Reviewerly/exHarmony huggingface dataset:
Table Tennis Forehand and Backhand Stroke Recognition Based on Neural Network

In the last few years, microcontroller producers started to produce SoC boards that are not only used to collect data from implemented sensors but also can be used for small neural networks implementation. The goal of this paper is to analyses the possibility of simple neural network implementation for sports monitoring but we will try to use the state of art technologies on that field. Sport monitoring devices can be used in most sports, but in this paper, the device that can recognize forehand and backhand strokes in table tennis will be developed. This task is not so complicated for development but the focus will be on the flexibility and possibility of using this system for other sports. According to the final test results in laboratory conditions, the system that has been developed is 96% accurate in table tennis forehand and backhand stroke recognition. Finally, in our implementation trained neural network was transferred to microcontroller and this approach opens some new possibilities that can be developed in future versions.
Deconstructing the Decentralization Trilemma

The vast majority of applications at this moment rely on centralized servers to relay messages between clients, where these servers are considered trusted third-parties.With the rise of blockchain technologies over the last few years, there has been a move away from both centralized servers and traditional federated models to more decentralized peer-to-peer alternatives.However, there appears to be a trilemma between security, scalability, and decentralization in blockchain-based systems.Deconstructing this trilemma using well-known threat models, we define a typology of centralized, federated, and decentralized architectures.Each of the different architectures has this trilemma play out differently.Facing a possible decentralized future, we outline seven hard problems facing decentralization and theorize that the differences between centralized, federated, and decentralized architectures depend on differing social interpretations of trust.
Switched-Capacitor Circuit Implementation of the Chaotic Neural Network Reservoir

A chaotic neural network reservoir has been proposed as an effective method for introducing various dynamics in the reservoir neural network. In this paper, we propose a method for implementing the chaotic neural network reservoir with a switched-capacitor (SC) circuit technique. Such a circuit consists of three SC chaotic neurons with discrete elements. We then confirm the functionality of the circuit through a discrimination task in which the network distinguishes sinusoidal and chaotic waveforms.
A personalized Recommendation Algorithm for College Books based on User Interest

Aiming at the problems of cold start, data sparseness, and similarity calculation bias in collaborative filtering algorithms, this paper proposes an interest-based university book recommendation algorithm. This algorithm solves the problem of the lack of scoring and the inability to use the collaborative filtering algorithm. At the same time, the combination of popularity and inverse popularity with similarity is considered to be closer to readers' behavioral characteristics. Experiments show that the algorithm is better than the traditional collaborative filtering recommendation algorithm, and has certain recommendation effect and practical value in the application of university libraries.
Finite State Machines for Building Believable Non-Playable Character in the Game of Khalid ibn Al-Walid

Abstract The character recognition game Khalid ibn al-Walid is an interactive Drama game application with the perspective of First Person Shooter introducing a hero in Islam named Khalid ibn al-Walid. To make the game interesting, the Non-Playable Character must be given artificial intelligence so that it can behave intelligently and make the game atmosphere more lively. One of the simple artificial intelligence that is applied in NPC games is Finite State Machine (FSM). In this game, FSM is applied to the enemy behavior of the character Khalid ibn al-Walid. The trial results show that NPC can behave independently and smartly in Search Player, Move To Player, Wait to Attack and Attack.
Countermeasure against Backdoor Attack on Neural Networks Utilizing Knowledge Distillation

A backdoor attack is a well-known security issue facing deep neural networks (DNNs).In a backdoor attack against DNNs for image classification, an adversary creates tampered data containing special marks ("poison data") and injects them into a training dataset.A DNN model that is trained with the tampered training dataset can achieve a high classification accuracy for clean (normal) input data, but the inference on the poisonous input data is misclassified to the adversarial target label.In this work, we propose a countermeasure against the backdoor attack by utilizing knowledge distillation in which the DNN model user distills a backdoored DNN model with clean unlabeled data.The distilled DNN model can be trained with clean knowledge on the backdoored model because the backdoor is not activated by clean data.Experimental results showed that the distilled model achieves high performance equiva lent to that of a clean model without a backdoor.
Multi-Task Pruning for Semantic Segmentation Networks

