bluuebunny/crossref_metadata_2025 huggingface dataset:
TIPS: Tracking Integer-Pointer Value Flows for C++ Member Function Pointers
Authors: Zou Changwei , He Dongjie , Sui Yulei , Xue Jingling
Published: July 2024

C++ is crucial in software development, providing low-level memory control for performance and supporting object-oriented programming to construct modular, reusable code structures. Consequently, tackling pointer analysis for C++ becomes challenging, given the need to address these two fundamental features. A relatively unexplored research area involves the handling of C++ member function pointers. Previous efforts have tended to either disregard this feature or adopt a conservative approach, resulting in unsound or imprecise results.

C++ member function pointers, handling both virtual (via virtual table indexes) and non-virtual functions (through addresses), pose a significant challenge for pointer analysis due to the mix of integers and pointers, often resulting in unsound or imprecise analysis. We introduce TIPS, the first pointer analysis that effectively manages both pointers and integers, offering support for C++ member function pointers by tracking their value flows. Our evaluation on TIPS demonstrates its accuracy in identifying C++ member function call targets, a task where other tools falter, across fourteen large C++ programs from SPEC CPU, Qt, LLVM, Ninja, and GoogleTest, while maintaining low analysis overhead. In addition, our micro-benchmark suite, complete with ground truth data, allows for precise evaluation of points-to information for C++ member function pointers across various inheritance scenarios, highlighting TIPS's precision enhancements.
Enhancing Function Name Prediction using Votes-Based Name Tokenization and Multi-task Learning
Authors: Zhang Xiaoling , Xu Zhengzi , Yang Shouguo , Li Zhi , Shi Zhiqiang , Sun Limin
Published: July 2024

Reverse engineers would acquire valuable insights from descriptive function names, which are absent in publicly released binaries. Recent advances in binary function name prediction using data-driven machine learning show promise. However, existing approaches encounter difficulties in capturing function semantics in diverse optimized binaries and fail to reserve the meaning of labels in function names. We propose Epitome, a framework that enhances function name prediction using votes-based name tokenization and multi-task learning, specifically tailored for different compilation optimization binaries. Epitome learns comprehensive function semantics by pre-trained assembly language model and graph neural network, incorporating function semantics similarity prediction task, to maximize the similarity of function semantics in the context of different compilation optimization levels. In addition, we present two data preprocessing methods to improve the comprehensibility of function names. We evaluate the performance of Epitome using 2,597,346 functions extracted from binaries compiled with 5 optimizations (O0-Os) for 4 architectures (x64, x86, ARM, and MIPS). Epitome outperforms the state-of-the-art function name prediction tool by up to 44.34%, 64.16%, and 54.44% in precision, recall, and F1 score, while also exhibiting superior generalizability.
How to Gain Commit Rights in Modern Top Open Source Communities?
Authors: Tan Xin , Gong Yan , Huang Geyu , Wu Haohua , Zhang Li
Published: July 2024

The success of open source software (OSS) projects relies on voluntary contributions from various community roles. Among these roles, being a committer signifies gaining trust and higher privileges in OSS projects. Substantial studies have focused on the requirements of becoming a committer in OSS projects, but most of them are based on interviews or several hypotheses, lacking a comprehensive understanding of committers' qualifications. To address this knowledge gap, we explore both the policies and practical implementations of committer qualifications in modern top OSS communities. Through a thematic analysis of these policies, we construct a taxonomy of committer qualifications, consisting of 26 codes categorized into nine themes, including "Personnel-related to Project", "Communication", and "Long-term Participation". We also highlight the variations in committer qualifications emphasized in different OSS community governance models. For example, projects following the "core maintainer model" place great importance on project comprehension, while projects following the "company-backed model" place significant emphasis on user issue resolution. Based on the above findings, we propose eight sets of metrics and perform survival analysis on two representative OSS projects to understand how these qualifications are implemented in practice. We find that the probability of gaining commit rights decreases as participation time passes. The selection criteria in practice are generally consistent with the community policies. Developers who submit high-quality code, actively engage in code review, and make extensive contributions to related projects are more likely to be granted commit rights. However, there are some qualifications that do not align precisely, and some are not adequately evaluated. This study enhances trust understanding in top OSS communities, aids in optimal commit rights allocation, and empowers developers' self-actualization via OSS engagement.
Predicting Code Comprehension: A Novel Approach to Align Human Gaze with Code using Deep Neural Networks
Authors: Alakmeh Tarek , Reich David , Jäger Lena , Fritz Thomas
Published: July 2024

