Reviewerly/exHarmony huggingface dataset:
Bayesian Quantile Estimation in Deconvolution

Estimating quantiles of a population is a fundamental problem of high practical relevance in nonparametric statistics. This chapter addresses the problem of quantile estimation in deconvolution models with known error distributions taking a Bayesian approach. We develop the analysis for error distributions with characteristic functions decaying polynomially fast, the so-called ordinary smooth error distributions that lead to mildly ill-posed inverse problems. Using Fourier inversion techniques, we derive an inequality relating the sup-norm distance between mixture densities to the Kolmogorov distance between the corresponding mixing cumulative distribution functions. Exploiting this smoothing inequality, we show that a careful choice of the prior law acting as an efficient approximation scheme for the sampling density leads to adaptive posterior contraction rates to the regularity level of the latent mixing density, thus yielding a new adaptive quantile estimation procedure.
VR and AR in Teaching 3D Environment Design for Video Games

VR (Virtual Reality) and AR (Augmented Reality) are two different technologies that bring alternative or projected realities to our life [1]. They allow us to imagine and simulate any non-real sense of space as if it were real [2]. When teaching 3D design, a common problem would be being forced to look at the design through a 2D screen trying to navigate around it. Especially to a person who is not a professional on the field [3]. In this research we aim to provide details on how VR and AR technologies can be useful to fill this gap and provide better cognition by simulating the feeling of the real space and thus, better and faster learning curve for 3D design students. The method of this research is both experimentation and observation. We started to use these technologies in numerous classes and, important results started to emerge as we observe the improvement on the student’s understanding of 3D space and geometry. The students’ impressions along with their ability to create 3D geometry are observed. There is a clear and a considerable gain in favor of students when it comes to understanding and visualizing 3D spaces for environmental design when experienced through a VR or AR device. This way, students gain a big benefit on understanding the basics of 3D space and they visualize their own ideas and creations better as they also gain better reference points to establish a basis for their design.
Comprehensive Evaluation of XSEDE’s Scientific Impact using Semantic Scholar Data

The United States science and engineering community faces multiple challenges related to funding and funding policies for science and engineering. A framework is needed to evaluate the impact of scientific facilities and instruments. In this paper, we demonstrate such an activity through our comprehensive work evaluating the scientific impact of XSEDE using the Semantic Scholar Data. In contrast to other studies, our study includes the bibliographic references of all recorded papers related to XSEDE over the entire performance period till March of 2021. This makes this study unique and distinguishes it from our earlier work while using (a) over 180 million papers as a comparison to our peer analysis, (b) include all publications reported, and (c) conduct the study repeatedly over several years.
Indoor Scene Simplification for Safe Navigation Using Saliency Map for the Benefit of Visually Impaired People

In computer vision and artificial intelligence fields, the construction of a performant system for scene interpretation is very important, especially for autonomous navigation applications. Currently used systems bring a lot of information that is sometimes not necessary to ensure safe navigation. In this chapter, we propose an approach that simplifies the observed scene as much as possible while ensuring autonomous navigation. Two main steps are involved: (i) highlighting salient regions in order to suppress background objects and (ii) employing the morphology operations to simplify scene interpretation. This approach is proposed for the benefit of visually impaired people (VIP) in order to navigate safely in their indoor environment only by having the necessary knowledge of the scene.
Considerations about quantities, units, and dimensions for interoperability

There are no generally agreed principles on how digital systems should handle data about physical quantities. From the late 19 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century, scientific equations have been expressed in a unit-independent form that hides the existence of measurement scales (units), and are governed by the rules of quantity calculus. To clarify the meaning of such unit-independent equations, this paper looks at a particular representation for quantity terms and shows that expressions for dimensions arise as an inherent part of the calculations. We consider how to broaden the interoperable properties of traditional quantity equations to support other types of physical data. Three aspects associated with the data are important: 1) the kind of quantity; 2) the measurement scale and the associated class of equivalent scales; 3) the mathematical operations that can be meaningfully applied to data. By considering these aspects, digital systems will be able to handle a wider range of physical data. Software examples are provided.
Virtual Cluster Model Based on Big Data Application

