Reviewerly/exHarmony huggingface dataset:
A Framework for Sharing Cultural Heritage Objects in Hybrid Virtual and Augmented Reality Environments

The emulation of social environments within which ideas, knowledge and interpretation are exchanged is a challenge for Extended Reality (XR) technologies. One aspect of the challenge is the concept of Extended Reality itself, and this within the broad spectrum of the physical and virtual reality continuum. As users settle down into the spectrum via their preferred devices, so must we investigate the viability of communication between users adopting different modes of XR. In this chapter, we discuss three attributes of virtual objects and explore the concept of a Hybrid Virtual and Augmented Reality (HVAR) environment. We look at how users from different realities could interact, engage and communicate in a shared space via objects. We believe that the use of HVAR environments is the way forward for connecting worlds, and that it will facilitate future communications around virtual objects, developing and flourishing across time, space and devices, much like how social media has facilitated user-generated contents, empowering individual interpretations and the formation of collective meanings. The concept of a hybrid space aims to gather communities from disparate backgrounds and cultures, and to facilitate discussions around objects of interest.
Cognitive Information Retrieval

Several existing search personalisation techniques tailor the returned results by using information about the user that often contains demographic data, query logs, or history of visited pages. These techniques still lack awareness about the user’s cognitive aspects like beliefs, knowledge, and search goals. They might return, for example, results that answer the query and fit the user’s interests but contain information that the user already knows. Considering the user’s cognitive components in the domain of Information Retrieval (IR) is still recognized as one of the “major challenges” by the IR community. This paper overviews my recent doctoral work on the exploration of the approaches to represent the user’s cognitive aspects (especially knowledge and search goals) and on the investigation of incorporating them into information retrieval systems. Knowing that those aspects are subject to constant change, the thesis also aims to consider this dynamic characteristic. The research’s objective is to better understand the knowledge acquisition process and the goal achievement task in an IR context. That will help search users find the information they seek for.
A Content Based and Collaborative Filtering Recommender System

This research proposes a new recommendation system for recommendation generation based on users' ratings and personal profiles. Motivated by existing studies, firstly we propose item-based collaborative filtering to recommend tourist spots based on users' rating. In addition, we incorporate the content-based filtering algorithm with Naïve Bayes Classifier, for recommendation generation. Detailed analysis of these proposed methods are discussed which will give a clear view on how the core part of the recommendation systems has been implemented. The proposed TRS was evaluated using several data sets to indicate its efficiency.
Large-Scale Empirical Study of Inline Assembly on 7.6 Million Ethereum Smart Contracts

Being the most popular programming language for developing Ethereum smart contracts, Solidity allows using inline assembly to gain fine-grained control.Although many empirical studies on smart contracts have been conducted, to the best of our knowledge, none has examined inline assembly in smart contracts.To fill the gap, in this paper, we conduct the first large-scale empirical study of inline assembly on more than 7.6 million open-source Ethereum smart contracts from three aspects, namely, source code, bytecode, and transactions after designing new approaches to tackle several technical challenges.Through a thorough quantitative and qualitative analysis of the collected data, we obtain many new observations and insights.Moreover, by conducting a questionnaire survey on using inline assembly in smart contracts, we draw new insights from the valuable feedback.This work sheds light on the development of smart contracts as well as the evolution of Solidity and its compilers.
Towards Multi-Scale Speaking Style Modelling with Hierarchical Context Information for Mandarin Speech Synthesis

Previous works on expressive speech synthesis focus on modelling the mono-scale style embedding from the current sentence or context, but the multi-scale nature of speaking style in human speech is neglected. In this paper, we propose a multi-scale speaking style modelling method to capture and predict multi-scale speaking style for improving the naturalness and expressiveness of synthetic speech. A multi-scale extractor is proposed to extract speaking style embeddings at three different levels from the ground-truth speech, and explicitly guide the training of a multi-scale style predictor based on hierarchical context information. Both objective and subjective evaluations on a Mandarin audiobooks dataset demonstrate that our proposed method can significantly improve the naturalness and expressiveness of the synthesized speech.
Graph Neural Network for Credit Card Fraud Detection