The better the code quality and the less complex the code, the easier it is for software developers to comprehend and evolve it. Yet, how do we best detect quality concerns in the code? Existing measures to assess code quality, such as McCabe’s cyclomatic complexity, are decades old and neglect the human aspect. Research has shown that considering how a developer reads and experiences the code can be an indicator of its quality. In our research, we built on these insights and designed, trained, and evaluated the first deep neural network that aligns a developer’s eye gaze with the code tokens the developer looks at to predict code comprehension and perceived difficulty. To train and analyze our approach, we performed an experiment in which 27 participants worked on a range of 16 short code comprehension tasks while we collected fine-grained gaze data using an eye tracker. The results of our evaluation show that our deep neural sequence model that integrates both the human gaze and the stimulus code, can predict (a) code comprehension and (b) the perceived code difficulty significantly better than current state-of-the-art reference methods. We also show that aligning human gaze with code leads to better performance than models that rely solely on either code or human gaze. We discuss potential applications and propose future work to build better human-inclusive code evaluation systems.
A Miss Is as Good as A Mile: Metamorphic Testing for Deep Learning Operators
Authors: Chen Jinyin , Jia Chengyu , Yan Yunjie , Ge Jie , Zheng Haibin , Cheng Yao
Published: July 2024

Deep learning (DL) is a critical tool for real-world applications, and comprehensive testing of DL models is vital to ensure their quality before deployment. However, recent studies have shown that even subtle deviations in DL operators can result in catastrophic consequences, underscoring the importance of rigorous testing of these components. Unlike testing other DL system components, operator analysis poses unique challenges due to complex inputs and uncertain outputs. The existing DL operator testing approach has limitations in terms of testing efficiency and error localization. In this paper, we propose
Meta
, a novel operator testing framework based on metamorphic testing that automatically tests and assists bug location based on metamorphic relations (MRs). Meta distinguishes itself in three key ways: (1) it considers both parameters and input tensors to detect operator errors, enabling it to identify both implementation and precision errors; (2) it uses MRs to guide the generation of more effective inputs (i.e., tensors and parameters) in less time; (3) it assists the precision error localization by tracing the error to the input level of the operator based on MR violations. We designed 18 MRs for testing 10 widely used DL operators. To assess the effectiveness of Meta, we conducted experiments on 13 released versions of 5 popular DL libraries. Our results revealed that Meta successfully detected 41 errors, including 14 new ones that were reported to the respective platforms and 8 of them are confirmed/fixed. Additionally, Meta demonstrated high efficiency, outperforming the baseline by detecting ∼2 times more errors of the baseline. Meta is open-sourced and available at https://github.com/TDY-raedae/Medi-Test.
LogSD: Detecting Anomalies from System Logs through Self-Supervised Learning and Frequency-Based Masking
Authors: Xie Yongzheng , Zhang Hongyu , Babar Muhammad Ali
Published: July 2024

Log analysis is one of the main techniques that engineers use for troubleshooting large-scale software systems. Over the years, many supervised, semi-supervised, and unsupervised log analysis methods have been proposed to detect system anomalies by analyzing system logs. Among these, semi-supervised methods have garnered increasing attention as they strike a balance between relaxed labeled data requirements and optimal detection performance, contrasting with their supervised and unsupervised counterparts. However, existing semi-supervised methods overlook the potential bias introduced by highly frequent log messages on the learned normal patterns, which leads to their less than satisfactory performance. In this study, we propose LogSD, a novel semi-supervised self-supervised learning approach. LogSD employs a dual-network architecture and incorporates a frequency-based masking scheme, a global-to-local reconstruction paradigm and three self-supervised learning tasks. These features enable LogSD to focus more on relatively infrequent log messages, thereby effectively learning less biased and more discriminative patterns from historical normal data. This emphasis ultimately leads to improved anomaly detection performance. Extensive experiments have been conducted on three commonly-used datasets and the results show that LogSD significantly outperforms eight state-of-the-art benchmark methods.
BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection
Authors: Pham Luan , Ha Huong , Zhang Hongyu
Published: July 2024

Detecting failures and identifying their root causes promptly and accurately is crucial for ensuring the availability of microservice systems. A typical failure troubleshooting pipeline for microservices consists of two phases: anomaly detection and root cause analysis. While various existing works on root cause analysis require accurate anomaly detection, there is no guarantee of accurate estimation with anomaly detection techniques. Inaccurate anomaly detection results can significantly affect the root cause localization results. To address this challenge, we propose BARO, an end-to-end approach that integrates anomaly detection and root cause analysis for effectively troubleshooting failures in microservice systems. BARO leverages the Multivariate Bayesian Online Change Point Detection technique to model the dependency within multivariate time-series metrics data, enabling it to detect anomalies more accurately. BARO also incorporates a novel nonparametric statistical hypothesis testing technique for robustly identifying root causes, which is less sensitive to the accuracy of anomaly detection compared to existing works. Our comprehensive experiments conducted on three popular benchmark microservice systems demonstrate that BARO consistently outperforms state-of-the-art approaches in both anomaly detection and root cause analysis.
Mining Action Rules for Defect Reduction Planning
Authors: Oueslati Khouloud , Laberge Gabriel , Lamothe Maxime , Khomh Foutse
Published: July 2024

