Reviewerly/exHarmony huggingface dataset:
Rewriting Conversational Utterances with Instructed Large Language Models

Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization, coding, and translation. In some cases, the results provided by LLMs are on par with those of human experts. These models' most disruptive innovation is their ability to perform tasks via zero-shot or few-shot prompting. This capability has been successfully exploited to train instructed LLMs, where reinforcement learning with human feedback is used to guide the model to follow the user's requests directly. In this paper, we investigate the ability of instructed LLMs to improve conversational search effectiveness by rewriting user questions in a conversational setting. We study which prompts provide the most informative rewritten utterances that lead to the best retrieval performance. Reproducible experiments are conducted on publicly-available TREC CAST datasets. The results show that rewriting conversational utterances with instructed LLMs achieves significant improvements of up to 25.2% in MRR, 31.7% in Precision@1, 27% in NDCG@3, and 11.5% in Recall@500 over state-of-the-art techniques.
Automated Alignment: Guiding Visual Generative AI for Brand Building and Customer Engagement

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Explainable AI Evaluation: A Top-Down Approach for Selecting Optimal Explanations for Black Box Models

Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI research resulted in diverse scholars possessing significant challenges in designing proper evaluation methods. This paper proposes a novel framework of a three-layered top-down approach on how to arrive at an optimal explainer, accenting the persistent need for consensus in XAI evaluation. This paper also investigates a critical comparative evaluation of explanations in both model agnostic and specific explainers including LIME, SHAP, Anchors, and TabNet, aiming to enhance the adaptability of XAI in a tabular domain. The results demonstrate that TabNet achieved the highest classification recall followed by TabPFN, and XGBoost. Additionally, this paper develops an optimal approach by introducing a novel measure of relative performance loss with emphasis on faithfulness and fidelity of global explanations by quantifying the extent to which a model’s capabilities diminish when eliminating topmost features. This addresses a conspicuous gap in the lack of consensus among researchers regarding how global feature importance impacts classification loss, thereby undermining the trust and correctness of such applications. Finally, a practical use case on medical tabular data is provided to concretely illustrate the findings.
Deep Learning Overview

Among the various machine learning algorithms, deep learning has recently been dramatically used in different scopes. Deep learning models have been significantly employed in effectively extracting hidden patterns from vast amounts of data and modeling interdependent variables to solve complex problems. Since this book aims to discuss the session-based recommender system approaches using deep learning models, brief explanations of various deep neural networks are provided in this chapter. For this purpose, the history, basic concepts, advantages/applications, and fundamental models of deep learning are discussed.
Healthy Personalized Recipe Recommendations for Weekly Meal Planning

Nowadays, in the pursuit of personalized health and well-being, dietary choices are critical. This paper introduces a novel recommendation system designed to provide users with personalized meal plans, consisting of breakfast, lunch, snack, and dinner, in alignment with their health history and preferences from other similar users. More specifically, our system exploits collaborative filtering first to identify other users with similar dietary preferences and uses this information to propose suitable recipes to individuals. The whole process is enhanced by analyzing the individual’s health history, including dietary restrictions, nutritional needs, and specific diet plans, such as low-carb or vegetarian. This ensures that the generated meal plans are not only aligned with the user’s taste but also contribute to the overall wellness of the user. A distinctive feature of our system is its dynamic adaptation feature, which enables users to make real-time adjustments to their meal plans based on their personal constraints and preferences, directly impacting future recommendations. We evaluate the usability of the system through a series of experiments on a large real-world data set of recipes, showing that our system is able to provide highly personalized, dynamic, and accurate recommendations.
Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual feasibility of the attack or the defense. Moreover, adversarial samples are often crafted in the “feature-space”, making the corresponding evaluations of questionable value. Simply put, the current situation does not allow to estimate the actual threat posed by adversarial attacks, leading to a lack of secure ML systems. We aim to clarify such confusion in this paper. By considering the application of ML for Phishing Website Detection (PWD), we formalize the “evasion-space” in which an adversarial perturbation can be introduced to fool an ML-PWD—demonstrating that even perturbations in the “feature-space” are useful. Then, we propose a realistic threat model describing evasion attacks against ML-PWD that are cheap to stage, and hence intrinsically more attractive for real phishers. After that, we perform the first statistically validated assessment of state-of-the-art ML-PWD against 12 evasion attacks. Our evaluation shows (i) the true efficacy of evasion attempts that are more likely to occur; and (ii) the impact of perturbations crafted in different evasion-spaces; Our realistic evasion attempts induce a statistically significant degradation (3–10% at p &lt; 0.05), and their cheap cost makes them a subtle threat. Notably, however, some ML-PWD are immune to our most realistic attacks ( p =0.22). Finally, as an additional contribution of this journal publication, we are the first to propose and empirically evaluate the intriguing case wherein an attacker introduces perturbations in multiple evasion-spaces at the same time . These new results show that simultaneously applying perturbations in the problem- and feature-space can cause a drop in the detection rate from 0.95 to 0. Our contribution paves the way for a much-needed re-assessment of adversarial attacks against ML systems for cybersecurity.
A critical literature review of security and privacy in smart home healthcare schemes adopting IoT &amp; blockchain: Problems, Challenges and Solutions

Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks. Recently, smart healthcare has leveraged smart home systems, thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner. However, PoA-based Blockchain DLT has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes. This review elicits some concerns, issues, and problems that have hindered the adoption of blockchain and IoT (BCoT) in some domains and suggests requisite solutions using the aging-in-place scenario. Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains. The study discusses recent findings, opportunities, and barriers, and provide recommendations that could facilitate the continuous growth of blockchain application in healthcare. Lastly, the study then explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process, including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing, as well as ethical trust in personal information disclosure, as a solution direction. The proposed authorisation framework could guarantee data ownership, conditional access management, scalable and tamper-proof data storage, and a more resilient system against threat models such as interception and insider attacks.
CMAIS-WOA: An Improved WOA with Chaotic Mapping and Adaptive Iterative Strategy

This paper proposes an improved whale optimization algorithm with chaotic mapping and adaptive iteration strategy (CMAIS-WOA). This algorithm addresses the issues of the WOA algorithm that is prone to local optimal solutions with low stability. CMAIS-WOA utilizes chaotic mapping to enhance the diversity and coverage of the initial population. Also, it adaptively adjusts the weight values based on the current distribution of whale populations and the fitness of search agents. In addition, CMAIS-WOA uses an improved nonlinear convergence factor to adjust the breadth-first and depth-first search during the optimization process. The performance of the proposed CMAIS-WOA is evaluated by using 13 classical benchmark functions and IEEE CEC2014 test suite. The experimental results show that CMAIS-WOA effectively improves the stability of the optimal solution and helps the algorithm to approach the global optimal solution. The method proposed in this paper contributes to the field of optimization which solves problems more powerfully and efficiently.
Psychological and Educational Factors of Digital Competence Optimization Interventions Pre- and Post-COVID-19 Lockdown: A Systematic Review

The rapid development of the ever-changing information and communication society demands skills from its members that allow access to and adapt to the various situations that they may face. To achieve this, it is essential to acquire a set of key competencies throughout different stages of life, among which we find digital competence. This systematic review aims to analyse, through a series of focal points and indicators, the internationally published interventions in the last ten years aimed at improving digital literacy and the acquisition of this competence by students in early childhood education, primary education, and higher education, as well as professionals from various fields. The procedure followed for the selection of the interventions has been documented and graphically represented according to the PRISMA statement, with searches conducted across various databases and journals. In total, 26 studies were selected, covering the period before, during, and after the COVID-19 health lockdown, and the influence of the lockdown on the development of digital competence was examined. The obtained results show the evolution of the selected interventions in terms of general aspects, instructional and evaluative procedures, fidelity, and encountered limitations. The results demonstrate a growing concern for the development of digital competence, amplified by the needs arising during the COVID-19 lockdown and evidenced by an increase in interventions aimed at this goal. It also showcases the relationship between adequate acquisition and the nurturing of other psychoeducational variables like motivation or satisfaction.
ASYMPTOTICALLY OPTIMAL ALGORITHM FOR PROCESSING SIDE RADIATION SIGNALS FROM MONITOR SCREENS ON LIQUID CRYSTAL STRUCTURES

