Computer Science

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Open Access
Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks
(2025-04-10) Gouge, Justin; Wang, Ping
Within the realm of federated learning (FL), adversarial entities can poison models, slowing down or destroying the FL training process. Therefore, attack prevention and mitigation are crucial for FL. Real-world scenarios may necessitate additional separation or abstraction between clients and servers. When considering multi-layer FL systems, which contain edge server layers, the structural differences warrant new strategies to handle adversaries. While existing works primarily address attack prevention and mitigation in conventional two-layer FL systems, research on attack prevention and mitigation in multi-layer federated learning systems remains limited. This thesis aims to address this gap by investigating the defense strategies in a multi-layered FL system. We propose new methods for anomaly detection and removal of attackers/adversarial entities from training in a multi-layer FL system. First, we train a variational autoencoder (VAE) using the model updates collected from the edge servers. This allows the VAE to discern between benign and adversarial model updates. Following that, we deploy the VAE to detect which edge servers at the cohort level contain malicious clients. Subsequently, we devise two malicious client exclusion strategies: the scoring-based method, which applies a score for each client based upon its appearances within cohorts labeled as benign or malicious, and the Bayesian-based method, which uses Bayesian inference to predict if a specific client is malicious based on the statistical performance of the autoencoder. Both approaches are aimed at mitigating potential harm caused by malicious clients during model training. The experimental results demonstrate the superiority of the proposed methods over previous works for traditional FL mitigation under a variety of scenarios.
Open Access
Tor User De-Anonymization: Client-Side Originating Watermark
(2025-04-10) Brown, Daniel; Vlajic, Natalija
Traditional techniques for Tor user de-anonymization through a side-channel by means of traffic-watermarks are generally implemented through utilization/modulation of server-side originating traffic (SSOW). However, the effectiveness of SSOW is often hindered by significant amounts of traffic noise that accumulates along Tor’s communication pathways. In this thesis, we outline the key ideas behind our novel user de-anonymization technique that utilizes client-side originating watermarks (CSOW). We describe some potential ways this scheme could be implemented in practice while not requiring the control of any Tor node or other resource. We also demonstrate significantly superior real-world performance of our CSOW approach vs. those previously discussed in the literature. Finally, we propose the use of Long Short-Term Memory (LSTM) DNN for the purpose of more effective watermark detection. The real-world experimentations demonstrate excellent potential of our proposed LSTM-Based CSOW watermark detection system to accurately de-anonymize Tor users while keeping the number of false positives (e.g., users mistakenly accused of wrongdoing) at an absolute 0.
Open Access
A Solution for Scale Ambiguity in Generative Novel View Synthesis
(2025-04-10) Forghani, Fereshteh; Brubaker, Marcus
Generative Novel View Synthesis (GNVS) involves generating plausible unseen views of a scene given an initial view and the relative camera motion between the input and target views using generative models. A key limitation of current generative methods lies in their susceptibility to scale ambiguity, an inherent challenge in multi-view datasets caused by the use of monocular techniques to estimate camera positions from uncalibrated video frames. In this work, we present a novel approach to tackle this scale ambiguity in multi-view GNVS by optimizing the scales as parameters in an end-to-end fashion. We also introduce Sample Flow Consistency (SFC), a novel metric designed to assess scale consistency across samples with the same camera motion. Through various experiments, we demonstrate our approach yields improvements in terms of SFC, providing more consistent and reliable novel view synthesis.
