Security vulnerabilities in smart contracts can have severe economic consequences. Existing smart contract vulnerability detection methods rely primarily on rigid rules defined by experts and have difficulty in detecting unknown vulnerabilities. This article proposes a new Anomalous Smart Contract Detector, named ASCD, to effectively detect known and unknown vulnerabilities in smart contracts. This is achieved by interpreting unknown vulnerabilities as code anomalies and detecting them with an anomaly detection technique named DeepSVDD. This is also attributed to a new design of feature extraction, in which we compile smart contract source codes into opcodes, extract semantic features from opcode sequences, and control flow features from control flow graphs. By joining LSTM and GIN, the semantic and control flow features are fused to offer a comprehensive representation of smart contracts suitable for anomaly detection. Extensive experiments were conducted to verify the ASCD model, and more than 30,000 smart contracts were tested. The new model offers a significantly better F1-score than existing methods in detecting known vulnerabilities and achieves a high accuracy of 77% in detecting unknown vulnerabilities.

Blockchains increasingly enable decentralized and trustworthy service execution. Smart contracts, i.e., self-executing programs on the blockchain, are essential for automating services. Yet, their inherent vulnerabilities can severely compromise the integrity and reliability of services. Existing detection methods are largely based on expert-defined rules, limiting their effectiveness to known vulnerability types and struggling with anomalous contracts that exhibit variant behaviors. Data imbalance further hinders the performance of deep learning-based approaches. This article presents TLSCG, a transfer learning-based framework that enhances smart contract vulnerability detection, particularly for unknown vulnerabilities, by leveraging a LSTM-based Variational Autoencoder (VAE) to enrich the training set with diverse vulnerable samples. TLSCG enhances the realism of generated contracts by incorporating a Bigram loss to preserve local semantic coherence and a Generative Adversarial Network (GAN)-based discriminator to ensure global structural consistency. Using transfer learning, the generative model can adapt to new vulnerability types, enriching training data and improving detector generalization. We further present OpTrans, a semantic- and structure-aware Transformer optimized for opcode sequence modeling. By integrating type-guided embeddings with a sparse and structure-aware attention mechanism, OpTrans effectively captures instruction semantics and execution structure. Experiments on real-world datasets show that TLSCG achieves 84% Macro-F1 in detecting unknown vulnerabilities, a 19% improvement over the state-of-the-art method Escort. Evaluations of generated samples in realism, anomalousness, and discrepancy confirm their value in improving detection.

This study aims to guide pre-service teachers in effectively utilizing Generative Artificial Intelligence (GAI) tools to enhance their Artificial Intelligence (AI) literacy. By ana-lyzing the impact of GAI on the AI literacy of pre-service teachers, this study proposes a “teacher-student-machine” triadic interactive teaching model based on self-organized learning and deep empowerment through GAI. Using an experimental class in a software engineering course as a case study, we construct an end-to-end experimental environment for the project-driven teaching lifecycle, covering requirement analysis, system design, development and implementation, as well as software testing and maintenance phases. GAI is used to support the completion of experimental tasks while fostering pre-service teachers' AI literacy in five core competencies: data literacy, digital communication and collaboration, critical thinking, computational thinking, and ethical literacy. The study demonstrates that this model effectively improves the AI literacy of pre-service teachers, provides a reference for innovating software engineering practice teaching, and offers a useful framework for the application of GAI in education.

SUMMARY: Single-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow, requiring significant expertise and time. To navigate this complexity, we have developed SCassist, an R package that leverages the power of the large language models (LLM's) to guide and enhance scRNA-seq analysis. SCassist integrates LLM's into key workflow steps, to analyze user data and provide relevant recommendations for filtering, normalization and clustering parameters. It also provides LLM guided insightful interpretations of variable features and principal components, along with cell type annotations and enrichment analysis. SCassist provides intelligent assistance using popular LLM's like Google's Gemini, OpenAI's GPT and Meta's Llama3, making scRNA-seq analysis accessible to researchers at all levels. AVAILABILITY AND IMPLEMENTATION: The SCassist package, along with the detailed tutorials, is available at GitHub. https://github.com/NIH-NEI/SCassist.

