Reviewerly/exHarmony huggingface dataset:
The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets

Cryptocurrencies have gained a lot of attention since Bitcoin was first proposed by Satoshi Nakamoto in 2008, highlighting the potential to play a significant role in e-commerce. However, relatively little is known about cryptocurrencies, their price behaviour, how quickly they incorporate new information and their corresponding market efficiency. To extend the current literature in this area, we develop four smart electronic Bitcoin markets populated with different types of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We apply the STGP technique to historical data of Bitcoin at the one-minute and five-minute frequencies to investigate the formation of Bitcoin market dynamics and market efficiency. Through a plethora of robust testing procedures, we find that both Bitcoin markets populated by high-frequency traders (HFTs) are efficient at the one-minute frequency but inefficient at the five-minute frequency. This finding supports the argument that at the one-minute frequency investors are able to incorporate new information in a fast and rationale manner and not suffer from the noise associated with the five-minute frequency. We also contribute to the e-commerce literature by demonstrating that zero-intelligence traders cannot reach market efficiency, therefore providing evidence against the hypothesis of Hayek (1945; 1968). One practical implication of this study is that we demonstrate that e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct behaviour-based market profiling.
A Crime Script Analysis of Child Sexual Exploitation Material Fora on the Darkweb

This study's aim is to contribute to the knowledge on the steps involved in child sexual exploitation material (CSEM) crimes committed in Darkweb CSEM communities. Due to the anonymous and illegal nature of these communities, academic research is scarce. This study provides a crime script analysis of member communication data from four CSEM Darkweb fora obtained by law enforcement. For cross-validation, suspect interviews from a relevant case file were analyzed. A step-by-step description of the crime process, starting with the preparations necessary to access Darkweb CSEM fora and ending with the postactivity behaviors of exiting the crime scene and preventing detection, is given, focusing on the casts, activities, probs, and personal and contextual requirements at each stage. The findings highlight the scope of the CSEM problem, as well as the influence the Darkweb has on the way the crime is committed. Suitable targets for law enforcement intervention are discussed.
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization

Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to its potential to solve complex classification and control problems. However, existing RL algorithms are rarely applied to real vehicles for two predominant problems: behaviors are unexplainable, and they cannot guarantee safety under new scenarios. This paper presents a safe RL algorithm, called Parallel Constrained Policy Optimization (PCPO), for two autonomous driving tasks. PCPO extends today’s common actor-critic architecture to a three-component learning framework, in which three neural networks are used to approximate the policy function, value function and a newly added risk function, respectively. Meanwhile, a trust region constraint is added to allow large update steps without breaking the monotonic improvement condition. To ensure the feasibility of safety constrained problems, synchronized parallel learners are employed to explore different state spaces, which accelerates learning and policy-update. The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.
Deep Learning Thermal Image Translation for Night Vision Perception

Context enhancement is critical for the environmental perception in night vision applications, especially for the dark night situation without sufficient illumination. In this article, we propose a thermal image translation method, which can translate thermal/infrared (IR) images into color visible (VI) images, called IR2VI. The IR2VI consists of two cascaded steps: translation from nighttime thermal IR images to gray-scale visible images (GVI), which is called IR-GVI; and the translation from GVI to color visible images (CVI), which is known as GVI-CVI in this article. For the first step, we develop the Texture-Net, a novel unsupervised image translation neural network based on generative adversarial networks. Texture-Net can learn the intrinsic characteristics from the GVI and integrate them into the IR image. In comparison with the state-of-the-art unsupervised image translation methods, the proposed Texture-Net is able to address some common challenges, e.g., incorrect mapping and lack of fine details, with a structure connection module and a region-of-interest focal loss. For the second step, we investigated the state-of-the-art gray-scale image colorization methods and integrate the deep convolutional neural network into the IR2VI framework. The results of the comprehensive evaluation experiments demonstrate the effectiveness of the proposed IR2VI image translation method. This solution will contribute to the environmental perception and understanding in varied night vision applications.
HashXor: A lightweight scheme for identity privacy of IoT devices in 5G mobile network

Mobile networks are set to play a crucial role as backbone network in IoT deployments due to their low cost, broad coverage, high bandwidth, and low latency. However, a challenging security issue prevalent in mobile networks across the various generations including 4G, is identity privacy. In a recent standard for 5G mobile network: 3GPP TS 33.501, an Elliptic Curve Integrated Encryption Scheme (ECIES) is adopted to tackle this issue. While this mechanism seems to provide a reasonable solution for modern smart phones, it may not be best suitable for resource constrained IoT devices because of the computationally intensive calculations involved in elliptic curve cryptography. In this paper, an alternative lightweight scheme for identity privacy of IoT devices in 5G mobile network is proposed. This new scheme called ‘HashXor’ requires only two Hash operations and three Xor operations for the IoT device to achieve its objective. The scheme is found safe and logically correct through security analysis and formal analysis (using AVISPA tool). Through an execution time analysis in the ATmega328P microchip, the scheme is found to be computationally efficient compared to ECIES and few other recent proposals in this area.
Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.
Sociotechnical imaginaries in the present and future university: a corpus-assisted discourse analysis of UK higher education texts

