Reviewerly/exHarmony huggingface dataset:
High-Efficiency Min-Entropy Estimation Based on Neural Network for Random Number Generators

Random number generator (RNG) is a fundamental and important cryptographic element, which has made an outstanding contribution to guaranteeing the network and communication security of cryptographic applications in the Internet age. In reality, if the random number used cannot provide sufficient randomness (unpredictability) as expected, these cryptographic applications are vulnerable to security threats and cause system crashes. Min-entropy is one of the approaches that are usually employed to quantify the unpredictability. The NIST Special Publication 800-90B adopts the concept of min-entropy in the design of its statistical entropy estimation methods, and the predictive model-based estimators added in the second draft of this standard effectively improve the overall capability of the test suite. However, these predictors have problems on limited application scope and high computational complexity, e.g., they have shortfalls in evaluating random numbers with long dependence and multivariate due to the huge time complexity (i.e., high-order polynomial time complexity). Fortunately, there has been increasing attention to using neural networks to model and forecast time series, and random numbers are also a type of time series. In our work, we propose several new and efficient approaches for min-entropy estimation by using neural network technologies and design a novel execution strategy for the proposed entropy estimation to make it applicable to the validation of both stationary and nonstationary sources. Compared with the 90B’s predictors officially published in 2018, the experimental results on various simulated and real-world data sources demonstrate that our predictors have a better performance on the accuracy, scope of applicability, and execution efficiency. The average execution efficiency of our predictors can be up to 10 times higher than that of the 90B’s for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>6</mn> </mrow> </msup> </mrow> </math> sample size with different sample spaces. Furthermore, when the sample space is over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </math> and the sample size is over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mrow> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mn>8</mn> </mrow> </msup> </mrow> </math> , the 90B’s predictors cannot give estimated results. Instead, our predictors can still provide accurate results. Copyright© 2019 John Wiley &amp; Sons, Ltd.
A low-cost autonomous navigation system for a quadrotor in complex outdoor environments

In this article, we design a low-cost navigation system for a quadrotor working in unknown outdoor environments. To reduce the computing burden of the quadrotor, we build a separated system and transfer the computing resources from onboard side to ground station side. Both sides’ communication is guaranteed by 5G wireless networks. We utilize a stereo camera to acquire point clouds and build Octomap for the quadrotor’s navigation. Then, the trajectory is generated in two stages. In the first stage, a modified RRT*-CONNECT algorithm is adopted to generate a set of collision-free waypoints. In the second stage, a curve fitting algorithm is utilized to get a smooth piecewise Bezier trajectory. The advantage of the proposed method is to optimize the path into a safe, smooth, and dynamically feasible trajectory in real time. The modules of the state estimation, dense mapping, and motion planning are integrated into a DJI Matrice 100 quadrotor. Finally, simulation and experiments are both conducted to show the validity and practicality of our method.
Exploiting Cross-Reality Technologies for Cultural Heritage Dissemination

One of the most challenging tasks in cross reality environment simulations is the generation of realistic and attractive worlds. The continuous evolution of computer game industry has a dramatic effect on such tasks as younger generations have higher expectations and demands in terms of realism. Virtual, Augmented, and mixed reality-based museums allow the efficient dissemination of cultural heritage thesaurus and are considered a popular application domain for cross reality environments due to their broad appeal. One of their primary scopes is to enrich user experience by introducing intuitive means of interaction with artefacts while offering knowledge in a more pleasant way than most of the traditional approaches. This chapter focuses on the development aspects of realistic simulations of cross reality environments for cultural heritage applications. It covers aspects related to modern 3D graphics and game engines, 3D digitization, and modelling. It discusses on the combination of these technologies in order to produce realistic, pleasant, and educative environments.
Freedom of Political Communication, Propaganda and the Role of Epistemic Institutions in Cyberspace

Abstract This article provides definitions of fake news, hate speech and propaganda, respectively. These phenomenon are corruptive of the epistemic norms, e.g. to tell the truth. It also elaborates on the right to freedom of communication and its relation both to censoring propaganda and to the role of epistemic institutions, such as a free and independent press and universities. Finally, it discusses the general problem of countering political propaganda in cyberspace and argues, firstly, that there is an important role for epistemic institutions in this regard and secondly, that social media platforms need to be redesigned since, as they stand and notwithstanding the benefits which they provide, they are a large part of the problem.
Use Public Wi-Fi? Fear Arouse and Avoidance Behavior

