摘要：
Classic deep learning methods for human activity recognition (HAR) from wearable sensors struggle with cross-person and cross-position challenges due to nonidentical data distributions caused by context variations (e.g., user, sensor placement). Existing solutions show promise but usually require extensive labeled data from source and target contexts, which is often unavailable in real-world scenarios. To address these limitations, we introduce semi-supervised context agnostic representation learning without target (SCAGOT), a novel semi-supervised approach that learns context-agnostic activity representations without relying on target context data. SCAGOT uses a dual-stream architecture with adversarial disentanglement and a contrastive clustering mechanism. This effectively separates context features from context-agnostic activity features, maximizing intraclass compactness and interclass separability in the activity representation space. In addition, a new inverse cross-entropy loss further refines the representations by removing residual context information. Extensive evaluations on four benchmark datasets demonstrate that SCAGOT outperforms state-of-the-art methods in cross-person and cross-position HAR, offering a promising solution for robust real-world activity recognition.
摘要：
Post-deduplication in traditional cloud environments primarily focuses on single-node, where delta compression is performed on the same deduplication node located on server side. However, with data explosion, the multi-node post-deduplication, also called global deduplication, has become a hot issue in research communities, which aims to simultaneously execute delta compression on data distributed across all nodes. Simply setting up single-node deduplication systems on multi-node environments would significantly affect storage utilization and incur secondary overhead from file migration. Nevertheless, existing global deduplication solutions suffer from lower data compression ratios and high computational overhead due to their resemblance detection's inherent limitations and overly coarse granularities. Similar blocks typically have high correlations between sub-blocks; inspired by this observation, we propose IBNR (Intra-Block Neighborhood Relationship-Based Resemblance Detection for High-Performance Multi-Node Post-Deduplication), which introduces a novel resemblance detection based on relationships between sub-blocks and determines the ownership of blocks in entry stage to achieve efficient global deduplication. Furthermore, the by-products of IBNR have shown powerful scalability by replacing internal resemblance detection scheme with existing solutions on practical workloads. Experimental results indicate that IBNR outperforms state-of-the-art solutions, achieving an average 1.99× data reduction ratio and varying degrees of improvement across other key metrics.
摘要：
Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines.
摘要：
Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial–temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can learn to identify potential regions of moving targets through motion saliency estimation and subsequently integrates the information into the popular object detection algorithms to design the retinal-inspired spiking neural network module for drone motion extraction and object detection architecture, which integrates motion and spatial features before object detection to enhance detection accuracy. To design and train the MG-SNN, we propose a new backpropagation method called Dynamic Threshold Multi-frame Spike Time Sequence (DT-MSTS), and establish a dataset for the training and validation of MG-SNN, effectively extracting and updating visual motion features. Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds.
通讯机构：
[Chen, YX ; Peng, GW; Zhang, Y ] U;Univ South China, Sch Chem & Chem Engn, Lab Optoelect Technol Low Dimens Nanomat, Hengyang 421001, Peoples R China.;Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.;Univ South China, Sch Resources Environm & Safety Engn, Hengyang 421001, Peoples R China.
关键词：
2e(−)-WOR-H(2)O(2);Density functional theory;Fe(2+)/Fe(3+) interconversion;SPF system;Synergistic oxidation and reduction
摘要：
Simultaneous organic pollutant oxidation and heavy metal ion reduction may enhance ocean and medical wastewater treatment. Here a self-cycling piezo-photocatalytic Fenton (SPF) system was developed using an Na-Sm bimetal-doped layered ferroelectric perovskite SrBi 2 Nb 2 O 9 (NS-SBNO) and Fe 2+ ions. The experimental results and density functional theory calculations demonstrated that bimetal doping induced an enhanced internal electric field, improving electron–hole pair separation and transmission. NS-SBNO exhibited increased adsorption energy and electron transfer capacity of H 2 O, leading to H 2 O 2 production at 497 μmol g −1 h −1 through a two-electron water oxidation reaction without any sacrificial reagent. Furthermore, compared with traditional Fenton systems, the SPF system afforded higher Fe 2+ /Fe 3+ interconversion efficiency and hydroxyl and superoxide radical yields, highlighting the system’s excellent redox capabilities. The SPF system also demonstrated enhanced oxidation and reduction of complex pollutants in wastewater [e.g., BPA, TC, 2, 4-DCP, NOR, LVX, U(VI), and Cr(VI)], demonstrating promising practical applicability.