Technology has dominated discourse on the future university and how digital technologies disrupting wider societal activities can be leveraged in higher education. To gain an insight into UK institutional perspective on technology adoption in teaching and learning and visions for the future, two corpora of text are analysed: Teaching Excellence Framework statements (n = 88) and university strategy documents (n = 88), totalling 1, 129, 736 words. Quantitative empirical analysis reveals that institutions write about how they 'use' technology for teaching and learning. Interpretative analysis found that technology is 'used' as an end in itself as well as a means for specific ends (such as assessment and feedback and flexible learning). Using concepts from science and technology studies and philosophy of technology, these perspectives are theorised as instrumentalist, substantivist and essentialist and problematised when viewing technology in education as apolitical, neutral and inevitable. Perceived neutrality ignores the many competing ideologies and interests at play. In this context, a dichotomy of 'pedagogy first' or 'technology-led' design is explored. Critical theory of technology is used to bridge these binary discourses which are described as reductive in a complex sociotechnical university assemblage.
A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

Abstract This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.
Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security

Industrial control systems (ICS) involve many key industries, which once attacked will cause heavy losses. However, traditional passive defense methods of cybersecurity have difficulty effectively dealing with increasingly complex threats; a knowledge graph is a new idea to analyze and process data in cybersecurity analysis. We propose a novel overall framework of data-driven industrial control network security defense, which integrated fragmented multisource threat data with an industrial network layout by a cybersecurity knowledge graph. In order to better correlate data to construct a knowledge graph, we propose a distant supervised relation extraction model ResPCNN-ATT; it is based on a deep residual convolutional neural network and attention mechanism, reduces the influence of noisy data in distant supervision, and better extracts deep semantic features in sentences by using deep residuals. We empirically demonstrate the performance of the proposed method in the field of general cybersecurity by using dataset CSER; the model proposed in this paper achieves higher accuracy than other models. And then, the dataset ICSER was used to construct a cybersecurity knowledge graph (CSKG) on the basis of analyzing specific industrial control scenarios, visualizing the knowledge graph for further security analysis to the industrial control system.
Blockchain for Diamond Industry: Opportunities and Challenges

In the recent years, the blockchain (BC) technology has been used in various applications ranging from financial sector to healthcare sector. Moreover, developing BC-based solutions for these applications has been an area of interest among the academia and industry professionals. Diamond has huge potential to become an investment asset, but there are some issues, which hinder the progress of the diamond industry, such as provenance, supply chain traceability, involvement of third party in the verification process, and reliability of transactions. BC seems to be a promising technology, which bridges the gap between the diamond industry and the burgeoning financial markets. Individuals are always confident that the crystals they bought are legitimate and that stolen property can be handed back to the legitimate owner easily. Motivated from these facts, in this article, we surveyed the adoption of BC in the diamond industry, and also present pros and cons of this integration. Then, we discuss issues of the diamond industry operations and based on the literature review, we suggest their probable countermeasures. Then, we analyze various open research issues and challenges associated with integrating the BC in the diamond industry. Finally, we present a case study on frameworks, such as Everledger and Tracr, which highlights the real-time challenges of integrating BC in the diamond Industry.
Tribe-charged system search for global optimization

In this paper, an improved metaheuristic algorithm called Tribe-Charged System Search is proposed for global optimization. In this improved algorithm, the main searching loop of the standard Charged System Search algorithm is modified to achieve a better convergence performance. In this algorithm, the searching phase of the algorithm is divided into three distinct phases called "Tribes" in which the searching process is conducted differently in each phase based on the communication allowance between tribes. These changes cause the algorithm focuses on high global searching for the early iterations while the concentrating in local search process is considered through the last iterations. In order to validate the performance of the new algorithm, 4 mathematical benchmark functions alongside the 3 well-known constrained problems and 2 engineering design problems are utilized. The performance of the new algorithm is compared to six other metaheuristic algorithms. The results of the mathematical, constrained and engineering problems prove that the new method is capable of providing very competitive results among the other metaheuristics.
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition

Collecting fine-grained labels usually requires expert-level domain knowledge and is prohibitive to scale up. In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples. The principle lies in that attribute features are shared among fine-grained sub-categories, and can be seamlessly transferred among images. Toward this goal, we propose an automatic attribute mining approach to discover attributes that belong to the same supercategory, and Attribute Mix is operated by mixing semantically meaningful attribute features from two images. Attribute Mix is a simple but effective data augmentation strategy that can significantly improve the recognition performance without increasing the inference budgets. Extensive experiments and ablation studies show that the proposed method consistently outperforms the state-of-the-art methods on challenging benchmarks including CUB-200-2011, FGVC-Aircraft, and Stanford Cars.
Hating journalism: Anti-press discourse and negative emotions toward journalism in Korea

