Reviewerly/exHarmony huggingface dataset:
A Guideline Study For Designing Virtual Reality Games

Virtual reality has great potential for immersive experiences that is not available in other mediums. Thus, it has a significant place in the entertainment and the game industry. However, creating a well-designed immersive experience can be extremely complicated due to the replacement of human perception from the real-world to an isolated virtual world. Understanding the essentials of virtual reality (VR) experiences and game design principles is necessary for designing an immersive VR game. Although there are many differences in design elements of VR games according to the experience that is wanted to be given to the player, many games also have common elements. In the line of this view, a guideline is aimed to be framed for VR game designers in the current study. For this purpose design pillars of VR experiences and game design principles were reviewed, and five VR games were analyzed. Games are highly plastic mediums that can be adapted to any environment and technology. Many game types have a close relationship in terms of game elements and design. However, the implementation of game elements from other mediums to the VR medium is challenging. Therefore, game design principles should be well-comprehended and implemented to the VR medium by considering the existed technology. Virtual reality takes the player and put him in a new virtual world. In this world, everything should be designed to be easily perceivable by the player. Thus, the human perception was reviewed as one of the design pillars. Virtual reality also offers an interactive experience that allows the user to affect the world. Like in traditional video games, players can interact and navigate in the virtual world. Therefore, interaction and navigation were reviewed as two other design pillars. Based on the reviewed design pillars, five VR games were analyzed. Then, a VR game design guideline is proposed based on common game design elements in those games
[Paper] User-selectable Event Summarization in Unedited Raw Soccer Video via Multimodal Bidirectional LSTM

A new method that generates user-selectable event summaries from unedited raw soccer videos is presented in this paper. Since there are more unedited raw soccer videos than broadcasted/distributed soccer videos and unedited videos have various viewers, it is necessary to analyze these videos for meeting the demands of various viewers. The proposed method introduces a multimodal CNN-BiLSTM architecture for analyzing unedited raw soccer videos. This architecture extracts candidate scenes for event summarization from unedited soccer videos and classifies these scenes into typical events. Finally, our method generates user-selectable event summaries by simultaneously considering the importance of candidate scenes and the event classification results. Experimental results using real unedited raw soccer videos show the effectiveness of our method.
On Explaining Your Explanations of BERT: An Empirical Study with Sequence Classification

BERT, as one of the pretrianed language models, attracts the most attention in recent years for creating new benchmarks across GLUE tasks via fine-tuning. One pressing issue is to open up the blackbox and explain the decision makings of BERT. A number of attribution techniques have been proposed to explain BERT models, but are often limited to sequence to sequence tasks. In this paper, we adapt existing attribution methods on explaining decision makings of BERT in sequence classification tasks. We conduct extensive analyses of four existing attribution methods by applying them to four different datasets in sentiment analysis. We compare the reliability and robustness of each method via various ablation studies. Furthermore, we test whether attribution methods explain generalized semantics across semantically similar tasks. Our work provides solid guidance for using attribution methods to explain decision makings of BERT for downstream classification tasks.
Dense spatio‐temporal stereo matching for intelligent driving systems

This paper addresses the problem of matching stereo images acquired by a stereo system mounted aboard an intelligent vehicle. The main idea behind the new method consists in involving temporal matching between a current stereo pair and its preceding one to achieve the spatial matching of the former stereo by involving the matching results obtained at the last frame. The proposed method is achieved in three main steps. First, an edge based disparity map is derived from the disparity map of the preceding frame, we call it the assisting disparity map (ADM). Second, for each scan-line, a set of local ranges and global ranges are deduced from the ADM to keep only potential matching candidates. Third, the matching is done on the basis of dynamic programming algorithm by involving in the resulting local and global ranges we get from the last step. The proposed approach has been tested on both real and synthetic stereo sequences and the results demonstrate its effectiveness.
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control

In application areas where data generation is expensive, Gaussian processes are a preferred supervised learning model due to their high data-efficiency. Particularly in model-based control, Gaussian processes allow the derivation of performance guarantees using probabilistic model error bounds. To make these approaches applicable in practice, two open challenges must be solved i) Existing error bounds rely on prior knowledge, which might not be available for many real-world tasks. (ii) The relationship between training data and the posterior variance, which mainly drives the error bound, is not well understood and prevents the asymptotic analysis. This article addresses these issues by presenting a novel uniform error bound using Lipschitz continuity and an analysis of the posterior variance function for a large class of kernels. Additionally, we show how these results can be used to guarantee safe control of an unknown dynamical system and provide numerical illustration examples.
Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

