Reviewerly/exHarmony huggingface dataset:
Optimalisasi Image Thresholding pada Optical Character Recognition Pada Sistem Digitalisasi dan Pencarian Dokumen

The administration activity in an institute is largerly done by using a paper based mailing and document as a media. Therefore, a great effort needs to be performed in the case of management and archiving, in the form of providing storage space through the categorizing system. Digitalization of document by scanning it into a digital image is one of the solution to reduce the effort to perform the work of archiving and categorizing such document. It also provide searching feature in the form of metadata, that is manually written during the digitalization process. The metadata can contains the title of document, summary, or category. The needs to manually input this metadata can be solved by utilizing Optical Character Recognition (OCR) that converts any text in the document into readable text storing in the database system. This research focused on the implementation of the OCR system to extract text in the scanned document image and performing optimization of the pre-processing stage which is Image Thresholding. The aim of the optimization is to increase OCR accuracy by tuning threshold value of given value sets, and resulting 0.6 as the best thresholding value. Experiment performed by processing text extraction towards several scanned document and achieving accuration rate of 92.568%.
BLOCKCHAIN TECHNOLOGY — INNOVATION AND SECURITY

Blockchain is one of the most revolutionary technologies of the 21st century, which is still under development, and whose potential is not yet fully exploited. Although blockchain gained importance in 2009, scientists and entrepreneurs are still at an early stage of understanding its mechanisms and fully appreciating its potential, especially from the perspective of the technical challenges and limitations of the technology. Blockchain fi nds a variety of applications, especially in areas that have so far been based on third-party transactions in order to maintain a certain level of trust. Although blockchain is a promising technology for the reorganisation of business processes and many industrial applications, it still has many weaknesses despite various implementations in many forms. An innovative element, and one of the most attractive functions, of blockchain is intelligent contracts, as they reduce or even completely eliminate the administrative costs associated with the lack of trust in the transaction. However, the existing software that is built on this infrastructure has many shortcomings and unfortunately, combined with the lack of maturity of the scripting language to write the contract representation in the computer language, leads to errors or gaps in security that are not noticed or addressed by the author of the script. So far, no blockchain-based system has been completely broken. Nevertheless, phishing is the main trend in the operation of criminals in blockchain networks. Research has shown that over $115 million has been stolen from nearly 17,000 victims in the Ethereum blockchain alone. It is estimated that in total about 10% of the money invested in Ethereum’s ICO has ended up in the hands of criminals.
The MARquette Visualization Lab (MARVL): An Immersive Virtual Environment for Research, Teaching and Collaboration

The MARquette Visualization Lab (MARVL) is a large-scale immersive virtual environment for research, teaching, collaboration and outreach at our mid-sized liberal arts university. MARVL consists of multiple display surfaces including an extra wide front wall and floor, and two side walls. This resource includes stereoscopic viewing, motion tracking and space for a large audience. MARVL's versatile configuration facilitates viewing of content by 30 people, while also projecting on the entire width of the floor. This feature uniquely facilitates comparative or separate content visible simultaneously via 'split mode' operation (two 3-sided environments), as well as detailed motion for applications such as gait analysis and performing arts. Since establishing the lab, its members have received numerous queries and requests pertaining to how system attributes and applications were determined, suggesting these and related decisions remain a challenge nearly 3 decades since the first CAVE was constructed. This paper provides an overview of MARVL including the processes used in identifying a diverse group of cross campus users, understanding their collective vision for potential use, and synthesizing this information to create the resource described above. The subsequent design, qualitative and quantitative approaches to vendor selection, and software decisions are then discussed. Steps implemented for dealing with simulator sickness and latency are presented along with current approaches being implemented for project development with end-users. Finally, we present results from the use of MARVL by several end-users identified in the early planning stages, and recent upgrades to the system.
Supervised attention for answer selection in community question answering

&lt;span class="fontstyle0"&gt;Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores &lt;/span&gt;&lt;span class="fontstyle2"&gt;matchLSTM &lt;/span&gt;&lt;span class="fontstyle0"&gt;for answer selection in CQA. A lexical gap in CQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into &lt;/span&gt;&lt;span class="fontstyle2"&gt;matchLSTM&lt;/span&gt;&lt;span class="fontstyle0"&gt;. Specifically, we leverage lexical-semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;
Using Embeddings to Predict Changes in Large Semantic Graphs

