Reviewerly/exHarmony huggingface dataset:
CTGAN-MOS: Conditional Generative Adversarial Network Based Minority-Class-Augmented Oversampling Scheme for Imbalanced Problems

This paper proposes a novel data augmentation scheme called the conditional generative adversarial network minority-class-augmented oversampling scheme (CTGAN-MOS) for solving class imbalance problems. Our methodology encompassed six key steps: data engineering using sophisticated pre-processing techniques, identifying the type of vulnerabilities present in the data, curating good quality synthetic data using the CTGAN model, the intelligent fusion of real and synthetic data, noise removal from the augmented data using coin-throwing algorithm, and building classifiers with the high-quality augmented data. Our scheme maintains higher structural similarity (data truthfulness) between the original and the resampled data by intelligently adding high-quality samples only to the minority class, whereas some augmentation techniques add records to the majority class, leading to poor-quality resampled data. Our scheme removes noisy samples from the data, which has remained unexplored in the CTGAN-based data augmentation. Furthermore, it augments data by adding fewer records compared to existing schemes, while offering comparable performance. Experiments are conducted on benchmark datasets to prove the feasibility of the proposed CTGAN-MOS in realistic scenarios. Results prove the improvement by CTGAN-MOS over existing state-of-the-art (SOTA) techniques in terms of accuracy, recall, precision, F1 score, and G-mean score. Specifically, the CTGAN-MOS has yielded accuracy values of 100% and 99.83% on two datasets which are higher than recent SOTA techniques. On average, it has yielded the 22.58% and 29.47% improvements w.r.t. G-mean score on two different datasets. On average, it adds 8.26% and 26.01% fewer records than the existing SOTA methods in the two datasets. Lastly, our scheme yields highly balanced confusion matrices compared to SOTA data augmentation techniques.
Coherently driven quantum features using a linear optics-based polarization-basis control

Quantum entanglement generation is generally known to be impossible by any classical means. According to Poisson statistics, coherent photons are not considered quantum particles due to the bunching phenomenon. Recently, a coherence approach has been applied for quantum correlations such as the Hong-Ou-Mandel (HOM) effect, Franson-type nonlocal correlation, and delayed-choice quantum eraser to understand the mysterious quantum features. In the coherence approach, the quantum correlation has been now understood as a direct result of selective measurements between product bases of phase-coherent photons. Especially in the HOM interpretation, it has been understood that a fixed sum-phase relation between paired photons is the bedrock of quantum entanglement. Here, a coherently excited HOM model is proposed, analyzed, and discussed for the fundamental physics of two-photon correlation using linear optics-based polarization-basis control. For this, polarization-frequency correlation in a Mach-Zehnder interferometer is coherently excited using synchronized acousto-optic modulators, where polarization-basis control is conducted via a selective measurement process of the heterodyne signals. Like quantum operator-based destructive interference in the HOM theory, a perfectly coherent analysis shows the same HOM effects of the paired coherent photons on a beam splitter, whereas individual output intensities are uniform.
Blockchain in accounting and auditing: unveiling challenges and unleashing opportunities for digital transformation in Egypt

Purpose This paper aims to examine the role of Blockchain in the accounting and auditing literature and profession. Specifically, the paper investigates auditors' perceptions about the role of blockchain in accounting and auditing and the perceived potential benefits and challenges of blockchain-based accounting systems in Egypt. Moreover, what are the capabilities required for successfully implementing blockchain-based accounting systems? Design/methodology/approach A mixed-method approach was adopted to achieve the research objectives. The qualitative study included 11 in-depth interviews with external auditors, and the results of the interviews and the literature review helped develop a survey collected from 58 auditors. Findings The findings revealed low-to-moderate awareness of Blockchain-based accounting systems. Also, there were significant differences between auditors from large audit firms and small-and-medium audit firms regarding the benefits and challenges associated with Blockchain-based accounting systems. Practical implications The results provide valuable insights for practitioners, researchers and policymakers. Originality/value Understanding blockchain-based accounting systems and the benefits and challenges associated with their application is crucial for developing effective strategies and frameworks to overcome barriers and realize the transformative potential of blockchain in the accounting and audit market.
Optimal Phase-Selective Entrainment of Heterogeneous Oscillator Ensembles

.We develop a framework to design optimal entrainment signals that entrain an ensemble of heterogeneous nonlinear oscillators, described by phase models, at desired phases. We explicitly take into account heterogeneity in both oscillation frequency and the type of oscillators characterized by different phase response curves. The central idea is to leverage the Fourier series representation of periodic functions to decode a phase-selective entrainment task into a quadratic program. We demonstrate our approach using a variety of phase models where we entrain the oscillators into distinct phase patterns. Also, we show how the generalizability gained from our formulation enables us to meet a wide range of design objectives and constraints, such as minimum-power, fast entrainment, and charge-balanced controls.Keywordsoptimal entrainmentphase modelsnonlinear oscillatorsMSC codes37N2537N3592B2534C1534C29
Biosignals meet Adaptive Systems

