Network (RNN) method with Long Short Term Memory (LSTM). Comments will be identified whether the comments have positive, neutral or negative sentiment. This study uses data as much as 1,473 data obtained from the results of crawling. The result of this research is a system that can classify sentiments. The level of testing accuracy obtained was 65% and the application accuracy rate obtained was 79.46%. Some of the obstacles in the process of sentiment analysis are the imbalanced dataset, so it is necessary to take additional steps in preparing the data for model training.
Interactive Word Completion for Morphologically Complex Languages

Text input technologies for low-resource languages support literacy, content authoring, and language learning.However, tasks such as word completion pose a challenge for morphologically complex languages thanks to the combinatorial explosion of possible words.We have developed a method for morphologically-aware text input in Kunwinjku, a polysynthetic language of northern Australia.We modify an existing finite state recognizer to map input morph prefixes to morph completions, respecting the morphosyntax and morphophonology of the language.We demonstrate the portability of the method by applying it to Turkish.We show that the space of proximal morph completions is many orders of magnitude smaller than the space of full word completions for Kunwinjku.We provide a visualization of the morph completion space to enable the text completion parameters to be fine-tuned.Finally, we report on a web services deployment, along with a web interface which helps users enter morphologically complex words and which retrieves corresponding entries from the lexicon.
Badminton Competition Aplication Using Website

Persatuan Bulu Tangkis Seluruh Indonesia (PBSI) is an organization that manages the game of badminton in Indonesia. The Palembang branch of PBSI has so far held a competition that has not used special software, from registration to the results of the competition. The problem that often occurs is the frequent occurrence of misinformation about the race. For this reason, an application will be made that can help accommodate the race administration, starting from the registration of the race to the results of the race. Application created using the website. The development method used in making applications is the Web Engineering method. The programming language used is using PHP and the database is used using MySql. The results of this study are expected to make it easier for athletes to see information about race events, match schedules, race registration, making it easier for badminton clubs to register their athletes in competitions held by PBSI
Nemes Békésy György emlékezete

Békésy arranged his experiments with great care and often sought the opinion of those around him. It indicates a very important personality trait: I think, he was fully aware of the limitations of his knowledge and endeavoured to expand it through community discussions. Like Hungarian personalities who went abroad, he changed his first name to Georg (instead of Hungarian György); he used the noble forename “von” characteristic for German-speaking regions, and this is how he appears in his dissertations. He used his surname in its Hungarian form, having accents on the letters é. In 1961, nobleman Georg von Békésy received the Nobel Prize in Medicine: “for his discoveries of the physical mechanisms of stimulation within the cochlea”. The essence of Békésy’s discovery is the clarification of the energy conversion process in the cochlea. He succeeded in designing devices for measuring all mechanical functions of the hearing organ and transferred them to numerical data. This study concerns the genealogy of Georg von Békésy.
Implementation of a Behavioral Recognition Sensor for Understanding Interactive Communication Situation of the Elderly

Studies in the field of epidemiology have shown that interactive communication with others is associated with a healthy life expectancy in the elderly population. However, for the most part, questionnaire surveys have been used as a method for analyzing communication situations, which lacks completeness and accuracy. In this study, we develop a behavioral recognition sensor to understand the state of interactive communication of the elderly quantitatively. This paper describes the implementation of a sensor that is capable of collecting the speech status.
EFL-Writing Activities Using WhatsApp Group: Students’ Perceptions during Study from Home

This research aims to investigate writing activities using the WhatsApp group during the study from home amid the COVID-19 pandemic and students’ perceptions about it. It is a qualitative research, in which the data were analyzed descriptively. This research was carried out at the Sentence-Based Writing class at IAIN Salatiga, Indonesia, in April - June 2020. This research participant involved 30 students. The researchers utilized a questionnaire and documentation for data collection. After conducting the research, the results revealed various students’ perceptions. 63% of the total students possessed a positive perception toward the writing activities using the WhatsApp group, and the rest 37% of students’ preferred face-to-face meetings and perceived writing using WhatsApp group as boring, confusing, and distracting. After analyzing the WhatsApp group activities, at least five writing activities were conducted: sharing and explaining materials, discussing materials, composing sentences, editing sentences, and posting writing products. Thus, this research can definitely provide teachers with knowledge regarding students' perceptions and writing activities carried out in the WhatsApp group.
CHILD-PARENT RELATIONS AS A FACTOR IN THE FORMATION OF PROFESSIONAL PREFERENCES IN PEDAGOGICAL DYNASTIES

The article raises the issue of the relationship of parent-child relationships and professional preferences in pedagogical dynasties. Particular attention is paid to the role of the family in the professional development of the individual. It has been suggested that there is a relationship between parent-child relationships and professional preferences.
Quantum walk processes in quantum devices

