A lightweight object detection algorithm based on YOLOv7 is proposed, optimized for deployment on resource-constrained drone platforms. Key improvements include optimizing the network structure, adjusting depth and channel dimensions, and integrating the Bi-directional Feature Pyramid Network (BiFPN) to enhance multi-scale feature fusion for improved detection accuracy and robustness. A 2-Level Haar Wavelet Transform (2-level HWT) replaces traditional strided convolutions, efficiently preserving spatial information for better feature extraction. Additionally, a novel Dual Cross Stage Partial Fusion (DCSPF) module is introduced to improve feature reuse and representation capacity by facilitating information flow across stages. The detection head is further enhanced with the Group Batch Normalization Reweighting Unit (GNRU) and the Adaptive Grouped Convolution Unit (AGCU) to dynamically adjust feature importance. Experimental results on the Vis-Drone2019 dataset show a 2.6% increase in mAP@50 and a 6.1% reduction in GFLOPs, validating the proposed approach's balance between accuracy and computational efficiency, suitable for drones with limited resources.

Federated learning (FL) enables multiple clients to collaboratively train a global model without disclosing their original data. However, traditional FL is susceptible to attacks from malicious clients who can disrupt the training process by uploading poisoned models to the server. In this paper, we introduce REDFL, a robust and efficient decentralized federated learning framework that employs committee consensus to effectively defend against poisoning attacks. Our framework not only enhances security but also improves the performance of the global model. In REDFL, we propose a dual model validation algorithm based on mutual information for precise validation of model updates. To evaluate the contributions made by clients to the global model, we develop a contribution calculation method that accounts for the varying roles of clients. Our proof of contribution mechanism enables a more accurate selection of honest clients as committee members. The committee validates the model updates from trainers, reaches a consensus on these updates, and ultimately aggregates a high-quality global model. Experimental results demonstrate that REDFL outperforms other Byzantine-tolerant algorithms in terms of global model accuracy and convergence speed. On the Non-IID dataset under a poisoning attack scenario, REDFL achieves an accuracy of 80%, showcasing its robustness and effectiveness.

This paper proposes an enhanced YOLOv7 algorithm specifically designed for small object detection on UAV platforms. The key improvements include the introduction of an optimized P2 detection layer, replacing the ineffective P5 layer for small object detection, and streamlining the network structure to reduce the number of parameters and computational cost. We introduce the Partial Agent Self-Attention (PASA) module to improve the aggregation of non-local information. Additionally, by incorporating depthwise separable convolutions and optimizing the structure, we further enhance the multi-scale feature extraction capability and computational efficiency of the Spatial Pyramid Pooling Cross-Stage Partial Connection (SPPCSPC) module. Furthermore, we propose a new feature extraction module, Faster Implementation of Enhanced CSP Bottleneck through Multiple Convolutions (FECM), which is implemented through multiple convolutions. We conduct experimental validation on the public Visdrone2019 and DroneVision-6 datasets. The experimental results show that the proposed method improves both precision and recall while reducing the number of parameters by 72%, providing a powerful solution for small object detection in complex UAV environments.

Speech carries a wealth of sensitive information, and many studies have investigated various methods of speech eavesdropping. Recent research has shown that even in soundproof indoor environments, speech systems can still be compromised by outdoor RF sensing technologies. However, existing approaches either rely heavily on prior knowledge of the target environment, fail when the primary sound source is occluded, or are limited to word-level classification. Consequently, they have not fully exposed the severe threats that RF sensing poses to speech privacy. To bridge these gaps, this paper presents Argus-ear, a mmWave-based speech eavesdropping system. By localizing and identifying sound sources throughout the target room, Argus-ear captures subtle vibrations from the most eavesdropping-worthy reflectors and reconstructs speech using deep neural networks. A series of techniques are proposed to enhance weak vibration sensing, suppress noise interference, and improve the fidelity of speech reconstruction. Extensive experiments demonstrate that Argus-ear can identify various types of sound sources and accurately reconstruct unconstrained vocabulary-level speech across different languages, speakers, and sound sources.

