Reviewerly/exHarmony huggingface dataset:
Beyond financial regulation of crypto-asset wallet software: In search of secondary liability

Since Bitcoin, the blockchain space considerably evolved. One crucial piece of software to interact with blockchains and hold private-public key pairs to distinct crypto-assets and securities are wallets. Wallet software can be offered by liable third-parties (‘custodians’) who hold certain rights over assets and transactions. As parties subject to financial regulation, they are to uphold Anti-money Laundering and Combating the Financing of Terrorist (AML/CFT) standards by undertaking Know-Your-Customer (KYC) checks on users of their services. In juxtaposition, wallet software can also be issued without the involvement of a liable third-party. As no KYC is performed and users have full ‘freedom to act’, such ‘non-custodial’ wallet software is popular in criminal undertakings. They are required to interact with peer-to-peer applications and organisations running on blockchains whose benefits are not the subject of this paper. To date, financial regulation fails to adequately address such wallet software because it presumes the existence of a registered, liable entity offering said software. As illustrated in the case of Tornado Cash, financial regulation fails to trace chains of secondary liability. Alas, the considered solution is a systematic surveillance of all transactions. Against this backdrop, this paper sets forth an alternative approach rooted in copyright law. Concepts that pertain to secondary liability prove of value to develop a flexible, principles-based approach to the regulation of non-custodial wallet software that accounts for both, infringing and non-infringing uses.
Subtleties in the trainability of quantum machine learning models

Abstract A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related field called variational quantum algorithms (VQAs). While both fields involve training a parametrized quantum circuit, there are crucial differences that make the results for one setting not readily applicable to the other. In this work, we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML. Consequently, our work has implications for several QML proposals in the literature. In addition, we provide theoretical and numerical evidence that QML models exhibit further trainability issues not present in VQAs, arising from the use of a training dataset. We refer to these as dataset-induced barren plateaus. These results are most relevant when dealing with classical data, as here the choice of embedding scheme (i.e., the map between classical data and quantum states) can greatly affect the gradient scaling.
Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things

The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of “selecting the feature with the lowest number of distinct values,” “selecting the feature with the lowest entropy,” and “selecting the feature with the highest entropy.” For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria.
Image Encryption Scheme Based on Orbital Shift Pixels Shuffling with ILM Chaotic System

Image encryption techniques protect private images from unauthorized access while they are being transmitted. Previously used confusion and diffusion processes are risky and time-consuming. Therefore, finding a solution to this problem has become necessary. In this paper, we propose a new image encryption scheme that combines the Intertwining Logistic Map (ILM) and Orbital Shift Pixels Shuffling Method (OSPSM). The proposed encryption scheme applies a technique for confusion inspired by the rotation of planets around their orbits. We linked the technique of changing the positions of planets around their orbits with the shuffling technique of pixels and combined it with chaotic sequences to disrupt the pixel positions of the plain image. First, randomly selected pixels from the outermost orbit are rotated to shift the pixels in that orbit, causing all pixels in that orbit to change their original position. This process is repeated for each orbit until all pixels have been shifted. This way, all pixels are randomly scrambled on their orbits. Later on, the scrambled pixels are converted into a 1D long vector. The cyclic shuffling is applied using the key generated by the ILM to a 1D long vector and reshaped into a 2D matrix. Then, the scrambled pixels are converted into a 1D long vector to apply cyclic shuffle using the key generated by the ILM. After that, the 1D long vector is converted into a 2D matrix. For the diffusion process, using ILM generates a mask image, which is then XORed with the transformed 2D matrix. Finally, a highly secure and unrecognizable ciphertext image is obtained. Experimental results, simulation analysis, security evaluation, and comparison with existing image encryption schemes show that it has a strong advantage in defending against common attacks, and the operating speed of this encryption scheme also performs excellently in practical image encryption applications.
Multiscale (microscopic to remote sensing) preliminary exploration of auriferous-uraniferous marbles: A case study from the Egyptian Nubian Shield

