Features Of Code Research Articles

Optimising source code vulnerability detection using deep learning and deep graph network

To enhance the effectiveness of vulnerability detection in software developed using C and C++ programming languages, our study introduces a novel correlation calculation method for analyzing and evaluating Code Property Graphs (CPG). The intelligent computation method proposed in this study comprises three key stages. In the first stage, we present a method for extracting features from the CPG source code. To accomplish this, we integrate three distinct data exploration methods: employing Graph Convolutional Neural (GCN) to extract node features from CPG, utilizing Convolutional Neural Network (CNN) to extract edge features from CPG, and finally employing the Doc2vec natural language processing algorithm to extract source code from CPG nodes. The second stage involves proposing a method for synthesizing CPG source code features. Building on the features acquired in the first stage, our paper introduces a synthesis and construction method to generate feature vectors for the source code. The final stage, stage three, executes the detection of source code vulnerabilities. The experimental results demonstrate that our proposed model in this study achieves higher efficiency compared to other studies, with an improvement ranging from 3% to 4%.

Dissecting Code Features: An Evolutionary Analysis of Kernel Versus Nonkernel Code in Operating Systems

ABSTRACTUnderstanding the evolution of software systems is crucial for advancing software engineering practices. Many studies have been devoted to exploring software evolution. However, they primarily treat software as an entire entity and overlook the inherent differences between subsystems, which may lead to biased conclusions. In this study, we attempt to explore variations between subsystems by investigating the code feature differences between kernel and nonkernel components from an evolutionary perspective. Based on three operating systems as case studies, we examine multiple dimensions, including the code churn characteristics and code inherent characteristics. The main findings are as follows: (1) The proportion of kernel code remains relatively small, and exhibits consistent stability across the majority of versions as systems evolve. (2) Kernel code exhibits higher stability in contrast to nonkernel code, characterized by a lower modification rate and finer modification granularity. The patterns of modification activities are similar in both kernel and nonkernel code, with a preference of changing code and a tendency to avoid the combination of adding and deleting code. (3) The cumulative code size and complexity of kernel files show an upward trajectory as the system evolves. (4) Kernel files exhibit a significantly higher code density and complexity than nonkernel files, featuring a greater number of code line, comments, and statements, along with a larger program length, vocabulary, and volume. Conversely, kernel functions prioritize modularity and maintainability, with a significantly smaller size and lower complexity than nonkernel functions. These insights contribute to a deeper understanding of the dynamics within operating system codebases and highlight the necessity of targeted maintenance strategies for different subsystems.

Stimulus-invariant aspects of the retinal code drive discriminability of natural scenes

Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells (RGCs), less is known about how populations form both flexible and reliable encoding in natural moving scenes. We record from the larval salamander retina responding to five different natural movies, over many repeats, and use these data to characterize the population code in terms of single-cell fluctuations in rate and pairwise couplings between cells. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the retinal output. while the single-cell activity adapts to different stimuli, the population structure captured in the sparse, strong couplings is consistent across natural movies as well as synthetic stimuli. We show that these interactions contribute to encoding scene identity. We also demonstrate that this structure likely arises in part from shared bipolar cell input as well as from gap junctions between RGCs and amacrine cells.

Кодекс профессиональной этики в контексте цифровизации

The transfer of professional activities to the digital space poses new ethical challenges for professional communities, which implies a rethinking of approaches to the formation of codes of professional ethics. The article reveals that professional codes are moving into the field of digital ethics, and this forces us to reconsider the existing experience of ethical and regulatory regulation. The distinctive features of codes of professional ethics and the current structure of the professional code in the aspect of digitalization problems are identified. The aim of the article is to identify the features of the content of codes of professional ethics in the context of digitalization. The scientific novelty of the article lies in the ethical analysis of the features of professional codes in the digital space. As a result, it is concluded that under the influence of digitalization, the functional significance of codes, their ethical content and moral meaning are changing; and prospects for ethical and regulatory regulation of professional activity in the context of digitalization are determined.

My turn or yours? Me-you-distinction in feature-based action planning.

