OpenRaiser/Intern-Atlas huggingface dataset:
Toward Integrating Semantic-aware Path Planning and Reliable Localization for UAV Operations
Venue: arXiv cs.RO 2024
Year: 2024

Localization is one of the most crucial tasks for Unmanned Aerial Vehicle systems (UAVs) directly impacting overall performance, which can be achieved with various sensors and applied to numerous tasks related to search and rescue operations, object tracking, construction, etc. However, due to the negative effects of challenging environments, UAVs may lose signals for localization. In this paper, we present an effective path-planning system leveraging semantic segmentation information to navigate around texture-less and problematic areas like lakes, oceans, and high-rise buildings using a monocular camera. We introduce a real-time semantic segmentation architecture and a novel keyframe decision pipeline to optimize image inputs based on pixel distribution, reducing processing time. A hierarchical planner based on the Dynamic Window Approach (DWA) algorithm, integrated with a cost map, is designed to facilitate efficient path planning. The system is implemented in a photo-realistic simulation environment using Unity, aligning with segmentation model parameters. Comprehensive qualitative and quantitative evaluations validate the effectiveness of our approach, showing significant improvements in the reliability and efficiency of UAV localization in challenging environments.
DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship
Venue: arXiv cs.HC 2024
Year: 2024

Although there have been automated approaches and tools supporting toxicity censorship for social posts, most of them focus on detection. Toxicity censorship is a complex process, wherein detection is just an initial task and a user can have further needs such as rationale understanding and content modification. For this problem, we conduct a needfinding study to investigate people's diverse needs in toxicity censorship and then build a ChatGPT-based censorship tool named DeMod accordingly. DeMod is equipped with the features of explainable Detection and personalized Modification, providing fine-grained detection results, detailed explanations, and personalized modification suggestions. We also implemented the tool and recruited 35 Weibo users for evaluation. The results suggest DeMod's multiple strengths like the richness of functionality, the accuracy of censorship, and ease of use. Based on the findings, we further propose several insights into the design of content censorship systems.
Causal Discovery and Classification Using Lempel-Ziv Complexity
Venue: arXiv cs.LG 2024
Year: 2024

Inferring causal relationships in the decision-making processes of machine learning algorithms is a crucial step toward achieving explainable Artificial Intelligence (AI). In this research, we introduce a novel causality measure and a distance metric derived from Lempel-Ziv (LZ) complexity. We explore how the proposed causality measure can be used in decision trees by enabling splits based on features that most strongly \textit{cause} the outcome. We further evaluate the effectiveness of the causality-based decision tree and the distance-based decision tree in comparison to a traditional decision tree using Gini impurity. While the proposed methods demonstrate comparable classification performance overall, the causality-based decision tree significantly outperforms both the distance-based decision tree and the Gini-based decision tree on datasets generated from causal models. This result indicates that the proposed approach can capture insights beyond those of classical decision trees, especially in causally structured data. Based on the features used in the LZ causal measure based decision tree, we introduce a causal strength for each features in the dataset so as to infer the predominant causal variables for the occurrence of the outcome.
On Arrival: Challenges and Opportunities Around Early-Stage Resettlement of Refugees in Australia
Venue: arXiv cs.HC 2024
Year: 2024

When refugees arrive in a host country, the form of immediate help and support they receive from various service providers sets the stage for successful settlement, integration, and social cohesion. This paper presents results from an exploratory study that investigated refugees perceptions of initial services received upon migration, in the first six months of their arrival. In collaboration with a refugee settlement services provider, we engaged 12 newly-arrived refugees in a qualitative study that employed a photo-diary study and semi-structured interviews. Based on our findings, we present refugees experiences over three phase, immediate services upon arrival, initial-settlement experiences, and ongoing settlement experiences. Through an in-depth unpacking of these phases, we show ongoing efforts and challenges associated with resettlement, and present implications for design for CSCW researchers.
Efficient Conflict Graph Creation for Time-Sensitive Networks with Dynamically Changing Communication Demands
Venue: arXiv cs.NI 2024
Year: 2024

Many applications of cyber-physical systems require real-time communication: manufacturing, automotive, etc. Recent Ethernet standards for Time Sensitive Networking (TSN) offer time-triggered scheduling in order to guarantee low latency and jitter bounds. This requires precise frame transmission planning, which becomes especially hard when dealing with many streams, large networks, and dynamically changing communications. A very promising approach uses conflict graphs, modeling conflicting transmission configurations. Since the creation of conflict graphs is the bottleneck in these approaches, we provide an improvement to the conflict graph creation. We present a randomized selection process that reduces the overall size of the graph in half and three heuristics to improve the scheduling success. In our evaluations we show substantial improvements in the graph creation speed and the scheduling success compared to existing work, updating existing schedules in fractions of a second. Additionally, offline planning of 9000 streams was performed successfully within minutes.
Adaptive Optimization of TLS Overhead for Wireless Communication in Critical Infrastructure
Venue: arXiv cs.CR 2024
Year: 2024