This study aims to study the internet security compliance behavior in a public Wi-Fi usage condition. We developed our research framework and hypotheses with the consideration of the protection motivation theory. Assembling questions from existing literature, we collected opinions from a group of college students who know and use public Wi-Fi. Using structural equation modeling, we examined the role of coping appraisal, threat appraisal, and fear to the avoidance behavior. Our findings are that fear is an important determinant to the user’s avoidance behavior and that coping appraisal is indirectly enhancing fear. Through this study, we provide support to use fear as a major control mechanism to the high-risk behavior in a less controlled environment. We also provided another perspective for how the fear is developed so that policy makers can have additional method to improve compliance behavior.
Modeling of furnace operation with a new adaptive data echo state network method integrating block recursive partial least squares

Thermal efficiency is an important index related to industrial furnace operation. Modeling of reheating furnace operation based on optimal thermal efficiency can greatly improve the actual furnace operation performance. However, some key variables in the complex chemical processes are very difficult to measure due to the nonlinearity, the disturbances, and the technological limitations, and the multicollinearity among several correlation variables will make the established model inaccurate. In this paper, a new adaptive data echo state network modeling method integrating block recursive partial least squares (BRPLS-ESN) is proposed. First, the Echo state network (ESN) is used to process the historical data, and the calculation of the weights are handled by partial least squares (PLS) instead of the least-squares, which will overcome the multicollinearity among certain variables. The block recursive method is also used during the weight calculation, which will help to reduce the computation time and occupied the memory of the proposed PLS based algorithm. The prediction results will be obtained lastly after multiple iterations. In order to verify the effectiveness of the proposed BRPLS-ESN method, an industrial reheating furnace is introduced to build an operational model using the proposed algorithm. The traditional ESN, the back-propagation network, and the support vector regression are also tested to compare with the proposed method, and the results show that the proposed BRPLS-ESN can obtain a better modeling effect.
Providing Equitable Access to Computing Education in Mississippi

To maintain competitiveness as a nation, the United States must broaden participation in computing education and career pathways. Integrating computational thinking and computer science in K-12 classrooms is becoming increasingly essential to the development of a responsible and innovative workforce. Further, with increased recognition of the need for computing competency, workforce development programs that target the emerging workforce that may not be on a college pathway and those citizens who want to retool for the digital economy are also essential. This study examines work by researchers and practitioners in Mississippi to engage all public school students and the workforce with computational thinking, computer science concepts, and cybersecurity, providing pathways for learning that make computing education accessible for all citizens. The state of Mississippi has the lowest median wage in the nation, and one of the lowest rates of STEM employment. With half of the public school children identified as African American, and half of them female, there is much opportunity for broadening participation in computing. Assessments from a K-12 teacher professional development programs will be discussed, in addition to outcomes from one year of a workforce development program.
Deep Reinforcement Learning with Model-Based Acceleration for Hyperparameter Optimization

Hyperparameter optimization is a key part of AutoML. In recent years, there have been successful hyperparameter optimization algorithms. However, these methods still face several challenges, such as high cost of evaluating large models or large datasets. In this paper, we introduce a new deep reinforcement learning architecture with model-based acceleration to optimize hyperparameters for any machine learning model. In this method, an agent constructed by a Long Short-Term Memory Network aims at maximizing the expected accuracy of a machine learning model on a validation set. To speed up training, we employ a model to predict the accuracy on a validation set instead of evaluating a machine learning model. To effectively train the agent and the predictive model, Real-Predictive-Real training process is proposed. Besides, to reduce the variance, we propose a bootstrap pool to guide the exploration in the search space. The experiment was carried out by optimizing hyperparameters of two widely used machine learning models: Random Forests and XGBoost. Experimental results show that the proposed method outperforms random search, Bayesian optimization, and Tree-structured Parzen Estimator in terms of accuracy, time efficiency and stability.
Protecting the Secrets: Advanced Technique for Active Tamper Detection Systems

Cryptographic modules and security circuits are the kernels of every piece of equipment that process security data. No matter where they are deployed, the equipment must be protected against physical attacks, as no one can access the electronic circuits that process the security data: firmware implementations, cryptographic keys and secret data. The most effective way to protect this kind of electronic circuits is to wrap them in a special conductive mesh and probe it with proper signals in order to detect intrusions. This paper provides a complete solution for intrusions detection: a system made of a special conductive mesh and an active tamper detection circuit. The conductive mesh consists of three layers that detect intrusions earlier than the effective penetration of the mesh. The active tamper detection circuit is designed to probe the mesh with signals that cannot be emulated by an adversary.
Enhancement of tripartite quantum correlation by coherent feedback control

