Reviewerly/exHarmony huggingface dataset:
Efficient Hardware Implementation for IoT Security System

With the evolution of IoT and its involvement in everyday life, the amount of data collected by IoT devices is increasing drastically and user's information is becoming more vulnerable to cyber-attacks. Thus, comes the importance of IoT security and the importance to have a flexible security system which doesn't affect the IoT devices power consumption and efficiency. In this paper an efficient dynamic security system that uses partial reconfiguration concept in zed board is presented to achieve better data security and faster generation of the encrypted data. Algorithm hopping is used to switch between three different cryptographic cyphers which are AEGIS, ASCON and DEOXYS-II. These ciphers are the main finalists in CAESAR competition as they achieved high security, medium security and low security respectively and each of these ciphers has its own significant attributes. The proposed design targets the improvement of reconfiguration time of the zed board using LZ4 (Lempel-Ziv4) compression and decompression techniques for the input bitstream files. Our research shows a decrease in the reconfiguration time of a minimum 61.3% decrease for the largest bit file in comparison to the previous works in the same field.
State of the art of consumer video surveillance standards in Russia and abroad

Abstract In recent years, video surveillance technology has been developing. The use of video surveillance has spread from national and social welfare to everyday life, and a number of consumer-grade video surveillance products have been created. However, there is a problem where the domestic market is still in a non-standard stage. Consumer-grade video surveillance system does not have specific standards, which seriously affect the consumer awareness of the consumer-grade video surveillance products. This article presents a video surveillance system and reveals the standards of an existing video surveillance system.
Comparative Study of Hardware Accelerated Convolution Neural Network on PYNQ Board

In recent years convolutional neural networks (CNNs) have been remarkably used in many applications, and they are the heart of many intelligent systems. The advancements in both new electronic design automation (EDA) tools and in new hardware development boards such as Python Productivity for Zynq (PYNQ) have significantly decreased the development time of CNNs. However, the short time-to-market is at the cost of implementation area, performance and power consumption. Over the last period, CNNs' energy consumption needs have skyrocketed dramatically. Thus, In this work, the authors conduct a comprehensive study on the power consumption of hardware accelerated CNN whether implemented using new EDAs High Level Synthesis (HLS) or the basic design abstraction of Register Transfer Level (RTL). Both methods are implemented on modern development boards from Xilinx (as PYNQ). Modern EDAs flow such as HLS does not represent the best environment for a good power consumption. The power consumption of the HLS implementation is six times more power than the RTL one. It is concluded that the new EDAs method have a deficiency to deliver highly efficient CNNs but it has the ability to deliver sufficient results within a very short period of time.
Challenges and Applications of Recommender Systems in E-Commerce

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.
The Need for Tidiness in Energy Data Analysis

The move to Smart Grids, has turned up a wealth of data improving the ability to understand various aspects of the network. Usage of this data offline (post trial) can be hindered by the structure of the data. Tidy principles result in a systematic structure beneficial for analysis. We argue tidiness of data is being overlooked in other work to organise energy data. The repositories of several power systems projects are examined to understand how the data is structured. The findings are summarized and recommendations made for improvements in tidiness. Future works in the area are suggested.
Significant Stakeholders: Toward an Agile Knowledge Management System in the Time of Coronavirus Crisis

Effective crisis response requires sophisticated knowledge management in organizations.Agile systems development capabilities for such crisis response systems are important, particularly for purposes of tailoring a crisis-oriented knowledge management system to a rapidly shifting threat landscape.We propose an architecture for achieving both of these ends in the form of an Agile Crisis Management System involving three specific stakeholders, and we discuss the steps, outcomes, and implications of such a system.
FA: A Fast Method to Attack Real-time Object Detection Systems

With the development of deep learning, image and video processing plays an important role in the age of 5G communication. However, deep neural networks are vulnerable: subtle perturbations can lead to incorrect classification results. Nowadays, adversarial attacks on artificial intelligence models have seen increasing interest. In this study, we propose a new method named FA to generate adversarial examples of object detection models. Based on the generative adversarial network (GAN), we combine the classification and location information to make the generated image look as real as possible. Experimental results on the PASCAL VOC dataset show that our method efficiently and quickly generates the image. Then, we test the transferability of adversarial samples on different datasets and object detection models such as YOLOv4, which also achieve certain transfer performance. Our work provides a basis for further exploring the defects of deep learning and improving the robustness of the systems.
Parameterizable LSB watermarking method with adaptive key generation