Simultaneous organic pollutant oxidation and heavy metal ion reduction may enhance ocean and medical wastewater treatment. Here a self-cycling piezo-photocatalytic Fenton (SPF) system was developed using an Na-Sm bimetal-doped layered ferroelectric perovskite SrBi 2 Nb 2 O 9 (NS-SBNO) and Fe 2+ ions. The experimental results and density functional theory calculations demonstrated that bimetal doping induced an enhanced internal electric field, improving electron–hole pair separation and transmission. NS-SBNO exhibited increased adsorption energy and electron transfer capacity of H 2 O, leading to H 2 O 2 production at 497 μmol g −1 h −1 through a two-electron water oxidation reaction without any sacrificial reagent. Furthermore, compared with traditional Fenton systems, the SPF system afforded higher Fe 2+ /Fe 3+ interconversion efficiency and hydroxyl and superoxide radical yields, highlighting the system’s excellent redox capabilities. The SPF system also demonstrated enhanced oxidation and reduction of complex pollutants in wastewater [e.g., BPA, TC, 2, 4-DCP, NOR, LVX, U(VI), and Cr(VI)], demonstrating promising practical applicability.
摘要：
Diagnosis and treatment of endometrial diseases are crucial for women's health. Over the past decade, ultrasound has emerged as a non-invasive, safe, and cost-effective imaging tool, significantly contributing to endometrial disease diagnosis and generating extensive datasets. The introduction of artificial intelligence has enabled the application of machine learning and deep learning to extract valuable information from these datasets, enhancing ultrasound diagnostic capabilities. This paper reviews the progress of artificial intelligence in ultrasound image analysis for endometrial diseases, focusing on applications in diagnosis, decision support, and prognosis analysis. We also summarize current research challenges and propose potential solutions and future directions to advance ultrasound artificial intelligence technology in endometrial disease diagnosis, ultimately improving women's health through digital tools.
摘要：
Ionic liquids (ILs) have emerged as new persistent pollutants in aquatic environments due to their notable ecotoxicity. Herein, In 2 Se 3 @Ag 3 PO 4 S-scheme piezo-photocatalyst with interfacial In-O and Se-P covalent bonds is prepared for efficient degradation of 1-butyl-2,3-dimethylimidazole bromide ([BMMIm]Br). The experimental results demonstrate that 98.3 % of [BMMIm]Br is degraded under piezo-photocatalysis. Notably, [BMMIm]Br functions as a hole sacrificial agent, leading to a simultaneous H 2 production rate of 582.7 μmol g −1 h −1 and a co-removal efficiency of 97.2 % for radioactive heavy metal ions of U(VI), achieving the "three birds with one stone" effect. Density functional theory (DFT) calculations indicate that the S-scheme structure, combined with the In-O and Se-P covalent bonds and the piezoelectric effect, effectively promotes the separation of electron/hole pairs while inhibiting carrier recombination dynamics, thereby enhancing piezo-photocatalytic performance. Moreover, systemic toxicity evaluations, including assessments of calculated toxicity, zebrafish growth, and bean sprout development, were conducted to verify the reduced toxicity of the degraded intermediates, highlighting the potential practical applications of this approach.
Ionic liquids (ILs) have emerged as new persistent pollutants in aquatic environments due to their notable ecotoxicity. Herein, In 2 Se 3 @Ag 3 PO 4 S-scheme piezo-photocatalyst with interfacial In-O and Se-P covalent bonds is prepared for efficient degradation of 1-butyl-2,3-dimethylimidazole bromide ([BMMIm]Br). The experimental results demonstrate that 98.3 % of [BMMIm]Br is degraded under piezo-photocatalysis. Notably, [BMMIm]Br functions as a hole sacrificial agent, leading to a simultaneous H 2 production rate of 582.7 μmol g −1 h −1 and a co-removal efficiency of 97.2 % for radioactive heavy metal ions of U(VI), achieving the "three birds with one stone" effect. Density functional theory (DFT) calculations indicate that the S-scheme structure, combined with the In-O and Se-P covalent bonds and the piezoelectric effect, effectively promotes the separation of electron/hole pairs while inhibiting carrier recombination dynamics, thereby enhancing piezo-photocatalytic performance. Moreover, systemic toxicity evaluations, including assessments of calculated toxicity, zebrafish growth, and bean sprout development, were conducted to verify the reduced toxicity of the degraded intermediates, highlighting the potential practical applications of this approach.
