Reviewerly/exHarmony huggingface dataset:
A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning

Besides accuracy, recent studies on machine learning models have been addressing the question on how the obtained results can be interpreted. Indeed, while complex machine learning models are able to provide very good results in terms of accuracy even in challenging applications, it is difficult to interpret them. Aiming at providing some interpretability for such models, one of the most famous methods, called SHAP, borrows the Shapley value concept from game theory in order to locally explain the predicted outcome of an instance of interest. As the SHAP values calculation needs previous computations on all possible coalitions of attributes, its computational cost can be very high. Therefore, a SHAP-based method called Kernel SHAP adopts a strategy that approximates such values with less computational effort. However, we see two weaknesses in Kernel SHAP: its formulation is difficult to understand and it does not consider further game theory assumptions that could reduce the computational cost. Therefore, in this paper, we propose a novel approach that addresses such weaknesses. Firstly, we provide a straightforward formulation of a SHAP-based method for local interpretability by using the Choquet integral, which leads to both Shapley values and Shapley interaction indices. Thereafter, we propose to adopt the concept of k-additive games from game theory, which contributes to reduce the computational effort when estimating the SHAP values. The obtained results attest that our proposal needs less computations on coalitions of attributes to approximate the SHAP values.
Man vs the machine in the struggle for effective text anonymisation in the age of large language models

Abstract The collection and use of personal data are becoming more common in today’s data-driven culture. While there are many advantages to this, including better decision-making and service delivery, it also poses significant ethical issues around confidentiality and privacy. Text anonymisation tries to prune and/or mask identifiable information from a text while keeping the remaining content intact to alleviate privacy concerns. Text anonymisation is especially important in industries like healthcare, law, as well as research, where sensitive and personal information is collected, processed, and exchanged under high legal and ethical standards. Although text anonymisation is widely adopted in practice, it continues to face considerable challenges. The most significant challenge is striking a balance between removing information to protect individuals’ privacy while maintaining the text’s usability for future purposes. The question is whether these anonymisation methods sufficiently reduce the risk of re-identification, in which an individual can be identified based on the remaining information in the text. In this work, we challenge the effectiveness of these methods and how we perceive identifiers. We assess the efficacy of these methods against the elephant in the room, the use of AI over big data. While most of the research is focused on identifying and removing personal information, there is limited discussion on whether the remaining information is sufficient to deanonymise individuals and, more precisely, who can do it. To this end, we conduct an experiment using GPT over anonymised texts of famous people to determine whether such trained networks can deanonymise them. The latter allows us to revise these methods and introduce a novel methodology that employs Large Language Models to improve the anonymity of texts.
Cyberbullying Conceptualization, Characterization and Detection in Social Media – A Systematic Literature Review

Social media has become the primary form of communication wherein users can share intimate moments online through photos, videos, or posts. At a glance, while this greatly improves interconnectivity between people, it also increases the propensity towards unrestricted acts of Cyberbullying, prompting the need for a data-centric detection system. Unfortunately, these sites generate much metadata, which begs the need for complex Machine Learning (ML) classifiers to categorize these acts accurately. Prior studies on the subject matter only target the topics of Conceptualization, Characterization, and Classification of Cyberbullying individually, so this research aims to provide a more holistic understanding of the subject matter in a continuous, synthesized format. This study found that Cyberbullying differs from Traditional Bullying in key areas of Repetition and Intention. Moreover, multimodal feature sets, as opposed to single feature sets, significantly improve ML classifiers' performance. Lastly, the selection of appropriate ML classifiers and performance metrics is context-dependent. The result of this study presents a consolidated view of relevant parties tackling different aspects of an ML-based automated Cyberbullying detection system so that those assigned tasks can approach them strategically
Quantum optical memory for entanglement distribution

Optical photons are powerful carriers of quantum information, which can be delivered in free space by satellites or in fibers on the ground over long distances. Entanglement of quantum states over long distances can empower quantum computing, quantum communications, and quantum sensing. Quantum optical memories are devices designed to store quantum information in the form of stationary excitations, such as atomic coherence, and are capable of coherently mapping these excitations to flying qubits. Quantum memories can effectively store and manipulate quantum states, making them indispensable elements in future long-distance quantum networks. Over the past two decades, quantum optical memories with high fidelities, high efficiencies, long storage times, and promising multiplexing capabilities have been developed, especially at the single-photon level. In this review, we introduce the working principles of commonly used quantum memory protocols and summarize the recent advances in quantum memory demonstrations. We also offer a vision for future quantum optical memory devices that may enable entanglement distribution over long distances.
An Intelligent Machine Learning Approach for Fraud Detection in Medical Claim Insurance: A Comprehensive Study

