Build failures are a major obstacle in RISC-V software migration, often involving complex interactions across logs, configurations, and environments. Traditional diagnostic tools struggle with the unstructured, multi-phase nature of build logs and lack semantic reasoning.We propose a two-stage framework for automated root cause analysis. RV-LAD compresses logs using template-based filtering and applies phase-aware anomaly detection via few-shot LLM prompting. MCTS-RCA integrates a domain-specific knowledge base with Monte Carlo Tree Search to perform LLM-guided multi-source reasoning under classification constraints.To support evaluation, we construct a curated dataset of 117 real-world RISC-V build failures, each annotated with logs, spec files, and repair records. Experiments show our approach achieves 75.2% diagnosis accuracy, surpassing previous LLM-based and rule-based methods. It also offers interpretable reasoning traces, enabling practical and transparent diagnosis. This work provides an effective and extensible solution for RCA in emerging software ecosystems like RISC-V, bridging large language models with domain-aware inference.

Large language models (LLMs) are increasingly integrated into software development workflows to accelerate code generation, but often produce insecure and uncontrollable code due to vulnerable training data and unconstrained decoding strategies. This poses severe risks in security-critical systems, where post-generation vulnerability detection and manual remediation incur significant overhead. While constrained decoding offers a practical mitigation strategy, existing methods suffer from degraded trustworthiness, constraint conflicts, and high latency—especially when enforcing multiple concurrent security constraints.We propose SCodeGen, a real-time constrained decoding framework designed to enforce fine-grained security controls during LLM code generation. To improve trustworthiness and controllability, SCodeGen introduces (1) a matching-length-aware logit modulation strategy that enhances trustworthiness and controllability without semantic disruption, and (2) a two-stage low-latency decoding architecture, which compiles constraint phrases into a runtime-enforceable constraint automaton (RCA) with precomputed logit bias vectors for efficient online decoding. Extensive evaluations on CodeGuard+ show that SCodeGen significantly improves secure pass rates under both single and multi-constraint settings, while maintaining latency comparable to unconstrained decoding. This work demonstrates a practical and scalable solution toward trustworthy LLM-assisted software development under security constraints.

Multimodal sarcasm is often used to express strong emotions online through the discrepancy of the literal-figurative scene across multi-modalities. Current researches retrofit transform-based pretrained language models to integrate text and image to detect sarcasm. However, these methods struggle to distinguish subtle semantic and emotional differences between image and text within the same instance. To address this issue, this paper proposes a new context-aware dual attention network that collaboratively performs textual and visual attentions using a shared memory module. This approach enables us to reason about the interconnected portions involving sarcasm in both text and image. Additionally, we use implicit context derived from multimodal commonsense graph to establish a holistic perspective that encompasses semantics and emotions across modalities. Finally, multi-view cross-modal matching technique is employed to effectively identify contradictions. We evaluate our method on the widely used HFM dataset and achieve 1.01% improvements on the F1-score. Extensive experiments demonstrate the effectiveness of the proposed method.

Within the realms of scientific computing, large-scale data processing, and artificial intelligence-powered computation, disparities in performance, which originate from differing code implementations, directly influence the practicality of the code. Although existing works tried to utilize code knowledge to enhance the execution performance of codes generated by large language models, they neglect code evaluation outcomes which directly refer to the code execution details, resulting in inefficient computation. To address this issue, we propose DSCT-Decode, an innovative adaptive decoding strategy for large language models, that employs a data structure named 'Code Token Tree' (CTT), which guides token selection based on code evaluation outcomes. DSCT-Decode assesses generated code across three dimensions---correctness, performance, and similarity---and utilizes a dynamic penalty-based boundary intersection method to compute multi-objective scores, which are then used to adjust the scores of nodes in the CTT during backpropagation. By maintaining a balance between exploration, through token selection probabilities, and exploitation, through multi-objective scoring, DSCT-Decode effectively navigates the code space to swiftly identify high-performance code solutions. To substantiate our framework, we developed a new benchmark, big-DS-1000, which is an extension of DS-1000. This benchmark is the first of its kind to specifically evaluate code generation methods based on execution performance. Comparative evaluations with leading large language models, such as CodeLlama and GPT-4, show that our framework achieves an average performance enhancement of nearly 30%. Furthermore, 30% of the codes exhibited a performance improvement of more than 20%, underscoring the effectiveness and potential of our framework for practical applications.

