Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Federated-Learning Framework for Defense against Backdoor Attacks

Federated learning (FL) is a technique that involves multiple participants who update their local models with private data and aggregate these models using a central server. Unfortunately, central servers are prone to single-point failures during the aggregation process, which leads to data leakage and other problems. Although many studies have shown that a blockchain can solve the single-point failure of servers, blockchains cannot identify or mitigate the effect of backdoor attacks. Therefore, this paper proposes a blockchain-based FL framework for defense against backdoor attacks. The framework utilizes blockchains to record transactions in an immutable distributed ledger network and enables decentralized FL. Furthermore, by incorporating the reverse layer-wise relevance (RLR) aggregation strategy into the participant’s aggregation algorithm and adding gradient noise to limit the effectiveness of backdoor attacks, the accuracy of backdoor attacks is substantially reduced. Furthermore, we designed a new proof-of-stake mechanism that considers the historical stakes of participants and the accuracy for selecting the miners of the local model, thereby reducing the stake rewards of malicious participants and motivating them to upload honest model parameters. Our simulation results confirm that, for 10% of malicious participants, the success rate of backdoor injection is reduced by nearly 90% compared to Vanilla FL, and the stake income of malicious devices is the lowest.
Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling

Abstract Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of “pegmatitic” (AUC = 0.962) and “alkalic” (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials.
Sentiment Analysis: Linguistic Potential of Preprocessing Regimentation

The article deals with the sentiment analysis regimentation as a relevant direction in automated natural language processing and its linguistic potential. Despite its impressive practical significance, the sentiment analysis still lacks reliable theoretical foundation. Although information technologies develop very fast, their fundamental foundations correlate with the linguistic system of knowledge. In fact, the methodological priority of the applied linguistics has no alternative with regard to the interdisciplinary specificity of the modern communication. The complex nature of this research made the authors appeal to the computer linguistics in order to provide a meta-description on the algorithmization and modeling of sentiment evaluation. The effectiveness of the relevant practice was conditioned by the optimal configuration of the procedure and an appropriate material evaluation. The preprocessing included identifying the meta-structure, defining its referentiality and level orientation, and choosing the analysis model. The authors described these main steps of the preprocessing algorithm, as well as the relevant practice. The study contributes to productive theoretical optimization of text sentiment analysis. In a broad context, the expedient disclosure of linguistic potential is relevant to the whole sphere of automated natural language processing.
Citizen-centric cybersecurity model for promoting good cybersecurity behaviour

The ultimate success of cybersecurity partly depends on good behaviours by Internet users. However, good cybersecurity behaviour is a major challenge among citizens because they lack cybersecurity knowledge in the use of the Internet and digital devices and generally, there is a misalignment between attitudes and behaviour of people which creates bristly tension in cyberspace. In an endeavour to promote cybersecurity behaviour, attitudes and behaviour of Internet users should be combined with knowledge. This study builds from the Protection Motivation Theory (PMT) to feed into the development of a citizen-centric cybersecurity model for promoting good cybersecurity behaviour. The theory assumes that threat appraisals and coping appraisals are the two foundational mechanisms underlying behavioural actions when individuals are faced with threatening events; hence, it provided a solid foundation for the conceptual model of the study. The model was tested on data collected from 464 participants. Each of the three independent latent variables (Attitude, Behavioral Intention and Cybersecurity Knowledge) has a P-value less than 0.05 in our situation, indicating that each of the independent latent factors affects Behavior. The findings of this study make a significant contribution both to theory and practice to implement a citizen-centric cybersecurity model.
Modeling variability in self-adapting robotic systems

