Reviewerly/exHarmony huggingface dataset:
Detection of counterfeit coins based on 3D height-map image analysis

Detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. Edge detection is one of the most widely used techniques to extract features from 2D images. However, in 2D images, the height information is missing, losing the hidden characteristics. In this paper, we propose a 3D approach to detect and analyze the precipice borders from the coin surface and extract significant features to train an ensemble classification system. To extract the features, we also propose Binned Borders in Spherical Coordinates (BBSC) to analyze different parts of precipice borders at different polar and azimuthal angles. The proposed method is robust even against degradation which appears on shiny coins after 3D scanning. Therefore, there is no need to restore the degraded images before the feature extraction process. Here, the system has been trained and tested with four types of Danish and two types of Chinese coins. We take advantage of stack generalization to classify the coins and add the reject option to increase the reliability of the system. The results illustrate that the proposed method outperforms other counterfeit coin detectors. The accuracy obtained by testing Danish 1990, 1991, 1996, and 2008 datasets are 98.6%, 98.0%, 99.8%, and 99.9% respectively. In addition, results for half Yuan Chinese 1942 and one Yuan Chinese 1997 were 95.5% and 92.2% respectively.
Instability of impulsive stochastic systems with application to image encryption

This paper concerns the stochastic instability of impulsive stochastic systems with application to image encryption. Based on comparison principle, the sufficient conditions for instability in probability of impulsive stochastic systems are obtained by the instability of continuous comparison system established by Lyapunov function. It also shows how to determine stochastic instability of impulsive stochastic systems when stability conditions fail. The effectiveness of theoretical results is verified by two numerical examples, whose chaotic signals are successfully used for image encryption.
Object oriented spatial analysis of natural concentration levels of chemical species in regional-scale aquifers

We address the problem of characterizing spatially variable Natural Background Levels (NBLs) of concentrations of chemical species of environmental concern in a large-scale groundwater body. Assessment of NBLs is critical to identify significant trends of (possibly hazardous) chemical concentrations in aquifer systems, the latter being typically associated with spatially heterogeneous hydrogeochemical characteristics. Our study considers the entire probability density function (PDF) of the concentration of the chemical species of interest as atom of the statistical analysis. These PDFs are estimated across a network of observation boreholes in the investigated spatial domain, and modeled as random points in a Bayes Hilbert space, in the context of Object Oriented Data Analysis. This approach enables one to take advantage of the entire information content provided by these objects for the purpose of spatial prediction and uncertainty quantification. As a key element of innovation, we investigate the use of depth measures for distributional data with the distinctive aims of (i) detecting central and outlying NBL distributions in the dataset, and (ii) building prediction regions for NBL distribution at unsampled locations. We illustrate the results of the proposed approach to the analysis of NBLs of a selected chemical species detected at an environmental monitoring network within a large-scale alluvial aquifer in Northern Italy.
A Hardware Accelerator for Language-Guided Reinforcement Learning

Reinforcement learning guided with language instructions can potentially improve efficiency and better align with overall system design goals. This article introduces a hardware-friendly architecture for training reinforcement learning equipped with natural language instructions.
Textbook Underflow: Insufficient Security Discussions in Textbooks Used for Computer Systems Courses

Introductory computer science courses, such as Computer Systems, could be used to provide the first exposure to computer security to students. However, prior work has shown that, in the US's top R1 universities, computer systems courses are not taught with security in mind. It was also shown that students and instructors use unsafe functions in their code, leading to security vulnerabilities. In this paper, we focused on the textbooks used for computer systems courses. We analyzed the discussion of security topics and the use of unsafe functions in the thirteen textbooks used in the top 30 R1 universities in the US for teaching computer systems. We show that many textbooks do not discuss security at all, while some limit their discussion to "undefined behavior'', ignoring that opportunity to discuss potential security issues associated with the undefined behavior. Furthermore, textbooks that talk about security continue using unsafe functions throughout (though not necessarily in vulnerable ways but also without any warning or explanation). We also show that many textbooks do not warn about unsafe functions they use or teach how to use them safely.
Group security using ECC

