Reviewerly/exHarmony huggingface dataset:
Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

Abstract This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM), including their strengths and weaknesses. A further analysis was carried out to determine to what extent existing PPDM/PPDSM methods address the trade-off between data mining accuracy and data privacy which is a significant concern in the area. The systematic literature review was conducted using data extracted from 104 primary studies from 5 reputed databases. The scope of the study was defined using three research questions and adequate inclusion and exclusion criteria. According to the results of our study, we divided existing PPDM methods into four categories: perturbation, non-perturbation, secure multi-party computation, and combinations of PPDM methods. These methods have different strengths and weaknesses concerning the accuracy, privacy, time consumption, and more. Data stream mining must face additional challenges such as high volume, high speed, and computational complexity. The techniques proposed for PPDSM are less in number than the PPDM. We categorized PPDSM techniques into three categories (perturbation, non-perturbation, and other). Most PPDM methods can be applied to classification, followed by clustering and association rule mining. It was observed that numerous studies have identified and discussed the accuracy-privacy trade-off. However, there is a lack of studies providing solutions to the issue, especially in PPDSM.
Mathematical nuances of Gaussian process-driven autonomous experimentation

Abstract The fields of machine learning (ML) and artificial intelligence (AI) have transformed almost every aspect of science and engineering. The excitement for AI/ML methods is in large part due to their perceived novelty, as compared to traditional methods of statistics, computation, and applied mathematics. But clearly, all methods in ML have their foundations in mathematical theories, such as function approximation, uncertainty quantification, and function optimization. Autonomous experimentation is no exception; it is often formulated as a chain of off-the-shelf tools, organized in a closed loop, without emphasis on the intricacies of each algorithm involved. The uncomfortable truth is that the success of any ML endeavor, and this includes autonomous experimentation, strongly depends on the sophistication of the underlying mathematical methods and software that have to allow for enough flexibility to consider functions that are in agreement with particular physical theories. We have observed that standard off-the-shelf tools, used by many in the applied ML community, often hide the underlying complexities and therefore perform poorly. In this paper, we want to give a perspective on the intricate connections between mathematics and ML, with a focus on Gaussian process-driven autonomous experimentation. Although the Gaussian process is a powerful mathematical concept, it has to be implemented and customized correctly for optimal performance. We present several simple toy problems to explore these nuances and highlight the importance of mathematical and statistical rigor in autonomous experimentation and ML. One key takeaway is that ML is not, as many had hoped, a set of agnostic plug-and-play solvers for everyday scientific problems, but instead needs expertise and mastery to be applied successfully. Graphical abstract
New maps of major oxides and Mg # of the lunar surface from additional geochemical data of Chang'E-5 samples and KAGUYA multiband imager data

In the past, global maps of major oxides and magnesium number (Mg #) on the lunar surface had been derived from spectral data of remote sensing images, combined with "ground truth" geochemical information from Apollo and Luna samples. These compositional maps provide insights into the chemical variations of different geologic units, revealing the regional and global geologic evolution. In this study, we produced new maps of five major oxides (i.e., Al2O3, CaO, FeO, MgO, and TiO2) and Mg # using imaging spectral data from the KAGUYA multiband imager (MI) and the one-dimensional convolutional neural network (1D-CNN) algorithm. We took advantage of recently acquired geochemical information from China's Chang'E-5 (CE-5) samples. We used the coefficients of determination (R2) and Root Mean Squared Error (RMSE) as model evaluation indicators. We compared the results with the previous machine learning algorithm models. Our study shows that the 1D-CNN algorithm model used in this study had a higher degree of fit and smaller dispersion between the "ground truth" value of geochemical information and the predicted value of spectral data. The 1D-CNN algorithm generally performs better in describing the complex nonlinear relationship between spectra and chemical components. In addition, we present regions of mare domes in Mairan Dome (43.76°N, 49.90°W) and irregular mare patches (IMPs) in Sosigenes (8.34°N, 19.07°E) to demonstrate the geologic implications of these new maps. With the highest spatial resolution (∼ 59 m/pixel), these new maps of five major oxides and Mg # will serve as an important guide in future studies of lunar geology.
Dynamic Resource Allocation Using an Adaptive Multi-Objective Teaching-Learning Based Optimization Algorithm in Cloud

