Reviewerly/exHarmony huggingface dataset:
µJSON, a Lightweight Compression Scheme for Embedded GNSS Data Transmission on IoT Nodes

Embedded computing is a sector in strong growth, driven by the increasing offer in the internet of things. Since position information is typically one of the intrinsic features of a sensory platform, the present work considers and analyzes the embedded lossless compression of localization data. A structural data scheme is proposed based on the number of occurrences of the independent coordinate components, named as &#x00B5;JSON. It is shown that, when considering a low-level language implementation on an embedded processor acquiring GNSS data, the proposed schema implies lower compression rates than its counterparts. An embedded test-bench platform is assembled, and several real case scenarios are considered for effectiveness and validation purposes.
Optimized multi‐label convolutional neural network using modified genetic algorithm for popularity based personalized news recommendation system

Abstract Classification of label‐specific users' diversified interests and incorporating social media with news media to address popular news articles which is the most formidable task in popularity‐based personalized news recommendation systems (PPNRS). To bring personalization to PPNRS, many remarkable features have to be considered from users user profiles to classify their interest. In this article, 13,346 features per user considered to classify their interest for 15 labels using multi‐label convolutional neural network (MLCNN). The efficiency of MLCNN model highly depends on its architecture through the tuning of its hyperparameters. Generally, researchers manually designed a constant CNN architecture for every label and verified the effectiveness, but this leads to an additional complexity as well as large computational resources were consumed. Moreover, designing the structure for all 15 labels leads to an increase in the network structure exponentially with an increase in labels. Hence, in this manuscript, MLCNN architectures optimized by implementing a novel approach modified genetic algorithm (MGA) with the help of introducing four novel crossover operators to strengthen CNN performance for users interest classification. Further, for the recommendation process, the label‐specific news articles were clustered from social media Facebook and Twitter feeds, and then most popular news articles determined from clusters along with label‐specific breaking news articles rendered from news feeds concerning users' interest. In addition to that, the reliability of the news articles also validated for recommendation process. The experimental result precisely proves that the proposed approach MGA attained an accuracy of 89.64%, 90.56%, 90.41%, and 91.79% for classifying users label specific interest and label‐wise recommendation accuracy attained 93.3%, 90%, 90% from Twitter, Facebook, and also from Newsfeed respectively.
Low-Cost Hardware Platform for Testing ML-Based Edge Power Grid Oscillation Detectors

This paper introduces a low-cost hardware testing platform designed to investigate the performance of a Machine Learning (ML)-based edge application developed to detect forced oscillations in power grids. The core of the ML application lies in a Convolutional Neural Network (CNN) model deployed on an ML edge device (NVIDIA Jetson TX2). The proposed platform consists of a method for real-time signal emulation using the WaveForms Software Development Kit (SDK) that defines low-voltage signals generated by Digilent’s Analog Discovery Board. The output of the signal generator is read by the Jetson board using an Analog-to-Digital Converter (ADC). Our experiments compare the performance of different ADCs when performing inference with the same CNN model. Additionally, we give an overview of the communication scheme that allows experiment automation, which is particularly useful when experiment design is time-consuming and laborious.
MA-GANet: A Multi-Attention Generative Adversarial Network for Defocus Blur Detection

Background clutters pose challenges to defocus blur detection. Existing approaches often produce artifact predictions in background areas with clutter and relatively low confident predictions in boundary areas. In this work, we tackle the above issues from two perspectives. Firstly, inspired by the recent success of self-attention mechanism, we introduce channel-wise and spatial-wise attention modules to attentively aggregate features at different channels and spatial locations to obtain more discriminative features. Secondly, we propose a generative adversarial training strategy to suppress spurious and low reliable predictions. This is achieved by utilizing a discriminator to identify predicted defocus map from ground-truth ones. As such, the defocus network (generator) needs to produce 'realistic' defocus map to minimize discriminator loss. We further demonstrate that the generative adversarial training allows exploiting additional unlabeled data to improve performance, a.k.a. semi-supervised learning, and we provide the first benchmark on semi-supervised defocus detection. Finally, we demonstrate that the existing evaluation metrics for defocus detection generally fail to quantify the robustness with respect to thresholding. For a fair and practical evaluation, we introduce an effective yet efficient AUFβ metric. Extensive experiments on three public datasets verify the superiority of the proposed methods compared against state-of-the-art approaches.
Shor Algorithm

