OpenRaiser/Intern-Atlas huggingface dataset:
On the effectiveness of smartphone IMU sensors and Deep Learning in the detection of cardiorespiratory conditions
Venue: arXiv cs.LG 2024
Year: 2024

This research introduces an innovative method for the early screening of cardiorespiratory diseases based on an acquisition protocol, which leverages commodity smartphone's Inertial Measurement Units (IMUs) and deep learning techniques. We collected, in a clinical setting, a dataset featuring recordings of breathing kinematics obtained by accelerometer and gyroscope readings from five distinct body regions. We propose an end-to-end deep learning pipeline for early cardiorespiratory disease screening, incorporating a preprocessing step segmenting the data into individual breathing cycles, and a recurrent bidirectional module capturing features from diverse body regions. We employed Leave-one-out-cross-validation with Bayesian optimization for hyperparameter tuning and model selection. The experimental results consistently demonstrated the superior performance of a bidirectional Long-Short Term Memory (Bi-LSTM) as a feature encoder architecture, yielding an average sensitivity of $0.81 \pm 0.02$, specificity of $0.82 \pm 0.05$, F1 score of $0.81 \pm 0.02$, and accuracy of $80.2\% \pm 3.9$ across diverse seed variations. We also assessed generalization capabilities on a skewed distribution, comprising exclusively healthy patients not used in training, revealing a true negative rate of $74.8 \% \pm 4.5$. The sustained accuracy of predictions over time during breathing cycles within a single patient underscores the efficacy of the preprocessing strategy, highlighting the model's ability to discern significant patterns throughout distinct phases of the respiratory cycle. This investigation underscores the potential usefulness of widely available smartphones as devices for timely cardiorespiratory disease screening in the general population, in at-home settings, offering crucial assistance to public health efforts (especially during a pandemic outbreaks, such as the recent COVID-19).
CycleGAN with Better Cycles
Venue: arXiv cs.CV 2024
Year: 2024

CycleGAN provides a framework to train image-to-image translation with unpaired datasets using cycle consistency loss [4]. While results are great in many applications, the pixel level cycle consistency can potentially be problematic and causes unrealistic images in certain cases. In this project, we propose three simple modifications to cycle consistency, and show that such an approach achieves better results with fewer artifacts.
Avoiding Generative Model Writer's Block With Embedding Nudging
Venue: arXiv cs.LG 2024
Year: 2024

Generative image models, since introduction, have become a global phenomenon. From new arts becoming possible to new vectors of abuse, many new capabilities have become available. One of the challenging issues with generative models is controlling the generation process specially to prevent specific generations classes or instances . There are several reasons why one may want to control the output of generative models, ranging from privacy and safety concerns to application limitations or user preferences To address memorization and privacy challenges, there has been considerable research dedicated to filtering prompts or filtering the outputs of these models. What all these solutions have in common is that at the end of the day they stop the model from producing anything, hence limiting the usability of the model. In this paper, we propose a method for addressing this usability issue by making it possible to steer away from unwanted concepts (when detected in model's output) and still generating outputs. In particular we focus on the latent diffusion image generative models and how one can prevent them to generate particular images while generating similar images with limited overhead. We focus on mitigating issues like image memorization, demonstrating our technique's effectiveness through qualitative and quantitative evaluations. Our method successfully prevents the generation of memorized training images while maintaining comparable image quality and relevance to the unmodified model.
An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication
Venue: arXiv cs.CL 2024
Year: 2024

Machine translation models are still inappropriate for translating chats, despite the popularity of translation software and plug-in applications. The complexity of dialogues poses significant challenges and can hinder crosslingual communication. Instead of pursuing a flawless translation system, a more practical approach would be to issue warning messages about potential mistranslations to reduce confusion. However, it is still unclear how individuals perceive these warning messages and whether they benefit the crowd. This paper tackles to investigate this question and demonstrates the warning messages' contribution to making chat translation systems effective.
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model
Venue: arXiv cs.CV 2024
Year: 2024