Abstract This paper presents a perspective on Biosignal-Adaptive Systems (BAS) which automatically adapt to user needs by continuously interpreting their biosignals and by providing transparent feedback, thereby keeping the user in the loop. The major hallmark of the described BAS is the low latency with which biosignals are processed, interpreted, and applied to perform rapid system adaptation, providing the user in the loop with immediate feedback on the BAS’s understanding of his or her condition. In contrast to explicit user input or the interpretation of observable behavior, the rapid system adaptation relies on biosignals, which in context of a concrete application can be interpreted as implicit signals of user needs. Recently, great advances have been made in sensor integration into smart devices, making it possible to collect vasts amounts of multimodal biosignal data. Furthermore, powerful machine learning methods enable rapidly processing and learning from such data. We argue that the time has come to harness the full spectrum of low-latency processing of biosignals to understand user needs and to apply this information to deliver adaptive systems accordingly. However, this will just be the beginning: real-time signal processing in combination with ubiquitous devices that are always connected to huge processing and storage capacities allow systems to provide users (and bystanders) with instant and transparent feedback and adaptations for the recognized needs. In the future, such systems could run 24/7 to assist users @home, @work, and @play from the cradle to the grave. Thus, BAS must be human-centric to curate data, archive information, learn from experience, and adapt to individual users throughout their lifespan. In this position paper, we present the concept of BAS with its key building blocks, provide selected examples of BAS from our research, and articulate selected challenges for future research.
Security-enhanced public-key authenticated searchable encryption

Public key authenticated encryption with keyword search (PAEKS) is an asymmetric searchable encryption technique secure against inside keyword guessing attacks. The security properties of PAEKS are modeled via ciphertext indistinguishability (CI) and trapdoor indistinguishability (TI). Nowadays, CI security has been extended to enable the attacker to challenge two keyword tuples (MCI security), and even obtain ciphertext embedded with any keyword (fully MCI security). However, similar extensions to TI security have not yet been achieved from bilinear pairings. In this work, we address this problem by focusing not only on ciphertext privacy, but also on trapdoor privacy. In particular, we adopt the enhanced TI-security, namely fully MTI security. We begin by conducting a detailed comparison of the fully MTI security with current TI models. Subsequently, we design a PAEKS scheme from bilinear pairings that initially achieves both fully MTI and fully MCI security, thereby offering a stronger security guarantee than other existing PAEKS schemes. To optimize the practical efficiency, we adopt various techniques: accelerating the online efficiency by shifting most computational cost offline; leveraging parallel computing to speed up the test algorithm. Consequently, our optimized scheme is competitive with other PAEKS schemes optimized using similar techniques.
A Symbolic Approach to Discrete Structural Optimization Using Quantum Annealing

With the advent of novel quantum computing technologies and the new possibilities thereby offered, a prime opportunity has presented itself to investigate the practical application of quantum computing. This work investigates the feasibility of using quantum annealing for structural optimization. The target problem is the discrete truss sizing problem—the goal is to select the best size for each truss member so as to minimize a stress-based objective function. To make the problem compatible with quantum annealing devices, the objective function must be translated into a quadratic unconstrained binary optimization (QUBO) form. This work focuses on exploring the feasibility of making this translation. The practicality of using a quantum annealer for such optimization problems is also assessed. A method is eventually established to translate the objective function into a QUBO form and have it solved by a quantum annealer. However, scaling the method to larger problems faces some challenges that would require further research to address.
$$\text {Muckle}+$$: End-to-End Hybrid Authenticated Key Exchanges

End-to-end authenticity in public networks plays a significant role. Namely, without authenticity, the adversary might be able to retrieve even confidential information straight away by impersonating others. Proposed solutions to establish an authenticated channel cover pre-shared key-based, password-based, and certificate-based techniques. To add confidentiality to an authenticated channel, authenticated key exchange (AKE) protocols usually have one of the three solutions built in. As an amplification, hybrid AKE (HAKE) approaches are getting more popular nowadays and were presented in several flavors to incorporate classical, post-quantum, or quantum-key-distribution components. The main benefit is redundancy, i.e., if some of the components fail, the primitive still yields a confidential and authenticated channel. However, current HAKE instantiations either rely on pre-shared keys (which yields inefficient end-to-end authenticity) or only support one or two of the three above components (resulting in reduced redundancy and flexibility). In this work, we present an extension of a modular HAKE framework due to Dowling, Brandt Hansen, and Paterson (PQCrypto'20) that does not suffer from the above constraints. While their instantiation, dubbed Muckle, requires pre-shared keys (and hence yields inefficient end-to-end authenticity), our extended instantiation called $$\text {Muckle}+$$ utilizes post-quantum digital signatures. While replacing pre-shared keys with digital signatures is rather straightforward in general, this turned out to be surprisingly non-trivial when applied to HAKE frameworks (resulting in adapted proof techniques).
Breaking the Quadratic Barrier: Quantum Cryptanalysis of Milenage, Telecommunications’ Cryptographic Backbone