Resource allocation is a non-polynomial complete problem in the cloud data center that selects the proper resources to execute many fine computational granularity tasks. Customer requirements and capacity of applications change frequently. To bridge the gap between frequently changing customer requirement and available infrastructure for the services, we propose a dynamic resource allocation strategy using an adaptive multi-objective teaching-learning based optimization (AMO-TLBO) algorithm in Cloud computing. To improve the exploration and exploitation capacities, AMO-TLBO introduces the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. Moreover, a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive is used. The objectives of AMO-TLBO include minimizing makespan, cost and maximizing utilization using well-balanced load across virtual machines. The evaluation results show that the proposed algorithm outperforms TLBO, MOPSO and NSGA-II algorithms in terms of different performance metrics.
Vehicle Detection in High-Resolution Aerial Images with Parallel RPN and Density-Assigner

Vehicle detection in aerial images plays a significant role in many remote sensing applications such as city planning, road construction, and traffic control. However, detecting vehicles in aerial images remains challenging due to the existence of tiny objects, the scale variance within the same type of vehicle objects, and dense arrangement in some scenarios, such as parking lots. At present, many state-of-the-art object detectors cannot generate satisfactory results on vehicle detection in aerial images. The receptive field of the current detector is not fine enough to handle the slight scale variance. Moreover, the densely arranged vehicles will introduce ambiguous positive samples in label assignment and false predictions that cannot be deleted by NMS. To this end, we propose a two-stage framework for vehicle detection that better leverages the prior attribution knowledge of vehicles in aerial images. First of all, we design a Parallel RPN that exploits convolutional layers of different receptive fields to alleviate the scale variation problem. To tackle the densely arranged vehicles, we introduce a density-based sample assigner in the vehicle-intensive areas to reduce low-quality and occluded positive samples in the training process. In addition, a scale-based NMS is proposed to filter out redundant proposals hierarchically from different levels of the feature pyramid. Moreover, we construct two challenging vehicle detection datasets based on the AI-TOD and xView datasets which contain many tiny objects. Extensive experiments on these two datasets demonstrate the effectiveness of our proposed method.
Sociophysics: Social collective behavior from the physics point of view

The problem of inferring the underlying structure from available data is important to understanding and controlling the dynamic behavior of complex systems. Although many efforts have been put forward to address this problem, their performance remains limited due to the lack of effective application of structure prior knowledge. Inspired by the powerful feature extraction capabilities of deep learning, this paper develops a complex network structure inference method based on least square generative adversarial network (SI-LSGAN) to promote the network structure inference performance. Specifically, we first construct the problem of network structure inference in the least squares paradigm. Then, a generator neural network is trained to fit the nonlinear mapping relationship between the measurement data and network structure. Next, a discriminator neural network is trained to distinguish the generated samples by incorporating the characteristics of degree distribution and binarization of network structure. Following the adversarial training principle, the generator and discriminator promote each other, and the inference result that conforms to the prior knowledge of the target network can be obtained from the observable data. Experimental results have demonstrated the effectiveness in the fusion of structure prior knowledge, which improves the inference accuracy and efficiency.
Quantum kernel methods for solving regression problems and differential equations

We propose several approaches for solving regression problems and differential equations (DEs) with quantum kernel methods. We compose quantum models as weighted sums of kernel functions, where variables are encoded using feature maps and model derivatives are represented using automatic differentiation of quantum circuits. While previously quantum kernel methods primarily targeted classification tasks, here we consider their applicability to regression tasks, based on available data and differential constraints. We use two strategies to approach these problems. First, we devise a mixed model regression with a trial solution represented by kernel-based functions, which is trained to minimize a loss for specific differential constraints or datasets. Second, we use support vector regression that accounts for the structure of differential equations. The developed methods are capable of solving both linear and nonlinear systems. Contrary to prevailing hybrid variational approaches for parametrized quantum circuits, we perform training of the weights of the model classically. Under certain conditions this corresponds to a convex optimization problem, which can be solved with provable convergence to global optimum of the model. The proposed approaches also favor hardware implementations, as optimization only uses evaluated Gram matrices, but require a quadratic number of function evaluations. We highlight trade-offs when comparing our methods to those based on variational quantum circuits such as the recently proposed differentiable quantum circuits approach. The proposed methods offer potential quantum enhancement through the rich kernel representations using the power of quantum feature maps, and start the quest towards provably trainable quantum DE solvers.
Video steganography: recent advances and challenges