In 1994, a mathematician named Peter Shor surprised the world by publishing an efficient quantum algorithm for finding the prime factors of very large numbers, which is the basis for today’s data encryption. This discovery spurred the further development of quantum computing, making Shor’s algorithm one of the most important algorithms.
Improvement in accuracy for dimensionality reduction and reconstruction of noisy signals. Part I: The case of random signals

Our work addresses an improvement in accuracy for dimensionality reduction and reconstruction of random signals. The proposed transform targets noisy signals. This is because in the case of highly noisy signals, the known optimal methods might produce a large associated error. Let x, y and u be a source signal with m components, observed noisy signal with n components and reduced signal with k components, respectively, and let c=k/min{m,n} be a reduction ratio (RR) where k ≤ min {m, n}. If RR is fixed then the error associated with the known methods cannot be improved. The purpose of this paper is the development of an approach which leads to the better performance than that of the well-known fundamental techniques. The associated accuracy is improved by an optimal choice of an “auxiliary” random signal v (called the injection) and its dimensionality q, and by the increase in the number of matrices to optimize compared to the known transforms. At the same time, the total number of entries of the matrices is less than for the known related transforms. The proposed method contains, in particular, a special operation Q which transforms the large covariance matrix to the block diagonal form with smaller blocks. The above circumstances make the proposed technique numerically faster than the known related transforms. Numerical examples are provided that illustrate the above advantages.
An Efficient and Accurate 3D Multiple-Contextual Semantic Segmentation Network for Medical Volumetric Images

Convolutional neural networks have become popular in medical image segmentation, and one of their most notable achievements is their ability to learn discriminative features using large labeled datasets. Two-dimensional (2D) networks are accustomed to extracting multiscale features with deep convolutional neural network extractors, i.e., ResNet-101. However, 2D networks are inefficient in extracting spatial features from volumetric images. Although most of the 2D segmentation networks can be extended to three-dimensional (3D) networks, extended 3D methods are resource and time intensive. In this paper, we propose an efficient and accurate network for fully automatic 3D segmentation. We designed a 3D multiple-contextual extractor (MCE) to simulate multiscale feature extraction and feature fusion to capture rich global contextual dependencies from different feature levels. We also designed a light 3D ResU-Net for efficient volumetric image segmentation. The proposed multiple-contextual extractor and light 3D ResU-Net constituted a complete segmentation network. By feeding the multiple-contextual features to the light 3D ResU-Net, we realized 3D medical image segmentation with high efficiency and accuracy. To validate the 3D segmentation performance of our proposed method, we evaluated the proposed network in the context of semantic segmentation on a private spleen dataset and public liver dataset. The spleen dataset contains 50 patients' CT scans, and the liver dataset contains 131 patients' CT scans.
Information Fusion

Information fusion is a data-driven technique to combine data from multiple sources, which can generate improved information in higher quality and accuracy for detection, inference, or characterization of an object than a single sensor alone.
The Project Method in Teaching Future Mathematics Teachers

This paper deals with the problem of using the project method in teaching future mathematics teachers. The new educational strategy implements self-education with the help of developing technologies, the goal of which is not only to bring knowledge to students, but also to identify and develop the creative interests and abilities of each student, to stimulate his/her independent productive learning activities. The authors showed that one of such technologies is project training, which involves joint learning and cognitive activity of students, having a common goal, agreed ways of working. This paper notes also the need to use project technology, which consists in developing the ability of future teachers to have an analytical, creative thinking; self-acquisition of missing math knowledge from various sources; thinking, based on knowledge of mathematical facts, the laws of science; and ability to work in a team, performing various social roles.
Ray Acoustics

The concept of a wavefront plays a central role in that branch of acoustical theory known as geometrical acoustics or ray acoustics.
Model-Checking Speculation-Dependent Security Properties: Abstracting and Reducing Processor Models for Sound and Complete Verification