Understanding and predicting how large knowledge graphs change over time is as difficult as it is useful. An important subtask to address this artificial intelligence challenge is to characterize and predict three types of nodes: add-only nodes that can solely add up new edges, constant nodes whose edges remain unchanged, and del-only nodes whose edges can only be deleted. In this work, we improve previous prediction approaches by using word embeddings from NLP to identify the nodes of the large semantic graph and build a Logistic Regression model. We tested the proposed model in different versions of DBpedia and obtained the following prediction improvements on F1 measure: up to 10% for add-only nodes, close to 15% for constant nodes, and close to 22% for del-only nodes.
Chatbot development using Java tools and libraries

The aim of this paper is the development of an intelligent programming robot for help with coding, using Java tools and libraries. This paper will examine the development process of Java software bots and their general objective. For the purpose of the successful development of an intelligent programming robot, the paper will provide theoretical guidance in selecting frameworks and development tools and assist in the development process, training and testing. It will compare existing software development tools for creating the bot that will quickly and reliably answer programming questions to users in the form of a conversation. As a result, it will provide a thorough foundation for the complete development of a chatbot.
Quantum and Approximation Algorithms for Maximum Witnesses of Boolean Matrix Products

The problem of finding maximum (or minimum) witnesses of the Boolean product of two Boolean matrices (MW for short) has a number of important applications, in particular the all-pairs lowest common ancestor (LCA) problem in directed acyclic graphs (dags). The best known upper time-bound on the MW problem for [Formula: see text] Boolean matrices of the form [Formula: see text] has not been substantially improved since 2006. In order to obtain faster algorithms for this problem, we study quantum algorithms for MW and approximation algorithms for MW (in the classical computational model). Some of our quantum algorithms are input or output sensitive. Our fastest quantum algorithm for the MW problem, and consequently for the related problems, runs in time [Formula: see text], where [Formula: see text] satisfies the equation [Formula: see text] and [Formula: see text] is the exponent of the multiplication of an [Formula: see text] matrix by an [Formula: see text] matrix. Next, we consider a relaxed version of the MW problem (in the classical model) asking for reporting a witness of bounded rank (the maximum witness has rank 1) for each non-zero entry of the matrix product. First, by adapting the fastest known algorithm for maximum witnesses, we obtain an algorithm for the relaxed problem that reports for each non-zero entry of the product matrix a witness of rank at most [Formula: see text] in time [Formula: see text] Then, by reducing the relaxed problem to the so called [Formula: see text]-witness problem, we provide an algorithm that reports for each non-zero entry [Formula: see text] of the product matrix [Formula: see text] a witness of rank [Formula: see text], where [Formula: see text] is the number of witnesses for [Formula: see text], with high probability. The algorithm runs in [Formula: see text] time, where [Formula: see text].
Evaluating Models for Virtual Forestry Generation and Tree Placement in Games.

A handful of approaches have been previously proposed to generate procedurally virtual forestry for virtual worlds and computer games, including plant growth models and point distribution methods. However, there has been no evaluation to date which assesses how effective these algorithms are at modelling real-world phenomena. In this paper we tackle this issue by evaluating three algorithms used in the generation of virtual forests - a randomly uniform point distribution method (control), a plant competition model, and an iterative random point distribution technique. Our results show that a plant competition model generated more believable content when viewed from an aerial perspective. We also found that a randomly uniform point distribution method produced forest visualisations which were rated highest in playability and photorealism, when viewed from a first-person perspective. Our results indicate that when it comes to believability, the relationship between viewing perspective and procedural generation algorithm is more important than previously thought.
Electrical Modeling of Two Tube Vocal Tract for Voice Pathology Detection

Voice pathology is interrelated to a vocal folds complication, and the vocal tract area which is attached to vocal folds shows a variable shapes in case of voice pathology. This variable shapes are tested to make the division of healthy and pathological voices. So, Length and area of vocal tract are used for finding electrical parameters using transmission line theory by considering the vocal tract as a sequence of tubes. The proposed work is used voice database from the Saarbrucken Voice Database (SVD) and real-time data from the Maharishi Markandeshwar (M.M) hospital, Mullana. The proposed method achieves a higher accuracy of 91.7 percent with electrical parameters compared to 88.2 percent with the acoustic parameters and 85.3 percent with physical parameters in the detection of voice pathology.
Research on Image Emotion Classification Based on Gene Expression