Multi-object tracking (MOT) is a critical technology in computer vision, designed to detect multiple targets in video sequences and assign each target a unique ID per frame. Existed MOT methods excel at accurately tracking multiple objects in real-time across various scenarios. However, these methods still face challenges such as poor noise resistance and frequent ID switches. In this research, we propose a novel ConsistencyTrack, joint detection and tracking(JDT) framework that formulates detection and association as a denoising diffusion process on perturbed bounding boxes. This progressive denoising strategy significantly improves the model's noise resistance. During the training phase, paired object boxes within two adjacent frames are diffused from ground-truth boxes to a random distribution, and then the model learns to detect and track by reversing this process. In inference, the model refines randomly generated boxes into detection and tracking results through minimal denoising steps. ConsistencyTrack also introduces an innovative target association strategy to address target occlusion. Experiments on the MOT17 and DanceTrack datasets demonstrate that ConsistencyTrack outperforms other compared methods, especially better than DiffusionTrack in inference speed and other performance metrics. Our code is available at https://github.com/Tankowa/ConsistencyTrack.
VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification
Venue: arXiv cs.LG 2024
Year: 2024

Vertical Federated Learning (VFL) focuses on handling vertically partitioned data over FL participants. Recent studies have discovered a significant vulnerability in VFL to backdoor attacks which specifically target the distinct characteristics of VFL. Therefore, these attacks may neutralize existing defense mechanisms designed primarily for Horizontal Federated Learning (HFL) and deep neural networks. In this paper, we present the first backdoor defense, called VFLIP, specialized for VFL. VFLIP employs the identification and purification techniques that operate at the inference stage, consequently improving the robustness against backdoor attacks to a great extent. VFLIP first identifies backdoor-triggered embeddings by adopting a participant-wise anomaly detection approach. Subsequently, VFLIP conducts purification which removes the embeddings identified as malicious and reconstructs all the embeddings based on the remaining embeddings. We conduct extensive experiments on CIFAR10, CINIC10, Imagenette, NUS-WIDE, and BankMarketing to demonstrate that VFLIP can effectively mitigate backdoor attacks in VFL. https://github.com/blingcho/VFLIP-esorics24
Exploring Selective Layer Fine-Tuning in Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning (FL) has emerged as a promising paradigm for fine-tuning foundation models using distributed data in a privacy-preserving manner. Under limited computational resources, clients often find it more practical to fine-tune a selected subset of layers, rather than the entire model, based on their task-specific data. In this study, we provide a thorough theoretical exploration of selective layer fine-tuning in FL, emphasizing a flexible approach that allows the clients to adjust their selected layers according to their local data and resources. We theoretically demonstrate that the layer selection strategy has a significant impact on model convergence in two critical aspects: the importance of selected layers and the heterogeneous choices across clients. Drawing from these insights, we further propose a strategic layer selection method that utilizes local gradients and regulates layer selections across clients. The extensive experiments on both image and text datasets demonstrate the effectiveness of the proposed strategy compared with several baselines, highlighting its advances in identifying critical layers that adapt to the client heterogeneity and training dynamics in FL.
Semantic and goal-oriented edge computing for satellite Earth Observation
Venue: arXiv cs.NI 2024
Year: 2024

The integration of Semantic Communications (SemCom) and edge computing in space networks enables the optimal allocation of the scarce energy, computing, and communication resources for data-intensive applications. We use Earth Observation (EO) as a canonical functionality of satellites and review its main characteristics and challenges. We identify the potential of the space segment, represented by a low Earth orbit (LEO) satellite constellation, to serve as an edge layer for distributed intelligence. Based on that, propose a system architecture that supports semantic and goal-oriented applications for image reconstruction and object detection and localization. The simulation results show the intricate trade-offs among energy, time, and task-performance using a real dataset and State-of-the-Art (SoA) processing and communication parameters.
Red Team Redemption: A Structured Comparison of Open-Source Tools for Adversary Emulation
Venue: arXiv cs.CR 2024
Year: 2024

Red teams simulate adversaries and conduct sophisticated attacks against defenders without informing them about used tactics in advance. These interactive cyber exercises are highly beneficial to assess and improve the security posture of organizations, detect vulnerabilities, and train employees. Unfortunately, they are also time-consuming and expensive, which often limits their scale or prevents them entirely. To address this situation, adversary emulation tools partially automate attacker behavior and enable fast, continuous, and repeatable security testing even when involved personnel lacks red teaming experience. Currently, a wide range of tools designed for specific use-cases and requirements exist. To obtain an overview of these solutions, we conduct a review and structured comparison of nine open-source adversary emulation tools. To this end, we assemble a questionnaire with 80 questions addressing relevant aspects, including setup, support, documentation, usability, and technical features. In addition, we conduct a user study with domain experts to investigate the importance of these aspects for distinct user roles. Based on the evaluation and user feedback, we rank the tools and find MITRE Caldera, Metasploit, and Atomic Red Team on top.
Hierarchical Blockmodelling for Knowledge Graphs
Venue: arXiv cs.AI 2024
Year: 2024

