As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fundus images can significantly enhance DR grading performance by expanding the field of view (FOV). However, there is a long-tailed distribution problem in fundus image analysis, i.e., a high prevalence of mild DR grades and a low prevalence of rare ones (e.g., cases of high severity), which presents a significant challenge to developing a unified model capable of detecting rare or unseen DR grades not encountered during training. In this paper, we propose ProME-DR, a Prompt-driven zero-shot DR grading framework, which leverages prompt Matching and Emulating to recognize the unseen DR categories and views beyond the training set. ProME-DR disentangles the training process into two stages to learn generalized knowledge for novel DR disease grading. Initially, ProME-DR leverages two sets of prompt units to capture semantic and inter-view consistency knowledge via a split-and-mask manner, gathering instance-level DR visual clues. Subsequently, it constructs a concept-aware emulator to generate context prompt units, linking extensible knowledge learned from the previously seen DR attributes for zero-shot DR grading. Extensive experiments conducted on eight datasets and various scenarios confirm the superiority of ProME-DR.

Abstract Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers often rely on LLMs to verify if code implementation satisfy task requirements, thereby ensuring code robustness and accuracy. However, it remains unclear whether LLMs can reliably determine code against the given task descriptions, which is usually in a form of natural language specifications. In this paper, we uncover a systematic failure of LLMs in matching code to natural language requirements. Specifically, with widely adopted benchmarks and unified prompts design, we demonstrate that LLMs frequently misclassify correct code implementation as non-compliant or defective. Surprisingly, we find that more detailed prompt design, particularly with those requiring explanations and proposed corrections, leads to higher misjudgment rates, highlighting critical reliability issues for LLM-based code assistants. We further analyze the mechanisms driving these failures and evaluate the reliability of rationale-required judgments. Building on these findings, we propose a Fix-guided Verification Filter that treats the model proposed fix as executable counterfactual evidence, and validates the original and revised implementations using benchmark tests and spec-constrained augmented tests. Our results expose previously under-explored limitations in LLM-based code review capabilities, and provide practical guidance for integrating LLM-based reviewers with safeguards in automated review and development pipelines.

Abstract With the refinement of 3D imaging modality, cryo-electron tomography (cryo-ET) has emerged as a powerful technique for the structural analysis of macromolecular complexes at near-atomic resolution. Recent advancements in volumetric segmentation methods applied to cryo-ET datasets have garnered significant attention within the biomedical sector. However, existing methods rely heavily on manually labeled data, which demands highly specialized expertise, making fully supervised approaches less feasible for cryo-ET images. To address this, a number of unsupervised domain adaptation (UDA) techniques have been developed to improve segmentation network performance using unlabeled data. Nevertheless, directly applying these methods to cryo-ET image segmentation presents two major challenges: 1) the source dataset, usually obtained through simulation, contains a fixed level of noise, while the target dataset, being directly collected from raw-data from the real-world scenario, has unpredictable noise levels; 2) the source data used for training typically consists of known macromolecules, in contrast, the target domain data are often unknown, causing the model to be biased towards those known macromolecules, leading to a domain shift problem. To address such challenges, in this paper, we introduce a voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation, and a denoised pseudo-labeling strategy based on the improved bilateral filter to alleviate the domain shift problem. Additionally, we further consider a scenario that is more in line with the real world, where the target (experimental) dataset might not be accessible during training or has a very small sample size, and we present a voxel-wise zero-shot domain adaptation (ZSDA) approach, named Vox-ZSDA. In Vox-ZSDA, we introduce a self-supervised graph learning strategy to eliminate any dependency on the target data, accompanied by a dynamic graph contrastive learning technique to enhance the model’s sensitivity to macromolecular structures to boost the segmentation performance. More importantly, we construct the first UDA and ZSDA cryo-ET subtomogram segmentation benchmark on three experimental datasets. Extensive experimental results on multiple benchmarks and newly curated real-world datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA and ZSDA methods.

Abstract Accurate carbon price forecasting is essential for market efficiency, effective climate policy design, and strategic decision-making by firms managing carbon assets. Carbon prices reflect a dynamic interplay of emission quotas, economic conditions, and policy expectations, directly influencing emission reduction incentives and investment in green technologies. However, existing models often treat data input variables independently and struggle with the non-stationary, multi-scale nature of carbon price signals. This paper proposes SW-HyDEC, a structure-aware hybrid framework that integrates Correlation-based Feature Selection (CFS), Recursive Feature Elimination (RFE), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and a Hybrid-Kernel Extreme Learning Machine (Hybrid-KELM). To enhance the training convergence and robustness, model parameters are optimized using a Sobol-sequence enhanced Whale Optimization Algorithm (Sobol-WOA). Experimental results on real-world carbon market datasets demonstrate that SW-HyDEC significantly outperforms mainstream statistical, machine learning, and deep learning baselines in terms of accuracy, generalization, and stability.

The interpretability of deep neural networks is crucial for understanding model decisions in various applications, including computer vision. In this paper, we propose AttEXplore+, a unified adversarial attribution framework built upon AttEXplore that connects transferable adversarial exploration with gradient-path attribution. Rather than treating transferable attacks merely as additional attack choices, AttEXplore+ reformulates them as gradient acquisition operators for constructing smoother and more structured decision-boundary exploration paths. We instantiate AttEXplore++ with validated operators such as MIG and GRA, and conduct extensive experiments on five models, including CNNs (Inception-v3, ResNet-50, VGG16) and vision transformers (MaxViT-T, ViT-B/16), using the ImageNet dataset. Our method achieves an average performance improvement of 7.57% over AttEXplore and 32.62% compared to other state-of-the-art interpretability algorithms. Using insertion and deletion scores as evaluation metrics, we show that adversarial transferability plays a vital role in enhancing attribution results. Furthermore, we explore the impact of randomness, perturbation rate, noise amplitude, and diversity probability on attribution performance, demonstrating that AttEXplore++ provides more stable and reliable explanations across various models. We release our code at: https://github.com/KxPlaug/ATTEXPLOREP .

