Reviewerly/exHarmony huggingface dataset:
Constructing New NLFSR Functions with Optimal Periods

Pseudorandom bit generators are essential components in many security applications. The security of the system relies on the security of its components. Feedback shift registers are commonly used to generate pseudorandom bits. Nonlinear feedback shift registers (NLFSRs) are known to be more secure than the linear ones. However, there is no mathematical foundation on how to construct NLFSR feedback functions with optimal periods. This article considers a new type of NLFSR capable of constructing feedback functions of degree 3 with optimal periods. Using their construction method, the authors propose new functions of this type.
Assessing User Experience (UX) Interacting With Car Catalog Application Using Multi-touch Tabletop Display

Current car catalogue applications could well support multi-touch interactions but still suffer from poor usability and user experience (UX). These two aspects are important for design considerations as they could influence customers' decision making when purchasing the cars. This paper aims to present the design and evaluation of a 3D car catalogue application for car industry using tabletop display. The idea is to provide a platform to conduct an exploratory study in assessing how well UX could be supported by the application. The 3D car catalogue application is designed and developed based on existing design guidelines and collaborative activity theory. This design provides a proof of concept to leverage the use of table-top technology for car catalogue applications. In order to evaluate UX of the application, an empirical method is conducted using a set of UX questionnaires. The study findings provide reliability of the questionnaires. User feedback on UX reveals that the application supports co-located collaboration and usability but still lacks in providing better emotional aspect around tabletop display.
Learning Cascaded Context-Aware Framework for Robust Visual Tracking

Context information on each corner of the whole image is useful for visual tracking. However, some trackers may not be able to model such information, this will result in suboptimal performance. To directly model fully context information is intractable since first the region of the foreground is relatively small, the structure of foreground is lost for some part by straightforwardly aware. Second, the target may share a similar structure of the surrounding distractors. To this end, we propose a cascaded context-aware framework based on two networks that progressively model the foreground and background of the various targets over time. The first network pays attention to the most discriminative information within the whole context and coarser structure of the target, the second network focuses on the self-structure information of the target. Depending on the output of these two networks-the final context-aware map, we can generate the bounding box of the target flexibly. Extensive experiments on 3 popular benchmarks demonstrate the robustness of the proposed CAT tracker.
Decoding Erasures for Nonsystematic RS Codes

In the past, RS codes were initially encoded in nonsystematic forms to account for general RS codes that transform all components in a codeword. To avoid information leakage, RS code can also be encoded in nonsystematic forms like the general RS code-based cryptography. This work proposes an algorithm to correct erasures in nonsystematic RS codes for the first time, in which Gaussian elimination is used to calculate the error values. Meanwhile, RS decoding in the frequency domain is reorganized to account for nonsystematic RS codes, in which the first Fourier transform is left for encoding and disappear in the decoder. Then the nonsystematic codes are received and pass only one Fourier transform in RS decoding. The first RS decoding is able to correct erasures in nonsystematic codewords, while the other one utilizes the nonsystematic forms to improve the efficiency of systematic RS decoding. Both scenarios are based on Fourier transforms in the finite field over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ). The proposed algorithms and architectures can be implemented by reconfigurable logic, which measures the efficiency and examines the validity of decoding nonsystematic RS codes.
Fast Non-dominated Sorting Genetic Algorithm with Three Crossover Individuals for Network Topology Optimization in Industrial Internet of Things

With the deep integration of information and communication, people's demand for more reliable network topology structure is becoming higher and higher. Multi-objective model of switched industrial Ethernet topology structure is a recently proposed mode, which has been proved to have a flexible network structure and high communication efficiency. However, the fast non-dominated sorting algorithm II (NSGA-II), which is used to tackle the mode above, has the drawback that multiple repeatedly selected parent individuals may result in low diversity of the offspring population. To tackle this issue, this paper proposes to employ three parent individuals to generate offspring individuals. Experimental results on ZDT test suit demonstrate that the proposed method can evidently improve NSGA-II. Based on the further experiment results on multi-objective model of switched industrial Ethernet topology structure, it can be concluded that the proposed method also has better performance in tackling practical problem.
Visualisation of Distributed Systems Simulation Made Simple

