Reviewerly/exHarmony huggingface dataset:
A physical memristor-based chaotic system and its application in colour image encryption scheme

Abstract This work proposes a physical memristor ( TaOx ) based new 4D chaotic system with 3D multi-scroll, no equilibrium point, spiking behaviour, coexistence bursting oscillation and multistability. Using this physical memristor-based chaotic system, a novel and efficient colour image encryption algorithm has been developed using a unique box scrambling method and bit-wise XOR operations. Many interesting and new dynamics of a material-based memristive chaotic system are reported here, like 3D multi-scroll chaotic attractors, bursting characteristics, multistability, a neuronal system like spiking behaviours etc using Lyapunov spectrum and bifurcation plots. It is observed that the number of scrolls is changed with the total simulation time. This novel memristive chaotic system has limit cycles with controllable spikes and bursting oscillation. In addition, the system shows chaotic bursting oscillation under a different set of parameters and initial conditions. The coexistence of the bursting phenomena is studied here. The bursting and spiking characteristic is important for material-based memristors in neuromorphic applications. 3D Chaotic multi-scroll and multistability properties make the image encryption method more efficient and secure. Such characteristics are rare in physical memristor-based chaotic systems and using this, the image encryption algorithm is also rare in recent findings. Therefore, a new secure image encryption algorithm for colour images is proposed here, based on the unique box scrambling method, bitwise XOR operation and pseudo-random number generation using the proposed memristive chaotic system. Various tests like NPCR, UACI, histogram analysis, correlation study, information entropy analysis, robustness against external noise, etc have been performed to check the algorithm’s robustness and efficiency and test the capability to resist statistical and differential attacks.
CollabCoder: A GPT-Powered WorkFlow for Collaborative Qualitative Analysis

Collaborative Qualitative Analysis (CQA) process can be time-consuming and resource-intensive, requiring multiple discussions among team members to refine codes and ideas before reaching a consensus. We introduce CollabCoder, a system leveraging Large Language Models (LLMs) to support three CQA stages: independent open coding, iterative discussions, and the development of a final codebook. In the independent open coding phase, CollabCoder provides AI-generated code suggestions on demand and allows users to record coding decision-making information (e.g. keywords and certainty) as support for the process. During the discussion phase, CollabCoder helps to build mutual understanding and productive discussion by sharing coding decision-making information within the team. It also helps to quickly identify agreements and disagreements through quantitative metrics, in order to build a final consensus. During the code grouping phase, CollabCoder employs a top-down approach for primary code group recommendations, reducing the cognitive burden of generating the final codebook. The source code for CollabCoder can be accessed via GitHub at https://github.com/gaojie058/CollabCoder.
A Survey on X.509 Public-Key Infrastructure, Certificate Revocation, and Their Modern Implementation on Blockchain and Ledger Technologies

Cyber-attacks are becoming more common against Internet users due to the increasing dependency on online communication in their daily lives. X.509 Public-Key Infrastructure (PKIX) is the most widely adopted and used system to secure online communications and digital identities. However, different attack vectors exist against the PKIX system, which attackers exploit to breach the security of the reliant protocols. Recently, various projects (e.g., Let’s Encrypt and Google Certificate Transparency) have been started to encrypt online communications, fix PKIX vulnerabilities, and guard Internet users against cyber-attacks. This survey focuses on classical PKIX proposals, certificate revocation proposals, and their implementation on blockchain as well as ledger technologies. First, we discuss the PKIX architecture, the history of the World Wide Web, the certificate issuance process, and possible attacks on the certificate issuance process. Second, a taxonomy of PKIX proposals, revocation proposals, and their modern implementation is provided. Then, a set of evaluation metrics is defined for comparison. Finally, the leading proposals are compared using 15 evaluation metrics and 13 cyber-attacks before presenting the lessons learned and suggesting future PKIX and revocation research.
Sentiment Analysis of News Headlines Based on Sentiment Lexicon and Deep Learning

The faster-growing economy and technologies make people run for their daily needs, people all over the world run of time to do their work and return home, and even some people are in lack the valuable time they spend with their families. In such a fast-moving life people run out of time to know the detailed news and read newspapers. This led to the development of mobile apps that update news in short message formats. Later only the headlines of the news articles were shared through the internet and apps. This gave sophisticated news knowing experience to the people as they can get through the news headline on the go. Later to make this process more convenient one can know whether the news is positive, neutral or negative. As per their interest in the particular matter, people can be able to view the news, they wanted by knowing sentiment analysis of the particular news. This process of sentiment analysis of news headlines can be achieved through one of the sentiment analysis techniques called lexicon sentiment and using Deep learning algorithms.
Biomembrane‐Based Memcapacitive Reservoir Computing System for Energy‐Efficient Temporal Data Processing

Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting input features and mapping them into higher dimensional spaces. Physical reservoirs have been realized using spintronic oscillators, atomic switch networks, volatile memristors, etc. However, these devices are intrinsically energy‐dissipative due to their resistive nature, increasing their power consumption. Therefore, memcapacitive devices can provide a more energy‐efficient approach. Herein, volatile biomembrane‐based memcapacitors are leveraged as reservoirs to solve classification tasks and process time series in simulation and experimentally. This system achieves a 99.6% accuracy for spoken‐digit classification and a normalized mean square error of in a second‐order nonlinear regression task. Furthermore, to showcase the device's real‐time temporal data processing capability, a 100% accuracy for an epilepsy detection problem is achieved. Most importantly, it is demonstrated that each memcapacitor consumes an average of 41.5 fJ of energy per spike, regardless of the selected input voltage pulse width, while maintaining an average power of 415 fW for a pulse width of 100 ms, orders of magnitude lower than those achieved by state‐of‐the‐art devices. Lastly, it is believed that the biocompatible, soft nature of our memcapacitor renders it highly suitable for computing applications in biological environments.
A Comprehensive Survey of Recommender Systems Based on Deep Learning

With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic.
Changes in solar resource intermittency and reliability under Australia’s future warmer climate

The dependency of photovoltaic (PV) power generation on meteorological parameters can impact power production due to weather-induced variability. During the day, fluctuations in radiation introduce intermittency in power generated, raising reliability and grid stability issues at higher penetration levels. Long-term future resource assessment provides an effective tool for estimating resource reliability and future intermittency essential for pre-feasibility site assessments around the world. Australia has high solar power capacity, with several solar farms in operational and developmental stage. Using Australia as a case study, this research aims to understand Australia's solar resource distribution and variability using regional climate model projections under a high emission scenario. Results indicate an abundance of solar resource power density in Australia, especially in the North (450-500Wm−2). The solar resource will be more reliable in Eastern Australia in the future with ∼ 5% increase in resource density. Results suggest reduction in intermittency (∼20-minute lull periods) in the East with increase in clear-sky days/year in the future (∼20 days/year). Resource assessment of Sun Cable and New England solar farm located in Australia, revealed the future scope of increase in clear-sky days at the sites. This long-term future solar variability analysis can help identify regions in Australia where PV systems will be least susceptible to losses due to intermittency. Furthermore, this study will help in critical decision-making processes like planning storage systems, site selection, opportunities to create hybrid solar farms with the co-existence of solar and wind technology, etc., to mitigate the risks associated with future intermittent PV power generation.
A Sinh Cosh optimizer

Currently, meta-heuristic algorithms have been widely studied and applied, but balancing exploration and exploitation remains a challenge. In this study, a novel meta-heuristic algorithm named Sinh Cosh Optimizer (SCHO) is proposed based on the mathematical inspiration of the characteristics of Sinh and Cosh. SCHO includes four steps: two different phases of exploration and exploitation, the bounded search strategy, and the switching mechanism. SCHO is compared with eight meta-heuristic algorithms for the 23 benchmark functions at different dimensions and CEC 2014, and its strong performance is validated. The efficiency and robustness of SCHO are verified by qualitative analysis, convergence curves, and two statistical tests. Furthermore, five engineering problems are presented. Source codes of SCHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/130734-a-sinh-cosh-optimizer.
Improved local models and new Bell inequalities via Frank-Wolfe algorithms

In Bell scenarios with two outcomes per party, we algorithmically consider the two sides of the membership problem for the local polytope: Constructing local models and deriving separating hyperplanes, that is, Bell inequalities. We take advantage of the recent developments in so-called Frank-Wolfe algorithms to significantly increase the convergence rate of existing methods. First, we study the threshold value for the nonlocality of two-qubit Werner states under projective measurements. Here, we improve on both the upper and lower bounds present in the literature. Importantly, our bounds are entirely analytical; moreover, they yield refined bounds on the value of the Grothendieck constant of order three: $1.4367\ensuremath{\leqslant}{K}_{G}(3)\ensuremath{\leqslant}1.4546$. Second, we demonstrate the efficiency of our approach in multipartite Bell scenarios, and present local models for all projective measurements with visibilities noticeably higher than the entanglement threshold. We make our entire code accessible as a julia library called BellPolytopes.jl.
An external attention-based feature ranker for large-scale feature selection

