Reviewerly/exHarmony huggingface dataset:
Private Keys Based Lightweight Multi-Objective Cipher Development and Evaluation

Information exchange and digital communication reflect basic processes and functionality of wide purpose hardware and software systems. For correct and reliable information exchange access control has to be explicitly implemented. A cipher is a digital communication processing extension and methodology for accurate access control, security and confidence incorporation into arbitrary data transfers. Lightweight ciphers are particularly suitable for the purpose in low power, fast and compact electronic devices (such as system on a chip) and within moderate amount of data exchange in large and small software systems. In this work we have provided actual classification of encryption and decryption schemes (ciphers), reviewed recent lightweight ciphers developed as well as designed, implemented, evaluated and discussed completely novel symmetric, highly secure and attack prone lightweight encryption and decryption scheme (cipher), which provides excellent protection from unauthorized access of any kind of data flowing through any channel.
Analysis of Noisy Image Lossy Compression by BPG

Acquired remote sensing images are often large and distorted by noise. On the one hand, this leads to the need to compress them. On the other hand, the presence of noise should be taken into account at the image compression stage and while evaluating the compression efficiency. Our work investigates the noise influence on the BPG method of image compression with losses supposing that data are distorted by additive Gaussian noise of different intensity. Within the framework of this study, the PSNR metric (peak signal to noise ratio, measured in dB) was chosen as a criterion for evaluating the quality of compression. It is shown that three typical dependences of the metric calculated between compressed and noise-free images are possible. Depending on the situation, there are different recommendations for setting a parameter that controls compression (PCC). In particular, if there is an optimal operating point (OOP), the compression should be done in its neighborhood. The examples of dependences of PSNR on PCC for several test images are given. It is shown that existence of OOP and its position depend on image complexity and noise intensity.
Makkah Landmark Recognition Using Deep Learning

Makkah is the most important historical city in the world for Muslims. Makkah visitors may find difficulties to obtain information about famous and historical places they visit. This project aims to develop a website that helps visitors and tourists to know about the landmarks and monuments of Makkah Al-Mukarramah city. This is done by taking a photo of archaeological landmarks then it is recognized by the system, then the system plays a sound clip talking about the recognized landmark and interacting with the user through a chatbot. This is done for the purpose of enriching and provide more details to the users by using artificial intelligence techniques which are image recognition, retrieval chatbot, and text to speech. This system achieved its goals and showed a great performance in terms of high accuracy numbers and correct chatbot responses.
New Word Discovery Algorithm for News

With rapid popularization of various mobile phone applications, many new words appeared on the Internet, resulting in the ineffective segmentation of these new words. This paper proposes a new method, which can effectively identify new words in the news. Firstly, the collected corpus is pre-processed and then proceed name entities recognition. At the same time, the candidate words are filtered through dictionary filtering, and finally the candidate words are filtered by mutual information and minimum branch entropy. Through experimental verification, it can be found that the algorithm for identifying new words proposed in this paper can well recognize new words on the Internet.
Conclusions

This section concludes the book and proposes the future outlook, which can be explored further to initiate new research avenues.
Fast On-Road Object Detector on ROS-Based Mobile Robot

The application environment of mobile robot is gradually expanding from indoor to outdoor. Vision-based detection, which acquires traffic information through the camera, is a state-of-the-art auxiliary technology. In this paper, a robotic middleware Robot Operating System (ROS) is applied to detect object and control application based on embedded processor. And, we present an effective On-road object detector which is suitable for embedded GPU by improving the performance of Single Shot MultiBox Detector (SSD). Our approach is to construct detection network by using depth-wise separable convolution for saving computing resource and present multi-category clustering to adjust the generated default boxes for optimizing accuracy. Experiments on KITTI dataset show that the proposed network runs 2.1 times faster than original SSD network on embedded GPU and maintains 71% mean average precision. Finally, a mobile robot is designed based on the detector and controller to demonstrate On-road assisted driving intuitively.
Creative Technologies and Interdisciplinary Collaboration

Many narratives have been established around Aspen Movie Map: about the interdisciplinary research lab where it was created, its pioneering interactive interface, or the concept of user empowerment and virtual travel. A predecessor of Google Street view and other navigation technology, it was one of the first tools where users could navigate freely through a virtual map. The creativity and innovation of this project resulted from artists and engineers working collaboratively. The question of the work practices and co-creative exchange between artists and technologists has not been analyzed in depth. ‘Collaboration’ and ‘innovation’ are often not clearly defined but presumed as positive attributes in themselves. However, this project along with other early art and technology collaborations has been critically reviewed in its emphasis on novel interfaces and disregard of a potential socio-political or cultural impact.
COCOA: Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains

Recent progress towards designing models that can generalize to unseen domains (i.e domain generalization) or unseen classes (i.e zero-shot learning) has embarked interest towards building models that can tackle both domain-shift and semantic shift simultaneously (i.e zero-shot domain generalization). For models to generalize to unseen classes in unseen domains, it is crucial to learn feature representation that preserves class-level (domain-invariant) as well as domain-specific information. Motivated from the success of generative zero-shot approaches, we propose a feature generative framework integrated with a COntext COnditional Adaptive (COCOA) Batch-Normalization layer to seamlessly integrate class-level semantic and domain-specific information. The generated visual features better capture the underlying data distribution enabling us to generalize to unseen classes and domains at test-time. We thoroughly evaluate our approach on established large-scale benchmarks – DomainNet, DomainNet-LS (Limited Sources) – as well as a new CUB-Corruptions benchmark, and demonstrate promising performance over baselines and state-of-the-art methods. We show detailed ablations and analysis to verify that our proposed approach indeed allows us to generate better quality visual features relevant for zero-shot domain generalization.
Research on fast detection algorithm of traffic road target

Aimed at the characteristics of the diversity of traffic road targets and the complexity and variability of detection scenarios, it is difficult for general target detection algorithms to find an ideal balance between accuracy and speed, resulting in slower detection speed and higher false detection rate. An improved YOLOv4 traffic road target detection algorithm has been proposed to solve these problems. Firstly, a Context Exploitation method is introduced to reduce information loss at the highest level feature map in FPN. Secondly, the residual feature augmentation method is adopted to enhance the feature extraction of the convolutional layer of the YOLOv4 neck, which greatly improves the detection speed and also obtains an increase in accuracy. Finally, the augmented PANet is used to improve the feature fusion method and enhance the representation ability of the feature map. Compared with other classic methods on the VOC and TT100K datasets for road target detection, it is found that the improved YOLOv4 algorithm can effectively reduce the false detection rate of small targets and significantly improve the accuracy and speed of detection. Experimental results show that the improved YOLOv4 algorithm has an average accuracy of 2.42% higher than original YOLOv4 algorithm on detection, and the detection speed reaches 61.5 frames/s.
Analysis of structure and material in the southwest of the Talnakh ore cluster

Abstract The issues of metallogenetic modeling of ore-forming systems for search purposes when studying sides and deep horizons of the mined deposits are considered. The object of the study is the geological and geochemical conditions of ore accumulation of various mineral composition and the concentration of useful components in various sections of the Talnakh ore-forming system. Mineral and geochemical zoning of the deposits reflect the degree of contamination of primary silicate and magmatic melts with crustal material. On the western and southern sides of the ore-forming system, ore mineralization is represented by low-sulfur associations of chalcopyrite minerals and is specialized in Cu–Ni–Co. The geochemical analysis of isotopes δ 34 S, 40 Ar/ 36 Ar, δ 65 Cu, 87 Sr/ 86 Sr suggests that the process of ore formation on the sides of the Talnakh ore cluster occurs under intense influence of the crust (sulfur source evaporite rocks) and near-surface water on the fluid in melting-fractionated sulphide magma enriched with copper, which results in the formation of chalcopyrite ore.
Improvisation through Performance-installation

This article builds on Richards’s work to formulate an understanding of the emerging practice of performance-installation, which embraces sound art, DIY electronic music and maker culture. A number of key points are addressed in this formulation: making/unmaking and ‘public making’; the assembling/disassembling of sonic devices and artefacts in the performance space; sound ‘through’ materials and materials at hand; and site and architectural features as material. In addition, readymade actions, illustrated by George Brecht’s The Cabinet , and the relationship between audience and performer are also presented. The article continues by outlining practice undertaken by the authors comprising two UK tours: ‘Sacrificial Floors’, 2018 with Tetsuya Umeda; and ‘Points of Failure’, 2020. Derek Bailey’s idea of ‘instrumental impulse’ is extended to include the concepts of expanded and reduced instruments that encompass ‘instrument as object’ and ‘raw material as instrument’. ‘Instrument’ is also viewed as a distributed mesh-like structure in which collective improvisation may occur. Borrowing from Ingold, ‘wayfaring’ is used to describe improvising with materials found ‘along the way’. Silent actions, uncontrollable instruments, unstable systems, performative failures, reimagined affordance of objects, ‘playing with resource’ and ‘improvising inside electronics’ are all addressed in relation to performance-installation. Finally, the authors explore the idea of ‘virtuosity in listening’, and question sonic autonomy and self-expression in improvisation and how ‘attending to sound’ can be an active part of improvised performance.
Lightweight Encryption Using Chaffing and Winnowing with All-or-Nothing Transform for Network-on-Chip Architectures

