Reviewerly/exHarmony huggingface dataset:
Implementation of Windows Interface for Disabled Persons

The technology is continuously advancing day by day and has transformed the personal computer (PC) from a very simple computing gadget to a highly advanced multi-processing and a multi-functional device. It is now capable of connecting and processing different devices at different places simultaneously. This improvement brings the system advancement for disabled persons so that they can be self-governed. This paper focuses on various interfacing techniques utilizing windows Operating System to assist the disabled persons and proposes a technique named as “Windows Interface for Mobility impaired and Blind people” (WIMB). Proposed model assists the disabled individuals (having blindness, mobility-impartment and deafness) to control real-life utilities through Microsoft Windows OS (Operating System). Furthermore, few available, applications are also reviewed and compared with the proposed technique.
Discriminative Recognition of Point Cloud Gesture Classes through One-Shot Learning

In this paper, we introduce a one-shot learning approach to extend learned point cloud gesture categories into recognizing newly introduced categories after training the original model. This approach is based on learning the discrimination between gesture classes of point cloud data, making it possible to recognize new classes without retraining the original deep neural network (DNN) model. We develop a temporal variant of the PointNet model referred to as Temporal PointNet or TPoinNet, which consumes a sequence of raw point clouds from a low-cost depth sensor and outputs the class of the sequence. We use a multitask strategy where the model learns the classification of a point cloud sequence and a Euclidean space discrimination between the classified sequence and another sequence of a different class. The new model is able to classify and map the point clouds sequence inputs into a Euclidean space where the distances between the gesture sequences correspond to the gesture similarities. We present results on a point cloud dataset and the MSR Action 3D dataset showing the discrimination of new gesture categories with a high precision.
Siamese Score: Detecting Mode Collapse for GANs

Despite large strides in terms of generative adversarial networks (GANs) for image generation, evaluating and comparing GANs remains an open question. Several measures have been introduced, however, there is no consensus in terms of the best score. In this paper, we delve into the widely-used metric Inception Score (based on KL divergence), revealing that it fails to detect intra-class mode collapse. Meanwhile, Wasserstein distance has received much attention in comparing distributions in recent years but suffers heavy computational burden in high dimensional space. Our idea is that we can find specific embedding space where Euclidean distance could mimic Wasserstein distance to solve the heavy computational problem. This space can be found using a Siamese network, which could be trained quickly because of shared weights. We also apply several proposed new techniques to get better image embedding. To evaluate our proposed metric (Siamese Score), we simulate mode collapse using K-means clustering performed on real data set. To further validate it, we perform an empirical study on several GAN models and use the generated images to do the task. Experiments show that Siamese Score can detect mode collapse and is time-efficient compared with Inception Score and we think our score can be complementary to Inception Score.
Estimating the Unknown Parameters of a Random Number Generator Based on a Chaotic Jerk System

A novel estimating system is proposed to find out the security weaknesses of a random number generator (RNG) based on a chaotic jerk system. Convergence of the estimating system is proved using auto-synchronization. Unknown parameter of the target RNG is revealed where the public information are the design of the RNG and a scalar time series observed from the target chaotic jerk system. Simulation and numerical results verifying the feasibility of the estimating system are given such that, next bit can be predicted while the same output bit sequence of the RNG can be regenerated.
An optimal, purely functional implementation of the Garsia–Wachs algorithm

The Garsia–Wachs algorithm is an algorithm for building a binary leaf tree whose cost is as small as possible. The problem and the algorithm are described in more detail below, but the task is essentially the same as that of building a Huffman coding tree with the added constraint that the fringe of the tree has to be exactly the given list of inputs (in Huffman coding, the fringe of the tree can be any permutation of the input). As we will show below, the Garsia–Wachs algorithm can be implemented with a linearithmic running time—a running time of O ( n log n ) steps for an input of length n , the same time bound as for Huffman coding.
"Meanings" Based Human Centered Design of Systems

There is a renewed interest in Systems Thinking and Systems Design in the practice and pedagogy of Design. Theoretical work to propose new design theories or methods of design are creating discussion on first principles and/or a general lack of it. Methods to map, model, understand the critical information needed for design of complex systems are being sought by practitioners and academics alike such as Organizational Semiotics and Ethical Value Based Design practices. This paper posits a hybrid approach based in traditional Human Systems Integration (HSI) approach and Organizational Semiotics. We believe that creating ability to incorporate perceived meanings and values of stakeholders will yield a much more empathetic and flexible design of systems.
Identify the Relevant Pages of Book to be Indexed Using Naive Bayes Classification Method

