Reviewerly/exHarmony huggingface dataset:
Does Learning Method Matter in Cyber Security Behaviour? Spaced Vs. Massed e-Learning

Besides acquiring knowledge and skills, learning can also have a behavior change purpose. The case of learning compliance and cyber hygiene is an example. It remains a largely unexplored question whether learning methods affect the degree of intention to follow the learned behavior. Spaced learning has been established as a useful presentation style for the retention of information and building skills, but many organisations continue to use a massed e-learning format. This study compares three presentation formats (spaced, massed, and mixed) using a newly developed e-learning tool for the European General Data Protection Regulation (GDPR). Mixed methods are used to examine differences as well as to explore user perceptions and preferences. Forty-seven participants completed baseline tests and were randomly allocated to one of the three presentation style conditions. All participants completed immediate posttests, delayed post-tests, and behavioral intention questionnaires. Despite some large observed differences between individuals, the univariate analyses showed that the gain scores between immediate post-test and delayed post-tests were not significantly different between participants across the three presentation formats. The qualitative analysis revealed emerging themes of presentation, delivery, messenger effects, and motivations.
Sampling-based Sparse Format Selection on GPUs

Sparse Matrix-Vector Multiplication (SpMV) is an important kernel in numerous computational disciplines. The overall performance of SpMV is highly dependent on the storage format of the sparse matrix. This has led to much interest in recent years on automatically choosing the appropriate format, typically using machine learning techniques and training a model using a large number of matrices. However, these methods have limitations in practice - besides the dependency on obtaining a large number of sparse matrices of training and expensive overheads of the training, they usually have limited prediction ability across architectures. In this paper, we take a very distinct approach to the same problem. This approach involves obtaining samples from the original matrix, executing the kernel using these samples, and selecting the best format. However, our approach requires obtaining representative samples that can help understand performance associated with using a specific format on the full matrix, which turns out to be challenging. Based on the storage properties and processing granularity associated with different formats, we develop three novel sampling schemes: Row Cropping sampling, Random Warp sampling, and Diagonal Aligning sampling. These sampling methods are designed by observing that certain factors tend to be critical for performance associated with a particular format, and thus preserving that factor through sampling. Experimental results using nearly 2000 matrices demonstrate that our approach delivers high efficiency without the expensive training process, and it is easy to migrate across architectures. At the same time, our approach achieves comparable prediction accuracy with the state-of-art methodologies, and even outperforms them in certain cases (especially for predicting on some of the largest matrices we use). Through our work, we also offer new insights into the performance achieved using different formats on GPUs.
SENSELET++: A Low-cost Internet of Things Sensing Platform for Academic Cleanrooms

Sensory IoT (Internet of Things) networks are widely applied and studied in recent years and have demonstrated their unique benefits in various areas. In this paper, we bring the sensor network to an application scenario that has rarely been studied - the academic cleanrooms. We design SENSELET++, a low-cost IoT sensing platform that can collect, manage and analyze a large amount of sensory data from heterogeneous sensors. Furthermore, we design a novel hybrid anomaly detection framework which can detect both time-critical and complex non-critical anomalies. We validate SENSELET++ through the deployment of the sensing platform in a lithography cleanroom. Our results show the scalability, flexibility, and reliability properties of the system design. Also, using real-world sensory data collected by SENSELET++, our system can analyze data streams in real-time and detect shape and trend anomalies with a 91% true positive rate.
Learning Representation with Q-irrelevance Abstraction for Reinforcement Learning

In order to improve the performance of deep reinforcement learning (DRL) algorithm in high-dimensional observation environments, we propose a new auxiliary task to learn representations to aggregate task-relevant information of observations. Inspired by Q-irrelevance abstraction, our auxiliary task trains a deep Q-network (DQN) to predict the true Q value distribution over all discrete actions. Then we use the output of DQN to train the encoder to discriminate states with different Q values. The encoder is used as the representation of proximal policy optimization (PPO). The resulting algorithm is called as Q-irrelevance Abstraction for Reinforcement Learning (QIARL). After training, the encoder can aggregate states with similar Q value distributions together for any policy and any action. Thus the encoder can encode the important information that is relevant to reinforcement learning task. We test QIARL in four Procgen environments compare with PPO, A2C and Rainbow. The experimental results show QIARL outperforms the other three algorithms.
Topographic Levelling from the Point of View of Active Learning Using the Flipped Classroom Model

