OpenRaiser/Intern-Atlas huggingface dataset:
Safe and Reliable Training of Learning-Based Aerospace Controllers
Venue: arXiv cs.AI 2024
Year: 2024

In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these models limits their applicability in aerospace systems and safety-critical domains, in which a single mistake can have dire consequences. In this paper, we present novel advancements in both the training and verification of DRL controllers, which can help ensure their safe behavior. We showcase a design-for-verification approach utilizing k-induction and demonstrate its use in verifying liveness properties. In addition, we also give a brief overview of neural Lyapunov Barrier certificates and summarize their capabilities on a case study. Finally, we describe several other novel reachability-based approaches which, despite failing to provide guarantees of interest, could be effective for verification of other DRL systems, and could be of further interest to the community.
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Venue: arXiv cs.DC 2024
Year: 2024

Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without exposing their local data. One of the major challenges in FL is the presence of uneven data distributions across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. To address the performance degradation issue incurred by such data heterogeneity, clustered federated learning (CFL) shows its promise by grouping clients into separate learning clusters based on the similarity of their local data distributions. However, state-of-the-art CFL approaches require a large number of communication rounds to learn the distribution similarities during training until the formation of clusters is stabilized. Moreover, some of these algorithms heavily rely on a predefined number of clusters, thus limiting their flexibility and adaptability. In this paper, we propose {\em FedClust}, a novel approach for CFL that leverages the correlation between local model weights and the data distribution of clients. {\em FedClust} groups clients into clusters in a one-shot manner by measuring the similarity degrees among clients based on the strategically selected partial weights of locally trained models. We conduct extensive experiments on four benchmark datasets with different non-IID data settings. Experimental results demonstrate that {\em FedClust} achieves higher model accuracy up to $\sim$45\% as well as faster convergence with a significantly reduced communication cost up to 2.7$\times$ compared to its state-of-the-art counterparts.
Neuromimetic metaplasticity for adaptive continual learning
Venue: arXiv cs.NE 2024
Year: 2024

Conventional intelligent systems based on deep neural network (DNN) models encounter challenges in achieving human-like continual learning due to catastrophic forgetting. Here, we propose a metaplasticity model inspired by human working memory, enabling DNNs to perform catastrophic forgetting-free continual learning without any pre- or post-processing. A key aspect of our approach involves implementing distinct types of synapses from stable to flexible, and randomly intermixing them to train synaptic connections with different degrees of flexibility. This strategy allowed the network to successfully learn a continuous stream of information, even under unexpected changes in input length. The model achieved a balanced tradeoff between memory capacity and performance without requiring additional training or structural modifications, dynamically allocating memory resources to retain both old and new information. Furthermore, the model demonstrated robustness against data poisoning attacks by selectively filtering out erroneous memories, leveraging the Hebb repetition effect to reinforce the retention of significant data.
The Emperor is Now Clothed: A Secure Governance Framework for Web User Authentication through Password Managers
Venue: arXiv cs.CR 2024
Year: 2024

Existing approaches to facilitate the interaction between password managers and web applications fall short of providing adequate functionality and mitigation strategies against prominent attacks. HTML Autofill is not sufficiently expressive, Credential Management API does not support browser extension password managers, and other proposed solutions do not conform to established user mental models. In this paper, we propose Berytus, a browser-based governance framework that mediates the interaction between password managers and web applications. Two APIs are designed to support Berytus acting as an orchestrator between password managers and web applications. An implementation of the framework in Firefox is developed that fully supports registration and authentication processes. As an orchestrator, Berytus is able to authenticate web applications and facilitate authenticated key exchange between web applications and password managers, which as we show, can provide effective mitigation strategies against phishing, cross-site scripting, inline code injection (e.g., by a malicious browser extension), and TLS proxy in the middle attacks, whereas existing mitigation strategies such as Content Security Policy and credential tokenisation are only partially effective. The framework design also provides desirable functional properties such as support for multi-step, multi-factor, and custom authentication schemes. We provide a comprehensive security and functionality evaluation and discuss possible future directions.
Inference Performance Optimization for Large Language Models on CPUs
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs) have shown exceptional performance and vast potential across diverse tasks. However, the deployment of LLMs with high performance in low-resource environments has garnered significant attention in the industry. When GPU hardware resources are limited, we can explore alternative options on CPUs. To mitigate the financial burden and alleviate constraints imposed by hardware resources, optimizing inference performance is necessary. In this paper, we introduce an easily deployable inference performance optimization solution aimed at accelerating LLMs on CPUs. In this solution, we implement an effective way to reduce the KV cache size while ensuring precision. We propose a distributed inference optimization approach and implement it based on oneAPI Collective Communications Library. Furthermore, we propose optimization approaches for LLMs on CPU, and conduct tailored optimizations for the most commonly used models. The code is open-sourced at https://github.com/intel/xFasterTransformer.
Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram
Venue: arXiv cs.AI 2024
Year: 2024