Prompt learning has emerged as an efficient tuning paradigm for fine-tuning powerful pre-trained models on downstream tasks in specific domains. Existing efforts mainly focus on dataset-level implicit embeddings by introducing extra learnable parameters instead of fully fine-tuning large-scale visual models. However, we find that these static post-training prompts are not flexible enough to adapt various input instances within the same dataset, which might lead to the loss of the model's generalization capability. To leverage the meaningful contextual information of each input instance, in this paper, we propose a straightforward yet effective method, termed CoMoDP, to enhance visual prompt learning withContext-ModulatedDiffusionPrompts. Specifically, CoMoDP is a dual-visual prompting scheme that comprises two key components:(i) a unified visual prompt designer, producing dataset-level implicit embedding as unified prompts for efficient adaptation without corrupting the underlying information of the original image; and(ii) a diffusion prompt simulator, leveraging diffusion model's meticulous understanding of semantic structure and texture edges in the images to dynamically generate instance-level implicit embedding as diffusion prompts for input samples. Moreover, to reduce the overfitting of prompts, we also introduce momentum alignment, a self-regulating strategy that restricts the optimization region of prompts in both feature and logit spaces. Extensive experiments on various standard and few-shot datasets demonstrate that our method brings substantial improvements and yields strong domain generalization performance, compared to the state-of-the-art methods. We also demonstrate both zero-shot and out-of-distribution performance to establish the utility of our dual-visual prompting scheme CoMoDP and the efficiency of each component, without involving excessive parameters.

In the modern era where software plays a pivotal role, software security and vulnerability analysis are essential for secure software development. Fuzzing test, as an efficient and tra-ditional software testing method, has been widely adopted across various domains. Meanwhile, the rapid development in Large Language Models (LLMs) has facilitated their application in the field of software testing, demonstrating remarkable performance. As existing fuzzing test techniques are not fully automated and software vulnerabilities continue to evolve, there is a growing interest in leveraging large language models to generate fuzzing test. In this paper, we present a systematic overview of the developments that utilize large language models for the fuzzing test. To our best knowledge, this is the first work that covers the intersection of three areas, including LLMs, fuzzing test, and fuzzing test generated based on LLMs. A statistical analysis and discussion of the literature are conducted by summarizing the state-of-the-art methods up to date of the submission. Our work also investigates the potential for widespread deployment and application of fuzzing test techniques generated by LLMs in the future, highlighting their promise for advancing automated software testing practices.

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET image segmentation tasks remains challenging due to two main issues: 1) the source dataset, usually obtained through simulation, contains a fixed level of noise, while the target dataset, directly collected from raw-data from the real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars. In contrast, the target domain data are often unknown, causing the model to be biased towards those known macromolecules, leading to a domain shift problem. To address such challenges, in this work, we introduce a voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on the improved Bilateral Filter to alleviate the domain shift problem. More importantly, we construct the first UDA cryo-ET subtomogram segmentation benchmark on three experimental datasets. Extensive experimental results on multiple benchmarks and newly curated real-world datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods.

The rapid development of large language models (LLMs) has revolutionized software testing, particularly fuzz testing, by automating the generation of diverse and effective test inputs. This advancement holds great promise for improving software reliability. Meanwhile, the introduction of MOJO, a high-performance AI programming language blending Python’s usability with the efficiency of C and C++, presents new opportunities to enhance AI model scalability and programmability. However, as a new language, MOJO lacks comprehensive testing frameworks and a sufficient corpus for LLM-based testing, which exacerbates model hallucination. In this case, LLMs will generate syntactically valid but semantically incorrect code, significantly reducing the effectiveness of fuzz testing. To address this challenge, we propose MOJOFuzzer, the first adaptive LLM-based fuzzing framework designed for zero-shot learning environments of emerging programming languages. MOJOFuzzer integrates a mutil-phase framework that systematically eliminates low-quality generated inputs before execution, significantly improving test case validity. Furthermore, MOJOFuzzer dynamically adapts LLM prompts based on runtime feedback for test case mutation, enabling an iterative learning process that continuously enhances fuzzing efficiency and bug detection performance. Our experimental results demonstrate that MOJOFuzzer significantly enhances test validity, API coverage, and bug detection performance, outperforming traditional fuzz testing and state-of-the-art LLM-based fuzzing approaches. Using MOJOFuzzer, we have conducted a first large-scale fuzz testing evaluation of MOJO, uncorvering 13 previous unknown bugs. This study not only advances the field of LLM-driven software testing but also establishes a foundational methodology for leveraging LLMs in the testing of emerging programming languages.

Recent studies have demonstrated outstanding capabilities of large language models (LLMs) in software engineering tasks, including code generation and comprehension. While LLMs have shown significant potential in assisting with coding, LLMs are vulnerable to adversarial attacks. In this paper, we investigate the vulnerability of LLMs to imperceptible attacks. This class of attacks manipulate source code at the character level, which renders the changes invisible to human reviewers yet effective in misleading LLMs’ behaviour. We devise these attacks into four distinct categories and analyse their impacts on code analysis and comprehension tasks. These four types of imperceptible character attacks include coding reordering, invisible coding characters, code deletions, and code homoglyphs. To assess the robustness of state-of-the-art LLMs, we present a systematic evaluation across multiple models using both perturbed and clean code snippets. Two evaluation metrics, model confidence using log probabilities of response and response correctness, are introduced. The results reveal that LLMs are susceptible to imperceptible coding perturbations, with varying degrees of degradation highlighted across different LLMs. Furthermore, we observe a consistent negative correlation between perturbation magnitude and model performance. These results highlight the urgent need for robust LLMs capable of manoeuvring behaviours under imperceptible adversarial conditions.

Deep Neural Networks (DNNs) have demonstrated remarkable success across various applications, yet some studies reveal their vulnerability to backdoor attacks, where attackers manipulate models under specific conditions using triggers. It significantly compromise the model integrity. Addressing this critical security issue requires robust defence mechanisms to ensure the reliability of DNN models. However, most existing defence mechanisms heavily rely on specialized defence datasets, which are often difficult to obtain due to data privacy and security concerns. This highlights the urgent need for effective data-free defence strategies. In this work, we propose Lipschitzness Precise Pruning (LPP), a novel data-free backdoor defence algorithm that leverages the properties of Lipschitz function to detect and mitigate backdoor vulnerabilities by pruning neurons with strong backdoor correlations while fine-tuning unaffected neurons. Our approach optimizes the computation of the Lipschitz constant using dot product properties, allowing for efficient and precise identification of compromised neurons without the need of clean defence data. This method addresses the limitations of existing data-free defences and extends the scope of backdoor mitigation to include fully connected layers, ensuring comprehensive protection of DNN models. As our approach does not require data exchange, it can be implemented efficiently and effectively in diverse environments. Extensive experiments demonstrate that LPP outperforms state-of-the-art defence approaches without the need for additional defence datasets. We release our code at: https://github.com/LMBTough/LPP

Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collaborative filtering, and deep learning, have achieved impressive results in recommendation systems. However, the cold-start and sparse-data scenarios are still challenging to deal with. Existing solutions either generate pseudo-interaction sequence, which often introduces redundant or noisy signals, or rely heavily on semantic similarity, overlooking dynamic shifts in user motivation. To address these limitations, this paper proposes a novel recommendation framework, termed M-LLM3REC, which leverages large language models for deep motivational signal extraction from limited user interactions. M-LLM3REC comprises three integrated modules: the Motivation-Oriented Profile Extractor (MOPE), Motivation-Oriented Trait Encoder (MOTE), and Motivational Alignment Recommender (MAR). By emphasizing motivation-driven semantic modeling, M-LLM3REC demonstrates robust, personalized, and generalizable recommendations, particularly boosting performance in cold-start situations in comparison with the state-of-the-art frameworks.

Federated learning aims at training models collaboratively across participants while protecting privacy. However, one major challenge for this paradigm is the data heterogeneity issue, where biased data preferences across multiple clients, harming the model's convergence and performance. In this paper, we first introduce powerful diffusion models into the federated learning paradigm and show that diffusion representations are effective steers during federated training. To explore the possibility of using diffusion representations in handling data heterogeneity, we propose a novel diffusion-inspired Federated paradigm with Diffusion Representation Collaboration, termed FedDifRC, leveraging meaningful guidance of diffusion models to mitigate data heterogeneity. The key idea is to construct text-driven diffusion contrasting and noise-driven diffusion regularization, aiming to provide abundant class-related semantic information and consistent convergence signals. On the one hand, we exploit the conditional feedback from the diffusion model for different text prompts to build a text-driven contrastive learning strategy. On the other hand, we introduce a noise-driven consistency regularization to align local instances with diffusion denoising representations, constraining the optimization region in the feature space. In addition, FedDifRC can be extended to a self-supervised scheme without relying on any labeled data. We also provide a theoretical analysis for FedDifRC to ensure convergence under non-convex objectives. The experiments on different scenarios validate the effectiveness of FedDifRC and the efficiency of crucial components.

Recommender systems play a pivotal role in personalized content delivery but still face significant challenges in sparse interaction scenarios such as cold start conditions. Recent works have explored the integration of large language models (LLMs) into recommendation tasks. It has shown early promise but also reveals some limitations, such as hallucinations and overconfident outputs due to a lack of targeted modeling process. This paper introduces DAMA, a novel framework for Dual Alignment for Motivation-Aware recommendation. DAMA is the first framework to systematically incorporate LLM alignment into recommender system modeling. It employs a dual alignment mechanism that jointly optimizes semantic representations from two dimensions: user motivation modeling and product attribute modeling. By leveraging natural language feedback, DAMA facilitates a structured and interpretable alignment process. Extensive experimental results on multiple real-world datasets demonstrate that DAMA significantly improves recommendation performance, especially in cold start scenarios. We anticipate that DAMA will pioneer a new alignment-centric and semantically grounded paradigm for recommender systems.

Out-of-distribution (OOD) detection is essential for enhancing the robustness and security of deep learning models in unknown and dynamic data environments. Gradient-based OOD detection methods, such as GAIA, analyse the explanation pattern representations of in-distribution (ID) and OOD samples by examining the sensitivity of model outputs w.r.t. model inputs, resulting in superior performance compared to traditional OOD detection methods. However, we argue that the non-zero gradient behaviors of OOD samples do not exhibit significant distinguishability, especially when ID samples are perturbed by random perturbations in high-dimensional spaces, which negatively impacts the accuracy of OOD detection. In this paper, we propose a novel OOD detection method called S & I based on layer Splitting and gradient Integration via Adversarial Gradient Attribution. Specifically, our approach involves splitting the model’s intermediate layers and iteratively updating adversarial examples layer-by-layer. We then integrate the attribution gradients from each intermediate layer along the attribution path from adversarial examples to the actual input, yielding true explanation pattern representations for both ID and OOD samples. Experiments demonstrate that our S & I algorithm achieves state-of-the-art results, with the average FPR95 of 29.05% (ResNet34)/38.61% (WRN40) and 37.31% (BiT-S) on the CIFAR100 and ImageNet benchmarks, respectively. Our code is available at: https://github.com/LMBTough/S-Ihttps://github.com/LMBTough/S-I

Credit card fraud detection (CCFD) is a critical application of Machine Learning (ML) in the financial sector, where accurately identifying fraudulent transactions is essential for mitigating financial losses. ML models have demonstrated their effectiveness in fraud detection task, in particular with the tabular dataset. While adversarial attacks have been extensively studied in computer vision and deep learning, their impacts on the ML models, particularly those trained on CCFD tabular datasets, remains largely unexplored. These latent vulnerabilities pose significant threats to the security and stability of the financial industry, especially in high-value transactions where losses could be substantial. To address this gap, in this paper, we present a holistic framework that investigate the robustness of CCFD ML model against adversarial perturbations under different circumstances. Specifically, the gradient-based attack methods are incorporated into the tabular credit card transaction data in both black- and white-box adversarial attacks settings. Our findings confirm that tabular data is also susceptible to subtle perturbations, highlighting the need for heightened awareness among financial technology practitioners regarding ML model security and trustworthiness. Furthermore, the experiments by transferring adversarial samples from gradient-based attack method to non-gradient-based models also verify our findings. Our results demonstrate that such attacks remain effective, emphasizing the necessity of developing robust defenses for CCFD algorithms.

With the advancement of edge computing, federated learning (FL) displays a bright promise as a privacy-preserving collaborative learning paradigm. However, one major challenge for FL is the data heterogeneity issue, which refers to the biased labeling preferences among multiple clients, negatively impacting convergence and model performance. Most previous FL methods attempt to tackle the data heterogeneity issue locally or globally, neglecting underlying class-wise structure information contained in each client. In this paper, we first study how data heterogeneity affects the divergence of the model and decompose it into local, global, and sampling drift sub-problems. To explore the potential of using intra-client class-wise structural knowledge in handling these drifts, we thus propose Federated Learning with Structural Knowledge Collaboration (FedSKC). The key idea of FedSKC is to extract and transfer domain preferences from inter-client data distributions, offering diverse class-relevant knowledge and a fair convergent signal. FedSKC comprises three components: i) local contrastive learning, to prevent weight divergence resulting from local training; ii) global discrepancy aggregation, which addresses the parameter deviation between the server and clients; iii) global period review, correcting for the sampling drift introduced by the server randomly selecting devices. We have theoretically analyzed FedSKC under non-convex objectives and empirically validated its superiority through extensive experimental results. Our code is at https://github.com/hwang52/FedSKC.

Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers increasingly rely on LLMs to assess whether system code implementation satisfy task requirements, thereby enhancing code robustness and accuracy. However, it remains unclear whether LLMs can reliably determine whether the code complies fully with the given task descriptions, which is usually natural language specifications. In this paper, we uncover a systematic failure of LLMs in evaluating whether code aligns with natural language requirements. Specifically, using widely adopted benchmarks, we employ unified prompts to judge code correctness. Our results reveal that LLMs frequently misclassify correct code implementations as either "not satisfying requirements" or containing potential defects. Surprisingly, more complex prompting, especially when leveraging prompt engineering techniques involving explanations and proposed corrections, leads to higher misjudgment rate, which highlights the critical reliability issues in using LLMs as code review assistants. We further analyze the root causes of these misjudgments, and propose two improved prompting strategies for mitigation. For the first time, our findings reveals unrecognized limitations in LLMs to match code with requirements. We also offer novel insights and practical guidance for effective use of LLMs in automated code review and task-oriented agent scenarios.

Federated learning (FL) aims to train models collaboratively across clients without sharing data for privacy-preserving.However, one major challenge is the data heterogeneity issue, which refers to the biased labeling preferences at multiple clients.A number of existing FL methods attempt to tackle data heterogeneity locally (e.g., regularizing local models) or globally (e.g., fine-tuning global model), often neglecting inherent semantic information contained in each client.To explore the possibility of using intra-client semantically meaningful knowledge in handling data heterogeneity, in this paper, we propose Federated Learning with Semantic-Aware Collaboration (FedSC) to capture client-specific and class-relevant knowledge across heterogeneous clients.The core idea of FedSC is to construct relational prototypes and consistent prototypes at semantic-level, aiming to provide fruitful class underlying knowledge and stable convergence signals in a prototype-wise collaborative way.On the one hand, FedSC introduces an inter-contrastive learning strategy to bring instance-level embeddings closer to relational prototypes with the same semantics and away from distinct classes.On the other hand, FedSC devises consistent prototypes via a discrepancy aggregation manner, as a regularization penalty to constrain the optimization region of the local model.Moreover, a theoretical analysis for FedSC is provided to ensure a convergence guarantee.Experimental results on various challenging scenarios demonstrate the effectiveness of FedSC and the efficiency of crucial components.Our code is at https://github.com/hwang52/FedSC.

Black-box attacks play a pivotal role in adversarial attacks. However, existing approaches often focus predominantly on attacking from a data-centric perspective, neglecting crucial aspects of the models. To address this issue, we propose a novel approach in this paper, coined Decision Boundary Adaptation (DBA). Our approach innovatively adopts a model-centric viewpoint, leveraging operations on the model to attain properties that enhance transferability. We observe that a flatter curvature of the statistical manifold, influenced by both samples and model parameters, leads to stronger transferability of the adversarial attacks. To leverage this, we introduce the concept of local flatness, providing an evaluation method for local flatness property along with a detailed mathematical proof. Additionally, we demonstrate a consistent relationship between local flatness, the model’s decision boundary, and the gradient descent process, showing how flatness can be achieved through gradient descent at the model parameter level. Through extensive evaluation using state-of-the-art adversarial attack techniques, our DBA approach significantly enhances the black-box attack capabilities of all the tested adversarial attack methods. The implementation of our method is available at https://github.com/LMBTough/DBA.

• We propose MVGFormer, a multi-view fusion framework with a dual-stream encoder guided by a visual graph. • We design two decoder variants: MF for hierarchical feature fusion and P3DA for multi-scale representation. • We introduce a view-masked self-supervised strategy that reconstructs masked views from remaining ones. • MVGFormer achieves superior performance over existing state-of-the-art cryo-ET segmentation methods. Cryo-Electron Tomography (cryo-ET) is a cutting-edge 3D imaging technology that enables detailed examination of biological macromolecular structures at near-atomic resolution. Recent deep learning applications on cryo-ET, such as cryo-ET segmentation, have drawn widespread interest for their potential to improve particle alignment, classification, and other tasks. However, current methods heavily rely on convolutional architectures, which prioritize local information while neglecting the global structural information inherent in cryo-ET data. Transformer-based models, known for their large receptive field, have become the de-facto design for 2D vision tasks due to their ability to effectively capture global information. This approach is also well-suited for 3D tasks, given the complex nature of 3D objects. Based on this, we extend 2D vision transformers into 3D and propose a novel transformer-based framework for cryo-ET segmentation, named MVGFormer. MVGFormer introduces a multi-view perspective fusion transformer encoder, which captures rich global structural information from multiple perspectives using unique positional embeddings. To enhance contextual awareness, we design a parallel context encoder that builds a visual graph to guide attention. We further introduce two complementary 3D decoders: multi-level feature fusion (MF) and parallel atrous convolutions (P3DA), which together capture multi-scale structural cues for precise segmentation. Furthermore, we introduce a view-masked self-supervised learning strategy to reinforce the effectiveness of the multi-view design and improve the model’s representation capability. To our knowledge, MVGFormer is the first transformer-based model for cryo-ET segmentation. We empirically evaluate MVGFormer on six cryo-ET datasets across three different tasks. Extensive experimental results demonstrate its superiority over state-of-the-art 3D segmentation methods.

The security guarantee of AI-enabled software systems (particularly using deep learning techniques as a functional core) is pivotal against the adversarial attacks exploiting software vulnerabilities. However, little attention has been paid to a systematic investigation of vulnerabilities in such systems. A common situation learned from the open source software community is that deep learning engineers frequently integrate off-the-shelf or open-source learning frameworks into their ecosystems. In this work, we specifically look into deep learning (DL) framework and perform the firstsystematicstudy of vulnerabilities in DL systems through a comprehensive analysis of identified vulnerabilities from Common Vulnerabilities and Exposures (CVE) and open-source DL tools, including TensorFlow, Caffe, OpenCV, Keras, and PyTorch. We propose a two-stream data analysis framework to explore vulnerability patterns from various databases. We investigate the unique DL frameworks and libraries development ecosystems that appear to be decentralized and fragmented. By revisiting the Common Weakness Enumeration (CWE) List, which provides the traditional software vulnerability related practices, we observed that it is more challenging to detect and fix the vulnerabilities throughout the DL systems lifecycle. Moreover, we conducted a large-scale empirical study of 3,049 DL vulnerabilities to better understand the patterns of vulnerability and the challenges in fixing them.

