Reviewerly/exHarmony huggingface dataset:
Laplace Transform, Non-Constant Coefficients Differential Equations and Applications to Riccati Equation

In this paper, the Laplace Transform is used to find explicit solutions of a family of second order Differential Equations with non-constant coefficients.For some of these equations, it is possible to find the solutions using standard techniques of solving Ordinary Differential Equations.For others, it seems to be very difficult indeed impossible to find explicit solutions using traditional methods.The Laplace transform could be an alternative way.An application on solving a Riccati Equation is given.Recall that the Riccati Equation is a non-linear differential equation that arises in many topics of Quantum Mechanics and Physics.
Extended Generic Process Model For Analysis MITM Attack Based On Evil Twin

Abstract The high of criminal level related to the field of networking, is a threat that is quite dangerous MITM. The perpetrators of this attack use a fake AP (access point) with different gateway settings that are legitimate AP, so this type of attack becomes quite difficult to detect. This is due to the lack of standard operating procedures (SOP) in handling this case. This research was conducted with the aim of making a forensic model based on the analysis stages in the Evil Twin based MITM case using the user side based live forensic method. The results of the forensic investigation in the study resulted in an investigative model of ENFGP (Extended NFGP) which was divided into 10 stages and consisted of 30 completion steps, obtained through the process of testing and implementation methods in the MITM based Evil Twin attack and further testing based on several previous forensic models
Innovative Activities of National Libraries of the CIS Countries

The article considers the results of study of current trends in the innovative activities of 15 national libraries of the Commonwealth of Independent States (CIS). In research, the author applied the methods of polling, comparative analysis, expert review, analysis of websites and printed sources in order to detect the promising areas for international library cooperation and partnership in the CIS. The author underlines the relevance of this research connected with the need to analyse the strategies of the CIS national libraries developed up to date. The article presents information on technology innovation, changing processes and reconfiguring products and services, as well as on organizational and management advanced innovation. The core programs of all national libraries of the CIS countries are focusing on e-library development, including coordination activities on the creation of national electronic libraries in the countries, full-text databases, as well as projects promoting international academic movement Open Access. The article presents the studies on scientific description and creation of soft copies of the unique national documentary monuments, as well as development of information portals and websites. Organizational and management innovation of national libraries of the CIS countries focuses on structural changes. These transformations promote creation of virtual reading rooms, electronic resource rooms, book and book-printing museums, as well as educational projects — training centres, including distance learning centres, and schools for conservation and restoration of manuscripts. Based on the analysis of empirical data, the author concludes that the priority innovative projects of national libraries cooperation are focused on provision of remote access to the most complete volume of document collections available in the CIS countries; these projects increase satisfaction of the needs of users of national libraries and the whole population in the relevant information.
Visualization Techniques for Data on 3D Grids

Simulations of physical processes are frequently used to study a continuous three-dimensional volume. In order to apply computational methods, the continuous volume is approximated by a discrete grid. Simulations are typically organized to compute variables at each location on the three-dimensional grid. Visualizing the results requires exploring the data with a variety of presentation techniques. Our movie illustrates applying these techniques to a dataset generated by a Raleigh-Taylor astrophysics simulation. However, these general-purpose approaches can be applied to a 3D compute grid produced by other applications. We have implemented the visualization using the VisIt software, based on the Visualization Toolkit (VTK), with Python scripting.
Sparse Actor-Critic: Sparse Tsallis Entropy Regularized Reinforcement Learning in a Continuous Action Space

In case of deep reinforcement learning (RL) algorithms, to achieve high performance in complex continuous control tasks, it is necessary to exploit the goal and at the same time explore the environment. In this paper, we introduce a novel off-policy actor-critic reinforcement learning algorithm with a sparse Tsallis entropy regularizer. The sparse Tsallis entropy regularizer has the effect of maximizing the expected returns while maximizing the sparse Tsallis entropy for its policy function. Maximizing the sparse Tsallis entropy makes the actor to explore the large action and state space efficiently, thus it helps us to find the optimal action at each state. We derive the iteration update rules and modify a policy iteration rule for an off-policy method. In experiments, we demonstrate the effectiveness of the proposed method in continuous reinforcement learning problems in terms of the convergence speed. The proposed method outperforms former on-policy and off-policy RL algorithms in terms of the convergence speed and performance.
Low Complexity Receiver Design Using Deep Neural Network Based on Compact Sparse AutoEncoder