Binding accounts propose that action planning involves temporarily binding codes of the action's unique features, such as its location and duration. Such binding becomes evident when another action (B) is initiated while maintaining the Action Plan A. Action B is usually impaired if it partially overlaps with the planned Action A (as opposed to full or no feature overlap). In Experiment 1, in which participants bimanually operated two keys, we replicated these partial overlap costs. In Experiment 2, two participants sat side by side, each handling one key. We tested whether Action B would be affected by duration overlap with the planned Action A of another person similarly as by duration overlap with a planned Action A of the participant's other hand. Here, we found no partial overlap costs. However, in Experiment 3, proposing a common reward yielded partial overlap costs. This suggests that in joint action planning, another person's action plan can impact own actions through feature binding, but only with sufficient incentives to corepresent the other's actions (i.e., when goal achievement depends on both participants' performance). This furthers the understanding of how we represent other people's yet-to-be-executed action plans alongside our own. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deep Semantics-Enhanced Neural Code Search

Code search uses natural language queries to retrieve code snippets from a vast database, identifying those that are semantically similar to the query. This enables developers to reuse code and enhance software development efficiency. Most existing code search algorithms focus on capturing semantic and structural features by learning from both text and code graph structures. However, these algorithms often struggle to capture deeper semantic and structural features within these sources, leading to lower accuracy in code search results. To address this issue, this paper proposes a novel semantics-enhanced neural code search algorithm called SENCS, which employs graph serialization and a two-stage attention mechanism. First, the code program dependency graph is transformed into a unique serialized encoding, and a bidirectional long short-term memory (LSTM) model is used to learn the structural information of the code in the graph sequence to generate code vectors rich in structural features. Second, a two-stage attention mechanism enhances the embedded vectors by assigning different weight information to various code features during the code feature fusion phase, capturing significant feature information from different code feature sequences, resulting in code vectors rich in semantic and structural information. To validate the performance of the proposed code search algorithm, extensive experiments were conducted on two widely used code search datasets, CodeSearchNet and JavaNet. The experimental results show that the proposed SENCS algorithm improves the average code search accuracy metrics by 8.30 % (MRR) and 17.85% (DCG) and compared to the best baseline code search model in the literature, with an average improvement of 14.86% in the SR@1 metric. Experiments with two open-source datasets demonstrate SENCS achieves a better search effect than state of-the-art models.

Feature Coding and Graph via Transformer: Different Granularities Classification for Aircraft

Against the background of the sky, imaging and perception of aircraft are crucial for various vision applications. Thanks to the ever-evolving nature of the convolutional neural network (CNN), it has become easier to distinguish and recognize different types of aircraft. Nevertheless, accurate classification for sub-categories of aircraft still poses great challenges. On one hand, fine-grained recognition focuses on exploring and studying such problems. On the other hand, aircraft under different sub-categories and granularities put forward higher requirements for feature representation to classify, which led us to rethink the in-depth application of features. We noticed that information in the swin-transformer effectively represents the features in neural network layers, fully showcasing encoding and indexing for information. Through further research based on this, we proposed a better understanding of encoding and reuse for features, and innovatively performed feature coding graphically for classification. In this paper, our approach shows the effects on aircraft feature representation and classification, manifested from the flexible recognition effect at different aircraft category granularities, and outperforms other famous fine-grained classification models on this vision task. Not only did the approach we proposed demonstrate adaptability to aircraft at different classification granularities, but it also revealed the mechanisms and characteristics of feature encoding under different sample space partitions for classification. The relationship between the oriented representation of aircraft features and various classification granularities, which is manifested through different classification criteria, shows that feature coding and graph construction via the transformer opens a new door for specific defined classification tasks where objects are divided under various partition criteria, and provides another perspective on calculation and feature extraction in fine-grained classification.

GMN+: A Binary Homologous Vulnerability Detection Method Based on Graph Matching Neural Network with Enhanced Attention

The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the same weaknesses across multiple platforms. Deep learning has emerged as a promising approach for detecting homologous vulnerabilities in binary code due to their automated feature extraction and high efficiency. However, existing deep learning methods often struggle to capture deep semantic features in binary code, limiting their effectiveness. To address this limitation, this paper presents GMN+, which is a novel graph matching neural network with enhanced attention for detecting homologous vulnerabilities. This method comprehensively considers the information contained in instructions and incorporates types of input instruction. Masked Language Modeling and Instruction Type Prediction are developed as pre-training tasks to enhance the ability of GMN+ in extracting semantic information from basic blocks. GMN+ utilizes an attention mechanism to focus concurrently on the critical semantic information within functions and differences between them, generating robust function embeddings. Experimental results indicate that GMN+ outperforms state-of-the-art methods in various tasks and achieves notable performance in real-world vulnerability detection scenarios.