University education has experienced an increase in the use of ICT-related tools, to some extent driven by the pandemic situation that occurred during the past academic years 2019/20 and 2020/21. Face-to-face small group practices that required the use of specific equipment by students have been relegated to other types of activities that allow keeping an adequate distance between individuals. At this point, active methodologies acquire a fundamental role for large group virtual theoretical classes, when having to keep focussed for one hour and a half on the traditional masterful method, thus becoming a true challenge. This paper presents the work carried out to complement the teaching of the subject Graphic Engineering and Topography, and more precisely, in the area related to topographic levelling to incorporate the flipped classroom model as a pilot experience in some topics. The follow-up of this experience has been performed by means of activity reports and a satisfaction survey.
Application Virtualization: An Agent Encapsulation of Software in Virtual Machines to Archive the Execution Performance in Hosts

Virtualization is now a key technology for increasing the utilization of physical hardware, and many software programs are benefiting from it. Virtual machine software has become more and more demanding in terms of virtual machine performance. Nowadays, the performance improvement of software inside a virtual machine comes from it of the virtual machine hardware, which is far less than the performance improvement of the same software on the physical machine. We propose Application Virtualization to improve the execution of virtual machine software inside the virtual machine. It can be implemented on existing virtualization platforms to simplify the deployment difficulty and execution efficiency of software inside virtual machines and providing a common way to invoke host software.
Highlighting Demanding Aspects of Operating Systems for Improved Efficiency

Today&#x2019;s life of mankind is mostly dependent on computers, and their success in our lives is due to strong and lifelike operating systems installed on these very modern machines. with the passage of time machines called hardware are becoming very strong. As we are aware that these machines are run by operating systems, we need such updated operating systems which can have maximum output from these powerful machines. we have to update our operating systems to make this hardware speedier and more elegant. if these are addressed competently, the level of utilization of computers /hardware can be boosted to an optimum level. There are deficiencies in even most modern operating systems, which open the doors for more opportunities to follow a line of investigation. the areas where extensive research is a need in the era, are reliability and security. we explore, why modern OS underperform and provide their solutions. The research directions are also set for the future. Finally, we portray Singularity, an assessment project at Microsoft Research that is building another working system to examine these troubles. Characteristic breakers three unequivocal arrangement decisions to grow structure dependability and improve system security, plan, and extension. This paper aimed to bounce a statement on the genuine issues and execution issues looked at by programming experts and specialists dealing with Distributed Programming systems.
An RDBMS-only architecture for web applications

Multi-tier architectures have been the de facto standard for web applications, leaving little room for alternative solutions. Despite this, there is diversity in the proposals, especially in the tiers' number, size, and responsibilities. In particular, the database-centric approach aims to implement application logic and behavior within an RDBMS. In this work, we present, model, and propose to extend the database-centric approach into an RDBMS-only architecture, where the whole multi-tiered application is implemented in the database server. We present a characterization and description of the architecture and an early prototype that implements the proposal. It is important to note that both the database-centric and the proposed RDBMS-only architectures are a particular case of a three-layered model that needs to be differentiated from monolithic systems. Our preliminary results show that this approach is not only feasible but also advisable in some cases.
Looking at the Digitized World from Different Perspectives

We are living disruptive times, experiencing unprecedented large scale societal emergencies that are amplifying the combined pressures of high potential digital enablers. The sudden gigantic adoption of distance working and distance learning at a global scale, with the consequent hybridization of local/remote, presence/distance, human/machine relational dimensions.
Pre-Optimization of High Dimensional Extreme Learning Machine with Cooperative Coevolution

Extreme Learning Machine (ELM) is a special type of single hidden layer feedforward neural network, which uses pseudo-inverse to compute weights of the output layer, and is often faster than the gradient-based methods. However, due to the random initialization of input weights and biases, the performance of this algorithm is not always stable, and is less effective in large-scale applications. Therefore, in this paper, based on an effective large-scale optimization algorithm, i.e., cooperative coevolutionary particle swarm optimization (CCPSO), an improved hybrid ELM learning algorithm, named CCPSO-ELM, is proposed, where the input weights and the hidden layer biases are optimized using CCPSO. Compared with the traditional ELM algorithm, as well as ELM optimized by traditional PSO, the CCPSO-ELM is more likely to avoid local optima, has smaller optimization errors, and is more robust against noises. The results are verified by experiments on two different types of problems, i.e., large-scale multivariate function approximation and pattern classification.
Breast Cancer Prediction Using Different Classification Algorithms with Various Feature Selection Strategies

