Reviewerly/exHarmony huggingface dataset:
DABAC: Smart Contract-Based Spatio-Temporal Domain Access Control for the Internet of Things

With the advent of IoT technology, the dynamic nature of IoT devices has introduced new obstacles to access control. It is essential to consider the security requirements of the actual physical environment, rendering the traditional access control approach centered on the information space. In the IoT ecosystem, there are several issues such as the dynamics of devices frequently entering and leaving, the lack of computing and storage capacity, and distributed deployment. To address these challenges, this paper proposes the Domain Attribute Based Access Control(DABAC) that incorporates domain elements to implement the physical location limitation of dynamic devices. Moreover, an intelligent gateway is utilized to divide the physical area and act as a proxy to achieve regional device management, automatic networking of devices in the domain, and the dynamic expansion of the sensor network resulting from device entry or exit. Then, given the distributed deployment of devices, smart contracts are employed to deploy access control mechanisms and construct a trusted environment to mitigate threats such as single points of failure. Finally, the DABAC is implemented on the Ethereum platform, simulating a smart medical situation. The experimental results demonstrate that the proposed solution effectively addresses the problem of access control of device dynamics in an untrusted IoT environment while maintaining system security.
General implementation of quantum physics-informed neural networks

Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics. By integrating knowledge of physical laws and processes in Partial Differential Equations (PDEs), fast convergence and effective solutions are obtained. Since training modern Machine Learning (ML) systems is a computationally intensive endeavour, using Quantum Computing (QC) in the ML pipeline attracts increasing interest. Indeed, since several Quantum Machine Learning (QML) algorithms have already been implemented on present-day noisy intermediate-scale quantum devices, experts expect that ML on reliable, large-scale quantum computers will soon become a reality. However, after potential benefits from quantum speedup, QML may also entail reliability, trustworthiness, safety, and security risks. To solve the challenges of QML, we combine classical information processing, quantum manipulation, and processing with PINNs to accomplish a hybrid QML model named quantum based PINNs.
Creating fiber-embedded photonic circuitry by liquid-phase structuring of multi-material cores

Multi-material fibers are a promising platform for integrating nanoscale structures into macroscale photonic systems due to their unique aspect ratio: fiber cores can be kilometers long and sub-micrometric in their cross-section simultaneously. We are introducing Very Large-Scale Integration for Fibers (or, in short, VLSI-Fi) – manufacturing of integrated photonic circuits in a fiber analogous to VLSI from the microelectronics realm. VLSI-Fi starts with a thermal draw of the 3D printed preform, defining the cross-sectional geometry of the fiber, followed by the axial patterning of the fiber cores into arrays of integrated devices using a material-selective spatially coherent capillary breakup1,2 . Additional control over the photonic and electronic properties of devices is accomplished through segregation-driven control of doping. The result is a fiber-embedded integrated photonic circuit with user-defined 3D architecture providing the desired functionality. We argue that the capillary breakup of a viscous thread, nonlinear and often chaotic, becomes predictable if the axial symmetry of the thread viscosity is broken. We found that the capillary breakup of semiconducting fiber cores initiated by feeding the fiber through a spot-like liquefaction zone results in deterministic photonic and optoelectronic structures, such as gratings and spherical resonators. As a proof of concept, we demonstrate a selective breakup of a silicon core in fiber with one silicon and one vanadium core into an array of spherical silicon resonators, with a vanadium electrode flanking those resonators for electrical tuning of their resonant frequencies. Such cascaded resonators are a nontrivial example of photonic circuitry implemented in fiber using a non-CMOS approach.
Contrastive knowledge integrated graph neural networks for Chinese medical text classification

This paper aims at medical text classification, where texts describe medicines, diseases, or other medical topics. This field is still challenging since medical texts contain intensive specialization and terminology, which require professional semantic and structured knowledge to classify. Based on our observations, medical knowledge graph (KG) can provide such knowledge although they may be ambiguous. To this end, we propose contrastive knowledge integrated graph neural networks (ConKGNN) to make full use of the above knowledge. Specifically, the proposed method builds two graphs for a medical text, i.e. text graph and text-specific subgraph, containing the text information and relevant KG information, respectively. Two graphs are merged into a united graph, which is jointly modeled by graph neural networks (GNN). In this way, our approach adequately learns interactions between neighbors. Meanwhile, it promotes the mutual influences between text and KG. We further propose graph-based supervised contrastive learning. By randomly cutting off nodes from the text graph, an augmented united graph is obtained. Learning it in a contrastive way could enhance the robustness of introducing KG information. Comprehensive experiments are conducted on five Chinese medical datasets and experimental results show our model outperforms strong baselines remarkably. Consequently, our model can serve as an efficient medical text classifier with excellent performance. We release the code at https://github.com/nolongernome/ConKGNN.
Joint leaf-refinement and ensemble pruning through $$L_1$$ regularization

