arXiv subjects

Yan Jia

Publications and source records attributed to Yan Jia.

At least 37 records · Page 2Linked to original sources

arXiv2025

The case for delegated AI autonomy for Human AI teaming in healthcare

In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act autonomously for a subset of patient cases whilst serving a supportive role in other subsets of patient cases based on defined delegation criteria. By leveraging the complementary strengths of both humans and AI, it aims to deliver greater overall performance than existing human-AI teaming models. It ensures safe handling of patient cases and potentially reduces clinician review time, whilst being mindful of AI tool limitations. After setting the approach within the context of current human-AI teaming models, we outline the delegation criteria and apply them to a specific AI-based tool used in histopathology. The potential impact of the approach and the regulatory requirements for its successful implementation are then discussed.

cs.AI↗

arXiv2024

Upstream and Downstream AI Safety: Both on the Same River?

Traditional safety engineering assesses systems in their context of use, e.g. the operational design domain (road layout, speed limits, weather, etc.) for self-driving vehicles (including those using AI). We refer to this as downstream safety. In contrast, work on safety of frontier AI, e.g. large language models which can be further trained for downstream tasks, typically considers factors that are beyond specific application contexts, such as the ability of the model to evade human control, or to produce harmful content, e.g. how to make bombs. We refer to this as upstream safety. We outline the characteristics of both upstream and downstream safety frameworks then explore the extent to which the broad AI safety community can benefit from synergies between these frameworks. For example, can concepts such as common mode failures from downstream safety be used to help assess the strength of AI guardrails? Further, can the understanding of the capabilities and limitations of frontier AI be used to inform downstream safety analysis, e.g. where LLMs are fine-tuned to calculate voyage plans for autonomous vessels? The paper identifies some promising avenues to explore and outlines some challenges in achieving synergy, or a confluence, between upstream and downstream safety frameworks.

cs.CY↗

arXiv2024

Simple But Not Secure: An Empirical Security Analysis of Two-factor Authentication Systems

To protect users from data breaches and phishing attacks, service providers typically implement two-factor authentication (2FA) to add an extra layer of security against suspicious login attempts. However, since 2FA can sometimes hinder user experience by introducing additional steps, many websites aim to reduce inconvenience by minimizing the frequency of 2FA prompts. One approach to achieve this is by storing the user's ``Remember the Device'' preference in a cookie. As a result, users are only prompted for 2FA when this cookie expires or if they log in from a new device. To understand and improve the security of 2FA systems in real-world settings, we propose SE2FA, a vulnerability evaluation framework designed to detect vulnerabilities in 2FA systems. This framework enables us to analyze the security of 407 2FA systems across popular websites from the Tranco Top 10,000 list. Our analysis and evaluation found three zero-day vulnerabilities on three service providers that could allow an attacker to access a victim's account without possessing the victim's second authentication factor, thereby bypassing 2FA protections entirely. A further investigation found that these vulnerabilities stem from design choices aimed at simplifying 2FA for users but that unintentionally reduce its security effectiveness. We have disclosed these findings to the affected websites and assisted them in mitigating the risks. Based on the insights from this research, we provide practical recommendations for countermeasures to strengthen 2FA security and address these newly identified threats.

cs.CR↗

arXiv2024

Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense

Deep Neural Networks (DNNs) have been widely used in many areas such as autonomous driving and face recognition. However, DNN model is fragile to backdoor attack. A backdoor in the DNN model can be activated by a poisoned input with trigger and leads to wrong prediction, which causes serious security issues in applications. It is challenging for current defenses to eliminate the backdoor effectively with limited computing resources, especially when the sizes and numbers of the triggers are variable as in the physical world. We propose an efficient backdoor defense based on evolutionary trigger detection and lightweight model repair. In the first phase of our method, CAM-focus Evolutionary Trigger Filter (CETF) is proposed for trigger detection. CETF is an effective sample-preprocessing based method with the evolutionary algorithm, and our experimental results show that CETF not only distinguishes the images with triggers accurately from the clean images, but also can be widely used in practice for its simplicity and stability in different backdoor attack situations. In the second phase of our method, we leverage several lightweight unlearning methods with the trigger detected by CETF for model repair, which also constructively demonstrate the underlying correlation of the backdoor with Batch Normalization layers. Source code will be published after accepted.