Spectre and Meltdown attacks in modern microprocessors represent a new class of attacks that have been difficult to deal with. They underline vulnerabilities in hardware design that have been going unnoticed for years. This shows the weakness of the state-of-the-art verification process and design practices. These attacks are OS-independent, and they do not exploit any software vulnerabilities. Moreover, they violate all security assumptions ensured by standard security procedures, (e.g., address space isolation), and, as a result, every security mechanism built upon these guarantees. These vulnerabilities allow the attacker to retrieve leaked data without accessing the secret directly. Indeed, they make use of covert channels, which are mechanisms of hidden communication that convey sensitive information without any visible information flow between the malicious party and the victim. The root cause of this type of side-channel attacks lies within the speculative and out-of-order execution of modern high-performance microarchitectures. Since modern processors are hard to verify with standard formal verification techniques, we present a methodology that shows how to transform a realistic model of a speculative and out-of-order processor into an abstract one. Following related formal verification approaches, we simplify the model under consideration by abstraction and refinement steps. We also present an approach to formally verify the abstract model using a standard model checker. The theoretical flow, reliant on established formal verification results, is introduced and a sketch of proof is provided for soundness and correctness. Finally, we demonstrate the feasibility of our approach, by applying it on a pipelined DLX RISC-inspired processor architecture. We show preliminary experimental results to support our claim, performing Bounded Model-Checking with a state-of-the-art model checker.
AI-Driven Complaint Management System

Citizens Complaint is important information reflecting citizen's sound. Our main objective of the organization is to give valuable and productive feedback to the citizens. The design we proposed for an AI-driven logging portal will have the strength to minimize citizens' worry and additionally, it can inspire people to promote our country by logging complaints on our website. In this paper, we propose a new model that is an AI-driven logging portal where we try to improve communication between citizens and government, and we try to make transparent communication to make your country a better place to live. There are different services for different types of complaints in your web portal. These services are used by numerous citizens on basis of their grievances. We have created a framework that can recognize citizens' problems and provide timely feedback to citizens. This system can recognize grievances by identifying and commenting on each complaint that has been raised. The concern of citizens is treated according to the priority in this portal. That is a problem depending on the seriousness of the situation that will be prioritized.
Comments on Paper: “Depositional history and provenance of cratonic ‘Purana’ basins in southern India: A multipronged geochronology approach to the Proterozoic Kaladgi and Bhima basins” by Joy et al., 2018. Geological Journal, https://doi.org/10.1002/gj.3415

The paper by Joy et al. (2018) contains several generalizations and appears to have followed a flawed analytical technique. We seek clarifications from the authors on specific points to ensure the credibility of the conclusions drawn by them.
Improved Machine Learning Assisted (Related-key) Differential Distinguishers For Lightweight Ciphers

At CRYPTO 2019, Gohr first proposes a deep learning based attack on round-reduced Speck32/64. It is an all-in-one differential approach under the Markov assumption. Then Baksi presents the method for non-Markov ciphers and applies it to Gimli by simulating the all-in-one differentials. However, all studies are still only for single-key differential distinguishers and the selection of input difference is based on traditional cryptanalysis. Inspired by the work of Gohr and Baksi, we extend and apply machine learning techniques to related-key differential distinguishers for the first time and propose a novel approach to develop (related-key) differential distinguishers without using prior cryptanalysis. We experimentally show that the differences with low Hamming weights are more suitable for building distinguishers. Then we present an exhaustive algorithm and a greedy algorithm to find an appreciable difference for the distinguisher. Finally, to obtain a suitable machine model for distinguishers, we adopt a Bayesian optimization tool named Hyperopt for parameter optimization and model selection. As proof of works, we apply our method to round-reduced Speck32/64, Present64/80 and get some improved cryptanalysis results.
Development of an Information System for Organizing and Monitoring the Educative Process of Students on the Basis of Cloud Technologies

The article considers the development of an information system for organizing and monitoring the educative process of students on the basis of cloud technologies. In the course of the research, the analysis of the requirements of the standards for the information and educational environment, the analysis of factors affecting the quality of the learning process, the analysis of the elements of the process of organizing and monitoring the students' education, analysis of popular cloud services, SWOT-analysis of the information system were carried out. The main results of the research are presented in the article. As a result of the research, an information system for organizing and monitoring the educative process of students on the basis of cloud technologies has been developed. This system meets all the requirements mentioned above. It includes an electronic journal of students' progress and attendance records, methodological materials and tasks for self-study of disciplines, a testing system, communication with teachers, a calendar of events and other elements.
Signal Sampling and Quantization