Although there are a great number of adversarial attacks on deep learning based classifiers, how to attack object detection systems has been rarely studied. In this paper, we propose a Half-Neighbor Masked Projected Gradient Descent (HNM-PGD) based attack, which can generate strong perturbation to fool different kinds of detectors under strict constraints. We also applied the proposed HNM-PGD attack in the CIKM 2020 AnalytiCup Competition, which was ranked within the top 1% on the leaderboard. We release the code at https://github.com/YanghaoZYH/HNM-PGD.
It will certainly be found that some words are literally repeated: Horecký’s hypersyntax

Abstract The article reflects the linguistic work of Ján Horecký in connection with hyper syntax and text linguistics. In his work Základy jazykovedy (Outline of linguistics) Ján Horecký remarks (1974, p. 90) that one of the principles of text construction is the literal repetition of certain words. We discuss this Horecký’s assumption and describe its consequences for the langue parole opposition and the concepts of textual isotopy and textual cohesion . The main task of the article is to examine Horeský’s assumption. For these purposes, we present an authorship attribution analysis of literary works by two Slovak authors: Svetozár Hurban Vajanský and Martin Kukučín. We focus on low‐frequency lexicon, i.e. hapax legomena, which are supposed to be independent of the authorial style (e.g. Binongo, 2003) and should reflect random circumstances of communication (de Saussure, 1996, p. 50; Bloomfield, 1933, p. 170). This means that if the structure of the text were to be affected by the repetition of certain words, the low frequency layer of the lexicon should contain evidence of this repetition with a low degree of dependence on the content and style of the literary work (Baayen, 1996). The analysis and its presentation is based on separate processing of hapax legomena and their n‐grams, cosine dissimilarity and multidimensional scaling (Torgerson, 1952). Contrary to the general notion of the text structure, we conclude that the authorial texts are based on the repetition of certain word forms and word forms combinations (by n‐gram analysis), even in the level of low‐frequency words.
Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique

Absolute moment block truncated coding (AMBTC) is a lossy image compression technique aiming at low computational cost, and has been widely studied. Previous studies have investigated the performance improvement of AMBTC; however, they often over describe the details of image blocks during encoding, causing an increase in bitrate. In this paper, we propose an efficient method to improve the compression performance by classifying image blocks into flat, smooth, and complex blocks according to their complexity. Flat blocks are encoded by their block means, while smooth blocks are encoded by a pair of adjusted quantized values and an index pointing to one of the k representative bitmaps. Complex blocks are encoded by three quantized values and a ternary map obtained by a clustering algorithm. Ternary indicators are used to specify the encoding cases. In our method, the details of most blocks can be retained without significantly increasing the bitrate. Experimental results show that, compared with prior works, the proposed method achieves higher image quality at a better compression ratio for all of the test images.
Using Blockchain and Distributed Machine Learning to Manage Decentralized but Trustworthy Disease Data

Public health surveillance systems for infectious diseases, that can turn pandemic, need a regular intervention for diagnosis, treatment and control. For an effective disease monitoring and control system, there is a need to model a solution for organizing, sharing and analyzing the disease data with trusted, privacy-preserving and interoperable methodologies to improve the outreach, time and cost-effectiveness for disease-control and treatment interventions. Blockchain has emerged as one of the promising technologies owing to its unique features like decentralization, transparency, immutability, data provenance and cryptography. The primary aim of this study is to perform a bibliometric analysis of literature on the applications of blockchain in disease data management systems. Secondly, it is aimed to survey the suitability of existing blockchain platforms for disease data storage, sharing and analytics. This study also explored literature on the design of privacy-preserving machine learning models on blockchain applications for collaborative learning. Conclusively, the presented survey opens opportunities to the building of privacy-preserving, trusted, interoperable and inferring disease data management systems with blockchain and machine learning.
An Account of the ISO/IEC Standardization of the Simon and Speck Block Cipher Families

Abstract Simon and Speck are two block cipher families published in 2013 by the US National Security Agency (NSA). These block ciphers, targeting lightweight applications, were suggested in 2015 to be included in ISO/IEC 29192-2 Information technology—Security techniques—Lightweight cryptography—Part 2: Block ciphers . Following 3.5 years of deliberations within ISO/IEC JTC 1 they were rejected in April 2018. This chapter provides an account of the ISO/IEC standardization process for Simon and Speck.
Spatiotemporal feature based convolutional neural network for violence detection