Open Access
Dual-Agent Deep Reinforcement Learning Approach to GPU Job Scheduling
(2025-04-10) Shao, Yiming; An, Aijun
Public cloud GPU clusters are increasingly used for distributed deep learning tasks, making the job scheduler critical for minimizing job waiting and completion times. However, scheduling is inherently complex and NP-hard. Current approaches typically address job scheduling and GPU allocation separately, leading to suboptimal performance. DRL-based scheduling methods, while flexible, often overlook two challenges. Firstly, they focus on minimizing the total job completion time and ignore fairness in waiting times. Secondly, distributed training speed is significantly influenced by GPU communication costs, often overlooked. To address this, we introduce AttentiveSched, a DRL-based framework that simultaneously optimizes job selection and GPU assignment. AttentiveSched considers cluster topology for informed scheduling. Its two agents (job and GPU) use attention mechanisms to capture global relationships in the input sequence. By addressing fairness, job completion time, and communication costs in its rewards, AttentiveSched outperforms heuristics-based, meta-heuristics-based, and other DRL-based schedulers on real-world datasets.
Open Access
From Discrete to Continuous: Learning 3D Geometry from Unstructured Points by Random Continuous Space Queries
(2025-04-10) Jia, Meng; Kyan, Matthew J.
In this dissertation, we focus on generalizing recent point convolution methods and building well-behaved point-cloud 3D shape features to achieve more robust, invariant, and versatile implicit neural representations (INR) of 3D shapes. In recent efforts to explore point-cloud based learning methods to improve 3D shape analysis, there has been much attention paid to the use of INR-based frameworks. Existing methods, however, mostly formulate models with an encoder-decoder architecture that incorporates a global shape embedding space, which often fails to model fine-grained local details efficiently, limiting overall generalization performance. To overcome this problem, we propose a convolutional feature space sampling operation (Dual-Feature Sampling or DFS) and develop a novel INR learning framework (Stochastic Continuous Function Learning or SCFL). This framework is first adapted and evaluated for its use in surface reconstruction of generic objects from sparsely sampled point clouds, which is a task that has been extensively used to bench-mark INR 3D shape learning methods. This study demonstrates impressive capabilities of our method, namely: 1) an ability to faithfully recover fine details and uncommon shape characteristics; 2) improved robustness to point-cloud rotation; 3) flexibility to handle different levels of sparsity in the input point clouds; 4) significantly better generalization in the presence of unseen shape categories. In addition, the proposed DFS operator proposed for this framework is well-formulated and general enough that it can be easily made compatible for integration into existing systems designed to address more complex 3D shape tasks. In this work, we harness this powerful ability to represent shape, within a newly proposed SCFL-based occupancy network, applied to shape based processing problems in medical image registration and segmentation. Specifically, our network is adapted and applied to two different, traditionally challenging problems: 1) liver image-to-physical registration; and 2) tumour-bearing whole brain segmentation. In both of these tasks, significant deformation can severely degrade and hinder performance. We illustrate however, that accuracy in both tasks can be considerably improved over baseline methods using our proposed network. Finally, through the course of the investigations conducted, an intensive effort has been made throughout the dissertation to review, analyze and offer speculative insights into the features of these proposed innovations, their role in the configurations presented, as well as possible utility in other scenarios and configurations that may warrant future investigation. It is our hope that the work in this dissertation may help to spark new ideas to advance the state of the art in learning-based representation of 3D shapes and encourage more interest in novel applications of INR to solve real-world problems.
Open Access
Automatic Instantiation of Assurance Cases from Patterns using Large Language Models
(2025-04-10) Odu, Oluwafemi John; Belle, Alvine Boaye
Justifying the correct implementation of mission-critical systems' non-functional requirements (e.g., safety, and security) is crucial to prevent system failure. The latter could have severe consequences such as the death of people and financial losses. Assurance cases can be used to prevent system failure. They are structured sets of arguments supported by evidence, demonstrating that a system’s non-functional requirements have been correctly implemented. Assurance case patterns serve as templates derived from previous successful assurance cases, aimed at facilitating the creation of new assurance cases. Despite the use of these patterns to generate assurance cases, their instantiation remains a largely manual and error-prone process that heavily relies on domain expertise. Thus, exploring techniques to support their automatic instantiation becomes crucial. To address this, our thesis explores the literature on assurance case patterns to understand recent advancements and trends characterizing that literature. Then we investigated the potential of Large Language Models (LLMs) in automating the generation of assurance cases that comply with specific assurance case patterns. Our findings suggest that LLMs can generate assurance cases that comply with the given patterns. However, this study also highlights that LLMs may struggle with understanding some nuances related to pattern-specific relationships. While LLMs exhibit potential in the automatic generation of assurance cases, their capabilities still fall short compared to human experts. Therefore, a semi-automatic approach to instantiating assurance cases may be more advisable at this time.