This paper focuses on channel pruning for semantic segmentation networks. There are a large number of works to compress and accelerate deep neural networks in the classification task (e.g., ResNet-50 on ImageNet), but they cannot be straightforwardly applied to the semantic segmentation network that involves an implicit multi-task learning problem. To boost the segmentation performance, the backbone of semantic segmentation network is often pre-trained on a large scale classification dataset (e.g., ImageNet), and then optimized on the desired segmentation dataset. Hence to identify the redundancy in segmentation networks, we present a multi-task channel pruning approach. The importance of each convolution filter w.r.t the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. In addition, we develop an alternative scheme for optimizing importance scores of filters in the entire network. Experimental results on several benchmarks illustrate the superiority of the proposed algorithm over the state-of-the-art pruning methods. Notably, we can obtain an about $2\times$ FLOPs reduction on DeepLabv3 with only an about $1\%$ mIoU drop on the PASCAL VOC 2012 dataset and an about $1.3\%$ mIoU drop on Cityscapes dataset, respectively.
R<sup>3</sup>MAT: A Rapid and Robust Graph Generator

One of the main problems when developing graph-based applications is the availability of large and representative datasets.The lack of real graphs has motivated the development of software tools for generating synthetic graphs.R-MAT is a data generation method that was designed to produce synthetic graphs whose characteristics resemble those occurring in real networks.Although the generation model defined by R-MAT is easy to understand, its implementation is not trivial and it has intrinsic memory restrictions that makes the generation of very large graphs difficult.This paper studies the practical implementation of R-MAT.We discuss the issues of the original implementation which works with the adjacency matrix of the graph and analyze the size of the resulting graph obtained with the R-MAT model.Then, we introduce and experimentally evaluate R 3 MAT, an alternative implementation for R-MAT based on an array of degrees.These experiments show that (i) our R 3 MAT is able to generate graphs of hundred million nodes and billion edges in a single machine, (ii) our method preserves the characteristic power-law distribution of the edge degrees present in real-world graphs, and (iii) R 3 MAT has the best performance in the current state of the art, when considering a single modest computer in a sequential fashion.
Achieving fair sampling in quantum annealing

Sampling all ground states of a Hamiltonian with equal probability is a desired feature of a sampling algorithm, but recent studies indicate that common variants of transverse field quantum annealing sample the ground state subspace unfairly. In this note, we present perturbation theory arguments suggesting that this deficiency can be corrected by employing reverse annealing-inspired paths. We confirm that this conclusion holds in simulations of previously studied small instances with degeneracy, as well as larger instances on quantum annealing hardware.
Library consortia contributing to college affordability: collection and OER initiatives in the Consortium of Academic and Research Libraries in Illinois

Purpose The Consortium of Academic and Research Libraries in Illinois (CARLI) consists of 128 libraries. This paper aims to present an analysis of collection and open educational resources (OER) initiatives undertaken by CARLI over the past decade that contribute to improving college affordability. Design/methodology/approach After reviewing important literature pertaining to library consortia and college affordability, this paper presents a detailed case study of CARLI’s collection and OER initiatives. Findings Owing to their economies of scale, library consortia have the potential to make substantial contributions to improving college affordability. Originality/value Compared to the efforts of individual libraries to improve college affordability, library consortium efforts have received far less attention in the professional literature. The work of the CARLI consortium documented here can provide a template for other library consortia that are working to improve college affordability.
Examining the Relationship between Individual Innovativeness and Digital Nativeness Levels of Teachers

It is thought that individuals who are open to innovation and development will have no difficulty in adapting to digital life. The aim of this study is to determine individual innovativeness and digital nativeness levels of teachers and to reveal the relationship between these two variables. Correlational survey model was used in the research. The research was carried out on 815 teachers working in state secondary schools in Turkey in 2018. Within the scope of the research, a questionnaire was applied to the teachers to collect their levels of personal innovativeness and digital nativeness and their personal information. According to the data obtained from the research, it can be said that both individual innovativeness and digital nativeness levels of male teachers are higher than female teachers. It was found that the levels of digital nativeness of IT teachers were higher than Mathematics, Turkish, Fine Arts and Religious Culture and Moral Education teachers. As a result of the analysis, it was found that there is a positive and moderate relationship between digital nativeness and individual innovativeness. It is concluded that individual innovativeness is a significant predictor of digital nativeness and that dimensions of individual innovativeness explain 26% of digital nativeness.
An enhanced LSB-based quantum audio watermarking scheme for nano communication networks