Automatic violence detection is one of the most interesting tasks in video surveillance applications. It is essential to ensure human safety by preventing fatal accidents. In this paper, we propose a spatiotemporal feature based on convolutional neural network and optical flow information for violence detection. Our descriptor, named distribution-based CNN feature (DCNN), estimates first the joint distribution of the optical flow magnitude and orientation around STIP points in the aim of modeling the motion structure. Then, it extracts deep features using the pretrained ResNet50 network. Our DCNN feature is finally fed into the SVM classifier for training. We evaluate the proposed descriptor on two challenging benchmark datasets designed for violence detection in both uncrowded and crowded scenes. For the two datasets, our DCNN feature shows significant improvement compared to the state-of-the-art descriptors.
Modern Unmanned Technology in Archaeogeophysical Studies

Summary Modern geophysical methods, such as magnetometry, electrotomography, electromagnetic profiling, georadar, are widely used in practice and provide important information about the location and structure of individual archaeological sites or monuments in general. Recent technological advances in the field of unmanned technologies open up prospects for a significant increase in the productivity and information content of geophysical surveys. This article is devoted to a review of the world practice of the use of unmanned technologies in archaeological research, a description of practical examples of the use of orthophotography and aeromagnetometry with UAVs for archeological problems, as well as a description of the prospects for the development of aerial versions of equipment for electromagnetic profiling.
Attention Guided Dialogue State Tracking with Sparse Supervision

Existing approaches to Dialogue State Tracking (DST) rely on turn level dialogue state annotations, which are expensive to acquire in large scale. In call centers, for tasks like managing bookings or subscriptions, the user goal can be associated with actions (e.g.~API calls) issued by customer service agents. These action logs are available in large volumes and can be utilized for learning dialogue states. However, unlike turn-level annotations, such logged actions are only available sparsely across the dialogue, providing only a form of weak supervision for DST models. To efficiently learn DST with sparse labels, we extend a state-of-the-art encoder-decoder model. The model learns a slot-aware representation of dialogue history, which focuses on relevant turns to guide the decoder. We present results on two public multi-domain DST datasets (MultiWOZ and Schema Guided Dialogue) in both settings i.e. training with turn-level and with sparse supervision. The proposed approach improves over baseline in both settings. More importantly, our model trained with sparse supervision is competitive in performance to fully supervised baselines, while being more data and cost efficient.
Analysis Of Android Based Mobile Blocking Application Design Using Mit App Inventor

Mobile phone or cellphone is a device that can’t be separated and left by anyone. Every day, almost every chance we see someone always holding a cellphone in every activity, whether working with full cell phone support or just mere communication needs. However, for mobile users who are busy, driving, in worship or in other circumstances, often get incoming calls or SMS that are not cold. This study presents a solution to politely block or reject incoming calls or reply automatically to SMS. The solution is in the form of developing an Android-based cell phone blocking application which becomes a backroud process to handle each event and then automatically processes it by the application. The selection of the Android platform for mobile devices because the operating system is based on the Linux kernel which is designed for mobile devices touch screen and with open source support makes it easy for application developers to freely modify and distribute in a well-known market place. From a series of experiments and analysis of applications developed, the overall application can work well with a fast response supported by simple operation methods and designs that are easy to understand so the user is greatly helpful.
AN ITERATIVE METHOD OF SOLVING A GAME

Abstract : In the paper, demonstration is made of the validity of an iterative procedure suggested by George W. Brown for a two-person game. This method corresponds to each player choosing in turn the best pure strategy against the accumulated mixed strategy of his opponent up to then.
HOW DO STUDENTS REALLY INTERACT? AN INVESTIGATION OF LITHUANIAN STUDENTSâ€™ INTERACTIONS VIA SMARTPHONE APPS

Purpose â€“ This mixed-method study investigated the ways smartphone apps promote studentsâ€™ interaction, collaboration and learning performance. Methodology â€“ A total of 160 respondents from a Lithuanian university were identified using convenience sampling. A survey was conducted to explore the frequent activities involving interaction, collaboration and learning performance. Focus group interviews were also conducted to determine the apps used in their interactions. Findings â€“ The quantitative results suggest that the smartphone apps promote students' interaction, collaboration and improved learning performance. Qualitative results indicate three types of apps that are commonly used for interactions by the undergraduates in Lithuania, namely educational, communication and entertainment apps. Significance â€“ The findings suggest that the instructions can play a constructive role in mediating the learnersâ€™ interactions on smartphone apps. As such, the study creates space for instructors to further craft pedagogical practices to better account for learnersâ€™ access to smartphone apps. It is hoped that this study will provide a path for educational solutions for instructors and practitioners.
Scaling client-specific equivalence checking via impact boundary search