Abstract The back-of-book index is a component that is often found in non-fiction books. The back-of-book index contains important terms and page numbers where the terms appear in the book, it is useful for helping readers find certain term directly, without having to search on every page. Usually, the back-of-book index is compiled by the author or a professional indexer, although currently there are several automatic indexing applications available. To determine a term on a page worth to be indexed requires knowledge and expertise of the author whom better understand the book’s context. Hence, generating a good back-of-book index is a task that requires great effort, knowledge and cost. Therefore, the back-of-book index is very possible containing indexed terms that refer to irrelevant page numbers due to human error or the weakness of the indexing application. This study aims to identify the relevance of the pages to be indexed in the back-of-book index, using Naive Bayes Classification. The testing result shows that the approach with Naive Bayes Classification produces an average precision value of 74.02 percent and 100 percent for the recall value. The average precision value that more than 50 percent indicates that the naive bayes classification approach is capable to identify the relevant and irrelevant pages.
Average Payoff Criterion

We are now interested in analyzing the average payoff game, that is, the game in ( 1.3 ) with the long-run expected average payoff in (1.13): $$\displaystyle J(x,\pi ^{1},\pi ^{2}):=\liminf \limits _{n\rightarrow \infty }\frac {1}{n} E_{x}^{\pi ^{1},\pi ^{2}}\sum _{t=0}^{n-1}r(x_{t},a_{t},b_{t}) {} $$
A Novel Algorithm of Cooperative Foraging for Swarm Robot Based on Neural Network

Swarm robotics is an emerging discipline that has a great deal of potential real-world applications. Swarm robotics aims to produce robust, scalable, and flexible self-organizing behaviors through local interactions of large numbers of simple robots. In this paper, a novel pheromone model of swarm foraging behavior is developed based on a neural network. The output of the neurons corresponds to the density or pheromone. The output diffuses to the neighboring neurons through their local connections. The neural network is updated based on the proposed evaporation model. The evolution of the neural network can mimic the features of pheromones. Simulation experiments are performed under different foraging scenarios. The experimental results verify the effectiveness of the proposed pheromone model.
Single Color Image Dehazing Based on Two Fast Variational Models

Total Variation (TV) has been proven to effectively restrain the effect of noise in image processing. Multichannel TV (MTV) is proposed by extending TV model to adapt the case in color image processing. In order to effectively improve the performance of image restoration, we proposed to simultaneously consider denoising and dehazing by integrating MTV model with dark channel prior, called H-MTV model. With a better information preservation in detail, edge and texture of the image, nonlocal information is jointly considered with H-MTV model as NL-H-MTV model. Additionally, the two fast algorithms, namely dual bregman iteration and split bregman iteration, are respectively used for solving the H-MTV and the NL-H-MTV model, leading to a fast and accurate convergence. Experimental results on the several different images show that the performance of restoration using proposed methods are superior to those compared state-of-art methods.
Decision System Modelling for "Saboteur" using Heuristics and Means-Ends Analysis

Saboteur is a card game played by several players. Players are divided into goldminers and saboteurs who prevents goldminers from finding gold. Each player knows his/her role, but he/she does not know who has the same role. Gold searching process starts by making a path of seven cards horizontally from a single point of reference to the gold card's position. Saboteur is classified as an incomplete information card game with mechanisms that requires complex decision making. This paper proposes an approach to model a decision system for Saboteur using Heuristics and Means-End Analysis. Implementation was purely done in Java language. To test it, we performed benchmarking for the proposed system against previous intelligent agent models. The result is much better.
Data Security

Data security in the information age is a critical facet in the integrity and reliability of the various information systems making up value structures of businesses, organizations etc. Aside from professionals directly involved with securing data within these systems, the importance of data security is not readily apparent to the everyday user of devices in the information systems. The purpose of this literature review is to highlight challenges related to data security and business information systems in conjunction with digital literacy. An extensive literature review was conducted with the aim of identifying and describing scenarios of technology misuse as well as vulnerabilities in vital business information systems. A gap in awareness continues to plague those who leverage information systems for its myriad uses because everyday users will in most cases dismiss data security advice as alarmist or jargon-laden. This falls in line with a 2018 cyber security survey from Statista which showed that 22% of data security tasks was preventing malware while 17% of tasks were dedicated to preventing social engineering and phishing attacks. This literature review will describe possible data insecurity solutions as well as potential areas of further research. The paper will point out the importance of digital literacy as well as recommendations for its improvement in society and also ongoing research in that regard. The essence of this literature review is to identify certain everyday information systems such as decision support systems and transaction processing systems; while pointing out vulnerabilities and threat nature i.e. technical or non-technical and also demonstrating the importance of digital literacy and lack thereof.
Random Convolutional Neural Network Based on Distributed Computing with Decentralized Architecture