Open Access
Intruders' Behavior Unveiled: A Dual-Tier Behavior-driven Model for Malicious Activity Detection in IoT Network Using Graph Learning
(2025-04-10) Shafi, MohammadMoein; Habibi Lashkari, Arash
In recent years, IoT technology has transformed smart homes, with most households now including several IoT devices that provide convenience and automation. However, the security of these smart homes is paramount, as vulnerabilities can expose residents to risks like unauthorized access, data breaches, and operational disruption. Network-based threats pose a particularly critical risk due to the numerous vulnerabilities in wireless communication between devices, making it possible for attackers to intercept data or do malicious activities. While traditional intrusion detection systems exist, they are often ineffective in detecting zero-day attacks and lack the ability to identify malicious patterns across diverse threat scenarios due to limited diversity in their detection models. Moreover, these systems are not designed to fully detect all types of intrusions, especially those involving both external network activities and internal IoT communications among smart home devices. This gap is made worse by the challenges in creating specialized IoT datasets that cover a diverse set of malicious activities and data types, which require extensive technical knowledge, a diverse range of devices, and expertise in capturing, executing, and labeling attack scenarios. Such datasets are crucial for data-driven intrusion detection systems. Addressing these challenges, this thesis introduces a dual-tier detection system that effectively can zero-day attacks, and is designed in a way to be scalable for learning the behavior of diverse malicious activities. the proposed solution leverages data from both the smart home hub’s internet connection and the internal network communication of IoT devices to detect and profile malicious activities using a novel graph learning approach. Furthermore, to support this research, we have created the largest IoT smart home dataset, incorporating real-world data from over 50 devices and more than 100 carefully designed attack scenarios, captured over a five-month period. The analysis of this dataset and the performance of our detection model demonstrate promising results, providing a valuable resource and foundation for advancing smart home IoT security.
Open Access
Underwater gesture-based human-to-robot communication
(2025-04-10) Codd-Downey, Robert Frank; Jenkin, Michael
Underwater human to robot interaction presents significant challenges due to the harsh environment, including reduced visibility from suspended particulate matter and high attenuation of light and electromagnetic waves generally. Divers have developed an application-specific gesture language that has proven effective for diver-to-diver communication underwater. Given the wide acceptance of this language for underwater communication, it would seem an appropriate mechanism for diver to robot communication as well. Effective gesture recognition systems must address several challenges. Designing a gesture language involves balancing expressiveness and system complexity. Detection techniques range from traditional computer vision methods, suitable for small gesture sets, to neural networks for larger sets requiring extensive training data. Accurate gesture detection must handle noise and distinguish between repeated gestures and single gestures held for longer durations. Reliable communication also necessitates a feedback mechanism to allow users to correct miscommunications. Such systems must also deal with the need to recognize individual gesture tokens and their sequences, a problem that is hampered by the lack of large-scale labelled datasets of individual tokens and gesture sequences. Here these problems are addressed through weakly supervised learning and a sim2real approach that reduces by several orders of magnitude the effort required in obtaining the necessary labelled dataset. This work addresses this communication task by (i) developing a traditional diver and diver part recognition system (SCUBANetV1+), (ii) using this recognition within a weak supervision approach to train SCUBANetV2, a diver hand gesture recognition system, (iii) SCUBANetV2 recognizes individual gestures, and provides input to the Sim2Real trained SCUBALang LSTM network which translates temporal gesture sequences into phrases. This neural network pipeline effectively recognizes diver hand gestures in video data, demonstrating success in structured sequences. Each of the individual network components are evaluated independently, and the entire pipeline evaluated formally using imagery obtained in both the open ocean and in pool environments. As a final evaluation, the resulting system is deployed within a feedback structure and evaluated using a custom unmanned unwatered vehicle. Although this work concentrates on underwater gesture-based communication, the technology and learning process introduced here can be deployed in other environments for which application-specific gesture languages exist.