Aiming at the problem of image emotion classification, this paper proposes an image emotion classifier based on gene expression programming algorithm. Firstly, we extract HSV spatial histogram and SIFT features from the image, then integrate them with K-means clustering and spatial pyramid matching. Finally, we design chromosome and fitness function based on image emotional features, then implement the emotional classification algorithm based on GEP. The experimental results show that GEP has a good recognition effect, and the accuracy is improved by 6% compared with traditional SVM algorithm.
Libraries, Lectures, and Talk Shows: Some Russian Internet Resources for Research, Teaching, and Learning

Ethnocentrism affects perception and choices, and scholars, teachers, and students may not see or seek the “global” in our “global information revolution,” especially in such vast monolingual social spaces as the United States and the Anglophone world. Russia illuminates how unfortunate this state of affairs is because perceptions of Russia are so negative; whereas online information and open access there are so plentiful. Elibrary.ru offers free, full-text access to Russia’s periodical scholarship in all disciplines. Postnauka.ru is a lecture platform for Russian academics that rivals TED Talks in scale and production quality. Kul’tura is a federal television channel devoted entirely to education. Academic talk shows constitute part of the programming there and elsewhere. High-quality and reliable online tools such as these can enhance “Western” scholars’ and educators’ resource repertoires.
Geometry-Aware Video Object Detection for Static Cameras.

In this paper we propose a geometry-aware model for video object detection. Specifically, we consider the setting that cameras can be well approximated as static, e.g. in video surveillance scenarios, and scene pseudo depth maps can therefore be inferred easily from the object scale on the image plane. We make the following contributions: First, we extend the recent anchor-free detector (CornerNet [17]) to video object detections. In order to exploit the spatial-temporal information while maintaining high efficiency, the proposed model accepts video clips as input, and only makes predictions for the starting and the ending frames, i.e. heatmaps of object bounding box corners and the corresponding embeddings for grouping. Second, to tackle the challenge from scale variations in object detection, scene geometry information, e.g. derived depth maps, is explicitly incorporated into deep networks for multi-scale feature selection and for the network prediction. Third, we validate the proposed architectures on an autonomous driving dataset generated from the Carla simulator [5], and on a real dataset for human detection (DukeMTMC dataset [28]). When comparing with the existing competitive single-stage or two-stage detectors, the proposed geometry-aware spatio-temporal network achieves significantly better results.
Cyberbullying Detection on Multiple SMPs Using Modular Neural Network

As reported in recent statistics, the rate of cyberbullying on social media platforms(SMPs) is increasing daily along with affected population in the teenage and young generation worldwide. There is an urgent need to curb cyberbullying online and help curb its adverse effects on young minds and their psychology. Deep Learning in Artificial Intelligence can prove to be a benefactor in detection of such instances. The existing cyberbullying detection techniques attempt to detect either similar type of bullying or target only one social media platform. The proposed model, hence, will attempt to curb both the given bottlenecks. We propose a model that detects varied cyberbullying instances on multiple SMPs, essentially using individual detection models of three social media platforms Wikipedia, Formspring, Twitter synergically, to make a single prediction.
Quantum Delocalized Interactions

Classical mechanics obeys the intuitive logic that a physical event happens at a definite spatial point. Entanglement however, breaks this logic by enabling interactions without a specific location. In this work we study these delocalised-interactions. These are quantum interactions that create less locational information than would be possible classically, as captured by the disturbance induced on some spatial superposition state. We introduce quantum games to capture the effect and demonstrate a direct operational use for quantum concurrence in that it bounds the non-classical performance gain. We also find a connection with quantum teleportation, and demonstrate the games using an IBM quantum processor.
A Context-Aware and Technology-Assisted Informal Caregiver Selection Method to support Medical Emergency

Communities globally are experiencing an increasing number of patients with medical conditions and elderly people, living alone, who need immediate attention and support in case of medical emergency incidents. At times, even calling an ambulance becomes difficult for them and even then ambulance may turn up late causing to their sufferings. On the other hand, ambulances may turn up only to find a situation a non-emergency one not requiring an ambulance but that costs them money and wastes their time. In context to such situations, informal caregivers, in forms of friends, families and neighbours, can initially attend the patients and elderly people before the arrival of ambulance. Selection of appropriate informal caregivers is critical for a patient or a particular medical emergency situation and in this paper we propose a context-aware recommendation system (CARS) to recommend appropriate informal caregivers based on a list of pre-identified context information of informal caregivers, patients and elderly people. CARS operates as part of the technology-assisted medical emergency framework introduced by us. This work discusses the different phases of CARS, namely, context dimension, context acquisition and processing and context recommendation, and explains the entire informal caregiver recommendation procedure of CARS. Preliminary simulation results have shown that our proposed CARS perform better than other such existing systems.
Synchronous Reality: Enhancing Sensory Perception in Immersive VR