In this letter, we demonstrate a novel strategy for designing a low complexity deep neural network (DNN) receiver. The compact-stacked Autoencoder (CSAE) receiver is designed based on the proposed neuron and layer numbers selection (NLNS) methodology. Compared with other DNN-based receiver, the CSAE receiver has low complexity but can achieve superior performance. Simulation results show that CSAE receiver can achieve or even outperform state of the art accuracy, and furthermore, the proposed receiver provides a robust performance against the phase offset, carrier frequency offset (CFO), and imperfect channel state information (ICSI).
Fine-Grained 3D-Attention Prototypes for Few-Shot Learning

In the real world, a limited number of labeled finely grained images per class can hardly represent the class distribution effectively. Due to the more subtle visual differences in fine-grained images than simple images with obvious objects, that is, there exist smaller interclass and larger intraclass variations. To solve these issues, we propose an end-to-end attention-based model for fine-grained few-shot image classification (AFG) with the recent episode training strategy. It is composed mainly of a feature learning module, an image reconstruction module, and a label distribution module. The feature learning module mainly devises a 3D-Attention mechanism, which considers both the spatial positions and different channel attentions of the image features, in order to learn more discriminative local features to better represent the class distribution. The image reconstruction module calculates the mappings between local features and the original images. It is constrained by a designed loss function as auxiliary supervised information, so that the learning of each local feature does not need extra annotations. The label distribution module is used to predict the label distribution of a given unlabeled sample, and we use the local features to represent the image features for classification. By conducting comprehensive experiments on Mini-ImageNet and three fine-grained data sets, we demonstrate that the proposed model achieves superior performance over the competitors.
LrGAN: A Compact and Energy Efficient PIM-Based Architecture for GAN Training

As a powerful unsupervised learning method, Generative Adversarial Network (GAN) plays an essential role in many domains. However, training a GAN imposes four more challenges: (1) intensive communication caused by complex train phases of GAN; (2) much more ineffectual computations caused by peculiar convolutions; (3) more frequent off-chip memory accesses for exchanging intermediate data between the generator and the discriminator; and (4) high energy consumption of unnecessary fine-grained MLC programming. In this article, we propose LrGAN, a PIM-based GAN accelerator, to address the challenges of training GAN. We first propose a zero-free data reshaping scheme for ReRAM-based PIM, which removes the zero-related computations. We then propose a 3D-connected PIM, which can reconfigure connections inside PIM dynamically according to dataflows of propagation and updating. After that, we propose an approximate weight update algorithm to avoid unnecessary fine-grain MLC programming. Finally, we propose LrGAN based on these three techniques, providing different levels of accelerating GAN for programmers. Experiments show that LrGAN achieves 47.2×, 21.42×, and 7.46× speedup over FPGA-based GAN accelerator, GPU platform, and ReRAM-based neural network accelerator respectively. Besides, LrGAN achieves 13.65×, 10.75×, and 1.34× energy saving on average over GPU platform, PRIME, and FPGA-based GAN accelerator, respectively.
Weighted Voting-Based Effective Free-Viewing Attention Prediction On Web Image Elements

Abstract Quantifying and predicting the user attention on web image elements finds applications in synthesis and rendering of elements on webpages. However, the majority of the existing approaches either overlook the visual characteristics of these elements or do not incorporate the users’ visual attention. Especially, obtaining a representative quantified attention (for images) from the attention allocation of multiple users is a challenging task. Toward overcoming the challenge for free-viewing attention, this paper introduces four weighted voting strategies to assign effective visual attention (fixation index (FI)) for web image elements. Subsequently, the prominent image visual features in explaining the assigned attention are identified. Further, the association between image visual features and the assigned attention is modeled as a multi-class prediction problem, which is solved through support vector machine-based classification. The analysis of the proposed approach on real-world webpages reveals the following: (i) image element’s position, size and mid-level color histograms are highly informative for the four weighting schemes; (ii) the presented computational approach outperforms the baseline for four weighted voting schemes with an average accuracy of 85% and micro F1-score of 60%; and (iii) uniform weighting (same weight for all FIs) is adequate for estimating the user’s initial attention while the proportional weighting (weight the FI in proportion to its likelihood of occurrence) extends to the latter attention prediction.
Proposed Mathematical Modeling of Small Remotely Operated Vehicle (ROV) Movement

Abstract In designing of Remotely Operated Vehicle (ROV), the most important issue is how to formulate the movements of the vehicle. This paper proposes the mathematical model of ROV movement. Combination of statics disciplines and dynamics of ROV movement are needed to formulate the mathematical model of ROV. Statics refers to the forces and moments that work on the physical system around the equilibrium point, dynamics refers to the effect of force on the movement of the object. Linear mathematical models analyzed can be combined or separated by taking into account the circumstances surrounding environment such as velocity vector of the linear state of the vehicle body (surge, sway and up) and angular (roll, yaw and pitch). Simulation results show that the movement of vehicles, both basic and combined movements have reliable characteristics.
Classification of Gender from Face Images and Voice