Nowadays security is main issue during transmission of data. Among many cryptographic methods, ECC is the public key asymmetric cryptosystem which provides faster computation over smaller size in comparison to other asymmetric key cryptosystems. In this paper, we have proposed a group security algorithm using the ECC cryptography algorithm. The group security is applied to ECC in terms of m-gram selection called ECC m-gram selection. Due to the group security implementation in terms of common grams, processing speed will be faster in comparison to individual item security. We have also made the comparison study between the traditional ECC algorithm with the proposed group security algorithm using generalized frequent-common gram selection for depicting lesser time requirements to achieve better security for the whole process.
A LSTM-Based Channel Fingerprinting Method for Intrusion Detection

Intrusion detection is a crucial issue for 5th generation (5G) access networks to securely support various services. Traditional cryptographic key-based solutions are not suitable for severe resources-constrained networks, such as the Internet of Things (IoT). In this paper, we propose a lightweight intrusion detection mechanism by exploring physical layer attributes that are unique and difficult to impersonate. Specifically, a long-short memory network (LSTM) is employed as an intelligent classifier to distinguish different transmitters based on channel state information (CSI) features. Then we develop a comprehensive 5G NR channel detection model based on LSTM under dynamic channel conditions to identify malicious attacks by intelligently analyzing CSI. The simulation results demonstrate that the proposed solution improves detection accuracy and successfully prevent systems from spoofing attacks.
Is Secure Coding Education in the Industry Needed? An Investigation Through a Large Scale Survey

The Department of Homeland Security in the United States estimates that 90% of software vulnerabilities can be traced back to defects in design and software coding. The financial impact of these vulnerabilities has been shown to exceed 380 million USD in industrial control systems alone. Since software developers write software, they also introduce these vulnerabilities into the source code. However, secure coding guidelines exist to prevent software developers from writing vulnerable code. This study focuses on the human factor, the software developer, and secure coding, in particular secure coding guidelines. We want to understand the software developers' awareness and compliance to secure coding guidelines and why, if at all, they aren't compliant or aware. We base our results on a large-scale survey on secure coding guidelines, with more than 190 industrial software developers. Our work's main contribution motivates the need to educate industrial software developers on secure coding guidelines, and it gives a list of fifteen actionable items to be used by practitioners in the industry. We also make our raw data openly available for further research.
Virtual Shadow Rendering for Maintaining Situation Awareness in Proximal Human-Robot Teaming

One focus of augmented reality (AR) in robotics has been on enriching the interface for human-robot interaction. While such an interface is often made intuitive to interact with, it invariably imposes novel objects into the environment. In situations where the human already has a focus, such as in a human-robot teaming task, these objects can potentially overload our senses and lead to degraded teaming performance. In this paper, we propose using AR objects to solely augment natural objects to avoid disrupting our natural senses while adding critical information about the current situation. In particular, our case study focuses on addressing the limited field of view of humans by incorporating persistent virtual shadows of robots for maintaining situation awareness in proximal human-robot teaming tasks.
Signature Texture Features Extraction Using GLCM Approach in Android Studio

Abstract Signature Features Extraction is a method for deriving informative values of image signature for indexing and signature identification. It is a dimensionality reduction of image data to be manageable for processing. Image texture is a key spatial attribute used for feature extraction and image coding. However, Android environment lack of efficient algorithm for automatic extracting texture features which may cause serious security issues and unreliability problem in the Android application (app). The challenge in Signature Features Extraction of a mobile app is to be as robust and stable as possible. For that, the present paper presents an efficient algorithm for extracting signature texture features using a gray level co-occurrence matrix (GLCM). The image signature is quantized into five texture features of energy feature, entropy feature, contrast feature, dissimilarity feature, and homogeneity feature. The processes developed in the android studio environment to be used in mobile phone cell applications as a tool for signature detection. The results show that the present method provides significant results due to obtained average values and acceptable computational time.
Enhanced Product Recommendations based on Seasonality and Demography in Ecommerce

Today, the social network environments are trending to list the customers with the product recommendations. The social recommendations are generated by recommender system based on product ratings and comments. There has been number of work towards improving the accuracy of recommendations generated by recommender systems. These tend to make the system to narrow the suggestion of product variety. The system should get evolved to the trend of generating diversity of recommendation so that the customer can explore the wide variety of products. The diversity of user demographic in social network makes the recommendation system can be applied to introduce variety of product recommendation. The seasonality of product is emerging trend in recommendation system to actively seek out the right product at right time. The work focuses on investigating the efficiency of recommender system, in generating the diverse suggestions for E-commerce dataset.
A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.
Dive into Systems