In recent years, digital multimedia including image, audio, and video are extended to quantum computation and quantum networks. Consequently, copyright protection of digital multimedia in quantum networks becomes a significant issue. As an important security technology, quantum watermarking is an appropriate solution, which embeds copyright information into the carrier signal. This work presents an enhanced least significant bit (LSB) based audio watermarking using reflected gray code. In the presented scheme, the watermark image is first scrambled using a new scrambling method presented in this paper, which modifies pixel values for scrambling instead of changing pixel positions. The scrambled image is then converted into a qubit sequence to comply with one-dimension audio signal, and the qubits are embedded into carrier quantum audio signal using an embedding key. In embedding phase, every four low qubit of target audio sample replaced with their nearest even or odd number in reflected gray code, based on the value of embedded qubit. For every procedure of the proposed scheme, the quantum circuit and complexity analysis is presented. Experimental results prove that the proposed scheme has good transparency, capacity and security.
Multiple Device Controlled Design for Implementing Telepresence Robot in Schools

Telepresence robots have shown their capabilities to perform remote teaching. In this research, the telepresence robot was designed and developed specifically for teaching from a remote location. The robot was intended to surpass the limitation of the normal teleconference software regarding the interaction with students. The robot incorporated movement and face display that help create an in-person environment. To use the robot for teaching, the servers were set up to exchange video data between the robot and the software on the remote teacher’s side, and to drive the robot’s wheels. On the remote teacher’s side, the software was designed and developed as a web application to view the classroom environment, control the robot, and control the teaching media within one screen. This enabled the teacher to fully control the equipment while delivering a lecture or talking to students. The robot was tested and evaluated in a real school setting with secondary school students.
Chapter 5 Federated Query Processing

Big data plays a relevant role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Semantic web technologies have also experienced great progress, and scientific communities and practitioners have contributed to the problem of big data management with ontological models, controlled vocabularies, linked datasets, data models, query languages, as well as tools for transforming big data into knowledge from which decisions can be made. Despite the significant impact of big data and semantic web technologies, we are entering into a new era where domains like genomics are projected to grow very rapidly in the next decade. In this next era, integrating big data demands novel and scalable tools for enabling not only big data ingestion and curation but also efficient large-scale exploration and discovery. Federated query processing techniques provide a solution to scale up to large volumes of data distributed across multiple data sources. Federated query processing techniques resort to source descriptions to identify relevant data sources for a query, as well as to find efficient execution plans that minimize the total execution time of a query and maximize the completeness of the answers. This chapter summarizes the main characteristics of a federated query engine, reviews the current state of the field, and outlines the problems that still remain open and represent grand challenges for the area.
Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm

Corona virus outbreaks that occur in almost all countries in the world have an impact not only in the health sector, but also in other sectors such as tourism, finance, transportation, etc. This raises a variety of sentiments from the public with the emergence of corona virus as a trending topic on Twitter social media. Twitter was chosen by the public because it can disseminate information in real time and can see market reactions quickly. This research uses "tweet" data or public tweet related to "Corona Virus" to see how the sentiment polarity arises. Text mining techniques and three machine learning classification algorithms are used, including Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) to build a tweet classification model of sentiments whether they have positive, negative, or neutral polarity. The highest test results are generated by the Support Vector Machine (SVM) algorithm with an accuracy value of 76.21%, a precision value of 78.04%, and a recall value of 71.42%.Keywords: Machine Learning, Corona Virus, Twitter, Sentiment Analysis.
Selective Encryption of Image by Number Maze Technique

Due to enormous increase in the usage of computers and mobiles, today's world is currently flooded with huge volumes of data.This paper is primarily focused on multimedia data and how it can be protected from unwanted attacks.Sharing of multimedia data is easy and very efficient, it has been a customary practice to share multimedia data but there is no proper encryption technique to encrypt multimedia data.Sharing of multimedia data over unprotected networks using DCT algorithm and then applying selective encryption-based algorithm has never been adequately studied.This paper introduces a new selective encryption-based security system which will transfer data with protection even in unauthenticated network.Selective encryption-based security system will also minimize time during encryption process which there by achieves efficiency.The data in the image is transmitted over a network is discriminated using DCT transform and then it will be selectively encrypted using Number Puzzle technique, and thus provides security from unauthorized access.This paper discusses about numeric puzzle-based encryption technique and how it can achieve security and integrity for multimedia data over traditional encryption technique.
Current state and trends of the mobile library applications (the case of the Bavarian State Library)