Defect reduction planning plays a vital role in enhancing software quality and minimizing software maintenance costs. By training a black box machine learning model and “explaining” its predictions, explainable AI for software engineering aims to identify the code characteristics that impact maintenance risks. However, post-hoc explanations do not always faithfully reflect what the original model computes. In this paper, we introduce CounterACT, a Counterfactual ACTion rule mining approach that can generate defect reduction plans without black-box models. By leveraging action rules, CounterACT provides a course of action that can be considered as a counterfactual explanation for the class (e.g., buggy or not buggy) assigned to a piece of code. We compare the effectiveness of CounterACT with the original action rule mining algorithm and six established defect reduction approaches on 9 software projects. Our evaluation is based on (a) overlap scores between proposed code changes and actual developer modifications; (b) improvement scores in future releases; and (c) the precision, recall, and F1-score of the plans. Our results show that, compared to competing approaches, CounterACT’s explainable plans achieve higher overlap scores at the release level (median 95%) and commit level (median 85.97%), and they offer better trade-off between precision and recall (median F1-score 88.12%). Finally, we venture beyond planning and explore leveraging Large Language models (LLM) for generating code edits from our generated plans. Our results show that suggested LLM code edits supported by our plans are actionable and are more likely to pass relevant test cases than vanilla LLM code recommendations.
Are Human Rules Necessary? Generating Reusable APIs with CoT Reasoning and In-Context Learning
Authors: Mai Yubo , Gao Zhipeng , Hu Xing , Bao Lingfeng , Liu Yu , Sun JianLing
Published: July 2024

Inspired by the great potential of Large Language Models (LLMs) for solving complex coding tasks, in this paper, we propose a novel approach, named Code2API, to automatically perform APIzation for Stack Overflow code snippets. Code2API does not require additional model training or any manual crafting rules and can be easily deployed on personal computers without relying on other external tools. Specifically, Code2API guides the LLMs through well-designed prompts to generate well-formed APIs for given code snippets. To elicit knowledge and logical reasoning from LLMs, we used chain-of-thought (CoT) reasoning and few-shot in-context learning, which can help the LLMs fully understand the APIzation task and solve it step by step in a manner similar to a developer. Our evaluations show that Code2API achieves a remarkable accuracy in identifying method parameters (65%) and return statements (66%) equivalent to human-generated ones, surpassing the current state-of-the-art approach, APIzator, by 15.0% and 16.5% respectively. Moreover, compared with APIzator, our user study demonstrates that Code2API exhibits superior performance in generating meaningful method names, even surpassing the human-level performance, and developers are more willing to use APIs generated by our approach, highlighting the applicability of our tool in practice. Finally, we successfully extend our framework to the Python dataset, achieving a comparable performance with Java, which verifies the generalizability of our tool.
FeatMaker: Automated Feature Engineering for Search Strategy of Symbolic Execution
Authors: Yoon Jaehan , Cha Sooyoung
Published: July 2024

We present FeatMaker, a novel technique that automatically generates state features to enhance the search strategy of symbolic execution. Search strategies, designed to address the well-known state-explosion problem, prioritize which program states to explore. These strategies typically depend on a ”state feature” that describes a specific property of program states, using this feature to score and rank them. Recently, search strategies employing multiple state features have shown superior performance over traditional strategies that use a single, generic feature. However, the process of designing these features remains largely manual. Moreover, manually crafting state features is both time-consuming and prone to yielding unsatisfactory results. The goal of this paper is to fully automate the process of generating state features for search strategies from scratch. The key idea is to leverage path-conditions, which are basic but vital information maintained by symbolic execution, as state features. A challenge arises when employing all path-conditions as state features, as it results in an excessive number of state features. To address this, we present a specialized algorithm that iteratively generates and refines state features based on data accumulated during symbolic execution. Experimental results on 15 open-source C programs show that FeatMaker significantly outperforms existing search strategies that rely on manually-designed features, both in terms of branch coverage and bug detection. Notably, FeatMaker achieved an average of 35.3% higher branch coverage than state-of-the-art strategies and discovered 15 unique bugs. Of these, six were detected exclusively by FeatMaker.
Bloat beneath Python’s Scales: A Fine-Grained Inter-Project Dependency Analysis
Authors: Drosos Georgios-Petros , Sotiropoulos Thodoris , Spinellis Diomidis , Mitropoulos Dimitris
Published: July 2024

Modern programming languages promote software reuse via package managers that facilitate the integration

of inter-dependent software libraries. Software reuse comes with the challenge of dependency bloat, which

refers to unneeded and excessive code incorporated into a project through reused libraries. Such bloat exhibits

security risks and maintenance costs, increases storage requirements, and slows down load times. In this work,

we conduct a large-scale, fine-grained analysis to understand bloated dependency code in the PyPI ecosystem.