The method of parameterizable digital watermarks LSB-embedding with adaptive key generation is proposed. It uses the predefined number of least significant bits for secure hiding of digital watermarks of a special class. Several functional parameters (algorithms) can be used in the method to improve the reliability of informationhiding. Experiments on the estimation of the resistance to various attacks are conducted to obtain the recommendations of parameters selection for the proposed method.
Semisupervised Clustering by Queries and Locally Encodable Source Coding

Source coding is the canonical problem of data compression in information theory. In a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">locally encodable</i> source coding, each compressed bit depends on only few bits of the input. In this paper, we show that a recently popular model of semisupervised clustering is equivalent to locally encodable source coding. In this model, the task is to perform multiclass labeling of unlabeled elements. At the beginning, we can ask in parallel a set of simple queries to an oracle who provides (possibly erroneous) binary answers to the queries. The queries cannot involve more than two (or a fixed constant number of) elements. Now the labeling of all the elements (or clustering) must be performed based on the noisy query answers. The goal is to recover all the correct labelings while minimizing the number of such queries. The equivalence to locally encodable source codes leads us to find lower bounds on the number of queries required in variety of scenarios. We provide querying schemes based on pairwise ‘same cluster’ queries - and pairwise AND queries, and show provable performance guarantees for each of the schemes.
HABEm: Hierarchical Attribute Based Encryption with Multi-Authority for the Mobile Cloud Service

For the mobile cloud service, the computation and power resources of the mobile terminal are limited. And the attribute-based encryption algorithm has the uncertainty of the attribute expiration time, which highly likely results in the leakage of user privacy, and causes a huge waste of computation and bandwidth resources. To deal with these problems, a hierarchical attribute-based encryption with multi-authority for the mobile cloud service (HABEm) is proposed in this paper. A hierarchical multi-level authorization mechanism, referring to different levels of authorities, manages different attributes of mobile terminal. A proxy is introduced to delegate the high complexity decryption algorithm to improve the decryption efficiency of the mobile terminal. The mobile terminal performs attribute revocation through the authorization authority when the rank of the mobile terminal is changed. Based on the deterministic assumption of the standard model, HABEm is proved to be CPA-safe in theory. The experiment results show that HABEm has higher decryption performance and is very suitable for the mobile cloud service environment.
Application of the DIC Technique to Remote Control of the Hydraulic Load System

Displacements or deformations of materials or structures are measured with linear variable differential transducers (LVDT), fibre optic sensors, laser sensors, and confocal sensor systems, while strains are measured with electro-resistant tensometers or wire strain gauges. Measurements significantly limited to a point or a small area are the obvious disadvantage of these measurements. Such disadvantages are eliminated by performing measurements with optical techniques, such as digital image correlation (DIC) or electronic speckle pattern interferometry (ESPI). Many devices applied to optical measurements only record test results and do not cooperate with the system that exerts and controls load. This paper describes the procedure for preparing a test stand involving the Digital Image Correlation system ARAMIS 6M for remote-controlled loading. The existing hydraulic power pack (ZWICK-ROELL) was adapted by installing the modern NI cRIO-9022 controller operating under its own software developed within the LABVIEW system. The application of the DIC techniques to directly control load on the real structure is the unquestionable innovation of the described solution. This led to the elimination of errors caused by the test stand susceptibility and more precise relations between load and displacements/strains which have not been possible using the previous solutions. This project is a synergistic and successful combination of civil engineering, computer science, automatic control engineering and electrical engineering that provides a new solution class. The prepared stand was tested using two two-span, statically non-determinable reinforced concrete beams loaded under different conditions (force or displacement). The method of load application was demonstrated to affect the redistribution of bending moments. The conducted tests confirmed the suitability of the applied technique for the remote controlling and recording of test results. Regardless of the load control method (with force or displacement), convergent results were obtained for the redistribution of bending moments. Force-controlled rotation of the beam section over the support was over 50% greater than rotation of the second beam controlled with an increase in the displacement.
Context-aware quantum simulation of a matrix stored in quantum memory

