Reviewerly/exHarmony huggingface dataset:
SAC-FACT: Soft Actor-Critic Reinforcement Learning for Counterfactual Explanations

Explainable AI (XAI) techniques are essential for improving the interpretability of machine learning models, which are generally regarded as black boxes. Among the various XAI techniques, Counterfactual (CF) explanations have a distinctive advantage, as they can be generated post-hoc while still preserving the complete fidelity of the underlying model. The generation of feasible and actionable CFs is a challenging task, which is typically tackled by solving a constrained optimization problem. One of the primary difficulties lies in developing a system that is model-agnostic, capable of producing optimal CFs, and scalable to large datasets. While several methods, including linear programming and heuristic approaches, have been proposed to generate CFs, the potential of reinforcement learning (RL) in this task has not been thoroughly explored. This is particularly important as generating CFs requires handling complex constraints and exploring a large search space, which are challenges that RL may be well-suited to address. In this paper, we model the problem of generating CFs as an RL problem and exploit the Soft Actor-Critic (SAC) RL architecture to build an RL-based system for generating CFs. We refer to our SAC RL-based approach for generating CFs by SAC-FACT (Soft Actor-Critic for Counterfactual Explanations). We model the reward function of SAC-FACT to adhere to desirable characteristics of generating CFs such as diversity, proximity, sparsity and validity. We evaluate the effectiveness of the proposed approach by performing extensive experiments on four real-world datasets. We prove the effectiveness and convergence of the SAC. Moreover, we compare SAC-FACT state-of-the-art explainers, namely, DiCE and SingleCF, in terms of validity, diversity, and proximity. Experimental results show that SAC-FACT is an applicable option for generating CFs.
Comparing the Predictive Power of Five Models on Bitcoin Volatility

There are many models available for predicting the volatility of Bitcoin, but there is limited research on the comparison of these models. This paper compares the predictive ability of five basic models in terms of mean baseline, random walk, GARCH (1, 1), fully connected neural networks (NN), and long short-term memory (LSTM) of Bitcoin volatility using two metrics, RMSPE and RMSE. The Bitcoin data covers the period from January 1, 2016, to February 30, 2023, with a total of 2607 records. The results show that the predictive abilities of the five models vary greatly, which is not entirely consistent with people's expectations, which LSTM and random walk model are better than others. This also provides basic data for exploring future predictive models.
Understanding Cyber Terrorism with a Special Focus on Zero-Day Attacks

The world is becoming increasingly digitalized and automated that adds to ease and comfort to everyone, but threats of security, data leakage, website hacking, attacks, phishing, zero-day attacks and other forms of cyber-terrorisms are also becoming major concerns not only for organizations, businesses and society but also for the government across the globe. This paper is an attempt to understand basics of cyber-terrorism with special focus on zero-day attacks. This paper is a preliminary literature review attempt of these concepts and looks at the growing destructions economically and financially by zero-day attacks. This paper attempts to explore various types of zero-day attacks, steps taken by various international organizations to deal with these issues and certain recommendations made by them. In addition, the paper looks at the impact of these externalities on policy making and society. As cyber-security gains importance as an agenda item for the business and policy makers, we intend to look deeper into this aspect that has the power to threaten national security, public life, economic and financial stability of developed, developing and even under-developing economies.
Secure Stitch: Unveiling the Fabric of Security Patterns for the Internet of Things

The design of the Internet of Things (IoT) system is a complex process, not only in terms of the balance between resource consumption and extensive functionality but also in the context of security. As various technical devices are now widespread and have access to all kinds of critical information, they become one of the main targets for attackers. Consequently, it is vital to consider the IT security aspect during the development of any system. A practical way to do it is to use security patterns. There are many different patterns that can address particular problems, but not all of them are suitable due to the wide range of requirements in such systems. In this paper, we present a systematic collection and categorisation of IoT-applicable security patterns and analyse gaps in recent research works related to security. We provide a catalogue of 61 patterns organised in a top-down approach that follows the World Forum's IoT Architecture Reference Model, this collection is able to play an important role in the future development of secure IoT solutions.
Do Intermediate Feature Coalitions Aid Explainability of Black-Box Models?