With critical infrastructure increasingly relying on wireless communication, using end-to-end security such as TLS becomes imperative. However, TLS introduces significant overhead for resource-constrained devices and networks prevalent in critical infrastructure. In this paper, we propose to leverage the degrees of freedom in configuring TLS to dynamically adapt algorithms, parameters, and other settings to best meet the currently occurring resource and security constraints in a wireless communication scenario. Consequently, we can make the best use of scarce resources to provide tightened security for wireless networks in critical infrastructure.
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
Venue: arXiv cs.LG 2024
Year: 2024

Federated Graph Learning (FGL) aims to collaboratively and privately optimize graph models on divergent data for different tasks. A critical challenge in FGL is to enable effective yet efficient federated optimization against multifaceted graph heterogeneity to enhance mutual performance. However, existing FGL works primarily address graph data heterogeneity and perform incapable of graph task heterogeneity. To address the challenge, we propose a Federated Graph Prompt Learning (FedGPL) framework to efficiently enable prompt-based asymmetric graph knowledge transfer between multifaceted heterogeneous federated participants. Generally, we establish a split federated framework to preserve universal and domain-specific graph knowledge, respectively. Moreover, we develop two algorithms to eliminate task and data heterogeneity for advanced federated knowledge preservation. First, a Hierarchical Directed Transfer Aggregator (HiDTA) delivers cross-task beneficial knowledge that is hierarchically distilled according to the directional transferability. Second, a Virtual Prompt Graph (VPG) adaptively generates graph structures to enhance data utility by distinguishing dominant subgraphs and neutralizing redundant ones. We conduct theoretical analyses and extensive experiments to demonstrate the significant accuracy and efficiency effectiveness of FedGPL against multifaceted graph heterogeneity compared to state-of-the-art baselines on large-scale federated graph datasets.
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning
Venue: arXiv cs.CV 2024
Year: 2024

In this study, 0.5m high resolution satellite datasets over Indian urban region was used to demonstrate the applicability of deep learning models over Ahmedabad, India. Here, YOLOv7 instance segmentation model was trained on well curated trees canopy dataset (6500 images) in order to carry out the change detection. During training, evaluation metrics such as bounding box regression and mask regression loss, mean average precision (mAP) and stochastic gradient descent algorithm were used for evaluating and optimizing the performance of model. After the 500 epochs, the mAP of 0.715 and 0.699 for individual tree detection and tree canopy mask segmentation were obtained. However, by further tuning hyper parameters of the model, maximum accuracy of 80 % of trees detection with false segmentation rate of 2% on data was obtained.
Imagining Better AI-Enabled Healthcare Futures: The Case for Care by Design
Venue: arXiv cs.HC 2024
Year: 2024

We find ourselves on the ever-shifting cusp of an AI revolution -- with potentially metamorphic implications for the future practice of healthcare. For many, such innovations cannot come quickly enough; as healthcare systems worldwide struggle to keep up with the ever-changing needs of our populations. And yet, the potential of AI tools and systems to shape healthcare is as often approached with great trepidation as celebrated by health professionals and patients alike. These fears alight not only in the form of privacy and security concerns but for the potential of AI tools to reduce patients to datapoints and professionals to aggregators -- to make healthcare, in short, less caring. This infixated concern, we - as designers, developers and researchers of AI systems - believe it essential we tackle head on; if we are not only to overcome the AI implementation gap, but realise the potential of AI systems to truly augment human-centred practices of care. This, we argue we might yet achieve by realising newly-accessible practices of AI healthcare innovation, engaging providers, recipients and affected communities of care in the inclusive design of AI tools we may yet enthusiastically embrace to materialise new opportunities for increasingly meaningful and effective care.
BlindexTEE: A Blind Index Approach towards TEE-supported End-to-end Encrypted DBMS
Venue: arXiv cs.CR 2024
Year: 2024