Our analysis is the first to focus on different granularity levels, including bloated dependencies, bloated files,

and bloated methods. This allows us to identify the specific parts of a library that contribute to the bloat. To do

so, we analyze the source code of 1,302 popular Python projects and their 3,232 transitive dependencies. For

each project, we employ a state-of-the-art static analyzer and incrementally construct the fine-grained project

dependency graph (FPDG), a representation that captures all inter-project dependencies at method-level.

Our reachability analysis on the FPDG enables the assessment of bloated dependency code in terms of several

aspects, including its prevalence in the PyPI ecosystem, its relation to software vulnerabilities, its root causes,

and developer perception. Our key finding suggests that PyPI exhibits significant resource underutilization:

more than 50% of dependencies are bloated. This rate gets worse when considering bloated dependency code

at a more subtle level, such as bloated files and bloated methods. Our fine-grained analysis also indicates

that there are numerous vulnerabilities that reside in bloated areas of utilized packages (15% of the defects

existing in PyPI). Other major observations suggest that bloated code primarily stems from omissions during

code refactoring processes and that developers are willing to debloat their code: Out of the 36 submitted pull

requests, developers accepted and merged 30, removing a total of 35 bloated dependencies. We believe that

our findings can help researchers and practitioners come up with new debloating techniques and development

practices to detect and avoid bloated code, ensuring that dependency resources are utilized efficiently.
Dependency-Induced Waste in Continuous Integration: An Empirical Study of Unused Dependencies in the npm Ecosystem
Authors: Weeraddana Nimmi Rashinika , Alfadel Mahmoud , McIntosh Shane
Published: July 2024

Modern software systems are increasingly dependent upon code from external packages (i.e., dependencies). Building upon external packages allows software reuse to span across projects seamlessly. Package maintainers regularly release updated versions to provide new features, fix defects, and address security vulnerabilities. Due to the potential for regression, managing dependencies is not just a trivial matter of selecting the latest versions. Since it is perceived to be less risky to retain a dependency than remove it, as projects evolve, they tend to accrue dependencies, exacerbating the difficulty of dependency management. It is not uncommon for a considerable proportion of external packages to be unused by the projects that list them as a dependency. Although such unused dependencies are not required to build and run the project, updates to their dependency specifications will still trigger Continuous Integration (CI) builds. The CI builds that are initiated by updates to unused dependencies are fundamentally wasteful. Considering that CI build time is a finite resource that is directly associated with project development and service operational costs, understanding the consequences of unused dependencies within this CI context is of practical importance.

In this paper, we study the CI waste that is generated by updates to unused dependencies. We collect a dataset of 20,743 commits that are solely updating dependency specifications (i.e., the package.json file), spanning 1,487 projects that adopt npm for managing their dependencies. Our findings illustrate that 55.88% of the CI build time that is associated with dependency updates is only triggered by unused dependencies. At the project level, the median project spends 56.09% of its dependency-related CI build time on updates to unused dependencies. For projects that exceed the budget of free build minutes, we find that the median percentage of billable CI build time that is wasted due to unused-dependency commits is 85.50%. Moreover, we find that automated bots are the primary producers of dependency-induced CI waste, contributing 92.93% of the CI build time that is spent on unused dependencies. The popular Dependabot is responsible for updates to unused dependencies that account for 74.52% of that waste. To mitigate the impact of unused dependencies on CI resources, we introduce Dep-sCImitar, an approach to cut down wasted CI time by identifying and skipping CI builds that are triggered due to unused-dependency commits. A retrospective evaluation of the 20,743 studied commits shows that Dep-sCImitar reduces wasted CI build time by 68.34% by skipping wasteful builds with a precision of 94%.
A Novel Point Cloud Registration Method for Multimedia Communication in Automated Driving Metaverse
Authors: Yong Binbin , Lei Ming , Shen Jun , Zhi Peng , Zhao Rui , Zhou Qingguo
Published: July 2024

The development of the Metaverse offers more possibilities for autonomous driving. This is mainly reflected in the fact that the scene reconstructed based on multiple sensors can help the autonomous vehicle establish a Metaverse world based on its own real situation. When multiple vehicles build such a Metaverse world in the same scene, they can exchange their information including the perception of the vehicle’s condition and the surrounding environment, which means the creation of a Metaverse world containing all vehicles. Thus, an indirect Human-Human Multimedia Communications based on the Metaverse is realized, in which the autonomous driving system acts as a multimedia to provide a medium for the exchange of information between vehicles. The establishment of the Metaverse is based on stable and high-quality scene reconstruction. Accurate scene information can bring high quality Human-Human Multimedia Communications. Achieving this requires accurate scene reconstruction and vehicle positioning, both of which depend on accurate point cloud registration. In this work, we propose a robust registration method that is based on semantic information and scaling constraints. Our method consists of three steps. Firstly, we filter out points that might mislead the point cloud registration process by leveraging semantic information. Secondly, we obtain a more accurate initial matrix using TEASER++, which is based on semantic information and feature descriptors. Finally, we use semantic information and scaling to constrain the nearest neighbor matching and filter out error matches to obtain a higher quality registration. By following these steps, our method overcomes the memory problem faced by TEASER++ when there are large-scale point clouds, and greatly reduces its running time. Meanwhile, our algorithm achieved superior point cloud registration results compared to two state-of-the-art robust registration techniques: Globally optimal Iterative Closest Point (Go-ICP) and Generalized Iterative Closest Point (GICP).
Fair Federated Learning with Multi-Objective Hyperparameter Optimization
Authors: Wang Chunnan , Shi Xiangyu , Wang Hongzhi
Published: July 2024