Open Access
Normalized Moments for Photo-realistic Style Transfer
(2025-04-10) Canham, Trevor Dalton; Brown, Michael S.
Style transfer, the operation of matching appearance features between source and target images, is a complex and highly subjective problem. Due to the profundity of the concept of artistic style, the optimal solution is ill-defined, so the variety of approaches that have been proposed represent partial solutions to varying degrees of efficiency, usability and appearance of results. In this work a photo-realistic style transfer method for image and video is proposed that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of features. As a proxy for mimicking the effects of camera color rendering or post processing, the employed features (the first through fourth order moments of the color distribution) represent important cues for visual adaptation and pre-attentive processing. The method is evaluated on the above criteria in a series of application relevant experiments and is shown to have results of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that it compared very well with the state-of-the-art (deep learning, optimal transport, etc.) The computational complexity of the algorithm is low, and a numerical implementation that is amenable for real-time video application is proposed and demonstrated. Finally, general recommendations for photo-realistic style transfer are discussed.
Open Access
Gaze-Contingent Multi-Modal and Multi-Sensory Applications
(2024-11-07) Vinnikov, Margarita; Allison, Robert
Gaze-contingent displays are applications that are driven by the user's gaze. They are an important tool for many multi-modal and multi-sensory domains. They can be used to precisely control the retinal image in real time to study visual control of natural behaviour through experimentation, or to improve user experience in virtual reality applications. In this dissertation, I explored the application of gaze-contingent display technology to dierent models and senses and evaluate whether such applications can be useful for simulation, psychophysical research and human-computer interaction. Specically, I have looked at the visual gaze-contingent display and an audio gaze-contingent display. I examined the effects of simulated visual defects on user's perception and control of self-motion during locomotion. I found that gaze-contingent display simulations of visual defects signicantly altered visual patterns and impaired the accuracy and precision of judgement of heading. I also examined the impact of simulating gaze-contingent depth-of-field for monocular and stereoscopic displays. The experimental data showed that the alleviation of negative eects associated with stereo displays depends on the user's age and the types of scenes that are viewed. Finally, I simulated gaze-contingent audio displays that imitated the cocktail party effect. My audio enhancement techniques turned to be very benecial for applications that have to deal with user's attention to multiple sources of sounds (speakers) such as teleconferences and social games. Finally, in this dissertation, I demonstrated that gaze-contingent systems can be used in many aspects of virtual system design and if combined together (used for multiple cues and senses) can be a very powerful tool for augmenting and improving the overall user experience.
Open Access
Leveraging the Power of Images: Image Recommendation to Enhance Issue Reports
(2024-11-07) Tan, Xuchen; Nayebi, Maleknaz
The trend of sharing images and image-based social networks has eventually changed the landscape of social networks. As a result, this shift has impacted social coding platforms, and previous studies showed that image sharing has become increasingly popular among software developers. However, most developers’ productivity assistance tools predominantly rely on textual content only. To enhance issue reports, this study focuses on three primary objectives: (i) identifying issue reports that benefit from image sharing and processing in Bugzilla, (ii) identifying the type of image that would improve the bug report, and (iii) conducting a comprehensive qualitative and quantitative evaluation of the tool’s performance and impact. The quantitative evaluation demonstrates that our tool achieves an average recall of 78% and an average F1-score of 74% in predicting the necessity of including image attachments in issue reports. Moreover, our qualitative evaluation of software developers showed that 75% of the developers found the overall design and recommendations of our method practically useful for issue reporting. This study, along with its associated dataset and methodology, represents the first research on recommending images to developers for enhanced issue report communication. Our results illuminate a promising trajectory for enhanced and visual productivity tools for developers.