In the big data of complex application scenarios, there are many kinds of mixed computing mode jobs, in order to work, virtual cluster needs to maintain a variety of computing modes. In the calculation of the complexity of the load, time-varying, in terms of resource utilization, virtual cluster is not high. In order to improve the global resource utilization, this utilization is based on the big data application virtual cluster, the flexible resource management strategy is adopted to absorb the sudden change of resource demand when multiple computing modes are mixed. On the basis of docker container technology, change of operation according to requirements is proposed according to the change of job requirements. According to the dynamic requirements of computing load on resources, computing form based on virtual cluster is adjusted in real time, including the size and type of the calculation node. This model can not only realize the dynamic customization of user execution environment, but also achieves the purpose of peak shifting calculation. The simulation results show that the CPU utilization is increased by 5.3%, which is the result of the model using virtual nodes, it also optimizes the execution efficiency of computer jobs.
Towards Visually Intelligent Agents (VIA): A Hybrid Approach

Service robots can undertake tasks that are impractical or even dangerous for us - e.g., industrial welding, space exploration, and others. To carry out these tasks reliably, however, they need Visual Intelligence capabilities at least comparable to those of humans. Despite the technological advances enabled by Deep Learning (DL) methods, Machine Visual Intelligence is still vastly inferior to Human Visual Intelligence. Methods which augment DL with Semantic Web technologies, on the other hand, have shown promising results. In the lack of concrete guidelines on which knowledge properties and reasoning capabilities to leverage within this new class of hybrid methods, this PhD work provides a reference framework of epistemic requirements for the development of Visually Intelligent Agents (VIA). Moreover, the proposed framework is used to derive a novel hybrid reasoning architecture, to address real-world robotic scenarios which require Visual Intelligence.
Sharing Identity Information on Dark Web Drug Boards

This chapter uses Elfreda Chatman’s concept of small worlds and Reijo Savolainen’s concept of Way of Life to examine identity information sharingInformation sharing on a Finnish Dark WebDark Web drug trading image boardDrug trading, image board. Based on a curated set of posts, it shows how people’s identity information sharingInformation sharing on such anonymous fora often centres around issues of trust and safety. The chapter discovers that like other small world virtual communities, these too are of information poor environments, where some participants are seen as outsiders and some as insiders, based on factors such as age, ethnicity, and information sharingInformation sharing.
Performance Analysis of Particle Swarm Optimization and Firefly Algorithms with Benchmark Functions

In recent years swarm mentality algorithms are usually created with nature inspiration to keep their popularity. One of these optimization techniques is particle swarm optimization, and the other one is firefly algorithm. Firefly algorithm process is working with the lower light intensity directed to higher intensities principle. Particle swarm optimization based on the positions of individuals; swarm keeps following the individual who have great position. This article explains with mathematical testing functions of minimum international points, particle swarm optimization and firefly algorithm. Tried to specify that which function is working better with which algorithm. Also, this research tried to recognize that different parameters are changing the result or not.
A Robust Multi-Server Two Factor Remote User Authentication Scheme Using Smartphone and Biometric

Recently, many multifactor remote user authentication schemes have emerged to cover-up the security weaknesses of single factor user authentication systems. Among these schemes, the two-factor multi-server user authentication scheme has drawn a considerable amount of attentions of researchers. Subsequently, many two-factor user authentication schemes for multi-server architecture have been introduced in recent past. However, they were unable to prevent the security vulnerabilities like password guessing attack, user impersonation attack, privileged insider attack, server masquerading attack, denial of service attack, replay attack, etc. To address these security deficiencies, we propose a robust two-factor remote user authentication scheme for multi-server architecture using smartphone and biometric that can prevent all the major security vulnerabilities shown by existing schemes. We use ProVerif to demonstrate that our scheme fulfils all the required security properties. Through a comprehensive heuristic security and performance analysis, we show that our proposed scheme can overcome drawbacks of existing systems.
Access to user data stored by organizations—divides surrounding information security professionals in Chinese IT organizations

With the rapid development of computer-based information systems in China, securing organizational systems as information assets is central to achieving a strategic advantage. Because information security (IS) experts with database access are the weakest link in the IS chain, scholarly contributions are needed in this important area. The interpretations of widespread data breaches committed by organizations’ information security experts have significant grounding. Drawing on social control theory, we propose a research framework to examine formal control, informal control, and self-control systems that can affect IS professionals’ perceptions and IS behaviors in Chinese IT organizations. We use in-depth interviews with IS professionals in Wuhan, Beijing, and Shenzhen to answer research questions relating to the research framework. The study’s primary contribution is to go beyond the extant IS research, which concentrates on sanction-based deterrence, and identify multiple dimensions rooted in social control perspectives. Based on interviews with practitioners and researchers, this study further advances our understanding of IS control deviance using information management and communication-related approaches.
Effect of Delay in EOG Signals for Eye Movement Recognition