This work introduces the notion of intermediate concepts based on levels structure to aid explainability for black-box models. The levels structure is a hierarchical structure in which each level corresponds to features of a dataset (i.e., a player-set partition). The level of coarseness increases from the trivial set, which only comprises singletons, to the set, which only contains the grand coalition. In addition, it is possible to establish meronomies, i.e., part-whole relationships, via a domain expert that can be utilised to generate explanations at an abstract level. We illustrate the usability of this approach in a real-world car model example and the Titanic dataset, where intermediate concepts aid in explainability at different levels of abstraction.
A Study on Growth-Finance Nexus: Evidence From the BRICS Nations

This article investigates the nexus of Economic Growth and the Financial Development for BRICS (Brazil, Russia, India, China and South-Africa) nations, within the period 1990–2020. The financial development is segregated into the parts of financial market and institution (Access, Depth and Efficiency). We implemented the Auto-Regressive Distributed Lag test and the results depicts that in BRICS region in long run, overall, it shows financial institution depth and efficiency has positive impact on GDP, while the Financial Institution access Financial Market efficiency, can have a negative impact on the GDP. It states that the institutions in the BRICS regions contribute more to the economy and the financial market is not that much efficient in the BRICS regions as they are the emerging nations. In short run, financial institution has a negative impact on the economy. In individual case the selected nations have mixed relationship. To investigate the directional causality the Dumitrescu Hurlin Panel Causality Test was performed, and the results showed bi-directional causality between financial institution depth and economy and unidirectional causality between financial institution access, financial market depth and efficiency. Thus, it shows that financial development is the major factor in the BRICS nation’s economic growth and certain strict policy implications will enhance the economic development of these nations.
An underwater image enhancement model combining physical priors and residual network

Abstract Deep learning‐based methods have achieved notable performance for underwater image enhancement. However, previous studies are mostly focused on pursuing high similarity between the original image and the target, which incurs performance drop when the models are used for real‐world images. A new framework for underwater image enhancement is proposed to improve the generalization performance of enhancement. First, the coordinate attention module is integrated into the backbone network, which serves as a pre‐trained model, to strengthen the feature extraction capability of the network. Second, the backbone is finetuned by physical prior knowledge and real‐world images, in an unsupervised manner, to realize generalization from artificial images to real‐world images. Furthermore, a model protection mechanism is designed to guarantee the successful execution of the training. The experimental results indicate that the proposed method provides a powerful pre‐trained backbone network and the finetuning strategy can further solve the color distortion and improve the image sharpening, especially in the harsh real environment. Compared with relevant methods, the UCIQE and NIQE are, respectively, 0.525 and 4.149, with a 0.009–0.095 increase in UCIQE and a 0.256–1.032 decrease in NIQE compared to other methods.
Impact of Consensus Protocols on the Efficiency of Registration and Authentication Process in Self-sovereign Identity

Self-Sovereign Identity (SSI), allows each entity to control its identities with minimal data disclosure to ensure privacy, through its concepts of Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), and Zero-Knowledge Proof (ZKP) protocols. The trust system is based on distributed ledger technologies such as blockchain, that provide a tamper proof and correct record of data. Registration and authentication in the SSI depend on the read-write operations that occur on the distributed ledger. In this work, we analyze the impact of distributed protocols on the read/write operations through various efficiency metrics, and therefore, take a step towards deciding which protocols can be suitable for the decentralized identity system. Moreover, by analyzing the properties of consensus algorithms, we propose an efficient protocol for the read operation adapted to the SSI requirements.
Applying Digital Technologies to Financial Product Marketing

This article listed some of the current challenges that are faced in the area of applying digital technologies in marketing of financial products. Possible solutions to these challenges are suggested as well as the results. The vision of the future of this area is also discussed at the end of this article.
Applications of Artificial Intelligence and Machine Learning for Diagnosis, Prediction, and Smart Health Care

In health care, machine learning (ML) algorithms harness the things of health data offered by the Internet of Things (IoT) to enhance patient outcomes. Such approaches offer both potential applications and some significant challenges. There are many domains in ML, but three domains that are commonly used are Natural Language Processing (NLP) for medical papers, medical imaging, and genetic information. Maximum of these disciplines deal with diagnosis, detection, and prediction. The most common types of applications are treatment recommendations after diagnoses, administrative tasks, patients’ adherence, and involvement. While there are numerous circumstances inside which AI can do healthcare activities equally as good as possible, if not faster than, and quicker than humans, obstacles will prevent the occupation of healthcare providers that become fully automated for a long time. A substantial infrastructure of medical devices now provides data, but the supporting infrastructure is lacking.
TLS Fingerprinting “A Passive Concept of Identification”