Federated learning (FL) is an attractive paradigm for privacy-aware distributed machine learning, which enables clients to collaboratively learn a global model without sharing clients’ data. Recently, many strategies have been proposed to improve the generality of the global model and thus improve FL effect. However, existing strategies either ignore the fairness among clients or sacrifice performance for fairness. They cannot ensure that the gap among clients is as small as possible without sacrificing federated performance. To address this issue, we propose
ParetoFed
, a new local information aggregation method dedicated to obtaining better federated performance with smaller gap among clients. Specifically, we propose to use multi-objective hyperparameter optimization (HPO) algorithm to gain global models that are both fair and effective. Then, we send Pareto Optimal global models to each client, allowing them to choose the most suitable one as the base to optimize their local model.
ParetoFed
not only make the global models more fair but also make the selection of local models more personalized, which can further improve the federated performance. Extensive experiments show that
ParetoFed
outperforms existing FL methods in terms of fairness, and even achieves better federated performance, which demonstrates the significance of our method.
On Elastic Language Models
Authors: Zhang Chen , Wang Benyou , Song Dawei
Published: July 2024

Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. While their large scales ensure capacity, they also hinder deployment. Knowledge distillation offers an opportunity to compress a large language model to a small one, in order to reach a reasonable latency-performance tradeoff. However, for scenarios where the number of requests (e.g., queries submitted to a search engine) is highly variant, the static tradeoff attained by the compressed language model might not always fit. Once a model is assigned with a static tradeoff, it could be inadequate in that the latency is too high when the number of requests is large, or the performance is too low when the number of requests is small. To this end, we propose an elastic language model (
ElasticLM
) that elastically adjusts the tradeoff according to the request stream. The basic idea is to introduce a compute elasticity to the compressed language model, so that the tradeoff could vary on-the-fly along a scalable and controllable compute. Specifically, we impose an elastic structure to equip
ElasticLM
with compute elasticity and design an elastic optimization method to learn
ElasticLM
under compute elasticity. To serve
ElasticLM
, we apply an elastic schedule. Considering the specificity of information retrieval, we adapt
ElasticLM
to dense retrieval and reranking, and present an
ElasticDenser
and an
ElasticRanker,
respectively. Offline evaluation is conducted on a language understanding benchmark GLUE, and several information retrieval tasks including Natural Question, Trivia QA and MS MARCO. The results show that
ElasticLM
along with
ElasticDenser
and
ElasticRanker
can perform correctly and competitively compared with an array of static baselines. Furthermore, an online simulation with concurrency is also carried out. The results demonstrate that
ElasticLM
can provide elastic tradeoffs with respect to varying request stream.
Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential Modeling
Authors: Zou Jie , Sun Aixin , Long Cheng , Kanoulas Evangelos
Published: July 2024

In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies among them. However, most of existing CRSs neglect these potential sequential dependencies. In this article, we first propose a Transformer-based sequential conversational recommendation method, named TSCR, to model the sequential dependencies in the conversations to improve CRS. In TSCR, we represent conversations by items and the item-related entities, and construct user sequences to discover user preferences by considering both the mentioned items and item-related entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Meanwhile, in certain domains, knowledge graphs formed by the items and their related entities are readily available, which provide various different kinds of associations among them. Given that TSCR does not benefit from such knowledge graphs, we then propose a knowledge graph enhanced version of TSCR, called TSCRKG. In specific, we leverage the knowledge graph to offline initialize our model TSCRKG, and augment the user sequence of conversations (i.e., sequence of the mentioned items and item-related entities in the conversation) with multi-hop paths in the knowledge graph. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines, and the enhanced version TSCRKG further improves recommendation performance on top of TSCR.
Graph Augmentation Empowered Contrastive Learning for Recommendation
Authors: Xu Lixiang , Liu Yusheng , Xu Tong , Chen Enhong , Tang Yuanyan
Published: July 2024

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the
G
raph
A
ugmentation
E
mpowered
C
ontrastive
L
earning
(GAECL)
for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models.
PriorMSM: An Efficient Acceleration Architecture for Multi-Scalar Multiplication
Authors: Liu Changxu , Zhou Hao , Dai Patrick , Shang Li , Yang Fan
Published: July 2024

