Reviewerly/exHarmony huggingface dataset:
Equivariant Contrastive Learning for Sequential Recommendation

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be sub-optimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., feature-level dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.
✨ Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations

Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derived from local historical news. However, this approach lacks a global perspective, failing to account for users’ hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY (Global-LOcal news Recommendation sYstem), which combines global representations learned from other users with local representations to enhance personalized recommendation systems. We accomplish this by constructing a Global-aware Historical News Encoder, which includes a global news graph and employs gated graph neural networks to enrich news representations, thereby fusing historical news representations by a historical news aggregator. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations1.
Examining AI and Systemic Factors for Improved Chatbot Sustainability

Chatbots link companies and users, increase conversions, reduce labor costs, and provide answers based on big data. Since COVID-19, demand for non-face-to-face services has increased. Despite expectations, chatbot use is inconsistent and satisfaction is low. This study identifies factors for improving the sustainability of chatbot services by considering artificial intelligence factors (personalization, anthropomorphism, social presence) and systemic factors (responsiveness, compatibility). The confirmatory factor analysis and structural equation model of the measurement model were analyzed using Smart PLS 3.3. Two hypotheses were rejected because the effect on expectation-confirmation was not statistically significant. This study presents implications for future chatbot research and development.
PrivacyEAFL: Privacy-Enhanced Aggregation for Federated Learning in Mobile Crowdsensing

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key exchange protocol. Secondly, we design a data encoding and aggregating method with data packing which can reduce the computation cost and communication overhead for the system. Thirdly, as the number of participants’ samples are dynamically changeable in MCS, we design a sample number protection method that can implement the security and privacy of the number of training samples owned by participants. Finally, we provide the experimental results on real-world datasets (i.e, MNIST and Car Evaluation) with crowdsensing devices under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Raspberry-Pi 4B</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Redmi-K30 Pro</i> , respectively, and the results demonstrate that our scheme is more efficient and practical in secure and privacy-enhanced model aggregation for federated learning in mobile crowdsensing.
The Coming Decades of Quantum Simulation

Contemporary quantum technologies face major difficulties in fault tolerant quantum computing with error correction, and focus instead on various shades of quantum simulation (Noisy Intermediate Scale Quantum, NISQ) devices, analogue and digital quantum simulators and quantum annealers. There is a clear need and quest for such systems that, without necessarily simulating quantum dynamics of some physical systems, can generate massive, controllable, robust, entangled, and superposition states. This will, in particular, allow the control of decoherence, enabling the use of these states for quantum communications (e.g. to achieve efficient transfer of information in a safer and quicker way), quantum metrology, sensing and diagnostics (e.g. to precisely measure phase shifts of light fields, or to diagnose quantum materials). In this Chapter we present a vision of the golden future of quantum simulators in the decades to come.
Medical image diagnosis based on adaptive Hybrid Quantum CNN

Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum-classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This is followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness is based on the development of an optimizer with a loss base adaptive momentum. Several algorithms are developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms are tested against a Kaggle brain dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.
Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network

Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instability) can easily collapse the performance of error-aware designs. What’s worse, users can even not be aware of the performance degradation caused by the change in noise. To address both issues, in this paper we use Quantum Neural Network (QNN) as a vehicle to present a novel compression-aided framework, namely QuCAD, which will adapt a trained QNN to fluctuating quantum noise. In addition, with the historical calibration (noise) data, our framework will build a model repository offline, which will significantly reduce the optimization time in the online adaption process. Emulation results on an earthquake detection dataset show that QuCAD can achieve 14.91% accuracy gain on average in 146 days over a noise-aware training approach. For the execution on a 7-qubit IBM quantum processor, ibm-jakarta, QuCAD can consistently achieve 12.52% accuracy gain on earthquake detection.
High-Precision Cluster Federated Learning for Smart Home: An Edge-Cloud Collaboration Approach

Owing to the strong protection of data privacy, federated learning (FL) has become a key method for achieving intelligent decision making in smart homes. However, under realistic conditions, such as differentiated requirements and heterogeneous service environments, FL in smart homes faces the problem of non-independent and identically distributed (non-IID) data and uneven computing power, which leads to the poor adaptability of global models. To address this issue, this study proposes a cluster FL architecture based on edge-cloud collaboration. First, a Gaussian mixture model-based cluster FL is proposed to improve the model accuracy by clustering features on the FL dataset and ensuring an independent identical distribution of the data. Subsequently, a model training strategy based on edge-cloud collaboration is proposed to achieve the sharing of edge-cloud computing power by split learning, which provides sufficient computing power for model training. The simulation results show that the proposed architecture improves the accuracy of global models while ensuring normal network service provision.
Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections

Using 3D CNNs on high-resolution medical volumes is very computationally demanding, especially for large datasets like UK Biobank, which aims to scan 100,000 subjects. Here, we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of 3D volumes leads to reasonable test accuracy (mean absolute error of about 3.5 years) when predicting age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20-50 s using a single GPU, which is two orders of magnitude faster than a small 3D CNN. This speedup is explained by the fact that 3D brain volumes contain a lot of redundant information, which can be efficiently compressed using 2D projections. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.
An Overview of Python Libraries for Data Science

In this era Python is the most popular as well as in -demand language for Data Science due to the number of libraries available for data processing, analysis and data visualization. The aim of this review paper is to give the overview of different available libraries. For this we grouped 48 different libraries in 3 different categories which are Data Collection, Data Analysis &amp; Processing and Data Visualization. For comparison we use the GitHub community base (Stars, Forks and commits) as well as their properties and functionalities.
In situ trace element compositions of sulfides constraining the genesis of the worldclass Shuangjianzishan Ag-Pb-Zn deposit, NE China

The worldclass Shuangjianzishan Ag-Pb-Zn deposit, containing proven reserves of >21,665 t of silver @128.5 g/t, >3.3 Mt of lead @1.6 wt%, and >1.9 Mt of zinc @0.6 wt%, is the largest silver deposit in Asia. This giant Ag mineralization system has attracted extensive attention since its discovery. However, the incorporation mechanism of Ag in ore sulfides and the deposit genetic type are still greatly debated. We have conducted in situ LA-ICP-MS trace element analyses for sulfides in this deposit to address the above issues. Three paragenetic stages of mineralization were recognized at Shuangjianzishan, including pyrite + quartz + K-feldspar stage (I), galena + sphalerite + Ag-bearing sulfosalt + quartz + calcite + chlorite + sericite stage (II), and post-ore quartz stage (III). Most sulfides and silver minerals were deposited in stage II. In situ time-resolved depth profiles, principal component analysis (PCA), and correlation plots of trace element for sulfides indicate that trace elements are dominantly present in sulfides as solid solutions and nano-inclusions, and less as micro-inclusions. For silver, the most critical element in the studied deposit, its substitution mechanisms vary largely among sphalerite, pyrite, and galena. In sphalerite, Ag enters the crystal lattice associated with coupled substitution of Zn by Fe, Sn, Sb, and Ga. In pyrite, Ag enters the lattice through the coupled substitution of Fe by Cu, Zn, and Sn. In galena, Ag enters lattice by the coupled substitution of Pb by Sb and Zn. Based on the GGIMFis geothermometer calculations, the temperatures for ore stage II range from 290° to 333 ℃ (mean of 319 ℃), which are almost identical to the results of the sulfur isotope geothermometry and fluid inclusion thermometry from the previous studies. Furthermore, the trace element distributions in the Shuangjianzishan sulfides, e.g., the enrichment of Fe, Mn, Sn, Cu, Ag, Sb and Pb and the depletion of Ga, Ge, Co and In in sphalerite, as well as the geological and geochemical features, infer that the studied deposit is similar to the typical epithermal type deposit. We therefore propose that the worldclass Shuangjianzishan Ag-Pb-Zn deposit is an epithermal deposit, and this contribution indicates that in situ LA-ICP-MS trace element analysis of sulfides could help shed light on identifying the genetic type for the hydrothermal ore deposits.
Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean

Mineral prospectivity mapping constitutes an efficient tool for delineating areas of highest interest to guide future exploration. Multiple knowledge-driven approaches have been applied for the creation of prospectivity maps for deep-sea ferromanganese (Fe-Mn) crusts over the last decades. The results of a data-driven approach making use of an extensive data collection exercise on occurrences of Fe-Mn crusts in the World Ocean and recent increase in global marine datasets are presented. A Random Forest machine learning algorithm is applied, and results compared with previously established expert-driven maps. Optimal predictive conditions for the algorithm are observed for (i) a forest size superior to a hundred trees, (ii) a training dataset larger than 10%, and (iii) a number of predictors to be used as nodes superior to two. The confusion matrix and out-of-bag errors on the remaining unused data highlight excellent predictive capabilities of the trained model with a prediction accuracy for Fe-Mn crusts of 87.2% and 98.2% for non-crusts locations, with a Kohen’s K index of 0.84, validating its application for prediction at the World scale. The slope of the seafloor, sediment thickness, sediment type, biological productivity, and abyssal mountain constitute the five strongest explanatory variables in predicting the occurrence of Fe-Mn crusts. Most ‘hand-drawn’ knowledge-driven prospective areas are also considered prospective by the random forest algorithm with notable exceptions along the coast of the American continent. However, poor correlation is observed with knowledge-driven GIS-based criterion mapping as the Random Forest considers un-prospective most target areas from the GIS approach. Overall, the Random Forest prediction performs better in predicting a high chance of Fe-Mn crust occurrence in ISA licensed area than the GIS approach, which constitutes an external validation of the predictive quality of the random forest model.
Infinity augmented state Kalman filter and its application in unknown input and state estimation