cs.CR↗

arXiv2023

Meta-Path Based Attentional Graph Learning Model for Vulnerability Detection

In recent years, deep learning (DL)-based methods have been widely used in code vulnerability detection. The DL-based methods typically extract structural information from source code, e.g., code structure graph, and adopt neural networks such as Graph Neural Networks (GNNs) to learn the graph representations. However, these methods fail to consider the heterogeneous relations in the code structure graph, i.e., the heterogeneous relations mean that the different types of edges connect different types of nodes in the graph, which may obstruct the graph representation learning. Besides, these methods are limited in capturing long-range dependencies due to the deep levels in the code structure graph. In this paper, we propose a Meta-path based Attentional Graph learning model for code vulNErability deTection, called MAGNET. MAGNET constructs a multi-granularity meta-path graph for each code snippet, in which the heterogeneous relations are denoted as meta-paths to represent the structural information. A meta-path based hierarchical attentional graph neural network is also proposed to capture the relations between distant nodes in the graph. We evaluate MAGNET on three public datasets and the results show that MAGNET outperforms the best baseline method in terms of F1 score by 6.32%, 21.50%, and 25.40%, respectively. MAGNET also achieves the best performance among all the baseline methods in detecting Top-25 most dangerous Common Weakness Enumerations (CWEs), further demonstrating its effectiveness in vulnerability detection.

cs.SE↗

arXiv2023

Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims at extracting opinionated aspect terms in review texts and determining their sentiment polarities, which is widely studied in both academia and industry. As a fine-grained classification task, the annotation cost is extremely high. Domain adaptation is a popular solution to alleviate the data deficiency issue in new domains by transferring common knowledge across domains. Most cross-domain ABSA studies are based on structure correspondence learning (SCL), and use pivot features to construct auxiliary tasks for narrowing down the gap between domains. However, their pivot-based auxiliary tasks can only transfer knowledge of aspect terms but not sentiment, limiting the performance of existing models. In this work, we propose a novel Syntax-guided Domain Adaptation Model, named SDAM, for more effective cross-domain ABSA. SDAM exploits syntactic structure similarities for building pseudo training instances, during which aspect terms of target domain are explicitly related to sentiment polarities. Besides, we propose a syntax-based BERT mask language model for further capturing domain-invariant features. Finally, to alleviate the sentiment inconsistency issue in multi-gram aspect terms, we introduce a span-based joint aspect term and sentiment analysis module into the cross-domain End2End ABSA. Experiments on five benchmark datasets show that our model consistently outperforms the state-of-the-art baselines with respect to Micro-F1 metric for the cross-domain End2End ABSA task.

cs.AI↗

arXiv2022

Towards Comprehensively Understanding the Run-time Security of Programmable Logic Controllers: A 3-year Empirical Study

Programmable Logic Controllers (PLCs) are the core control devices in Industrial Control Systems (ICSs), which control and monitor the underlying physical plants such as power grids. PLCs were initially designed to work in a trusted industrial network, which however can be brittle once deployed in an Internet-facing (or penetrated) network. Yet, there is a lack of systematic empirical analysis of the run-time security of modern real-world PLCs. To close this gap, we present the first large-scale measurement on 23 off-the-shelf PLCs across 13 leading vendors. We find many common security issues and unexplored implications that should be more carefully addressed in the design and implementation. To sum up, the unsupervised logic applications can cause system resource/privilege abuse, which gives adversaries new means to hijack the control flow of a runtime system remotely (without exploiting memory vulnerabilities); 2) the improper access control mechanisms bring many unauthorized access implications; 3) the proprietary or semi-proprietary protocols are fragile regarding confidentiality and integrity protection of run-time data. We empirically evaluated the corresponding attack vectors on multiple PLCs, which demonstrates that the security implications are severe and broad. Our findings were reported to the related parties responsibly, and 20 bugs have been confirmed with 7 assigned CVEs.

cs.CR↗

arXiv2022

LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge

This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022. Track2 is a speech recognition task without limits on the scope of model size. Our main points include deep learning based speech enhancement, text-to-speech based speech generation, training data augmentation via various techniques and speech recognition model fusion. We compared and fused the hybrid architecture and two kinds of end-to-end architecture. For end-to-end modeling, we used models based on connectionist temporal classification/attention-based encoder-decoder architecture and recurrent neural network transducer/attention-based encoder-decoder architecture. The performance of these models is evaluated with an additional language model to improve word error rates. As a result, our system achieved 10.2\% character error rate on the challenge test set data and ranked third place among the submitted systems in the challenge.