Federated learning (FL) offers innovative solutions for privacy-preserving distributed machine learning (ML). Different from centralized data collection algorithms, FL enables participants to locally train their model and only share the model updates for aggregation. Since private data never leaves the end node, FL effectively mitigates privacy leakage during collaborative training. Despite its promising potential, FL is vulnerable to various attacks due to its distributed nature, affecting the entire life cycle of FL services. These threats can harm the model's utility or compromise participants' privacy, either directly or indirectly. In response, numerous defense frameworks have been proposed, demonstrating effectiveness in specific settings and scenarios. To provide a clear understanding of the current research landscape, this article reviews the most representative and state-of-the-art threats and defense frameworks throughout the FL service life cycle. We start by identifying FL threats that harm utility and privacy, including those with potential or direct impacts. Then, we dive into the defense frameworks, analyze the relationship between threats and defenses, and compare the trade-offs among different defense strategies. We subsequently revisit these studies to evaluate their practicality in real-world scenarios and conclude by summarizing existing research bottlenecks and outlining future directions. We hope this survey sheds light on trustworthy FL research and contributes to the FL community.

The recent performance leap of Large Language Models (LLMs) opens up new opportunities across numerous industrial applications and domains. However, the potential erroneous behavior (e.g., the generation of misinformation and hallucination) has also raised severe concerns for the trustworthiness of LLMs, especially in safety-, security- and reliability-sensitive industrial scenarios, potentially hindering real-world adoptions. While uncertainty estimation has shown its potential for interpreting the prediction risks made by classic machine learning (ML) models, the unique characteristics of recent LLMs (e.g., adopting self-attention mechanism as its core, very largescale model size, often used in generative contexts) pose new challenges for the behavior analysis of LLMs. Up to the present, little progress has been made to better understand whether and to what extent uncertainty estimation can help characterize the capability boundary of an LLM, to counteract its undesired behavior, which is considered to be of great importance with the potential wide-range applications of LLMs across industry domains. To bridge the gap, in this paper, we initiate an early exploratory study of the risk assessment of LLMs from the lens of uncertainty. In particular, we conduct a large-scale study with as many as twelve uncertainty estimation methods and eight general LLMs on four NLP tasks and seven programming-capable LLMs on two code generation tasks to investigate to what extent uncertainty estimation techniques could help characterize the prediction risks of LLMs. Our findings confirm the potential of uncertainty estimation for revealing LLMs’ uncertain/nonfactual predictions. The insights derived from our study can pave the way for more advanced analysis and research on LLMs, ultimately aiming at enhancing their trustworthiness.

To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the ax- ioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the ex- isting attribution methods present challenges for effective in- terpretation and efficient computation. In this work, we in- troduce MFABA, an attribution algorithm that adheres to ax- ioms, as a novel method for interpreting DNN. Addition- ally, we provide the theoretical proof and in-depth analy- sis for MFABA algorithm, and conduct a large scale exper- iment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art at- tribution algorithms. The effectiveness of MFABA is thor- oughly evaluated through the statistical analysis in compar- ison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA.

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. To facilitate model safety, transfer-based attacks employ surrogate models to craft adversarial examples. In this work, we firstly study the intricate mechanisms of such attacks. We observe a correlation between the sharpness of decision boundaries in model sensitive regions and overfitting during adversarial training, which hampers the adversarial examples' transferability. To address this issue, we propose a novel approach termed Frequency-Guided Sample Relevance Attack (FGSRA). Specifically, we leverage frequency information to explore similar sensitive regions across different models, thereby generating neighborhood samples. Additional similarity weights are subsequently introduced to assess the adversarial contribution of the neighborhood samples. A hybrid gradient is then obtained to thoroughly exploit neighborhood information within input samples. Extensive experiments demonstrate the prominent performance of our approach. Compared to other state-of-the-art benchmarks on surrogate model Inc-v3, our method has an average improvement of 27.21% for normally trained CNNs and 42.1% for adversarially trained CNNs. Moreover, we achieve an average improvement of 24.6% for ViTs. Our code is available at:https://github.com/LMBTough/FGSRA

The way research and business manage and utilize knowledge is undergoing a significant transformation, driven by Artificial Intelligence (AI). Deep learning and machine learning are emerging as powerful tools for optimizing knowledge management systems, leading to more informed and productive development. AI offers unique solutions for organizations struggling with information overload and inefficient knowledge transfer. These AI models can significantly improve data management and utilization. Imagine an AI-powered system that streamlines onboarding processes, provides precise answers to various queries, and even captures the valuable tacit knowledge (implicit skills and expertise) often residing within individuals. AI bridges the gap between explicit knowledge (easily documented information) and tacit knowledge, fostering a more comprehensive and accessible knowledge base. However, such AI systems solicit trustworthy and responsible approaches to mitigate potential misuse and malfunction. In this workshop, we aim to gather researchers and engineers from academia and industry to discuss the latest advances in trustworthy and responsible AI solutions for information and knowledge management systems.

Federated Learning (FL) is a promising technology that enables multiple participants collaboratively train a joint model without sharing their local data. Owing the privacy protection nature, FL has attracted interests from industry, leading to its deployment across diverse domains such as smartphones, institutions, and Internet of Things (IoTs). Despite the development of various FL algorithms aimed at enhancing FL performance from multiple perspectives, their evaluation typically hinges on a single metric like accuracy, failing to account for the unique demands of different use cases. Thus, how to comprehensively evaluate an FL algorithm and determine the most suitable candidate for a designated use case remains an open question. To address this research gap, we introduce the Holistic Evaluation Metrics (HEM) for federated learning. In this work, we consider the application scenarios of IoT, Smartphones, and Institutions as the principal FL use cases. Subsequently, we determine the components of the evaluation metric and the corresponding importance vector for each use case. The HEM index is then generated by integrating these metric components with their importance vectors. We evaluate various FL algorithms in different use cases through HEM, our experimental results demonstrate that HEM can effectively evaluate and select the appropriate FL algorithms in different use cases.

