Reviewerly/exHarmony huggingface dataset:
Exploring Synergy of Blockchain and 6G Network for Industrial Automation

Automating industrial tasks has become critical for organizations due to the inefficiencies and risks associated with conventional procedures. The proliferation of connected devices and machines brings forth a range of security concerns, including data tampering and unauthorized access. Establishing confidential and trustworthy communication between these devices becomes particularly difficult as they rely on the open channel of the Internet. Numerous established security measures, such as antivirus software, access control systems, intrusion detection systems (IDS), and intrusion prevention systems (IPS), have been identified in the literature as facing issues like centralized vulnerabilities, latency challenges, reliability issues, and single points of failure. As technology advances rapidly, the convergence of emerging technologies holds immense potential to revolutionize industrial automation. Among these promising technologies, blockchain and 6G stand out for their immense potential. With its decentralized and tamperproof nature, blockchain has disrupted various sectors, while 6G offers unprecedented connectivity and lightning-fast data transfer speeds. Motivated by these developments, this paper explores the potential impact of integrating blockchain technology with 6G in industrial automation, paving the way for the future of smart factories and intelligent supply chains. Our proposed work aims to provide a holistic understanding of this emerging amalgamation’s key benefits, challenges, offered solutions, and prospects.
Leading-edge Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Internet of Things (IoT) technologies for enhanced wastewater treatment systems

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Spoofing and Manipulating Order Books with Learning Algorithms

This paper proposes a dynamic model of the limit order book to test if a trading algorithm will learn to spoof the order book. We derive testable conditions that are simple to implement and to interpret. Our results show that as a market maker becomes more tolerant to bearing inventory risk, the learning algorithm will find optimal strategies that spoof the book more frequently. Spoofing occurs for two reasons: to induce mean reversion in inventory to an optimal level, and to execute round-trip trades with limit orders at a higher probability than was otherwise likely to occur. The conditions are tested with order book data from Nasdaq and we show that market conditions are conducive for an algorithm to learn to spoof the order book. Finally, when two market makers use learning algorithms to trade, their algorithms can learn to coordinate their spoofing.
The Use of Information Technologies in Crime Investigation

The digitization of various spheres of state and public life not only gives rise to new ways of committing crimes, but also presents new opportunities for crime investigation work. In the modern world, the products of digitization are an important source of evidentiary and search information, they also act as an instrument of detecting and studying digital traces in crime investigation. A separate sphere of research is algorithms of artificial intelligence, and the possibilities of using them in criminalistics and the investigation of criminal activities. Serial crimes are used as an example to showcase the possibility of applying AI algorithms to construct a search portrait of a serial offender, to find serial crimes in a mass of unsolved crimes, and to identify the suspect. Further improvement of preliminary investigation is impossible without the use of modern digital technologies, which sets new tasks for the science of criminalistics.
Forensics Doctrine of the Mechanisms of Trace Formation: Current State, Development Directions

The author points out the key problem of the forensic doctrine of the mechanisms of trace formation – the object of cognition is limited to material traces only, which is still reflected in the unjustified situation when the clauses of traceology are identical to the clauses of forensic trace study in the “Criminalistic technique” section. The author also highlights active research in the sphere of analyzing ideal and digital traces, which is combined with the ambivalent place that they occupy in the system of criminalistics. In this connection, it is stated that criminalistics lacks a unified interpretation of the term “trace” and understanding of the types of traces. The author argues for the necessity of modernizing the clauses of the forensic doctrine on the mechanisms of trace formation by widening the understanding of the objects of cognition in this theory, improving its research and conceptual basis. A new systemic structure of the doctrine is suggested, and the author’s own definitions of its key concepts are presented.
Jogging-in-Place: Exploring Body-Steering Methods for Jogging in Virtual Environments

Walk-in-Place is an established locomotion technique for walking in virtual environments, as it incorporates body motion similar to regular walking. Although there is extensive research on the performance and experience of walking in virtual reality spaces, it typically requires minimal-to-moderate physical movement, e.g., stepping forward and turning around, to explore virtual spaces. Therefore, the question we ask ourselves in this work is how user experience and performance are affected when the user is actively moving in place. In this paper, we explored three body-steering methods for jogging-in-place in virtual environments: (1) head-, (2) hand-, and (3) torso-based. To investigate the performance of the proposed body-steering methods for jogging in virtual reality, we conducted a controlled lab experiment (N = 12) to assess task completion time, number of steps, and VR sickness. We discovered that hand- and torso-based methods require fewer steps than the head-based method, and the torso-based is slower than the other two. Moreover, the number of collisions and virtual reality sickness were comparable among the methods.
User Reviews as a Reporting Mechanism for Emergent Issues Within Social VR Communities