Medical claim insurance fraud poses a significant challenge for insurance companies and the healthcare system, leading to financial losses and reduced efficiency. In response to this issue, we present an intelligent machine- learning approach for fraud detection in medical claim insurance to enhance fraud detection accuracy and efficiency. This comprehensive study investigates the application of advanced machine learning algorithms for identifying fraudulent claims within the insurance domain. We thoroughly evaluate several candidate algorithms to select an appropriate machine learning algorithm, considering the unique characteristics of medical claim insurance data. Our chosen algorithm demonstrates superior capabilities in handling fraud detection tasks and is the foundation for our proposed intelligent approach. Our proposed approach incorporates domain knowledge and expert rules, augmenting the machine learning algorithm to address the intricacies of fraud detection within the insurance context. We introduce modifications to the algorithm, further enhancing its performance in detecting fraudulent medical claims. Through an extensive experimental setup, we evaluate the performance of our intelligent machine-learning approach. The results indicate significant accuracy, precision, recall, and F1-score improvements compared to traditional fraud detection methods. Additionally, we conduct a comparative analysis with other machine learning algorithms, affirming the superiority of our approach in this domain. The discussion section offers insights into the interpretability of the experimental findings and highlights the strengths and limitations of our approach. We conclude by emphasizing the significance of our research for the insurance industry and the potential impact on the healthcare system's efficiency and cost-effectiveness.
On the role of entanglement in qudit-based circuit compression

Gate-based universal quantum computation is formulated in terms of two types of operations: local single-qubit gates, which are typically easily implementable, and two-qubit entangling gates, whose faithful implementation remains one of the major experimental challenges since it requires controlled interactions between individual systems. To make the most of quantum hardware it is crucial to process information in the most efficient way. One promising avenue is to use higher-dimensional systems, qudits, as the fundamental units of quantum information, in order to replace a fraction of the qubit-entangling gates with qudit-local gates. Here, we show how the complexity of multi-qubit circuits can be lowered significantly by employing qudit encodings, which we quantify by considering exemplary circuits with exactly known (multi-qubit) gate complexity. We discuss general principles for circuit compression, derive upper and lower bounds on the achievable advantage, and highlight the key role played by entanglement and the available gate set. Explicit experimental schemes for photonic as well as for trapped-ion implementations are provided and demonstrate a significant expected gain in circuit performance for both platforms.
Video Object Detection From Compressed Formats for Modern Lightweight Consumer Electronics

The rapid rise of technological advancements led to the increased consumption of electronic gadgets. This change expedited the requirement for sustainable technologies to meet the growing consumer requirements with minimum computational costs. Nowadays, video content shares a large proportion of the internet bandwidth. Object Detection from the videos is essential in various real-time applications. Traditionally, the videos are decoded to the raw format for detection tasks. This analytics process can be more efficient if the detection tasks are carried out from compressed video formats instead of raw video. The compressed format of the videos, produced by modern deep learning-based approaches, contains both semantic and motion information in easily consumable formats. Based on the same notion, a video compression cum object detection network has been proposed in this paper, which consumes the compressed videos for carrying out detection tasks. The proposed network comprises an already-designed video compression network, which has been extended to incorporate object detection capabilities. The proposed network has been experimented with a standard ImageNet VID dataset, and the results show fast and efficient object detection from the compressed videos. Coupled with temporal features, the proposed model achieves significantly better mAP of 44.3 w.r.t. 36.7 and 39.6 from YOLOv5-s and YOLOX-s models, respectively. The comparative results have shown incremental improvement in the detection tasks from the compressed videos, making it sustainable for its application in modern lightweight consumer electronic devices.
Digital image watermarking based on hybrid FRT-HD-DWT domain and flamingo search optimisation

The image watermarking topologies afford a promising solution in digital media copyright protection. However, it is essential to take into account the robustness of watermarking methods. Therefore, in this paper, a digital image watermarking technique based on finite ridgelet transform (FRT), discrete wavelet transform (DWT), and Hessenberg decomposition (HD) is proposed. The FRT and HD methods are hybridised with DWT to enhance the embedding capacity. Likewise, the embedding strength factor is also optimised through a flamingo search algorithm (FSA). After embedding the extraction process is carried out with deep belief neural (DBN) network. The experiments are conducted on four kinds of host images like Lena, house, baboon, and Barbara in MATLAB platform. The results are compared with different models in terms of peak signal to noise ratio (PSNR), normalisation coefficient (NC), and structural similarity index measure (SSIM) under various geometric attacks.
RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Low-light image enhancement methods have limitations in addressing issues such as color distortion, lack of vibrancy, and uneven light distribution and often require paired training data. To address these issues, we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network (RF-Net), which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms. This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images. In the first stage, we design a multi-scale feature extraction module based on Retinex theory, capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images. In the second stage, an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features, and the generated images are fused with the original input images to complete the low-light image enhancement. Experiments show the effectiveness and rationality of each module designed in this paper. And the method reconstructs the details of contrast and color distribution, outperforms the current state-of-the-art methods in both qualitative and quantitative metrics, and shows excellent performance in the real world.
Best practices for portfolio optimization by quantum computing, experimented on real quantum devices