Non-preemptive rigid gang scheduling combines the efficiency of parallel execution with the reduced overhead of non-preemptive scheduling. This approach is particularly advantageous for parallel hardware accelerators, such as Google's Edge Tensor Processing Unit (TPU), which is widely used for deep neural network (DNN) inference on embedded systems. This paper studies sporadic global non-preemptive fixed-priority (NP-FP) rigid gang scheduling, which is well-suited for DNN applications in Edge TPU pipelines. Each gang task spawns a fixed number of threads that must execute concurrently across distinct processing units. We introduce the first carry-in limitation technique specifically designed for gang task response time analysis, addressing the unique challenges posed by intra-task parallelism. This technique is formulated as a generalized knapsack problem, and we develop both a linear programming relaxation and a dynamic programming approach to solve it under different time complexities. Additionally, we propose the first optimal priority assignment policy for NP-FP gang schedulability tests. Our proposed schedulability analysis and optimal priority assignment policy are evaluated through extensive experiments, including both synthetic task sets and a case study using DNN benchmarks on commercial off-the-shelf Edge TPU accelerators. The results demonstrate that the proposed approaches effectively enhance the state-of-the-art global NP-FP gang schedulability tests, achieving improvements of up to 57.9% for synthetic task sets and 76.7% for Edge TPU benchmarks. Furthermore, we conduct an ablations study to examine the impact of different algorithmic components in the proposed technique, providing valuable insights for future research.

Non-preemptive rigid gang scheduling combines the performance benefits of parallel execution with the low overhead of non-preemptive scheduling and rigid task programming model. This approach appears particularly well-suited for parallel hardware accelerators where the context switch and migration overheads are critical and should be avoided. One of the most notable examples today is Google's Edge Tensor Processing Unit (TPU) used for neural network inference on embedded boards. The paper studies sporadic non-preemptive rigid gang scheduling applied to multi-TPU edge AI accelerators. Each gang task spawns a fixed number of threads that must execute simultaneously on distinct processing units. We consider non-preemptive fixed-priority gang (NP-FP-Gang) scheduling and propose the first carry-in limitation for gang task response time analysis. The gang task carry-in limitation differs from conventional sequential tasks due to the intra-task parallelism. We formulate it as a generalized knapsack problem and develop a linear programming relaxation and a dynamic programming approach to solve the problem under different time complexities. The performance of the proposed schedulability analysis is evaluated through randomly generated synthetic task sets and a case study using neural network benchmarks executed on commercial off-the-shelf multi-TPU edge AI accelerators. The evaluation results show that the proposed response time analysis effectively improves the state of-the-art NP-FP-Gang schedulability test even by 85.7% for the Edge TPU benchmarks in particular.

Timing correctness is crucial in a multi-criticality real-time system, such as an autonomous driving system. It has been recently shown that these systems can be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions like schedule randomization cannot protect against such attacks, often limited by the system’s real-time nature. This article presents “ SchedGuard++ ”: a temporal protection framework for Linux-based real-time systems that protects against posterior schedule-based attacks by preventing untrusted tasks from executing during specific time intervals. SchedGuard++ supports multi-core platforms and is implemented using Linux containers and a customized Linux kernel real-time scheduler. We provide schedulability analysis assuming the Logical Execution Time (LET) paradigm, which enforces I/O predictability. The proposed response time analysis takes into account the interference from trusted and untrusted tasks and the impact of the protection mechanism. We demonstrate the effectiveness of our system using a realistic radio-controlled rover platform. Not only is “ SchedGuard++ ” able to protect against the posterior schedule-based attacks, but it also ensures that the real-time tasks/containers meet their temporal requirements.