Social virtual reality (VR) will potentially change online communities, transforming how people meet, interact, and socialise online. However, issues surrounding the growing threat of harassment (from verbal to cyber-physical assault) posed within social VR platforms are increasingly being noted within research.Yet, while prior research has successfully captured singular qualitative insights (e.g. identifying individual types of harassment or target user groups) it is essential that systems be developed to monitor emergent threats to community cohesion, including harassment, in a platform independent manner. In this paper, we explore whether user reviews are a valid and useful source of data around harassment incidences in social VR, a crucial first step towards platform agnostic, large scale and longitudinal monitoring of emerging harassment in social VR communities through user reviews posted about social VR applications.. We analysed 1000 user reviews posted on the Meta Quest store page for the social VR platform Rec Room and found 114 reviews report experiencing and/or witnessing harassment during use. Our results establish the validity of analysing user reviews as a means of monitoring harassment in social VR communities and we close by discussing their use in large scale, longitudinal monitoring systems.
Augmenting People, Places &amp; Media: The Societal Harms Posed by Everyday Augmented Reality, and the Case for Perceptual Human Rights

Everyday Augmented Reality (AR) displays, with wearable, fashionable, all-day form factors, may one day supplant our reliance on physical displays, heralding new capabilities in augmented intelligence and perception, communication, productivity, and more. Such technology has the potential to become as fundamental to our daily lives as smartphones are today, empowering users, communities, business, governments, and others to alter, augment, diminish or otherwise mediate our perception of reality. For social good, this technology can enable augmenting expression of social identity to better represent our 'authentic' self, and virtually enhancing real-world social spaces to encourage greater community ownership and social cohesion. For social harm however, everyday AR could facilitate and amplify manipulation, information disorder (e.g. dis-information), censorship and coercion in our day-to-day experience of reality. In this essay, we consider some of the key societal changes (and ethical challenges) posed by the adoption of everyday AR, and argue that everyday AR will provoke the need for new human rights to be considered alongside proposed neurorights and existing and envisaged digital human rights, around: who can mediate reality (perceptual autonomy); what elements of reality are permissible to alter/augment (perceptual agency); and governing permissible intent regarding why we augment the user's perception of reality, in particular considering tensions in cognitive autonomy (e.g. manipulation) and perceptual integrity (e.g. information disorder).
BlocklyVR: Exploring Block-based Programming in Virtual Reality

As programming is typically a static activity in front of a screen, we perform an initial exploration around the capabilities of block-based programming in the immersive space using Virtual Reality (VR) to make an early charting on how programming could involve moving the programmer's body. We created a block-based programming interface in a VR space called BlocklyVR based on the existing Blockly programming environment. To investigate programmer performance and experience in BlocklyVR, we conducted a controlled lab experiment (N = 20) with eight programming tasks that covered mathematical operations, if-statements, and function creation. Our initial exploration contributes by classifying movement types made by BlocklyVR programmers and reflecting on how these movements are related to the programming tasks. Additionally, our data suggests that participant performance in BlocklyVR was not affected compared to the 2D Blockly, even if participants were physically moving in VR space. We also found that the virtual reality sickness was marginal. Lastly, we identified four types of interaction that can potentially be employed by VR designers and developers aiming to convert a static task, like programming at a desk, into a "mobile" immersive experience.
Design for Science: Proposing an Interactive Circular 2-Level Algorithm

Finding important scientific results in the actual glut of information have never been more challenging. In the context of digital scientific libraries, there is a gap in the design of exploratory interfaces to support the discovery and visualization of scientific knowledge, as well as new ways to foster cooperation between the main actors in science, namely researchers. Science is defined by a social dimension where communities of researchers cooperate with each other. Researchers play a key role as curators of important scientific articles by sharing with their peers what they read. This kind of behavior already happens on social platforms, however without a logic and medium that supports and maps these social dynamics. Circular layouts are well-known structures to visualize relations between entities, and as opposed to force-directed layouts, are a more effectively and user-friendly solution for visualize relations. This article presents a novel 2-level circular structure to map and support cooperation in science communities. The 2-level circular layout was designed and implemented using D3.js and it is able to portray the relations between two different entities, the scientific community that falls within a specific knowledge domain (outer circle), and the scientific articles shared by this community (inner circle). The 2-level circular structure provide an overview plus details of the most read and shared scientific articles within a community. The preliminary results provide interesting results and pave the way for future research on the topic. The link to access the visualization and code can be found at the end of the article.
Automated threat modelling and risk analysis in e-Government using BPMN