This paper investigates the infinity augmented state Kalman filter (IASKF) to obtain the unknown input and state estimation for systems with the rank-deficient matrix. With the aid of input model, which is described as the random walk process with finite covariance, the augmented state Kalman filter (ASKF) is established. Then in light of matrix limiting, the IASKF is deduced from the ASKF as the covariance of the input signal tends to infinity. It is shown that the previous unbiased minimum-variance (UMV) algorithm and recursive three-step filter (RTSF) are the special cases of the proposed IASKF algorithm. Moreover, the condition of existence of the IASKF is given. The present work gives a new insight into unknown input and state estimation. It indicates that the ASKF can also be used to estimate the unknown input and state by only choosing a proper covariance matrix of the input signal. A simulation example is given to illustrate the effectiveness of the proposed results.
Predictors of financial sustainability for cryptocurrencies: An empirical study using a hybrid SEM-ANN approach

Cryptocurrencies stand as a significant financial innovation, poised to transform traditional financial systems by facilitating faster, cost-efficient, and inclusive value transfers. Understanding their sustainability is essential in comprehending their potential to reshape established financial frameworks. Hence, this study aimed to identify the factors predicting the financial sustainability of cryptocurrencies. The study proposed a model based on the “Expectation Confirmation Theory” (ECT) and tested the model using a multi-analytical approach by combining “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN). The sample of the study included 1649 participants, ranging in age from 17 to 70. Results indicated that perceived risk, regulation, price volatility, innovativeness, and confirmation of expectations significantly predicted perceived usefulness, which in turn significantly predicted satisfaction with cryptocurrencies. The findings highlighted that users' degree of confirmation of expectations significantly influenced their satisfaction with and perceived usefulness of cryptocurrencies. The specified paths within the model accounted for 61 % and 74 % of the variance in perceived usefulness and financial sustainability, respectively. In comparison to the results obtained through SEM analysis, the deep ANN multi-layer perceptron displayed superior performance in predicting perceived usefulness. This was evident from its enhanced predictive accuracy, achieving averages of 87.34 % for training and 87.76 % for testing.
Chinese intermediate English learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing

ChatGPT is a publicly available chatbot that can quickly generate texts on given topics, but it remains unknown whether the chatbot is as good as or even superior to human writers in all aspects of writing, and whether the chatbot's writing quality can be significantly improved by updating commands. To investigate these issues, this study compared writing performance on a narrative topic by ChatGPT and Chinese intermediate English (CIE) learners so as to reveal the chatbot's advantages and disadvantages in writing. The data were analyzed by virtue of the five discourse components using Coh-Metrix (a special instrument for analyzing language discourse), and the results revealed that ChatGPT performed better than human writers in narrativity, word concreteness, and referential cohesion, but worse in syntactic simplicity and deep cohesion in its initial version. After its commands were updated, the resulting version was better in its syntactic simplicity, but it still lagged far behind the CIE learners' writing in deep cohesion. In addition, a correlation analysis of the discourse components suggests that narrativity was correlated with referential cohesion for both ChatGPT and the human writers, but the correlations varied within each group.
Quantum networks with neutral atom processing nodes

Abstract Quantum networks providing shared entanglement over a mesh of quantum nodes will revolutionize the field of quantum information science by offering novel applications in quantum computation, enhanced precision in networks of sensors and clocks, and efficient quantum communication over large distances. Recent experimental progress with individual neutral atoms demonstrates a high potential for implementing the crucial components of such networks. We highlight latest developments and near-term prospects on how arrays of individually controlled neutral atoms are suited for both efficient remote entanglement generation and large-scale quantum information processing, thereby providing the necessary features for sharing high-fidelity and error-corrected multi-qubit entangled states between the nodes. We describe both the functionality requirements and several examples for advanced, large-scale quantum networks composed of neutral atom processing nodes.