TLS (transport layer security) fingerprinting relies on the assumption that different HTTPS clients (programs, PC programs, etc.) support different computations. The TLS focuses on providing security to insurance (characterization), dependability, and utilizing cryptography, similar to announcements and around granting system applications. As demonstrated by this show detail, TLS includes two layers: the TLS record show and the TLS Hand-shake show. This suggests the once-over of maintained codes and developments that a client gives to the server during the TLS Hand-shake. TLS fingerprinting is the detailed verification of a client considering the fields in its Client Howdy msg. during a TLS Hand-shake. Two or three typical motivations behind TLS fingerprinting: to collect information about a client on the Web, such as working system or program interpretation. There are three main elements which helps in achieving the TLS purpose: encryption, confirmation, and reliability. Encryption covers the data being moved from outcasts. Approval ensures that the social affairs exchanging information are who they assure to be: typical applications that use TLS to consolidate Web programs, messaging, email, and voice-over IP.TLS is ordinarily executed on top of TCP to scramble Application Layer shows like HTTP, FTP, SMTP, and IMAP. Nonetheless, it may be done on UDP, DCCP, and SCTP (for instance, for VPN- and Taste-based applications used). This is known as Datagram Transport Layer Security (DTLS), not entirely set in stone in RFCs 6083, 6347, and 5238.
CAREER DEVELOPMENT CHALLENGES IN STRENGTHENING THE PERFORMANCE OF THE POLRI BAINTELKAM

The role of Baintelkam Polri is critically important in maintaining national security stability, identifying and responding to complex and varied security threats. In carrying out its duties, Baintelkam Polri plays a crucial role in gathering information, conducting intelligence analysis, and providing strategic recommendations to support national security policies. However, in the execution of its responsibilities, the Police Baintelkam faces numerous challenges stemming from various aspects, including the rapid advancements in information and communication technology. This technological progress poses a significant challenge to the Police Baintelkam as it opens up new opportunities for cybercriminals and terrorist groups to operate online. Consequently, addressing this threat necessitates more intensive efforts in monitoring and gathering information. The theories employed in this study encompass human resource management, career management, job satisfaction, and SWOT analysis. The research method adopted a qualitative approach, utilizing data collection techniques such as interviews, observations, and data documentation. The findings of this study underscore the pivotal role of career development within the National Police's Security Intelligence Agency (Baintelkam) in enhancing the performance of its members and upholding professionalism in the execution of security intelligence tasks. In an ever-evolving intelligence landscape, career coaching assists members in remaining relevant and effective. Consequently, career development strategies hold significant importance in surmounting challenges and fortifying the performance of the National Police's Security Intelligence Agency (Baintelkam). The overarching objective of career development is to enhance the capabilities, qualifications, motivation, and performance of members, equipping them to address complex and evolving situations in the realm of security intelligence.
A Markov Chain Monte Carlo approach for the estimation of photovoltaic system parameters

Knowledge of the installation parameters of photovoltaic systems is essential in the context of grid management: by relating these parameters to performance data, forecasting models may be optimised to improve the management of power flow into the grid. In the case of small residential systems, these parameters are often not available. We present a novel method for determining the azimuth (ϕ), tilt (θ) and rated power (P) of photovoltaic systems, using openly available data over the course of 2016–2018 of 12 photovoltaic systems in PVOutput. This method consists of two steps: firstly we identify a candidate list of clear days by computing descriptive statistics of a larger set of 80 PVOutput system profiles. In the second step we compare the observed clear-day profiles, of the aforementioned 12 systems, with modelled clear-sky profiles from the PVLib library. The fits are performed employing a Markov Chain Monte Carlo (MCMC) approach, implemented with the Emcee package: the most favoured parameters and their associated uncertainties, for any given day, are obtained by sampling from the posterior assuming a Gaussian sampling distribution. The results for our 12 systems are in good agreement with the PVOutput metadata.
A Defeasible Description Logic for Abduction

In this work we introduce a defeasible Description Logic for abductive reasoning. Our proposal exploits a fragment of a probabilistic extension of a Description Logic of typicality, whose semantics corresponds to a natural extension of the well established mechanism of rational closure extended to Description Logics. The presence of typicality assertions that can be non-monotonically inferred from a knowledge base, corresponding to those belonging to its rational closure, avoids the need of an explicit selection of abducibles.
ABALearn: An Automated Logic-Based Learning System for ABA Frameworks