Electrooculogram (EOG) signals as a part of human-controlled interface (HCI) is proposed for detecting the relevant information in EOG with and without delay in movement of eyes. The performance of eye movements is studied with the accuracy in identification of information along with single and double blink. The algorithm consists of a simple first order derivative, threshold windowing technique, and pattern recognition. The EOG pattern recognition was studied with time domain features mean value (MV) and ensemble of MV and zero crossing (ZC). The highest average classification accuracy of 85% and 84.4% is obtained from continuous movement of eyes for three classes (L, R, DB and L, R, SB) with two time-domain features. Further, the accuracy of 90% and 88% from two eye movement detection is obtained.
Unsupervised Activity Modelling in a Video

In today’s modern era, human activity recognition is widely used in video surveillance for various purposes like safety and security. This proposed work is also used in health care system, entertainment environment. In the unsupervised activity recognition, it recognize the activity in current frame and compares it to the previous frame. If there is any sudden change in activity then it gives the current frame number. The proposed system is basically divided into three phases: Pre-processing, Feature extraction, and Recognition. Gaussian Mixture Model is used to recognise moving object by using background subtraction. Then I apply some rules to recognize the different activities. I create two types of datasets i.e. two activity and three activity dataset. This approach shows better results in both the datasets.
Employing Adaptive Just-In-Time-Learning in a Transfer Learning Frame for Soft-Sensor Design

Online learning methods, such as moving window (MW) and just in time learning (JITL), have been proposed in the literature to remedy concept drift problem deteriorating soft-sensor performance. While these methods are effective against different types of drifts, a single method may not be sufficient in combating against heterogeneous concept drifts. In the current study, we propose combining MW and JITL methods within a transfer learning frame coupled with a relevant instance selection method to improve the prediction accuracy offered by either method. The proposed method involves i) forming a relevant sample of historical observations via backward elimination of the clusters composed of the extended nearest neighbors of the query point, ii) constructing a task transferred JITL model via kernel ridge regression, and iii) using a transductive MW learner. Employing the proposed method on two publicly available real benchmark datasets yields highly accurate predictions, showing convenience for industrial applications.
Usability Assessment of the XiaoAi Touch Screen Speaker

With the development of modern economy and technology, people's living standards are constantly improving, and more intelligent products have come into people's vision. People's requirements for product availability are also increasing. This research is carried out with an intelligent speaker called Xiao MI XiaoAi touch screen speaker and the main and specific functions of it was measured through a series of tasks. And this experiment was described as a summary usability assessment. Through a variety of usability evaluation methods, the most troublesome part of Xiao Ai speakers was experimented to get and tried to give some solutions and new ideas. In addition, the participants were divided into three group of users in this experiment: novice, occasional and expert to ensure that the experimental results are authentic and credible.
TFHE Parameter Setup for Effective and Error-Free Neural Network Prediction on Encrypted Data

With the rise of Cloud and Big Data technologies, Machine Learning as a Service (MLaaS) receives much attention, too. However, in some sense, the current situation resembles the era of wild digitalization, where security was pushed to the sideline. Back then, the problem was mostly about misunderstanding of severe consequences that insecure digitalization might bring. To date, common awareness of security has improved significantly, however, currently we are facing rather a technological challenge. Indeed, we are still missing a competitive and satisfactory solution that would secure MLaaS. In this paper, we contribute to the very recent line of research, which utilizes a Fully Homomorphic Encryption (FHE) scheme by Chillotti et al. named TFHE. It has been shown that TFHE is particularly suitable for securing MLaaS. In addition, its security relies on the famous LWE problem, which is considered quantum-proof. However, it has not been studied yet how all the TFHE parameters are to be set. Hence we provide a thorough analysis of error propagation through TFHE homomorphic computations, based on which we derive constraints on the parameters as well as we suggest a convenient representation of internal objects. We particularly focus on effective resource utilization in order to achieve the best performance of any prospective implementation.
Deblurring Algorithm Using Alternating Low Rank Augmented Lagrangian with Iterative Priors

Abstract The paper focuses on the Enhanced Augmented Lagrangian method with sparse regularization for image deblurring. The method suggested by ALTERNATING LOW RANK AUGMENTED LAGRANGIAN WITH ITERATIVE A PRIOR is novel in the following ways. (i) Faster convergence leading to speeder execution through rank regulations (ii) using derivatives and low rank together as regularization priors (iii) penalty and regularization weights ensure that each iteration hits a global minimum with a steep descent. The proposed method begins with the lowest rank matrix, which is the sparsest matrix available. The final deblurred result is very successful in achieving good dB improvements through rank regulation.
Deep Learning for Bibliographic Catalogue Assisting System

