Reviewerly/exHarmony huggingface dataset:
The Pseudo-DF Approach for Learning Huge-scale Data

Abstract As the advent of the big data era, huge-scale data continuously appears in various fields of science, commerce, industry and society. More algorithms/methods/approaches are urgently required to learn huge-scale data collected from different applications/backgrounds. Therefore, the Pseudo Data Flow (Pseudo-DF) approach with ensemble ReOS-ELMs is proposed in this paper. The Pseudo-DF approach randomly divides a huge-scale data set into K ( K &gt;1) non-overlapping data chucks, and a Pseudo-DF is constructed by these data chucks. The computation of a huge-scale data is changed into that of a Pseudo-DF with smaller-scale chucks, the computational burden will be much reduced. Then, the ensemble Regularized OS-ELMs (ReOS-ELMs) based on Different random Hidden-node Parameters (DiffHPs) is presented to learn a Pseudo-DF, which is a recursive leaning algorithm possessing the advantages of low computational burden, high accuracy, well generalization and stability, and strong robustness. Lastly, experiments are performed to validate the effectiveness of the proposed approach.
Research on Video Target Tracking Algorithm Based on Particle Filter and CNN

Target recognition and tracking technology has become a core technology for UAVs to visually perceive and understand the battlefield environment. Therefore, this paper proposes an algorithm to study UAVs tracking video targets. In the framework proposed, according to the motion model, the particle filter is used to predict the target position at each frame of the image sequence, the input of the CNN are those particles that round the position predicted, and the adaptive correlation filter is learned on the output of each layer of the CNN to encode the target appearance, and then through a correlation filter maintains the long-term memory of the target’s appearance. Finally, the output of the CNN and the correlation filter is used to determine the particle weights, and the target position of the current sequence of the image sequence is calculated based on the particles and their weight. By using the Visual Tracker Benchmark v1.0 to test and evaluate the algorithm, we can find that the algorithm has good tracking performance.
Personalized Transaction Kernels for Recommendation Using MCTS

We study pairwise preference data to model the behavior of users in online recommendation problems. We first propose a tensor kernel to model contextual transactions of a user in a joint feature space. The representation is extended to all users via hash functions that allow to effectively store and retrieve personalized slices of data and context. In order to quickly focus on the relevant properties of the next item to display, we propose the use of Monte-Carlo tree search on the learned preference values. Empirically, on real-world transaction data, both the preference models as well as the search tree exhibit excellent performance over baseline approaches.
Optical convolution based computational method for low-power image processing

Convolutional neural networks (CNNs) is becoming a critical role for deep learning-based computer vision applications. Through CNN we can extract meaningful information out of massive sensor data. Vision applications use this information to analyze the mainstream trends of the data and take immediate action based on these trends. However, CNN's energy consumption and bandwidth limitations make it difficult for CNN network systems to deploy in mobile systems with stringent energy limits. In this paper, we explore the simulation and the hardware method of optical convolution for low power image processing, which is inspired by the bio-image sensor Angle sensitive pixel (ASP). This optical computation method may be used to substitute the first convolution layer of the CNN network due to its energy-saving features and speed of light processing time. We adopt two-layer of customized transmission grating to perform this optical convolution computing. By generating the Talbot effect, the two-layer grating structure can perform optical convolution computation using Gabor wavelet filters, which will cause zero electrical power. We demonstrate both simulation and experiment results for optical convolution through our algorithm and prototype system, the convolution results can extract different meaningful information about the original image, which is very similar to edge filtering. This optical operation will hopefully be used to replace the first convolution layer of CNN since it can effectively reduce both the consumption of the energy power and the performing time.
Computational modeling of trust factors in imaging data using reinforcement learning

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous decision-making algorithms. The approach relies on the implementation of a neural network based reinforcement learning paradigm known as adaptive critic design to model an adaptive decision making process that is regulated by a quantitative measure of risk associated with each possible decision. Specifically, this work expands on the risk-directed exploration strategies of reinforcement learning to obtain quantitative risk factors for an automated object recognition process in the presence of imprecise data. Accordingly, this work addresses the challenge of automated risk quantification based on the confidence of the decision model and the nature of given data. Additionally, further analysis into risk directed policy development for improved object recognition is presented.
Hot Code Patching in CPython