Multi-Scalar Multiplication (MSM) is a computationally intensive task that operates on elliptic curves based on
GF(P)
. It is commonly used in zero-knowledge proof (ZKP), where it accounts for a significant portion of the computation time required for proof generation. In this article, we present PriorMSM, an efficient acceleration architecture for MSM. We propose a Priority-Based Scheduling Mechanism (PBSM) based on a multi-FIFO and multi-bank architecture to accelerate the implementation of MSM. By increasing the pairing success rate of internal points, PBSM reduces the number of bubbles in the pipeline of point addition (PADD), consequently improving the data throughput of the pipeline. We also introduce an advanced parallel bucket aggregation algorithm, leveraging PADD’s fully pipelined characteristics to significantly accelerate the implementation of bucket aggregation. We perform a sensitivity analysis on the crucial parameter of window size in MSM. The results indicate that the window size of the MSM significantly impacts its latency. Area-Time Product (ATP) metric is introduced to guide the selection of the optimal window size, balancing the performance and cost for practical applications of subsequent MSM implementations. PriorMSM is evaluated using the TSMC 28 nm process. It achieves a maximum speedup of 10.9× compared to the previous custom hardware implementations and a maximum speedup of 3.9× compared to the GPU implementations.
Investigating the Temperature Dependency of Trimethyl Aluminum Assisted Atomic Surface Reduction of Li and Mn-Rich NCM
Authors: Evenstein Eliran , Taragin Sarah , Saha Arka , Noked Malachi , Rosy
Published: July 2024

Most next-generation electrode materials are prone to interfacial degradation, which eventually spreads to the bulk and impairs electrochemical performance. One promising method for reducing interfacial degradation is to surface engineer the electrode materials to form an artificial cathode electrolyte interphase as a protective layer. Nevertheless, the majority of coating techniques entail wet processes, high temperatures, or exposure to ambient conditions. These experimental conditions are only sometimes conducive and can adversely affect the material structure or composition. Therefore, we investigate the efficacy of a low-temperature, facile atomic surface reduction (ASR) using trimethylaluminum vapors as a surface modification strategy for Li and Mn-rich NCM (LMR-NCM). The results presented herein manifest that the extent of TMA-assisted ASR is temperature-dependent. All tested temperatures demonstrated improved electrochemical performance. However, ASR carried out at temperatures >100 °C was more effective in preserving the structural integrity and improving the electrochemical performance. Electrochemical testing revealed improved rate capabilities, cycling stability, and capacity retention of ASR-treated LMR-NCM. Additionally, post-cycling high-resolution scanning electron microscopy analysis verified that after extended cycling, ASR carried out at T > 100 °C showed no cracks or cleavage, demonstrating the efficiency of this method in preventing surface degradation.
The Effect of Oxygen Vacancy Defects on the Structure and Electrochemical Behaviors of LiMn0.65Fe0.35PO4 Cathode
Authors: Zhang Jingpeng , Ke Xiwen , Wang Yong , Xue Juanjuan
Published: July 2024

The presence of oxygen vacancy defects significantly impacts the crystal structure and electrochemical attributes of phosphate cathodes. In this investigation, LiMn0.65Fe0.35PO4 materials with varying levels of oxygen vacancy defects were synthesized via hydrogen plasma-induced reduction. It was observed that the content of oxygen vacancy defects on the crystal surface increased proportionately with the rise in hydrogen (H2) flow rate. Notably, the LMFP-3 sample, prepared with an H2 flow rate of 10 ml min−1, demonstrated superior electrochemical performance, characterized by a 159.7 mAh g−1 discharge capacity at 0.1 C and a remarkable 99.8% capacity retention at 5 C after 200 cycles. This enhancement in electrochemical performance is attributed to the improved intrinsic conductivity of the LiMn0.65Fe0.35PO4 material due to the presence of oxygen vacancy defects. However, it is important to note that an excessively high H2 flow rate can lead to the formation of Fe2P impurities, which hinder lithium ion (Li+) diffusion. Furthermore, theoretical calculations conducted using density functional theory provide a rational explanation for the observed improvement in electronic conductivity. The introduction of oxygen vacancy defects results in a significant reduction in the Band gap, which is highly beneficial for enhancing the intrinsic conductivity of the LiMn0.65Fe0.35PO4 materials.
An Amperometric Type NO2 Sensor Utilizing La0.7Sr0.3MnO3−δ
-NiO Composite Sensing Electrode
Authors: Zhao Tengteng , Wang Chaoyu , Dai Lei , Meng Weiwei , Liu Yongguang , Li Yuehua , Wang Ling
Published: July 2024