The mobile technology gained widespread use and became an essential part of all advanced industries over the past decade. Libraries also cannot ignore them, so mobile library applications have become one of the most relevant technological innovations in library activities. Now more and more libraries offer their websites in the mobile version. However, the capabilities of library mobile applications are not limited to providing access to basic services on the library website. The aim of the article is to analyse the functionality of specially developed applications of the Bavarian State Library (FRG), which are aimed at solving specific tasks. On the basis of a wide range of domestic and mainly German sources (articles, monographs, web resources), the author analyses the aspects of using the four mobile applications in the practice of one of the largest German libraries. It is shown that mobile applications can help to reveal unique publications stored in special collections of the library. It is noted that mobile application can be a navigator in a multi-storey library building with a developed infrastructure. The author stresses that the above tasks are achieved due to the extended functionality of the applications: user geolocation, augmented reality, effective reading modes as well as Bluetooth data transfer technology. For the first time, the author considers the mobile applications used in German library practice, which are aimed at meeting special needs of users (study of topography of the region, acquaintance with works of cultural and artistic value, navigation inside the library building). The analysed tendencies of use of the mobile applications can be of interest also for domestic libraries which strive to increase efficiency of work through implementation of mobile technology.
CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers

Deep Convolutional Neural Networks (CNNs) are empirically known to be invariant to moderate translation but not to rotation in image classification. This paper proposes a deep CNN model, called CyCNN, which exploits polar mapping of input images to convert rotation to translation. To deal with the cylindrical property of the polar coordinates, we replace convolution layers in conventional CNNs to cylindrical convolutional (CyConv) layers. A CyConv layer exploits the cylindrically sliding windows (CSW) mechanism that vertically extends the input-image receptive fields of boundary units in a convolutional layer. We evaluate CyCNN and conventional CNN models for classification tasks on rotated MNIST, CIFAR-10, and SVHN datasets. We show that if there is no data augmentation during training, CyCNN significantly improves classification accuracies when compared to conventional CNN models. Our implementation of CyCNN is publicly available on https://github.com/mcrl/CyCNN.
A Study of ‘Hear Me Later’ VR Content Production to Improve the Perception of the Visually-Impaired

This study was conducted to improve the education method for improving perception awareness of the visually-impaired.'Hear me later' was designed and implemented based on VR content that allows the visually-impaired experience in the eyes and environment.The main target is from middle and high school students to adolescents in their twenties.It is consisted of a student, the user's daily life with waking up at home in the morning, going to school, taking classes at school, and disembarking home late in the dark.In addition, 10 quests are placed on each map to induce users' participation and activity.These quests are a daily activity for non-disabled people, but it is an activity to experience uncomfortable activity for visually impaired people.In order to verify the effect of 'Hear me later', 8 participants in their early teens to early 20s' perception of visually impaired people was measured through pre and post evaluation of VR contents experience.In order to verify the effect of'Hear me later', 8 participants in their early teens to early 20s' perception 1 "학부생 주저자 논문임" of visually impaired people was measured through pre-post evaluation of VR experiences.As a result, it was found that in the post-evaluation of VR contents experience, the perception of the visually impaired was increased by 30% compared to the pre-evaluation.In particular, misunderstandings and changes in prejudice toward the visually impaired were remarkable.Through this study, the possibility of a VR-based disability experience education program that can freely construct space-time and maximize the experience was verified.In addition, it laid the foundation to expand it to various fields of improvement of the disabled.
Rotational-XOR Rectangle Cryptanalysis on Round-Reduced Simon

Recently, Ashur and Liu introduced the Rotational-XOR-difference approach which is a modification of rotational cryptanalysis, for an ARX cipher Speck (Ashur and Liu, 2016). In this paper, we apply the Rotational-XOR-difference (RXD) approach to a non-ARX cipher Simon and evaluate its security. First, we studied how to calculate the probability of an RXD for bitwise AND operation that the round function of Simon is based on unlike Speck is on modular addition. Next, we prove that two RXD trails can be connected such that it becomes possible to construct a boomerang/rectangle distinguisher similar to the case using differential characteristics. Finally, we construct related-key rectangle distinguishers for round-reduced versions of Simon with block lengths of 32, 48, and 64, and we suggest a five- or six-round key recovery attack. To our knowledge, it is the first attempt to apply the notion of rotational cryptanalysis for a non-ARX cipher. Although our attack does not show the best results for Simon thus far, the attempt here to define and apply a new cryptanalytic characteristic is meaningful, and we expect further improvements and applications to other ciphers to be made in subsequent studies.