Autonomous robots operating in everyday environments, such as hospitals, private houses, and public roads, are context-aware self-adaptive systems, i.e. they exploit knowledge about their resources and the environment to trigger runtime adaptation, so that they exhibit a behavior adequate to the current context. For these systems, context-aware self-adaptation requires to design the robot control application as a dynamically reconfigurable software architecture and to specify the adaptation logic for reconfiguring its variable aspects (e.g. the modules that implement various obstacle detection algorithms or control different distance sensors) according to specific criteria (e.g. enhancing robustness against variable illumination conditions). Despite self-adaptation is an intrinsic capability of autonomous robots, ad-hoc approaches are used in practice to design reconfigurable robot architectures. In order to enhance system maintainability, the control logic and the adaptation logic should be loosely coupled. For this purpose, the adaptation logic should be defined against an explicit representation of software variability in the robot control architecture. In this paper we propose a modeling approach, which consists in explicitly representing robot software variability with the MARTE::ARM-Variability metamodel, which has been designed as an extension of the UML MARTE profile. We evaluate the applicability of the proposed approach by exemplifying the software architecture design of a robot navigation framework and by analyzing the support provided by the ROS infrastructure for runtime reconfiguration of its variable aspects.
City Hall Has Been Hacked! The Financial Costs of Lax Cybersecurity

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Quantitative Verification and Strategy Synthesis for BDI Agents

Belief-Desire-Intention (BDI) agents feature probabilistic outcomes, e.g. the chance an agent tries but fails to open a door, and non-deterministic choices: what plan/intention to execute next? We want to reason about agents under both probabilities and non-determinism to determine, for example, probabilities of mission success and the strategies used to maximise this. We define a Markov Decision Process describing the semantics of the Conceptual Agent Notation (Can) agent language that supports non-deterministic event, plan, and intention selection, as well as probabilistic action outcomes. The model is derived through an encoding to Milner's Bigraphs and executed using the BigraphER tool. We show, using probabilistic model checkers PRISM and Storm, how to reason about agents including: probabilistic and reward-based properties, strategy synthesis, and multi-objective analysis. This analysis provides verification and optimisation of BDI agent design and implementation.
The Venue Code: Digital Surveillance, Spatial (Re)organization, and Infrastructural Power During the Covid Pandemic in China

China has insisted on a zero-Covid policy for almost three years, deploying many disciplinary containment measures and surveillance tools. This chapter examines a surveillance tool called the "venue codeVenue code" which is claimed to improve contact tracing of Covid outbreaks. My research is based on ethnography and discourse analysis of online textual and visual materials, capturing the integration of the venue code into China's Covid containment regime. I analyze four aspects of the practice: bureaucratic structures, integration, spatial (re)organization, and technologies. I argue that the venue code mediates residents' experience of cities and restricts urban mobility through the introduction of physical and digital infrastructureInfrastructure, while granting local actors more power. By doing so, this study draws attention to the ways in which the responsibility for achieving the zero Covid objective was shifted onto local actors and Chinese citizens, and state presence in communities and despotic powerDespotic power were strengthened.
Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access

A central task in control theory, artificial intelligence, and formal methods is to synthesize reward-maximizing strategies for agents that operate in partially unknown environments. In environments modeled by gray-box Markov decision processes (MDPs), the impact of the agents’ actions are known in terms of successor states but not the stochastics involved. In this paper, we devise a strategy synthesis algorithm for gray-box MDPs via reinforcement learning that utilizes interval MDPs as internal model. To compete with limited sampling access in reinforcement learning, we incorporate two novel concepts into our algorithm, focusing on rapid and successful learning rather than on stochastic guarantees and optimality: lower confidence bound exploration reinforces variants of already learned practical strategies and action scoping reduces the learning action space to promising actions. We illustrate benefits of our algorithms by means of a prototypical implementation applied on examples from the AI and formal methods communities.
A decentralized model to ensure traceability and sustainability of the food supply chain by combining blockchain, IoT, and machine learning

Many food contamination incidents have occurred during the last decade which has proven the failure of the food supply chain management system to track the food, money, and information movement within the food supply chain. Many models have been established. This paper presents the design and implementation of the new model providing real-time data acquisition, monitoring, and storing on a tamper-proof blockchain of the main food supply movement. This system is using smart contracts that are deployed on the Ethereum blockchain to allow every participant to transact securely with other FSC players. IoT networks are implemented in different workplaces to gather multiple data about food status without human involvement to ensure transparency by different sensors. Machine learning models are established to ensure the correctness of the collected data and help drive decision making within the application or businesses.
Establishment of NLP-Based Greenwashing Pattern Detection Service