In recent years, deep learning has made great progress in image classification and detection. Popular deep learning algorithms rely on deep networks and multiple rounds of back-propagations. In this paper, we propose two approaches to accelerate deep networks. One is expanding the width of every layer. We reference to the Extreme Learning Machine, setting big number of convolution kernels to extract features in parallel. It can obtain multiscale features and improve network efficiency. The other is freezing part of layers. It can reduce back-propagations and speed up the training procedure. From the above, it is a random convolution architecture that network is proposed for image classification. In our architecture, every combination of random convolutions extracts distinct features. Apparently, we need a lot of experiments to choose the best combination. However, centralized computing may limit the number of combinations. Therefore, a decentralized architecture is used to enable the use of multiple combinations.
PT-GA-IRIAL: Enhanced Energy Efficient Approach to Select Migration VMs for Load Balancing in Cloud Computing Environment

Cloud computing is a very well known technology for all business people, software developers, end-users, and so on. Significant researches are going on to balance the cloud load. The migration of heavily loaded Virtual Machines (VMs) into lightly loaded Physical Machines (PMs) balances the Cloud load. In Resource Intensity Aware Load Balancing (RIAL) method, based on the weight of resources under utilization, it selected the VMs from heavily loaded PMs for migration and chosen the lightly loaded PMs as destination. An Improved RIAL was proposed to consider both lightly and heavily loaded PMs as destination. Later it was enhanced in the proposed Power Consumption Aware- Traffic Aware- IRIAL (PT-IRIAL) method with the consideration of power consumption, temperature and traffic measures to select the VMs for migration and select PMs for destination. From all these, in this current paper, the crossover and mutation process of GA is utilized to optimally select the migration VMs and choose the destination PMs. Thus this GA based load optimization algorithm optimally maps the migration VMs with the destination PMs efficiently.
Conceptual Framework of an Intelligent Tutor for Teaching English Grammar to High School Students

This paper presents a framework for an intelligent tutoring system (ITS) to teach English grammar to high school students. The framework includes scripts to teach a student about a particular topic followed by assessment related to the topic. It also provides learning material in the form of gifs and videos to supplement text for better understanding. Deep learning methods have been used to automatically fetch answers to Multiple Choice Questions (MCQs) based assessments. Machine learning algorithms have been used to automatically detect student's learning styles for language. The framework also includes analyzing cognitive thinking level of the student according to bloom's order of thinking skills.
Verified security of BLT signature scheme

The majority of real-world applications of digital signatures use timestamping to ensure non-repudiation in face of possible key revocations. This observation led Buldas, Laanoja, and Truu to a server-assisted digital signature scheme built around cryptographic timestamping.
Financial Contagion Effect and Investor Behavior in African Financial Markets During the 2007–09 Global Financial Crisis

This paper examines contagion effect on 10 African financial markets. These markets can be considered risky as they carry additional political and economic risks. They are also a lot less integrated with the US as depicted in financial integration literature. A consequence of this is that African financial markets, generally have much lower correlations with the US market. The distinguishing characteristic of the 2007-09 crisis is its long nature characterised by a series of sub-shocks. The insight that this paper gives is that, a multiple-event crisis that consists of a series of shocks, leads us to examine contagion as a series of events. Using correlation coefficient analysis, evidence of contagion is found in different crisis periods which mostly disappeared following the adjustment for heteroscedasticity bias. Our results do point to investors’ herding behaviour as the main driving force for contagion in our sample.
Modeling GUI Widgets from Use Case Elaboration

Machine Learning (ML) algorithms have drastically taken technology to the next advanced levels. Therefore, we have proposed a methodology using supervised machine learning algorithm for rapid Graphical User Interface (GUI) development by employing Unified Modeling Language (UML) Use Case (UC) model of Library Management System's case study. Our aim of the study is to reduce the gap between initial phases of (SDLC) for rapid development in terms of time and cost, by minimizing the development efforts, for higher stakeholder's satisfaction. In this paper, we have used extended form of use case descriptions to map and predict the most suitable User Interface (UI) element against each use case. We have categorical data with known classes and used binary class classification technique to train our dataset. Python is used to create applied predictive models, to predict most appropriate classes against each use-case. We have applied both binary and multi class classification on our dataset. Following classifiers i.e. Naïve Bayes (NB), Decision Tree, Cross Validation (CV), Random Forest (RF) and Logistic Regression (LR) Algorithms used for binary classes to statistically analyze predicted results. We have acquired 94% accuracy, when applied NB and RF Algorithms on data, which concludes that creating UI's from UML UC model can improve efficiency in terms of time and cost.
Development and Various Critical Testing Operational Frameworks in Data Acquisition for Cyber Forensics