Using cloud-based applications comes with privacy implications, as the end-user looses control over their data. While encrypting all data on the client is possible, it largely reduces the usefulness of database management systems (DBMS) that are typically built to efficiently query large quantities of data. We present BlindexTEE, a new component that sits between the application business-logic and the database. BlindexTEE is shielded from malicious users or compromised environments by executing inside an SEV-SNP confidential VM, AMD's trusted execution environment (TEE). BlindexTEE is in charge of end-to-end encryption of user data while preserving the ability of the DBMS to efficiently filter data. By decrypting and re-encrypting data, it builds blind indices, used later on to efficiently query the DBMS. We demonstrate the practicality of BlindexTEE with MySQL in several micro- and macro-benchmarks, achieving overheads between 36.1% and 462% over direct database access depending on the usage scenario.
Real-time and Downtime-tolerant Fault Diagnosis for Railway Turnout Machines (RTMs) Empowered with Cloud-Edge Pipeline Parallelism
Venue: arXiv cs.NI 2024
Year: 2024

Railway Turnout Machines (RTMs) are mission-critical components of the railway transportation infrastructure, responsible for directing trains onto desired tracks. For safety assurance applications, especially in early-warning scenarios, RTM faults are expected to be detected as early as possible on a continuous 7x24 basis. However, limited emphasis has been placed on distributed model inference frameworks that can meet the inference latency and reliability requirements of such mission critical fault diagnosis systems. In this paper, an edge-cloud collaborative early-warning system is proposed to enable real-time and downtime-tolerant fault diagnosis of RTMs, providing a new paradigm for the deployment of models in safety-critical scenarios. Firstly, a modular fault diagnosis model is designed specifically for distributed deployment, which utilizes a hierarchical architecture consisting of the prior knowledge module, subordinate classifiers, and a fusion layer for enhanced accuracy and parallelism. Then, a cloud-edge collaborative framework leveraging pipeline parallelism, namely CEC-PA, is developed to minimize the overhead resulting from distributed task execution and context exchange by strategically partitioning and offloading model components across cloud and edge. Additionally, an election consensus mechanism is implemented within CEC-PA to ensure system robustness during coordinator node downtime. Comparative experiments and ablation studies are conducted to validate the effectiveness of the proposed distributed fault diagnosis approach. Our ensemble-based fault diagnosis model achieves a remarkable 97.4% accuracy on a real-world dataset collected by Nanjing Metro in Jiangsu Province, China. Meanwhile, CEC-PA demonstrates superior recovery proficiency during node disruptions and speed-up ranging from 1.98x to 7.93x in total inference time compared to its counterparts.
Fast Fixes and Faulty Drivers: An Empirical Analysis of Regression Bug Fixing Times in the Linux Kernel
Venue: arXiv cs.SE 2024
Year: 2024

Regression bugs refer to situations in which something that worked previously no longer works currently. Such bugs have been pronounced in the Linux kernel. The paper focuses on regression bug tracking in the kernel by considering the time required to fix regression bugs. The dataset examined is based on the regzbot automation framework for tracking regressions in the Linux kernel. According to the results, (i) regression bug fixing times have been faster than previously reported; between 2021 and 2024, on average, it has taken less than a month to fix regression bugs. It is further evident that (ii) device drivers constitute the most prone subsystem for regression bugs, and also the fixing times vary across the kernel's subsystems. Although (iii) most commits fixing regression bugs have been reviewed, tested, or both, the kernel's code reviewing and manual testing practices do not explain the fixing times. Likewise, (iv) there is only a weak signal that code churn might contribute to explaining the fixing times statistically. Finally, (v) some subsystems exhibit strong effects for explaining the bug fixing times statistically, although overall statistical performance is modest but not atypical to the research domain. With these empirical results, the paper contributes to the efforts to better understand software regressions and their tracking in the Linux kernel.
Deep Learning on 3D Semantic Segmentation: A Detailed Review
Venue: arXiv cs.CV 2024
Year: 2024

In this paper an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D Semantic Segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of the 3DSS deep learning methods is ambiguous. Based on the taxonomy schemes of 9 existing review papers, a new taxonomy scheme of the 3DSS deep learning methods is proposed, aiming to standardize it and improve the comparability and clarity across related studies. Furthermore, an extensive overview of the available 3DSS indoor and outdoor datasets is provided along with their links. The core part of the review is the detailed presentation of recent and former 3DSS deep learning methods and their classification using the proposed taxonomy scheme along with their GitHub repositories. Additionally, a brief but informative analysis of the evaluation metrics and loss functions used in 3DSS is included. Finally, a fruitful discussion of the examined 3DSS methods and datasets, is presented to foster new research directions and applications in the field of 3DSS. Supplementary, to this review a GitHub repository is provided (https://github.com/thobet/Deep-Learning-on-3D-Semantic-Segmentation-a- Detailed-Review) including a quick classification of over 400 3DSS methods, using the proposed taxonomy scheme.
Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security
Venue: arXiv cs.CV 2024
Year: 2024