NO2 is an important pollutant of automobile engines and industrial fuels, making it important to quantitatively monitor and control. An amperometric-type NO2 gas sensor was fabricated using yttria-stabilized zirconia (YSZ) electrolyte with a bi-layered structure and La0.7Sr0.3MnO3−δ
-xNiO (LSMO-xNiO, x = 0–0.75) composite sensing electrode (SE) prepared by impregnation method in combination with self-demixing. The samples were characterized using SEM, XRD, and XPS, and their performance as sensors was tested. LSMO-xNiO composite SE particles were formed by de-mixing in the process of treating the precursor at high temperatures and are uniformly dispersed in the YSZ porous backbone. With the increase of NiO content, the SE particles become significantly large. At 450 °C–600 °C, the response currents at a fixed potential have a linear relationship with the NO2 concentrations at 25–700 ppm. Combining stability and sensitivity, the voltage was fixed to −0.25 V. The introduction of NiO into the LSMO sensing electrode effectively improves the performance of the sensor. The sensor based on LSMO-0.5NiO has the highest sensitivity (0.0405 μA/ppm) at 550 °C. Simultaneously, the sensor exhibits good anti-interference ability for CH4, CO2, O2, and NO, but has obvious cross-sensitivity to H2 and NH3. In addition, the response current of the sensor change with the increase of RH.
A Novel Electrochemical Sensing Based on Cerium Oxide/Nitrogen-Doped Reduced Graphene Oxide for Sensitive Detection of Acetaminophen
Authors: Qi Xue , Wang Ziyin , Yuan Honglin , Gao Hongmin , Ren Xinshui , Chen Hua , Zhang Hehua , Chang Dong , Pan Hongzhi
Published: July 2024

Acetaminophen (ACP), a common analgesic and antipyretic medication, can harm the liver when overdosed and its metabolites can contaminate the environment, so it is necessary to monitor the concentration precisely and reliably. In this work, we successfully synthesized cerium oxide/nitrogen-doped reduced graphene oxide (CeO2/N-rGO) composite nanomaterials using a one-step hydrothermal method. Using composite nanomaterials, we created an electrochemical sensing detection platform for ACP detection. The synthesized materials were characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy. The constructed electrochemical sensor exhibits good ACP detection ability under the synergistic effect of CeO2 and N-rGO. Under optimal experimental conditions, the sensor displayed a linear range for the detection of ACP of 1 ∼ 200 μM and the lowest detection limit of 0.79 μM, exhibiting outstanding selectivity, stability, and repeatability. Furthermore, the sensor was effectively applied to detect ACP in tap water samples, which offers a wide range of possible applications in actual sample testing.
Reducing Manganese Dissolution in Electrolytic Manganese Dioxide Electrodes in NaOH Electrolyte
Authors: Wu Xinsheng , Whitacre Jay F.
Published: July 2024

Previous attempts to enhance the stability and performance of MnO2-based cathodes for use in aqueous alkaline electrolytes, primarily KOH-based, have relied on a range of additives. This work demonstrates that the fast capacity decay of the MnO2-based cathode materials in alkaline electrolytes is mainly due to spontaneous manganese dissolution when cycling through the second-electron reaction voltage range. Reducing relative electrolyte content and using carbon materials that have a high specific surface area suppresses manganese dissolution and thus extends the cycle life of the electrode material while reducing overall battery costs. Moreover, reducing the size of the MnO2 particles and decreasing the cycling rate are found to increase manganese dissolution and negatively impact the performance of the electrode material, indicating a sensitivity to material surface area. Lastly, Fe-MnO2-based low-cost battery chemistry was also demonstrated based on the second electron reaction of the MnO2 in an electrolyte lean environment, which could be promising for grid-level energy storage.
Glucocorticoids impair T lymphopoiesis after myocardial infarction
Authors: Shane Danielle X. , Konovalova Daria M. , Rajendran Harishkumar , Yuan Sarah Y. , Ma Yonggang
Published: July 2024

The thymus is essential for maintaining whole body T-cell output. Thymic injury can adversely affect T lymphopoiesis and T-cell immune response. This study demonstrates that MI induces thymocyte apoptosis and compromises T lymphopoiesis, resulting in fewer releases of mature thymocytes to the spleen. This process is mediated by glucocorticoids secreted by adrenal glands. Therefore, targeting glucocorticoids represents a novel approach to attenuate post-MI thymic injury.
Nurses’ Experiences from Patient Safety Incidents of Hospitalized Children: A Qualitative Study
Authors: Lee Haeyoung , Kim Yujeong
Published: July 2024