We introduce ABALearn, an automated algorithm that learns Assumption-Based Argumentation (ABA) frameworks from training data consisting of positive and negative examples, and a given background knowledge. ABALearn’s ability to generate comprehensible rules for decision-making promotes transparency and interpretability, addressing the challenges associated with the black-box nature of traditional machine learning models. This implementation is based on the strategy proposed in a previous work. The learnt ABA frameworks can be mapped onto logic programs with negation as failure. The main advantage of this algorithm is that it requires minimal information about the learning problem and it is also capable of learning circular debates. Our results show that this approach is competitive with state-of-the-art alternatives, demonstrating its potential to be used in real-world applications where low user expertise is available. Overall, this work contributes to the development of automated learning techniques for argumentation frameworks in the context of Explainable AI (XAI) and provides insights into how such learners can be applied to make predictions.
An Ontological Modelling of Reason-Based Preferences

We present an ontological framework for the reason-based model of individual preferences introduced by F. Dietrich and C. List. According to this perspective, an agent prefers x to y if and only if the importance of the reasons motivating x outweighs the importance of the reasons motivating y. Firstly, we represent motivating reasons as concepts in Description Logic, to enable a rich ontological theory that provides a clear and shareable semantics of reasons. Secondly, we present a model to express preferences on combinations of reasons. Finally, we discuss how preferences on alternatives depend on preferences on motivating reasons. We present the framework in a knowledge-dependent way, meaning that the ontological background constrains the definable preferences on alternatives and reasons.
Deriving Dependency Graphs from Abstract Argumentation Frameworks

Abstract Argumentation Frameworks (AFs) are used, in the field of Artificial Intelligence, to evaluate the justification state of conflicting information, thus allowing the development of automatic reasoning techniques and systems. Complex argumentative processes such as decision-making and negotiation, which take place over time, can be modelled through the Concurrent Language for Argumentation, a formalism for handling concurrent interactions between intelligent agents that use an AF as shared memory. In this paper, we first show how AFs can be interpreted as dependency graphs by exploiting the relation between arguments induced by the attacks. Then, we describe a methodology for obtaining a procedure that generates the given AF. Such a procedure allows to dynamically represent dialogues and other forms of interaction that brought to the instantiation of the specified AF.
ReConf: An Automatic Context-Based Software Reconfiguration Tool for Autonomous Vehicles Using Answer-Set Programming

A significant challenge in the domain of autonomous vehicles is to ensure a reliable and safe operation in a multitude of contexts. As a consequence, autonomous vehicles must be capable of handling various context changes, such as changing weather conditions as well as software and hardware faults, without human support. To address this issue, we introduce a context-based software configuration tool for autonomous vehicles, called ReConf, which is embedded into Aptus, a generic framework for extending system architectures of autonomous vehicles with a self-managing functionality proposed in previous work. ReConf reconfigures the autonomous driving system in case the context changes by means of a reasoning component based on answer-set programming in order to determine system configurations that fulfill the requirements of the current context.
Scaling Large Language Models to the Extreme: Neural Semantic Processing of Multiple Tasks in Italian

This paper explores the potential of utilizing a unified neural model to tackle multiple and complex semantic processing tasks in the Italian language. We applied a state-of-the-art instruction-tuned Decoder-only Large Language Model to the recent EVALITA 2023 [17] challenge, which encompassed 13 different tasks and 22 subtasks across diverse semantic dimensions, such as Affect Detection, Authorship Analysis, Computational Ethics, Named Entity Recognition, Information Extraction, and Discourse Coherence. Our approach focuses on representing tasks using natural language instructions, for which prompts to the model are designed able to define the process as well as the desired responses. Notably, this single neural model achieved first place in 41% of the subtasks and demonstrated top-three performance in 64% of them. A dedicated experiment was also conducted to investigate the degree of linguistic generalization achieved by the LLM specifically, through instruction-tuning it with limited sets of training data. Results suggest that instruction-tuning is still required to capture dependencies between input and output even in such LLMs.
Mastering the Card Game of Jaipur Through Zero-Knowledge Self-Play Reinforcement Learning and Action Masks

Jaipur is a challenging two-player score-based strategy game where the players take turns to trade and sell cards for points, with the objective of having more points than the opponent at the end of the game. This game contains multiple factors which make self-play learning challenging, such as being partially observable, having stochastic actions, and having a very large action space of 25,469 possible discrete actions. Moreover, the game contains both immediate and long-term rewards, and the players have the possibility of adopting different strategies as the game is adversarial. In this work we benchmark the state-of-the-art PPO, A2C, DQN and DDQN reinforcement learning algorithms using self-play without any domain knowledge and starting from random play. Due to the large action space of the game, we propose to use action masks. The policy generated by each algorithm was evaluated quantitatively against typical Jaipur scores, and also qualitatively by checking which actions each agent was selecting. The results show that all the algorithms converged to policies that played the game strongly, with the PPO algorithm obtaining the best results.