Enhancing Vibration Detection in ϕ-OTDR Through Image Coding and Deep Learning-Driven Feature Recognition

Enhancing Vibration Detection in ϕ-OTDR Through Image Coding and Deep Learning-Driven Feature Recognition

Figurative Expressions in Undang-Undang Melaka and Undang-Undang Laut: A Reflection of Malay Intellectual Tradition

The exploration of figurative expressions aims to identify the conceptual frameworks in traditional Malay texts, specifically focusing on the Undang-Undang Melaka (Laws of Melaka) and the Undang-Undang Laut (Maritime Laws). These legal codes feature a variety of figurative expressions, such as idioms, similes, and metaphors, which serve to clarify legal principles, societal standards, and cultural values of the Malay people at that time. This study aims to examine how the use of figurative expressions contributes to understanding the development of intellectual frameworks of the Malay community. Interdisciplinary methodologies, including historical research and literary analysis are employed to provide a comprehensive view of how figurative expressions contribute to the exploration of intellectual frameworks in traditional Malay manuscripts. The use of figurative language to convey legal and societal norms reflects the intricate thought processes of the Malay community. These linguistic features reveal the Malay intellectual tradition, which intertwines legal, ethical, and cultural elements into a cohesive narrative, highlighting the importance of language in shaping and preserving cultural identity.

Auditory Competition and Stimulus Selection across Spatial Locations from Midbrain to Forebrain in Barn Owls.

Barn owls enable investigation of neural mechanisms underlying stimulus selection of concurrent stimuli. The audiovisual space map in the optic tectum (OT), avian homolog of the superior colliculus, encodes relative strength of concurrent auditory stimuli through spike response rate and interneuronal spike train synchrony (STS). Open questions remain regarding stimulus selection in downstream forebrain regions lacking topographic coding of auditory space, including the functional consequences of interneuronal STS on interregional signaling. To this end, we presented concurrent stimuli at different locations and manipulated relative strength while simultaneously recording neural responses from OT and its downstream thalamic target, nucleus rotundus (nRt), in awake barn owls of both sexes. Results demonstrated that broadly spatially tuned nRt units exhibit different spike response patterns to competition depending on spatial tuning preferences. Modeling suggests nRt units integrate convergent inputs from distant locations across midbrain map regions. Additionally, STS within nRt reflects the temporal properties of the strongest stimulus. Furthermore, interregional STS between OT and nRt was strongest when spatial tuning overlap between units across regions was large and when the strongest stimulus location during competition was favorable for units in both regions. Additionally, though gamma oscillations synthesized within OT are weakly propagated within nRt, average gamma power across regions correlates with strength of interregional STS. Overall, we demonstrate that nRt integrates inputs across distant areas of OT, retains spatial information through differences in strength of inputs from various locations of the midbrain map across neurons, and prioritizes coding of identity features to the strongest sound.

OpenSBLI v3.0: High-fidelity multi-block transonic aerofoil CFD simulations using domain specific languages on GPUs

OpenSBLI is an automatic code-generation framework for compressible Computational Fluid Dynamics (CFD) simulations on heterogeneous computing architectures (previous release: Lusher et al. (2021) [4]). OpenSBLI is coupled to the Oxford Parallel Structured (OPS) Domain Specific Language (DSL), which uses source-to-source translation to enable parallel execution of the code on large-scale supercomputers, including multi-GPU clusters. To date, OpenSBLI has largely been applied to compressible turbulence and shock-wave/boundary-layer interactions on very simple geometries comprised of single mesh blocks with essentially orthogonal grid lines. OpenSBLI has been extended in this new release to target strongly curvilinear cases, including transonic aerofoils using multi-block grids. In addition to multi-block mesh support, more efficient numerical shock-capturing methods and filters have been added to the codebase. Improvements to post-processing, reduced-dimension data output, and coupling to a modal decomposition library are also included. A set of validation cases are presented to showcase the new code features. Furthermore, state-of-the-art wide-span transonic aerofoil simulations on up to N=2.5×109 grid points demonstrate that wider aspect ratios can alter buffet predictions and increase the regularity of the low-frequency shock oscillations by accommodating fully-developed trailing edge flow separation. Spectral Proper Orthogonal Decomposition (SPOD) analysis showed that overly-narrow aerofoil simulations contain additional domain-dependent energy content at a Strouhal number of St≈3 associated with wake modes.