摘要：
Recently, Oblivious Storage has been proposed to prevent privacy leakage from user access patterns, which obfuscates and makes it computationally indistinguishable from the random sequences by fake accesses and probabilistic encryption. The same data exhibits distinct ciphertexts. Thus, it seriously impedes cloud providers' efforts to improve storage utilization to remove user redundancy, which has been widely used in the existing cloud storage scenario. Inspired by the successful adoption of removing duplicate data in cloud storage, we attempt to integrate obliviousness, remove redundancy, and propose a practical oblivious storage, PEO-Store. Instead of fake accesses, introducing delegates breaks the mapping link between a valid access pattern and a specific client. The cloud interacts only with randomly authorized delegates. This design leverages non-interactive zero-knowledge-based redundancy detection, discrete logarithm problem-based key sharing, and secure time-based delivery proof. These components collectively protect access pattern privacy, accurately eliminate redundancy, and prove the data delivery among delegates and the cloud. Theoretical proof demonstrates that, in our design, the probability of identifying the valid access pattern with a specific client is negligible. Experimental results show that PEO-Store outperforms state-of-the-art methods, achieving an average throughput of up to 3 times faster and saving 74% of storage space.
摘要：
The concept of data locality is crucial for distributed systems (e.g., Spark and Hadoop) to process Big Data. Most of the existing research optimized the data locality from the aspect of task scheduling. However, as the execution container of Spark's tasks, the executor launched on different nodes can directly affect the data locality achieved by the tasks. This article tries to improve the data locality of tasks by executor allocation in Spark framework. First, because of different communication modes at stages, we separately model the communication cost of tasks for transferring input data to the executors. Then formalize an optimal executor allocation problem to minimize the total communication cost of transferring all input data. This problem is proven to be NP-hard. Finally, we present a greed dropping heuristic algorithm to provide solution to the executor allocation problem. Our proposals are implemented in Spark-3.4.0 and its performance is evaluated through representative micro-benchmarks (i.e., WordCount, Join, Sort) and macro-benchmarks (i.e., PageRank and LDA). Extensive experiments show that the proposed executor allocation strategy can decrease the network traffic and data access time by improving the data locality during the task scheduling. Its performance benefits are particularly significant for iterative applications.
摘要：
U-network is a comprehensive convolutional neural network that is widely utilized in medical image segmentation domain. However, it is not accurate enough in detail segmentation and resulting in unsatisfactory segmentation results. To solve this problem, this paper proposes an enhanced U-network that combines an improved Pyramid Pooling Module (PPM) and a modified Convolutional Block Attention Module (CBAM). Its whole network is U-Net architecture, where the PPM is improved by reducing the number of bin species and increasing the pooling connection multiples. It is used in the downsampling part of the network, which can extract input image features of various dimensions. And the CBAM is modified by using $1\times 1$ convolutional layers instead of the original fully connected layers. It is used in the upsampling part of the network, which can combine convolution and attention mechanism. This pays attention to the image from two aspects of space and channel. Besides, the network is trained with novel RGB training to further improve the segmentation ability of the network. Experimental results show that our network outperforms traditional U-shaped segmentation networks by 30% to 40% in metrics Dice, IoU, MAE, and BFscore respectively. What‘s more, it is better than U-Net ++, U2-Net, ResU-Net, ResU-Net++, and UNeXt in terms of segmentation effect and training time.
摘要：
<jats:p>It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmation results for high-severity defects. Therefore, a multi-feature fusion defect automatic confirmation method is proposed. The label generation method solves the dimensionality disaster problem caused by multi-feature fusion by fusing features with strong correlations, improving the classifier’s performance. This method extracts node features and basic path features from the program dependency graph and designs high-severity contextual defect confirmation labels combined with contextual features. Finally, an optimized Support Vector Machine is used to train the automatic detection model for high-severity defects. This study uses open-source programs to manually implant defects for high-severity defect confirmation verification. The experimental results show that compared with existing methods, this model significantly improves the efficiency of confirming high-severity defects.</jats:p>
通讯机构：
[Zhigang Xu; Shiguang Zhang; Wenlong Tian; Hongmu Han; Xinhua Dong] S;[Haitao Wang; Zhiqiang Zheng] N;Narcotics Control Bureau of Department of Public Security of Guangdong Province,Guangzhou 510050,null,China<&wdkj&>School of Computer Science and Technology,University of South China,Hengyang 421001,China<&wdkj&>School of Computer Science,Hubei University of Hubei University of Technology,Wuhan 430068,China
关键词：