This qualitative study aimed at exploring nurses’ experiences concerning patient safety incidents among hospitalized children in South Korea. From August 4 to 12, 2023, data were collected through in‐depth individual interviews involving 14 clinical nurses. Employing thematic analysis, we identified 8 themes, which coalesced into three theme clusters: “challenges in pediatric patient safety nursing due to patient and caregiver characteristics,” “emotional changes in nurses following patient safety incidents,” and “sincere desire to prevent patient safety incidents in pediatric patients” The findings underscored that nurses experience significant burdens related to patient safety, emphasizing the necessity for robust support from caregivers, healthcare institutions, and national policies. Consequently, it is imperative to develop and implement programs and policies to foster a secure care environment for pediatric patients. Nurse managers and organizations must proactively design healthcare systems and related policies that prioritize safely protecting pediatric patients and nurses alike from patient safety incidents, considering the characteristics of pediatric patients and the experiences of the nurses caring for them.
Fixed‐Time Stable Gradient Flows for Optimal Adaptive Control of Continuous‐Time Nonlinear Systems
Authors: Niroomand Mahdi , Kardehi Moghaddam Reihaneh , Modares Hamidreza , Naghibi Sistani Mohammad-Bagher
Published: July 2024

This paper introduces an inclusive class of fixed‐time stable continuous‐time gradient flows (GFs). This class of GFs is then leveraged to learn optimal control solutions for nonlinear systems in fixed time. It is shown that the presented GF guarantees convergence within a fixed time from any initial condition to the exact minimum of functions that satisfy the Polyak–Łojasiewicz (PL) inequality. The presented fixed‐time GF is then utilized to design fixed‐time optimal adaptive control algorithms. To this end, a fixed‐time reinforcement learning (RL) algorithm is developed on the basis of a single network adaptive critic (SNAC) to learn the solution to an infinite‐horizon optimal control problem in a fixed‐time convergent, online, adaptive, and forward‐in‐time manner. It is shown that the PL inequality in the presented RL algorithm amounts to a mild inequality condition on a few collected samples. This condition is much weaker than the standard persistence of excitation (PE) and finite duration PE that relies on a rank condition of a dataset. This is crucial for learning‐enabled control systems as control systems can commit to learning an optimal controller from the beginning, in sharp contrast to existing results that rely on the PE and rank condition, and can only commit to learning after rich data samples are collected. Simulation results are provided to validate the performance and efficacy of the presented fixed‐time RL algorithm.
Using Spatial‐Temporal Attention for Video Quality Evaluation
Authors: Chi Biwei , Su Ruifang , Chen Xinhui
Published: July 2024

With the rapid development of media, the role of video quality assessment (VQA) is becoming increasingly significant. VQA has applications in many domains. For example, in the field of remote medical diagnosis, it can enhance the quality of video communication between doctors and patients. Besides, in sports broadcasting, it can improve video clarity. Within VQA, the human visual system (HVS) is a crucial component that should be taken into consideration. Considering that attention is guided by goal‐driven and top‐down factors, such as anticipated locations or some attractive frames within the video, we propose a blind VQA algorithm based on spatial‐temporal attention model. Specifically, we first use two pretrained convolutional networks to extract low‐level static‐dynamic fusion features. Then, a spatial attention‐guided model is established to get more representative features of frame‐level quality perception. Next, through a temporal attention‐guided model, the video‐level features are obtained. Finally, the features are fed into a regression model to calculate the final video quality score. The experiments conducted on seven VQA databases reach the state‐of‐the‐art performance, demonstrating the effectiveness of our proposed method.
Molar–Incisor Hypomineralization Is Associated with the Prevalence of Thinness among Schoolchildren in Communities with Different Fluoride Levels in the Drinking Water
Authors: García Pérez Alvaro , Villanueva Gutiérrez Teresa , González-Aragón Pineda Alvaro Edgar , Murillo Santos Karla Lizbeth , Pérez Pérez Nora Guillermina
Published: July 2024

Objective. To examine the association between molar–incisor hypomineralization (MIH) and the prevalence of thinness among Mexican schoolchildren in communities with different fluoride levels in the drinking water. Methods. A cross‐sectional study on Mexican children (n = 488) selected from two communities presenting different concentrations of fluoride in the drinking water (1.0–1.40 ppm/F). The World Health Organization (WHO) growth standards were used to calculate BMI‐for‐age z‐scores, with BMI z‐score cutoff points of <−2.0, >+1.0, >+2.0 recommended for defining thinness, being overweight, and obesity. The presence and severity of MIH were evaluated using the European Academy of Paediatric Dentistry (EAPD) criteria. Multiple logistic regression analyses were used to assess the association, adjusting for confounders. Results. The proportion of children presenting thinness, being overweight, and obesity was 8.2%, 23.6%, and 28.7%, respectively, while 21.5% of the schoolchildren had MIH, classified, by severity, as 9.6% mild, 6.4% moderate, and 5.5% severe. Of those schoolchildren presenting thinness, 16.2% had MIH, and only 6.0% did not (p  < 0.001). Finally, schoolchildren presenting thinness were more likely to present MIH (OR = 2.76 (CI 95% 1.33–5.73); p = 0.006) than children with a normal BMI. Conclusion. The present study found a relationship between thinness and the presence of MIH in schoolchildren, indicating the need for strategies and interventions aimed at preventing and controlling micronutrient deficiencies in the child population.