Prioritising primary care patients with unexpected weight loss for cancer investigation: diagnostic accuracy study (update)

ObjectiveTo quantify the predictive value of unexpected weight loss for cancer according to patient’s age, sex, smoking status, and concurrent clinical features (symptoms, signs, and abnormal blood test results).DesignDiagnostic accuracy...

Robustness Analysis of QR – Code Based and Geolocation Based Attendance System

The use of QR-code and GPS technology in the attendance system also has several advantages, such as making the attendance process easier for students, monitoring employee attendance accurately and efficiently, and improving the quality of student work. Therefore, it is important to compare the efficiency of the QR-code attendance method with Geolocation to find out which technology is more effective and efficient in improving the attendance system. In this research the author used the K-means Clustering method. The K-means method is a non-hierarchical data grouping method that attempts to partition existing data into two or more 13 groups. This method partitions data into groups so that data with the same characteristics is included in other groups. The research results show that Cluster 1 has a centeroid that is close to the QR Code features (3,4,4), namely moderate efficiency in application implementation, relatively high ease of use, and system robustness with high data accuracy. Cluster 2 has a centeroid with Geolocation features (3,3,3), namely moderate efficiency and flexibility, moderate ease of use, and system robustness with moderate data accuracy. Thus, after obtaining comparison results between QR code and Geolocation in the lecture attendance process, researchers can recommend the best system to use in terms of user aspects and needs. If the user needs a presence system that prioritizes ease of use and robustness of the system, the user is suited to using the QR-Code system because the usability and durability aspects are relatively high. Meanwhile, if the user prioritizes efficiency and flexibility in the process, it is best to use a presence system in the form of Geolocation, because the results of this research show that the efficient aspect of Geolocation is higher

Implementation of Software for Learning Message Transmission Using Morse Code

The article presents the implementation of software for training message transmission using Morse code. This is a method of sign coding when letters of the alphabet, numbers, punctuation marks and other symbols are represented as sequences of short and long signals called dots and dashes. It is intended for transmission over serial communication channels. A unique feature of Morse code is the ability to be encoded and decoded by humans without the use of special terminal devices. The paper shows a software implementation of processing input characters using a telegraphic key, it includes: various mathematical requirements and conditions that allow you adjusting to each user, provided that his input speed will either fall or remain unchanged. However, it is possible to adjust the minimum duration threshold in such a way that the system will slowly adapt to a slow increase in input speed. At the same time, the higher the value of the minimum threshold, the faster the adaptation is; however, it will fall within the limit of the maximum input speed that the device can recognize. During the implementation of the program, a flow chart of message transliteration, signal reception and transmission was developed. The whole program implements the following functions: a simple and intuitive translator, with audio output, a simulator for consolidating the skill of entering Morse code using a key; consolidating the skills of recognition and decoding by ear. To create the final application for training, the Processing toolkit was chosen, since its key parameter when choosing a development tool has a simple writing syntax to increase the speed of writing code, as well as the ability of the environment to work with the communication channel of the microcontroller.

Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals

Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features of a patient status at a single time point. We developed a self-attention-based model to predict MACE within 3 years from time series data utilizing numerous features in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient data through code mapping and feature selection by the calculated importance using Xgboost. We established operational definitions and categories for diagnoses, medications, and laboratory tests to streamline scattered codes, enhancing clinical interpretability across hospitals. This resulted in reduced feature size and improved data quality for transfer learning. The pre-trained model demonstrated an increase in AUROC after transfer learning, from 0.564 to 0.821. Furthermore, to validate the effectiveness of the predicted scores, we analyzed the data using traditional survival analysis, which confirmed an elevated hazard ratio for a group with high scores.