In this paper a storage method and a context-aware circuit simulation idea are presented for the sum of block diagonal matrices. Using the design technique for a generalized circuit for the Hamiltonian dynamics through the truncated series, we generalize the idea to (0-1) matrices and discuss the generalization for the real matrices. The presented circuit requires $O(n)$ number of quantum gates and yields the correct output with the success probability depending on the number of elements: for matrices with $poly(n)$, the success probability is $1/poly(n)$. Since the operations on the circuit are controlled by the data itself, the circuit can be considered as a context aware computing gadget. In addition, it can be used in variational quantum eigensolver and in the simulation of molecular Hamiltonians.
Smart engagement for smart cities: Design patterns for digitally augmented, situated community engagement

Public digital technologies are increasingly pervading cities around the world, creating a new phenomenon of “urban spamming,” which exposes urban dwellers to vast amounts of commercial advertising and other forms of digital content. Over the years a large body of work has focused on how these technologies can be harnessed to engage urban dwellers in local issues and to give them a platform to share their opinions. Despite this, situated digital technologies created for community engagement with “best intentions in mind” are not always relevant to the context of where they are located. These technologies are often unable to adapt to the variable conditions of the space and changing needs of urban dwellers within them. This issue of relevance is amplified due to the increasing hyperconnectedness of modern society, where information has become readily available through personal smart devices. While there is much understanding of how situated digital technologies can be designed so that they are relevant to the context of place and space, there is currently no up-to-date set of principles that define how they should be designed to be relevant to individual urban dwellers within their respective communities. To address this gap, we propose a smart engagement approach to investigate the factors that make situated digital technologies relevant to individual urban dwellers. From a discussion of previous work and reflection of our own studies, we then present six design patterns within the context of making situated digital technologies relevant.
A Jaccards Similarity Score Based Methodology for Kannada Text Document Summarization

The techniques for better Information Retrieval (IR) have become indispensable as the amount of information available on the World Wide Web is steadily increasing. A method based on Jaccards' similarity score for Kannada language text document summarization is devised, which produces a coherent summary of a given document. The corpus used in this work is prepared from the Kannada web portals called as kanaja and kannadawebdunia. The results obtained are compared with keyword based and sentence ranking based methods used for summarization Results obtained are satisfactory
WITHDRAWN: Image classification based on high-dimensional feature retrieval intelligent algorithm

High dimensional biomedical are searching from query image is retrieved from the for information-related information related diseases, this information includes central highlights accurate and successful, and a large number of available study highlights for help. Content-Based Imagery Search (CBIR) has been one of the most important research areas in the last few years. Image recovery is a framework used to find compatible highlights for query images. The main motive behind the CBIR strategy is to achieve accurate and quick results. Question images with this technique are obtained from a high accuracy and information site. These include pre-preparation, extraction, optimization, and adjustment, especially as different improvements have occurred. Preparations are abrupt changes, sieving running image in question, and restored using shadow. Descriptor is used to highlight. Improved sieve type size change feature change and faster. The Genetic Algorithm (GA) improvement is used to enhance free images customization. It has the Euclidean expression adjusted. These information images are free highlights intimacy measurements
High performance GPU primitives for graph-tensor learning operations

Graph-tensor learning operations extend tensor operations by taking the graph structure into account, which have been applied to diverse domains such as image processing and machine learning. However, the running time of graph-tensor operations increases rapidly with the number of nodes and the dimension of data on nodes, making them impractical for real-time applications. In this paper, we propose a GPU library called cuGraph-Tensor for high-performance graph-tensor learning operations, which consists of eight key operations: graph shift (g-shift), graph Fourier transform (g-FT), inverse graph Fourier transform (inverse g-FT), graph filter (g-filter), graph convolution (g-convolution), graph-tensor product (g-product), graph-tensor SVD (g-SVD) and graph-tensor QR (g-QR). cuGraph-Tensor supports scalar, vector, and matrix data processing on each graph node. We propose optimization techniques on computing, memory accesses, and CPU–GPU communications that significantly improve the performance of the graph-tensor learning operations. Using the optimized operations, cuGraph-Tensor builds a graph data completion application for fast and accurate reconstruction of incomplete graph data. In the experiments, the proposed graph learning operations achieve up to 142.12× speedups versus CPU-based GSPBOX and CPU MATLAB implementations running on two Xeon CPUs. The graph data completion application achieves up to 174.38× speedups over the CPU MATLAB implementation, and up to 3.82× speedups with better accuracy over the GPU-based tensor completion in the cuTensor-tubal library.
Efficient Binary Multi-view Subspace Learning for Instance-Level Image Retrieval