This paper presents our experiences, motivations, and goals for developing Dive into Systems [17], a new, free, online textbook that introduces computer systems, computer organization, and parallel computing. Our book's topic coverage is designed to give readers a gentle and broad introduction to these important topics. It teaches the fundamentals of computer systems and architecture, introduces skills for writing efficient programs, and provides necessary background to prepare students for advanced study in computer systems topics. Our book assumes only a CS1 background of the reader and is designed to be useful to a range of courses as a primary textbook for courses that introduce computer systems topics or as an auxiliary textbook to provide systems background in other courses. Results of an evaluation from students and faculty at 18 institutions who used a beta release of our book show overwhelmingly strong support for its coverage of computer systems topics, its readability, and its availability. Chapters are reviewed and edited by external volunteers from the CS education community. Their feedback, as well as that of student and faculty users, is continuously incorporated into its online content. We anticipate releasing version 1.0 of the book in spring of 2021, and a release candidate is currently available at https://diveintosystems.org.
Cost analysis of amazon web services – From an eye of architect and developer

The basic concept to use any cloud platform is to reduce the overall cost involved in the development, maintenance, and required infrastructure as per the need. However, in case we are not wary of the basic principles behind the usage of the cloud platform, the cost advantage goes haywire and organizations failed to realize the benefits of services available on the cloud platform. An attempt is made in this paper to conceptualize mathematical model for cost computation and to bring many other hidden attributes which may play a very significant role in determining the overall cost. To parameterize the AWS Costing, in this endeavor four factors are considered to build the mathematical model to arrive at a high-level cost of using a cloud platform.
Components of Smart Chatbot Academic Model for a University Website

In current days, Chatbot has been increasingly in demand and become popular among the communities of media, end-users, and even vendors. There are two types of Chatbot which are Smart Chatbot and Simple Chatbot. The Smart Chatbot provides more functions and it is the most used in current days. As the Smart Chatbot has becomes the tool that eases the interaction between the human and the site, most of the companies, institutions, and even the individual have look for it. This is including the universities. Having a Chatbot embedded in the University Website is able to increase the service level and the interaction between the stakeholders and the university itself. However, there is lacking in the guideline on the general or core components that must have before developing a Smart Chatbot for the University Websites. Hence, to overcome the problem, this paper comes out with a research study on identifying the components needed in developing a Smart Chatbot for University Website. For now, the research work is focusing on the academic category only on the website. As the first step of the research process, the list of components of the Smart Chatbot Academic model was gained through Literature Review from the year 2017 to 2020. The identified components will be next evaluated through a survey process. The idea of the work is to develop a Smart Chatbot Academic model that will be significant as a guideline for the University to develop its own Smart Chatbot.
A Rule Based System for Bangla Voice and Text to Bangla Sign Language Interpretation

It is very difficult in Bangladesh to make communication between Sign and Non-Sign people even though in the entire world because most of the Non-Sign people do not understand the Sign language. To create effective communication between Sign and Non-Sign people, we have proposed a rule-based system for Bangla Voice and text to Bangla sign language interpretation. Voice and Bangla Text are taken as input by the proposed method and this input text is parsed based on Bangla linguistics. After parsing, the system links and displays the signs with the corresponding trained animated Bangla Signs. The 2D animation for the model is trained with 14 animated Bangla Numeral Signs (0-9, Hundred, Thousand, Lakh, Core). The system is being evaluated using 100 different lengths of Bangla Composite Numeral. The system achieves the interpretation average accuracy of 96.03% for voice, 100% for Bangla Text as the input of Bangla Composite Numeral, and Bangla Sentences with a computation cost of 9.5ms per sign.
Understanding Demographic Risk Factors for Adverse Outcomes in COVID-19 Patients: Explanation of a Deep Learning Model