This paper introduces the concept of Synchronous Reality whereby a combined sensory physical and virtual installation is created. The research is presented examining how the practice work evolved to establish the concepts which make up a Synchronous Reality installation. The research ultimately focused on creating a sensory landscape to complement the virtual layer of the installation, thus making a coherent whole which encompasses Synchronous Reality. The findings and conclusions indicate that this type of installation can increase the immersive quality and presence for the participant. The important considerations when making such an installation are also presented. The paper also touches upon a brief examination of the wider issues of how immersive virtual reality affects the digital economy and telecommunications.
Analysis of Student Academic Performance and Social Media Activities by Using Data Mining Approach

This study aims to analysis the relationship of student academic performance to the activity of using social media. The method used in this research is a data mining approach to analysis its connectedness. Data mining techniques used are association and classification. As for the needs of the data used comes from student academic data sourced fromAcademic Information Systemand social media activity data comes from Instagram using Application Programming Interface (API) for get data automatically. Data requirements for academic achievement, i.e. grade-point averagre (GPA), duration of study, and faculty. As for identifying the data of social media activity, i.e.number of post (feed), number of following & follower, date & time post, and caption.The results of the analysis in this study indicate a relationship between academic achievement and the activity of using social media.
Knowledge-Based Matching of n-ary Tuples

An increasing number of data and knowledge sources are accessible by human and software agents in the expanding Semantic Web. Sources may differ in granularity or completeness, and thus be complementary. Consequently, they should be reconciled in order to unlock the full potential of their conjoint knowledge. In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar. This task is challenging since knowledge units can be heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we focus on matching n-ary tuples in a knowledge base with a rule-based methodology. To alleviate heterogeneity issues, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples from four different real-world sources. Results highlight noteworthy agreements and particularities within and across sources.
J-Analyzer: A Software for Computer-Assisted Analysis of Antônio Carlos Jobims Songs

The present paper describes structure and functioning of J-Analyzer, a computational tool for assistedanalysis. It integrates a research project intended to investigate the complete song collection by Brazilian composer Antônio Carlos Jobim, focusing on the aspect of harmonic transformation. The program is used to determine the nature of transformational relations between any chordal pair of chords present in a song, as well as the structure of the chords themselves.
The Influence of School Literacy Movement and Reading Habit on Student’s Achievement

This study aimed at determining whether there is an influence of the school literacy movement and reading habit on the student’s learning achievement of SD Negeri 31 Pulau Rimau. The sample of this research is fifth grade of 50 students. The data were collected using questionnaire, interviews and documentation. Data were analyzed using the SPSS program. The results showed 1) there was a significant influence of school literacy movement on student’s achievement; 2) there is a significant influence of reading habit on student’s learning achievement; and 3) there is a significant influence of the school literacy movement and the reading habit together on the student’s learning achievement.
Evolutionary simplicial learning as a generative and compact sparse framework for classification

Dictionary learning for sparse representations has been successful in many reconstruction tasks. Simplicial learning is an adaptation of dictionary learning, where subspaces become clipped and acquire arbitrary offsets, taking the form of simplices. Such adaptation is achieved through additional constraints on sparse codes. Furthermore, an evolutionary approach can be chosen to determine the number and the dimensionality of simplices composing the simplicial, in which most generative and compact simplicials are favored. This paper proposes an evolutionary simplicial learning method as a generative and compact sparse framework for classification. The proposed approach is first applied on a one-class classification task and it appears as the most reliable method within the considered benchmark. Most surprising results are observed when evolutionary simplicial learning is considered within a multi-class classification task. Since sparse representations are generative in nature, they bear a fundamental problem of not being capable of distinguishing two classes lying on the same subspace. This claim is validated through synthetic experiments and superiority of simplicial learning even as a generative-only approach is demonstrated. Simplicial learning loses its superiority over discriminative methods in high-dimensional cases but can further be modified with discriminative elements to achieve state-of-the-art performance in classification tasks.