Modeling spatial and temporal dependencies is essential for achieving accurate air quality prediction. Current air quality prediction models often overlook the underlying causal relationships in the data and primarily focus on statistical correlations. As a result, these models lack sufficient predictive power for medium- and long-term forecasts. This paper introduces causality into air quality prediction and proposes a Causal Spatio-Temporal Convolutional Network (CSTCN). We utilize an attention mechanism to automatically assign attention weights to each air quality monitoring site, enabling the capture of causal relationships between sites in the spatial dimension. Conducting tests on the identified relationships ensures the causality of the data in space. Furthermore, convolution operations are applied to extract the spatio-temporal features of the monitoring stations, while also utilizing causal convolution to ensure the causality of the data over time. The experiments conducted on the Beijing air quality dataset demonstrate that CSTCN exhibits outstanding performance in medium- and long-term predictions.

Deep Neural Network (DNN) classifiers easily yield high confidence for Out-of-Distribution (OOD) examples beyond the training distribution, i.e., In-Distribution (ID), leading to classification errors. Detecting and rejecting various OOD examples is crucial for the reliability of DNNs. More challenging, well-built detections can also suffer from being re-bypassed by adversarial attacks perturbing unseen OOD examples. Some existing works introduce adversarial training on the auxiliary outliers to improve the robustness of OOD detection. However, in this work, we find that applying adversarial training on the auxiliary outliers is insufficient to make the detection robust to strong adaptive attacks. To fix this bug of OOD detection, we propose a semi-supervised adversarial training approach, RobDet, which mines adversarially perturbed ID examples from within the neighborhood of clean ID ones as auxiliary outliers and uses multiple "other" classes to train them together with other auxiliary clean and adversarially perturbed outliers to enhance the robustness of OOD detection without significantly sacrificing the performance on clean OOD examples. Experiments show that RobDet has a significant advantage in detecting malicious OOD examples generated by strong adaptive attacks while maintaining advanced performance in detecting clean OOD examples.

Environment configuration is the basis for software reuse, enabling developers to reuse specific functions.However, the lack of uniform practice in dependency declaration specifications of Python projects can cause problems for developers trying to install third-party libraries.Existing package management tools are often inadequate to help fix these problems.Fixing these errors requires expensive hours and domain knowledge for developers.To help address related problems, some studies focus on well-maintained and popular Python projects about dependency conflict problems caused by PIP's installation rules.However, many projects in the wild are outside of this scope.We carefully investigate 110 issues in 110 projects in the wild.Based on the comprehensive study, we design and implement EasyPip to automatically detect and fix problems in Python dependency declaration files.Dif-

Big data processing is now the primary mission in remote sensing processing, fortunately, cloud computing provides a feasible approach to perform it efficiently. But the work of resource provisioning, scheduling, and scaling is still inevitable in most cloud computing solutions, it poses a considerable challenge to data analyst. The emerging serverless architecture presents a new paradigm to provide a cloud service, the user only needs to upload function codes and leaves all the other server management jobs to the service provider. It reveals a new possibility of remote sensing processing. This paper presents FaasRS, a framework to process remote sensing images upon serverless platform. FaasRS is built on AWS Lambda, it exposes only simple APIs to operate images, and builds DAG for user’s algorithm. FaasRS splits task by splitting the image into small tiles based on geospatial region, and uses each Lambda worker to perform the computation for one tile. To reduce the redundant operations, we also make optimizations based on the algorithm DAG. FaasRS shows favorable performance and scalability in our evaluation. In the comparison with Spark and Ray, FaasRS shows a significant performance improvement in different type of RS processing jobs.

Heterogeneous Information Network (HIN) is a graph that contains variety of nodes and their relationships. It can provide abundant auxiliary information for the feature engineering of the recommendation model and thus help to improve its recommendation performance. Most work applying the auxiliary information is to calculate node similarities over meta-paths or meta-graphs of HIN and then recommend based on those similarities through matrix factorization or other analogous recommendation algorithms. In this paper, we propose a novel meta-graph embedding based deep learning recommendation model, MGRec. Types of meta-graphs of HIN are embedded as input features through multiple same structured Attention-enhanced CNNs, which help to learn the weight of each node and get a more accurate vector representation of the meta-graph. Besides, a Wide&Multi-Deep structured recommendation framework is designed to learn both the shallow and deep interactions among features, in which multiple independent deep modules are used to learn the distinguishable correlation degree of each type of meta-graph to the target user and item to highlight the distinguishable contribution of each meta-graph to the recommendation. Experiments on two real-world datasets show that, compared with other popular recommendation models, our MGRec model achieves the best performance in multiple evaluation metrics.

Channel pruning is a commonly used model compression in convolutional neural network. The structured pruning using sparse constraints can automatically learn the importance of parameters during the training process by imposing sparse constraints on parameters. However, existing pruning methods based on sparse constraints cannot process the final convolutional layer of the residual module with complex connections. Due to the existence of residual connection, if the final convolutional layer of the residual module is pruned, the sparse channel of the feature map from residual connection does not correspond to the feature map from module output, which will cause the parameters to be unable to be pruned. This paper studies this problem and proposes an identity association group pruning algorithm, which we call IGP. IGP groups the parameters and channels that generate the corresponding feature maps, uses Group Lasso to sparse the same group of parameters as a whole, and forces the sparseness of the parameters with sparse correlation to be consistent with each other. Experiments show that when IGP compresses ResNet56 60% parameters, the model performance only drops 0.36 %, which is better than the existing pruning method based on sparse constraints. In the case of high compression ratio, IGP can compresses ResNet-50 compressesed with 87% parameters and the performance drops only 0.76%, which is 5.17 % higher than the existing methods.

Deep learning (DL) has been widely adopted in many safety-critical scenarios. Deep neural networks (DNNs) usually play the core part in these DL systems. Existing studies have shown that DNNs can suffer from various vulnerabilities, and cause severe consequences. To improve the testing adequacy of DNNs, researchers have proposed several coverage criteria, e.g., neuron coverage in DeepXplore. The prediction result of a DNN is jointly determined by the outputs of neurons and the connection weights that they connect into next-level neurons. However, existing coverage criteria use only the output of a neuron to determine the activation state of the neuron and ignore the connection weights it emits.In this paper, we propose DeepCon, a novel contribution coverage. In DeepCon, we define a term contribution as the combination of the output of a neuron and the connection weight it emits, and use the contribution coverage to gauge the testing adequacy of DNNs. DeepCon can thoroughly cover both neurons and the connection weights they emit and can scale well to large DNNs. We further propose a contribution coverage guided test generation approach, DeepCon-Gen, which can automatically generate tests and activate inactivated contributions of DNNs. We evaluate DeepCon and DeepCon-Gen on five different DNNs over two popular datasets. The experimental results show that DeepCon can well present the testing adequacy of these DNNs. DeepCon-Gen can effectively activate the inactivated contributions, and 62.6% of the generated tests can lead to mispredictions.

Patient similarity measuring plays a significant role in many healthcare applications, such as cohort study and treatment comparative effectiveness research. Existing methods mainly rely on supervised metric learning method to study patient similarity from Electronic Health Records (EHRs), facing the challenge of differentiating patients with a large number of fine-grained disease categories. Deep metric learning has gained noticeable success in fine-grained image categorization problem, however, it cannot be directly applied to classification of patients with hierarchical disease labels. In this paper, we present a novel three layer patient similarity deep metric learning framework (PSDML) by optimizing quadruple loss improved from triplet loss, to learn an embedding distance for disease classification among the patients. The context semantic relation of multi diagnosis labels encoding by ICD-10 is taken into account to compute the supervised distance of patients. To solve the diagnosis class imbalance, patient tuples that violate deep metric learning framework loss constraints are chosen prior as samples to accelerate the convergence of the neural network. We conducted KNN multi label classification experiment using the learned similarity metric on the real EHRs about stroke disease collected by Chinese Stroke Data Center. The results demonstrate substantial improvement over the baselines.

Big data applications that rely on relational databases gradually expose limitations on scalability and performance. In recent years, Hadoop ecosystem has been widely adopted as an evolving solution. This paper presents the migration of a legacy data analytics application in a provincial data center. The target platform follows "no one size fits all" method. Considering different workloads, data storage is hybrid with distributed file system (HDFS) and distributed NoSQL database. Beyond the architecture re-design, we focus on the problem of data model transformation from relational database to NoSQL database. We propose a query-aware approach to free developers from tedious manual work. The approach generates query-specific views (NoView) for NoSQL and re-structures the views to align with NoSQL's data model. Our results show that the migrated application achieves high scalability and high performance. We believe that our practice provides valuable insights (such as NoSQL data modeling methodology), and the techniques can be easily applied to other similar migrations.

Out of memory (OOM) errors occur frequently in data-intensive applications that run atop distributed data-parallel frameworks, such as MapReduce and Spark. In these applications, the memory space is shared by the framework and user code. Since the framework hides the details of distributed execution, it is challenging for users to pinpoint the root causes and fix these OOM errors. This paper presents a comprehensive characteristic study on 123 real-world OOM errors in Hadoop and Spark applications. Our major findings include: (1) 12% errors are caused by the large data buffered/cached in the framework, which indicates that it is hard for users to configure the right memory quota to balance the memory usage of the framework and user code. (2) 37% errors are caused by the unexpected large runtime data, such as large data partition, hotspot key, and large key/value record. (3) Most errors (64%) are caused by memory-consuming user code, which carelessly processes unexpected large data or generates large in-memory computing results. Among them, 13% errors are also caused by the unexpected large runtime data. (4) There are three common fix patterns (used in 34% errors), namely changing the memory/dataflow-related configurations, dividing runtime data, and optimizing user code logic. Our findings inspire us to propose potential solutions to avoid the OOM errors: (1) providing dynamic memory management mechanisms to balance the memory usage of the framework and user code at runtime; (2) providing users with memory+disk data structures, since accumulating large computing results in in-memory data structures is a common cause (15% errors).

Most recent distributed file systems have adopted architecture with an independent metadata server cluster. However, potential multiple hotspots and flash crowds access patterns often cause a metadata service that violates performance Service Level Objectives. To maximize the throughput of the metadata service, an adaptive request load balancing framework is critical. We present a distributed cache framework above the distributed metadata management schemes to manage hotspots rather than managing all metadata to achieve request load balancing. This benefits the metadata hierarchical locality and the system scalability. Compared with data, metadata has its own distinct characteristics, such as small size and large quantity. The cost of useless metadata prefetching is much less than data prefetching. In light of this, we devise a time period-based prefetching strategy and a perfecting-based adaptive replacement cache algorithm to improve the performance of the distributed caching layer to adapt constantly changing workloads. Finally, we evaluate our approach with a hadoop distributed file system cluster.

Rule execution is the core step of rule-based semantic web reasoning. However, most existing approaches are centralized, which cannot scale out to reason big semantic web datasets. In this paper, we described a kind of semantic web rule execution mechanism using MapReduce programming model, which not only can handle RDFS and OWL ter Horst semantic rules, but also can be used in SWRL reasoning. Theoretical analysis is present on the scalability of this rule execution mechanism. Result shows that it can scale well as Mapreduce framework.

Map-Reduce is a powerful solution for processing and analyzing large-scale data. Just as Hadoop and Spark are able to deal with terabyte data and even more. Users only need to complete "map" and "reduce" function, the Map-Reduce framework can finish variety jobs. But many machine learning and data mining algorithms cannot leverage the Map-Reduce framework or it would take large efforts to modify the algorithm itself. This issue can be explained by the following ways: 1. Map-Reduce is a batch operation so that most of Map-Reduce frameworks do not built-in to support iteration. 2. Map-Reduce is absolutely parallel, each vertex cannot obtain all records, so none of them could get the global optimal model. In this paper, we proposed a job management tool to enable the Map-Reduce framework to support iteration, called "de-parallel". This make the Map-Reduce framework like Hadoop so that Map-Reduce could run more algorithms and support more various tasks. In addition, our tool does not modify the Map-Reduce framework itself. In face MR-Runner interacts with Map-Reduce framework like a "client", therefore MR-Runner could be deployed in any single PC instead of Map-Reduce cluster. We also abstract the mainly interface related to Map-Reduce frameworks, this makes our tool portable to the representative Map-Reduce frameworks.

Everyone has a characteristic pattern of daily activities. This study applies cluster analysis to identify a computer user's daily behavior patterns based on 1000 China users' 4-weeks software and web usage. Clustering models are built for 4 different behavior definition methods with different time period divisions and feature measurement selections. With these patterns, we build classification models to predict new users' daily behavior pattern with their half day activity logs. For example, if we know one user use computer for entertainment in the morning, we can predict his behavior in the afternoon and evening. The prediction model can be used to recommend suitable items to users according to their current behavior status. Our method can get 92.5% prediction correctness for the best.

Mashup tools allow end users graphically build complex mashups using pipes to connect web data sources into a data flow. Because end users are of poor technical expertise, the designed data flows may be inefficient. This paper targets on enhancing the performance of mashups via automatically refactoring the structure of its data flows. First a set of operational semantics features are selected for annotating the operators in data flows and refactoring rules are defined to generate all candidate semantics equivalent data flows. Then a heuristic algorithm is described for accurately searching the data flow of minimal execution time by constructing a partially ordered set of data flows based on their cost estimation. This approach is applicable to general mashup data flows without knowing complete operational semantics of their operators and the efficiency improvement is demonstrated by experiments.