Abstract Ensembles are among the state-of-the-art in many machine learning applications. With the ongoing integration of ML models into everyday life, e.g., in the form of the Internet of Things, the deployment and continuous application of models become more and more an important issue. Therefore, small models that offer good predictive performance and use small amounts of memory are required. Ensemble pruning is a standard technique for removing unnecessary classifiers from a large ensemble that reduces the overall resource consumption and sometimes improves the performance of the original ensemble. Similarly, leaf-refinement is a technique that improves the performance of a tree ensemble by jointly re-learning the probability estimates in the leaf nodes of the trees, thereby allowing for smaller ensembles while preserving their predictive performance. In this paper, we develop a new method that combines both approaches into a single algorithm. To do so, we introduce $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> regularization into the leaf-refinement objective, which allows us to jointly prune and refine trees at the same time. In an extensive experimental evaluation, we show that our approach not only offers statistically significantly better performance than the state-of-the-art but also offers a better accuracy-memory trade-off. We conclude our experimental evaluation with a case study showing the effectiveness of our method in a real-world setting.
MEP-3M: A large-scale multi-modal E-commerce product dataset

The product categories are vital for the E-commerce platforms due to the core applications on automatic product category assignment, personalized product recommendations, etc. In this paper, we construct a large-scale Multi-modal E-commerce Products classification dataset MEP-3M, which is large-scale, hierarchical-categorized, multi-modal, fine-grained, and long-tailed. Statistically, MEP-3M consists of over 3 million products, thus achieves the largest data scale in comparison to the existing E-commerce product datasets. The products in MEP-3M are represented in three modalities: image, textual description, and OCR text, and labeled with tree-like labels. The third level labels are extremely fine-grained. In addition, we exploit four novel practical tasks on this dataset, Product classification, Hierarchical Product Classification, Fine-grained Product Classification, and Product Representation Learning. For each task, we present some image-only, text-only, and multi-modal baseline performances for further researches. The MEP-3M dataset will be released at https://github.com/ChenDelong1999/MEP-3M.
Developing Artificial Intelligence-Based Content Creation: Are EU Copyright and Antitrust Law Fit for Purpose?

Abstract Artificial intelligence offers promising applications for content production. However, their development faces significant copyright issues because it involves reproduction of protected subject matter and requires datasets so large that obtaining licences from all rightholders is unfeasible. These issues potentially hinder technological development and content production. On the other hand, some AI applications can threaten the interests and incentives of those who create works and subject matter that are protected by related rights. This article examines whether EU copyright and antitrust law are capable of addressing these challenges. It identifies possibilities and obstacles in applying exceptions for text and data mining (TDM) and temporary copying to the development of artificial creativity (AC) applications. The article also examines mechanisms by which EU antitrust law facilitates access to copyright-protected training materials and licences – an important complement to the copyright exceptions. While copyright and antitrust law enable the development of AC in certain situations, their tools are limited to particular types of AI applications, certain categories of subject matter and specific market conditions, and are subject to requirements concerning the development process as well as considerable legal uncertainty. Copyright and antitrust law also remain largely toothless against contractual and technological restraints, while recent EU initiatives dealing with data access also provide little relief in this regard.
Stochastic quantum Krylov protocol with double-factorized Hamiltonians

We propose a class of randomized quantum Krylov diagonalization (rQKD) algorithms capable of solving the eigenstate estimation problem with modest quantum resource requirements. Compared to previous real-time evolution quantum Krylov subspace methods, our approach expresses the time evolution operator ${e}^{\ensuremath{-}i\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{H}\ensuremath{\tau}}$ as a linear combination of unitaries and subsequently uses a stochastic sampling procedure to reduce circuit depth requirements. While our methodology applies to any Hamiltonian with fast-forwardable subcomponents, we focus on its application to the explicitly double-factorized electronic-structure Hamiltonian. To demonstrate the potential of the proposed rQKD algorithm on near-term quantum devices, we provide numerical benchmarks for a variety of molecular systems with circuit-based state-vector simulators including the effects of sampling noise, achieving ground-state energy errors of less than $1 \mathrm{kcal} {\mathrm{mol}}^{\ensuremath{-}1}$ with circuit depths orders of magnitude shallower than those required for low-rank deterministic Trotter-Suzuki decompositions.
Description of reaction and vibrational energetics of CO2–NH3 interaction using quantum computing algorithms