Open Access
Empirical Analysis and Enhancement of Machine Learning Software Documentation
(2024-11-07) Sharuka Promodya Thirimanne; Nayebi, Maleknaz; Datta, Suprakash
As machine learning gains popularity, individuals from diverse fields and skill levels integrate it into their workflows. However, many lack software engineering experience, impacting the usability of documentation. Additionally, the current machine learning documentation and its issues are insufficiently addressed in the literature. This thesis comprises two papers. In the first paper, we compared the content and design differences of TensorFlow tutorials and analyzed the profiles of users who asked questions about them. We also developed a comprehensive taxonomy of TensorFlow documentation issues. In the second paper, we examined the potential of leveraging generative AI to augment machine learning documentation. We proposed a method to augment TensorFlow API documentation by addressing documentation-related questions using large language models. This thesis highlights the need for machine learning documentation to accommodate diverse skill levels as its use expands across domains and showcases the potential of generative AI to automate documentation augmentation.
Open Access
Anonymity in Developer Communities: Insights from Developer Perceptions and Stack Overflow Profiles
(2024-11-07) Lemango, Elim Yoseph; Nayebi, Maleknaz
This thesis consists of two studies: an interview study with 34 early-career developers and a mining study analyzing 130,000 developer profiles. The interview study examines developers' definitions of anonymity, their preferences for anonymity, and their engagement with privacy policies. It also explores whether presenting privacy policies using contextual integrity principles improves understanding of the privacy policies. The developers from the interview study defined anonymity as withholding identifiable information like name, location, and professional background. The mining study investigates how much information developers share across platforms and the ease of retrieving their professional profiles. Our findings show that using Stack Overflow location and screen name in LinkedIn searches narrows down profiles, but cross-linking Stack Overflow profile data information with GitHub or Twitter adds noise. This research provides valuable insights into how developers define anonymity, and how that affects their behaviour when using social coding platforms.
Open Access
Image White Balance for Multi-Illuminant Scenes
(2024-11-07) Arora, Aditya; Derpanis, Konstantinos G.
Performing white-balance (WB) correction for scenes with multiple illuminants remains a challenging task in computer vision. Most previous methods estimate per-pixel scene illumination directly in the RAW sensor image space. Recent work explored an alternative fusion strategy, where a neural network fuses multiple white-balanced versions of the input image processed to sRGB using pre-defined white-balance settings. Inspired by this line of work, we present two contributions targeting fusion-based multi-illuminant WB correction. First, we introduce a large-scale multi-illumination dataset rendered from RAW images to support training fusion models and evaluation. The dataset comprises over 16,000 sRGB images with ground truth sRGB white-balance corrected images. Next, we introduce an attention-based architecture to fuse five white-balance settings. This architecture yields an improvement of up to 25% over prior work.
Open Access
Symmetry-based monocular 3D vehicle ground-truthing for traffic analytics
(2024-11-07) Tran, Trong Thao; Elder, James
3D object detection is critical for autonomous driving and traffic analytics. Current research relies on LiDAR-derived ground truth for training and evaluation. However, LiDAR ground truth is expensive and usually inaccurate in the far field due to sparse LiDAR returns. Assuming a fully calibrated camera and a 3D terrain model, we explore whether inexpensive RGB imagery can be used to obtain 3D ground truth based on the bilateral symmetry of motor vehicles. From manually annotated symmetry points and tire-ground contact points, we infer a vertical symmetry plane and 3D point cloud to estimate vehicle location, pose, and dimensions. These estimates are input into a probabilistic model derived from a standard public motor vehicle dataset to form maximum a posteriori estimates of remaining dimensions. Evaluations on a public traffic dataset show that this novel symmetry-based approach is more accurate than LiDAR-based ground-truthing on single frames and comparable to LiDAR-based methods that propagate information across frames.
