Reviewerly/exHarmony huggingface dataset:
A Case Study of the Capital One Data Breach

In an increasingly regulated world, with companies prioritizing a big part of their budget for expenses with cyber-security protections, why have all of these protection initiatives and compliance standards not been enough to prevent the leak of billions of data points in recent years? New data protection and privacy laws and recent cyber-security regulations, such as the General Data Protection Regulation (GDPR) that went into effect in Europe in 2018, demonstrate a strong trend and growing concern on how to protect businesses and customers from the significant increase in cyber-attacks. Are current legislation, regulations and compliance standards sufficient to prevent further major data leaks in the future? Does the flaw lie in the existing compliance requirements or in how companies manage their protections and enforce compliance controls? The purpose of this research was to answer these questions by means of a technical assessment of the Capital One data breach incident which occurred at one of the largest financial institutions in the U.S. This incident was selected as a case study to understand the technical modus operandi of the attack, map out exploited vulnerabilities, and identify the related compliance requirements, that existed. The National Institute of Standards and Technology (NIST) Cyber-security Framework, version 1.1, as a basis for analysis because it is required by the regulatory bodies of the case study and it is an agnostic framework widely used in the global industry to provide cyber threat mitigation guidelines. The results of this research and the case study will help government entities, regulatory agencies, companies and managers in understanding and applying recommendations to establish a more mature cyber-security protection and governance ecosystem for the protection of organizations and individuals.
Wireless Hand Gesture Recognition for an Automatic Fan Speed Control System: Rule-Based Approach

Wireless communication may be between humans or machines is very much prevailing at these times. One of the most known methods is the use of Bluetooth. Bluetooth is perfect for short-range wireless communication and is a feature that can be found on phones, computers, and other electronic devices. In this study, it aims to create a wireless hand gestured controlled fan. The Bluetooth module is the main component used for transmitting and receiving information. The study used a gyro sensor in order to determine the change in coordinates of the hand in which the speed of the rotation of the fan will be dependent on it. It is connected to a microcontroller, specifically an Arduino in which coded functions have been used. Hand gesture recognition has been done with an overall testing accuracy of 98.61%. Wireless hand gesture fan speed control system has been effective and possible using the coordinates produced by the Bluetooth and gyroscope axis.
An Approach for Human Machine Interaction using Dynamic Hand Gesture Recognition

Gesture recognition is one of the most challenging area of research in computer vision. The interface for human computer interaction can easily be created using the gesture recognition. This paper suggests a novel as well as robust approach of dynamic hand gesture recognition for the human machine interaction system. Here, the system is designed to work in real time with images captured through web camera during experimentation. In RGB and HSV color space, skin color modeling is done for segmenting geometrical approximation of hand using contours. The region of interest (ROI) is used to locate the hand within the image. Fingers are counted using the contour defects and centroid tracking is used to track the hand over the sequence of frames. In experiments, the eight types of dynamic hand gestures are performed by user and these gestures are successfully recognized by the system. This proposed methodology is functioning admirably in term of recognition accuracy up to 95 % for dynamic hand gesture recognition.
The Effect of a Blended Learning Course of Visual Literacy for In-service Teachers

Aim/Purpose: The purpose of this study was to examine the effects of a 20-hour blended learning visual literacy course applied to in-service teachers. For this purpose, we designed the course to train the educators and the instrument to measure the level of visual literacy of participants before and after the intervention. Then, we found the differences. Background: Visual literacy is essential for improving visual communication skills on in-service teachers because they use and construct visual material permanently. Hence, they need to be trained for developing visual literacy taking into account their pace of life and specific needs. Methodology: We employed a quasi-experimental one-group pretest-posttest design. The area of study is Social Science, specifically Education. The population was in-service teachers who work in private schools in the north zone of Quito, Ecuador. The convenience sampling method was used to conduct this pilot study of 51 teachers at one private school. The visual literacy course was designed based on the ACRL Visual Literacy Competency Standards. The differences in the level of visual literacy were measured through a visual literacy test of 45 items made for this specific purpose. Differences between pretest and posttest were found after performing paired samples t-test on collected data. Contribution: This research contributes to visual literacy research focused on in-service teacher’s instruction. This practical study was based on a complete proposal for training and evaluated the visual literacy level of in-service teachers. Findings: Findings show that there are statistically significant differences in pretest and posttest scores, so teachers improved their level of visual literacy after the 20-hour blended learning visual literacy course. Recommendations for Practitioners: Practitioners should adapt the length of the training course to the teachers’ schedules. They should review the course themes and the items in the visual literacy test to know about the specific content to be taught along the course. Recommendation for Researchers: Researchers who want to replicate a similar study should have a bigger group of participants and, if possible, they should have a control group. Impact on Society: This study indicates that teachers could improve their level of visual literacy after attending a well-structured training course. Thus, it is crucial to offer in-service teachers the opportunity to improve their visual communication skills through a concrete learning process adapted to their schedules and life. Future Research: Future research should focus on evaluating before and after the treatment, through practical projects, the previous and acquired knowledge of in-service teachers.
A mobile robot path planning algorithm based on improved A*