In this paper, we investigate the use of probabilistic graphical models, specifically stochastic blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs. These models, seldom used in the Semantic Web community, decompose a graph into a set of probability distributions. The parameters of these distributions are then inferred allowing for their subsequent sampling to generate a random graph. In a non-parametric setting, this allows for the induction of hierarchical clusterings without prior constraints on the hierarchy's structure. Specifically, this is achieved by the integration of the Nested Chinese Restaurant Process and the Stick Breaking Process into the generative model. In this regard, we propose a model leveraging such integration and derive a collapsed Gibbs sampling scheme for its inference. To aid in understanding, we describe the steps in this derivation and provide an implementation for the sampler. We evaluate our model on synthetic and real-world datasets and quantitatively compare against benchmark models. We further evaluate our results qualitatively and find that our model is capable of inducing coherent cluster hierarchies in small scale settings. The work presented in this paper provides the first step for the further application of stochastic blockmodels for knowledge graphs on a larger scale. We conclude the paper with potential avenues for future work on more scalable inference schemes.
Agricultural On-Demand Networks for 6G enabled by THz Communication
Venue: arXiv cs.NI 2024
Year: 2024

The transforming process in the scope of agriculture towards Smart Agriculture is an essential step to fulfill growing demands in respect to nourishment. Crucial challenges include establishing robust wireless communication in rural areas, enabling collaboration among agricultural machines, and integrating artificial intelligence into farming practices. Addressing these challenges necessitates a consistent communication system, with wireless communication emerging as a key enabler. Cellular technologies, as 5G and its successor 6G, can offer a comprehensive solution here. Leveraging technologies following the ITU-R M. 2160 recommendation like THz communication, low-latency wireless AI, and embedded sensing, can provide a flexible and energy-efficient infrastructure. This paper introduces on-demand networks based on the OpenRAN approach and a 7.2 functional split. By implementing THz front-hauling between components, a flexible application of 5G or future 6G networks can be realized. Experiments demonstrate that THz communication is suitable for data transmission over the eCPRI interface, particularly in terms of data rate, thereby reducing the need for wired alternatives such as fiber optic cables. Furthermore, limitations such as limited range are discussed, and possible initial solutions are presented. The integration of the OpenRAN standard further enhances flexibility, which is crucial in dynamic agricultural environments. This research contributes to the ongoing discourse on the transformative potential of 6G-enabled wireless communication in shaping the future of smart agriculture.
Protecting Privacy in Federated Time Series Analysis: A Pragmatic Technology Review for Application Developers
Venue: arXiv cs.CR 2024
Year: 2024

The federated analysis of sensitive time series has huge potential in various domains, such as healthcare or manufacturing. Yet, to fully unlock this potential, requirements imposed by various stakeholders must be fulfilled, regarding, e.g., efficiency or trust assumptions. While many of these requirements can be addressed by deploying advanced secure computation paradigms such as fully homomorphic encryption, certain aspects require an integration with additional privacy-preserving technologies. In this work, we perform a qualitative requirements elicitation based on selected real-world use cases. We match the derived requirements categories against the features and guarantees provided by available technologies. For each technology, we additionally perform a maturity assessment, including the state of standardization and availability on the market. Furthermore, we provide a decision tree supporting application developers in identifying the most promising technologies available matching their needs. Finally, existing gaps are identified, highlighting research potential to advance the field.
Different Facets for Different Experts: A Framework for Streamlining The Integration of Qualitative Insights into ABM Development
Venue: arXiv cs.MA 2024
Year: 2024

A key problem in agent-based simulation is that integrating qualitative insights from multiple discipline experts is extremely hard. In most simulations, agent capabilities and corresponding behaviour needs to be programmed into the agent. We report on the architecture of a tool that disconnects the programmed functions of the agent, from the acquisition of capability and displayed behaviour. This allows multiple different domain experts to represent qualitative insights, without the need for code to be changed. It also allows a continuous integration (or even change) of qualitative behaviour processes, as more insights are gained. The consequent behaviour observed in the model is both, more faithful to the expert's insight as well as able to be contrasted against other models representing other insights.
Partial and weighted matrix multiplication
Venue: arXiv cs.CC 2024
Year: 2024