In knowledge distillation, a primary focus has been on transforming and balancing multiple distillation components. In this work, we emphasize the importance of thoroughly examining each distillation component, as we observe that not all elements are equally crucial. From this perspective, we decouple the Kullback-Leibler (KL) divergence into three unique elements: Binary Classification Divergence (BCD), Strong Correlation Divergence (SCD), and Weak Correlation Divergence (WCD). Each of these elements presents varying degrees of influence. Leveraging these insights, we present the Correlation-Aware Knowledge Distillation (CAKD) framework. CAKD is designed to prioritize the facets of the distillation components that have the most substantial influence on predictions, thereby optimizing knowledge transfer from teacher to student models. Our experiments demonstrate that adjusting the effect of each element enhances the effectiveness of knowledge transformation. Furthermore, evidence shows that our novel CAKD framework consistently outperforms the baseline across diverse models and datasets. Our work further highlights the importance and effectiveness of closely examining the impact of different parts of distillation process.

Vision Transformers (ViTs) have been widely used in various domains. Similar to Convolutional Neural Networks (CNNs), ViTs are prone to the impacts of adversarial samples, raising security concerns in real-world applications. As one of the most effective black-box attack methods, transferable attacks can generate adversarial samples on surrogate models to directly attack the target model without accessing the parameters. However, due to the distinct internal structures of ViTs and CNNs, adversarial samples constructed by traditional transferable attack methods may not be applicable to ViTs. Therefore, it is imperative to propose more effective transferability attack methods to unveil latent vulnerabilities in ViTs. Existing methods have found that applying gradient regularization to extreme gradients across different functional regions in the transformer structure can enhance sample transferability. However, in practice, substantial gradient disparities exist even within the same functional region across different layers. Furthermore, we find that mild gradients therein are the main culprits behind reduced transferability. In this paper, we introduce a novel Gradient Normalization Scaling method for fine-grained gradient editing to enhance the transferability of adversarial attacks on ViTs. More importantly, we highlight that ViTs, unlike traditional CNNs, exhibit distinct attention regions in the frequency domain. Leveraging this insight, we delve into exploring the frequency domain to further enhance the algorithm's transferability. Through extensive experimentation on various ViT variants and traditional CNN models, we substantiate that the new approach achieves state-of-the-art performance, with an average performance improvement of 33.54\% and 42.05\% on ViT and CNN models, respectively. Our code is available at: https://github.com/LMBTough/GNS-HFA.

Due to the real-world noise and human-added perturbations, attaining the trustworthiness of deep neural networks (DNNs) is a challenging task. Therefore, it becomes essential to offer explanations for the decisions made by these non-linear and complex parameterized models. Attribution methods are promising for this goal, yet its performance can be further improved. In this paper, for the first time, we present that the decision boundary exploration approaches of attribution are consistent with the process for transferable adversarial attacks. Specifically, the transferable adversarial attacks craft general adversarial samples from the source model, which is consistent with the generation of adversarial samples that can cross multiple decision boundaries in attribution. Utilizing this consistency, we introduce a novel attribution method via model parameter exploration. Furthermore, inspired by the capability of frequency exploration to investigate the model parameters, we provide enhanced explainability for DNNs by manipulating the input features based on frequency information to explore the decision boundaries of different models. Large-scale experiments demonstrate that our \textbf{A}ttribution method for \textbf{E}xplanation with model parameter e\textbf{X}ploration (AttEXplore) outperforms other state-of-the-art interpretability methods. Moreover, by employing other transferable attack techniques, AttEXplore can explore potential variations in attribution outcomes. Our code is available at: https://github.com/LMBTough/ATTEXPLORE.

Deep neural networks (DNNs) have achieved state-of-the-art performance across various applications. However, ensuring the reliability and trustworthiness of DNNs requires enhanced interpretability of model inputs and outputs. As an effective means of Explainable Artificial Intelligence (XAI) research, the interpretability of existing attribution algorithms varies depending on the choice of reference point, the quality of adversarial samples, or the applicability of gradient constraints in specific tasks. To thoroughly explore the attribution integration paths, in this paper, inspired by the iterative generation of high-quality samples in the diffusion model, we propose an Iterative Search Attribution (ISA) method. To enhance attribution accuracy, ISA distinguishes the importance of samples during gradient ascent and descent, while clipping the relatively unimportant features in the model. Specifically, we introduce a scale parameter during the iterative process to ensure the features in next iteration are always more significant than those in current iteration. Comprehensive experimental results show that our method has superior interpretability in image recognition tasks compared with state-of-the-art baselines. Our code is available at: https://github.com/LMBTough/ISA

In the field of artificial intelligence, AI models are frequently described as 'black boxes' due to the obscurity of their internal mechanisms. It has ignited research interest on model interpretability, especially in attribution methods that offers precise explanations of model decisions. Current attribution algorithms typically evaluate the importance of each parameter by exploring the sample space. A large number of intermediate states are introduced during the exploration process, which may reach the model's Out-of-Distribution (OOD) space. Such intermediate states will impact the attribution results, making it challenging to grasp the relative importance of features. In this paper, we firstly define the local space and its relevant properties, and we propose the Local Attribution (LA) algorithm that leverages these properties. The LA algorithm comprises both targeted and untargeted exploration phases, which are designed to effectively generate intermediate states for attribution that thoroughly encompass the local space. Compared to the state-of-the-art attribution methods, our approach achieves an average improvement of 38.21% in attribution effectiveness. Extensive ablation studies in our experiments also validate the significance of each component in our algorithm. Our code is available at: https://github.com/LMBTough/LA/

Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data, i.e., images, text, and audio. Accordingly, its promising performance has led to the GAN-based adversarial attack methods in the white-box and black-box attack scenarios. The importance of transferable black-box attacks lies in their ability to be effective across different models and settings, more closely aligning with real-world applications. However, it remains challenging to retain the performance in terms of transferable adversarial examples for such methods. Meanwhile, we observe that some enhanced gradient-based transferable adversarial attack algorithms require prolonged time for adversarial sample generation. Thus, in this work, we propose a novel algorithm named GE-AdvGAN to enhance the transferability of adversarial samples whilst improving the algorithm's efficiency. The main approach is via optimising the training process of the generator parameters. With the functional and characteristic similarity analysis, we introduce a novel gradient editing (GE) mechanism and verify its feasibility in generating transferable samples on various models. Moreover, by exploring the frequency domain information to determine the gradient editing direction, GE-AdvGAN can generate highly transferable adversarial samples while minimizing the execution time in comparison to the state-of-the-art transferable adversarial attack algorithms. The performance of GE-AdvGAN is comprehensively evaluated by large-scale experiments on different datasets, which results demonstrate the superiority of our algorithm. The code for our algorithm is available at: https://github.com/LMBTough/GE-advGAN.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