Academic libraries play a major role in providing the information and resources to support formal and informal learning. In order to provide the circulation service, librarians have to deal with the cataloguing process after acquisition. Cataloguing has been a major workload process that requires the intellectuals of librarians. With different experiences of the librarians and the complexity of the content, the quality of cataloguing information and the time spending is out of control. This study developed a catalogue assisting model to reduce the bottleneck of assigning subject access fields in bibliographic records which presumed as the most difficult task in the cataloguing process. The Neural Network models were built by applying the words appearing in the title and table of contents of bibliographic records as the input and predict the list of suggested subjects. The performance of the models was evaluated through the value of precision, recall, and the percentage of bibliographic records that correctly assigned at least 1 subject. The experimental results suggested that combining the suggested subject list obtained from the title word and table of content word models provides better results than using only an individual model.
Class Attendance System Using Viola-Jones Algorithm and Principal Component Analysis

Abstract Face recognition is one of the numerous biometrics approaches that can be implemented by smart and automated attendance management systems. The individual identity can be determined by the unique representation of the face structure of each individual face and it cannot be lost, stolen, or reproduced in the same way as other types of identification. Thus, this work is motivated to propose a class attendance system based on face recognition. With current approaches such as passwords, access cards, and identification numbers, face recognition can be used to prevent theft and fraud which can significantly reduce the chances of system hacking. In this proposed work, initially, video framing has been implemented by activating the Universal Serial Bus (USB) camera through a user-friendly interface which was created with Graphical user interfaces (GUI) in the MATLAB software. The image of each student’s face that was snapped by using the USB camera will be stored in a dataset. The dataset then will be divided into the training set and testing set. In the detection process, the Viola-Jones algorithm is utilized to detect and segment the image of student’s face from the video frame. Next, the scaling of the size of the images is carried out to prevent the loss of information in the pre-processing phase. Then, the Principal Component Analysis (PCA) is utilized in the face recognition process in order to extract the features from facial images.
Exploring and Presenting Security Measures in Big Data Paradigm

Big Data is a field that provides different ways to analyze and to extract information and hidden patterns. It also helps to deal with the data sets which are complex and larger in size. In many cases, data offers greater statistical power while the data with higher complexity leads to a higher false discovery rate. At the current time due to the key concepts like volume, variety, and velocity which are associated with Big Data, privacy and security are the biggest challenges in this field. So in this chapter, we have discussed different types of issues and solutions related to security and privacy in Big Data. In the field of Big Data, privacy is the liberty to control how personal information is collected, organized, and used. Whereas security in Big Data refers to the process of protecting information from destructive forces and from the unwanted actions of unauthorized users, such as a cyberattack or a data breach. In this field, privacy and security both are very important issues. The security model of Big Data is not recommended for complex applications and as a result of which by default it gets disabled. But in the absence of this model, the data can be easily compromised. So through this chapter, we have tried to highlight the privacy threats, issues and challenges of Big Data. Several techniques required to maintain data security have also been covered in brief.
Path Planning for a Network of Robots with Distributed Multi-Objective Linear Programming

In this paper, the problem of target pursuit and obstacle avoidance in a network of robots is considered. No single robot in the network is aware of all the obstacles in the arena, and therefore, knowledge of obstacle avoidance constraints is distributed across the network. A distributed multi-objective linear programming form of the problem is derived and its efficient solutions identified. This is broken down into a set of distributed linear programs that are solved at each robot, which is substantially computationally cheaper than nonlinear optimization. Numerical examples are used to demonstrate the efficacy of the proposed method in a network of robots navigating through a dynamic environment.
Geochemical Features of the Distribution of Ore Elements in the Polymetal Ores of Mehmanian Deposit (Azerbaijan Part of the Lesser Caucasus)

By using the results of geochemical research methods, the geochemical features of distribution of basic ore elements in Mehmanian polimetal deposit ores have been considered in the article. The definition features of main ore elements were given, close positive and negative relations between elements pairs were defined by correlation analysis according to the selected elements. On the basis of analysis of current analytical information results complexity has been studied, geochemical condition of localization of productive sulphide mineralization in the generation stage of ore formation has been analyzed. Behaviour features of elements have been investigated, the interaction between them has been clarified and based on their statistical analysis of geochemical features of the deposit has been made. The dependence features of one ore element from the other were determined in various types of ores of the deposit.