Automated acknowledgement of a human gender is pivotal for various frameworks like data recovery, human–machine collaboration that processes human source data. Automatic gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Gender identification has been a widely researched area, but face attribute recognition from facial images still remains challengeable. We propose a methodology for automatic gender classification based on feature extraction from facial images. Our methodology includes three main iterations: preprocessing, feature extraction, and classification. Texture features, geometric moments, and histogram values of facial images are used to train the system. The eminent and efficient features are selected and trained using suitable machine learning technique classifiers, namely SVM, AdaBoost and random forest. The developed model is used to identify the gender of an individual with an accuracy of approximately 95% and can be deployed in various scenarios like hospital registration process, sports selection process, and airports to identify the gender of a person.
Online product search using gray level co-occurrence matrix, color moments, and histogram of oriented gradients for content based image retrieval

Abstract Usually an information retrieval system uses text as its query for retrieving the results. However, the system can also use images, as opposed to text, for the search query. This technique commonly is called content based image retrieval. In this paper, we explore the retrieval of fashion products such as hats, sneakers, t-shirts, flat shoes, and dresses from images using color, texture, and shape features to represent the characteristics of the query images. The color, texture, and shape features are color moments, gray level co-occurrence matrix, and histogram of oriented gradients, respectively. The feature extraction process produces vectors with statistical feature value which can then be clustered to retrieve images with high degree of similarity. Comparing those feature representations for the retrieval system, it is shown that shape feature is the best representation.
Difficulty Curve-Based Procedural Generation of Scrolling Shooter Enemy Formations

Abstract In any game where the player must face large formations of enemies, the way the formations are formed significantly influences the difficulty level of the game. As proper difficulty levels are essential to player’s enjoyment, the enemy formations need to be arranged properly. In this paper we experimented with how to procedurally generate enemy formations in a vertical scrolling shooter game while keeping the difficulty levels proper. Generations of the formations were done with genetic algorithm where the fitness function calculated two variables: difficulty curve, which reflects a formation’s difficulty level, and enemy variety. The difficulty curve took into account difficulty points, which measured the danger levels of on-screen enemies throughout the game’s duration. The fitness of difficulty curve of an enemy formation was acquired through point-by-point comparison between the curve and a human-designed one, which represented the ideal difficulty curve as intended by the game’s developer, and calculation of the root-mean-square error. The genotype of an individual was represented by a 5x40 grid containing enemy genes and empty space genes. We ran 300 generations in total and each was done in 100 iterations and had 40 individual genotypes. The results show that the amount of enemy genes in initial genotypes influences the population’s fitness progression. We then discuss the implications of our findings and possible directions of future researches.
AIIS: The SIGIR 2020 Workshop on Applied Interactive Information Systems

Nowadays, intelligent information systems, especially the interactive information systems (e.g., conversational interaction systems like Siri, and Cortana; news feed recommender systems, and interactive search engines, etc.), are ubiquitous in real-world applications. These systems either converse with users explicitly through natural languages, or mine users interests and respond to users requests implicitly. Interactivity has become a crucial element towards intelligent information systems. Despite the fact that interactive information systems have gained significant progress, there are still many challenges to be addressed when applying these models to real-world scenarios. This half day workshop explores challenges and potential research, development, and application directions in applied interactive information systems. We aim to discuss the issues of applying interactive information models to production systems, as well as to shed some light on the fundamental characteristics, i.e., interactivity and applicability, of different interactive tasks. We welcome practical, theoretical, experimental, and methodological studies that advances the interactivity towards intelligent information systems. The workshop aims to bring together a diverse set of practitioners and researchers interested in investigating the interaction between human and information systems to develop more intelligent information systems.
Emotion Mining of Indonesia Presidential Political Campaign 2019 using Twitter Data

Abstract The presidential election campaign in Indonesia which was held in 2019 attracted the interest of the Indonesian people. People expressing their emotion through social media, one of which is Twitter. Supervised learning is used to classify user’s emotion of that issues. The goal of this research is extracting the emotional content in tweets. The interest is in whether the text is an expression of Twitter users’ about the presidential candidates and whether this online forum represents the election results. For this purpose, text mining techniques are performed and we will compare Naive Bayes Classifier and SVM in classification process. The result showed that Naive Bayes Classifier and SVM have good performance for classifiying text and each of classifier have not outperform the others. The result also showed that stemming step in text pre-processing do not give any significant result in model accuracy, because twitter data use informal languange like abbreviation or slang.
Selection of a Virtual Machine Within a Scheduler (Dispatcher) Using Enhanced Join Idle Queue (EJIQ) in Cloud Data Center