Simulation and programming of current quantum computers as Noisy Intermediate-Scale Quantum (NISQ) devices represent a hot topic at the border of current physical and information sciences. The quantum walk process represents a basic subroutine in many quantum algorithms and plays an important role in studying physical phenomena. Simulating quantum walk processes is computationally challenging for classical processors. With an increasing improvement in qubits fidelity and qubits number in a single register, there is a potential to improve quantum walks simulations substantially. However, efficient ways to simulate quantum walks in qubit registers still have to be explored. Here, we explore the relationship between quantum walk on graphs and quantum circuits. Firstly, we discuss ways to obtain graphs provided quantum circuit. We then explore techniques to represent quantum walk on a graph as a quantum circuit. Specifically, we study hypercube graphs and arbitrary graphs. Our approach to studying the relationship between graphs and quantum circuits paves way for the efficient implementation of quantum walks algorithms on quantum computers.
Systematic Review on Ethical Issues in Cloud Computing

Cloud computing is the new age of computing systems which swiftly gaining attention and traction in business. It offers technology services online through web-based tools and applications, rather than a direct connection to a server. There are several ethical issues in cloud computing which raise concerns to organizations in adopting cloud computing to their settings such as data privacy. In most cases, these ethical issues are depending on specific applications and settings. The main purpose of this paper is to identify ethical issues in cloud computing in people, process, and technology perspectives. The paper describes how the systematic review was conducted, how ethical issues were collected and analyzed and it concludes by discussing means of managing them in general. As a result, there are 25 cloud computing ethical issues identified which are categorized into three categories based on Leavitt’s Diamond. Most issues identified are in the Technology category, followed by Process and finally People.
Machine Translation Pre-training for Data-to-Text Generation - A Case Study in Czech.

While there is a large body of research studying deep learning methods for text generation from structured data, almost all of it focuses purely on English. In this paper, we study the effectiveness of machine translation based pre-training for data-to-text generation in non-English languages. Since the structured data is generally expressed in English, text generation into other languages involves elements of translation, transliteration and copying - elements already encoded in neural machine translation systems. Moreover, since data-to-text corpora are typically small, this task can benefit greatly from pre-training. Based on our experiments on Czech, a morphologically complex language, we find that pre-training lets us train end-to-end models with significantly improved performance, as judged by automatic metrics and human evaluation. We also show that this approach enjoys several desirable properties, including improved performance in low data scenarios and robustness to unseen slot values.
Human Activity Recognition Using Smart phones

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context infor-mation about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repos-itory. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.
MODERN APPROACHES TO TRAINING OF PEDAGOGICAL SPECIALISTS OF MUSICAL ARTS

The content of approaches to the professional training of teachers of music arts is determined by a number of objective factors due to the realities of modern life.They pose challenges to standardized education systems, stimulating the study and implementation of foreign educational traditions and innovative practices in the domestic space that would adapt the system of national higher education to adequate functioning on a global scale.The purpose of the present investigation is to study modern domestic and global approaches to training of pedagogical specialists in music arts as part of the intensification of globalization processes.As a result, these approaches will stimulate the integration of educational space, and ensure increased dynamics of using information and media teaching tools.Methods of observation, description, complex analysis, functional method, method of comparison, method of system analysis, method of generalization and method of scientific abstraction have been used.The defining feature of modern approaches to training of pedagogical specialists in music arts is the tendency to globalization and integration of educational space.This will further determine the expanded involvement of distance learning methods, informatization of educational space, attitude to lifelong learning and, in general, a conscious and self-motivated attitude towards the process of acquiring knowledge and obtaining the necessary professional skills.Globalization processes of a music teacher in the education intensify the weight of art education and training of a pedagogical specialist of musical arts as a carrier and conductor of world cultural values.However, against this background, we should not forget about music as a source of national education of the younger generations.Therefore, a balance between national and globalization dominants is needed in the methodology of teaching music disciplines in the course of university training of music teachers.
Chaotic-based Particle Swarm Optimization with Inertia Weight for Optimization Tasks

Among variety of meta-heuristic population-based search algorithms, particle swarm optimization (PSO) with adaptive inertia weight (AIW) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. Although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may be trapped in a local minimum through a limited number of iterations. To increase its diversity as well as enhancing its exploration ability, this paper inserts a chaotic factor, generated by three chaotic systems, along with a perturbation stage into AIW-PSO to avoid premature convergence, especially in complex nonlinear problems. To assess the proposed method, a known optimization benchmark containing nonlinear complex functions was selected and its results were compared to that of standard PSO, AIW-PSO and genetic algorithm (GA). The empirical results demonstrate the superiority of the proposed chaotic AIW-PSO to the counterparts over 21 functions, which confirms the promising role of inserting the randomness into the AIW-PSO. The behavior of error through the epochs show that the proposed manner can smoothly find proper minimums in a timely manner without encountering with premature convergence.
Double Layered Text Encryption using Beaufort and Hill Cipher Techniques