Open Access
An Axiomatic Perspective on Anomaly Detection
(2024-11-07) Wyke, Chester Samuel; Urner, Ruth
A major challenge for both theoretical treatment and practical application of unsupervised learning tasks, such as clustering, anomaly detection or generative modeling, is the inherent lack of quantifiable objectives. Choosing methods and evaluating outcomes is then often a matter of ad-hoc heuristics or personal taste. Anomaly detection is often employed as a preprocessing step to other learning tasks, and unsound decisions for this task may thus have far-reaching consequences. In this work, we propose an axiomatic framework for analyzing behaviours of anomaly detection methods. We propose a basic set of desirable properties (or axioms) for distance-based anomaly detection methods and identify dependencies and (in-)consistencies between subsets of these. In addition, we include empirical results, which demonstrate the benefits of this axiomatic perspective on behaviours of anomaly detection methods. Our experiments illustrate how some commonly employed algorithms violate, perhaps unexpectedly, a basic desirable property. Namely, we highlight a material problem with a commonly used method called Isolation Forest, related to infinite bands of space likely to be labelled as inliers that extend infinitely far away from the training data. Additionally, we experimentally demonstrate that another common method, Local Outlier Factor, is vulnerable to adversarial data poisoning. To conduct these experimental evaluations, a tool for dataset generation, experimentation and visualization was built, which is an additional contribution of this work.
Open Access
Neural Document Segmentation Using Weighted Sliding Windows with Transformer Encoders
(2024-11-07) Abbasi, Saeed; An, Aijun; Davoudi, Heidar
The subdivision of documents into semantically coherent segments is a fundamental challenge in Natural Language Processing (NLP), with notable applications in information retrieval and question answering. Effective text segmentation is crucial for enhancing Retrieval-Augmented Generation (RAG) systems by providing coherent segments that improve the contextual accuracy of responses. We introduce a weighted sliding window framework, WeSWin, that effectively segments arbitrarily long documents using Transformers. WeSWin consists of overlapping document partitioning followed by the weighted aggregation of multiple sentence predictions within the generated windows, ensuring that sentences with larger context visibility contribute more to the ultimate label. Additionally, we propose a multi-task training framework, WeSWin-Ret, which combines text segmentation with an auxiliary sentence retrieval task. This approach injects query-awareness into the embedding space of the shared Transformer, resulting in improved segmentation performance. Extensive experiments demonstrate that our methods outperform state-of-the-art approaches. On the Wiki-727k benchmark, both our WeSWin and WeSWin-Ret models surpass existing works based on BERT, RoBERTa, or LongFormer Transformers. Notably, our RoBERTa baseline often matches LongFormer’s performance while being significantly more efficient in training and inference. We validate our model’s robustness on domain-specific segmentation benchmarks, including en_city, en_disease, and an industrial automotive dataset, demonstrating generalizability across domains. Lastly, our model proves to be highly effective in enhancing down-stream RAG applications by providing cohesive chunks for knowledge retrieval.
Open Access
IoT network Malicious Behaviour Profiling Based on Explainable AI Using LSTM and SHAP
(2024-11-07) Niktabe, Sepideh; Habibi Lashkari, Arash
The proliferation of IoT devices has enhanced connectivity but exposed networks to new cyber threats, particularly from botnets. Detecting and identifying malicious data is critical for early threat detection, understanding botnet attack patterns, and deploying countermeasures. This research proposes an IoT Bot detection and identification profiling model using XAI. The proposed model introduces a novel feature selection techqnique with the XGBoost algorithm and a correlation-based feature selection technique to enhance efficiency. An optimized LSTM neural network enables accurate bot detection and identification, with hyperparameters selected using the Bayesian Optimization algorithm. SHAP analysis provides insightful individual and collective bot characteristic profiles. The model’s performance was evaluated using the augmented BCCC-Aposemat-Bot-IoT-24 dataset, built upon the Aposemat-Bot-IoT-23 dataset, and compared against established models assessed primarily on the same dataset in previous research. The results showed that the proposed model performed comparably to these models, with distinct advantages, including handling sequential and time-series data, managing imbalanced datasets, and providing explainable insights into botnet behavior. The model’s design also emphasizes computational efficiency, making it potentially suitable for deployment in resource-constrained environments.