Some language runtimes such as Java Hotspot or the virtual machine for Squeak/Smalltalk support edit-and-continue debugging, which allows developers to make changes to a program while it is running. This capability is especially useful for recovering from errors in a program. However, it is not supported by CPython, the reference interpreter for Python.
Interactive show physics through all senses

This contribution describes a long-term project Physics through All Senses which is organized by the Department of Physics Education. This 90 minute-long performance is intended for secondary school students. About 25 demonstrational experiments which often require participation of the audience are divided into groups inspired by human senses. This performance is not devised as a lecture with exact theoretical background, but as an interactive show engaging student. Another part of this contribution describes the examples of specific experiments from this performance and video contest which has been organized since September 2017 via social networks.
Cryptographic Strength Evaluation of AES S-box Variants

In recent years, researchers have constructed AES s-box variants by customising the values of the parameters in the equation of AES s-box. Strength of these s-box variants is evaluated against the cryptographic properties like avalanche effect, nonlinearity and bit independence criteria, however, there are other important cryptographic properties of s-box, which need to be evaluated before adopting the s-box. In this paper, all types of AES s-box variants are reproduced and evaluated against ten cryptographic properties. The results of five properties, differential and linear probability, nonlinearity, balance and algebraic degree remains the same for any s-box variant due to isomorphic equivalence nature of the variants; whereas, strict avalanche effect, bit independence criteria, correlation immunity, cyclic and fix-point properties showed different results for different s-box variant. Finally, the results are compared with the standard AES s-box and conclusion of overall security of s-box variants with respect to selected properties is conferred.
A Novel Strategy for Short-Term Prediction of Fading Channel

Abstract In this paper, a novel prediction strategy based on firefly algorithm, echo state network and Savitzky-Golay filter for short-term prediction of fading channel is proposed and introduced in detail. This hybrid strategy includes two parts, that is, optimization part and repairing part. In the former, parameters of echo state network are optimized by firefly algorithm so that an optimal prediction echo state network is obtained, then in order to improve performances in prediction strategy, prediction sample of echo state network is repaired by repairing strategy based on Savitzky-Golay filter. Finally, the classic Rayleigh channel is tested and performances are analysed and compared with classic AR, which verified the validity and correctness of our proposed prediction strategy.
Extending Modular Semantics for Bipolar Weighted Argumentation (Extended Abstract)

This extended abstract summarizes the key results from [10].
Analysis for Visualizing Actors' Experiences in E-learning Spaces to Create an Active Learner's Profile

During the past ten years, the authors have developed a blended learning space provided with web 2.0 features, which offers unique opportunities for the study, analysis and interpretation of students' behavior so as to identify teaching methods and techniques that are adequate to the big data era. They are investigated with a mix of web usage mining techniques and formal concept analysis. The present article explores a novel approach, which will allow for the automatic discovery of concepts from students' behavior patterns that are generated as a reaction to teacher thinking and behavior patterns. The main objective of the study is to create a structure of meta-attributes needed by students in order to gain knowledge and skills (detection and prediction patterns) in a learning environment where support and guidance is provided by a more “knowledgeable other”. The results are expected to provide a minimal set of attributes that students need in order to be considered active participants in the learning process fostered within a wiki-based learning environment.
Development of Parameter Measurement Method of Information Systems

Measurement and analysis of information systems parameters is a necessary element of their research. Unlike technical systems, in information systems parameters need to be selected basing on the analysis of their functional characteristics. In this work are reviewed peculiarities of information systems parameters selection. For such systems it is important is not only to measure some values, but also setting boundaries, scale etc. In this work is researched the concealment parameter. This parameter describes the level of ability to detect the hidden message in digital environment, providing that according to the concealment parameter it is invisible. To solve the task are used statistical methods of digital environment analysis. In the work, the scale of the concealment parameter is determined and method of measurement of its value is reviewed. The research provides tools to use this parameter and illustrate an example of conduction of measurements in information systems.
An Inherently-Typed Formalization for Featherweight Java

Featherweight Java is one of the most popular calculi which specify object-oriented programming features. It has been used as the basis for investigating novel language functionalities, as well as to specify and understand the formal properties of existing features for languages in this paradigm. However, when considering mechanized formalization, it is hard to find an implementation for languages with complex structures and binding mechanisms as Featherweight Java. In this paper we explore an inherently-typed approach to formalize Featherweight Java, implementing the static and dynamic semantics in Agda using dependent types, and then replicating it in Coq (the latter using the Equations plug-in). Using this approach, the interpreter is correct by construction, since the type checker of the host language is responsible for verifying type safety, thus avoiding repetitions of proofs and error checking.
Performance Analysis and Parameter Optimization of the Optimal Fixed-Point Quantum Search