Cloud technology is a fast-growing technology which supports wide range of services with less cost. The majority of the costly services like ERP software tools, hardware equipments, and packages can be availed here with the basic concept of ‘What and How much we used, per for that only.’ There are different concerns to be considered in cloud computing for researchers; they are load balancing, security issues, and availability of resources. Join idle queue (JIQ) is a dynamic algorithm used for virtual machine allocation for incoming jobs within a datacenter. In that JIQ algorithm, some issues were found. In this paper, we proposed enhancements in JIQ algorithm and used in optimized load balancing across the virtual machines in the data center.
Mobile game Application for Religion engagement

Religion is a part of a fundamental human right to serve and worship their God according to their religion and belief. Mobile game application along with the development of mobile phone technology such as a smartphone, tablet, and laptop, can be used to increase the player or user interest to engage with religion thing. Using the mobile game application in mobile gadgets will expand quickly to install and use for religion engagement. The player can learn religion things with fun and entertain ways, where the player does not realize that they do something serious in an unserious way. This paper will explain the model system design for mobile game application for learning Catholicism in fun and entertaining way and for the first development was limited to only three Catholic catechisms such as bible learning, church lesson, and liturgy celebration. For the early development, three types of games were developed such as True or False game, Scramble words game, and multiple-choice game. The mobile game model was designed with a use case diagram, storyboard, and class diagram, where for the current implementation the mobile game application was implemented using a ten tables database.
Quantum Computing for Solving Spatial Optimization Problems

Ever since Shor’s quantum factoring algorithm was developed, quantum computing has been pursued as a promising and powerful approach to solving many computationally complex problems such as combinatorial optimization and machine learning. As an important quantum computing approach, quantum annealing (QA) has received considerable attention. Extensive research has shown that QA, exploiting quantum-mechanical effects such as tunneling, entanglement and superposition, could be much more efficient in solving hard combinatorial optimization problems than its classical counterpart—simulated annealing. Recent advances in quantum annealing hardware open the possibility of empirical testing of QA against the most challenging computational problems arising in geospatial applications. This chapter demonstrates how to employ QA to solve NP-hard spatial optimization problems through an illustrative example of programming a p-median model and a case study on spatial supply chain optimization. The research findings also address the short- and long-term potential of quantum computing in the future development of high-performance computing for geospatial applications.
Intelligent virtual agent, learning how to reach a goal by making the least number of compromises

Abstract The learning process in the Q-learning algorithm is characterized by maximizing a single, numerical reward signal. However, there are tasks for which the requirements toward the way to reach a goal are complex. This paper proposes a modification to the Q-learning algorithm. In order to make the Q-learning agent find the optimal path to the goal by meeting particular complex criteria, the use of measures model (a model of environment criteria), represented as a new memory matrix, is introduced. If the goal cannot be reached by following the pre-set criteria, the learning agent can compromise a given criterion. The agent makes the least possible number of tradeoffs in order to reach the goal. If the criteria are arranged by their level of importance, then the agent can choose more in number and more acceptable compromises. The aim of the modification is to empower the learning agent to control the way of reaching a goal. The modified algorithm has been applied to training smart shopping-cart learning agents. The tests show improvement in their behaviour.
A Distributed Solution to the Multi-robot Task Allocation Problem Using Ant Colony Optimization and Bat Algorithm

The multi-robot task allocation problem is a very active search field in autonomous multi-robot systems. It consists of three elements: (i) a set of tasks requiring some supports, (ii) a set of robots offering some supports and (iii) an objective function. The solution of such a problem is to assign tasks to robots, while optimizing the considered objective function. We consider search and rescue scenarios: tasks are survivors, robots are unmanned aerial vehicles and the objective function is to rescue the maximum number of survivors. We propose a fully distributed solution made up of the following two main steps: (i) the first step uses ant colony optimization to construct overlapping task bundles for each robot; and (ii) the second step uses bat algorithm to construct disjoint task bundles for each robot. This solution has been implemented using Java programming language and JADE multi-agent platform. Simulation results show the efficiency and scalability of our solution, in terms of makespan values; and the quality of our solution is very close to the quality of an optimal solution (the difference is only 3.17%), in terms of objective function.