Abstract Since their recent first record within the Egyptian Nubian Shield, auriferous and uraniferous marbles (Au = 0.98–2.76 g/t; U = 133–640 g/t) have rarely been addressed, despite not only their probable economic importance but also the fact that it is a new genetic style of gold and uranium mineralization in the Nubian Shield rocks. This is mainly attributed to the inadequate localization of these marbles within harsh terrains, as well as the cost and time spent with conventional fieldwork for their identification compared to the main lithological components of the Nubian Shield. On the contrary, remote sensing and machine learning techniques save time and effort while introducing reliable feature identification with reasonable accuracy. Consequently, the current research is an attempt to apply the well-known machine learning algorithm (Support vector Machine—SVM) over Sentinel 2 remote sensing data (with a spatial resolution of up to 10 m) to delineate the distribution of auriferous-uraniferous marbles in the Barramiya-Daghbagh district (Eastern Desert of Egypt), as a case study from the Nubian Shield. Towards better results, marbles were accurately distinguished utilizing ALOS PRISM (2.5 m) pan-sharpened Sentinel 2 data and well-known exposures during fieldwork. With an overall accuracy of more than 90%, a thematic map for auriferous-uraniferous marbles and the major rock units in the Barramiya-Daghbagh district was produced. Marbles are spatially related to ophiolitic serpentinite rocks, as consistent with their genesis within the Neoproterozoic oceanic lithosphere. Field and petrographic investigations have confirmed the newly detected Au and U-bearing zones (impure calcitic to impure dolomitic marbles in Wadi Al Barramiya and Wadi Daghbagh areas and impure calcitic marble in Gebel El-Rukham area). Additionally, X-ray diffraction (XRD), back-scattered electron images (BSEIs), and Energy Dispersive X-ray spectroscopy (EDX) results were integrated to verify our remote sensing results and petrographic investigations. Different times of mineralization are indicated, ranging from syn-metamorphism (gold in Wadi Al Barramiya and Gebel El-Rukham) to post-metamorphism (gold in Wadi Daghbagh and uranium in all locations). Based on the application of geological, mineralogical, machine learning and remote sensing results for the construction of a preliminary exploration model of the auriferous-uraniferous marble in the Egyptian Nubian Shield, we recommend a detailed exploration of Au and U-bearing zones in Barramiya-Dghbagh district and applying the adopted approach to other districts of similar geological environments.
Critical Schrödinger Cat Qubit

A bosonic code is proposed that makes use of both two-photon loss and Kerr nonlinearity, offering an enhanced performance over a broad range of detuning values and resistance to the random frequency shifts arising in multiple-qubit operations.
Eye-Gaze-Guided Vision Transformer for Rectifying Shortcut Learning

Learning harmful shortcuts such as spurious correlations and biases prevents deep neural networks from learning meaningful and useful representations, thus jeopardizing the generalizability and interpretability of the learned representation. The situation becomes even more serious in medical image analysis, where the clinical data are limited and scarce while the reliability, generalizability and transparency of the learned model are highly required. To rectify the harmful shortcuts in medical imaging applications, in this paper, we propose a novel eye-gaze-guided vision transformer (EG-ViT) model which infuses the visual attention from radiologists to proactively guide the vision transformer (ViT) model to focus on regions with potential pathology rather than spurious correlations. To do so, the EG-ViT model takes the masked image patches that are within the radiologists’ interest as input while has an additional residual connection to the last encoder layer to maintain the interactions of all patches. The experiments on two medical imaging datasets demonstrate that the proposed EG-ViT model can effectively rectify the harmful shortcut learning and improve the interpretability of the model. Meanwhile, infusing the experts’ domain knowledge can also improve the large-scale ViT model’s performance over all compared baseline methods with limited samples available. In general, EG-ViT takes the advantages of powerful deep neural networks while rectifies the harmful shortcut learning with human expert’s prior knowledge. This work also opens new avenues for advancing current artificial intelligence paradigms by infusing human intelligence.
Legal Challenges to Generative AI, Part I

Questioning the legality of using in-copyright works for training data and producing outputs derived from copyrighted training data.
Code-based Cryptography in IoT: A HW/SW Co-Design of HQC