This chapter investigates the sampling process, sampling theory, and the signal reconstruction process. It also includes practical considerations for anti-aliasing filter and anti-image filter and signal quantization.
Dependence

This paper discusses different kinds of dependency. For numerically valued variables our discussion centers on the maximal correlation coefficient and its cousin the monotone correlation coefficient. We show how to calculate the maximal correlation coefficient in the case the random variables take on a finite set of values. For non-numerically valued variables our discussion centers on information theoretic measures related to mutual information and we describe some that are also metrics. We visually illustrate the difference between these two kinds of measures with a texture example that computes the joint probability image: an image in which the gray level of each pixel is the joint probability of the gray levels of the pixels in its neighborhood. Neighborhoods can be regular such as 5 × 5 or they can be irregular. Finally, we discuss manifold methods for classification: the N-tuple method, the subspace classifiers, the subspace ensemble classifiers, including the graphical model for representing the class conditional probability distribution. We describe a procedure to convert an N-tuple classifier to a graphical model classifier. We also conjecture that there is new form of a universal approximation theorem by which not too complex classification functions from measurement space to the set of classes can be approximately represented in the form of a subspace classifier using multiple subspaces such as the N-tuple method.
Recent advances in particle swarm optimization analysis and understanding

tutorial Recent advances in particle swarm optimization analysis and understanding Share on Authors: AP Engelbrecht University South Africa University South AfricaView Profile , CW Cleghorn University of Pretoria South Africa University of Pretoria South AfricaView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 747–774https://doi.org/10.1145/3377929.3389850Online:08 July 2020Publication History 2citation86DownloadsMetricsTotal Citations2Total Downloads86Last 12 Months34Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Semantic Web, Ontology, and Linked Data

Enormous amount of information is being produced every day and get consumed according to the needs of human being. Semantic web and ontology represent information which are machine processable and understand the semantics present among the entities. Ontology can be represented as Knowledge Organization and data modelling tool. Librarians are designated as “Information Custodian” or “Knowledge Keepers”. Implication and application of concepts in LIS can play big role in shaping knowledge-based services and mining and inferring them in better way in future. Ontology and semantic web is the future of LIS as speculated by several professionals and experts. This chapter would delineate a basic overview of Semantic Web, Ontology and linked data.
Individual Simulations

We develop an individual simulation technique that explicitly makes use of particular properties/structures of a given adversary's functionality. Using this simulation technique, we obtain the following results. The core conceptual idea underlying our individual simulation technique is an observation of the existence of nearly optimal extractors for all hard distributions: For any NP-instance(s) sampling algorithm, there exists a polynomial-size witness extractor (depending on the sampler's functionality) that almost outperforms any circuit of a-priori bounded polynomial size in terms of the success probability.
Support Vector Machines

Support vector machines perhaps has the best mathematical pedigree of any statistical learning procedure. It was originally developed as a classifier that maximizes a somewhat different definition of a margin, which leads to a novel "hinge" loss function. Also distinctive is the use of kernels in place of the usual design matrix. The kernels allow for very complicated linear basis expansions derived from the full set of predictors. Support vector machines competes well with other statistical learning classifiers, but because kernels are N × N, support vector machines does not scale well. That can be a major problem in the era of big data.
ETO Meets Scheduling: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem

Evolutionary transfer optimization(ETO) serves as "a new frontier in evolutionary computation research", which will avoid zero reuse of experience and knowledge from solved problems in traditional evolutionary computation. In schedule-ing applications via ETO, a highly competitive "meeting" framework between them could be constituted towards both intelligent scheduling and green scheduling, especially for carbon neutrality within the context of China.To the best of our knowledge, our study on scheduling here, is the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> work of ETO for complex optimization when multi-objective problem "meets" single-objective problems in combinatorial case (not multitasking optimization). More specifically, key knowledge like positional building blocks clustered, could be learned and transferred for permutation flow shop scheduling problem (PFSP). Empirical studies on well-studied benchmarks validate relatively firm effectiveness and great potential of our proposed ETO-PFSP framework.