Client-specific equivalence checking (CSEC) is a technique proposed previously to perform impact analysis of changes to downstream components (libraries) from the perspective of an unchanged system (client). Existing analysis techniques, whether general (regression verification, equivalence checking) or special-purpose, when applied to CSEC, either require users to provide specifications, or do not scale. We propose a novel solution to the CSEC problem, called 2clever, that is based on searching the control-flow of a program for impact boundaries. We evaluate a prototype implementation of 2clever on a comprehensive set of benchmarks and conclude that our prototype performs well compared to the state-of-the-art.
Toward a Self-Adaptive Cyberdefense Framework in Organization

Organizations suffer more than ever from the inability to securely manage the information system, despite their myriad efforts. By introducing a real cyberattack of a bank, this research analyzes the characteristics of modern cyberattacks and simulates the dynamic propagation that makes them difficult to manage. It develops a self-adaptive framework that through simulation, distinctly improves cyberdefense efficiency. The results illustrate the discrepancies of the previous studies and validate the use of a time-based self-adaptive model for cybersecurity management. The results further show the significance of human and organizational learning effects and a coordination mechanism in obtaining a highly dependable cyberdefense setting. This study also provides an illuminating analysis for humans to position themselves in the collaborations with increasingly intelligent agents in the future.
Scheduling of multiple mobile robots based on time windows and improved ant colony algorithm

In view of the low efficiency and easy to fall into local optimum in the current path planning research, an improved ant colony algorithm is proposed. In the ant colony algorithm, the difference between the previous generation best path and the current iteration best path has dynamic change information, which improves the efficiency of the algorithm by feeding forward the pheromone generated by the dynamic change information; Aiming at the path conflict problem of mobile robot, a multi robot cooperative obstacle avoidance strategy based on time window algorithm is proposed. Firstly, the calculation method of multi robot conflict area is studied. Secondly, the time window arrangement method of two or more paths crossing is given, which realizes the efficient cooperation of multi robot in the workshop.
Decision Trees in Large Data Sets

Data mining is the process of obtaining information, which is used to identify and define the relationships between data of different qualities.One of the important problems encountered in this process is the classification process in large data sets.Extensive research has been done to find solutions to this classification problem and different solution methods have been introduced.Some decision tree algorithms are among the structures that can be used effectively in this field.In this article, various decision tree structures and algorithms used for classification process in large data sets are discussed.Along with the definitions of the algorithms, the similarities and existing differences between them were determined, their advantages and disadvantages were investigated.
Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation

Unsupervised domain adaptation (UDA) methods have shown their promising performance in the cross-modality medical image segmentation tasks. These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images. However, when there exists a large domain shift between source and target domains, we argue that this asymmetric structure could not fully eliminate the domain gap. In this paper, we present a novel deep symmetric architecture of UDA for medical image segmentation, which consists of a segmentation sub-network, and two symmetric source and target domain translation sub-networks. To be specific, based on two translation sub-networks, we introduce a bidirectional alignment scheme via a shared encoder and private decoders to simultaneously align features 1) from source to target domain and 2) from target to source domain, which helps effectively mitigate the discrepancy between domains. Furthermore, for the segmentation sub-network, we train a pixel-level classifier using not only original target images and translated source images, but also original source images and translated target images, which helps sufficiently leverage the semantic information from the images with different styles. Extensive experiments demonstrate that our method has remarkable advantages compared to the state-of-the-art methods in both cross-modality Cardiac and BraTS segmentation tasks.
Image Retrieval based Convolutional Neural Network

At the present time, everyone is interested in dealing with images in different fields such as geographic maps, medical images, images obtaining by Camera, microscope, telescope, agricultural field photos, paintings, industrial parts drawings, space photos, etc. Content Based Image Retrieval (CBIR) is an efficient retrieval of relevant images from databases based on features extracted from the image. Follow the proposed system for retrieving images related to a query image from a large set of images, based approach to extract the texture features present in the image using statistical methods (PCA, MAD, GLCM, and Fusion) after pre-processing of images. The proposed system was trained using 1D CNN using a dataset Corel10k which widely used for experimental evaluation of CBIR performance the results of proposed system shows that the highest accuracy is 97.5% using Fusion (PCA, MAD), where the accuracy is 95% using MAD, 90% using PCA. The performance result is acceptable compared to previous work.