CO2 capture is critical to solving global warming. Amine-based solvents are extensively used to chemically absorb CO2. Thus, it is crucial to study the chemical absorption of CO2 by amine-based solvents to better understand and optimize CO2 capture processes. Here, we use quantum computing algorithms to quantify molecular vibrational energies and reaction pathways between CO2 and a simplified amine-based solvent model—NH3. Molecular vibrational properties are important to understanding kinetics of reactions. However, the molecule size correlates with the strength of anharmonicity effect on vibrational properties, which can be challenging to address using classical computing. Quantum computing can help enhance molecular vibrational calculations by including anharmonicity. We implement a variational quantum eigensolver (VQE) algorithm in a quantum simulator to calculate ground state vibrational energies of reactants and products of the CO2 and NH3 reaction. The VQE calculations yield ground vibrational energies of CO2 and NH3 with similar accuracy to classical computing. In the presence of hardware noise, Compact Heuristic for Chemistry (CHC) ansatz with shallower circuit depth performs better than Unitary Vibrational Coupled Cluster. The “Zero Noise Extrapolation” error-mitigation approach in combination with CHC ansatz improves the vibrational calculation accuracy. Excited vibrational states are accessed with quantum equation of motion method for CO2 and NH3. Using quantum Hartree–Fock (HF) embedding algorithm to calculate electronic energies, the corresponding reaction profile compares favorably with Coupled Cluster Singles and Doubles while being more accurate than HF. Our research showcases quantum computing applications in the study of CO2 capture reactions.
Predicting Software Cohesion Metrics with Machine Learning Techniques

The cohesion value is one of the important factors used to evaluate software maintainability. However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually. Although there are many static code analysis tools, not every tool measures every metric. The user should apply different tools for different metrics. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest (RF)) to solve them alternatively. We created two datasets utilizing two different open-source software projects. According to the obtained results, for the LCOM2 and TCC metrics, the KNN algorithm provided the best results, and for LCC and LSCC metrics, the REPTree algorithm was the best. However, out of all the metrics, RF, REPTree, and KNN had close performances with each other, and therefore any of the RF, REPTree, and KNN techniques can be used for software cohesion metric prediction.
Robust Semi-Quantum Summation over a Collective-Dephasing Noise Channel

Quantum summation is one of the various applications in secure multi-party computation. However, most of the existing quantum summation protocols assume that the participants possess all the quantum devices. Considering future applications, the capability of the participants must be adjusted before it can be put into practical use. Although Boyer et al. proposed that the semi-quantum environment could be used to solve this problem; another practical problem is the interference by noise. In 2022, Ye et al. proposed a two-party semi-quantum summation (SQS) protocol resistant to the interference of collective noise, in which two classical participants can accomplish the summation of their private binary sequences with the assistance of a quantum semi-honest third party. They proved that their SQS protocol is resistant to various eavesdropping attacks. This paper unveils two risks of information leakage in Ye et al.’s SQS protocol. If the aforementioned security issues are not resolved, Ye et al.’s SQS protocol may not be able to perform private quantum computations securely. Fortunately, the SQS protocol against the collective-dephasing noise proposed in this study is free from the issue of information leakage as well as resistant to various quantum attacks. In addition, the quantum efficiency of the SQS protocol proposed in this study is four times higher than that of Ye et al.’s SQS protocol, which can effectively improve the quantum utilization rate.
Cross-domain recommendation with Multi-Auxiliary Domains via Consistent and Selective Cluster-Level Knowledge Transfer