With the rapid development of network payment, events of electronic transaction fraud take place often and result in lots of losses to customers and merchants. Therefore, transaction fraud detection is very important, and many detection methods have been studied and applied based on big data and machine learning. Some focused on the relationship of transactions, some on the relationship of original transaction features, and some on the dynamic changes of behaviours of users. However, there is few method to comprehensively characterise them. Hence, we design a weighted multiple graph called Transaction Graph (TG), and use Graph Neural Network (GNN) and TG to train a detection model so that the above characters can be represented comprehensively and thus the detection performance is enhanced obviously. We first construct a group of rules that are represented by a group of logical propositions and used to describe some transaction characters, and assign different weights for these rules to reflect their different importances. In our TG, a node corresponds to a transaction record. If two records satisfy a rule, then the two nodes corresponding to the two records are connected by an edge whose weight is equal to the weight of the rule. Based on our TG, we propose a new sampling policy which can sample neighbour nodes with more useful information. Additionally, we propose a new attention mechanism for GNN in order to decrease the interference of abnormal samples and increase influence of normal ones. Our experiments on a big dataset of real transactions illustrate the advantages of our method compared with some state-of-the-art ones.
Trajectory Privacy Preserving for Continuous LBSs in VANET

Location-based services (LBSs) support various applications in vehicular ad hoc network (VANET). However, location/trajectory privacy becomes a serious concern for LBSs. Existing location/trajectory privacy-preserving schemes rarely take the attack model of adversaries into consideration, and the cost for achieving privacy has not been carefully studied. To deal with these problems, this study proposes a collaborative trajectory obfuscation scheme based on analyzing the attack model and designs a privacy-preserving efficiency metric that balances the achieved privacy and the cost. Through simulation, the effects of the density of vehicles using the same LBSs on the performance of our design and an existing scheme are investigated. The performance comparison results validate the effectiveness and efficiency of our scheme.
Reduce unrelated Knowledge through Attribute Collaborative signal for knowledge graph recommendation

Knowledge graph (KG), as an auxiliary information, plays an important role in the recommendation system, which effectively solves the sparsity and cold start problems of collaborative filtering algorithms. The recommendation algorithm that introduces the propagation mechanism on the KG has been a great success, it enriches the representation of users and items by aggregating multi-hop neighbors. However, the existing KG-based propagation recommendation algorithm aggregating all entity information cannot guarantee the improvement of recommendation results, because entity information in KG is not all helpful to recommend appropriate items to users. Indiscriminately aggregating the entity information in the neighborhood allows the learned embedding representation to be influenced by its unrelated entities.
Estimating the Attack Surface from Residual Vulnerabilities in Open Source Software Supply Chain

Software supply chain security has now become a critical concern in the software industry (and beyond) following the large impact of recent attacks: hackers injected malicious code into Solarwinds components and Octopus scanner, which eventually infected a wide range of downstream dependencies, affecting a massive number of users. Since supply chain vulnera-bilities are a well-known concern, especially with open source systems, approaches in the literature mainly focus on identifying and patching such vulnerability. Frequently, however, a vulnerability patch is not immediately propagated to earlier releases that have been inherited by dependents, leaving residual vulnerabilities in supply chains. Our work addresses this challenge and develops a simple approach to iteratively explore the attack surface of supply chain residual vulnerabilities in open source projects. We have assessed our search scheme on 50 GitHub-hosted projects having high stars and forks: we mine their bug fix commits and identify buggy package versions to track the affected dependents and estimate the potential attack surface. We find that many projects fix their vulnerable issues by update their dependency versions, and version inheritance is a significant cause of supply chain attacks for open source projects.
Linear Laser Scanning Measurement Method Tracking by a Binocular Vision

The 3D scanning of a freeform structure relies on the laser probe and the localization system. The localization system, determining the effect of the point cloud reconstruction, will generate positioning errors when the laser probe works in complex paths with a fast speed. To reduce the errors, in this paper, a linear laser scanning measurement method is proposed based on binocular vision calibration. A simple and effective eight-point positioning marker attached to the scanner is proposed to complete the positioning and tracking procedure. Based on this, the method of marked point detection based on image moment and the principle of global coordinate system calibration are introduced in detail. According to the invariance principle of space distance, the corresponding points matching method between different coordinate systems is designed. The experimental results show that the binocular vision system can complete localization under different light intensities and complex environments, and that the repeated translation error of the binocular vision system is less than 0.22 mm, while the rotation error is less than 0.15°. The repeated error of the measurement system is less than 0.36 mm, which can meet the requirements of the 3D shape measurement of the complex workpiece.
Chapter 3 Paradigms to make smart city using blockchain