The existing hashing methods mainly handle either the feature based nearest-neighbour search or the category-level image retrieval, whereas a few efforts are devoted to instance retrieval problem. Besides, although multi-view hashing methods are capable of exploring the complementarity among multiple heterogeneous visual features, they heavily rely on massive labeled training data, and somewhat affects the real-world applications. In this paper, we propose a binary multi-view fusion framework for directly recovering a latent Hamming subspace from the multi-view features. More specifically, the multi-view subspace reconstruction and the binary quantization are integrated in a unified framework so as to minimize the discrepancy between the original multi-view high-dimensional Euclidean space and the resulting compact Hamming subspace. In addition, our method is amenable to efficient iterative optimization for learning a compact similarity-preserving binary code. The resulting binary codes demonstrate significant advantage in retrieval precision and computational efficiency at the cost of limited memory footprint. More importantly, our method is essentially an unsupervised learning scheme without any labeled data involved, and thus can be used in the cases when the supervised information is unavailable or insufficient. Experiments on public benchmark and large-scale datasets reveal that our method achieves competitive retrieval performance comparable to the state-of-the-art and has excellent scalability in large-scale scenario.
Putting Jewellery and Accessories on a 3D Face Model Generated from 2D Image

Most of the work on adding accessories onto faces has been done in 2D. These methods use deep learning models and directly process the 2D images. In this paper, we perform this process in 3D space. This is done by converting 2D images into 3D face models and then placing the 3D model of the accessory on this face model. The automation has been done by using feature points and Procrustes analysis. We have also performed similar automation in Blender using the python API. The accessories include hats, glasses and Indian jewellery in various colours containing various diamonds and gemstones.
Most General Variant Unifiers

Equational unification of two terms consists of finding a substitution that, when applied to both terms, makes them equal modulo some equational properties.Equational unification is of special relevance to automated deduction, theorem proving, protocol analysis, partial evaluation, model checking, etc.Several algorithms have been developed in the literature for specific equational theories, such as associative-commutative symbols, exclusive-or, Diffie-Hellman, or Abelian Groups.Narrowing was proved to be complete for unification and several cases have been studied where narrowing provides a decidable unification algorithm.A new narrowing-based equational unification algorithm relying on the concept of the variants of a term has been developed and it is available in the most recent version of Maude, version 2.7.1, which provides quite sophisticated unification features.A variant of a term t is a pair consisting of a substitution σ and the canonical form of tσ .Variant-based unification is decidable when the equational theory satisfies the finite variant property.However, it may compute many more unifiers than the necessary and, in this paper, we explore how to strengthen the variantbased unification algorithm implemented in Maude to produce a minimal set of most general variant unifiers.Our experiments suggest that this new adaptation of the variant-based unification is more efficient both in execution time and in the number of computed variant unifiers than the original algorithm available in Maude.
Solving for multi-class using orthogonal coding matrices

A common method of generalizing binary to multi-class classification is the error correcting code (ECC). ECCs may be optimized in a number of ways, for instance by making them orthogonal. Here we test two types of orthogonal ECCs on seven different datasets using three types of binary classifier and compare them with three other multi-class methods: 1 vs. 1, one-versus-the-rest and random ECCs. The first type of orthogonal ECC, in which the codes contain no zeros, admits a fast and simple method of solving for the probabilities. Orthogonal ECCs are always more accurate than random ECCs as predicted by recent literature. Improvments in uncertainty coefficient (U.C.) range between 0.4--17.5% (0.004--0.139, absolute), while improvements in Brier score between 0.7--10.7%. Unfortunately, orthogonal ECCs are rarely more accurate than 1 vs. 1. Disparities are worst when the methods are paired with logistic regression, with orthogonal ECCs never beating 1 vs. 1. When the methods are paired with SVM, the losses are less significant, peaking at 1.5%, relative, 0.011 absolute in uncertainty coefficient and 6.5% in Brier scores. Orthogonal ECCs are always the fastest of the five multi-class methods when paired with linear classifiers. When paired with a piecewise linear classifier, whose classification speed does not depend on the number of training samples, classifications using orthogonal ECCs were always more accurate than the other methods and also faster than 1 vs. 1. Losses against 1 vs. 1 here were higher, peaking at 1.9% (0.017, absolute), in U.C. and 39% in Brier score. Gains in speed ranged between 1.1% and over 100%. Whether the speed increase is worth the penalty in accuracy will depend on the application.