Distributed (computing) systems come in various sizes and scale. They range from a single workstation computer with several processors, a cluster of compute nodes (servers) to a federation of geographically distributed data centres with millions of servers. Job scheduling is a fundamental aspect for data centre efficiency. In this paper, we present ds-viz as a visualisation aid for ds-sim, a recently developed distributed systems simulator. In particular, ds-viz significantly helps leverage the evaluation and analysis of scheduling algorithms that ds-sim facilitates to design. We show the effectiveness of these tools with some examples.
Points calculation system on taekwondo martial arts based on wi-fi

In Taekwondo matches, a calculation system is needed to help the judge carry out his duties. The system of one of the South Korean martial arts, is designed using a Wi-Fi device that has six buttons, consisting of three buttons for blue contestants and three buttons for red contest, respectively, one button for add three points, one button for add two points and one button for add one points as input. The last point will be given by the operator if each judge's points have the same value given by a minimum of two of the three judges.
A Novel Topological Descriptor for ASL

Hand gesture recognition is a challenging problem in human computer interaction. A familiar category of this problem is American sign language recognition. In this paper, we study this problem from a topological point of view. We introduce a novel topological feature to capture and represent the shape properties of a hand gesture. The method is invariant to changes in rotation, scale, noise, and articulations. Due to the lack of ASL image database with all variations and signs, we introduced a database consisting of 520 images of 26 ASL gestures, with different rotation and deformations. Experimental results show that this algorithm can achieve a higher performance in comparison with state of the art methods.
Thai Scene Graph Generation from Images and Applications

The scene graph is used to represent the semantics of images or visual understanding. It has been used frequently for image retrieval and image generation tasks. We develop a scene graph generator tool from a single image. This tool creates a scene graph representing Thai language. The main methodology contains 3 steps: image captioning, scene graph parser, and machine translation. We propose an application of chat bot demonstrating the use of the generated scene graph data. Our experimental results show the metric values of the machine translator and caption generator. For the translator model, we use BLEU, GLEU, WER and TER scores. Those scores are calculated during the different process in preparing the input data. In the first step, the translator model accepts a single word which is mapped to translated words in a local language, and we compose them into a sentence. In the second step, we send the sentence to the translator model. We also measure METEOR, ROUGE, CIDEr and SPICE scores for the performance of the caption generator. For the overall system score calculated by CIDEr and GLEU scores, we obtain 0.5393 and 0.6028 respectively.
Mathematical Fundamentals II: Abstract Algebra

Ordinary arithmetic has the basic operations of addition, subtraction, multiplication, and division defined over the integers and the real numbers. Similar operations can be defined over other mathematical structures—certain subsets of integers, polynomials, matrices, and so forth. This chapter is a short discussion on such generalizations. The first section of the chapter is an introduction to two types of abstract mathematical structures that are especially important in cryptography: groups and fields. The second section consists of a review of ordinary polynomial arithmetic. The third section draws on the first two sections and covers polynomial arithmetic over certain types of fields. And the last section is on the construction of some fields that are especially important in cryptography.
Online CAPTCHA Replacement through Face Detection

The sensitive information stored on the internet should not be easily accessible by any unauthorized personnel, it should be readily available only to the authorized people. Thus integrity, security and confidentiality need to be maintained. There needs to be a security check before any kind of access is granted to any kind of sensitive information. CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) is a popular way for authenticating a user. The main goal of any technique is to recognize whether it is a human who is accessing the content or a robot via some recognition techniques. The advancement in recognition technologies has enabled humans to develop bots which can now bypass common authentication techniques which have been in use for a long time. CAPTCHA has been in use since a very long time and has some serious drawbacks. Facial recognition alongside with artificial intelligence can overcome drawbacks like difficulty in solving, ambiguity, and sometimes easy prey for trained bots of CAPTCHA and can easily replace it.
Landmark-based Multi-Points Warping Approach to 3D Facial Expression Recognition in Human

Expression in H-sapiens plays a remarkable role when it comes to social communication. The identification of this expression by human beings is relatively easy and accurate. However, achieving the same result in 3D by machine remains a challenge in computer vision. This is due to the current challenges facing facial data acquisition in 3D: such as lack of homology and complex mathematical analysis for facial point digitization. This study proposes facial expression recognition in human with the application of Multi-points Warping for 3D facial landmark. The results indicate that Fear expression has the lowest recognition accuracy while Surprise expression has the highest recognition accuracy. The classifier achieved a recognition accuracy of 99.58%.
Web based Accounting Integrated Management System (AIMS) over Cloud using Mean Stack