We learn in everyday life that data is created and marketed at a rate that could not be seen a few years back. At present, when data is increasing exponentially over the successfully opened media, its consistent quality is crucial. To make the data robust and to promote exchange, we rely on a great figure of mediators, which confirms the details of building “trust” among the working organizations. Confirmation of the exchange rate for banks is another matter to create a system that is maintaining belief. This mediator that people trust had a great deal in our work. Surprisingly, in the day and year when data is readily available, these mediators existence will give new era to utilize the datasets. A newly launched blockchain is an appropriate record development that stores information over different systems securely to enable dispersed trades by making a reliable wellspring of fact disintermediating the indirect legislative body of the trust. This new system understands an adjustment in context in such a way wherein we develop faith and have far-attained penalty. Blockchain is prepared to modify how it will amuse and will influence everyone in trade (banking, power, preparing, social protection, etc.). The open region is no uncommon case. The organization and open fragment have an urgent necessity to be total, safe, credible and dependable in order swap crossways with a variety of fields. Blockchain advancement is ascending as a gadget for administration, mainly to restructure the data based on reasons.
Substitution Attacks against Message Authentication

This work introduces Algorithm Substitution Attacks (ASAs) on message authentication schemes. In light of revelations concerning mass surveillance, ASAs were initially introduced by Bellare, Paterson and Rogaway as a novel attack class against the confidentiality of encryption schemes. Such an attack replaces one or more of the regular scheme algorithms with a subverted version that aims to reveal information to an adversary (engaged in mass surveillance), while remaining undetected by users. While most prior work focused on subverting encryption systems, we study options to subvert symmetric message authentication protocols. In particular we provide powerful generic attacks that apply e.g. to HMAC or Carter–Wegman based schemes, inducing only a negligible implementation overhead. As subverted authentication can act as an enabler for subverted encryption (software updates can be manipulated to include replacements of encryption routines), we consider attacks of the new class highly impactful and dangerous.
Data Storage

While research data has become integral to the scholarly endeavour, a number of challenges hinder its development, management and dissemination. This chapter follows the life cycle of research data, by considering aspects ranging from storage and preservation to sharing and legal factors. While it provides a wide overview of the current ecosystem, it also pinpoints the elements comprising the modern research sharing practices such as metadata creation, the FAIR principles, identifiers, Creative Commons licencing and the various repository options. Furthermore, the chapter discusses the mandates and regulations that influence data sharing and the possible technological means of overcoming their complexity, such as blockchain systems.
Developing 4IR Engineering Entrepreneurial Skills in Polytechnic Students. A Conceptual Framework

The Fourth Industrial Revolution (4IR) which is technological driven has brought a lot of opportunities those with entrepreneurial mindset as most of the skills demanded in the 4IR are entrepreneurial. This calls approaches that can lead to the development of engineering entrepreneurs that can drive the 4IR. To this effect, this study explored experts’ opinions on the most relevant entrepreneurial skills for the 4IR engineers as well as the best teaching methods to be adopted for the teaching of entrepreneurship education in Nigerian polytechnics. A two-round Delphi interview was used to arrive at 19 entrepreneurial skills with experts’ consensus of Wa = 0.821 and 8 innovative teaching methods with experts’ consensus of Wa = 0. 925. The results from the findings were used to develop a conceptual framework for the development of 4IR Entrepreneurial skills.
Distinctly Trained Multi-Source CNN for Multi-Camera Based Vehicle Tracking System

In the last few years the exponential rise in the demands or robust surveillance systems have revitalized academia-industries to achieve more efficient vision based computing systems. Vision based computing methods have been found potential for the different surveillance purposes such as Intelligent Transport System (ITS), civil surveillance, defense and other public-private establishment security. However, computational complexities turn-out to be more complicate for ITS under occlusion where multiple cameras could be synchronized together to track certain target vehicle. Classical texture, color based approaches are confined and often leads false positive outcome thus impacting decision making efficiency. Considering this as motivation, in this paper a highly robust and novel Distinctly Trained Multi-Source Convolutional Neural Network (DCNN) has been developed that exhibits pre-training of the real-time traffic videos from multiple cameras to track certain targeted vehicle. Our proposed DCNNvehicle tracking model encompasses multiple shared layers with multiple branchesof the source-specific layers. In other words, DCNN is implemented on each camera or source where it performs feature learning and enables a set of features shared by each camera, which is then learnt to identify Region of Interest (ROI) signifying the “targeted vehicle”. Our proposed DCNNmodel trains each source input iteratively to achieve ROI representations in the shared layers. To perform tracking in a new sequence, DCNNforms a new network by combining the shared layers in the pre-trained DCNN with a new binary classification layer, which is updated online. This process enables online tracking by retrieving the ROI windows arbitrarily sampled near the previous ROI state. It helps achieving real-time vehicle tracking even under occlusion and dynamic background conditions.
Deep Learning for NLP