An asymptotically optimal compatible algorithm for detecting side radiation signals from the monitor screen on liquid crystal structures and estimating the duration of image immutability on the monitor screen is found, which will better intercept information from monitor screens. The structure of a special technical intelligence tool is justified as a maximum likelihood algorithm for a finite number of unknown quadrature amplitudes of the information leakage signal from the monitor screen on liquid crystal structures.
General quantum algorithms for Hamiltonian simulation with applications to a non-Abelian lattice gauge theory

With a focus on universal quantum computing for quantum simulation, and through the example of lattice gauge theories, we introduce rather general quantum algorithms that can efficiently simulate certain classes of interactions consisting of correlated changes in multiple (bosonic and fermionic) quantum numbers with non-trivial functional coefficients. In particular, we analyze diagonalization of Hamiltonian terms using a singular-value decomposition technique, and discuss how the achieved diagonal unitaries in the digitized time-evolution operator can be implemented. The lattice gauge theory studied is the SU(2) gauge theory in 1+1 dimensions coupled to one flavor of staggered fermions, for which a complete quantum-resource analysis within different computational models is presented. The algorithms are shown to be applicable to higher-dimensional theories as well as to other Abelian and non-Abelian gauge theories. The example chosen further demonstrates the importance of adopting efficient theoretical formulations: it is shown that an explicitly gauge-invariant formulation using loop, string, and hadron degrees of freedom simplifies the algorithms and lowers the cost compared with the standard formulations based on angular-momentum as well as the Schwinger-boson degrees of freedom. The loop-string-hadron formulation further retains the non-Abelian gauge symmetry despite the inexactness of the digitized simulation, without the need for costly controlled operations. Such theoretical and algorithmic considerations are likely to be essential in quantumly simulating other complex theories of relevance to nature.
Not all information security-related stresses are equal: the effects of challenge and hindrance stresses on employees’ compliance with information security policies

Information security-related stresses (SRSs) are widely considered to play a negative role in the workplace, motivating employees' violation of information security policies (ISPs). However, researchers have neglected to challenge SRS and its role in promoting positive security actions. Therefore, this study aimed to explore the effect of both challenge and hindrance SRSs on employees' intention to comply with ISPs and the moderating mechanisms of regulatory focus in the relationship between SRSs and ISP compliance. Using survey data from 489 employees in Chinese enterprises, we applied a PLS-SEM method to test hypotheses. Our study found that the hindrance SRS had a negative effect on ISP compliance intention, differently, challenge SRS motivates employees to comply with ISPs. Our study also found prevention focus acted as a positive moderator in the relationship between hindrance SRS and compliance intention, while promotion focus had no effect on this relationship; Promotion focus positively moderated the relationship between challenge SRS and compliance intention, and prevention focus negatively moderated the relationship between challenge SRS and compliance intention. These findings provide new knowledge by examining the different effects and the boundary conditions to understand how two types of SRS influence employees' informaiton security dicisions.
Unveiling the Power of Visible-Thermal Video Object Segmentation