The integration of vehicle edge computing (VEC) and microservice architectures improves real-time data processing and computational optimization in the Internet of Vehicles. Specifically, in high-traffic areas, the dynamic deployment and request routing of microservices with complex data dependencies within vehicle clusters can effectively reduce the computational load on edge devices. However, existing research has primarily focused on efficiently utilizing vehicular resources, overlooking the dynamic nature of vehicular cluster networks and the additional communication costs arising from data dependencies between microservices. Therefore, we propose a joint service deployment and request routing problem for vehicle collaboration. We first design a vehicle-road collaborative service framework assisted by temporary vehicle workers, expanding available resources and coverage by deploying microservice instances on selected temporary vehicle nodes. Second, recognizing the dependency between service deployment and request routing, we propose a dual-timescale service-deployment and request-routing policy. On a long timescale, a microservice-aware deployment method optimizes request selection and response time. On a short timescale, we propose a decentralized, swarm intelligence-based collaborative request routing method that constructs a response threshold model through agent interaction, thereby enhancing the collaborative optimization capability of the system. Finally, experimental results using real datasets show that our method outperforms other approaches in reducing request response time when communication costs are taken into account.

Security vulnerabilities in smart contracts can have serious economic consequences. Existing smart contract vulnerability detection methods rely primarily on strict rules defined by experts, making current research limited to detecting specific known vulnerabilities and difficult to deal with other types of anomalous contracts (i.e., the variants of contracts with potentially known vulnerabilities). The imbalance of a smart contract dataset also affects the effectiveness of deep learning-based methods. This paper proposes a new transfer learning-based, anomalous smart contract generation (TLSCG) method for abnormal contract detection, aimed at effectively detecting known vulnerabilities and other anomalous contracts. This method trains a smart contract operation code sequence generation model and improves the authenticity of generating smart contracts by adding semantic regularization terms to the loss function. Through transfer learning, the generative model can be readily extended to new types of vulnerabilities, obtaining known vulnerabilities and anomalous contract generation models, expanding training data, improving the generalization of detection models, and enabling detection models to detect anomalous contracts. By using a real smart contract dataset for validation, experiments show that the proposed method can effectively improve the generalization of the model in detecting known vulnerabilities. Compared to the latest rule-based vulnerability detection tools, the accuracy of anomalous contract detection is improved by 40% and the F1 score is improved by 24%.

Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

Digital assets involved in smart contracts are on the rise. Security vulnerabilities in smart contracts have resulted in significant losses for the blockchain community. Existing smart contract vulnerability detection techniques have been typically single-purposed and focused only on the source code or opcode of contracts. This paper presents a new smart contract vulnerability detection method, which extracts features from different levels of smart contracts to train machine learning models for effective detection of vulnerabilities. Specifically, we propose to extract 2-gram features from the opcodes of smart contracts and token features from the source code using a pre-trained CodeBERT model, thereby capturing the semantic information of smart contracts at different levels. The 2-gram and token features are separately aggregated and then fused and input into machine-learning models to mine the vulnerability features of contracts. Over 10,266 smart contracts are used to verify the proposed method. Widespread reentrancy, timestamp dependence, and transaction-ordering dependence vulnerabilities are considered. Experiments show the fused features can help significantly improve smart contract vulnerability detection compared to the single-level features. The detection accuracy is as high as 98%, 98% and 94% for the three vulnerabilities, respectively. The average detection time is 0.99 second per contract, indicating the proposed method is suitable for automatic batch detection of vulnerabilities in smart contracts.