Open Access
Robust Representation Learning Solutions for Wireless Sensing Applications
(2024-11-07) Barahimi, Borna; Tabassum, Hina
WiFi sensing, a technique for utilizing wireless signals for monitoring human activities and environmental conditions, holds substantial potential in diverse applications including human activity recognition (HAR). It offers a powerful, continuous, and non-intrusive monitoring solution. This technology eliminates the need for wearable sensors, and even functions outside the line-of-sight. However, the large-scale deployment of WiFi sensing faces several challenges: (1) limited computational power in WiFi devices, (2) the cost and complexity of annotating channel state information (CSI) data, and (3) ensuring model generalization across different environments. The first part of the thesis addresses the limited computation power of edge devices by developing a Real-time Sensing and Compression Network (RSCNet). RSCNet is a cloud-based architecture designed to alleviate computational constraints on edge devices. It achieves this through efficient CSI compression at the edge and subsequent sensing and reconstruction in the cloud. RSCNet employs window-based CSI compression and LSTM-based recurrent blocks, significantly reducing computational demands and communication overheads while maintaining high sensing accuracy. The second part of the thesis addresses the issue of limited labeled data by developing self-supervised learning (SSL) method, namely Context-Aware Predictive Coding (CAPC) method. CAPC combines contrastive predictive coding with the Barlow Twins method, enhancing the model's ability to learn robust representations from unlabeled CSI time-series data. This approach improves model generalization, particularly when labeled data is scarce. CAPC also introduces a novel augmentation technique, dual view, which isolates free space propagation information from hardware distortions, further enhancing representation quality for WiFi sensing applications. Through extensive evaluations, this thesis demonstrates the effectiveness of both RSCNet and CAPC. RSCNet achieves results on par with the state-of-the-art performance in HAR tasks while drastically reducing computational burdens on edge devices. CAPC outperforms baseline SSL approaches and traditional supervised methods, showcasing its superior generalization capabilities in unseen environments. The dual view augmentation further enhances CAPC's performance by reducing electronic distortions. This thesis concludes that RSCNet and CAPC contribute significantly to the advancement of robust and practical wireless sensing technologies. These frameworks address critical challenges in the field, paving the way for wider adoption of WiFi sensing in real-world applications.
Open Access
A Novel Vulnerable Smart Contracts Profiling Method Based on Advanced Genetic Algorithm Using Penalty Fitness Function
(2024-11-07) HajiHosseinKhani, Sepideh; Habibi Lashkari, Arash
With the advent of blockchain networks, there has been a transition from traditional contracts to Smart Contracts (SCs), which are crucial for maintaining trust within these networks. Previous methods for analyzing SC vulnerabilities typically lack accuracy and effectiveness, struggling to detect complex vulnerabilities due to limited data availability. This study introduces a novel approach to detecting and profiling SC vulnerabilities, featuring two components: a new analyzer named BCCC-SCsVulLyzer and an advanced Genetic Algorithm (GA) profiling method. The BCCC-SCsVulLyzer extracts 240 features, while the enhanced GA employs techniques such as Penalty Fitness Function and Adaptive Mutation Rate to profile vulnerabilities. Additionally, this work introduces a new dataset, BCCC-SCsVul-2024, with 111,897 Solidity source code samples for practical validation. Three taxonomies are established to enhance the efficiency of profiling techniques. Our approach demonstrated superior precision and accuracy, proving efficient in time and space complexity. The profiling technique also makes the model highly transparent and explainable, highlighting the potential of GA-based profiling to improve SC vulnerability detection and enhance blockchain security.