In a paper published in 1981, Schönhage showed that large total matrix multiplications can be reduced to powers of partial matrix multiplication tensors, which correspond to the bilinear computation task of multiplying matrices with some of the entries fixed to be zero. It was left as an open problem to generalize the method to the case when the multiplication is also partial in the sense that only a subset of the entries need to be computed. We prove a variant of a more general case: reducing large weighted matrix multiplications to tensor powers of a partial matrix multiplication in the sense that every entry of the result is a partial version of the inner product of the corresponding row and column of the factors that would appear in the usual matrix product. The implication is that support rank upper bounds on partial matrix multiplication tensors in this general sense give upper bounds on the support rank exponent of matrix multiplication.
220 GHz Urban Microcell Channel Measurement and Characterization on a University Campus
Venue: arXiv cs.IT 2024
Year: 2024

Owning abundant bandwidth resources, the Terahertz (THz) band (0.1-10~THz) is envisioned as a key technology to realize ultra-high-speed communications in 6G and beyond wireless networks. To realize reliable THz communications in urban microcell (UMi) environments, propagation analysis and channel characterization are still insufficient. In this paper, channel measurement campaigns are conducted in a UMi scenario at 220~GHz, using a correlation-based time domain channel sounder. 24 positions are measured along a road on the university campus, with distances ranging from 34~m to 410~m. Based on the measurement results, the spatial consistency and interaction of THz waves to the surrounding environments are analyzed. Moreover, the additional loss due to foliage blockage is calculated and an average value of 16.7~dB is observed. Furthermore, a full portrait of channel characteristics, including path loss, shadow fading, K-factor, delay and angular spreads, as well as cluster parameters, is calculated and analyzed. Specifically, an average K-factor value of 17.5 dB is measured in the line-of-sight (LoS) case, which is nearly two times larger than the extrapolated values from the 3GPP standard, revealing weak multipath effects in the THz band. Additionally, 2.5 clusters on average are observed in the LoS case, around one fifth of what is defined in the 3GPP model, which uncovers the strong sparsity in THz UMi. The results and analysis in this work can offer guidance for system design for future THz UMi networks.
Shot Segmentation Based on Von Neumann Entropy for Key Frame Extraction
Venue: arXiv cs.CV 2024
Year: 2024

Video key frame extraction is important in various fields, such as video summary, retrieval, and compression. Therefore, we suggest a video key frame extraction algorithm based on shot segmentation using Von Neumann entropy. The segmentation of shots is achieved through the computation of Von Neumann entropy of the similarity matrix among frames within the video sequence. The initial frame of each shot is selected as key frames, which combines the temporal sequence information of frames. The experimental results show the extracted key frames can fully and accurately represent the original video content while minimizing the number of repeated frames.
Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation
Venue: arXiv cs.CR 2024
Year: 2024

Backdoor attacks present a serious security threat to deep neuron networks (DNNs). Although numerous effective defense techniques have been proposed in recent years, they inevitably rely on the availability of either clean or poisoned data. In contrast, data-free defense techniques have evolved slowly and still lag significantly in performance. To address this issue, different from the traditional approach of pruning followed by fine-tuning, we propose a novel data-free defense method named Optimal Transport-based Backdoor Repairing (OTBR) in this work. This method, based on our findings on neuron weight changes (NWCs) of random unlearning, uses optimal transport (OT)-based model fusion to combine the advantages of both pruned and backdoored models. Specifically, we first demonstrate our findings that the NWCs of random unlearning are positively correlated with those of poison unlearning. Based on this observation, we propose a random-unlearning NWC pruning technique to eliminate the backdoor effect and obtain a backdoor-free pruned model. Then, motivated by the OT-based model fusion, we propose the pruned-to-backdoored OT-based fusion technique, which fuses pruned and backdoored models to combine the advantages of both, resulting in a model that demonstrates high clean accuracy and a low attack success rate. To our knowledge, this is the first work to apply OT and model fusion techniques to backdoor defense. Extensive experiments show that our method successfully defends against all seven backdoor attacks across three benchmark datasets, outperforming both state-of-the-art (SOTA) data-free and data-dependent methods. The code implementation and Appendix are provided in the Supplementary Material.
microYOLO: Towards Single-Shot Object Detection on Microcontrollers
Venue: arXiv cs.CV 2024
Year: 2024

This work-in-progress paper presents results on the feasibility of single-shot object detection on microcontrollers using YOLO. Single-shot object detectors like YOLO are widely used, however due to their complexity mainly on larger GPU-based platforms. We present microYOLO, which can be used on Cortex-M based microcontrollers, such as the OpenMV H7 R2, achieving about 3.5 FPS when classifying 128x128 RGB images while using less than 800 KB Flash and less than 350 KB RAM. Furthermore, we share experimental results for three different object detection tasks, analyzing the accuracy of microYOLO on them.