Abstract Global path planning of mobile robot aims to provide a safe and smooth path for mobile robot navigation. Traditional A * algorithm is planning the path of more turns, and not smooth. Moreover, for the u-shaped terrain, the path stick to the obstacles. Aiming at the shortcomings of A* algorithm, cosine distance is selected as the heuristic function, and direction information is added and normalized. The 36-order neighborhood search matrix is selected to solve the problem of fitting u-bend. In addition, a post-processing method based on Bessel curve is proposed. Make the planned path curvature change continuously. Simulation results show the effectiveness of the improved algorithm.
Performance observation of mammograms using an improved dynamic window based adaptive median filter

Breast cancer is one of the vital causes of an increase in the rate of mortality of women in developing countries. Detection of masses in the breast tissue by the radiologists is a significant step in the process of identification of cancer in the breast. Pre-processing in mammogram has played a major role in identifying such masses in the breast. Mammography has emerged as the popular screening approach for early identification of masses in the breast. In this manuscript, we have introduced an improved Dynamic Window based Adaptive Median Filter (DWAMF) for removal of impulse noise. DWAMF algorithm is implemented for removal of noise based on its size in the direction to enhance the image standard. The experimental results of DWAMF has been compared with Standard Median Filter (MF), and its observed that the DWAMF gives better result and higher value along with the introduction of threshold value with the increased percentage of noise. To compare the performance of the studies on filter methods, the Mean square Error (ME), Peak signal to Noise Ratio (PNR) and Structural Content (SC) measures are used to evaluate and compare the performance of the studied models.
A Wide &amp; Deep Learning Sharing Input Data for Regression Analysis

The Wide& Deep learning which combinesthe wide component and deep component shown good performance in recommendation systems. However, the Wide & Deep learning lack of a research and experimental result on regression analysis. In this paper, we experiment the Wide & Deep learning on regression analysis and we also present new Wide & Deep structure named WDSI which have better performance than the existing Wide & Deep learning in regression analysis. The wide component and deep component of the WDSI sharing a same input data. This strategy can reduce burden on a hand-crafted variables and it have better performance than the Wide & Deep learning in regression analysis. We also show that the WDSI outperforms a traditional machine-learning and deep-learning models in regression analysis.
Intelligent video analysis: A Pedestrian trajectory extraction method for the whole indoor space without blind areas

Pedestrian trajectory extraction is an important part of intelligent monitoring, which is of great significance to many fields such as statistics on pedestrian flow and density, population behavior analysis, abnormal behavior detection, etc. However, it is quite challenging to extract pedestrian trajectory without blind areas in the whole space due to the limited view angle of ordinary cameras. So far, no efficient method has been proposed to deal with this problem. In this paper, we propose a pedestrian trajectory extraction method based on a single fisheye camera, which can realize no blind areas pedestrian trajectory extraction in the whole interior space. First, the fisheye camera with a perspective of 180∘ is adopted in our work which can realize the entire space monitoring without blind areas and avoid object matching among multiple cameras. Then, the deep convolutional neural network, the Kalman Filter algorithm, and the Hungarian algorithm are combined for pedestrian head detection and tracking. In order to calculate the coordinates of the trajectory points according to the obtained head position, we propose a novel pedestrian height estimation method for fisheye cameras. Finally, the pedestrian trajectory points are calculated based on the detected head position and the estimated height. The performance of the proposed pedestrian trajectory extraction method has been evaluated by a variety of experiments. The experimental results show that the trajectories of multiple pedestrians can be extracted simultaneously through the method proposed in this paper, and the average error of the trajectory points is less than 5.07 pixels in the 512×512 images.
Authoring-by-Doing: Animating Work Instructions for Industrial Virtual Reality Learning Environments