eess.AS↗

arXiv2022

The Role of Explainability in Assuring Safety of Machine Learning in Healthcare

Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against which to assess validity. This problem is exacerbated by the "opaque" nature of ML where the learnt model is not amenable to human scrutiny. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human-interpretable representations of ML models which can help users to gain confidence and build trust in the ML system. However, little work explicitly investigates the role of explainability for safety assurance in the context of ML development. This paper identifies ways in which XAI methods can contribute to safety assurance of ML-based systems. It then uses a concrete ML-based clinical decision support system, concerning weaning of patients from mechanical ventilation, to demonstrate how XAI methods can be employed to produce evidence to support safety assurance. The results are also represented in a safety argument to show where, and in what way, XAI methods can contribute to a safety case. Overall, we conclude that XAI methods have a valuable role in safety assurance of ML-based systems in healthcare but that they are not sufficient in themselves to assure safety.

cs.LG↗

arXiv2022

Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words

Wake-up word detection models are widely used in real life, but suffer from severe performance degradation when encountering adversarial samples. In this paper we discuss the concept of confusing words in adversarial samples. Confusing words are commonly encountered, which are various kinds of words that sound similar to the predefined keywords. To enhance the wake word detection system's robustness against confusing words, we propose several methods to generate the adversarial confusing samples for simulating real confusing words scenarios in which we usually do not have any real confusing samples in the training set. The generated samples include concatenated audio, synthesized data, and partially masked keywords. Moreover, we use a domain embedding concatenated system to improve the performance. Experimental results show that the adversarial samples generated in our approach help improve the system's robustness in both the common scenario and the confusing words scenario. In addition, we release the confusing words testing database called HI-MIA-CW for future research.

cs.SD↗

arXiv2021

CGNN: Traffic Classification with Graph Neural Network

Traffic classification associates packet streams with known application labels, which is vital for network security and network management. With the rise of NAT, port dynamics, and encrypted traffic, it is increasingly challenging to obtain unified traffic features for accurate classification. Many state-of-the-art traffic classifiers automatically extract features from the packet stream based on deep learning models such as convolution networks. Unfortunately, the compositional and causal relationships between packets are not well extracted in these deep learning models, which affects both prediction accuracy and generalization on different traffic types. In this paper, we present a chained graph model on the packet stream to keep the chained compositional sequence. Next, we propose CGNN, a graph neural network based traffic classification method, which builds a graph classifier over automatically extracted features over the chained graph. Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification. CGNN is quite robust in terms of the recall and precision metrics. We have extensively evaluated the parameter sensitivity of CGNN, which yields optimized parameters that are quite effective for traffic classification.

cs.LG↗

arXiv2021

The DKU System Description for The Interspeech 2021 Auto-KWS Challenge

This paper introduces the system submitted by the DKU-SMIIP team for the Auto-KWS 2021 Challenge. Our implementation consists of a two-stage keyword spotting system based on query-by-example spoken term detection and a speaker verification system. We employ two different detection algorithms in our proposed keyword spotting system. The first stage adopts subsequence dynamic time warping for template matching based on frame-level language-independent bottleneck feature and phoneme posterior probability. We use a sliding window template matching algorithm based on acoustic word embeddings to further verify the detection from the first stage. As a result, our KWS system achieves an average score of 0.61 on the feedback dataset, which outperforms the baseline1 system by 0.25.

eess.AS↗

arXiv2021

Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)

Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains such as healthcare , automotive and manufacturing, exhibit high degrees of autonomy and are safety critical. Establishing justified confidence in ML forms a core part of the safety case for these systems. In this document we introduce a methodology for the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). AMLAS comprises a set of safety case patterns and a process for (1) systematically integrating safety assurance into the development of ML components and (2) for generating the evidence base for explicitly justifying the acceptable safety of these components when integrated into autonomous system applications.

