Reviewerly/exHarmony huggingface dataset:
Quantum Technologies II: Cryptography, Blockchains, and Sensing

This chapter further accentuates the aspect of quantum technology, built upon the basics of quantum information that is dealt in the previous chapter. A brief review is made on two primary quantum information processing tasks, being quantum communication and quantum sensing. Quantum communication is additionally divided into quantum cryptography and quantum blockchain, respectively. In quantum cryptography, the feature of security in quantum cryptosystems is commented upon. Quantum blockchain encompasses the nature and the networking of the framework to witness decentralized, distributed and public digital ledger in quantum domain. Finally, the emergence of quantum sensing from particles/systems that are described by quantum mechanics, like photons, is introduced.
A University Portrait System Incorporating Academic Social Network

There are more than 2800 higher education institutions in China, all of which have a wealth of basic attributes and introductory information. However, by investigating common university and college information service platforms, we find a problem that users cannot quickly access key information. Inspired by user profile and corporate portraits, we propose a university portrait system incorporating academic social networks. We first collect two types of data, then utilize text mining techniques integrated with statistics-based methods and topic-based methods to extract features and generate tags of universities. Additionally, we incorporate data related to the universities on the academic social network SCHOLAT.COM including scholars, academic news, courses and academic organizations to enrich our university portraits.
Harnessing the x86 Intermediate Rings for Intra-Process Isolation

Modern applications often involve the processing of sensitive information. However, the lack of privilege separation within the user space leaves sensitive application secrets such as cryptographic keys just as unprotected as a ”hello world” string. Cutting-edge hardware-supported security features are being introduced. However, the features are often vendor-specific or lack compatibility with older generations of the processors. The situation leaves developers with no portable solution to incorporate protection for the sensitive application component. We propose LOTRx86, a fundamental and portable approach for user-space privilege separation. Our approach creates a more privileged user execution layer called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PrivUser</i> by harnessing the underused intermediate privilege levels on the x86 architecture. The PrivUser memory space, a set of pages within process address space that are inaccessible to user mode, is a safe place for application secrets and routines that access them. We implement the LOTRx86 ABI that exports the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privcall</monospace> interface to users to invoke secret handling routines in PrivUser. This way, sensitive application operations that involve the secrets are performed in a strictly controlled manner. The memory access control in our architecture is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privilege-based</i> , accessing the protected application secret only requires a change in the privilege, eliminating the need for costly remote procedure calls or change in address space. We evaluated our platform by developing a proof-of-concept LOTRx86-enabled web server that employs our architecture to securely access its private key during an SSL connection. We conducted a set of experiments, including a performance measurement on the PoC on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">both</i> Intel and AMD PCs, and confirmed that LOTRx86 incurs only a limited performance overhead.
A Survey on Computational Intelligence Techniques in Learning and Memory

Learning and Memory is a branch of artificial intelligence that studies the critical brain functions in order to create novel computational intelligence techniques and methods focused on learning and memory. As a consequence, human intelligence demands a review on computational intelligence techniques in learning and memory. This paper explains why without learning, the goal of achieving human intelligence is still a long bit away. This paper discusses hippocampus learning, human learning and memory, hidden markov model, behavioral plasticity in learning and memory, PET and fMRI, as well as the neurological illnesses. Here, we outline the important work done in the domain of learning as well as memory. This paper examines the evolution of learning and memory during several decades. This study also discusses the merits and limitations of several learning and memory models and techniques.
Factors Influencing Cybercrime Reporting Behaviour in South African State-Owned Entities

Cybercrime may destabilise organisations and society due to the social, financial, emotional, psychological, and physical impacts. The purpose of this paper was to investigate cybercrime reporting behaviour and the factors that influence it. South African state-owned entities were the focus of attention given their strategic role, which requires that attention be given to improving their cybersecurity practices, such as cybercrime reporting in an increasingly digital society. The conceptual framework was developed using themes from the cybercrime literature, and the Theory of Planned Behaviour (TPB) as a lens. The study used a quantitative method, and data was collected online using a questionnaire survey. One hundred and three complete responses were received from employees working in South African state-owned entities. Factors that were identified as influencing cybercrime reporting behaviour were self-efficacy and facilitating conditions.
VCI Construction and Shifting Strategy Based Steganography for 3D Images