Geometry problem solving (GPS) requires capacities of multi-modal understanding, multi-hop reasoning and theorem knowledge application. In this paper, we propose a neural-symbolic model for plane geometry problem solving (PGPS), named PGPSNet-v2, with three key steps: modal fusion, reasoning process and knowledge verification. In modal fusion, we leverage textual clauses to express fine-grained structural and semantic content of geometry diagram, and fuse diagram with textual problem efficiently through structural-semantic pre-training. For reasoning, we design an explicable solution program to describe the geometric reasoning process, and employ a self-limited decoder to generate solution program autoregressively. To reduce solution errors, a multi-level theorem verifier is proposed to eliminate solutions that do not match geometric principles, alleviating the hallucination of the neural model. We also construct a large-scale geometry problem dataset called PGPS9K, containing fine-grained annotations of textual clauses, solution program and involved knowledge tuples. Extensive experiments on datasets Geometry3K and PGPS9K show that our PGPSNet solver outperforms existing symbolic and neural solvers in GPS performance, while maintaining good explainability and reliability, and the solver components (fusion, reasoning, verification) are all justified effective.
CATP: Context-Aware Trajectory Prediction with Competition Symbiosis
Venue: arXiv cs.LG 2024
Year: 2024

Contextual information is vital for accurate trajectory prediction. For instance, the intricate flying behavior of migratory birds hinges on their analysis of environmental cues such as wind direction and air pressure. However, the diverse and dynamic nature of contextual information renders it an arduous task for AI models to comprehend its impact on trajectories and consequently predict them accurately. To address this issue, we propose a ``manager-worker'' framework to unleash the full potential of contextual information and construct CATP model, an implementation of the framework for Context-Aware Trajectory Prediction. The framework comprises a manager model, several worker models, and a tailored training mechanism inspired by competition symbiosis in nature. Taking CATP as an example, each worker needs to compete against others for training data and develop an advantage in predicting specific moving patterns. The manager learns the workers' performance in different contexts and selects the best one in the given context to predict trajectories, enabling CATP as a whole to operate in a symbiotic manner. We conducted two comparative experiments and an ablation study to quantitatively evaluate the proposed framework and CATP model. The results showed that CATP could outperform SOTA models, and the framework could be generalized to different context-aware tasks.
TDML -- A Trustworthy Distributed Machine Learning Framework
Venue: arXiv cs.CR 2024
Year: 2024

Recent years have witnessed a surge in deep learning research, marked by the introduction of expansive generative models like OpenAI's SORA and GPT, Meta AI's LLAMA series, and Google's FLAN, BART, and Gemini models. However, the rapid advancement of large models (LM) has intensified the demand for computing resources, particularly GPUs, which are crucial for their parallel processing capabilities. This demand is exacerbated by limited GPU availability due to supply chain delays and monopolistic acquisition by major tech firms. Distributed Machine Learning (DML) methods, such as Federated Learning (FL), mitigate these challenges by partitioning data and models across multiple servers, though implementing optimizations like tensor and pipeline parallelism remains complex. Blockchain technology emerges as a promising solution, ensuring data integrity, scalability, and trust in distributed computing environments, but still lacks guidance on building practical DML systems. In this paper, we propose a \textit{trustworthy distributed machine learning} (TDML) framework that leverages blockchain to coordinate remote trainers and validate workloads, achieving privacy, transparency, and efficient model training across public remote computing resources. Experimental validation demonstrates TDML's efficacy in overcoming performance limitations and malicious node detection, positioning it as a robust solution for scalable and secure distributed machine learning.
Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture
Venue: arXiv cs.CL 2024
Year: 2024