Measurement-free coherent feedback control is promising to manipulate various quantum optical systems for preparing nonclassical states of light. In this paper, three different coherent feedback control systems are constructed based on cascaded four-wave mixing processes. By utilizing a beam splitter as the feedback controller, we theoretically investigate the enhancement of tripartite quantum correlation and its pairwise correlation. The absorption effect of Rb vapor cells and the loss of optical propagation in the coherent feedback loop are also taken into account. The quantum correlation in the three structures can be characterized by the degree of relative intensity squeezing of the output fields. We find that the tripartite quantum correlation can be maximally enhanced by an optimal feedback ratio. In addition, two of the three pairwise correlations in the quantum regime can also be improved by tuning the strength of feedback, while the other pairwise correlation remains in the classical regime. Our results pave the way for the experimental implementation and may find potential applications in quantum communication and quantum metrology.
Drug usage duration classification using Extreme Learning Machine based on personality features

Determining the duration of drug consumption is essential for the success of treatment for drug abuse since the effectivity of such a program depends on the duration of the treatment. One promising set of features to identify the duration of drug consumption is personality features called Revised NEO Personality Inventory (NEO PI-R). In this paper, the Extreme Learning Machine model is employed to perform the classification. The model is trained and tested using 10- fold mechanism to verify the effectivity of the classification. The accuracy of the classifier differs, depending on the type of drug, with the maximum accuracy of 86.31% and the minimum one of 36.65%.
Accelerating the Calculation of Friedman Test Tables on Many-Core Processors

The Friedman Test has been proposed in 1937 to analyze tables of ranks, like those arising from a wine contest. If we have N judges and k wines, the standard problem is to analyze a table of N rows and k columns holding the opinion of the judges. The Friedman's Test is used to accept/reject the null hypothesis that all the wines are equivalent. Friedman offered an asymptotically valid approximation as well as exact tables for low k and N. The accuracy of the asymptotic approximation for moderate k and N was low, and extended tables were required. The published ones were mostly computed using Monte Carlo techniques. The effort required to compute the extended tables for the case without ties was significant (over 100 years of CPU time) and an alternative using many-core processors is described here for the general case with ties. The solution can be used also for other similar tests which yet lack for large enough tables.
Systemic Risk Document Classification on Indonesian News Articles using Deep Learning and Active Learning

Indonesian online news articles are growing fastly in this decade. One of the information is about economic news, including the information on financial systemic risk. In order to get information on financial systemic risk in real time, the task on systemic risk document classification should be done automatically. Here, we employ deep learning and active learning to classify systemic risk document automatically. We use 15 classes of financial systemic risk, such as defined before by Bank of Indonesia. The task is a multi-label classification, where a text document may contain more than 1 information of systemic risk. For the deep learning strategy, we've conducted several experiments of CNN, Bi-LSTM and Bi-GRU. We've also compared it with two steps of classification. In the experimental result, using 1752 documents as the training data and 228 documents as the testing data, the highest F1 score was achieved by using Bi-LSTM topology with one classification step and large common corpus as the resource for the word embedding. The highest F1 score was 45.37% for 15 classes with probability threshold defined as 0.15. In the two steps of classification, the first classification for 2 classes (contain risk information or not), the accuracy was 82.46%. To handle the limited data, we've conducted active learning to select the next candidate to be labeled as training data. In the experiment, for 420 new data with each iteration of 20 new data, the results showed that using active learning couldn't improve the performance.
Non-linear filtering for bilinear stochastic differential systems: A Stratonovich perspective

Bilinear stochastic differential equations have found applications to model turbulence in autonomous systems as well as switching uncertainty in non-linear dynamic circuits. In signal processing and control literature, bilinear stochastic differential equations are ubiquitous, since they capture non-linear qualitative characteristics of dynamic systems as well as offer closed-form solutions. The novelties of the paper are two: we weave bilinear filtering for the Stratonovich stochasticity. Then this paper unfolds the usefulness of bilinear filtering for switched dynamic systems. First, the Stratonovich stochasticity is embedded into a vector ‘bilinear’ time-varying stochastic differential equations. Then, coupled non-linear filtering equations are achieved. Finally, the non-linear filtering results are applied to an appealing bilinear stochastic Ćuk converter circuit. This paper also encompasses a system of coupled bilinear filtering equations for the vector input Brownian motion case. This paper brings the notions of systems theory, that is, bilinearity, Stratonovich stochasticity, non-linear filtering techniques and switched electrical networks together. Numerical simulation results are presented to demonstrate that the proposed bilinear filter can achieve much better and accurate filtering performance than the conventional Extended Kalman Filter (EKF).
Data Exfiltration Techniques and Data Loss Prevention System