This paper says how powerful a cloud based accounting management tool can be, which also provides platform independent services. This paper also observes the tools of MEAN stack (MongoDB, Express.js, Angular.js, Node.js) together with Material design. It also takes readers to the libraries like Bcryptjs, Body parser, Cors, JWT.
ALTEE: Constructing Trustworthy Execution Environment for Mobile App Dynamically

TEE(Trusted Execution Environment) has became one of the most popular security features for mobile platforms. Current TEE solutions usually implement secure functions in Trusted applications (TA) running over a trusted OS in the secure world. Host App may access these secure functions through the TEE driver. Unfortunately, such architecture is not very secure. A trusted OS has to be loaded in secure world to support TA running. Thus, the code size in secure world became large. As more and more TA is installed, the secure code size will be further larger and larger. Lots of real attack case have been reported [1]. In this paper, we present a novel TEE constructing method named ALTEE. Different from existing TEE solutions, ALTEE includes secure code in host app, and constructs a trustworthy execution environment for it dynamically whenever the code needs to be run.
Region Based Convolutional Neural Network for Smart Driving Monitoring System

The following topics are dealt with: feature extraction; learning (artificial intelligence); image classification; photovoltaic power systems; power grids; diseases; Matlab; support vector machines; power engineering computing; convolutional neural nets.
The Influence of the Leading Hemisphere on the Learning Foreign Language

The relevance of the chosen topic is determined by the interest of teachers in effective foreign language teaching in the conditions of modern globalization and intercultural interactions. For competent and effective language learning, it is necessary to take into account the leading hemisphere of the brain. Based on the functions of the hemispheres, it is possible to rationally draw up a training plan that will give results and facilitate the learning process of language acquisition.
Effect of Sampling Strategies on Fine-grained Emotion Classification in Microblog Text

This study investigates the effect of diverse training samples on machine learning model performance for fine-grained emotion classification. Using four different sampling strategies (random sampling, sampling by topic and two variations of sampling by user), we found the class distribution of 28 emotion categories to differ across the samples produced by each sampling strategy. However, combining different sampling strategies is complementary in generating sufficiently diverse training examples for the emotion classifiers. Based on support vector machine (SVM) and Bayesian network learning algorithms, our findings show that a classifier trained on combined data from the four sampling strategies performs better and is more generalizable than a classifier trained only on data from a single sampling strategy. Demonstrating how the diversity of the training samples affect the performance of emotion classifiers is the main contribution of this study.
Training Ternary Neural Networks By Rectified L2 Regularization

This paper develops a neural network optimization algorithm: the rectified L2 regularization, which can be used to train ternary neural networks with weights of all layers constrained to -1, 0 and +1. It will analyze how to set the learning rate and penalty coefficient during the training phase. Compared with previous approaches, the rectified L2 regularization algorithm can be directly implemented on the open source machine learning framework, such as TensorFlow and PyTorch. The accuracy of the MNIST and Fashion-MNIST test datasets is 99.40% and 92.21%, respectively, which is close to the state-of-the-art accuracy of full precision neural networks with the model compression rate guaranteed.
Deep Ensemble Learning: A Communications Receiver Over Wireless Fading Channels

Deep learning algorithms have proven themselves powerful in different applications because of their ability of generalization. In this paper, we introduce a deep learning wireless communications receiver network that enables reliable data transmission over wireless multipath Rayleigh fading channels. Significant improvements in terms of Bit Error Rate (BER) are achieved in simulation for various channel models compared to the traditional receiver. We also present a training procedure that leads to better generalization of the network by dividing it into jointly trained subnetworks as a Deep Ensemble learner while leveraging the regularization by combining information from different subnetworks.
Fine-Grained Image Classification with Object-Part Model

Fine-grained image classification is used to identify dozens or hundreds of subcategory images which are classified in a same large category. This task is challenging due to the subtle inter-class visual differences. Most existing methods try to locate discriminative regions or parts of objects to develop an effective classifier. However, there are two main limitations: (1) part annotations or attribute descriptions are usually labor-intensive, and (2) it is less effective to find spatial relationship between the object and its parts. To alleviate these problems, we propose a novel object-part model that relies on an attention mechanism. The main improvements of our method are threefold: (1) an object-part spatial constraint which selects highly representative parts, able to keep parts both discriminative and integrative, (2) a novel heatmap generation method, able to represent comprehensively the discriminative parts by regions, and (3) a speed up of the part selection by filtering image patch candidates using a fine-tuned CNN. With these improvements, the proposed method achieves encouraging results compared to the state-of-the-art methods benchmarking on the Stanford Cars and Oxford-IIIT Pet datasets.
Building the Pornography Corpus for Bahasa Indonesia Based on TRUST+™ Positif Database