Toward On-Demand Transmission: Joint Feature and Image Coding With Reversible Neural Networks

Toward On-Demand Transmission: Joint Feature and Image Coding With Reversible Neural Networks

FMCF: A fusing multiple code features approach based on Transformer for Solidity smart contracts source code summarization

A smart contract is a software program executed on a blockchain, designed to facilitate functionalities such as contract execution, asset administration, and identity validation within a secure and decentralized ecosystem. Summarizing the code of Solidity smart contracts aids developers in promptly grasping essential functionalities, thereby enhancing the security posture of Ethereum-based projects. Existing smart contract code summarization works mainly use traditional information retrieval and single code features, resulting in suboptimal performance. In this study, we propose a fusing multiple code features (FMCF) approach based on Transformer for Solidity summarization. First, FMCF created contract integrity modeling and state immutability modeling in the data preprocessing stage to process and filter data that meets security conditions. At the same time, FMCF retains the self-attention mechanism to construct the Graph Attention Network (GAT) encoder and CodeBERT encoder, which respectively extract multiple feature vectors of the code to ensure the integrity of the source code information. Furthermore, the FMCF uses a weighted summation method to input these two types of feature vectors into the feature fusion module for fusion and inputs the fused feature vectors into the Transformer decoder to obtain the final smart contract code summarization. The experimental results show that FMCF outperforms the standard baseline methods by 12.45% in the BLEU score and maximally preserves the semantic information and syntax structures of the source code. The results demonstrate that the FMCF can provide a good direction for future research on smart contract code summarization, thereby helping developers enhance the security of development projects.

Enhancing chip design verification through AI-powered bug detection in RTL code

This paper presents a novel AI-driven approach for enhancing chip design verification through automated bug detection in Register Transfer Level (RTL) code. The proposed method integrates advanced machine learning techniques with domain-specific knowledge of chip design to address the challenges of increasing complexity and time-to-market pressures in modern integrated circuit development. Our system employs a comprehensive data preprocessing pipeline that effectively captures syntactic and semantic features of RTL code, feeding into an innovative attention-based neural network model. The model demonstrates superior bug detection accuracy across diverse design categories and bug types compared to traditional methods and existing AI-assisted approaches. Extensive experimental evaluation on a large-scale dataset of RTL designs, including both open-source projects and industry collaborations, validates the effectiveness of our method. The proposed approach achieves a 95% accuracy in bug detection, with a 28-35% reduction in verification time when applied to real-world chip design projects. The paper addresses the interpretability of AI decisions in the context of chip design, presenting novel visualization techniques that enhance trust and facilitate adoption among RTL designers. While acknowledging current limitations, we discuss future research directions, including integration with formal verification methods and extension to system-level verification scenarios. This work contributes significantly to AI-assisted chip design, paving the way for more efficient and reliable development of complex integrated circuits.

AugPersist: Automatically augmenting the persistence of coverage-based greybox fuzzing for persistent software

Fuzzing is one of the most successful approaches for verifying software functionalities and discovering security vulnerabilities. However, the software with persistent runtime characteristics (e.g., web service programs) cannot be effectively tested by current coverage-based greybox (CG) fuzzers, which strictly rely on the termination state of the target software to feed test cases synchronously and obtain code coverage. The present approach requires delicate analysis and modification of the target to eliminate its persistence, but leads to excessive non-essential restarts during testing, resulting in low throughput.To improve the convenience and efficiency of CG fuzzing for persistent software, we propose augmenting persistence (AugPersist) as a complementary method. AugPersist introduces the concept of persistent basic block (PBB) to leverage the inherent code features of persistent software. PBB can be found automatically and quickly before fuzzing based on the execution flow graph (EFG). On this basis, we develop a low- delay synchronous communication so that after regular test cases are fed into the target, the fuzzer can derive code coverage without rebooting the target, thus significantly minimizing extraneous restarts. Additionally, by utilizing the self-adaptive forkserver, we can dynamically adjust the re-execution point of the target to the PBB position, which further minimizes losses when test cases trigger exceptions and cause necessary restarts.To show the potential of augmenting persistence, we create two implementations, AFL-AugPersist and AFLNet-AugPersist, using AFL and AFLNet as baselines. We evaluate both with their respective baselines on different benchmarks. AFL-AugPersist makes stateless persistent software easier to be fuzzed than AFL and provides 4.9 × to 71.1 × throughput improvement compared to AFL. The throughput of AFLNet-AugPersist improves by a maximum of 210.0 × and a minimum of 3.3 × compared to AFLNet. These results show that AugPersist significantly contributes to the convenience and efficiency of CG fuzzing on persistent software.