Reviewerly/exHarmony huggingface dataset:
Stochastic Fast Gradient for Tracking

In recent applications, first-order optimization methods are often applied in the non-stationary setting when the minimum point is drifting in time, addressing a so-called parameter tracking, or non-stationary optimization (NSO) problem. In this paper, we propose a new method for NSO derived from Nesterov's Fast Gradient. We derive theoretical bounds on the expected estimation error. We illustrate our results with simulation showing that the proposed method gives more accurate estimates of the minimum points than the unmodified Fast Gradient or Stochastic Gradient in case of deterministic drift while in purely random walk all methods behave similarly. The proposed method can be used to train convolutional neural networks to obtain super-resolution of digital surface models.
A Modified Recursive Least Squares Algorithm with Forgetting and Bounded Covariance

Recursive least squares (RLS) is widely used in identification and estimation. An unfortunate weakness of RLS is the divergence of its covariance matrix in cases where the data are not sufficiently persistent. To solve this problem, [1] introduced the exponential forgetting and resetting algorithm (EFRA), whose covariance update equation is modified so that the covariance matrix remains bounded. Unfortunately, EFRA does not include RLS as a special or limiting case, and cannot easily approximate RLS estimates. In this paper, we derive a modified RLS variant of EFRA that includes RLS without forgetting as a limiting case, and that can closely approximate RLS with forgetting. An additional advantage of MRLS relative to EFRA is greater ease in choosing parameters to set the covariance bounds.
Soft Computing Methods based on Fuzzy, Evolutionary and Swarm Intelligence for Analysis of Digital Mammography Images for Diagnosis of Breast Tumors

Soft Computing Methods based on Fuzzy, Evolutionary and Swarm Intelligence for Analysis of Digital Mammography Images for Diagnosis of Breast Tumors
Operator Entanglement in Local Quantum Circuits I: Maximally Chaotic Dual-Unitary Circuits

The entanglement in operator space is a well established measure for the complexity of the quantum many-body dynamics. In particular, that of local operators has recently been proposed as dynamical chaos indicator, i.e. as a quantity able to discriminate between quantum systems with integrable and dynamics. For systems the local-operator entanglement is expected to grow linearly in time, while it is expected to grow at most logarithmically in the integrable case. Here we study local-operator entanglement in dual-unitary quantum circuits, a class of statistically solvable quantum circuits that we recently introduced. We identify a class of completely chaotic dual-unitary circuits where the local-operator entanglement grows linearly and we provide a conjecture for its asymptotic behaviour which is in excellent agreement with the numerical results. Interestingly, our conjecture also predicts a phase transition in the slope of the local-operator entanglement when varying the parameters of the circuits.
Enhanced Authentication System Performance Based on Keystroke Dynamics using Classification algorithms

Nowadays, most users access internet through mobile applications.The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems.Additional multi modal authentication procedures are needed to improve the security.Behavioral authentication is a way to authenticate people based on their typing behavior.It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively.Keystroke dynamic rhythm is one of these behavioral authentication methods.Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users.This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features.The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%.A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.
Development of Students' Programming Abilities With the Means of Non-Programming Disciplines and Activities

This article is dedicated to the topic of discovering effective ways of developing students' programming abilities with the means of non-programming disciplines and activities. The authors argue that the process of educating students in programming becomes effective if students participates not only in programming lessons themselves, but also dedicates a significant amount of time to other academic disciplines and extracurricular activities. For example, these other activities are solving number-theoretics and chess endgame problems. The authors find that these disciplines and activities provide efficient means for developing programming capacities and therefore, their methods are the essential prerequisites for programming course. The significance of the obtained results is that they provide an effective alternative approach to the teaching process in educational institutions where the traditional methodology does not bring the desired pedagogical effect.
Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis.CMF is performed by copying and pasting a part of an image into other portions of it.Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult.A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps.Firstly, the suspicious image is divided into overlapping blocks.Secondly, invariant features for each block are extracted using QPCET.Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching.Lastly, a new technique is introduced to reduce the flat region-mediated false matches.Experiments are performed on numerous images selected from the CoMoFoD database.MATLAB 2017b is used to employ the proposed method.Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method.Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression.Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate.The proposed approach may serve as a basis for accurate digital image forensic investigations.
Remote Distance Measurement from a Single Image by Automatic Detection and Perspective Correction