Recent advances in quantum computing pose a serious threat on the security of widely used public-key cryp-tosystems. Thus, new post-quantum cryptographic algorithms have been proposed as part of the associated US NIST process to enable secure, encrypted communication in the age of quantum computing. Many hardware accelerators for structured lattice-based algorithms have already been published to meet the strict power, area and latency requirements of low-power IoT edge de-vices. However, the security of these algorithms is still uncertain. Currently, many new attacks against the lattice structure are investigated to judge on their security. In contrast, code-based algorithms, which rely on deeply explored security metrics and are appealing candidates in the NIST process, have not yet been investigated to the same depth in the context of IoT due to the computational complexity and memory footprint of state-of-the-art software implementations. In this paper, we present to the best of our knowledge the first HW /SW co-design based implementation of the code-based Hamming Quasi Cyclic Key-Encapsulation Mechanism. We profile and evaluate this algorithm in order to explore the trade-off between software optimizations, tightly coupled hardware acceleration by instruction set extension and modular, loosely coupled accelerators. We provide detailed results on the energy consumption and performance of our design and compare it to existing implementations of lattice- and code-based algorithms. The design was implemented in two technologies: FPGA and ASIC. Our results show that code-based algorithms are valid alternatives in low-power IoT from an implementation perspective.
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-Shot Prompt Learning for Automatic Scoring in Science Education

Developing natural language processing (NLP) models to automatically score students’ written responses to science problems is critical for science education. However, collecting sufficient student responses and labeling them for training or fine-tuning NLP models is time and cost-consuming. Recent studies suggest that large-scale pre-trained language models (PLMs) can be adapted to downstream tasks without fine-tuning by using prompts. However, no research has employed such a prompt approach in science education. As students’ written responses are presented with natural language, aligning the scoring procedure as the next sentence prediction task using prompts can skip the costly fine-tuning stage. In this study, we developed a zero-shot approach to automatically score student responses via Matching exemplars as Next Sentence Prediction (MeNSP). This approach employs no training samples. We first apply MeNSP in scoring three assessment tasks of scientific argumentation and found machine-human scoring agreements, Cohen’s Kappa ranges from 0.30 to 0.57, and F1 score ranges from 0.54 to 0.81. To improve scoring performance, we extend our research to the few-shots setting, either randomly selecting labeled student responses at each grading level or manually constructing responses to fine-tune the models. We find that one task’s performance is improved with more samples, Cohen’s Kappa from 0.30 to 0.38, and F1 score from 0.54 to 0.59; for the two other tasks, scoring performance is not improved. We also find that randomly selected few-shots perform better than the human expert-crafted approach. This study suggests that MeNSP can yield referable automatic scoring for student-written responses while significantly reducing the cost of model training. This method can benefit low-stakes classroom assessment practices in science education. Future research should further explore the applicability of the MeNSP in different types of assessment tasks in science education and further improve the model performance. Our code is available at https://github.com/JacksonWuxs/MeNSP .
E-learning Portal Success in higher education organizations: A multi-group comparison

Purpose: This study provides an analytical framework based on the literature to evaluate the effectiveness of E-learning portals. The study aims to contrast the accessibility of e-learning portals from the perspectives of business and engineering students. Research methodology: Using convenience and snowball sampling methods with the help of Google polls, the data was acquired from 482 students, including engineering and business students. The study used a seven-point Likert-type questionnaire to gather data. The study employed PLS-SEM to do a multi-group analysis and examine group differences. Results: The results revealed user satisfaction predicts the system used for both models. Only e-service quality is the significant predictor of user satisfaction and the system used in the business model, but the system and information qualities are also predictors in the engineering model. Finally, the success of an e-learning portal is positively correlated with user satisfaction and system use. Limitations: This study consists of only a sample of 482 students. Future research may take into account using a bigger sample size. Additionally, this study mostly focused on how students perceived the efficacy of the e-learning portal while ignoring the viewpoints of the instructors and the institution, which may vary. Contribution: Higher administration in Bangladeshi educational institutions will find the study helpful in building policies and plans for the growth of e-learning portals. Novelty: This work validates the DeLone and McLean Model in the context of Bangladeshi undergraduate students. The study shows how business and engineering students' access to e-learning portals varies.
Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images

In recent years, the protection and management of water environments have garnered heightened attention due to their critical importance. Detection of small objects in unmanned aerial vehicle (UAV) images remains a persistent challenge due to the limited pixel values and interference from background noise. To address this challenge, this paper proposes an integrated object detection approach that utilizes an improved YOLOv5 model for real-time detection of small water surface floaters. The proposed improved YOLOv5 model effectively detects small objects by better integrating shallow and deep features and addressing the issue of missed detections and, therefore, aligns with the characteristics of the water surface floater dataset. Our proposed model has demonstrated significant improvements in detecting small water surface floaters when compared to previous studies. Specifically, the average precision (AP), recall (R), and frames per second (FPS) of our model achieved 86.3%, 79.4%, and 92%, respectively. Furthermore, when compared to the original YOLOv5 model, our model exhibits a notable increase in both AP and R, with improvements of 5% and 6.1%, respectively. As such, the proposed improved YOLOv5 model is well-suited for the real-time detection of small objects on the water’s surface. Therefore, this method will be essential for large-scale, high-precision, and intelligent water surface floater monitoring.
Object Detection in Drone Video with Temporal Attention Gated Recurrent Unit Based on Transformer