The Indonesian government has developed a database called TRUST+™ Positif which contains the list of blacklisted URLs. These URLs are blacklisted because they contain some prohibited materials or negative contents such as pornography, radicalism, fraud, racism, violence, gambling, and security threat. The government requires all the Internet Service Provider (ISP) in Indonesia to block Internet access to websites listed on TRUST+™ Positif database. The government expects this action will help reducing the spread of negative contents, especially pornography. One of the many ways to disseminate pornographic content is by publishing those articles in the websites. This research purpose is to provide the pornographic corpus in order to be the reference for pornography identification research. The corpus is built from 1,000 articles from 150 websites based on TRUST+™ Positif database. This research only extracts the sentences that related to pornography. The extraction is done based on the selected keywords. These keywords are generated by the most frequent words the articles and should be related to pornography. There are 447 keywords that have been selected manually. The result of this research is a pornographic corpus in Bahasa Indonesia that consists of 35,753 sentences.
Prewett Edge Detector Method for Content Extraction in Moving Pictures or Images

Contents are the significant document into images or videos. Different authors or scientist presents different strategies to separate the helpful contents from a picture or a moving picture but some it is very difficult to extract the contents because of picture quality. So we propose a substitute method to discover and compute the contents that is uncommonly planned for being associated with shading pictures with complex foundation. The emphasis is on an issue which is identified with contents extraction in video pictures. In this work, we present calculation for removing content in video, pictures using Prewett Edge Detection in terms of precision rate and recall rate. Distinctive types of shading pictures digitized from imparting TV are used to test the estimation and the unfathomable execution result was gotten. For execution examination of our proposed strategy, parameters, for example, precision rate and recall rates have been watched.
A Study of Image Processing Based Object Depth Estimation

Issue of object's depth estimation from binocular vision technique is studied in this paper. A binocular vision platform is set up to detect and estimate the object's depth. Mathematical derivation of object's depth from the two cameras of the proposed platform is re-visited to reveal the linkage between system parameters such as focal length and the estimation error. Both of simulation and experimental results are obtained for parametric analysis of estimation error. A comparison with Kinect is also given to demonstrate the superiority of the proposed design.
Improving big-data automotive applications performance through adaptive resource allocation

In automotive applications, connected vehicles (CVs) can collect various information (external temperature, speed, location, etc.) and send them to a central infrastructure for exploitation in a wide range of applications: Eco-Driving, fleet management, environmental monitoring, etc. Such applications are known to generate a massive volume of data that is processed in real or near real time (i.e., data streams) depending on the target application requirements. To handle this data volume, big data architectures, based on stream computing paradigm, are usually adopted. Within this paradigm, data are continuously processed by a set of operators (elementary operations) instances. Further, a streaming application can be modeled as a directed graph where vertices are operators instances and edges are data streams (i.e., continuous series of tuples, generated by an operator). The central challenge when developing streaming applications is the way to assign operators to given resources for optimal performances (i.e., resource allocation). We showed that straightforward allocation, when based on intrinsic data parallelism, do not yield satisfying results. We developed a novel approach that takes into account the specifics of the target application, on the one hand, and on the other hand the systems performance metrics we derived from the infrastructure. The proposed approach improves the throughput by 4% compared to the previous approach. In the automotive context, this number translates into 800k additional vehicles to be served by the infrastructure over the 20M of the expected number of connected vehicles by Groupe PSA.
Improved HyperD Routing Query Algorithm Under Different Strategies

A hypercube labeling model HyperD was introduced to solve the problem of excessive bandwidth consumption and long query time between various nodes. By analyzing and adopting different strategies to improve the routing query algorithm of HyperD, a relatively more efficient routing query algorithm is proposed, which reduces the cost of communication and accelerates the data transmission between nodes. In order to verify the effectiveness of the algorithm, experiments were performed on randomly selected HyperD networks of various sizes. The improved routing algorithm was compared with the known routing algorithms, the results show that the improved algorithm is more efficient, and it can find the optimal solution in valid time.