In this study, we introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Utilizing a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), our model architecture uniquely incorporates dual shared layers alongside modality-specific enhancements for comprehensive feature extraction. The system undergoes rigorous training with a joint loss function, optimizing for accuracy across diverse biometric inputs. Feature-level fusion via Principal Component Analysis (PCA) and classification through Gradient Boosting Machines (GBM) further refine the authentication process. Our approach demonstrates significant improvements in authentication accuracy and robustness, paving the way for advanced secure identity verification solutions.
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learning, large-scale models have garnered significant attention due to their exceptional performance. However, a key challenge in FL is the limitation imposed by clients with constrained computational and communication resources, which hampers the deployment of these large models. The Mixture of Experts (MoE) architecture addresses this challenge with its sparse activation property, which reduces computational workload and communication demands during inference and updates. Additionally, MoE facilitates better personalization by allowing each expert to specialize in different subsets of the data distribution. To alleviate the communication burdens between the server and clients, we propose FedMoE-DA, a new FL model training framework that leverages the MoE architecture and incorporates a novel domain-aware, fine-grained aggregation strategy to enhance the robustness, personalizability, and communication efficiency simultaneously. Specifically, the correlation between both intra-client expert models and inter-client data heterogeneity is exploited. Moreover, we utilize peer-to-peer (P2P) communication between clients for selective expert model synchronization, thus significantly reducing the server-client transmissions. Experiments demonstrate that our FedMoE-DA achieves excellent performance while reducing the communication pressure on the server.
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis
Venue: arXiv cs.CL 2024
Year: 2024

The evaluation of layer importance in deep learning has been an active area of research, with significant implications for model optimization and interpretability. Recently, large language models (LLMs) have gained prominence across various domains, yet limited studies have explored the functional importance and performance contributions of individual layers within LLMs, especially from the perspective of activation distribution. In this work, we propose the Activation Variance-Sparsity Score (AVSS), a novel metric combining normalized activation variance and sparsity to assess each layer's contribution to model performance. By identifying and removing approximately the lowest 25% of layers based on AVSS, we achieve over 90% of original model performance across tasks such as question answering, language modeling, and sentiment classification, indicating that these layers may be non-essential. Our approach provides a systematic method for identifying less critical layers, contributing to efficient large language model architectures.
A Survey on AI-driven Energy Optimisation in Terrestrial Next Generation Radio Access Networks
Venue: arXiv cs.NI 2024
Year: 2024

This survey uncovers the tension between AI techniques designed for energy saving in mobile networks and the energy demands those same techniques create. We compare modeling approaches that estimate power usage cost of current commercial terrestrial next-generation radio access network deployments. We then categorize emerging methods for reducing power usage by domain: time, frequency, power, and spatial. Next, we conduct a timely review of studies that attempt to estimate the power usage of the AI techniques themselves. We identify several gaps in the literature. Notably, real-world data for the power consumption is difficult to source due to commercial sensitivity. Comparing methods to reduce energy consumption is beyond challenging because of the diversity of system models and metrics. Crucially, the energy cost of AI techniques is often overlooked, though some studies provide estimates of algorithmic complexity or run-time. We find that extracting even rough estimates of the operational energy cost of AI models and data processing pipelines is complex. Overall, we find the current literature hinders a meaningful comparison between the energy savings from AI techniques and their associated energy costs. Finally, we discuss future research opportunities to uncover the utility of AI for energy saving.
Positive Experience Reflection for Agents in Interactive Text Environments
Venue: arXiv cs.CL 2024
Year: 2024

Intelligent agents designed for interactive environments face significant challenges in text-based games, a domain that demands complex reasoning and adaptability. While agents based on large language models (LLMs) using self-reflection have shown promise, they struggle when initially successful and exhibit reduced effectiveness when using smaller LLMs. We introduce Sweet&Sour, a novel approach that addresses these limitations in existing reflection methods by incorporating positive experiences and managed memory to enrich the context available to the agent at decision time. Our comprehensive analysis spans both closed- and open-source LLMs and demonstrates the effectiveness of Sweet&Sour in improving agent performance, particularly in scenarios where previous approaches fall short.
Combining Induction and Transduction for Abstract Reasoning
Venue: arXiv cs.LG 2024
Year: 2024

When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test outputs, e.g. using a neural network? We study this question on ARC by training neural models for induction (inferring latent functions) and transduction (directly predicting the test output for a given test input). We train on synthetically generated variations of Python programs that solve ARC training tasks. We find inductive and transductive models solve different kinds of test problems, despite having the same training problems and sharing the same neural architecture: Inductive program synthesis excels at precise computations, and at composing multiple concepts, while transduction succeeds on fuzzier perceptual concepts. Ensembling them approaches human-level performance on ARC.