Digital forensics is the science of preserving and analyzing digital data; this data can then be used in court cases as well as for crime detection and prevention. Digital forensics began in the 1970s and was initially used as a tool for fighting financial crime. Today, with computers and digital devices being an integral part of our professional and private lives, digital forensics are used/needed in a wide variety of disputes. Data Acquisitions is described and discuss different techniques or methodology obtain the data, facts, and figures from different resource and at a different level of the system.
A Survey of the Different Itemset Representation for Candidate Generation

Itemset representation is a pivotal part in association rule mining. The itemset representation is a way of how the itemset in the dataset are stored in the memory. There are different data structures used for storing such itemsets. Some of the common data structures used are linked list, array. The efficiency of the association rule mining algorithm largely depends on the way the itemsets are stored. The execution time and memory consumption of the itemsets plays a vital role for determining the performance of the mining algorithms. In this paper, a study of the data structures used for itemset representation is discussed. The different data structures are being tested on the different datasets for generation of candidate itemsets. The performance of the different data structures in candidate generation process is analysed.
Reform and Practice Path of Composite Personnel Cultivation Model for Postgraduates

Postgraduate education of professional degree aims to cultivate innovative talents with both theory and practice capabilities. In order to achieve the training goal and enhance students' engineering practical ability and innovative spirit, this paper proposed a reform plan, including design of integrated training methods for graduate education of professional degrees, reconstruction of core courses and teaching content, building of a three-stage personnel training process, carrying out of distinctive project training by a multi-level joint training support platform, and implementation of a school-enterprise collaborative process guidance and evaluation mechanism.
A Model for Analyzing Students' Mastery of Knowledge Points

Deeply mining the detailed data in the process of students' examination plays an important role in studying students' learning characteristics and realizing individualized learning. Due to the association between test questions mostly given by experts, the number of choosing path length on each question in the process of answering questions can not be accurately collected. In this paper, we propose a model named MASMKP(A Model for Analyzing Students' Mastery of Knowledge Points) based on text analysis in order to combine the examination questions with the knowledge points they belong to. By analyzing the relationship between the questions and combining the number of choosing path length on each question in the process of answering the questions, we can get the students' mastery of the knowledge points. Depending on this model, the text features of test questions are extracted firstly, and the test questions are classified into knowledge points according to the classification model. Then the number of choosing path length on each question is converted to the number of choosing path length on each knowledge point. The number of choosing path length reflects the students' mastery of the knowledge point to a certain extent. Students are clustered according to their mastery of each knowledge point using k-means algorithm, then students with different characteristics are obtained and personalized teaching is realized. The experimental results show that this model can extract and classify the features of students, and the accuracy and validity of this model are verified.
Discrete Glowworm Swarm Optimization Algorithm With Key Strategy Adjustment

A discrete glowworm swarm optimization algorithm with key strategy adjustment (KSA-DGSO) is proposed to solve the problems that the current discrete glowworm swarm optimization algorithm (DGSO) is lack of application features combined with practical problem, rigorous and accurate scientific basis, which is caused by updating the position of the individual glowworm according as the random probability. The KSA-DGSO algorithm is applied to search for the optimal control strategy of complex network security risk. The position update mechanism of the algorithm is reset according to the change case of the objective function with the key strategy, which prevent the algorithm into the local stalemate or cause a poor accuracy. The experiment results show that the optimal objective function is average reduced by about 2.55%-4.83% with the KSA-DGSO algorithm than the DGSO algorithm, and the optimal iteration number is average reduced by about 9.85%-12.80%. The superiority of the KSA-DGSO algorithm is validated by simulations, and the effect of the change for the control parameters to the complex network security risk optimal control is further analyzed from the simulated results.
Reverse Aging based on Face Rejuvenation using Inpainting of Facial Wrinkles

Facial reverse-aging problem is of significant importance in research community, which primarily focuses at formatting persons face to look more younger in images. Our reverse aging problem essentially considers face part. The proposed methodology consists of two parts. First, roughness in appearance of image is quantified using detected wrinkles in the input image. Wrinkle detection is followed by face rejuvenation using wrinkle inpainting. Face rejuvenation makes subject to look more younger. Our prime objective was to make subject at-least 10 year more younger than original. We have evaluated our system to find inter annotator agreement between judges using kappa statistical measure for describing the validity of complete system.