An important problem in data science, feature selection (FS) consists of finding the optimal subset of features and eliminating irrelevant or redundant features. The FS task on high-dimensional data is challenging for the FS methods currently available in the literature. To overcome this limitation, we propose a novel feature selection method called External Attention-Based Feature Ranker for Large-Scale Feature Selection (EAR-FS) whose function is based on the logic of an attention mechanism and a hybrid metaheuristic. EAR-FS comprises three interdependent modules: (1) in the training module design, a multilayer perceptron network endowed with an attention module is trained to fit the dataset; (2) in feature ranking by attention, the trained attention module is used for attention updating and to rank features according to their importance; 3) in subset generation, a two-stage heuristic approach is applied to determine a small number of features that still guarantee high-accuracy performance. The experimental benchmark comprised 26 datasets of small, large and very large sizes, ranging from 15 to 12,533 features. Experiments performed against the state-of-the-art algorithms of FS show that our algorithm is efficient at selecting a small number of features from large datasets while guaranteeing excellent levels of classification accuracy. For instance, EAR-FS demonstrated its capability to reduce the features of the 11 Tumor dataset by 97% while maintaining a classifier accuracy of over 93%.
Can AI chatbots help retain customers? An integrative perspective using affordance theory and service-domain logic

Chatbots' role in service contexts is changing to ensure better connectedness with customers in the digital marketing era. Thus, developing an understanding of how to enhance user perceptions and behaviors through interface design has become crucial. Using affordance theory and service-dominant logic as theoretical lenses, this study examines how chatbot affordances affect consumers' continuance intention through value-in-use. Data collected from 405 chatbot users of selected banks were analyzed using structural equation modeling. The results reveal that chatbot affordances positively influence personalization and experience. Furthermore, personalization influences switching costs and psychological ownership, whereas experience affects psychological ownership. Moreover, switching costs affect continuance intention through inattentiveness to alternatives. Finally, psychological ownership influences customer citizenship behavior, which impacts continuance intention.
Metaverse Key Requirements and Platforms Survey

The growing interest in the metaverse has led to an abundance of platforms, each with its own unique features and limitations. This paper’s objective is two-fold. First, we aim at providing an objective analysis of requirements that need to be fulfilled by metaverse platforms. We survey a broad set of criteria including interoperability, immersiveness, persistence, multimodal and social interaction, scalability, level of openness, configurability, market access, security, and blockchain integration, among others. Second, we review a wide range of existing metaverse platforms, and we critically evaluate their ability to meet the requirements listed. We identify their limitations, which must be addressed to establish fair, trustworthy, and interactive experiences within the metaverse ecosystem. Looking forward, we highlight the need for further research and development in areas such as decentralization, improved security and privacy measures, and the integration of emerging technologies like blockchain and AI, as essential building blocks for a resilient and secure metaverse.
Deep learning personalized recommendation-based construction method of hybrid blockchain model

Abstract This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.
Toward Trustworthy Metaverse: Advancements and Challenges

The Metaverse, a transformative digital realm, holds immense promise for reshaping industries and human interactions while potentially addressing global challenges and democratizing opportunities. However, it also introduces a spectrum of complexities that demand careful navigation. To establish trustworthiness within the Metaverse ecosystem, gaining a deep understanding of its applications, challenges, and existing solutions is imperative. In this comprehensive survey, we first delve into Metaverse applications, drawing insights from existing literature. Subsequently, we explore the diverse challenges the Metaverse presents, analyzing them through the lens of existing research. We then scrutinize the overall trustworthiness of the Metaverse environment and investigate existing solutions to previously identified challenges through a thorough review and analysis of pertinent literature. Lastly, we discussed future research directions aimed at fostering a trustworthy Metaverse environment. This comprehensive review can provide an overview of the Metaverse, its application domains, challenges, existing solutions and research directions for many multidisciplinary studies.
Neural Disentanglement of Query Difficulty and Semantics

Researchers have shown that the retrieval effectiveness of queries may depend on other factors in addition to the semantics of the query. In other words, several queries expressed with the same intent, and even using overlapping keywords, may exhibit completely different degrees of retrieval effectiveness. As such, the objective of our work in this paper is to propose a neural disentanglement method that is able to disentangle query semantics from query difficulty. The disentangled query semantics representation provides the means to determine semantic association between queries whereas the disentangled query difficulty representation would allow for the estimation of query effectiveness. We show through our experiments on the query performance prediction; and, query similarity calculation tasks that our proposed disentanglement method is able to show better performance compared to the state of the art.
Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers

Estimating query difficulty, also known as Query Performance Prediction (QPP), is concerned with assessing the retrieval quality of a ranking method for an input query. Most traditional unsupervised frequency-based models and many recent supervised neural methods have been designed specifically for predicting the performance of sparse retrievers such as BM25. In this paper we propose an unsupervised QPP method for dense neural retrievers which operates by redefining the well-known concept of query robustness i.e., a more robust query to perturbations is an easier query to handle. We propose to generate query perturbations for measuring query robustness by systematically injecting noise into the contextualized neural representation of each query. We then compare the retrieved list for the original query with that of the perturbed query as a way to measure query robustness. Our experiments on four different query sets including MS MARCO, TREC Deep Learning track 2019 and 2020 and TREC DL-Hard show consistently improved performance on linear and ranking correlation metrics over the state of the art.
Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.