Breast cancer has always been one of the most dangerous diseases that threaten women's lives. If the disease is not detected in the early stages, it can result in the death of the patient. The term breast cancer is referring to a malignant tumor that happened due to the unexpected development of breast's cells which can probably have the ability to spread through other different parts of the patient's body. The occurrence of cancer is often a result of the abnormal growth of cells in our bodies. Cancers generally are classified into two types, Benign (non-cancerous cell) and Malignant (cancerous cell). The earlier the cancer is diagnosed, the better the patient's chance of recovery. Being able to accurately predict breast cancer present in patients has always been an important issue for cancer researchers. Machine Learning (ML) and Data Mining (DM) have always been a point of interest in the scientific community in the hope that they can yield accurate results. We aimed in this study to predict the tumor at early stages using some classification algorithms. After the dataset was collected and the outliers and skewness in the data set were removed, different classification algorithms were applied, focusing on the effect of the feature selection step in the model building phase. After conducting multiple experiments, we got the best overall accuracy by Support Vector Machine (SVM) classifier based on feature selection using Recursive Feature Elimination (RFE) with Random Forest (RF) technique with an accuracy of 98.25%.
Development of Artificial Neural Networks for Cyclohexane Oxidation Data Analysis

Machine learning has been demonstrated to be a powerful technique in recent decades. Artificial neural networks are widely used in various fields of science and industry. In this paper, we examine in detail the use of artificial intelligence for catalysis studies. The example of the cyclohexane oxidation reaction reveals how this powerful technology helps to solve complex problems of catalysis prediction.
Simulation and forecasting of the influenza epidemic process using seasonal autoregressive integrated moving average model

Today's global COVID-19 pandemic has affected the spread of influenza. COVID-19 and influenza are respiratory infections and have several similar symptoms. They are, however, caused by various viruses; there are also some differences in the categories of people most at risk of severe forms of these diseases. The strategies for their treatment are also different. Mathematical modeling is an effective tool for controlling the epidemic process of influenza in specified territories. The results of modeling and forecasts obtained with the help of simulation models make it possible to develop timely justified anti-epidemic measures to reduce the dynamics of the incidence of influenza. The study aims to develop a seasonal autoregressive integrated moving average (SARIMA) model for influenza epidemic process simulation and to investigate the experimental results of the simulation. The work is targeted at the influenza epidemic process and its dynamic in the territory of Ukraine. The subjects of the research are methods and models of epidemic process simulation, which include machine learning methods, in particular the SARIMA model. To achieve the aim of the research, we have used methods of forecasting and have built the influenza epidemic process SARIMA model. Because of experiments with the developed model, the predictive dynamics of the epidemic process of influenza for 10 weeks were obtained. Such a forecast can be used by persons making decisions on the implementation of anti-epidemic and deterrent measures if the forecast exceeds the epidemic thresholds of morbidity. Conclusions. The paper describes experimental research on the application of the SARIMA model to the epidemic process of influenza simulation. Models have been verified by influenza morbidity in the Kharkiv region (Ukraine) in epidemic seasons for the time ranges as follows: 2017-18, 2018-19, 2019-20, and 2020-21. Data were provided by the Kharkiv Regional Centers for Disease Control and Prevention of the Ministry of Health of Ukraine. The forecasting results show a downward trend in the dynamics of the epidemic process of influenza in the Kharkiv region. It is due to the introduction of anti-epidemic measures aimed at combating COVID-19. Activities such as wearing masks, social distancing, and lockdown also contribute to reducing seasonal influenza epidemics.
Chaotic Neural Network with Legendre Function Self-feedback and Applications

A new transient chaotic neuron model which introduces Legendre function into the self-feedback term is constructed. The dynamic characteristics of the single neuron are analyzed by the time evolution graph and the inverted bifurcation graph of the largest Lyapunov exponent. The network parameters are set for settling the combinatorial optimization problem. The effectiveness of this model has been verified by the simulation results in nonlinear function optimization, traveling salesman problem (TSP).
Resolving Infeasibility in Linear Programs for the Frequent Itemset Hiding Problem

Hiding sensitive knowledge in transactional databases is a problem that can be addressed through the use of frequent itemset hiding. A well established and robust methodology for hiding sensitive itemsets in transactional databases is embodied through different database extension schemes. Having considered this field extensively in our past and recent research agenda, we proposed a constraint-based hiding model that relies on Border revision and Linear Programming. The Linear Program strives to find the optimal solution to our problem by suggesting which itemsets to add in the extended database for maximum security and data quality. Unfortunately, linear programs do not always have either feasible or optimal solutions, so in this paper we propose some alternate approaches in case of not feasible Linear Programs and suggest the most suitable ones, depending on the needs of the user.
Off-line Handwritten Verification Model for Processing Bank Checks Based on Truncated-SVD and Support Vector Machine (SVM)