The potential advent of large-scale quantum computers in the near future poses a threat to contemporary cryptography. One ubiquitous usage of cryptography is currently present in the vibrant field of cellular networks. The cryptography of cellular networks is centered around seven secret-key algorithms $$f_1, \ldots , f_5, f_1^{*}, f_5^{*}$$ , aggregated into an authentication and key agreement algorithm set. Still, to the best of our knowledge, these secret key algorithms have not yet been subject to quantum cryptanalysis. Instead, many quantum security considerations for telecommunication networks argue that the threat posed by quantum computers is restricted to public-key cryptography. However, various recent works have presented quantum attacks on secret key cryptography that exploit quantum period finding to achieve more than a quadratic speedup compared to the best known classical attacks. Motivated by this quantum threat to symmetric cryptography, this paper presents a quantum cryptanalysis for the Milenage algorithm set, the prevalent instantiation of the seven secret-key $$f_1, \ldots , f_5, f_1^{*}, f_5^{*}$$ algorithms that underpin cellular security. Building upon recent quantum cryptanalytic results, we show attacks that go beyond a quadratic speedup. Concretely, we provide quantum attack scenarios for all Milenage algorithms, including exponential speedups distinguishable by different quantum attack models. Our results do not constitute a quantum break of the Milenage algorithms, but they do show that Milenage suffers from structural weaknesses making it susceptible to quantum attacks.
Data Loss Prevention Solution for Linux Endpoint Devices

Endpoint data loss prevention (DLP) software monitors and protects data on the endpoint against accidental and malicious leakage. While the risk of such leakage is widely present in current systems, it is more so within the intelligent infrastructures due to potential impact, heterogeneity, and complexity. However, there is a significant gap in open solutions for wide Linux-based endpoints. Therefore, this paper discusses possible approaches towards Linux endpoint DLP solution, which would be widely available on Linux distributions, not relying on fragile assumptions and not undermining security controls. Namely, the focus is on audit and control of file system operations and external USB devices. The viable approaches are discussed, and a prototype solution is implemented using the ftrace framework for file system operations and combining the udev subsystem and the sysfs virtual file system for external USB devices. While the solution is demonstrated in scenarios involving various DLP channels, it also established a platform for further research based on the data from intercepted events.
The Influence of Anthropomorphic Cues on Patients’ Perceived Anthropomorphism, Social Presence, Trust Building, and Acceptance of Health Care Conversational Agents: Within-Subject Web-Based Experiment

The last decade has witnessed the rapid development of health care conversational agents (CAs); however, there are still great challenges in making health care CAs trustworthy and acceptable to patients.Focusing on intelligent guidance CAs, a type of health care CA for web-based patient triage, this study aims to investigate how anthropomorphic cues influence patients' perceived anthropomorphism and social presence of such CAs and evaluate how these perceptions facilitate their trust-building process and acceptance behavior.To test the research hypotheses, the video vignette methodology was used to evaluate patients' perceptions and acceptance of various intelligent guidance CAs. The anthropomorphic cues of CAs were manipulated in a 3×2 within-subject factorial experiment with 103 participants, with the factors of agent appearance (high, medium, and low anthropomorphic levels) and verbal cues (humanlike and machine-like verbal cues) as the within-subject variables.The 2-way repeated measures ANOVA analysis indicated that the higher anthropomorphic level of agent appearance significantly increased mindful anthropomorphism (high level>medium level: 4.57 vs 4.27; P=.01; high level>low level: 4.57 vs 4.04; P<.001; medium level>low level: 4.27 vs 4.04; P=.04), mindless anthropomorphism (high level>medium level: 5.39 vs 5.01; P<.001; high level>low level: 5.39 vs 4.85; P<.001), and social presence (high level>medium level: 5.19 vs 4.83; P<.001; high level>low level: 5.19 vs 4.72; P<.001), and the higher anthropomorphic level of verbal cues significantly increased mindful anthropomorphism (4.83 vs 3.76; P<.001), mindless anthropomorphism (5.60 vs 4.57; P<.001), and social presence (5.41 vs 4.41; P<.001). Meanwhile, a significant interaction between agent appearance and verbal cues (.004) was revealed. Second, the partial least squares results indicated that privacy concerns were negatively influenced by social presence (β=-.375; t312=4.494) and mindful anthropomorphism (β=-.112; t312=1.970). Privacy concerns (β=-.273; t312=9.558), social presence (β=.265; t312=4.314), and mindless anthropomorphism (β=.405; t312=7.145) predicted the trust in CAs, which further promoted the intention to disclose information (β=.675; t312=21.163), the intention to continuously use CAs (β=.190; t312=4.874), and satisfaction (β=.818; t312=46.783).The findings show that a high anthropomorphic level of agent appearance and verbal cues could improve the perceptions of mindful anthropomorphism and mindless anthropomorphism as well as social presence. Furthermore, mindless anthropomorphism and social presence significantly promoted patients' trust in CAs, and mindful anthropomorphism and social presence decreased privacy concerns. It is also worth noting that trust was an important antecedent and determinant of patients' acceptance of CAs, including their satisfaction, intention to disclose information, and intention to continuously use CAs.
Compression and reconstruction of flotation foam images based on generative adversarial networks