This paper proposes a novel method for locating objects in real space from a single remote image and measuring actual distances between them by automatic detection and perspective transformation.The dimensions of the real space are known in advance.First, the corner points of the interested region are detected from an image using deep learning.Then, based on the corner points, the region of interest (ROI) is extracted and made proportional to real space by applying warp-perspective transformation.Finally, the objects are detected and mapped to the real-world location.Removing distortion from the image using camera calibration improves the accuracy in most of the cases.The deep learning framework Darknet is used for detection, and necessary modifications are made to integrate perspective transformation, camera calibration, un-distortion, etc. Experiments are performed with two types of cameras, one with barrel and the other with pincushion distortions.The results show that the difference between calculated distances and measured on real space with measurement tapes are very small; approximately 1 cm on an average.Furthermore, automatic corner detection allows the system to be used with any type of camera that has a fixed pose or in motion; using more points significantly enhances the accuracy of real-world mapping even without camera calibration.Perspective transformation also increases the object detection efficiency by making unified sizes of all objects.
Problematic of the Assessment Activity within Adaptive E-Learning Systems

The purpose of this paper is to focus on the activity of assessment that takes a decisive place in the learning process and discuss the importance of this action for each participant of the process. After that, authors profile the desirable characteristics of an adaptive system and describe the importance of the assessment activity, the extent of its impact on the cognitive decision-making and the adaptation of learning within such systems. Later they present a model of e-assessment for an educational adaptive system, that can accept objects developed under open standards, by exploiting technology in the benefit of the pedagogy.
WEB-BASED LOGISTIC DEMAND INFORMATION SYSTEM DESIGN AT RAHARJA UNIVERSITY

In the present Information Technology (IT) is highly developed. Even in daily life, everything is computerized, such as in companies, schools, offices, shopping centers, and many more. Nearly the average has used a computerized system for service and information presentation. With this computerized system, it is expected to be able to facilitate in all aspects of life. The purpose of this study is to design a logistics goods demand system at Raharja College. The approach taken in this report is by direct observation through field studies. To examine how the procedure for processing logistical demand data at Raharja University. Data processing is very important to produce a logistical demand report. The logistical demand data processing system that runs currently is semi computerized using Microsoft office Excel. So that errors often occur in the calculation of logistics goods demand reports and the processing process becomes less effective in terms of time. This study uses a SWOT analysis method, system requirements elicitation, and system modeling using UML (Unified Modeling Language) to visualize, which is then implemented with the Hypertext Preprocessor (PHP) programming language with the MySQL-Server database as the database used. With the logistics demand system, it can make it easier for the finance department to produce accurate logistics goods demand reports with fast time, so that it can create effective and efficient performance, and can support evaluation in internal control for the leadership of the report.
Space-Efficient and Secure Substring Searchable Symmetric Encryption Using an Improved DAWG

A searchable symmetric encryption (SSE) scheme is a method which searches encrypted data without decrypting it. In this paper, we address the substring search problem such that for a set D of documents and a pattern p, we find all occurrences of p in D. Here a document and a pattern are defined as strings and are encrypted. A directed acyclic word graph (DAWG), which is a deterministic finite automaton, is known for solving a substring search problem on a plaintext. We improve a DAWG so that all transitions of a DAWG have distinct symbols and present a space-efficient and secure substring SSE scheme using an improved DAWG. The novel feature of an improved DAWG is that we can solve the substring search problem using only the labels of transitions. The proposed substring SSE scheme consists of an index with a simple structure and the size is O(n) for the total size n of documents.
Are people pixel-peeping 360&amp;#xB0; videos?