The prime goal of any steganography technique is to establish a digital covert communication, whereas, the hiding capacity is the big point of concern. The majority of the existing techniques and approaches investigates the trade-off between the hiding capacity and distortion. In this paper, we propose a novel information hiding technique in which we have explored a shifting strategy preceded by the vertex component interval or VCI construction mechanism to embed the confidential data within a 3-dimensional (3D) image effectively. Instead of using critical geometric way, we have used truncated space to decrease the hiding distortion and increase visual quality. Moreover, in this proposed method, the embedding capacity does not depend upon the shape of images that is why it can prevent the vertex reordering attack. Here, the truncation length is used to adjust the quality of the stego-image. Our innovative approach’s versatility and great performance have been proved in a variety of experiments.
A Workflow Scheduling Strategy for Reasoning Tasks of Autonomous Driving

In different periods of time, the real-time reasoning tasks generated by autonomous vehicles are scheduled within the tolerance time, which is an important problem to be solved in autonomous driving. Traditionally, tasks are arranged on the on-board unit (OBU), which results in a long time to complete. Heuristic algorithm is widely used in task scheduling, which often leads to premature convergence. Task scheduling in the edge environment can effectively reduce the completion time of tasks. A workflow scheduling strategy in edge environment is designed. To optimize the completion time of reasoning tasks, this paper proposes a Q-learning algorithm based on simulated annealing (SA-QL). Moreover, this paper comprehensively reflects the performance of SA-RL and PSO algorithm from four aspects. Experimental results show that SA-RL algorithm and PSO algorithm have good performance in feasibility and effectiveness. TD(0) algorithms show better performance of exploration, TD(λ) algorithms show that of convergence.
Practice and Evaluation of Engineering Education on Automotive Dynamic Performance Design Using Spreadsheet Software

In order to improve the ability to design exercise performance, which is the basic performance of traffic machinery, it is important to build an educational program that makes it easy to understand that various exercises are combined. It is also required to be able to prepare at low cost from the viewpoint of building a self-study environment for students to take. The purpose of this study is to develop a motor performance design tool for automobiles using spreadsheet software and to evaluate the learning effect by applying it to education. As a result, it promoted an intuitive understanding of the motor performance of automobiles, and a certain effect was seen in improving performance and promoting extracurricular activities. On the other hand, it was also shown that it is important to use the tool of this research as an introduction tool for beginners.
AGAPECert: An Auditable, Generalized, Automated, Privacy-Enabling Certification Framework With Oblivious Smart Contracts

This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and reporting real-time aggregate certification status without disclosing underlying private data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologies, and a real-time graph-based API standard to provide automated, oblivious, and auditable certification. Our technique allows a privacy-conscious data owner to run pre-approved Oblivious Smart Contract code in their own environment on their own private data to produce Private Automated Certifications. These certifications are verifiable, purely functional transformations of the available data, enabling a third party to trust that the private data must have the necessary properties to produce the resulting certification. Recently, a multitude of solutions for certification and traceability in supply chains have been proposed. These often suffer from significant privacy issues because they tend to take a" shared, replicated database" approach: every node in the network has access to a copy of all relevant data and contract code to guarantee the integrity and reach consensus, even in the presence of malicious nodes. In these contexts of certifications that require global coordination, AGAPECert can include a blockchain to guarantee ordering of events, while keeping a core privacy model where private data is not shared outside of the data owner's own platform. AGAPECert contributes an open-source certification framework that can be adopted in any regulated environment to keep sensitive data private while enabling a trusted automated workflow.
Security Culture in Industrial Control Systems Organisations: A Literature Review