Recommender systems are software tools and techniques designed to provide users with useful items suggestions. For most existing recommender systems, data sparsity is a challenging and common problem. The cross-domain recommender systems can mitigate the sparsity problem of data in a target domain by introducing auxiliary domain with relatively dense data. However, there are three challenging issues in the existing cross-domain recommender systems: (1) Most cross-domain recommender systems are designed for a single auxiliary domain, which may lead to insufficient knowledge transfer. However, in practice, there may be multiple auxiliary domains related to the target domain that are available; (2) Due to the distribution divergence between the auxiliary and target domains, inconsistent knowledge may be transferred to the target domain; (3) Because of the diversity among domains, they do not necessarily share exactly the same rating pattern. Moreover, not all cluster-level knowledge of auxiliary domains is beneficial to the target domain, and some cluster-level knowledge may be harmful to the target domain. To deal with the above three issues, we propose a Cross-Domain Recommendation with Multi-Auxiliary Domains via Consistent and Selective Cluster-Level Knowledge Transfer, called MD-CSKT. First, rating matrices from multiple auxiliary domains are decomposed jointly with a single target rating matrix to transfer richer knowledge from multiple auxiliary domains. Secondly, the Maximum Mean Discrepancy (MMD) regularization constraint is incorporated into the joint decomposition to reduce the distribution discrepancy between each auxiliary domain and the target domain, so as to achieve consistent cluster-level knowledge transfer. Thirdly, selective cluster-level knowledge transfer is realized by adaptively selecting of cluster-level rating knowledge from multiple auxiliary domains in the joint matrix factorization. The experimental results on six real-world datasets in three categories demonstrate that the presented MD-CSKT method substantially outperforms nine comparison methods and achieves the improvement of recommendation accuracy in the target domain. Compared with the state-of-the-art comparison methods, the overall performance of the MD-CSKT method is improved by more than 25%.
Sci-Net: scale-invariant model for buildings segmentation from aerial imagery

Buildings’ segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning-based methods in the literature can be applied to a fixed or narrow-range spatial resolution imagery. In practical scenarios, users deal with a broad spectrum of image resolutions. Thus, a given aerial image often needs to be re-sampled to match the spatial resolution of the dataset used to train the deep learning model, which results in a degradation in segmentation performance. To overcome this challenge, we propose, in this manuscript, scale-invariant neural network (Sci-Net) architecture that segments buildings from wide-range spatial resolution aerial images. Specifically, our approach leverages UNet hierarchical representation and dense atrous spatial pyramid pooling to extract fine-grained multi-scale representations. Sci-Net significantly outperforms state-of-the-art models on the open cities AI and the multi-scale building datasets with a steady improvement margin across different spatial resolutions.
‘You’re not a better person, but you look like you are.’ Hate speech and privilege: contributions from a participatory research study with teenagers

The current expansion of hate speech in different areas of public life poses a challenge for educators committed to developing their praxis from an intercultural approach. In this sense, the social privileges enjoyed by part of the population are exploited through these discourses with political and economic objectives that are incompatible with collective justice. To delve deeper into this issue, a participatory research study was carried out among 52 adolescents and nine educators with the aim of identifying the scenarios, agents and strategies related to these discourses, as well as to create collective strategies to address this social problem. Over a period of 9 months, dialogic workshops were conducted to identify relevant issues that would facilitate a pedagogical approach to addressing hate speech. This article presents results that clarify the role of privilege in educational reflection and action in the face of hate speech. It concludes with some reflections, questions and possible guidelines to promote critical literacy in educational scenarios that help question hate speech when it is based on the defence of inequality through naturalised privileges.
Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

Mode recognition is a basic task to interpret the behavior of multi-functional radar. The existing methods need to train complex and huge neural networks to improve the recognition ability, and it is difficult to deal with the mismatch between the training set and the test set. In this paper, a learning framework based on residual neural network (ResNet) and support vector machine (SVM) is designed, to solve the problem of mode recognition for non-specific radar, called multi-source joint recognition framework (MSJR). The key idea of the framework is to embed the prior knowledge of radar mode into the machine learning model, and combine the manual intervention and automatic extraction of features. The model can purposefully learn the feature representation of the signal on the working mode, which weakens the impact brought by the mismatch between training and test data. In order to solve the problem of difficult recognition under signal defect conditions, a two-stage cascade training method is designed, to give full play to the data representation ability of ResNet and the high-dimensional feature classification ability of SVM. Experiments show that the average recognition rate of the proposed model, with embedded radar knowledge, is improved by 33.7% compared with the purely data-driven model. Compared with other similar state-of-the-art reported models, such as AlexNet, VGGNet, LeNet, ResNet, and ConvNet, the recognition rate is increased by 12%. Under the condition of 0-35% leaky pulses in the independent test set, MSJR still has a recognition rate of more than 90%, which also proves its effectiveness and robustness in the recognition of unknown signals with similar semantic characteristics.