Many cyber-physical systems are offloading computation-heavy programs to hardware accelerators (e.g., GPU and TPU) to reduce execution time. These applications will self-suspend between offloading data to the accelerators and obtaining the returned results. Previous efforts have shown that self-suspending tasks can cause scheduling anomalies, but none has examined inter-task communication. This paper aims to explore self-suspending tasks' data chain latency with periodic activation and asynchronous message passing. We first present the cause for suspension-induced delays and worst-case latency analysis. We then propose a rule for utilizing the hardware co-processors to reduce data chain latency and schedulability analysis. Simulation results show that the proposed strategy can improve overall latency while preserving system schedulability.

Real-time systems have recently been shown to be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions such as schedule randomization lack the ability to protect against such attacks, often limited by the system's real-time nature. This paper presents “SchedGuard”: a temporal protection framework for Linux-based hard real-time systems that protects against posterior scheduler side-channel attacks by preventing untrusted tasks from executing during specific time segments. SchedGuard is integrated into the Linux kernel using cgroups, making it amenable to use with container frameworks. We demonstrate the effectiveness of our system using a realistic radio-controlled rover platform and synthetically generated workloads. Not only is SchedGuard able to protect against the attacks mentioned above, but it also ensures that the real-time tasks/containers meet their temporal requirements.

OBJECTIVE: De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where the model is applied) is different from the source domain (where the model is trained), the model often suffers a significant performance drop, commonly referred to as domain adaptation issue. In de-identification, domain adaptation issues can make the model vulnerable for deployment. In this work, we aim to close the domain gap by leveraging unlabeled data from the target domain. MATERIALS AND METHODS: We introduce a self-training framework to address the domain adaptation issue by leveraging unlabeled data from the target domain. We validate the effectiveness on 4 standard de-identification datasets. In each experiment, we use a pair of datasets: labeled data from the source domain and unlabeled data from the target domain. We compare the proposed self-training framework with supervised learning that directly deploys the model trained on the source domain. RESULTS: In summary, our proposed framework improves the F1-score by 5.38 (on average) when compared with direct deployment. For example, using i2b2-2014 as the training dataset and i2b2-2006 as the test, the proposed framework increases the F1-score from 76.61 to 85.41 (+8.8). The method also increases the F1-score by 10.86 for mimic-radiology and mimic-discharge. CONCLUSION: Our work demonstrates an effective self-training framework to boost the domain adaptation performance for the de-identification task for electronic health records.

The Unmanned aerial vehicles (UAVs) sector is fast-expanding. Protection of real-time UAV applications against malicious attacks has become an urgent problem that needs to be solved. Denial-of-service (DoS) attack aims to exhaust system resources and cause important tasks to miss deadlines. DoS attack may be one of the common problems of UAV systems, due to its simple implementation. In this paper, we present a software framework that offers DoS attack-resilient control for real-time UAV systems using containers: ContainerDrone. The framework provides defense mechanisms for three critical system resources: CPU, memory, and communication channel. We restrict attacker's access to CPU core set and utilization. Memory bandwidth throttling limits attacker's memory usage. By simulating sensors and drivers in the container, a security monitor constantly checks DoS attacks over communication channels. Upon the detection of a security rule violation, the framework switches to the safety controller to mitigate the attack. We implemented a prototype quadcopter with commercially off-the-shelf (COTS) hardware and open-source software. Our experimental results demonstrated the effectiveness of the proposed framework defending against various DoS attacks.

There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers. In this paper, we propose a new measure of local community structure, coupled with a two-phase algorithm that extracts all possible candidates first, and then optimizes the community hierarchy. We compare our results with previous methods on real world networks such as the co-purchase network from Amazon. Experimental results verify the feasibility and effectiveness of our approach.

Communities in social networks may overlap, with some hub nodes belonging to multiple communities. They may also have outliers, which are nodes that belong to no community. The criterion to locate hubs or outliers is network dependent. Previous methods usually require this information as input parameters, e.g., an expected number of communities, with no intuition or assistance. Here we present a visual data mining approach, which first helps the user to make appropriate parameter selections by observing initial data visualizations, and then finds and extracts overlapping community structures from the network. Experimental results verify the scalability and accuracy of our approach on real network data and show its advantages over previous methods.

Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common property of these networks is their community structure, considered as clusters of densely connected groups of vertices, with only sparser connections between groups. The identification of such communities relies on some notion of clustering or density measure. which defines the communities that can be found. However, previous community detection methods usually apply the same structural measure on all kinds of networks, despite their distinct dissimilar features. In this paper, we present a new community mining measure, Max-Min Modularity, which considers both connected pairs and criteria defined by domain experts in finding communities, and then specify a hierarchical clustering algorithm to detect communities in networks. When applied to real world networks for which the community structures are already known, our method shows improvement over previous algorithms. In addition, when applied to randomly generated networks for which we only have approximate information about communities, it gives promising results which shows the algorithm's robustness against noise.

Effectively organizing web search results into clusters is important to facilitate quick user navigation to relevant documents. Previous methods may rely on a training process and do not provide a measure for whether page clustering is actually required. In this paper, we reformalize the clustering problem as a word sense discovery problem. Given a query and a list of result pages, our unsupervised method detects word sense communities in the extracted keyword network. The documents are assigned to several refined word sense communities to form clusters. We use the modularity score of the discovered keyword community structure to measure page clustering necessity. Experimental results verify our method's feasibility and effectiveness.

As the volume of digitally accessible information grows, there is increasing pressure on the development of data visualization methods to enable humans to interpret that data. We provide a description of our WebViz system, as a tool to visualize both the structure and usage of web sites. We illustrate the use of our visualization paradigm by introducing polygonal graphs layered on top of our adaptation of radial disk trees. In our system, the structure of a web segment is rendered as a radial tree, and usage data can be extracted and layered as polygonal graphs. By interactively creating and adjusting these layers, a user can develop real time insight into the data. We present the system, show the idea of interactive visual operators, and provide some examples that help show the value of the specific visualization techniques, as well as the interactive use of those techniques.

Discovering web navigational trends and understanding data mining results is undeniably advantageous to web designers and web-based application builders. It is also desirable to interactively investigate web access data and patterns, to allows ad-hoc discovery and examination of patterns that are not apriori known. Visualizing the usage data in the context of the web site structure is of major importance, as it puts web access requests and their connectivity in perspective. Various visualization tools have been developed for this task, but often fail to provide visual data mining functionalities to generate new patterns. Here we present our visual data mining system, WebViz, which allows interactive investigation of web usage data within their structure context, as well as ad-hoc knowledge pattern discovery on web navigational behaviour.

Web site structures are complex to analyze. Cross-referencing the web structure with navigational behaviour adds to the complexity of the analysis. However, this convoluted analysis is necessary to discover useful patterns and understand the navigational behaviour of web site visitors, whether to improve web site structures, provide intelligent on-line tools or offer support to human decision makers. Moreover, interactive investigation of web access logs is often desired since it allows ad hoc discovery and examination of patterns not a priori known. Various visualization tools have been provided for this task but they often lack the functionality to conveniently generate new patterns. In this paper we propose a visualization tool to visualize web graphs, representations of web structure overlaid with information and pattern tiers. We also propose a web graph algebra to manipulate and combine web graphs and their layers in order to discover new patterns in an ad hoc manner.

A significant part of the Web mining problem is simply in understanding the value of any mining method. For example, the value of Web mining to improve user navigation is even more challenging if one can't visualize the differences over a large collection of Web pages or a significant structure within the existing Web. We present WebKIV, a tool we've developed to help us visualize our own results in Web mining. WebKIV combines strategies from several other Web visualization tools, to provide a single method of visualizing Web structure, and the results of Web mining on that structure. We summarize the value of Web visualization tools along the dimensions of scale (can one visualize small and large structures), navigation dynamics (can one visualize navigation dynamically or statically), and cumulative usage (can one distinguish individual and aggregate Web usage). We then show how WebKIV provides a way of visualizing the results of Web mining in a way that distinguishes properties along all three of these dimensions.