Industrial control systems (ICS) are a key element of a country’s critical infrastructure, which includes industries like energy, water, and transport. In recent years, an increased convergence of operational and information technology has been taking place in these systems, increasing their cyber risks, and making security a necessity. People are often described as one of the biggest security risks in ICS, and historic attacks have demonstrated their role in facilitating or deterring them. One approach to enhance the security of organisations using ICS is the development of a security culture aiming to positively influence employees’ security perceptions, knowledge, and ultimately, behaviours. Accordingly, this work aims to review the security culture literature in organisations which use ICS and the factors that affect it, to provide a summary of the field. We conclude that the factors which affect security culture in ICS organisations are in line with the factors discussed in the general literature, such as security policies and management support. Additional factors related to ICS, such as safety culture, are also highlighted. Gaps are identified, with the limited research coverage being the most prominent. As such, proposals for future research are offered, including the need to conduct research with employees whose roles are not security related.
Clustering-Based Semi-supervised Technique for Credit Card Fraud Detection

Among the high volume and frequency of credit card transactions in today’s world , the fraudulent ones must be flagged instantaneously. To establish its credibility the algorithm must do so with high sensitivity and minimum false alerts. We propose a light weight and easily re-trainable two stage semi-supervised approach to handle the problem. In the first phase we eliminate noisy data points (outliers) and extract core data points through a clustering technique. The extracted core points form the basis of training data. Set of core points are enhanced further to address the challenge of class imbalance (as fraudulent data is very less). Training set is used in the second phase to model the final classifier using k-D Tree. Experimental results on bench-marked dataset establish that the proposed model is well suited for real-time prediction of fraud transactions with high specificity.
N-Gram Feature Based Resume Classification Using Machine Learning

Shortlisting the right resume for the job is a tedious task for a job recruiter. For each post, thousands of applicants send their resumes; among all, very few resumes fit for the published job recruitment. Finding suitable resumes from a huge pool of resumes manually is a time-consuming process. This research addresses the above issue by suggesting a machine learning-based automated resume classification model. The automated resume classification model helps to classify the resume into different categories. Many classification models are used during the model development and found that the random forest classifier provides the most promising outcomes. The random forest classifier achieved the highest micro precision, recall, F1-score, and an accuracy value of 0.99 for the best case with the combination of uni- and bi-gram features.
Radar Emitter Recognition Based on Deep Feature Selection Network

Many artificial features have been extracted and applied to the recognition of radar emitter signals. It is worth studying which features play key roles in the recognition. The traditional sparse learning-based methods can achieve feature selection by setting some of the features to zero. However, linear model-based methods cannot deal with the nonlinearity. Deep neural networks (DNNs) have a strong ability to deal with nonlinearity of input variables.. However, the sparsification of DNNs is a challenge. This paper proposes a DNN based on importance measure (DNN-IM) for feature selection. The method combines the importance of weight and gradient to gradually delete features to achieve sparsification, and finally selects the optimal feature subset through the evaluation function containing redundancy measure. This method was applied to the feature selection of expert feature sets of radar emitter signals, and the results show that our method has superior performance.
“CGNet Swara Encourages Us to Participate and Bring About Change”: Analyzing the Role of Media and Development

Focusing on the idea of communication as an intrinsic part of culture and as a vehicle of transformation (Carey, 2002). A cultural approach to communication. McQuail’s reader in mass communication theory, 36–45.). A cultural approach to communication. McQuail’s reader in mass communication theory, 36–45.), this chapter seeks to look into the concept of citizen journalism as a powerful tool of development communication in India. (Melkote, S. R. (2003). Theories of development communication. International and development communication: A 21st-century perspective, 129–146.). Theories of development communication. International and development communication: A 21st-century perspective has underlined the key concepts in this field as communication, modernization, development, participation and empowerment, which also forms the basis of the dominant paradigm in the area. While these central ideas are intrinsically fused, this chapter is particularly interested in the ideas of participation, empowerment and the social process of development as envisaged by concepts of participatory action research (PAR) and how they are enhanced and encouraged through citizen journalism. Through qualitative in-depth interviews with the reporters, audiences and other stakeholders of the CGNET Swara; a citizen journalism outlet operating in extremely resource poor areas in India, this chapter hopes to contribute to this area by examining the role of citizen journalism and its contribution to social change by engaging communities and enabling them to become the main agents of this transformation.
Deep-learning Based Autoencoder Model for Label Distribution Learning