As safety remains a crucial concern throughout the development lifecycle of Large Language Models (LLMs), researchers and industrial practitioners have increasingly focused on safeguarding and aligning LLM behaviors with human preferences and ethical standards. LLMs, trained on extensive multilingual corpora, exhibit powerful generalization abilities across diverse languages and domains. However, current safety alignment practices predominantly focus on single-language scenarios, which leaves their effectiveness in complex multilingual contexts, especially for those complex mixed-language formats, largely unexplored. In this study, we introduce Multilingual Blending, a mixed-language query-response scheme designed to evaluate the safety alignment of various state-of-the-art LLMs (e.g., GPT-4o, GPT-3.5, Llama3) under sophisticated, multilingual conditions. We further investigate language patterns such as language availability, morphology, and language family that could impact the effectiveness of Multilingual Blending in compromising the safeguards of LLMs. Our experimental results show that, without meticulously crafted prompt templates, Multilingual Blending significantly amplifies the detriment of malicious queries, leading to dramatically increased bypass rates in LLM safety alignment (67.23% on GPT-3.5 and 40.34% on GPT-4o), far exceeding those of single-language baselines. Moreover, the performance of Multilingual Blending varies notably based on intrinsic linguistic properties, with languages of different morphology and from diverse families being more prone to evading safety alignments. These findings underscore the necessity of evaluating LLMs and developing corresponding safety alignment strategies in a complex, multilingual context to align with their superior cross-language generalization capabilities.
CHOP: Integrating ChatGPT into EFL Oral Presentation Practice
Venue: arXiv cs.HC 2024
Year: 2024

English as a Foreign Language (EFL) students often struggle to deliver oral presentations due to a lack of reliable resources and the limited effectiveness of instructors' feedback. Large Language Model (LLM) can offer new possibilities to assist students' oral presentations with real-time feedback. This paper investigates how ChatGPT can be effectively integrated into EFL oral presentation practice to provide personalized feedback. We introduce a novel learning platform, CHOP (ChatGPT-based interactive platform for oral presentation practice), and evaluate its effectiveness with 13 EFL students. By collecting student-ChatGPT interaction data and expert assessments of the feedback quality, we identify the platform's strengths and weaknesses. We also analyze learners' perceptions and key design factors. Based on these insights, we suggest further development opportunities and design improvements for the education community.
Distributed multi-robot potential-field-based exploration with submap-based mapping and noise-augmented strategy
Venue: arXiv cs.RO 2024
Year: 2024

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a Distributed Multi-Robot Potential-Field-Based Exploration (DMPF-Explore). In particular, we first present a Distributed Submap-Based Multi-Robot Collaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with Modified Wave-Front Distance and Colored Noises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability.
A Conceptual Framework for API Refactoring in Enterprise Application Architectures
Venue: arXiv cs.SE 2024
Year: 2024

Enterprise applications are often built as service-oriented architectures, where the individual services are designed to perform specific functions and interact with each other by means of well-defined APIs (Application Programming Interfaces). The architecture of an enterprise application evolves over time, in order to adapt to changing business requirements. This evolution might require changes to the APIs offered by services, which can be achieved through appropriate API refactorings. Previous studies on API refactoring focused on the effects on API definitions, with general considerations on related forces and smells. So far, instead, the development strategy for realising these refactorings has received little attention. This paper addresses exactly this aspect. We introduce a conceptual framework for the implementation of API refactorings. Our framework elicits that there are important trade-offs and choices, which significantly affect the efficiency, maintainability, and isolation properties of the resulting architecture. We validate our framework by implementing several refactorings that introduce established API patterns with different choices, which illustrates the guiding principles offered by our framework. Our work also elicits, for the first time, how certain programming language features can reduce friction in applying API refactoring and open up more architectural choices.
Missile detection and destruction robot using detection algorithm
Venue: arXiv cs.RO 2024
Year: 2024

This research is based on the present missile detection technologies in the world and the analysis of these technologies to find a cost effective solution to implement the system in Bangladesh. The paper will give an idea of the missile detection technologies using the electro-optical sensor and the pulse doppler radar. The system is made to detect the target missile. Automatic detection and destruction with the help of ultrasonic sonar, a metal detector sensor, and a smoke detector sensor. The system is mainly based on an ultrasonic sonar sensor. It has a transducer, a transmitter, and a receiver. Transducer is connected with the connected with controller. When it detects an object by following the algorithm, it finds its distance and angle. It can also assure whether the system can destroy the object or not by using another algorithm's simulation.
GothX: a generator of customizable, legitimate and malicious IoT network traffic
Venue: arXiv cs.CR 2024
Year: 2024