With the rapid upgrading and explosive growth of Internet of Things (IoT) devices in mobile-edge computing, more and more IoT applications with high resource requirements are developed and utilized. Meanwhile, there are large quantities of edge nodes (e.g., switches and edge servers) with limited resources, higher operating costs, and certain failure probabilities in the mobile-edge computing environment. Therefore, when an IoT application is split into multiple collaborative tasks and offloaded into multiple edge clouds, there is an urgent need to increase the availability level of the task allocation scheme and the resource utilization of edge servers under the condition of certain communication delay. In this article, we first present a joint optimization objective to evaluate the unavailability level, communication delay, and resource wastage while allocating the same batch of IoT applications to multiple edge clouds. We then propose an approach to minimize the joint optimization objective under the condition of certain communication delay. Finally, we performed a comprehensive simulation experiment analysis to demonstrate that our proposed approach is superior to other related approaches.

Internet of Things (IoT) devices have become an integral part of our lives and are increasingly used in almost every field. Subsequently, there are a large number of latency-sensitive IoT applications (e.g., face recognition and autonomous driving) targeted for mobile edge computing environments. These IoT applications are often split into multiple collaborative tasks and offloaded onto containers or virtual machines (VMs) with certain failure rates and recovery rates. If these containers or VMs are not deployed in the same edge servers, the bandwidth resources of edge clouds must be consumed to transfer data. These factors increase the completion time of IoT applications to different degrees, and then affect their reliability level. Therefore, there exists equilibrium between the reliability level and bandwidth consumption. In this article, we investigate the equilibrium of minimizing the bandwidth consumption of IoT applications while maximizing the reliability level of these IoT applications during task offloading. We propose a multiobjective optimization problem, and transform it to a single-objective optimization problem. Furthermore, we introduce two efficient approaches to acquire two near-optimal solutions. The results of simulation experiments demonstrate that our proposed approaches can observably enhance the reliability level and reduce the bandwidth consumption of IoT applications compared with other related approaches. Meanwhile, we also make a comparative analysis of our proposed approaches.

The abnormal detection of the workload sequence is designed to achieve intelligent operation and management of the cloud platform to improve operational efficiency. Due to the diversity of workload sequence variation patterns in the large-scale cloud, it is difficult for the traditional abnormal detection methods to extract features effectively, which leads to detecting anomalies inaccurately. In this paper, a deep unsupervised anomaly sequence detection model with fusion of spatial and temporal features of workload sequence (TS-DeepSVDD) is proposed. To extract the spatial and temporal features of the workload sequence, the model introduces convolutional recurrent neural network (CRNN) to improve network architecture with deep support vector data description (DeepSVDD). First, a convolutional neural network (CNN) module extracts the spatial features of the workload sequence. Second, for the acquired spatial features vectors, the bidirectional long short-term memory (BiLSTM) module extracts temporal features. Finally, the features vectors training support vector data description (SVDD) classifier fuses with the deep features of the workload sequence spatial and temporal features. TS-DeepSVDD extracts the deep features of the workload sequence from the spatial and temporal dimensions. It achieves a comprehensive description of the inherent law of the workload sequence, increasing the differentiation between normal and abnormal sequences. The simulation dataset and Google trace dataset are used respectively for verification. The results show TS-DeepSVDD can detect different abnormal sequences more accurately than the traditional unsupervised anomaly detection methods.

Constrained energy minimization (CEM) has been widely used for subpixel detection. It takes advantage of inverting the global sample correlation matrix R to suppress background so as to enhance detection of targets of interest. This paper presents a progressive band processing of CEM (PBP-CEM) which can perform CEM for target detection progressively band by band according to band sequential format. In doing so, a new concept, called causal band correlation matrix (CBCM), is introduced to replace the global sample correlation matrix R. It is a global correlation matrix formed by only those bands that were already visited up to the band currently being processed while excluding bands yet to be visited in the future. The proposed PBP-CEM allows CEM to be processed whenever bands are available, without waiting for completing band collection. With such an advantage, CEM has potential in data transmission and communication, specifically in satellite data processing.

Spectral cues are important for speech intelligibility, but they are distorted, in most popular multi-band compression algorithms for hearing aids, by being split into multiple bands and processed by different amplifiers. The paper presents a morphology-based method, which can preserve primary spectral cues and have low computation cost. Speech testing results demonstrate its efficiency.