The froth flotation is a beneficiation method that utilizes the differences in the physical and chemical properties of the surface of mineral particles to achieve the effective separation of different minerals. Recently, with the development of image processing and computer technology, machine vision has been widely used in mineral froth flotation. However, due to the high pixels of the flotation froth image and the limited storage space of the industrial computer, it is difficult to save a large amount of image data at the industrial site. To this end, a flotation foam image compression and reconstruction method is presented in this work. First, a lossless froth image is collected from the flotation cell in a gold hydrometallurgical plant. Then, the JPEG image compression algorithm compresses the original images to one in dozens. Under this situation, low-quality and small-volume images can be stored in an industrial computer easily. To reconstruct the compressed image, the generative adversarial networks (GAN) is used in this work, and RRDB and VGG19 are employed as the generative and discriminative models of the network, respectively. The loss function of the model is the sum of three losses, which are L1 loss, perceptual loss, and adversarial loss. Three methods for testing the texture features of the froth image are selected to verify the efficiency of the proposed method. The results show that the reconstructed image can effectively restore the details of the bubbles, reflect the image features, and has less noise and high visual similarity with the original image. The reconstructed images generated by low-resolution images with different compression qualities mostly maintain a restoration degree of more than 90% compared with the original images in three texture features.
Blockchain technology-based crypto assets: new insights into the evolution of the understanding of digital entrepreneurship

Purpose The study aims to shed light on the concepts most addressed in scientific research, which blockchain topics are of most interest, how relevant are these tools for academia, and how relevant are they? Design/methodology/approach The authors have developed a bibliometric study of scientific publications on blockchain made since 2016. For the analysis, the VOSViewer software version 1.6.19 has been used, which allows a statistical analysis of scientific publications on the subject. Findings The study manifest the relevance of Initial Coin Offering, growth of research interest in this field and the relevance of blockchain technology in the development of entrepreneurial projects. Originality/value This study provides a complete and updated picture of the scientific research on blockchain for the subsequent transfer of knowledge to the business world.
Graph-based methods for cervical cancer segmentation: Advancements, limitations, and future directions

Cervical cancer remains a significant health concern worldwide, where precise segmentation of cervical lesions is integral for effective diagnosis and treatment planning. This systematic review critically evaluates the application of graph-based methodologies for cervical cancer segmentation, identifying their potential, drawbacks, and avenues for future development. An exhaustive literature search across Scopus and PubMed databases resulted in 20 pertinent studies. These studies were assessed focusing on their implementation of graph-based techniques for cervical cancer segmentation, the utilized datasets, evaluation metrics, and reported precision levels. The review highlights the progressive strides made in the field, especially regarding the segmentation of intricate, non-convex regions and facilitating the detection and grading of cervical cancer using graph-based methodologies. Nonetheless, several constraints were evident, including a dearth of comparative performance analysis, reliance on high-resolution images, difficulties in specific boundary delineation, and the imperative for additional validation and diversified datasets. The review suggests future work to integrate advanced deep learning strategies for heightened accuracy, formulate hybrid methodologies to counteract existing limitations, and explore multi-modal fusion to boost segmentation precision. Emphasizing the explainability and interpretability of outcomes also stands paramount. Lastly, addressing critical challenges such as scarcity of annotated data, the need for real-time and interactive segmentation, and the segmentation of multiple objects or regions of interest remains a crucial frontier for future endeavors.