Recent progress integrates security requirements into BPMN, enhancing its framework. Extensions aim to seamlessly embed security concepts, yet the inherent ambiguity of security terms may lead to misinterpretations and vulnerabilities. Unfortunately, many business process experts lack the expertise to accurately interpret and integrate vital security concepts. In this study, we present an innovative automated methodology tailored to assist business process experts in identifying security threats and conducting risk assessments, particularly in the context of e-Government processes. Our approach streamlines the process, requiring only a business specialist to annotate BPMN entities with high-level, non-security-related information. Based on these annotations, potential threats to the system can be automatically identified. To develop our methodology, we leverage the standard BPMN annotation mechanism. From the annotated BPMN, the methodology utilises the ENISA Threat Landscape knowledge base for threat identification and employs the OWASP Risk Rating Methodology for risk assessment. To demonstrate the effectiveness of our approach, we applied it to a straightforward case study within the e-Government domain. Through this example, we illustrate how our methodology can be employed to ensure compliance with the General Data Protection Regulation and meet the mandatory Data Protection Impact Assessment requirements.
OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework

The constantly updating big data in the ocean engineering domain has challenged the traditional manner of manually extracting knowledge, thereby underscoring the current absence of a knowledge graph framework in such a special field. This paper proposes a knowledge graph framework to fill the gap in the knowledge management application of the ocean engineering field. Subsequently, we propose an intelligent question-answering framework named OEQA based on an ocean engineering-oriented knowledge graph. Firstly, we define the ontology of ocean engineering and adopt a top-down approach to construct a knowledge graph. Secondly, we collect and analyze the data from databases, websites, and textual reports. Based on these collected data, we implement named entity recognition on the unstructured data and extract corresponding relations between entities. Thirdly, we propose an intent-recognizing-based user question classification method, and according to the classification result, construct and fill corresponding query templates by keyword matching. Finally, we use T5-Pegasus to generate natural answers based on the answer entities queried from the knowledge graph. Experimental results show that the accuracy in finding answers is 89.6%. OEQA achieves in the natural answer generation in the ocean engineering domain significant improvements in relevance (1.0912%), accuracy (4.2817%), and practicability (3.1071%) in comparison to ChatGPT.
Prospecting Quantum Correlations and Examining Teleportation Fidelity in a Pair of Coupled Double Quantum Dots System

Abstract The topic of improving the ability of quantum systems to retain non‐local features and enhance the efficiency of quantum protocols continues. This study delves into the thermal investigation of quantum correlations and teleportation fidelity of a two‐qubit teleported state using excess electrons in a coupled double quantum dots system as a quantum channel. The study employs three reliable quantum quantifiers to prospect the resourcefulness and non‐classicality of the system. The findings suggest that preserving quantum correlations and optimizing teleportation fidelity require minimizing tunneling coupling and maximizing Coulomb interaction. The study has significant implications for quantum physics and its practical applications in quantum information processing.
Optimization of Virtual Machines in Cloud Environment

The proposed VMP-LR (Virtual Machine Placement-Load Rebalancing) has three main components, namely, Resource Request Handling Component, Placement Component and Load Monitoring Component. VMP-LR is designed using a two-phase methodology, where the first phase handles the tasks involved with Resource Request Handling and Placement Components, while Phase II handles the tasks of Load Monitoring Component. The algorithms proposed in both the phases are designed to handle multiple resource requests. Dur ing non-rush hours (low traffic), as the number of requests is minimal, the VMP-LR uses a simple enhanced round-robin method to place virtual machines (VMs) to physical machines (PMs). During rush hours (heavy traffic), the three queues created are handled using three separate hybrid scheduling and load- balancing algorithms to perform placement operation efficiently and accommodate high resource demands. To solve this issue, the load monitoring component is used. For this purpose, a hybrid algorithm that combines ant colony optimiza tion (ACO) with an artificial bee colony (ABC) algorithm is used. The proposed algorithms are implemented using CloudSim Simulator and evaluated using seven performance metrics. They are throughput, response time, resource utilization rate, power usage, load unbalancing rate, SLA Violation rate and migra tion rate. The average SLA violation rate of VMP-LR is 0.6% less as compared with FF (1.26%), BF (1.1%), RR (1.36%), GA (0.98%), ACO (1.16%) and PSO (1.01%). This shows that VMP-LR is a much-improved version among existing load balancing algorithm.
A Novel Unsupervised Outlier Detection Algorithm Based on Mutual Information and Reduced Spectral Clustering

Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised methods and unsupervised methods, suffer from the following problems: curse of dimensionality, lack of labeled data, and hyperparameter tuning. To address these issues, we present a novel unsupervised outlier detection algorithm based on mutual information and reduced spectral clustering, called MISC-OD (Mutual Information and reduced Spectral Clustering—Outlier Detection). MISC-OD first constructs a mutual information matrix between features, then, by applying reduced spectral clustering, divides the feature set into subsets, utilizing the LOF (Local Outlier Factor) for outlier detection within each subset and combining the outlier scores found within each subset. Finally, it outputs the outlier score. Our contributions are as follows: (1) we propose a novel outlier detection method called MISC-OD with high interpretability and scalability; (2) numerous experiments on 18 benchmark datasets demonstrate the superior performance of the MISC-OD algorithm compared with eight state-of-the-art baselines in terms of ROC (receiver operating characteristic) and AP (average precision).
IIM: an information interaction mechanism for aspect-based sentiment analysis

Term polarity co-extraction is an aspect-based sentiment analysis task, which has been widely used in the fields of user opinions extraction. It consists of two subtasks: aspect term extraction and aspect sentiment classification. Most existing studies solve aforesaid subtasks as independent tasks or simply unify the two subtasks without making full use of the relationship between tasks to mine the interaction of text information, which leads to low performance for practical applications. Meanwhile, the learning framework for these studies has a label drift phenomenon (LDP) in the process of predictive learning, increasing the learning error rate. To address the above problems, this study unifies subtasks and proposes a Unified framework based on the information interaction mechanism framework, called IIM. Specifically, we design an Information Interaction Channel (IIC) to construct closer semantic features to extract preliminary term-polarity unified labels from the perspective of basic semantics. For label inconsistency between aspect terms, a Position-aware Module (SAM) is proposed to alleviate the Label Drift Phenomenon (LDP). Moreover, we introduce a syntax-attention graph neural network (Syn-AttGCN) to model the syntactic structure of text and strengthen the emotional connection between aspect terms. The experimental results show that IIM outperforms most baselines. Meanwhile, the SAM module has a certain slowing effect on LDP.
Remote attestation of confidential VMs using ephemeral vTPMs

Trying to address the security challenges of a cloud-centric software deployment paradigm, silicon and cloud vendors are introducing confidential computing – an umbrella term aimed at providing hardware and software mechanisms for protecting cloud workloads from the cloud provider and its software stack. Today, Intel Software Guard Extensions (SGX), AMD secure encrypted virtualization (SEV), Intel trust domain extensions (TDX), etc., provide a way to shield cloud applications from the cloud provider through encryption of the application’s memory below the hardware boundary of the CPU, hence requiring trust only in the CPU vendor. Unfortunately, existing hardware mechanisms do not automatically enable the guarantee that a protected system was not tampered with during configuration and boot time. Such a guarantee relies on a hardware root of trust, i.e., an integrity-protected location that can store measurements in a trustworthy manner, extend them, and authenticate the measurement logs to the user (remote attestation).
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks

Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data. Recent research, however, has demonstrated the effectiveness of inference and poisoning attacks on FL. Mitigating both attacks simultaneously is very challenging. State-of-the-art solutions have proposed the use of poisoning defenses with Secure Multi-Party Computation (SMPC) and/or Differential Privacy (DP). However, these techniques are not efficient and fail to address the malicious intent behind the attacks, i.e., adversaries (curious servers and/or compromised clients) seek to exploit a system for monetization purposes. To overcome these limitations, we present a ledger-based FL framework known as FLEDGE that allows making parties accountable for their behavior and achieve reasonable efficiency for mitigating inference and poisoning attacks. Our solution leverages crypto-currency to increase party accountability by penalizing malicious behavior and rewarding benign conduct. We conduct an extensive evaluation on four public datasets: Reddit, MNIST, Fashion-MNIST, and CIFAR-10. Our experimental results demonstrate that (1) FLEDGE provides strong privacy guarantees for model updates without sacrificing model utility; (2) FLEDGE can successfully mitigate different poisoning attacks without degrading the performance of the global model; and (3) FLEDGE offers unique reward mechanisms to promote benign behavior during model training and/or model aggregation.