Network-on-Chip (NoC) fulfills the communication requirements of modern System-on-Chip (SoC) architectures. Due to the resource-constrained nature of NoC-based SoCs, it is a major challenge to secure on-chip communication against eavesdropping attacks using traditional encryption methods. In this paper, we propose a lightweight encryption technique using chaffing and winnowing (C&W) with all-or-nothing transform (AONT) that benefits from the unique NoC traffic characteristics. Our experimental results demonstrate that our proposed encryption technique provides the required security with significantly less area and energy overhead compared to the state-of-the-art approaches.
Multi-moving Target Recognition and Tracking Based on Mobile Robot Platform

Starting from the mobile robot platform, visual sensing system and intelligent monitoring system, this paper introduced some of the key technologies in detail which recognized and tracked multi-moving target based on the mobile robot platform. Intelligent monitoring system was designed to realize accurate detection and real-time tracking of multiple targets in complex scenes, and to expand the search and tracking range of cameras in the video surveillance system, in order to meet the urgent needs of intelligent video surveillance tasks in the field of security prevention, intelligent services and smart city construction.
Design of Automatic Speech Evaluation System of Professional English for the Navy based on Intelligent Recognition Technology

With the rapid development of information technology, the application scope of intelligent technology is increasingly expanding. Intelligent recognition technology has become a new trend in the reforming of foreign language teaching. This paper introduces the functional design and application of “Automatic Speech Evaluation System of Professional English for the Navy”. Through the analysis and comparison of the standard degree, correct degree and frequency of use of English pronunciation, the corresponding data results are output to promote the students' learning and mastering of practical English in the Navy and improve the learning effect.
Application blockchain in supply chain finance: a study on small and micro enterprises in Xi'an

Blockchain has the characteristics of decentralization, traceability and non-tampering and is very suitable for the study of supply chain finance. In supply chain finance, blockchain can alleviate the information asymmetry between small and micro enterprises and commercial banks, and reduce the financing cost of enterprises. This paper takes small and micro enterprises in Xi&#x0027;an as the research objects, and analyzes the influences of blockchain to supply chain finance. Based on the theories of blockchain and supply chain finance, this paper designs the supply chain financing mode based on blockchain. It is suggested that small and micro enterprises in Xi&#x2019; an should actively use blockchain technology to alleviate the financing dilemma.
A superpixel structure based visual attention target detection algorithm for SAR image

In this paper, a superpixel structure based visual attention target detection algorithm for synthetic aperture radar (SAR) image is proposed. The proposed algorithm uses visual attention to locate the region of interest (ROI) area, and then uses the corner detection to extract the structural features of the superpixels. Finally, the outlier detection algorithm is used to find the superpixel where the target is located. The proposed algorithm uses superpixel combined with visual attention. Moreover, the proposed algorithm uses superpixel structure information to detect the target. Due to the difference in the superpixel structure of the target and the background, the superpixel structure information can be used to distinguish the target and the background in the SAR image of the complex scene. Experimental results show that the proposed algorithm can effectively complete SAR image target detection in complex scenes.
An Evolutionary Based Recommendation Approach

A recommender system aims to satisfy its users by offering them relevant items (films, books, products, etc). This is done by comparing the items deemed satisfactory by the user with all of the items (content-based filtering), or by searching for similar users (collaborative filtering). We propose a genetic based approach to recommend relevant items without needing an explicit request from the user. Our approach looks for the most similar users using a genetic algorithm. Then, a recommendation space is constructed by grouping the items preferred by each similar user, and removing those preferred by the active user. After that, the system predicts a rating for each unrated item. The recommendation space will be reduced by keeping only relevant items using a threshold. An experimental study has been made in comparison with KNN algorithm. Experimental results seem interesting and show an improvement in precision, recall and F-Measure. Also Mean Absolute Error (MAE) has been reduced.
Online Learning for the Early Childhood and Elementary Education Student