Unmanned aerial vehicle (UAV) based object detection plays a pivotal role in civil and military fields. Unfortunately, the problem is more challenging than general visual object detection due to the significant appearance deterioration in images captured by drones. Considering that video contains more abundant visual features and motion information, a better idea for UAV based image object detection is to enhance target appearance in reference frame by aggregating the features in neighboring frames. However, simple feature aggregation methods will frequently introduce the interference of background into targets. To solve this problem, we proposed a more effective module, termed Temporal Attention Gated Recurrent Unit (TA-GRU), to extract effective temporal information based on recurrent neural networks and transformers. TA-GRU works as an add-on module to bring existing static object detectors to high performance video object detectors, with negligible extra computational cost. To validate the efficacy of our module, we selected YOLOv7 as baseline and carried out comprehensive experiments on the VisDrone2019-VID dataset. Our TA-GRU empowered YOLOv7 to not only boost the detection accuracy by 5.86% in the mean average precision (mAP) on the challenging VisDrone dataset, but also to reach a running speed of 24 frames per second (fps).
Configurable circular-polarization-dependent optoelectronic silent state for ultrahigh light ellipticity discrimination

Abstract Filterless light-ellipticity-sensitive optoelectronic response generally has low discrimination, thus severely hindering the development of monolithic polarization detectors. Here, we achieve a breakthrough based on a configurable circular-polarization-dependent optoelectronic silent state created by the superposition of two photoresponses with enantiomerically opposite ellipticity dependences. The zero photocurrent and the significantly suppressed noise of the optoelectronic silent state singularly enhance the circular polarization extinction ratio (CPER) and the sensitivity to light ellipticity perturbation. The CPER of our device approaches infinity by the traditional definition. The newly established CPER taking noise into account is 3–4 orders of magnitude higher than those of ordinary integrated circular polarization detectors, and it remains high in an expanded wavelength range. The noise equivalent light ellipticity difference goes below 0.009° Hz −1/2 at modulation frequencies above 1000 Hz by a light power of 281 μW. This scheme brings a leap in developing monolithic ultracompact circular polarization detectors.
Nonlinear dynamics of a three-dimensional discrete-time delay neural network

This paper examines a three-dimensional delayed discrete neural network model analytically and numerically to determine the existence of different types of bifurcations of the involved fixed points. The model exhibits different bifurcations such as pitchforks, flips, Neimark–Sackers, and flip-Neimark–Sackers. The critical coefficients are used to determine the structure of each bifurcation. The curves are calculated and plotted for each bifurcation when the parameters are changed. Further, these bifurcations are theoretically analyzed and numerically verified. From the obtained results, we observed that by drawing the curves associated with each bifurcation, the numerical simulations are consistent with the analytical results.
Deep reservoir computing based on self-rectifying memristor synapse for time series prediction

Herein, a self-rectifying resistive switching memristor synapse with a Ta/NbOx/Pt structure was demonstrated for deep reservoir computing (RC). The memristor demonstrated stable nonlinear analog switching characteristics, with a rectification ratio of up to 1.6 × 105, good endurance, and high uniformity. Additionally, the memristor exhibited typical short-term plasticity and dynamic synaptic characteristics. Based on these characteristics, a deep memristor RC system was proposed for time series prediction. The system achieved a low normalized root mean square error (NRMSE) of 0.04 in the time series prediction of the Henon map. Even at 90 °C, deep RC retains good predictive power with an NRMSE of only 0.07. This work provides guidance for efficient deep memristive RC networks to handle more complex future temporal tasks.
Accelerating Learned Sparse Indexes Via Term Impact Decomposition

Novel inverted index-based learned sparse ranking models provide more effective, but less efficient, retrieval performance compared to traditional ranking models like BM25.In this paper, we introduce a technique we call postings clipping to improve the query efficiency of learned representations.Our technique amplifies the benefit of dynamic pruning query processing techniques by accounting for changes in term importance distributions of learned ranking models.The new clipping mechanism accelerates top-k retrieval by up to 9.6× without any loss in effectiveness.