Abstract Video steganography approach enables hiding chunks of secret information inside video sequences. The features of video sequences including high capacity as well as complex structure make them more preferable for choosing as cover media over other media such as image, text, or audio. Video steganography is a prominent as well as the evolving field in the information security domain and significant number of video steganography methods are proposed in recent years. This article provides a comprehensive review of video steganography methods proposed in the literature. This article initially reviews various raw domain-based video steganography methods. In particular, the raw domain-based methods include spatial domain approaches such as least significant bits (LSB), transform domain-based methods such as discrete wavelet transform, discrete cosine transform, etc. Furthermore, the article looks into various compressed domain steganography methods. A critical comparative analysis is included in the article to analyze and contrast the steganography methods proposed in the literature. A brief description of various evaluation matrices for video steganography methods is provided in this article. Moreover, a brief introduction to steganalysis and video steganalysis is provided. The article concludes with a discussion focused on the limitations and challenges of the video steganography methods. Further, a brief insight into future directions in video steganography systems is provided.
Self‐Powered and Humidity‐Modulable Optoelectronic Synapse

Abstract With the advantage of high bandwidth, low power consumption, and low crosstalk from optical signal stimulation, optoelectronic synapses are attracting more and more attention. Herein, a self‐powered two‐terminal optoelectronic synapse based on a lead‐free Cs 2 AgBiBr 6 perovskite/P(VDF‐TrFE)/pentacene heterostructure is designed, which shows bidirectional responses to optical stimuli with different wavelengths ( λ = 445 nm/660 nm). This feature enables the device to emulate both excitatory and inhibitory synaptic behaviors in a fully optical pathway. Besides optical stimulus, humidity is used as an additional stimulus to modulate the synaptic performance of the device under 445 nm illumination. The device is successfully applied to stimulate the behavior of the weather tree forecast and visual learning and forgetting processes under different mood states via humidity regulation.
Tornado Cash and Blockchain Privacy: A Primer for Economists and Policymakers

It is difficult to retain privacy on a public blockchain.In contrast to popular belief, permissionless blockchains are completely transparent.All confirmed transactions are publicly observable and stored as part of the blockchain's history.The users' identities are only protected through the use of addresses that act as pseudonyms.This setup allows public blockchains to operate without any intermediaries and creates a system where everyone can mathematically verify the legitimacy and integrity of transactions as well as the current state of the ledger; but the setup raises severe privacy concerns.If someone obtains information that allows them to link a blockchain address to an entity, they may effectively observe that entity's entire transaction history and associated activity.Even if the entity uses multiple addresses, any link between these addresses may expose the fact that they belong to the same person.Moreover, the immutable and public nature of the data creates a setting where the data accrues over time and will always be available for analysis.The algorithms and tools to analyze the data will become more sophisticated, off-chain data more abundant, and computational constraints less relevant.
Certification of non-classicality in all links of a photonic star network without assuming quantum mechanics

Abstract Networks composed of independent sources of entangled particles that connect distant users are a rapidly developing quantum technology and an increasingly promising test-bed for fundamental physics. Here we address the certification of their post-classical properties through demonstrations of full network nonlocality. Full network nonlocality goes beyond standard nonlocality in networks by falsifying any model in which at least one source is classical, even if all the other sources are limited only by the no-signaling principle. We report on the observation of full network nonlocality in a star-shaped network featuring three independent sources of photonic qubits and joint three-qubit entanglement-swapping measurements. Our results demonstrate that experimental observation of full network nonlocality beyond the bilocal scenario is possible with current technology.
Simultaneously enhanced photon blockades in two microwave cavities via driving a giant atom