Early childhood students require hands-on and interactive learning activities. Technology is one of the best practices to provide engaging and interactive learning activities for students in early childhood to learn and practice in a repetitive manner to ensure mastery before moving to another level. Online learning has become a vital method of learning for students. Therefore, the use of technology needs to be interactive rather than passive and lecture based. Tools such as Google, web-based applications, and other digitally-interactive available technologies assist early childhood students to participate in the learning lessons.
Potential purchase intention capturing method based on consumer's interests mining in social networks

Traditional session-based recommendation systems usually capture users' potential purchase intention from their recent transaction records, which does not consider the impact of social networks on users' interests. This paper proposes a new method by mining consumption information in social networks. First of all, a novel method based on a recurrent neural network is designed to express the correlation between users' purchasing behaviors, which dynamically captures users' interests in different scenarios. Secondly, to more accurately obtain the interests of neighbor nodes in social networks, this paper designs a multi-layer recurrent neural network with residual structure, which enriches the consumption information of neighbor nodes, and at the same time allows the original feature information of nodes to be retained during the propagation process of the deep network. Finally, the two interests are merged into users' potential purchase intention, which improves the performance of recommendation systems. The experimental results prove the effectiveness of the two methods proposed in this paper. Their Recall and Normalized Discounted Cumulative Gain on Douban Movie and Delicious datasets both outperform the state-of-art models.
Multiple Kernel Clustering with Direct Consensus Graph Learning

Multiple kernel graph-based clustering (MKGC) has achieved impressive experimental results, primarily due to the superiority of multiple kernel learning (MKL) and the outstanding performance of graph-based clustering. However, many present MKGC methods face the following two disadvantages that pose challenges for further improving clustering performance: (1) these methods always rely on MKL to learn a consensus kernel from multiple base kernels, which may lose some important graph information since graph learning is the key to graph-based clustering, not kernel learning; (2) these methods perform affinity graph learning and subsequent graph-based clustering in two separate steps, which may not be optimal for clustering tasks. To tackle these problems, this paper proposes a new MKGC method for multiple kernel clustering. By directly learning a consensus affinity graph rather than a consensus kernel from multiple base kernels, the important graph information can be preserved. Moreover, by utilizing rank constraint, the cluster indicators are obtained directly without performing the k-means clustering and any graph cut technique. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed method.
Mental Health Detection Using Transformer BERT

The COVID-19 pandemic has affected the daily life of each individual drastically at global level. The adverse effects of the pandemic on an individual and people around them have created an anxious and depressive environment. The virus has changed the way of living for most people and increased the distance between individuals. As the COVID-19 spread, people have been constantly in bad mental health which includes fear, boredom, sadness, and stress. Based on this situation, in this chapter the authors have analysed the mental health of people affected due to COVID-19 by analyzing two parameters of mental health, boredom and stress, from social media posts by detecting different emotions and feelings expressed in the form of text. The authors have utilized the BERT pre-trained model on preprocessed data to create classification models of boredom, stress, and consequently, determining the emotion of the person. These models are used to determine the emotions (i.e., stress and boredom) during different stages of the COVID-19 pandemic.
Art Access in Pandemic Times:

With the spread of COVID-19, many cultural and artistic institutions around the world have been forced to temporally close doors to protect attendees from the transmission risks. These measures have instigated curators and creators to explore alternative ways to showcase artworks through the digitization of collections, thus allowing the general public to access art online during social isolation. This work, therefore, describes the digitization process of a Brazilian art gallery which adopted the 3D virtual tour format to grant access to its art exhibitions in both Web and virtual reality (VR) formats. Both the development process of the virtual gallery and its usability evaluation with participants of different backgrounds are presented. Although some suggestions for technical improvement were pointed out, preliminary results showed that there was general acceptance in using the technology as a functional digital resource to access artistic exhibitions remotely.
On Theory of Names to be Used in Semantics of References in Functional and Object-Oriented Languages

The long-standing problem of adequate formalization of local names in mathematical formulas and semantics of references in object-oriented languages taken on their own without objects, is discussed. Reasons why the existing approaches cannot be considered suitable solutions, are explained. An introduction is given to the relatively recent works on the theories of names and references of the group lead by Andrew Pitts. The concept of referential transparency, in which contextual equivalence is used instead of the usual equality of values, is analyzed. It is the main property, which these theories are based upon: such modified referential transparency is preserved when a purely functional language is extended with names and references as data. An outline of a constructive denotational semantics of the extended functional language is given. It is argued that the modified referential transparency, along with many other valuable properties, can be also preserved for mutable objects that change to a limited extent. This leads to a model of computation between functional and object-oriented ones, allowing for a deterministic parallel implementation.