The optimal fixed-point quantum search (OFPQS) algorithm [Phys. Rev. Lett. 113, 210501 (2014)] achieves both the fixed-point property and quadratic speedup over classical algorithms, which gives a sufficient condition on the number of iterations to ensure the success probability is no less than a given lower bound (denoted by 1 − δ 2 ). However, this condition is approximate and not exact. In this paper, we derive the sufficient and necessary condition on the number of feasible iterations, based on which the exact least number of iterations can be obtained. For example, when δ = 0 . 8 , iterations can be saved by almost 25%. Moreover, to find a target item certainly, setting directly 1 − δ 2 = 100 % , the quadratic advantage of the OFPQS algorithm will be lost, then, applying the OFPQS algorithm with 1 − δ 2 &lt; 100 % requires multiple executions, which leads to a natural problem of choosing the optimal parameter δ . For this, we analyze the extreme and minimum properties of the success probability and further analytically derive the optimal δ which minimizes the query complexity. Our study can be a guideline for both the theoretical and application research on the fixed-point quantum search algorithms.
An Experiment on Game Facet Combination\

Procedural Content Generation of game content has been vastly improved over the last years and is more and more adopted also in the game industry. It relies mostly on evolutionary and related optimization methods but usually only treats a single of the many available facets as visuals, levels, audio, etc. The problem of how to combine several facets of generation is largely unsolved, but nevertheless very important. One of its subproblems is that we currently do not know in advance how users will react to machine-generated combinations. Based on a simple maze game with exchangeable visuals and audio styles we test how users receive ‘usual’ and ‘unusual’ facet compositions by means of rank trace based annotations of their own play-throughs. By means of machine learning techniques, we establish a model in order to learn and predict user reactions. Understanding the effects of facet composition on the user is fundamental if we want to rise evolutionary generation of content to the next level.
Linear Prediction Algorithms for Lossless Audio Data Compression

The paper considers the use of such linear interpolation algorithms as LPC, FLPC, and Wise-LPC in the lossless audio data compression. In addition to the interpolation methods, the problems of best coding and optimal sampling window selection are investigated. The Wise-LPC algorithm is shown to allow a 1–5% improvement of audio signal compression against conventional LPC and FLPC approaches. The prediction error has a Laplace distribution, its variance decreasing smoothly and reaching “saturation” with the growing window width.
The Using of Thresholding and Region Merging Algorithm for Correcting the Multiple Choice Answer Sheets

Abstract Increasing technology in digital encourages the human to create computer aplication to process two-dimensional data (Citra). Image retrieval that is the form of test files uses CCD cameras with a fairly expensive price, such as CCTV cameras. In this research, the author uses the methods thresholding and region merging to correct the answer sheet with a tool Webcam that the form of Image and it is procesed for correction the answer multiple choice. in this study, the author focuses on the application of the thresholding method in which the purpose is to classify the gray degree value into two, namely black and white. the application of the region merging method is the process of combining regions less than the threshold. The use of webcam, thresholding methods and merging region is expected to facilitate all circles of agencies, especially in the education field in the correction sheet answer. It can increase the output of quality teachers.
An Efficient Short Blind Proxy Re-signatures Scheme

Abstract The existing proxy re-signature schemes have drawbacks in terms of privacy protection and signature efficiency. Our scheme is based on the proxy re-signature scheme, which combines the main idea of short blind signature and proposes the definition of short blind proxy re-signature scheme. Then, using the signature framework of the random oracle model, we propose a short blind proxy re-signature scheme based on the random oracle model, and prove the security of the scheme under the adaptive chosen-message attacks. The analysis shows that compared with the existing similar schemes, our scheme does not add additional verification information under the premise of shortening the signature length, and the applicability is better in the interactive transmission of devices with limited calculation and transmission performance.
Semantic representations for under-resourced languages

Distributional semantics studies methods for learning semantic representation of natural text. The semantic similarity between words and documents can be derived from this presentation which leads to other practical NLP applications such as collaborative filtering, aspect-based sentiment analysis, intent classification for chatbots and machine translation. Under-resourced language data is small in size. Small data implies not only small corpora, but also short documents within the corpus. In this paper we investigate the performance of word embedding techniques on two under-resourced languages. We investigate two topic models, LDA and DMM as well as a word embedding word2vec. We find DMM to perform better than LDA as a topic model embedding. DMM and word2vec perform similar in a semantic evaluation task of aligned corpora.
Motion Planning of a 5-DOF Anthropomorphic Robotic Arm Under ROS Environment