In this chapter, you implement deep learning for NLP. The following recipes are covered.
Detecting LDoS in NB-IoTs by using metaheuristic-based CNN

The number of IoT devices will grow explosively due to great potential of the NB-IoT so that the IoT network environment will become a hotbed of botnets. Various IoT devices and platforms will be threatened by DoS attacks. However, NB-IoT has the characteristics of lower speed so it is not an ideal environment for heavy traffic DoS attacks. Low-rate DoS is the main attack method in the NB-IoT environment. In this environment, the attacker can hide the attack packet for avoiding detection in a data stream so that the difficulty of detection will be increased greatly. There are already many CNN methods to identify the characteristics of this attack. However, these traditional methods will cause the amount of data to be insufficiently diverse. In order to improve the phenomenon of overfitting, this article uses simulated annealing to adjust the weight of the CNN to achieve better global search.
The Neural Correlates in the Presumed Extrasensory Faculties of the Medium and in the Perception on the Sacred

Starting from dozens of studies on neural correlates during mystical experiences and the use of extrasensory skills from the common sense, I proceeded to contextualize in a more scientific way the concept of extrasensory mediums and faculties, then analyzing the state of the art in relation to discoveries in the neuro scientific field concerning precisely the alleged extrasensory faculties possessed and the perception of the sacred in the subjects of the researches so far evaluated.From this examination there emerges a direct involvement of the cerebral areas of the frontal and temporal lobes and in general of the circuits connected to the limbic system.This thematic research has deepened the object of study concerning the phenomena of subjective perception; however, in the last part I proceeded instead to suggest a research-oriented research scheme not so much of the subjective profiles already known as much as the hypothesis of the existence of the entities perceived by the mediums, therefore an objective profile, in order to better know the external context of the medium.
Information Flow Security Models for Cloud Computing

In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajector... | Find, read and cite all the research you need on Tech Science Press
Smart Glove for Hearing-Impaired

Individuals communicate with one another to pass on their thoughts to the general population around them. There are 2.78% of the total populations of India who can’t speak. Gesture based communication is really a mode of correspondence for the general population who are either deaf or deaf-mute. Ordinary individuals don't become familiar with the gesture based communication. It causes conveyance gap between deafdumb and normal people. The past system of this project involved using image processing concept. But the downside of these past frameworks are projects were non portable and excessively costly. The aim behind this work is to build up a framework for perceiving the gesture based communication, which provides interaction between people who are deaf-dumb and normal people, thereby diminishing the interaction gap between them. Generally hearing-impaired people use linguistic communication based on hand gestures with specific movements to represent the ideas to others. The proposed glove is an robotic gadget that interprets American Sign Language Standard into text or speech in order to evacuate the information transmission gap between the mute and the ordinary public. This glove has been actualized with the assistance of flex sensors, accelerometer, microcontroller (Arduino Nano) and the Bluetooth chip.
An Efficient Signcryption Scheme for Secure Authentication using Hyper Elliptic Curve Cryptography and Keccak Hashing

The need for security is a challenging task nowadays due to the transition from wired to wireless networks, emergence of Internet of Things (IoT), Mobile Networks, Wireless Sensor Networks (WSN) and Radio Frequency Identification System (RFID). Generally wireless systems are prone to insecurity and resource (power) constraint, to deal with these challenges many solutions has been proposed in cryptography. One such important development is light weight cryptography particularly signcryption.Signcryption is a logical combination of digital signature and encryption in a single step therefore the cost of communication and computation is very less compared to the existing signature then encryption scheme. Till date many signcryption techniques were raised based on El-Gamal, RSA and Elliptic Curve Cryptography (ECC). The proposed research work highlights the limitations of existing signcryption based on ECC and it proposes the efficient light weight cryptographic scheme of signcryption based on Hyper Elliptic Curve Cryptography (HECC) and Keccak hashing. Further the proposed research work achieves all the security metrics such as confidentiality, integrity, non- repudiation, forward secrecy and public verifiability.