Introduction;Materials and Methods;Results;Discussion;Conclusion;Abstract;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interests;Authors’ Contributions;Funding Statement;Acknowledgements;Acknowledgments;Supplementary Materials;Reference;Dataset Description;Dataset Files;Abstract;Introduction;Introduction and Materials;Introduction and Methods;Materials;Materials and Methods;Methods;Results;Discussion;Results and Discussion;Discussion and Conclusion;Results and Conclusion;Conclusion;Conclusions;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interest;Authors’ Contributions;Funding Statement;Acknowledgements;Supplementary Materials;References;Appendix;Abbreviations;Preliminaries;Introduction and Preliminaries;Notation;Proof of Theorem;Proofs;Analysis of Results;Examples;Numerical Example;Applications;Numerical Simulation;Model;Model Formulation;Systematic Palaeontology;Nomenclatural Acts;Taxonomic Implications;Experimental;Synthesis;Overview;Characterization;Background;Experimental;Theories;Calculations;Model Verification;Model Implementation;Geographic location;Study Area;Geological setting;Data Collection;Field Testing;Data and Sampling;Dataset;Literature Review;Related Works;Related Work;System Model;Methods and Data;Experimental Results;Results and Analysis;Evaluation;Implementation;Case Presentation;Case Report;Search Terms;Case Description;Case Series;Background;Limitations;Additional Points;Case;Case 1;Case 2 etc.;Concern Details;Retraction Details;Copyright;Related Articles
摘要：
In the Internet of Things (IoT), data sharing security is important to social security. It is a huge challenge to enable more accurate and secure access to data by authorized users. Blockchain access control schemes are mostly one-way access control, which cannot meet the need for ciphertext search, two-way confirmation of users and data, and secure data transmission. Thus, this paper proposes a blockchain-aided searchable encryption-based two-way attribute access control scheme (STW-ABE). The scheme combines ciphertext attribute access control, key attribute access control, and ciphertext search. In particular, two-way access control meets the requirement of mutual confirmation between users and data. The ciphertext search avoids information leakage during transmission, thus improving overall efficiency and security during data sharing. Moreover, user keys are generated by the coalition blockchain. Besides, the ciphertext search and pre-decryption are outsourced to cloud servers, reducing the computing pressure on users and adapting to the needs of lightweight users in the IoT. Security analysis proves that our scheme is secure under a chosen-plaintext attack and a chosen keyword attack. Simulations show that the cost of encryption and decryption, keyword token generation, and ciphertext search of our scheme are preferable.
摘要：
Spark Streaming is an extension of the core Spark engine that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It treats stream as a series of deterministic batches and handles them as regular jobs. However, for a stream job responsible for a batch, data skew (i.e., the imbalance in the amount of data allocated to each reduce task), can degrade the job performance significantly because of load imbalance. In this paper, we propose an improved range partitioner (ImRP) to alleviate the reduce skew for stream jobs in Spark Streaming. Unlike previous work, ImRP does not require any pre-run sampling of input data and generates the data partition scheme based on the intermediate data distribution estimated by the previous batch processing, in which a prediction model EWMA (Exponentially Weighted Moving Average) is adopted. To lighten the data skew, ImRP presents a novel method of calculating the partition borders optimally, and a mechanism of splitting the border key clusters when the semantics of shuffle operators permit. Besides, ImRP considers the integrated partition size and heterogeneity of computing environments when balancing the load among reduce tasks appropriately. We implement ImRP in Spark-3.0 and evaluate its performance on four representative benchmarks: wordCount, sort, pageRank, and LDA. The results show that by mitigating the data skew, ImRP can decrease the execution time of stream jobs substantially compared with some other partition strategies, especially when the skew degree of input batch is serious.
摘要：
Qutrit is the natural extension of qubit in quantum information processing and has quite a few advantages that outperform qubit. In this paper, we investigate the feasibility of teleportation of an unknown qubit state, as well as an unknown qutrit state using a two-qutrit entangled pair. We show that by carefully constructing the measurement bases, both the qubit and the qutrit can be faithfully teleported from Alice to Bob with a two-qutrit maximally entangled state.
关键词：
exfoliation;few-atomic layers;nanosheets;sodium ion batteries;TiS2
摘要：
<jats:title>Abstract</jats:title><jats:p>Sodium ion batteries are now attracting great attention, mainly because of the abundance of sodium resources and their cheap raw materials. 2D materials possess a unique structure for sodium storage. Among them, transition metal chalcogenides exhibit significant potential for rechargeable battery devices due to their tunable composition, remarkable structural stability, fast ion transport, and robust kinetics. Herein, ultrathin TiS<jats:sub>2</jats:sub> nanosheets are synthesized by a shear‐mixing method and exhibit outstanding cycling performance (386 mAh g<jats:sup>−1</jats:sup> after 200 cycles at 0.2 A g<jats:sup>−1</jats:sup>). To clarify the variations of galvanostatic curves and superior cycling performance, the mechanism and morphology changes are systematically investigated. This facile synthesis method is expected to shed light on the preparation of ultrathin 2D materials, whose unique morphologies could easily enable their application in rechargeable batteries.</jats:p>