Abstract We propose a scheme to enhance photon blockades simultaneously in two microwave cavities connected by a giant atom. In the case that only one cavity is weakly driven, we observe that the enhanced photon blockades occur in both cavities at the same time, which is attributed to the anharmonic eigenenergy spectrum constructed by the resonantly coupled giant atom. When the cavity and the atom are simultaneously driven, the stronger photon blockades in two cavities can be successfully achieved by the destructive quantum interference of two-photon excitation. Interestingly, we find that high single-photon occupations can be obtained in both cases. Moreover, we give the optimal conditions for conventional and unconventional photon blockades through analytical calculations, which are in good agreement with numerical results. Our scheme opens a prospective path to achieve simultaneous photon blockades in two indirectly coupled cavities, and provides a promising method to generate the high-quality and brightness single photon source.
Effectiveness of VR-based training on improving occupants’ response and preparedness for active shooter incidents

Effective training for preparing building occupants for active shooter incidents is essential to reduce harm and fatalities. Virtual Reality (VR) technology has been increasingly used as a promising training method in many applications including emergency preparedness. However, previous studies for active shooter incidents have mainly focused on developing VR-based training prototypes. Thus, the effectiveness of VR-based training for active shooter incidents has yet to be rigorously studied. This study focuses on the effectiveness of VR-based training for active shooter preparedness compared to traditional training methods and investigates the factors behind the improved performance. To study the effectiveness of VR-based training, a total of 108 participants were recruited to experience video-based training, VR-based training with low interactivity, and VR-based training with high interactivity. Both objective and subjective measures were assessed. The results show that VR-based training is more effective than video-based training in improving participants' response performance and perceived preparedness for active shooter incidents. The immersion and interactivity of the VR-based training contribute to performance. Participants without previous training experience benefit more from VR-based training than participants who previously had received some training to prepare for active shooter incidents.
Blockchain technology for sustainable supply chains: a network cluster analysis and future research propositions

Achieving the sustainable goals of the United Nations requires improving supply chain sustainability (SSC). Blockchain technology (BCT) has attracted attention on a global level with the ability to transform supply chain management and sustainability efforts. Recognizing this, this study investigates how BCT plays a role in SSC. The current study looks into the importance of BCT in order to move supply networks towards sustainability by performing bibliometric analysis, and network cluster analysis. Through the literature review, the current literature was analyzed and future research directions were concluded. We begin our study by selecting 297 papers on the relevant subjects by applying various filters to the Web of Science (WoS) database. Influential individuals, journals, and organizations in this field were identified using bibliometric analysis. A network analysis was performed to identify influential co-author, and keywords, and for page rank, and cluster analysis. The network analysis was revealed ten distinct study clusters, and ten propositions were suggested from the analysis of these clusters. Additionally, a conceptual framework for the research was proposed, which can advise managers, practitioners, and researcher communities on the key trends and topics in this emerging research domain. Furthermore, to guide research scholars in this field, 33 future research directions were suggested.
The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71–0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74–0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82–0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.
Passport: Improving Automated Formal Verification Using Identifiers

Formally verifying system properties is one of the most effective ways of improving system quality, but its high manual effort requirements often render it prohibitively expensive. Tools that automate formal verification by learning from proof corpora to synthesize proofs have just begun to show their promise. These tools are effective because of the richness of the data the proof corpora contain. This richness comes from the stylistic conventions followed by communities of proof developers, together with the powerful logical systems beneath proof assistants. However, this richness remains underexploited, with most work thus far focusing on architecture rather than on how to make the most of the proof data. This article systematically explores how to most effectively exploit one aspect of that proof data: identifiers. We develop the Passport approach, a method for enriching the predictive Coq model used by an existing proof-synthesis tool with three new encoding mechanisms for identifiers: category vocabulary indexing, subword sequence modeling, and path elaboration. We evaluate our approach’s enrichment effect on three existing base tools: ASTactic, Tac, and Tok. In head-to-head comparisons, Passport automatically proves 29% more theorems than the best-performing of these base tools. Combining the three tools enhanced by the Passport approach automatically proves 38% more theorems than combining the three base tools. Finally, together, these base tools and their enhanced versions prove 45% more theorems than the combined base tools. Overall, our findings suggest that modeling identifiers can play a significant role in improving proof synthesis, leading to higher-quality software.