This paper deals with motion planning of a 5-DOF anthropomorphic robotic arm. The CAD model of the robot is built and converted into a URDF file for the simulation with the ROS platform. Sequentially, the motion planning of the manipulator is completed through the MoveIt! Package. The motion process is observed in RViz, with the planned result being transmitted to the servo controller to drive the real robotic arm to reach to the target point. Moreover, the extraction and matching of key feature points based on SIFT algorithm is studied for object detection to obtain the coordinates of the targeted object, in order to realize the pick-and-place operation for the material handling.
Food Packaging Search Application From Text Image In Android With Deep Convolutional Neural Network (DCNN) Method

Abstract Search Application Info on Food Packaging with Image Input based on Android is one application that can make it easier for users to see a description of the ingredients on food packaging. This application uses the Deep Convolutional Neural Network (DCNN) method which is able to show outstanding performance in the field of image recognition, especially in the field of character recognition, because DCNN has a performance capable of extracting high-level features. Detailed information on food packaging is usually written on food packaging with a small font size that makes it difficult for consumers to read it, and on food packaging there is also no selling price of the product. This makes it difficult for consumers who only want to know the information and prices of these food products. A method is needed to get a text version of the food packaging brand image and to become a search keyword on the food information service site. In this report, we will examine digital image processing especially in the field of pattern recognition to analyze the titles contained in food packaging in real time. This research will utilize smartphone cameras that are used to detect objects specifically for food packaging to see the price, composition, brand, net weight and description of the food packaging based on the food packaging detected.
WorkFlow Application Scheduling in Cloud Computing

Scheduling workflow applications in cloud computing is one of the most challenging issues. Workflow applications contain many distinct tasks and complex structures. Every single task might include processing, entering, and accessing storage and software functions. For all these reasons, it is considered to be an NP-hard optimization problem. Afterward, efficient scheduling algorithms are needed for the selection of appropriate resources in workflow execution, so task scheduling is the potential research area in this context. It is a part of a full-text study that systematically reviews the literature on workflow application in the context of cloud computing. Initial results are described by answering the two research questions. 41 articles were selected out of 225 research papers. Articles that answer selected research questions are included in this paper. Workflow scheduling purposes at assigning and managing the execution of tasks on to the shared resources that are controlled by the scheduler. We have expanded the scope of our study to include both workflow and optimization scheduling in terms of single and multi-objective optimization. This study explores that there is a need to develop an efficient task scheduling algorithm that maps appropriate tasks on to the VMs, while minimizing the cost overhead and maximize the performance of the cloud-based system.
Protecting Users from Phishing Email through Awareness and Training

Background/Objectives: Most of cyber-attacks start with a phishing emails by deceiving users into acquiring sensitive information.As long human are using the system, there will be a weak part that could be exploited.Many literatures indicates that the main reason of people had been the victim of phishing is a lack of awareness.Several approaches have been used to improve awareness of users; however, the realistic situations in some studies were not applied.Furthermore, environment and organization should be considered when design training methods.Methods/Statistical Analysis: The present study was conducted over three years using formative and summative assessments to evaluate students' awareness.This study investigated the most common ways in the classroom of educating students about email phishing email.Findings: This study presents the results of email phishing attacks and quizzes conducted to demonstrate how the education can be a powerful tool to increase awareness and protect students from email phishing.This paper also highlights the significance of educational through the classes to increase awareness of email phishing and other security threats.Improvements/ Applications: The work could be extended in the future to use more ways of teaching students against phishing attacks.
Physical model of coin acceptor

The basic principle of coin detection is to test the physical properties of the coin against known characteristics of acceptable coins. The coin acceptor analyses the coin according to its size, diameter, thickness, mass, metal composition and/or magnetism. The article discusses the possible principle of distinguishing Czech coins based on their influence on the magnetic field. In CAD SolidWorks a 3D model was created to fit a standard school coil. The model was printed by 3D printer Prusa i3 MK3S and supplemented with a magnetic field sensor from Vernier. After placing the coin into the core the changes of magnetic field magnitude are measured. There is measured the changes of magnitude of the magnetic field.