With the rise of Virtual Reality, gaming is about to reach a new level of realism. The industry sector has recognized the technology’s potential too, especially for learning and training of assembly procedures and maintenance tasks. Although gaming and manufacturing industry seem to have different requirements at the first sight, both worlds can benefit from each other. One of the most cumbersome tasks when creating a VR application is the authoring of content, more specific the animation of characters and interactable objects. This paper describes the prototype for a Virtual Reality-supported learning and training application and presents a concept to simplify the authoring process of content with additional focus on animating assembly procedures. Our idea is to record actions performed by an expert in the VR environment. Trainees can watch the recorded actions as semi-transparent “ghost” animation and interactively mimic the set of instructions. By this “authoring-by-doing” approach, we hope to accelerate content creation for industrial VR-learning scenarios.
Comparison of Speech Representations for Automatic Quality Estimation in Multi-Speaker Text-to-Speech Synthesis

We aim to characterize how different speakers contribute to the perceived output quality of multi-speaker Text-to-Speech (TTS) synthesis. We automatically rate the quality of TTS using a neural network (NN) trained on human mean opinion score (MOS) ratings. First, we train and evaluate our NN model on 13 different TTS and voice conversion (VC) systems from the ASVSpoof 2019 Logical Access (LA) Dataset. Since it is not known how best to represent speech for this task, we compare 8 different representations alongside MOSNet frame-based features. Our representations include image-based spectrogram features and x-vector embeddings that explicitly model different types of noise such as T60 reverberation time. Our NN predicts MOS with a high correlation to human judgments. We report prediction correlation and error. A key finding is the quality achieved for certain speakers seems consistent, regardless of the TTS or VC system. It is widely accepted that some speakers give higher quality than others for building a TTS system: our method provides an automatic way to identify such speakers. Finally, to see if our quality prediction models generalize, we predict quality scores for synthetic speech using a separate multi-speaker TTS system that was trained on LibriTTS data, and conduct our own MOS listening test to compare human ratings with our NN predictions.
Towards an Artificial Intelligence Aided Design Approach: Application to Anime Faces with Generative Adversarial Networks

Ever since the inception of Machine Learning and Artificial Intelligence, the basic motto for most of research works has been to bring the machines at par with human intelligence. Designing new products and artifacts is one of the many fields where it is very difficult to enable computing machines replicate human creativity and innovativeness. Design processes in engineering fields as well as in arts follow methodical series of steps to create new products. Due to high demands of customized products and services, competitors tend to shorten the time-to-market periods, using advanced Computer-Aided Design programs. These programs play important roles to assist designers in digitizing blueprints and automating repetitive tasks. However, they fail to boost designer creativity by generating or suggesting new ideas or designs based on existing products or their variants. In order to boost creativity in the entertainment industry, we propose in this paper a new approach based on unsupervised learning techniques to create variants of a given artifact or product blueprints. Within the field of designing new cartoon characters, our proposed approach relies on Generative Adversarial Neural Networks [1] to create new anime or cartoon faces on their own without any human intervention. It learns features and characteristics from an image training dataset and combines them to create new features and thus builds a new image which is not present in the training dataset. This applied approach attempts to not only help artists and designers to have a preview of the possible new and unique avatars but also would prevent any copyright infringements.
Comparison of Smart Contract Blockchains for Healthcare Applications.

Blockchain and smart contracts (i.e., computer code that can be run on blockchain) are increasingly popular for healthcare applications. However, only very few implementations exist because of the complexity of the technologies. Although there are tutorials and reviews to introduce blockchain and smart contracts, a pragmatic comparison of such platforms is needed. In this study, we addressed practical considerations while building a healthcare blockchain and smart contract system, by (1) comparing technical features of platforms, (2) selecting three platforms, (3) constructing blockchain networks, (4) testing the blockchains, and (5) summarizing the experience and time used for implementation by students. We evaluated Ethereum, Hyperledger Fabric, and MultiChain, and confirmed that the selection of a proper platform depends on the requirements of the application. The findings of our study can accelerate the process and reduce the risk of adopting blockchain technology in biomedical and healthcare domain.
MR-SimExCoverage: Multi-robot Simultaneous Exploration and Coverage