Label ambiguity in data is one of the serious challenges in Machine Learning. In such cases, the significance of each label matters. Label Distribution Learning (LDL) is a new way to deal with label ambiguity. In LDL, instances are defined through the degree to which it is represented by its labels or classes. In this paper, deep learning using autoencoders has been proposed for a computing this degree of labels. The autoencoder consists two parts i.e., encoder and decoder. The encoder compresses the input whereas the decoder decompresses it. The idea of compression is to learn relationship between patterns of data which remains unchanged. The idea of decompression is to recover the original input from the compressed input. Since autoencoders are semi-supervised we have appended a fully connected network for computing degree for each labels. Our results show that DLDL shows the least mean-square error among the contemporary methods.
A Dual Path Convolutional Network for Placental Vessel Segmentation in Fetoscopic Surgery

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat Twin-to-Twin Transfusion Syndrome (TTTS). In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow Field of View (FOV) of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. For this reason, we propose a novel encoder-decoder network with a dual-path structure to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the DSC, IoU and Pixel Accuracy by 5.80%, 8.39% and 0.62%, respectively.
A Conceptual Framework for Scaling and Security in Serverless Environments Using Blockchain and Quantum Key Distribution

Serverless computing is one of the most preferred deployment strategies in today’s world. It refers to a concept where execution environment is not predefined during deployment, but scaled based on demand. The concept has gained importance with improvements in cloud computing, which banks on this concept. Nowadays, cloud service providers are able to expose this concept as a cloud service itself, with application like GCP Cloud Functions and AWS Lambda. But as services go online, securing them becomes a challenge. Serverless computing was originally envisaged as a stateless environment, but now it has evolved to semi-stateful entities, making them prone to state analysis and hijacking. With current cryptosystems being outmatched at a fast pace, and quantum computing expected to break almost all cryptographic algorithms in the coming decade, it is imperative that serverless communications be encrypted in a much more secure fashion. A concept based on quantum key distribution and blockchain is proposed to improve application scaling and security. Blockchain platforms like Ethereum work based on smart contracts, which can execute pieces of code on the nodes and place results on the blockchain after consensus. The same can be adapted to serverless computing because smart contract codebase is deployed on the node itself, and is triggered by user requests. The user-defined function can then be deployed as a smart contract. When demand is huge, more nodes can be spun up, which contain the codebase for the smart contract, and they can initiate execution. In order to enhance security, the communication between endpoints can be protected by using Quantum Key Distribution, and even among the nodes, selections can be done using principles behind QKD. After execution is complete, the result is placed on the blockchain itself, which acts as a log of execution, as well as carry the result.
An Investigation into Educational Process Models for Teaching Secure Programming

Despite the many advantages that software applications provide in our daily lives, there are also numerous threats that target vulnerabilities in these applications. There is therefore a demand for new technologies and approaches to secure software development. Educational institutions are responsible for equipping computing graduates with the requisite secure programming knowledge, skills and abilities. However, despite various curricula guidelines being provided by the ACM and other professional bodies, many educational institutions have not successfully implemented such changes within their curricula. One of the problems is that the available curricula guidelines focus more on what secure programming concepts should be taught, rather than how. This paper therefore investigates how educational process models could be used for teaching secure programming. It further identifies various themes and sub-themes from different educational process models and argues how these can be used to teach secure programming.
Meaning Negotiation Based on Merged Individual Context Ontology and Part of Semantic Web Ontology

The meaning negotiation process has become a confusing term in the pragmatic web. It does not depend only on convenience to agree, but requires an excellent choice of terms used in the negotiated domain. The presented paper proposes a meaning negotiation model using ontologies merging into several domains and contextual situations. The idea is to design meaning negotiation scenarios using ontologies merging extended into several domains. The results obtained show the impact of merging in meaning negotiation on the multi-agent systems. The influence of merging is interpreted by the dependence between two variables: the meaning negotiation step numbers and the merging ontologies. The authors observe the difference between meaning negotiation step numbers with and without merging. They conclude that merging ontologies has a positive influence on meaning negotiation in different contextual situations of scenarios. To demonstrate the influence, an observable analytical study proof under statistical control is applied.