This study aims to establish a search service that provides greenwashing pattern detection and infographic of enterprises through AI-based NLP technology. The purpose of it is to provide decision indicators by checking data on greenwashing trends of companies by users, including investment institutions and general consumers. We used a deep learning model to adopted evaluation factors to score the ‘greenwashing’ and collected the title of media data with active monitoring of companies based on those factors. In addition, construct greenwashing sentence discrimination model and a corporate greenwashing scoring model using the BERT model. As a result, a service provided scores using data collected through its keywords through visualization and the process of calculation when users input the name of the company.
Reward Shaping from Hybrid Systems Models in Reinforcement Learning

Reinforcement learning is increasingly often used as a learning technique to implement control tasks in autonomous systems. To meet stringent safety requirements, formal methods for learning-enabled systems, such as closed-loop neural network verification, shielding, falsification, and online reachability analysis, analyze learned controllers for safety violations. Besides filtering unsafe actions during training, these approaches view verification and training largely as separate tasks. We propose an approach based on logically constrained reinforcement learning to couple formal methods and reinforcement learning more tightly by generating safety-oriented aspects of reward functions from verified hybrid systems models. We demonstrate the approach on a standard reinforcement learning environment for longitudinal vehicle control.
A Modern Recommendation System Survey in the Big Data Era

Recommendation Systems are a prominent field that is widely used and popular for assisting people in making appropriate automated selections. It is a mechanism that assists the users in identifying relevant information from a range of available data. In this paper, we create a survey of state-of-the-art recommendation models and focuses primarily on providing a concise summary of the many strategies for recommendation, ranging from traditional to modern approaches, to open the door for further investigation into recommendation systems. In addition, the specific domain of the recommendation models was also illustrated in this study.
Perception of the Environment

This chapter discusses the intricacies of cybersecurity agents’ perception. It addresses the complexity of perception and illuminates how perception is shaping and influencing the decision-making process. It then explores the necessary considerations when crafting the world representation and discusses the power and bandwidth constraints of perception and the underlying issues of AICA’s trust in perception. On these foundations, it provides the reader with a guide to developing perception models for AICA, discussing the trade-offs of each objective state approximation. The guide is written in the context of the CYST cybersecurity simulation engine, which aims to closely model cybersecurity interactions and can be used as a basis for developing AICA. Because CYST is freely available, the reader is welcome to try implementing and evaluating the proposed methods for themselves.
Adaptivity and Antifragility

A resilient system can survive attacks and failures by autonomously adapting and managing its own functionality. An antifragile system is not only resilient but is also able to enhance its capabilities and become more resilient as a result of endogenous and exogenous stressors. This makes antifragility a highly desirable property of cyber defense systems operating in dynamic, contested environments. In this chapter, we outline how antifragility can be achieved in AICA systems through self-management (self-adaptivity) and self-improvement. We introduce the concept of a self-* (S*) agent and – after elucidating the various self-* properties which such agents would be capable of realizing – we present a conceptual framework for S* multi-agent systems, encompassing S* agent architectures and macro−/micro-level design concepts, and describe a corresponding generic self-management/improvement approach. We then present an overview of AWaRE 2.0 as a concrete example of an S* system and middleware framework for antifragility. Throughout our exposition, we explain how S* agents and S* multi-agent systems are related to AICA agents and AICA cyber defense systems, and how the former can help the latter achieve resilience and antifragility. Finally, we discuss several key challenges related to coordination and agent organization for self-management and learning for self-improvement.
Insights into UK investment firms’ efforts to comply with MiFID II RTS 6 that governs the conduct of algorithmic trading