cs.LG↗

arXiv2021

A Framework for Assurance of Medication Safety using Machine Learning

Medication errors continue to be the leading cause of avoidable patient harm in hospitals. This paper sets out a framework to assure medication safety that combines machine learning and safety engineering methods. It uses safety analysis to proactively identify potential causes of medication error, based on expert opinion. As healthcare is now data rich, it is possible to augment safety analysis with machine learning to discover actual causes of medication error from the data, and to identify where they deviate from what was predicted in the safety analysis. Combining these two views has the potential to enable the risk of medication errors to be managed proactively and dynamically. We apply the framework to a case study involving thoracic surgery, e.g. oesophagectomy, where errors in giving beta-blockers can be critical to control atrial fibrillation. This case study combines a HAZOP-based safety analysis method known as SHARD with Bayesian network structure learning and process mining to produce the analysis results, showing the potential of the framework for ensuring patient safety, and for transforming the way that safety is managed in complex healthcare environments.

cs.LG↗

arXiv2021

The 2020 Personalized Voice Trigger Challenge: Open Database, Evaluation Metrics and the Baseline Systems

The 2020 Personalized Voice Trigger Challenge (PVTC2020) addresses two different research problems a unified setup: joint wake-up word detection with speaker verification on close-talking single microphone data and far-field multi-channel microphone array data. Specially, the second task poses an additional cross-channel matching challenge on top of the far-field condition. To simulate the real-life application scenario, the enrollment utterances are recorded from close-talking cell-phone only, while the test utterances are recorded from both the close-talking cell-phone and the far-field microphone arrays. This paper introduces our challenge setup and the released database as well as the evaluation metrics. In addition, we present a joint end-to-end neural network baseline system trained with the proposed database for speaker-dependent wake-up word detection. Results show that the cost calculated from the miss rate and the false alarm rate, can reach 0.37 in the close-talking single microphone task and 0.31 in the far-field microphone array task. The official website and the open-source baseline system have been released.

eess.AS↗

arXiv2020

Training Wake Word Detection with Synthesized Speech Data on Confusion Words

Confusing-words are commonly encountered in real-life keyword spotting applications, which causes severe degradation of performance due to complex spoken terms and various kinds of words that sound similar to the predefined keywords. To enhance the wake word detection system's robustness on such scenarios, we investigate two data augmentation setups for training end-to-end KWS systems. One is involving the synthesized data from a multi-speaker speech synthesis system, and the other augmentation is performed by adding random noise to the acoustic feature. Experimental results show that augmentations help improve the system's robustness. Moreover, by augmenting the training set with the synthetic data generated by the multi-speaker text-to-speech system, we achieve a significant improvement regarding confusing words scenario.

cs.LG↗

arXiv2020

Domain Aware Training for Far-field Small-footprint Keyword Spotting

In this paper, we focus on the task of small-footprint keyword spotting under the far-field scenario. Far-field environments are commonly encountered in real-life speech applications, causing severe degradation of performance due to room reverberation and various kinds of noises. Our baseline system is built on the convolutional neural network trained with pooled data of both far-field and close-talking speech. To cope with the distortions, we develop three domain aware training systems, including the domain embedding system, the deep CORAL system, and the multi-task learning system. These methods incorporate domain knowledge into network training and improve the performance of the keyword classifier on far-field conditions. Experimental results show that our proposed methods manage to maintain the performance on the close-talking speech and achieve significant improvement on the far-field test set.

eess.AS↗

arXiv2019

Discovering and Understanding the Security Hazards in the Interactions between IoT Devices, Mobile Apps, and Clouds on Smart Home Platforms

A smart home connects tens of home devices to the Internet, where an IoT cloud runs various home automation applications. While bringing unprecedented convenience and accessibility, it also introduces various security hazards to users. Prior research studied smart home security from several aspects. However, we found that the complexity of the interactions among the participating entities (i.e., devices, IoT clouds, and mobile apps) has not yet been systematically investigated. In this work, we conducted an in-depth analysis of five widely-used smart home platforms. Combining firmware analysis, network traffic interception, and blackbox testing, we reverse-engineered the details of the interactions among the participating entities. Based on the details, we inferred three legitimate state transition diagrams for the three entities, respectively. Using these state machines as a reference model, we identified a set of unexpected state transitions. To confirm and trigger the unexpected state transitions, we implemented a set of phantom devices to mimic a real device. By instructing the phantom devices to intervene in the normal entity-entity interactions, we have discovered several new vulnerabilities and a spectrum of attacks against real-world smart home platforms.

cs.CR↗