Abstract In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.
replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To help overcome these limitations, we developed replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation. We also show that it increases the subject-specificity and domain-invariance of the trained networks, thereby conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.
A fuzzy decision-making system for video tracking with multiple objects in non-stationary conditions

Computer vision remains challenged by tracking multiple objects in motion frames, despite efforts to improve surveillance, healthcare, and human-machine interaction. This paper presents a method for monitoring several moving objects in non-stationary settings for autonomous navigation. Additionally, at each phase, movement information between successive frames, including the new frame and the previous frame, is employed to determine the location of moving objects inside the camera's field of view, and the background in the new frame is determined. With the help of a matching algorithm, the Kanade–Lucas–Tomasi (KLT) feature tracker for each frame is determined. To get the new frame, we access the matching feature points between two subsequent frames, calculate the movement size of the feature points and the camera movement, and subtract the previous frame of moving objects from the current frame. Every moving object within the camera's field of view is captured at every moment and location. The moving items are categorized and segregated using fuzzy logic based on their mass center and length-to-width ratio. Our algorithm was implemented to investigate autonomous navigation surveillance of three types of moving objects, such as a vehicle, a pedestrian, a bicycle, or a motorcycle. The results indicate high accuracy and an acceptable time requirement for monitoring moving objects. It has a tracking and classification accuracy of around 75 % and processes 43 frames per second, making it superior to existing approaches in terms of speed and accuracy.
Photonic integrated neuro-synaptic core for convolutional spiking neural network

Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network (PSNN). However, they are separately implemented with different photonic materials and devices, hindering the large-scale integration of PSNN. Here, we propose, fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback (DFB) laser with a saturable absorber (DFB-SA). A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation. Furthermore, a four-channel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network, achieving a recognition accuracy of 87% for the MNIST dataset. The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.
Efficient classical algorithms for simulating symmetric quantum systems

In light of recently proposed quantum algorithms that incorporate symmetries in the hope of quantum advantage, we show that with symmetries that are restrictive enough, classical algorithms can efficiently emulate their quantum counterparts given certain classical descriptions of the input. Specifically, we give classical algorithms that calculate ground states and time-evolved expectation values for permutation-invariant Hamiltonians specified in the symmetrized Pauli basis with runtimes polynomial in the system size. We use tensor-network methods to transform symmetry-equivariant operators to the block-diagonal Schur basis that is of polynomial size, and then perform exact matrix multiplication or diagonalization in this basis. These methods are adaptable to a wide range of input and output states including those prescribed in the Schur basis, as matrix product states, or as arbitrary quantum states when given the power to apply low depth circuits and single qubit measurements.
DESIGNING AND EVALUATING DL-MODEL FOR VULNERABILITY DETECTION IN SMART CONTRACTS

Task features. Smart-contracts are programs that are stored in a distributed registry and execute code written in them in response to transactions addressed to them. Such smart- contracts are written in the Solidity programming language, which has a specific structure and syntax. The language was developed for the Ethereum platform. Having a specific structure, such languages are prone to certain vulnerabilities, the use of which can lead to large financial losses. Task statement. In this paper, a Deep Learning (DL) model is used to detect the vulnerabilities. Using the chosen approach and a properly specified input data structure, it is possible to detect complex dependencies between various program variables that contain vulnerabilities and bugs. Research results. Using well-defined experiments, this approach was investigated to better understand the model and improve its performance. The developed model classified vulnerabilities at the string level, using the Solidity corpus of smart-contracts as input data. The application of the DL model allows vulnerabilities of varying complexity to be identified in smart-contracts. Conclusions. Thus, the pipeline developed by us can capture more internal code information than other models. Information from software tokens, although semantically incapable of capturing vulnerabilities, increases the accuracy of models. The interpretability of the model has been added through the use of the attention mechanism. Operator accounting has shown significant performance improvements.
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains an open problem. In this work, we aim to provide a thorough answer to this question. Through extensive experiments on nine datasets across four IE tasks, we demonstrate that current advanced LLMs consistently exhibit inferior performance, higher latency, and increased budget requirements compared to fine-tuned SLMs under most settings. Therefore, we conclude that LLMs are not effective few-shot information extractors in general. Nonetheless, we illustrate that with appropriate prompting strategies, LLMs can effectively complement SLMs and tackle challenging samples that SLMs struggle with. And moreover, we propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs. In this paradigm, SLMs serve as filters and LLMs serve as rerankers. By prompting LLMs to rerank a small portion of difficult samples identified by SLMs, our preliminary system consistently achieves promising improvements (2.4% F1-gain on average) on various IE tasks, with an acceptable time and cost investment.