In recent years, machine learning-based anomaly detection (AD) has become an important measure against security threats from Internet of Things (IoT) networks. Machine learning (ML) models for network traffic AD require datasets to be trained, evaluated and compared. Due to the necessity of realistic and up-to-date representation of IoT security threats, new datasets need to be constantly generated to train relevant AD models. Since most traffic generation setups are developed considering only the author's use, replication of traffic generation becomes an additional challenge to the creation and maintenance of useful datasets. In this work, we propose GothX, a flexible traffic generator to create both legitimate and malicious traffic for IoT datasets. As a fork of Gotham Testbed, GothX is developed with five requirements: 1)easy configuration of network topology, 2) customization of traffic parameters, 3) automatic execution of legitimate and attack scenarios, 4) IoT network heterogeneity (the current iteration supports MQTT, Kafka and SINETStream services), and 5) automatic labeling of generated datasets. GothX is validated by two use cases: a) re-generation and enrichment of traffic from the IoT dataset MQTTset,and b) automatic execution of a new realistic scenario including the exploitation of a CVE specific to the Kafka-MQTT network topology and leading to a DDoS attack. We also contribute with two datasets containing mixed traffic, one made from the enriched MQTTset traffic and another from the attack scenario. We evaluated the scalability of GothX (450 IoT sensors in a single machine), the replication of the use cases and the validity of the generated datasets, confirming the ability of GothX to improve the current state-of-the-art of network traffic generation.
Development of an automatic modification system for generated programs using ChatGPT
Venue: arXiv cs.SE 2024
Year: 2024

In recent years, the field of artificial intelligence has been rapidly developing. Among them, OpenAI's ChatGPT excels at natural language processing tasks and can also generate source code. However, the generated code often has problems with consistency and program rules. Therefore, in this research, we developed a system that tests the code generated by ChatGPT, automatically corrects it if it is inappropriate, and presents the appropriate code to the user. This study aims to address the challenge of reducing the manual effort required for the human feedback and modification process for generated code. When we ran the system, we were able to automatically modify the code as intended.
Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
Venue: arXiv cs.LG 2024
Year: 2024

We study the problem of assessing the robustness of counterfactual explanations for deep learning models. We focus on $\textit{plausible model shifts}$ altering model parameters and propose a novel framework to reason about the robustness property in this setting. To motivate our solution, we begin by showing for the first time that computing the robustness of counterfactuals with respect to plausible model shifts is NP-complete. As this (practically) rules out the existence of scalable algorithms for exactly computing robustness, we propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees while preserving scalability. Remarkably, and differently from existing solutions targeting plausible model shifts, our approach does not impose requirements on the network to be analyzed, thus enabling robustness analysis on a wider range of architectures. Experiments on four binary classification datasets indicate that our method improves the state of the art in generating robust explanations, outperforming existing methods on a range of metrics.
Zero-Shot Class Unlearning in CLIP with Synthetic Samples
Venue: arXiv cs.CV 2024
Year: 2024

Machine unlearning is a crucial area of research. It is driven by the need to remove sensitive information from models to safeguard individuals' right to be forgotten under rigorous regulations such as GDPR. In this work, we focus on unlearning within CLIP, a dual vision-language encoder model trained on a massive dataset of image-text pairs using contrastive loss. To achieve forgetting we expand the application of Lipschitz regularization to the multimodal context of CLIP. Specifically, we ensure the smoothing of both visual and textual embeddings associated with the class intended to be forgotten relative to the perturbation introduced to the samples from that class. Additionally, importantly, we remove the necessity for real forgetting data by generating synthetic samples through gradient ascent maximizing the target class. Our forgetting procedure is iterative, where we track accuracy on a synthetic forget set and stop when accuracy falls below a chosen threshold. We employ a selective layers update strategy based on their average absolute gradient value to mitigate over-forgetting. We validate our approach on several standard datasets and provide thorough ablation analysis and comparisons with previous work.
Understanding the Semantic SQL Transducer
Venue: arXiv cs.DB 2024
Year: 2024

Nowadays we observe an evolving landscape of data management and analytics, emphasising the significance of meticulous data management practices, semantic modelling, and bridging business-technical divides, to optimise data utilisation and enhance value from datasets in modern data environments. In this paper we introduce and explain the basic formalisation of the Semantic SQL Transducer, a well-founded but practical tool providing the materialised lossless conceptual view of an arbitrary relational source data, contributing to a knowledge-centric data stack.