In this paper, we compare the influence of a higherresolution Head-Mounted Display (HMD) like HTC Vive Pro on 360&#xB0; video QoE to that obtained with a lower-resolution HMD like HTC Vive. Furthermore, we evaluate the difference in perceived quality for entertainment-type 360&#xB0; content in 4K/6K/8K resolutions at typical high-quality bitrates. In addition, we evaluate which video parts people are focusing on while watching omnidirectional videos. To this aim we conducted three subjective tests. We used HTC Vive in the first and HTC Vive Pro in the other two tests. The results from our tests are showing that the higher resolution of the Vive Pro seems to enable people to more easily judge the quality, shown by a minor deviation between the resulting quality ratings. Furthermore, we found no significant difference between the quality scores for the highest bitrate for 6K and 8K resolution. We also compared the viewing behavior for the same content viewed for the first time with the behavior when the same content is viewed again multiple times. The different representations of the contents were explored similarly, probably due to the fact that participants are finding and comparing specific parts of the 360&#xB0; video suitable for rating the quality.
Feedback alfa-rooting algorithm for medical image enhancement

This paper presents a new combined local and global transform domain-based feedback image enhancement algorithm for medical diagnosis, treatment, and clinical research. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. The block size and alfa-rooting parameters driven through optimization using the Agaian's cost function (image enhancement non-reference quality measure). The presented new approach allows enhancing MRI and CT images with uneven lighting and brightness gradient by preserving the local image features/details. Extensive computer simulations (CS) on real medical images are offered to gage the presented method. CS shows that our method improves the contrast and enhances the details of the medical images effectively compared with the current state-of-art methods.
On Fractional Decoding of Reed-Solomon Codes

We define a virtual projection of a Reed-Solomon code RS(ql, n, k) to an RS(q, n, k) Reed-Solomon code. A new probabilistic decoding algorithm that can be used to perform fractional decoding beyond the α- decoding radius is considered. An upper bound for the failure probability of the new algorithm is given, and the performance is illustrated by examples.
User Authentication for Smart Home Networks Based on Mobile Apps Usage

End-user devices, such as mobile phones and tablets, have become essential tools for accessing smart homes. Consequently, user authentication, one of the most important security factors, needs to be considered to prevent unauthorized access to home devices. Although mobile phones are equipped with different means of authentication such as fingerprint readers, these methods are only employed at the time of access; hence, countermeasures should be developed to overcome potential threats. This paper presents a continuous user authentication model based on apps access usage on mobile devices. To validate the presented model, two public real-world datasets collected from real users over a long period, are used. The model is evaluated for its ability to differentiate between users utilizing shared apps at the same daily intervals. Moreover, various classification approaches regarding legitimate user classification in compliance with the history of apps usage are evaluated. The results demonstrate the capacity of the presented method to authenticate users with high true positive and true negative rates.
Illumination invariant NIR face recognition using directional visibility

Biometric face recognition technology has received substantial attention in the past several years due to its potential for a wide variety of applications in both law enforcement and non-law enforcement fields. However, most current face recognition systems are designed for indoor and cooperative-user applications. Moreover, ambient lighting fluctuates greatly between days and among indoor and outdoor environments. Furthermore, illumination is the most significant factor affecting the appearance of faces. Most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. Furthermore, state-of-the-art techniques designed to combat this issue have very low accuracy. This paper attempts to combat the issue by proposing an illumination invariant near infrared face recognition architecture that consists of (1) generating a sequence of directional visibility images using quadrant and circular filters, (2) extracting Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features, and (3) performing SVM based classification. This technique a) improves the accuracy of the face recognition system, b) works under illumination variations, and c) does not need registration of face information. Furthermore, extensive computer simulations performed on the TUFTS (NIR) database and IIT Delhi NIR Face Database demonstrate that the proposed technique produces 94.52% and 80.41% respectively
Unification in Matching Logic