In recent years, the development of machine learning-based solutions for software services, particularly for source code, has grown rapidly. It is witnessed that many machine learning models for software services require the input of source code snippets in a desired form of abstract syntax tree (AST), which is mostly generated from an external tool. However, such data pre-processing tasks could be done by different engineering tools, and the impact of these tools towards final models is often neglected. In this work, we aim to investigate the source code engineering impacts towards machine learning-based software services. Three different types of parsing tools are identified, which are parser generator, parsing library and parser developed for a certain purpose. They are thoroughly evaluated towards the impacts on the prediction model of Code2Vec for the prediction task of the method name in Java language. The collective result on the Java-small dataset shows that the generated ASTs differ a lot in terms of source code structures and contents when using different parsing tools. The difference could influence the performance of the trained model significantly. Our result suggests that when machine learning models are implemented for software services, especially for code-related tasks, the selection of parsing tools should be thoroughly considered during the data pre-processing stage. While there are some interesting findings on Java-med and Java-small, we anticipate this work could provide some insights for better ML-based software service solutions from the perspective of source code engineering.

Machine learning models have made significant breakthroughs across various domains. However, it is crucial to assess these models to obtain a complete understanding of their capabilities and limitations and ensure their effectiveness and reliability in solving real-world problems. In this paper, we present a framework, termed ML-Compass, that covers a broad range of machine learning abilities, including utility evaluation, neuron analysis, robustness evaluation, and interpretability examination. We use this framework to assess seven state-of-the-art classification models on four benchmark image datasets. Our results indicate that different models exhibit significant variation, even when trained on the same dataset. This highlights the importance of using the assessment framework to comprehend their behavior.

The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model pruning. In this work, we have thoroughly considered the deep learning model pruning process with and without fine-tuning step, ensuring the model performance consistency. To achieve our objectives, we propose a methodology that employs adversarial attack methods to explore deep neural network parameters. This approach is combined with an innovative attribution algorithm to analyse the level of network neurons involvement. In our experiments, our approach can effectively quantify the importance of network neuron. We extend the evaluation through comprehensive experiments conducted on a range of datasets, including CIFAR-10, CIFAR-100 and Caltech101. The results demonstrate that, our method have consistently achieved the state-of-the-art performance over many existing methods. We anticipate that this work will help to reduce the heavy training and inference cost of deep neural network models where a lightweight deep learning enhanced service and system is possible. The source code is open source at https://github.com/LMBTough/FVW.

Deep neural networks (DNNs) have been shown vulnerable to interference from adversarial samples, leading to erroneous predictions. Investigating adversarial attacks can effectively improve the reliability as well as the performance of deep neural models in real-world applications. Since it is generally challenging to infer the parameters in black-box models, high transferability becomes an important factor for the success rate of an attack method. Recently, the Spectrum Simulation Attack method exhibits promising results based on the frequency domain. In light of SSA, we propose a novel attack approach in this paper, which achieves the best results among diverse state-of-the-art transferable adversarial attack methods. Our method aims to find a stationary point, which extends the ability to find multiple local optima with the optimal local attack effect. After finding the stationary point, a frequency-based search is employed to explore the best adversarial samples in the neighbouring space, utilmately determining the final adversarial direction. We compare our method against a variety of cutting-edge transferable adversarial methods. Extensive experiments validate that our method improves the attack success rate by 4.7% for conventionally trained models and 53.1% for adversarially trained models. Our code is available at https://github.com/LMBTough/FSPS

The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding. By embedding information into a noise-tolerant signal, such as audio, video, or images, digital watermarking and steganography techniques can be used to protect sensitive intellectual property (IP) and enable confidential communication, ensuring that the information embedded is only accessible to authorized parties. This survey provides an overview of recent developments in deep learning techniques deployed for data hiding, categorized systematically according to model architectures and noise injection methods. In addition, potential future research directions that unite digital watermarking and steganography on software engineering to enhance security and mitigate risks are suggested and deliberated. This contribution furthers the creation of a more trustworthy digital world and advances responsible artificial intelligence (AI).

Data-driven software engineering processes, such as vulnerability prediction heavily rely on the quality of the data used. In this paper, we observe that it is infeasible to obtain a noise-free security defect dataset in practice. Despite the vulnerable class, the non-vulnerable modules are difficult to be verified and determined as truly exploit free given the limited manual efforts available. It results in uncertainty, introduces labeling noise in the datasets and affects conclusion validity. To address this issue, we propose novel learning methods that are robust to label impurities and can leverage the most from limited label data; noisy label learning. We investigate various noisy label learning methods applied to software vulnerability prediction. Specifically, we propose a two-stage learning method based on noise cleaning to identify and remediate the noisy samples, which improves AUC and recall of baselines by up to 8.9% and 23.4%, respectively. Moreover, we discuss several hurdles in terms of achieving a performance upper bound with semi-omniscient knowledge of the label noise. Overall, the experimental results show that learning from noisy labels can be effective for data-driven software and security analytics.

Current machine-learning based software vulnerability detection methods are primarily conducted at the function-level. However, a key limitation of these methods is that they do not indicate the specific lines of code contributing to vulnerabilities. This limits the ability of developers to efficiently inspect and interpret the predictions from a learnt model, which is crucial for integrating machine-learning based tools into the software development work-flow. Graph-based models have shown promising performance in function-level vulnerability detection, but their capability for statement-level vulnerability detection has not been extensively explored. While interpreting function-level predictions through explainable AI is one promising direction, we herein consider the statement-level software vulnerability detection task from a fully supervised learning perspective. We propose a novel deep learning framework, LineVD, which formulates statement-level vulnerability detection as a node classification task. LineVD leverages control and data dependencies between statements using graph neural networks, and a transformer-based model to encode the raw source code tokens. In particular, by addressing the conflicting outputs between function-level and statement-level information, LineVD significantly improve the prediction performance without vulnerability status for function code. We have conducted extensive experiments against a large-scale collection of real-world C/C++ vulnerabilities obtained from multiple real-world projects, and demonstrate an increase of 105% in F1-score over the current state-of-the-art.

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

An intelligent industrial system is demanded for the development of Industry 4.0, which aims at providing efficient and intelligent computing service to increase the productivity. In-ternet of things become critical to achieve this goal by employing the sensors and connecting the objects over internet. In this study, we firstly investigate how the intelligent industrial service will be realised by constructing a five-layer framework based on our comprehensive field experiences. In detail, how the IoT sensor data are connected with the system and how the computational model is designed to improve the efficiency of the manufacturing system are discussed. Particularly, in this paper, the task of the defect identification of the steel is selected as our application on field. Since the longitudinal surface crack on the steel slab is a crucial indication suggesting the quality of continuous casting slab, how to discover the longitudinal surface crack on the slab in an early stage is of great significance. Traditional methods to detect the longitudinal surface crack have different drawbacks. Given the benefit of numerous IoT sensor data, we have proposed a novel computational model to incorporate the multiple factors of steel manufacturing system to improve the detection. Experiment evaluation has shown the efficiency and effectiveness of the model. In summary, we anticipate this work will contribute to an intelligent steel manufacturing system based on industrial IoT in building viable solutions, which benefit from the early stage identification and prediction of poor quality productions.

Research on protein-protein interactions (PPIs) data paves the way towards understanding the mechanisms of infectious diseases, however improving the prediction performance of PPIs of inter-species remains a challenge. Since one single type of sequence data such as amino acid composition may be deficient for high-quality prediction of protein interactions, we have investigated a broader range of heterogeneous information of sequences data. This paper proposes a novel framework for PPIs prediction based on Heterogeneous Information Mining and Ensembling (HIME) process to effectively learn from the interaction data. In particular, the proposed approach introduces an ensemble process together with substantial features that generate better performance of PPIs prediction task. The performance of the proposed framework is validated on real protein interaction datasets. The extensive experiments show that HIME achieves higher performance over all existing methods reported in literature so far.

Summary Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud‐based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host‐pathogen protein‐protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud‐based multiomics data is discussed. In particular, a novel two‐layer machine learning model, namely APEX2S, is proposed for discovery of the protein‐protein interactions data. The results show that our model can better learn and predict from the accumulated host‐pathogen protein‐protein interactions.

For classification tasks, such as protein-protein interactions (PPI), support vector machines (SVMs) have been continually utilised as a standard machine learning model. However, most practices in PPIs classifications are limited to common circumstances with small datasets and low feature dimensions, due to the big computation burden of kernel functions and quadratic optimization of SVM. Alternatively, these practical experiences might tend to employ a linear model once the dataset becomes larger, which may have exclusively lost the kernel function's potential. Since there are different defined kernels and various groups of hyperparameter, the time costs in estimating a best set of hyperparameter by traditional grid search are subsequently tremendous for PPI classification. To address this challenge, in this paper, we present a more efficient solution of hyperparameter estimation by gaining acceleration with GPU, which trains SVM efficiently and accurately with kernel functions calculation accelerated on various PPI datasets. The experiments are firstly conducted on PPI classification task, and we have exclusively evaluated the effectiveness on five public classification datasets. Our solution demonstrates a faster and more accurate performance comparing with the state-of-the-art.

Testable prediction outcomes generated by computational models based on available databases are the primary sources helping to design biological experiments. Although numerous databases have been designed by collecting data either only from literature manually or together with prediction outcomes from computational models, there is currently not a comprehensive data service framework delivering better insights for these results. In this paper, we introduce a biological sequence data service towards delivering deeper insights and helping better biological experiments design. The service includes following major components: a comprehensive database for storing biological data, data analytics tools for analysing biological data, and computational models for delivering testable prediction outcomes. Specifically, we present this service in a framework for studies on host-pathogen interactions. The design of this framework aims to improve the understanding of host-pathogen interactions. The relationships of hierarchical databases and their working mechanism, specifically between PPIs and DDIs, are also presented in this framework. Finally, the preliminary and practical experiences of building computational model for prediction is discussed.

Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out.

Nowadays more and more data are being sequenced and accumulated in system biology, which brings the data analytics researchers to a brand new era, namely `big data', to extract the inner relationship and knowledge from the huge amount of data. Bridging the gap between computational methodology and biology to accelerate the development of biology analytics has been a hot area. In this paper, we focus on these enormous amounts of data generated with the speedy development of high throughput technologies during the past decades, especially for protein-protein interactions, which are the critical molecular process in biology. Since pathogen-host protein-protein interactions are the major and basic problems for not only infectious diseases but also drug design, molecular level interactions between pathogen and host play very critical role for the study of infection mechanisms. In this paper, we built a basic framework for analyzing the specific problems about pathogen-host protein-protein interactions (PHPPI), meanwhile, we also presented the state-of-art deep learning method results on prediction of PHPPI comparing with other machine learning methods. Utilizing the evaluation methods, specifically by considering the high skewed imbalanced ratio and huge amount of data, we detailed the pipeline solution on both storing and learning for PHPPI. This work contributes as a basis for a further investigation of protein and protein-protein interactions, with the collaboration of data analytics results from the vast amount of data dispersedly available in biology literature.

The emergence of Cloud Services and Mobile Internet has influenced the society a lot, including the tourism industry. This paper proposes a design of backpacking service, not only aimed at the travelling routines, but also focus on the low-cost airlines. This kind of service aids backpackers with an effective travelling and satisfy the price requirement. With this low-cost airlines system, the backpackers can experience real e-Tourism and enjoy a better travel aided by the real-time information. According to the basic principles of low-cost airlines, this paper provides the backpackers with a definitely efficient way to check out a suitable flight itinerary under an acceptable price.

In most cases of safety-related systems, the network is an indispensable part. At this point, the system reliability is as important as the system communication quality. With the emergence of multi-core architectures, the first generation usually aims to provide reliable and deterministic computing resources. Therefore, with the boost requirement of reliability and throughput that cannot be satisfied by general single-core processors, the deployment of safety-related systems is transferred and processed multi-core environments. In this paper, we propose Reliable Communication Server on SPU (RCSoS), which is a server model for reliable communication utilizing SPU (Synergistic Processor Unit) in Cell/B.E (Cell Broadband Engine). It simulates SPU as a communication server and guarantees the reliability and determinacy by the isolation mode of SPU and contract model. We have implemented RCSoS in PlayStation 3, which dynamically adjust parameters, and inform applications on contract violations. Experiments show the performance of this model.