This study was to understand the impacts of three key demographic variables, age, gender, and race, on the adverse outcome of all-cause hospitalization or all-cause mortality in patients with COVID-19, using a deep neural network (DNN) analysis. We created a cohort of Veterans who were tested positive for COVID-19, extracted data on age, gender, and race, and clinical characteristics from their electronic health records, and trained a DNN model for predicting the adverse outcome. Then, we analyzed the association of the demographic variables with the risks of the adverse outcome using the impact scores and interaction scores for explaining DNN models. The results showed that, on average, older age and African American race were associated with higher risks while female gender was associated with lower risks. However, individual-level impact scores of age showed that age was a more impactful risk factor in younger patients and in older patients with fewer comorbidities. The individual-level impact scores of gender and race variables had a wide span covering both positive and negative values. The interaction scores between the demographic variables showed that the interaction effects were minimal compared to the impact scores associated with them. In conclusion, the DNN model is able to capture the non-linear relationship between the risk factors and the adverse outcome, and the impact scores and interaction scores can help explain the complicated non-linear effects between the demographic variables and the risk of the outcome.
PSF Estimation Method of Simple-Lens Camera Using Normal Sinh-Arcsinh Model Based on Noise Image Pairs

With the development of computational photography, single-lens camera combined with corresponding image deblurring algorithm is gradually becoming a new research direction, replacing complex modern optical imaging system such as single lens reflex (SLR) camera. For single-lens camera, the Point Spread Function (PSF) estimation accuracy will directly affect the image restoration effect. In this paper, we designed the simple-lens cameras with one, two and three lenses, respectively, and propose a robust and accurate PSF estimation method of simple-lens camera. The key point of estimation is to obtain the blur image and clear image pairs, which are necessary for non-blind deconvolution PSF estimation. Considering the structure characteristic of simple-lens camera, we take picture of original clear image displayed on the computer screen to get the image pairs through corner detection and color correction is made to remove color distortion. In addition, a few studies have shown that the PSF of the simple lens is close to the spatially deformed wedge, so we use a more reasonable Normal Sinh-Arcsinh (NSAS) model to fit the blur kernel and get its parameters by Powell algorithm. The experiment results have shown that the space-variant PSF estimated by the proposed method achieves better performance than the compared methods both qualitatively and quantitatively.
Chemistry Education (Rate of Reaction) via Eleaming Mobile Application

Learning medium for students in schools are very crucial as it will capture the student interest, thus make them understand about the topic. However, most of the time, students in school are learning in the traditional method, where all notes are obtained from textbooks provided for them. The theory explained in the textbooks make students confuse with what they had learn and the explanations is too details. This situation will affect the interest of students to study. With the growing of technology in almost every domain, sector, and industry revolution 4.0, mobile apps had become part of our life where the benefit of it also can be extend to the classroom. Thus, the use of mobile apps in education has several benefits by providing intuitive learning and attractive platforms. This research addresses the needs of mobile applications in learning environment. It will focus on development of a mobile apps for learning chemistry. This paper will consider the case study for a local secondary school in Malaysia. Usability test and Functionality test will be conducted to evaluate the function of the mobile apps. The methodology used to develop the mobile learning application is Mobile Application Development Life Cycle (MADLC) Model. The result form this research had shown that, mobile apps can help school students understanding the theory of specific topics in chemistry subject. Thus, mobile apps can be one of the learning tools, providing intuitive learning and attractive platforms, for students in school, which not only depending on their textbooks.
An Automatic System for Essay Questions Scoring based on LSTM and Word Embedding

Essay scoring is a significant task in education, especially online education. Manual essay scoring is a complex job, which limits the development of large-scale online education. In tradition, essays are all scored by human because computer program cannot understand what text means. Recent advance of natural language processing technology in artificial intelligence provides a way to score essays automatically. In our study, we create an automatic essay scoring (AES) system by using Long-Short Term Memory (LSTM) network and word embedding. We show an automated system that can rate essays in electronic text. We combine manually crafted features and Word2Vec embedding in training the model, which makes it more interpretable. We carefully tune the hyperparameter to improve the precision of our model. The LSTM network reaches a quadratic weighted Kappa score (QWK) of 0.95 ± 0.01, which outperforms many other rating systems. The AES system we design can greatly improve the efficiency of online education.
Get This!? Mixed Reality Improves Robot Communication Regardless of Mental Workload

We present the first experiment analyzing the effectiveness of robot-generated mixed reality gestures using real robotic and mixed reality hardware. Our findings demonstrate how these gestures increase user effectiveness by decreasing user response time during visual search tasks, and show that robots can safely pair longer, more natural referring expressions with mixed reality gestures without worrying about cognitively overloading their interlocutors.