Purpose The purpose of this paper is to examine the effectiveness of UK investment firms’ implementation of the requirements in Commission Delegated Regulation 2017/589 (more commonly known as “Regulatory Technical Standard 6” or “RTS 6”) that govern the conduct of algorithmic trading activities. Design/methodology/approach A qualitative examination of 19 semi-structured interviews with practitioners working for, or with, UK investment firms engaged in algorithmic trading activities. Findings The paper finds that practitioners generally have a good understanding of the requirements in RTS 6. Some lack knowledge of algorithms, coding and algorithmic strategies but have used best efforts to implement RTS 6. However, regulatory fatigue, complacency, cost pressures, governance in international groups, overreliance on external knowledge and generous risk parameter calibration threaten to undermine these efforts. Research limitations/implications The study’s findings are limited to the participants’ insights. Some areas of the RTS 6 regime attracted little comment from participants. Practical implications The paper proposes the introduction of mandatory algorithmic trading qualification requirements for key staff; the lessening of the requirements in RTS 6 for automated executors; and the introduction of a recognised software vendor regime to reduce duplication and improve coordination between market participants that deploy algorithmic trading systems. Originality/value To the best of the author’s knowledge, the study represents the first qualitative examination of firms’ implementation of the algorithmic trading regime in the second Markets in Financial Instruments Directive 2014/65/EU.
Pattern Encoding of Robust M-Array Driven by Texture Constraints

M-Array pattern encoding is widely used in structured light for 3D reconstruction due to its single projection and single imaging characteristics. The robustness of M-Array in 3D reconstruction can be assessed by the accuracy of the global positioning of the code-word and the density of the decodable feature points. This paper proposes a pattern encoding method of robust M-Array based on texture constraints. M-Arrays are formed by matching and splicing candidate sub-windows, which are composed of symbols obtained from an M-Sequence one by one. To construct an M-Array with more texture information, both the number of edges and the maximum rectangular area within the sub-window are constrained. A novel three-level backtracking mechanism reduces the time complexity. Four indicators are introduced to evaluate the robustness of the M-Array and used to compare the proposed method with existing methods. The results show that the proposed method has low similarity between adjacent sub-windows and high uniformity of symbol distribution globally, indicating better accuracy of sub-window decoding. A 3D reconstruction experiment in the actual scenes proves that the point cloud density and accuracy obtained by solving the M-Array pattern constructed by the method proposed are better.
Quality Control Strategy and Evaluation Algorithm for Noncontact Instrument Testing

The test quality of noncontact instruments is an important issue that has aroused social concern at present, which is also widely used in the power quality management of telecommunication systems. The fuzzy logic algorithm can be used in power quality testing to analyze the power quality of electrical equipment. Based on the advantage of the fuzzy logic algorithm, this paper studied the quality control strategy and evaluation algorithm of noncontact instrument testing and drew a conclusion. In terms of the investigation on the safety of power quality measurement, it was concluded that the method of noncontact instrument testing power quality improved the safety of voltage quality, current quality, power supply quality, and power consumption quality. In the investigation of the accuracy of the power quality measurement, it was concluded that the noncontact instrument was used to measure power quality, which greatly improved the accuracy of measurement. In terms of the investigation on the rapidity of the power quality measurement, it was concluded that the use of the noncontact instrument to test power quality could greatly improve the rapidity of current quality measurement and that the rapidity of the power quality measurement could be improved by more than 70%. In the aspect of economic investigation of the power quality measurement, it was concluded that the use of the noncontact instrument to test power quality could greatly improve the economic efficiency of the power quality measurement. In the aspect of fuzzy logic evaluation in power quality management, it was concluded that most of the power quality grades of the selected research objects were excellent. Therefore, the fuzzy logic method was very meaningful for power quality evaluation.
Modern Standard Arabic Speech Corpora: A Systematic Review

Speech processing applications have become integral components across various domains of modern life. The design and preparation of a reliable recognition system rely heavily on the availability of suitable speech databases. While numerous speech databases exist for English and other languages, the availability of comprehensive resources for Arabic language remains limited. In light of this, we conducted a systematic review aiming to identify, analyse, and classify existing Modern Standard Arabic speech databases. Through our review, we identified 27 publicly available databases and analysed an additional 80 subjective databases. These databases were thoroughly studied, classified based on their characteristics, and subjected to a detailed analysis of research trends in the field. This paper provides a comprehensive discussion on the diverse speech databases developed for various speech processing applications. It sheds light on the purposes and unique characteristics of Arabic speech databases, enabling researchers to easily access suitable resources for their specific applications. The findings of this review contribute to bridging the gap in available Arabic speech databases and serve as a valuable resource for researchers in the field.