In South Korea since the mid-2010s, the discourse on giraegi has prevailed. The word giraegi is a combination of gija, the Korean word for journalist, and tsuraegi, the Korean word for trash. By considering this distinctive discourse on giraegi as a negative emotional form of anti-press discourse, this paper explores the background logics and rationales behind the giraegi discourse that classifies journalists as trash, by focusing on the case of the networked public of # giraegi. Its analysis reveals that certain negative emotions toward the press – disgust, hate, and shame – are the sources of the formation and maintenance of the networked public of # giraegi and also the energy that directs the flow of their hostile messages about journalists and journalism. Moreover, journalists are considered ‘pollutants’ that tarnish Korean society and thereby something that needs to be eliminated. In connection with the growing research on hate speech against journalists on digital media, this research ultimately argues that this affective form of anti-journalistic discourse – mainly focusing on hate, disgust, and shame – can hardly bring about normative and constructive effects; its probable impact will be to exacerbate distrust and skepticism toward journalists and journalism itself.
A Public Platform for Virtual IoT-Based Monitoring and Tracking of COVID-19

The world is developing an app that alerts my smartphone when a COVID-19 (COrona VIrus Disease 19) confirmed case comes near me. However, regardless of what will be put to practical use first, the COVID-19 tracking system should satisfy the issues of legalization of location tracking and scalability as a public platform used by the world. Additional problems need solutions related to real-time authentication for information gathering, blind naming and privacy of tracked persons, and quality of service on the Query/Reply procedure. This paper proposes the Software-Defined Networking Controller-centric global public platform to monitor and track information for the COVID-19 relevant people and provide real-time information disclosure services to world-wide Centers for Disease Control and Prevention (CDCs) and regular users. The CDC manages a list of people who needs to be monitored related to the COVID-19 and forcibly installs COVID-19 virtual Internet of Things (vIoT) nodes in the form of applications on their smartphones. In addition to these nodes, the vIoT support nodes also engage as information providers to improve the quality of information services. The design of our platform aims to ensure confidentiality and authentication services giving individually different secret keys. In addition, our platform meets system scalability and reduces Query/Reply latency, where the platform accommodates a large number of world-wide CDCs and persons in control per CDC.
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems

Deep multi-task networks are of particular interest for autonomous driving systems. They can potentially strike an excellent trade-off between predictive performance, hardware constraints and efficient use of information from multiple types of annotations and modalities. However, training such models is non-trivial and requires balancing learning over all tasks as their respective losses display different scales, ranges and dynamics across training. Multiple task weighting methods that adjust the losses in an adaptive way have been proposed recently on different datasets and combinations of tasks, making it difficult to compare them. In this work, we review and systematically evaluate nine task weighting strategies on common grounds on three automotive datasets (KITTI, Cityscapes and WoodScape). We then propose a novel method combining evolutionary meta-learning and task-based selective backpropagation, for computing task weights leading to reliable network training. Our method outperforms state-of-the-art methods by a significant margin on a two-task application.
Public key encryption with equality test for Industrial Internet of Things system in cloud computing

Abstract Present day world have evolved from traditional environment to smart industries using IoT scheme which in turn forms Industrial Internet of Things (IIoT), which significantly elaborated by providing enhance integration using smart communication through IoT based sensors. IIoT has been providing cost reduction and enhancement in technology by bringing availability, flexibility and data sharing through real time scenario. Despite being unsecure environment of cloud, the privacy of data transfer and information confidentiality is guaranteed. In this context, this work presents a Public Key Encryption with Equality Test based on DLP with double decomposition problems over near‐ring. Computation Diffie‐Hellman is utilized in algebraic structure which involves DLP with Double Decomposition problem for proposing a Public Key Encryption with Equality Test which provides more security to the scheme. The proposed method is highly secure and it solves the problem of quantum algorithm attacks in IIoT systems. Further, the suggested system is significantly secure and it prevents the chosen‐ciphertext attack in type‐I rival and it is indistinguishable against the random oracle model for the type‐II rival. The recommended scheme is highly secure and the security analysis measures are comparatively stronger than existing techniques. Search time of the proposed scheme is 150 milliseconds for which the number of attributes is 50 and when comparing to the decryption time of the proposed model which is lower when compared to other existing scheme for 50 attributes.
A new density-based subspace selection method using mutual information for high dimensional outlier detection

Outlier detection in high dimensional data faces the challenge of curse of dimensionality, where irrelevant features may prevent detection of outliers. In this research, we propose a novel efficient unsupervised density-based subspace selection for outlier detection in the projected subspace. First, the Maximum-Relevance-to-Density algorithm(MRD) is proposed to select the relevant subspace based on the mutual information. Then, applying the concept of redundancy among features, we present an efficient relevant subspace selection method called minimum-Redundancy-Maximum-Relevance-to-Density (mRMRD). Finally, the degree of outlierness of data points in the corresponding relevant subspace is computed based on Local Outlier Factor(LOF). Experimental results on both real and synthetic data demonstrate that the proposed algorithms – based on MRD and mRMRD criteria – increase the accuracy of outlier detection while reducing computational complexity and execution time. Moreover, as the dimensionality increases, the accuracy of outlier detection on mRMRD-based relevant subspace is higher than MRD-based relevant subspace. This verifies that the proposed mRMRD-based subspace selection algorithm can efficiently select the subspace by considering the relevance between features.