The rising data demands of generative AI models, such as large language models (LLMs), underscore the value of utilizing edge device data in mobile computing, where federated learning (FL) provides a privacy-preserving approach via decentralized model training. To address data heterogeneity across edge devices, federated multi-view learning (FedMVL) has been proposed to improve global model performance by capturing consistency and complementarity among diverse data views. However, existing methods often assume ideal conditions and neglect data uncertainty in mobile edge devices environments. To overcome these challenges, we propose Federated Reliable Multi-view Classification (FedRMVL), a vertical FedMVL framework incorporates Subjective Logic for lightweight uncertainty quantification at local edge devices and applies the Dempster-Shafer combination rule for adaptive and reliable multi-view opinion fusion at the server. Additionally, a partial parameter-sharing strategy is introduced to address feature dimension heterogeneity during federated optimization. The effectiveness and robustness of FedRMVL are validated through theoretical analysis and extensive experiments on real-world datasets.

Federated learning is essential for enabling collaborative model training across decentralized data sources while preserving data privacy and security. This approach mitigates the risks associated with centralized data collection and addresses concerns related to data ownership and compliance. Despite significant advancements in federated learning algorithms that address communication bottlenecks and enhance privacy protection, existing works overlook the impact of differences in data feature dimensions, resulting in global models that disproportionately depend on participants with large feature dimensions. Additionally, current single-view federated learning methods fail to account for the unique characteristics of multi-view data, leading to suboptimal performance in processing such data. To address these issues, we propose a Self-expressive Hypergraph Based Federated Multi-view Learning method (FedMSGL). The proposed method leverages self-expressive character in the local training to learn uniform dimension subspace with latent sample relation. At the central side, an adaptive fusion technique is employed to generate the global model, while constructing a hypergraph from the learned global and view-specific subspace to capture intricate interconnections across views. Experiments on multi-view datasets with different feature dimensions validated the effectiveness of the proposed method.

Abstract One single code change can significantly influence a wide range of software systems and their users. For example, (a) adding a new feature can spread defects in several modules, while (b) changing an API method can improve the performance of all client programs. Unfortunately, developers often may not clearly know whether code changes are influential at commit time. This paper investigates influential software changes and proposes an approach to identify them immediately when they are applied. Our goals are to (a) identify existing influential changes (ICs) in software projects, (b) understand their characteristics, and (c) build a classification model of ICs to help developers find and address them early. We first conduct a post‐mortem analysis to discover existing influential changes by using intuitions (eg, changes referred by other changes). Then, we re‐categorize all identified changes through an open‐card sorting process. Subsequently, we conduct a survey with about 100 developers to finalize a taxonomy. Finally, from our ground truth, we extract features, including metrics such as the complexity of changes and file centrality in co‐change graphs to build machine learning classifiers. The experiment results show that our classification model with random samples achieves 86.8% precision, 74% recall, and 80.4% F‐measure, respectively.

peer reviewed

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has produced approaches and tools to identify piggybacked apps, the literature lacks a comprehensive investigation into such phenomenon. We fill this gap by: 1) systematically building a large set of piggybacked and benign apps pairs, which we release to the community; 2) empirically studying the characteristics of malicious piggybacked apps in comparison with their benign counterparts; and 3) providing insights on piggybacking processes. Among several findings providing insights analysis techniques should build upon to improve the overall detection and classification accuracy of piggybacked apps, we show that piggybacking operations not only concern app code, but also extensively manipulates app resource files, largely contradicting common beliefs. We also find that piggybacking is done with little sophistication, in many cases automatically, and often via library code.

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains.To overcome storage and processing challenges in time series mining, compression techniques are being used.In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data.This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data.From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This extended empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in smoothing and reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help to achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

We discuss the capability of a new feature set for malware detection based on potential component leaks (PCLs). PCLs are defined as sensitive data-flows that involve Android inter-component communications. We show that PCLs are common in Android apps and that malicious applications indeed manipulate significantly more PCLs than benign apps. Then, we evaluate a machine learning-based approach relying on PCLs. Experimental validations show high performance for identifying malware, demonstrating that PCLs can be used for discriminating malicious apps from benign apps.