Handwritten is widely used for personal identification and confirmation. It is utilized in numerous fields such as banking, e-business, access control, etc. Nonetheless, it is easily subjected to forgery. Studies have advanced in the field of handwritten verification and recognition. However, a handwritten verification system based on the Arabic language has received minimal attention. Therefore, an Arabic Off-line Verification Model (AOVM) specifically for bank check processing is suggested in this paper. The AOVM model has three major stages: preliminary processing, feature extraction using Truncated- Singular Value Decomposition (TSVD), verification using Polynomial, Linear, and Gaussian Support Vector Machine (SVM) classifiers. In the proposed model, the images of the text are normalized into uniform size at first. Consequently, the skeleton of the text is extracted for feature extraction of the handwritten using TSVD. The accuracy of the proposed model is evaluated on version 2 of the IFN/ENIT dataset of handwritten Arabic text using Polynomial, Linear, and Gaussian SVM classifiers. Moreover, the accuracy of the proposed system was compared with the result produced by a benchmark OHATRS, which is based on Principle Component Analysis (PCA) and SVM classifiers using several normalization sizes of Arabic text images. The experiential results show the effectiveness of the proposed model compared to the OHATRS model.
Intent Matching based Customer Services Chatbot with Natural Language Understanding

Customer service is the lifeblood of any business. Excellent customer service not only generates return business but also creates new customers. Looking at the demanding market to provide a 24/7 service to customers, many organisations are increasingly engaged in popular social media and text messaging platforms such as WhatsApp and Facebook Messenger in providing a 24/7 service to customers in the current demanding market. In this paper, we present an intent matching based customer services chatbot (IMCSC), which is capable of replacing the customer service work of sales personnel, whilst interacting in a more natural and human-like manner through the employment of Natural Language Understanding (NLU). The bot is able to answer the most common frequently asked questions and we have also integrated features for the processing and exporting of customer orders to a Google Sheet.
A Post-Quantum Secure e-Health System for the Data Management

The concept of a smart city as a broad concept implies a versatile collection, storage and processing of data for the urban community. Obviously, health data is prioritized. The collection of such data is made possible by wearable medical devices. Generally, the mentioned devices do not store any data they, at best, perform limited processing basic functions and have little or no additional processing the capabilities for the collected information. In this article is described an integrated distributed eHealth system that collects the health information from registered city residents and securely stores and processes medical information in the cloud by means of a comprehensive data privacy encryption model that is based on NTRU public-key cryptosystem. A hash-based digital signature mechanism is used to verify that medical data is correctly assigned to the person concerned. The hash-based digital signature scheme is improved by mean of the novel random number generator. The user data is collected by system using the client application module installed on the user’s smart watch or smartphone and safely transfers them to the cloud server. Similar processing of an encrypted data is occurred by means of Apache Spark service. The IBM OpenWhisk Programming Service is responsible for running event-based handlers. The prototype was tested in a real-world example involving five hundred citizens of Brasov, Romania.
S-DFP: shifted dynamic fixed point for quantized deep neural network training

Abstract Recent advances in deep neural networks have achieved higher accuracy with more complex models. Nevertheless, they require much longer training time. To reduce the training time, training methods using quantized weight, activation, and gradient have been proposed. Neural network calculation by integer format improves the energy efficiency of hardware for deep learning models. Therefore, training methods for deep neural networks with fixed point format have been proposed. However, the narrow data representation range of the fixed point format degrades neural network accuracy. In this work, we propose a new fixed point format named shifted dynamic fixed point (S-DFP) to prevent accuracy degradation in quantized neural networks training. S-DFP can change the data representation range of dynamic fixed point format by adding bias to the exponent. We evaluated the effectiveness of S-DFP for quantized neural network training on the ImageNet task using ResNet-34, ResNet-50, ResNet-101 and ResNet-152. For example, the accuracy of quantized ResNet-152 is improved from 76.6% with conventional 8-bit DFP to 77.6% with 8-bit S-DFP.
Overview of Echo State Networks using Different Reservoirs and Activation Functions

IoT devices are growing rapidly in number and networks become more complex than ever. Therefore, network traffic prediction is a topic that needs special attention, allowing network administrators to be able to continuously adjust network parameters. This paper is focused on Echo State Networks (ESN), a quite new type of Recurrent Networks which can be used for times series prediction. Different configurations were tested, and their performance were compared to those of classical algorithms such as Seasonal Autoregressive Moving Average (SARIMA), Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM).