The increasing penetration of renewable energy generation (REG) introduces significant uncertainty into power grids, posing heightened risks for cascading failures. In this paper, a Markov tree model is proposed to assess the risk of cascading failure in power grid with uncertain REG. The model captures the diverse failure paths caused by REG uncertainty, representing the cascading failure process as a sequence of state transitions with probabilities reflecting the likelihood of state transitions. To identify critical tripping branches during cascading failure propagation, a hybrid probability-interval method is introduced. Probabilistic power flow analysis identifies branches with overload risk, while interval positional relationships rank their severity. To improve the efficiency of risk assessment, a risk-based depth-first search (R-DFS) method is proposed. This method uses estimated risk indices to prioritize high-risk failure paths while pruning low-risk paths, significantly reducing simulation time while maintaining assessment accuracy. Compared with existing models, the proposed model balances simulation efficiency and accuracy, effectively identifying high-risk failure paths under REG uncertainty. Simulation results demonstrate the impact of threshold selection on the retention of high-risk paths and simulation performance, providing insights into managing cascading failure risks in power grid with high REG penetration.

In recent years, there has been increasing interest in using deep learning for binary analysis tasks. Particularly, Transformer-based pre-trained language models have attracted enormous attention and obtained encouraging results. Numerous research attempts modified the Transformer network architecture and designed new pre-training tasks explicitly tailored for individual downstream binary analysis tasks, and positive results were reported. However, it remains unclear whether these architectural changes and their associated pre-training tasks are beneficial to other downstream binary analysis tasks, and whether a vanilla Transformer model can perform equally well via fine-tuning.In order to provide guidance for future explorations in this direction, in this paper, we evaluate four custom Transformer-based models (i.e. jTrans, PalmTree, StateFormer, and Trex) and their pre-training tasks on four downstream applications. According to our evaluation results, we have the following surprising observations: aside from MLM (Masked Language Model), many existing pre-training tasks seem either too noisy or too challenging for the Transformer model to learn effectively; the vanilla BERT model is comparable or superior to these custom Transformers in all the four downstream applications. Moreover, our evaluation suggests that improvements in fine-tuning are generally more beneficial than introducing new pre-training tasks or making architectural modifications. Consequently, we conclude that recent architectural modifications and additional pre-training tasks for Transformer models may offer limited impact that does not sufficiently justify their associated costs.

Mobile device vendors develop their customized Android OS (termed downstream) based on Google Android (termed upstream) to support new features. During daily independent development, the downstream also periodically merges changes of a new release from the upstream into its development branches, keeping in sync with the upstream. Due to a large number of commits to be merged, heavy code conflicts would be reported if auto-merge operations failed. Prior work has studied conflicts in this scenario. However, it is still unclear about the coupling between the downstream and the upstream (We term this coupling as the dependency facade), as well as how merge conflicts are related to this coupling. To address this issue, we first propose the DepFCD to reveal the dependency facade from three aspects, including interface-level dependencies that indicate a clear design boundary, intrusion-level dependencies which blur the boundary, and dependency constraints imposed by the upstream non-SDK restrictions. We then empirically investigate these three aspects (RQ1, RQ2, RQ3) and merge conflicts (RQ4) on the dependency facade. To support the study, we collect four open-source downstream projects and one industrial project, with 15 downstream and 15 corresponding upstream versions. Our study reveals interesting observations and suggests earlier mitigation of merge conflicts through a well-managed dependency facade. Our study will benefit the research about the coupling between upstream and downstream as well as the downstream maintenance practice.

Software vulnerabilities are now reported unprecedentedly due to the recent development of automated vulnerability hunting tools. However, fixing vulnerabilities still mainly depends on programmers’ manual efforts. Developers need to deeply understand the vulnerability and affect the system’s functions as little as possible. In this paper, with the advancement of Neural Machine Translation (NMT) techniques, we provide a novel approach called SeqTrans to exploit historical vulnerability fixes to provide suggestions and automatically fix the source code. To capture the contextual information around the vulnerable code, we propose to leverage data-flow dependencies to construct code sequences and feed them into the state-of-the-art transformer model. The fine-tuning strategy has been introduced to overcome the small sample size problem. We evaluate SeqTrans on a dataset containing 1,282 commits that fix 624 CVEs in 205 Java projects. Results show that the accuracy of SeqTrans outperforms the latest techniques and achieves 23.3% in statement-level fix and 25.3% in CVE-level fix. In the meantime, we look deep inside the result and observe that the NMT model performs very well in certain kinds of vulnerabilities like CWE-287 (Improper Authentication) and CWE-863 (Incorrect Authorization).

This study proposes a new method of cloud detection—the Fusion Network Cloud Detection Method (FNCDM). It can capture fine-grained details of foreground objects and highlight hierarchical features more effectively than U-Net, SegNet, and UNet++. To identify new classes of cloud pixels, the FNCDM method employs the hard voting strategy to reclassify pixels from the prediction results of three different network models. The result shows that the FNCDM is able to identify clouds on different underlying surfaces with a high degree of accuracy, and the overall accuracy can reach 96%. The method has higher recognition accuracy for thin clouds, broken clouds and cloud boundaries, and the false rate and miss rate are below 2.11% and 9.43%, respectively.

Spartina alterniflora is one of the most important invasive alien plants in coastal wetlands in China, and its rapid expansion has caused serious negative ecological impacts. Current researches mainly have focused on the monitoring of S. alterniflora using moderate-resolution satellite images at the regional or landscape scale, while the monitoring capability of early invasion at the patch scale is limited, and it is difficult to obtain satellite images stably due to cloud and tidal interference. In this paper, we propose a semi-automatic monitoring framework for patch-scale monitoring of S. alterniflora based on deep learning techniques with unmanned aerial vehicle (UAV) RGB images. First, the spectral characteristics of S. alterniflora in the visible light band were analyzed, and Visible-band Difference Vegetation Index (VDVI) with the strongest stability for this feature type was selected for fusion. Second, a dataset of S. alterniflora on both sides of the Yellow River estuary was constructed and trained using the improved U-Net++ deep learning network. Finally, prediction and accuracy verification were carried out for some dense distribution areas of S. alterniflora. The experimental results show that the overall accuracy (OA) of the prediction results obtained by this method is 98.1%, and the kappa coefficient is 0.960, which is better than the traditional classification methods, and has certain significance for the patch scale monitoring of S. alterniflora.

Deep learning has demonstrated its strengths in numerous binary analysis tasks, including function boundary detection, binary code search, function prototype inference, value set analysis, etc. When applying deep learning to binary analysis tasks, we need to decide what input should be fed into the neural network model. More specifically, we need to answer how to represent an instruction in a fixed-length vector. The idea of automatically learning instruction representations is intriguing, but the existing schemes fail to capture the unique characteristics of disassembly. These schemes ignore the complex intra-instruction structures and mainly rely on control flow in which the contextual information is noisy and can be influenced by compiler optimizations. In this paper, we propose to pre-train an assembly language model called PalmTree for generating general-purpose instruction embeddings by conducting self-supervised training on large-scale unlabeled binary corpora. PalmTree utilizes three pre-training tasks to capture various characteristics of assembly language. These training tasks overcome the problems in existing schemes, thus can help to generate high-quality representations. We conduct both intrinsic and extrinsic evaluations, and compare PalmTree with other instruction embedding schemes. PalmTree has the best performance for intrinsic metrics, and outperforms the other instruction embedding schemes for all downstream tasks.

In recent years, Complex Network theory and graph algorithms have been proved to be effective in predicting software bugs. On the other hand, as a widely-used algorithm in Complex Network theory, k-core decomposition has been used in software engineering domain to identify key classes. Intuitively, key classes are more likely to be buggy since they participate in more functions or have more interactions and dependencies. However, there is no existing research uses k-core decomposition to analyze software bugs. To fill this gap, we first use k-core decomposition on Class Dependency Networks to analyze software bug distribution from a new perspective. An interesting and widely existed tendency is observed: for classes in k-cores with larger k values, there is a stronger possibility for them to be buggy. Based on this observation, we then propose a simple but effective equation named as top-core which improves the order of classes in the suspicious class list produced by effort-aware bug prediction models. Based on an empirical study on 18 open-source Java systems, we show that the bug prediction models' performances are significantly improved in 85.2 percent experiments in the cross-validation scenario and in 80.95 percent experiments in the forward-release scenario, after using top-core. The models' average performances are improved by 11.5 and 12.6 percent, respectively. It is concluded that the proposed top-core equation can help the testers or code reviewers locate the real bugs more quickly and easily in software bug prediction practices.

Over the past decades, numerous approaches were proposed to help practitioner to predict or locate defective files. These techniques often use syntactic dependency, history co-change relation, or semantic similarity. The problem is that, it remains unclear whether these different dependency relations will present similar accuracy in terms of defect prediction and localization. In this paper, we present our systematic investigation of this question from the perspective of software architecture. Considering files involved in each dependency type as an individual design space, we model such a design space using one DRSpace. We derived 3 DRSpaces for each of the 117 Apache open source projects, with 643,079 revision commits and 101,364 bug reports in total, and calculated their interactions with defective files. The experiment results are surprising: the three dependency types present significantly different architectural views, and their interactions with defective files are also drastically different. Intuitively, they play completely different roles when used for defect prediction/localization. The good news is that the combination of these structures has the potential to improve the accuracy of defect prediction/localization. In summary, our work provides a new perspective regarding to which type(s) of relations should be used for the task of defect prediction/localization. These quantitative and qualitative results also advance our knowledge of the relationship between software quality and architectural views formed using different dependency types.

Test case prioritization is widely used in testing with the purpose of detecting faults as early as possible. Most existing techniques exploit coverage to prioritize test cases based on the hypothesis that a test case with higher coverage is more likely to catch bugs. Statement coverage and function coverage are the two widely used coverage granularity. The former typically achieves better test case prioritization in terms of fault detection capability, while the latter is more efficient because it incurs less overhead. In this paper we argue that static information such as statement and function coverage may not be the best criteria for guiding dynamic executions. Executions that cover the same set of statements /functions can may exhibit very different behavior. Therefore, the abstraction that reduces program behavior to statement/function coverage can be too simplistic to predicate fault detection capability. We propose a new approach that exploits function call sequences to prioritize test cases. This is based on the observation that the function call sequences rather than the set of executed functions is a better indicator of program behavior. Test cases that reveal unique function call sequences may have better chance to encounter faults. We choose function instead of statement sequences due to the consideration of efficiency. We have developed and implemented a new prioritization strategy AGC (Additional Greedy method Call sequence), that exploit function call sequences. We compare AGC against existing test case prioritization techniques on eight real-world open source Java projects. Our experiments show that our approach outperforms existing techniques on large programs (but not on small programs) in terms of bug detection capability. The performance shows a growth trend when the size of program increases.

Convolutional Neural Network (CNN) has achieved success in Android malware detection and many other fields. However, the empirical evaluation of previous studies have shown that no single machine learning classifier is capable to provide the best accuracy in any context. In this paper, a new method for Android malware detection is proposed, we replace the single machine learning classifier in CNN with Adaptive Selection of Classifiers (ASC) to improve the performance of malware classification. We test our method on 1746 apk samples with 1000 malware, the result shows the accuracy of our approach performs 4.27% better than the state-of-art CNN model used in the current research.

Crowd intelligence opens up new ways for decision making in open environments, traditional decision making is unable to effectively make correct decisions in open environments. In this paper, positive and negative comparing method using linguistic scale is proposed to make decisions in the open environments with crowd intelligence. Firstly, the crowd participants compare the alternative with the corresponding positive and negative assessment points, and give their evaluations using linguistic scales form positive and negative views. The crowd participants' evaluations can be translated into Intuitionistic Fuzzy Numbers (IFNs). In the proposed methods, the evaluations given by the crowd participants do not depend on the pairwise comparisons of the alternatives, the consistent problem can be avoided. Secondly, the consensus measures between aggregating results and IFNs are proposed. Based on these concepts, the aggregating methods that without discarding any IFNs are proposed and studied. The studying results show that the proposed methods can improve the consensus measures between the aggregating result and evaluations given by crowd participants.

Network measures have been proved to be useful in predicting software defects. Leveraging the dependency relationships between software modules, network measures can capture various structural features of software systems. However, existing studies have relied on user-defined network measures (e.g., degree statistics or centrality metrics), which are inflexible and require high computation cost, to describe the structural features. In this paper, we propose a new method called node2defect which uses a newly proposed network embedding technique, node2vec, to automatically learn to encode dependency network structure into low-dimensional vector spaces to improve software defect prediction. Specifically, we firstly construct a program's Class Dependency Network. Then node2vec is used to automatically learn structural features of the network. After that, we combine the learned features with traditional software engineering features, for accurate defect prediction. We evaluate our method on 15 open source programs. The experimental results show that in average, node2defect improves the state-of-the-art approach by 9.15% in terms of F-measure.

Open educational resources (OERs) are essentially resources made freely available to the public for the purpose of enabling education. This paper examines the experiences of a collaborative team of interdisciplinary teachers and computer science students in their research and development of an OER designed to facilitate student note-making, and research. As expected, the team encountered, and overcame, a number of challenges, with both teachers and students learning from the experiences. The OER has been completed, and has been deployed in a full research study. The reflections from the team may help guide similar projects and other forms of student-centeredcollaboration and education.

Aggregation operators for intuitionistic fuzzy information, the popular methods in group decision, face the challenge in this area - counter-intuitive result (the decision result is conflict with people's intuition under specific inputs). In this paper, the Mixed Intuitionistic Fuzzy Aggregation Operators (MIFAOs) are proposed to relieve this problem. Firstly, the Bivariate Mixed Intuitionistic Fuzzy Aggregation Operators (BMIFAOs) are introduced based on the proposed extensions of t-conorms and t-norms. Some basic operational laws are proposed for Archimedean t-conorms and t-norms. Thus, the effects of unusual Intuitionistic Fuzzy Numbers (IFNs) would be decreased on final aggregating results. Secondly, the Multivariate Mixed Intuitionistic Fuzzy Aggregation Operators (MMIFAOs) are proposed, by extending BMIFAOs to higher dimensions. The basic operators are deduced for the Algebraic t-conorms and t-norms. Finally, an illustrative example is utilized to demonstrate the process of aggregating the IFNs by utilizing the proposed MMIFAOs in this paper. The results show that the proposed MIFAOs can relieve the counter-intuitive results for aggregation operators on unusual IFNs.

Linguistic pairwise comparison matrices are widely used in decision-making procedures. However, the matrices often give conflicting results when there are multiple criteria under consideration. Despite intensive research, achieving consistency of such matrices remains a daunting task. In this paper, a novel approach based on linguistic discrete region is proposed to address the challenge. Unlike existing methods that require a single value for each comparison, our approach allows a comparison to be expressed by a discrete region with multiple linguistic terms. Such front-end gives users more freedom to express their opinions. In the back-end, we propose an iterative searching algorithm that is able to achieve approximate optimal consistency for the comparison matrices with discrete region values. The final results are single-value matrices that not only guarantee approximate optimal consistency but comply with evaluators' intentions a well, as our approach does not modify any linguistic values like many existing methods. We have conducted extensive evaluations, and our empirical study confirms that the linguistic discrete region-based approach significantly improves the consistency of linguistic pairwise comparison matrices.

In the identity authentication, many advanced encryption techniques are applied to confirm and protect the user identity. Although the identity information is transmitted as cipher text in the Internet, the attackers can theft and fraud the identity by eavesdropping, cryptanalysis and forging. In this paper, a new identity authentication mechanism is proposed, which exploits the Timing Covert Channel (TCC) to transmit the identity information. TCC was originally a hacker technique to leak information under supervising, which uses the sending time of packets to indicate the information. In our method, the intervals between packets are applied to indicate the authentication tags. It is difficult for the attackers to eavesdrop, crack and forge the TCC identity, since the packets are too huge to analyze and the noise is different between the users and the attackers. A platform is designed to verify our proposed method. The experiment shows that the intervals and the thresholds are the key factors on the accuracy and efficiency. And it also proves our method is a secure way for identity information, which could be implanted on various network applications.

SUMMARY Millions of new malicious programs are produced by the mature industry of malware production. These programs have tremendous challenges on the signature‐based antivirus products. Machine learning techniques are applicable for detecting unknown malicious programs without knowing their signatures. In this paper, a layered classification method is developed to detect malwares with a two‐layer framework. The low‐level‐classifier is employed to identify whether the programs perform any malicious functions according to the API‐calls of the programs; the up‐level‐classifier is applied to detect malwares according to the function identification. A hybrid structure called Type‐Function, constituting of the classification results of low‐level‐classifier and up‐level‐classifier, is proposed to describe the malware. This method is compared with Naive Bayes, decision tree, and boosting using a comprehensive test dataset containing 16,135 malwares and 1800 benign programs. The experiments demonstrate that our method outperforms other algorithms in terms of detection accuracy. Moreover, the Type‐Function structure is proved as an unprejudiced and effective method for malware description. Copyright © 2011 John Wiley & Sons, Ltd.

Traditional classification algorithms can be limited in their performance on highly imbalanced and overlapping data sets, In this paper, we focus on modifying support vector machines (SVMs) to make it suitable for highly imbalanced and overlapping (HIO) classification. Based on the analysis of most SVM learning algorithms for imbalanced classification, we argue that in SVM-based algorithms, due to the linearity property of SVM, the key problem is that the increase of the number of correctly predicted minority samples will lead to even more majority samples be misclassified. Then a novel algorithm HIO-SVM is developed, it can recognize all minority samples while minimizing the error rate of majority ones. The proposed approach can identify the non-overlapping samples in one feature space, furthermore, by iteratively shifting kernel spaces, all non-overlapping samples in different kernel spaces are recognized. Because of the highly imbalanced distribution, the remaining overlapping samples can be regarded as minority. Then all minority samples can be predicted correctly and the error rate of majority samples can be guaranteed minimized simultaneously. Finally, numerous case studies show the properties and effectiveness of the proposed HIO-SVM algorithm.