One of the primary concerns of data security specialists is to mitigate insider threats and prevent data leaks. Often, unfortunately, insider threats go unnoticed. In most cases, the longer such activity goes unnoticed, the greater the resulting damages are likely to be. This paper provides an overview of the basic data exfiltration techniques that deal with file structures and were utilized in multiple scenarios in an attempt to bypass a Data Loss Prevention system. Details show which of the scenarios have been detected and which have not been by the Data Loss Prevention system. The paper also proposed solutions for the undetected scenarios.
Information Security Insider Threats in Organizations and Mitigation Techniques

This paper discusses the types of insider threats in organizations and their mitigation methods. The human factor is the major cause of security threats in organizations. A framework is designed in this study to prevent insider threats and the security aspects of digital library architecture are described. The technical and management approaches to prevent threats are discussed.
When Does Design Help Thinking, and When Does Design Thinking Help?

As I'm sitting in the audience of a software development day, where Johan Sanneblad (hiQ developer and business consultant) raves about the possibilities of ultrawideband (<i>IEEE Standard for Information Technology</i>, IEEE Standard 802.15.4a, 2007) and wireless personal area networks and the new chip that Apple put in their new iPhone, I realize that the focus on the technical perspective is really asking us to take a deeper look at the social implications of the technology we are designing. How can we design technology for people, together with people, considering impacts on other people? As often in the requirements corner, the answer includes taking multiple perspectives, a diverse set of stakeholders, ethical considerations, and creativity.
Ethical Issues in Artificial Intelligence in UAE

AI Is disrupting almost every industry in every country and has been said to have ushered the fourth industrial revolution. The development of AI, however, has given rise to a new ethical, societal and legal challenges requiring governments of countries to develop a legal framework to address those concerns over this technology. This study investigate these challenges in the context of UAE. The study concludes that UAE is a pioneer in dealing with these issues crafting law that governs all side of the technology at the earliest possible introduction.
Optimization of resources in Cloud Computing using Virtual Machine Consolidation

Underutilization and overutilization of Virtual Machine resources is a vital part of cloud computing. Migration of resources can handle it but it is necessary to predict the need for migration while also avoiding other issues. To tackle this the paper proposes using ACO (Ant Colony Optimization) for a well-planned VM migration while using different scheduling algorithms to reduce CPU overloading, minimizing the rate of SLA violations and to increase the energy efficiency. In this paper, we have researched some of the cloud consolidation and migration techniques such as Ant Colony Optimization and K-means Regression and summarized our findings. The proposed methodology includes initial allocation algorithm to allocate Virtual Machines (VMs) to Physical Machines (PMs) and thus optimizing the physical machines which are followed by the migration which makes use of ACO (Ant Colony Optimization) to migrate the Virtual Machine to the Physical Machine which is nearest thus saving time and bandwidth.
A State Assessment Method of Relay Protection Device Based on PSO optimizes SVM parameters and Cloud Model

In order to provide scientific maintenance basis for the state maintenance of the relay protection device, an improved method that PSO optimizes SVM (Support Vector Machines) parameters is proposed, and this method is applied to the state evaluation of the relay protection device. Firstly, according to the operational state data collected by the acquisition unit, the data is preprocessed to form a sample database, at the same time, the PSO is used to optimize the SVM kernel function parameter and error penalty factor. And the PSO-SVM state evaluation model is established to evaluate the problem. By calculating the distance from the point to the hyperplane, it is transformed into a classification problem, and the randomness and stable trend of the cloud model are used to realize the uncertainty mapping between the health degree and the comment domain, combine cloud model to simulate the opinions of different experts, combined with quantitative analysis and qualitative experience. So that the evaluation result is more in line with the actual situation. The simulation result shows that the proposed method can effectively evaluate the operating status of the protection device and provide maintenance basis for operation and maintenance personnel.