Data is critical, especially in the current cyber era. Maintaining data confidentiality and security is one thing that must be done so that the data owned cannot be used by other parties who are not interested. Cryptography is one solution that can be utilized. In cryptography, there are various methods, including the Beaufort Cipher. The Beaufort cipher method is a cryptographic method using simple calculations. This method's weakness is to use mathematical calculations that are quite simple and include methods that are already quite old. Despite this fact, this method is still one of the popularly used in securing data. One way to improve data security by using this method is through integration with other cryptographic methods, one of them is the hill cipher. In this research, the encryption process is known as Double Layered Encryption. The encryption process will be done in two layers, namely using Beaufort cipher and Hill cipher. The quality of the proposed encryption process is measured using an Avalanche Effect (AE) value. The results show that the proposed method has a higher Avalanche Effect than the Beaufort cipher only and Hill cipher only methods.
Exploring NMF and LDA Topic Models of Swedish News Articles

The ability to automatically analyze and segment news articles by their content is a growing research field. This thesis explores the unsupervised machine learning method topic modeling applied on ...
Development of an Autonomous Agent based on Reinforcement Learning for a Digital Fighting Game

In this work, an autonomous agent based on reinforcement learning is implemented in a digital fighting game. The implemented agent uses Fusion Architecture for Learning, COgnition, and Navigation (FALCON) and Associative Resonance Map (ARAM) neural networks. The experimental results show that the autonomous agent is able to develop game strategies using the experience acquired in the matches, and achieves a winning rate of up to 90% against an agent with fixed behavior.
PENGARUH MODEL PEMBELAJARAN PROJECT BASED LEARNING TERHADAP HASIL BELAJAR SISWA KELAS V SD SAWAH BESAR 01

This research aims to examine the effect of project-based learning through biopore learning models on the learning outcomes of elementary school students in grade V, to examine the effect of project-based learning through biopori learning outcomes on learning outcomes of elementary school students. This study used a pre-experimental design in the form of a one-group pretest-posttestdesign. The study population was all students in grade V SD Negeri Sawah Besar 01 Semarang, totaling 40 children. The sample of all students in grade V SD Negeri Sawah Besar 01 Semarang, totaling 40 students, namely 23 male students and 17 female students obtained by simple random sampling. The independent variable is the project based learning model, while the dependent variable is the learning outcome. Data were collected by interview, test, and documentation. The research hypothesis was tested with Liliefors using the Microsoft Office Excel program. The conclusions of the research results are: There is a positive influence on the learning model of project based learning on the learning outcomes of students in class V SD Sawah Besar 01.
When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network

In online customer service applications, multiple chatbots that are specialized in various topics are typically developed separately and are then merged with other human agents to a single platform, presenting to the users with a unified interface.Ideally the conversation can be transparently transferred between different sources of customer support so that domain-specific questions can be answered timely and this is what we coined as a Bot-Agent symbiosis.Conversation transition is a major challenge in such online customer service and our work formalises the challenge as two core problems, namely, when to transfer and which bot or agent to transfer to and introduces a deep neural networks based approach that addresses these problems.Inspired by the net promoter score (NPS), our research reveals how the problems can be effectively solved by providing user feedback and developing deep neural networks that predict the conversation category distribution and the NPS of the dialogues.Experiments on realistic data generated from an online service support platform demonstrate that the proposed approach outperforms state-of-the-art methods and shows promising perspective for transparent conversation transition.
Perceptions of open innovation at CERN: an explorative study

Innovation is seen as a crucial factor to drive businesses to grow, stand out and to meet customer needs. However, most research on open innovation focuses on commercial businesses. Here, we study how research institutes, specifically CERN, establish innovation as their driving force, primarily through internal and external openness. To explore how interdisciplinary collaboration and open innovation is perceived by CERN’s constituents, mixed-form interviews have been conducted. A majority is found to be in favour of interdisciplinary collaboration, and acknowledges the impact of open innovation. Future work may continue to explore the validity of these sentiments among a wider audience.
Improving multi-view facial expression recognition through two novel texture-based feature representations

Although several automatic computer systems have been proposed to address facial expression recognition problems, the majority of them still fail to cope with some requirements of many practical application scenarios. In this paper, one of the most influential and common issues raised in practical application scenarios when applying automatic facial expression recognition system, head pose variation, is comprehensively explored and investigated. In order to do this, two novel texture feature representations are proposed for implementing multi-view facial expression recognition systems in practical environments. These representations combine the block-based techniques with Local Ternary Pattern-based features, providing a more informative and efficient feature representation of the facial images. In addition, an in-house multi-view facial expression database has been designed and collected to allow us to conduct a detailed research study of the effect of out-of-plane pose angles on the performance of a multi-view facial expression recognition system. Along with the proposed in-house dataset, the proposed system is tested on two well-known facial expression databases, CK+ and BU-3DFE datasets. The obtained results shows that the proposed system outperforms current state-of-the-art 2D facial expression systems in the presence of pose variations.