In this paper, we present a novel problem of simultaneous exploration and area coverage by multiple cooperating mobile robots. As the robots cover an initially unknown region, they perform intermittent exploration of the region and build a map, which in turn is used to plan the coverage path. We use a Voronoi partition based multi-robot coverage strategy using the Manhattan distance metric to solve the coverage problem and a frontier based exploration strategy for exploration mapping. We provide results of simulation using Matlab/V-rep environments to demonstrate the proposed multi-robot simultaneous exploration and coverage (MR-SimExCoverage) problem using the spanning tree based coverage (STC) algorithm.
Privacy preserving sentiment analysis on multiple edge data streams with Apache NiFi

Sentiment analysis, also known as opinion mining, plays a big role in both private and public sector Business Intelligence (BI); it attempts to improve public and customer experience. Nevertheless, de-identified sentiment scores from public social media posts can compromise individual privacy due to their vulnerability to record linkage attacks. Established privacy-preserving methods like k-anonymity, l-diversity and t-closeness are offline models exclusively designed for data at rest. Recently, a number of online anonymization algorithms (CASTLE, SKY, SWAF) have been proposed to complement the functional requirements of streaming applications, but without open-source implementation. In this paper, we present a reusable Apache NiFi dataflow that buffers tweets from multiple edge devices and performs anonymized sentiment analysis in real-time, using randomization. The solution can be easily adapted to suit different scenarios, enabling researchers to deploy custom anonymization algorithms.
A Graph Theory Approach to Fuzzy Rule Base Simplification

Fuzzy inference systems (FIS) gained popularity and found application in several fields of science over the last years, because they are more transparent and interpretable than other common (black-box) machine learning approaches. However, transparency is not automatically achieved when FIS are estimated from data, thus researchers are actively investigating methods to design interpretable FIS. Following this line of research, we propose a new approach for FIS simplification which leverages graph theory to identify and remove similar fuzzy sets from rule bases. We test our methodology on two data sets to show how this approach can be used to simplify the rule base without sacrificing accuracy.
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models

Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the data, but result in serious and potentially dangerous prediction errors. To address this issue, practitioners often use adversarial training to learn models that are robust against such attacks at the cost of higher generalization error on unperturbed test sets. The conventional wisdom is that more training data should shrink the gap between the generalization error of adversarially-trained models and standard models. However, we study the training of robust classifiers for both Gaussian and Bernoulli models under $\ell_\infty$ attacks, and we prove that more data may actually increase this gap. Furthermore, our theoretical results identify if and when additional data will finally begin to shrink the gap. Lastly, we experimentally demonstrate that our results also hold for linear regression models, which may indicate that this phenomenon occurs more broadly.
Blockchain for Cybersecurity: A Comprehensive Survey

Blockchain is a decentralized ledger used to securely exchange digital currency, perform deals and transactions. Each member of the network has access to the latest copy of encrypted ledger so that they can validate a new transaction. Various features such as decentralization, trustworthiness, trackability and immutability are provided by the blockchain technology. This paper provides the blockchain architecture and explains the concept, characteristics, need of Blockchain in Security, how Bitcoin works and to enhance the security in the field of IoT. It attempts to highlights the role of Blockchain in shaping the future of Cyber Security, Cryptocurrency and adoption of IoT. This paper explains the need of blockchain technology in various technical fields and shows various advantages over conventional system.
Sparse GPU Kernels for Deep Learning

Scientific workloads have traditionally exploited high levels of sparsity to accelerate computation and reduce memory requirements. While deep neural networks can be made sparse, achieving practical speedups on GPUs is difficult because these applications have relatively moderate levels of sparsity that are not sufficient for existing sparse kernels to outperform their dense counterparts. In this work, we study sparse matrices from deep learning applications and identify favorable properties that can be exploited to accelerate computation. Based on these insights, we develop high-performance GPU kernels for two sparse matrix operations widely applicable in neural networks: sparse matrix-dense matrix multiplication and sampled dense-dense matrix multiplication. Our kernels reach 27% of single-precision peak on Nvidia V100 GPUs. Using our kernels, we demonstrate sparse Transformer and MobileNet models that achieve 1.2-2.1x speedups and up to 12.8x memory savings without sacrificing accuracy.