Despite recent progress, Video Object Segmentation (VOS) remains challenging in complex situations such as low light and dark scenes. In this paper, we tackle the visibility limitations by introducing thermal information as auxillary for VOS. Specifically, we generate a hybrid benchmark dataset for Visible-Thermal VOS, named VisT300, which contains 300 challenging videos with visible light and thermal frames and corresponding object mask annotations. Besides, a Visible-Thermal integration Network, named as VTiNet, is proposed to use both cross-modal and cross-frame propagation for accurate video object segmentation. It is advantageous in two aspects: 1) effective cross-modal feature fusion and propagation for strong expressions on visible, thermal, and fused modalities; 2) effective modality-sensitive memory bank enables preserving the most valuable historical contexts in each modality. Extensive experiments demonstrate our VTiNet outperforms the state-of-the-art VOS works by a large margin (over 5% than RGB SotAs in Mean J&F). Our preliminary research clearly recovers that importing complementary modalities can effectively increase the strength of models to achieve robust segmentation in challenging scenarios. Data and code are released at https://github.com/yjybuaa/vtinet, and we hope this work will promote the progress of visible-thermal VOS.
Examining the Impact of Team Dynamics in Agile Project Management Success in Software Development: A Systematic Literature Review

Agile project management has become a popular approach in software development due to its focus on collaboration, flexibility, and customer satisfaction. However, the success of agile projects relies heavily on effective team dynamics. This systematic literature review aims to identify and analyze the factors that contribute to team dynamics and their impact on agile project success in software development. The review includes a selection of 20 studies published between 2017 and 2022, which were analyzed using thematic synthesis. The findings reveal that effective communication, collaboration, skills, and knowledge are the primary team dynamics factors that contribute to agile project success. However, the role of diversity in team dynamics is less explored and requires further research. In addition, most of the reviewed studies use cost, customer satisfaction, quality, and timely completion as the main indicators of project success, but there is a need to consider other factors that are specific to these four indicators. Overall, this review emphasizes the importance of team dynamics in agile project success and highlights the need for a more comprehensive understanding of the impact of team dynamics on project success.
Neural Network-Based Hammerstein Model Identification of a Lab-Scale Batch Reactor

This paper focuses on two types of neural network-based Hammerstein model identification methods for the acrylamide polymerization reaction of a batch reactor process. The first neural-based identification type formulates the weights of the multilayer network directly as parameters of the nonlinear static and linear dynamic blocks of the Hammerstein model and trains the weights using a gradient-based backpropagation algorithm. In the second identification type, the nonlinear static block of the Hammerstein model is framed as a single hidden-layer feedforward network and both nonlinear and linear block parameters are trained using an extreme learning machine, where the training procedure is exempted from gradient calculation. The primary focus of the paper is neural-based model identification of a complex nonlinear system, which facilitates ease of linear/nonlinear controller design with good learning speed and less computations. A future work toward the machine learning-based nonlinear model predictive controller implementation using the Jetson Orin Nano board is also described.
Leveraging Online Communities for Building Social Capital in University Libraries

This study investigates the development of social capital in university libraries using the Fudan University Medical Library (FUML) as the case. The authors first use the SWOT matrix to analyze the FUML based on librarian interviews, official websites, and previous literature. Next, they construct a social capital evaluation framework for university libraries with four dimensions (degree of user demand, level of trust, visibility, and status in users' minds). Guided by the framework, our findings indicate that FUML's user demands in recent years are optimistic, though trust, visibility, and library status vary in users' minds. Thus, they suggest some strategies to help improve patron-library relations through online communities, such as using social media, multi-online channel user feedback, and improving related employee training. This study provides insights into how university libraries can build online relations from social capital concepts. Scant studies have applied social capital to investigate the relationship between university libraries and students, especially in East Asia.
Quantum-probabilistic Hamiltonian learning for generative modeling and anomaly detection

The Hamiltonian of an isolated quantum-mechanical system determines its dynamics and physical behavior. This study investigates the possibility of learning and utilizing a system's Hamiltonian and its variational thermal state estimation for data analysis techniques. For this purpose, we employ the method of quantum Hamiltonian-based models for the generative modeling of simulated Large Hadron Collider data and demonstrate the representability of such data as a mixed state. In a further step, we use the learned Hamiltonian for anomaly detection, showing that different sample types can form distinct dynamical behaviors once treated as a quantum many-body system. We exploit these characteristics to quantify the difference between sample types. Our findings show that the methodologies designed for field theory computations can be utilized in machine learning applications to employ theoretical approaches in data analysis techniques.