Matching Logic is a framework for specifying programming language semantics and reasoning about programs. Its formulas are called patterns and are built with variables, symbols, connectives and quantifiers. A pattern is a combination of structural components (term patterns), which must be matched, and constraints (predicate patterns), which must be satisfied. Dealing with more than one structural component in a pattern could be cumbersome because it involves multiple matching operations. A source for getting patterns with many structural components is the conjunction of patterns. Here, we propose a method that uses a syntactic unification algorithm to transform conjunctions of structural patterns into equivalent patterns having only one structural component and some additional constraints. We prove the soundness and the completeness of our approach, and we provide sound strategies to generate certificates for the equivalences.
Memory Nets: Knowledge Representation for Intelligent Agent Operations in Real World

In this paper, we introduce Memory Nets, a knowledge representation targeted at Autonomous Intelligent Agents (IAs) operating in real world. The main focus is on a knowledge base (KB) that on the one hand is able to leverage the large body of openly available semantic information, and on the other hand allows to incrementally accumulate additional knowledge from situated interaction. Such a KB can only rely on operable semantics fully contained in the knowledge base itself, avoiding any type of hidden semantics in the KB attributes, such as human-interpretable identifier. In addition, it has to provide means for tightly coupling the internal representation to real-world events. We propose a KB structure and inference processes based on a knowledge graph that has a small number of link types with operational semantics only, and where the main information lies in the complex patterns and connectivity structures that can be build incrementally using these links. We describe the basic domain independent features of Memory Nets and the relation to measurements and actuator capabilities as available by autonomous entities, with the target of providing a KB framework for researching how to create IAs that continuously expand their knowledge about the world.
Automatic Irony Detection using Feature Fusion and Ensemble Classifier

With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text.Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task.It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text.The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text.In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features.The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method.In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets.The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers.The experimental result shows the proposed approach significantly out-performs the conventional methods.
Scientific Knowledge Communication in Online Q&amp;A Communities: Linguistic Devices as a Tool to Increase the Popularity and Perceived Professionalism of Knowledge Contributions

With the popularity of question-and-answer (Q&A) communities, widespread dissemination of scientific knowledge has become more viable than ever before.However, those contributing highquality professional scientific knowledge are confronted with the challenge of making their contributions popular, since nonexpert readers may not recognize the importance of their contributions given the massive amount of information available online.In this study, we show that nonexpert readers are capable of evaluating the professionalism of content contributed in such communities as well as experts.However, we discovered that a salient discrepancy exists between the content nonexperts favor and the content they perceive as professional.In line with studies that have suggested that writing techniques play an important role in how expert content is received by laypersons, we investigated how the use of linguistic devices affects both the perceived professionalism and the popularity of contributions in Q&A communities.Based on both secondary data and a scenario-based survey, we identified specific linguistic devices that can increase content popularity without reducing perceived professionalism.Additionally, we revealed linguistic devices that increase popularity at the expense of perceived professionalism in this context.Finally, we conducted a laboratory experiment to more firmly establish the causal effects of the linguistic device use.The triangulated findings have important implications for both research and practice on communicating scientific knowledge in Q&A communities.
Modern Methods for Analyzing Malware Targeting Control Systems

Industrial control systems are critical infrastructure of nation. ICSs are sensor-actuator networks that control physical systems. The core components are Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA), distributed control systems (DCS). Traditional ICS had specialized hardware without Internet connection. Nowadays ICS are commodity computers comes with high configuration and internet connection which makes it defenseless for most common attacks. Defensive mechanism are limited because ICSs are not using typical solutions like anti-viruses. They developed a malware-tolerant ICS network architecture that operate in secure manner even if attacker can attack on some of components. They provide ProVerif proofs to show the correctness of the network protocol. They added self-healing mechanism they implemented it on top of FreeRTOS and ARM TrustZone. The architecture automatically repair ordinary and malicious faults is known as self-healing. Governmental organizations recommend a strategy called “defense in depth” which tries to deploy defenses at every layer of the network. But author of paper use new approach. They distribute trust over each component on the network so malware cannot break the security policies. This approach is called malware tolerant.