DrWASI : LLM-assisted Differential Testing for WebAssembly System Interface Implementations

For the problem that the early fault information of diesel engine system is weak and difficult to identify and diagnose, an early fault diagnosis method based on cross-point frequency response and static and dynamic information fusion was proposed for the assembly quality of diesel engine system. The dynamic vibration response signal and static cross-point frequency response signal of the diesel engine system were collected by reasonable layout of measuring points. After CEEMD reconstruction and de-noising, the sample entropy and approximate entropy were extracted as characteristic parameters of the dynamic signal, and the frequency response features were extracted from the static signal. The static and dynamic information of the two kinds of information was integrated by PCA. The optimized support vector machine is used to identify the dynamic information and the static and dynamic fusion information respectively. The results show that this method can effectively detect the assembly quality of key components of diesel engine system, and the accuracy of diagnosis is up to 95%, and the recognition rate after static and dynamic information fusion is better than that of dynamic information. The method presented in this paper has a good application prospect in the assembly quality inspection and early fault diagnosis of diesel engine system.

In this paper, we propose BiTDB, a built-in Trusted Execution Environment (TEE) database for embedded systems, to realize higher system availability while ensuring data confidentiality. With BiTDB, dilemmas that the state-of-the-art research work on secure embedded databases has to face can be significantly reduced and eliminated, including (i) complicated research and realization on searchable encryption algorithms (SEA), (ii) limited support to all database operations, and (iii) almost none of specific design and optimizations toward built-in TEE embedded databases. Through BiTDB, all database operations can process plaintext in TEE instead of retrieving ciphertext by developing complicated SEAs. To enable BiTDB to handle database files in Rich Execution Environment (REE) as local ones, we extend the TEE OS with generic file I/O libraries. Then, we contribute three critical optimizations to significantly reduce redundant memory and file operations between TEE and REE, and BiTDB achieve better system performance and availability in embedded systems. Finally, we have implemented the prototype system based on OP-TEE and SQLite for several typical platforms, including virtualization and hardware environments. The TPC-H test shows BiTDB can achieve 85% (on average) of the original database performance while guaranteeing data confidentiality and integrity.

Internet‐scale distributed systems often replicate data at multiple geographic locations to provide low latency and high availability, despite node and network failures. According to the CAP theorem, low latency and high availability can only be achieved at the cost of accepting weak consistency. The conflict‐free replicated data type (CRDT) is a framework that provides a principled approach to maintaining eventual consistency among data replicas. CRDTs have been notoriously difficult to design and implement correctly. Subtle deep bugs lie in the complex and tedious handling of all possible cases of conflicting data updates. We argue that the CRDT design should be formally specified and model checked, to uncover deep bugs which are beyond human reasoning. The implementation further needs to be systematically tested. On the one hand, the testing needs to inherit the exhaustive nature of the model checking and ensures the coverage of testing. On the other hand, the testing is expected to find coding errors which cannot be detected by design level verification. Toward the challenges above, we propose the model‐checking‐driven explorative testing (MET) framework. At the design level, MET uses TLA+ to specify and model check CRDT designs. At the implementation level, MET conducts model‐checking‐driven explorative testing, in the sense that the test cases are automatically generated from the model‐checking traces. The system execution is controlled to proceed deterministically, following the model‐checking trace. The explorative testing systematically controls and permutes all nondeterministic choices of message reorderings. We apply MET in our practical development of CRDTs. The bugs in both designs and implementations of CRDTs are found. As for bugs which can be found by traditional testing techniques, MET greatly reduces the cost of fixing the bugs. Moreover, MET can find subtle deep bugs which cannot be found by existing techniques at a reasonable cost. Based on our practical use of MET, we discuss how MET provides us with sufficient confidence in the correctness of our CRDT designs and implementations.Conflict‐free replicated data type (CRDT) is a framework that provides a principled approach to maintaining eventual consistency among data replicas in distributed systems. CRDTs have been notoriously difficult to design and implement correctly. We propose model‐checking‐driven explorative testing (MET) framework for dealing with such problem. We apply MET in our practical development of CRDTs. MET successfully finds subtle deep bugs and provides us with sufficient confidence in the correctness of our CRDT designs and implementations.

This research presents a comprehensive framework for building customized chatbots empowered by large language models (LLMs) to summarize documents and answer user questions. Leveraging technologies such as OpenAI, LangChain, and Streamlit, the framework enables users to combat information overload by efficiently extracting insights from lengthy documents. This study discussed the framework's architecture, implementation, and practical applications, emphasizing its role in enhancing productivity and facilitating information retrieval. Through a step-by-step guide, this research has demonstrated how developers can utilize the framework to create end-to-end document summarization and question-answering applications.

Dynamic analysis extracts information that describes the structure of the runtime behavior of a software system. This information, typically represented in the form of execution traces, is useful in many software modernization activities, such as reverse engineering and program analysis. The Architecture-Driven Modernization initiative has defined standards to support the modernization process in the context of Model-Driven Engineering (MDE). However, to date, there is no standard for representing runtime information that allows the specification of dynamic information independent of any programming language. In light of this, we present TRACEM, a metamodel for representing trace information under a standard representation that would allow traces to become a new domain in software engineering. The purpose of defining TRACEM is to complement an MDE framework for software modernization that aims to integrate static and dynamic analysis techniques during the reverse engineering process. This article contains a description of TRACEM and a case study that illustrates how dynamic information combined with static information allows for improving the whole reverse engineering process.

A declarative approach to detecting design patterns from Java execution traces and source code

International audience

INFORMATION-BASED EVACUATION EXPERIMENT AND ITS CELLULAR AUTOMATON SIMULATION

We investigated segregant development for FePt high-density magnetic recording media using large language models (LLMs) and identified new fluoride-based materials. By increasing the temperature of the softmax function to select probable vocabularies, we successfully incorporated the prior knowledge involved in LLM training into the material selection process (exploratory generation) and identified LaF3 as an optimal material. Meanwhile, we independently conducted sputtering deposition of FePt-LaF3 nanogranular samples and verified whether the LLM could reproduce the results. The inhomogeneity in the surface chemical composition of FePt-LaF3 in the non-equilibrium state of sputtering were also reproduced by the LLM, leading to the identification of AlF3 as an alternative segregant. Structures and magnetic properties strongly dependent on the sputtering apparatus cannot be reproduced by simple LLM predictions, necessitating interactive data exchange between physical and cyber experiments. However, phenomena that can occur in physical experiments are almost accurately reproduced by LLM, demonstrating the usefulness of LLM predictions in material development. LLMs that generate responses from the most likely vocabulary tend to select well-known materials. To identify unknown materials as candidates, techniques such as exploratory generation or inputting unexpected results are required.

Real-time monitoring of source zone of light non-aqueous liquids (LNAPLs) is important for preventing accidental pollution and taking effective underground pollution remediation measures. As a high-precision monitoring technology, cross-hole electrical resistivity tomography (CHERT) has been widely used to obtain static information regarding underground stratigraphic structures and dynamic information regarding fluid motion. The time-lapse data processing method can be used to obtain pollution zone dynamic change information. Since the leakage rate directly affects the diffusion range of pollution, this study simulated the initial evolution process of LNAPL pollution source regions under two different leakage rates. The time-lapse monitoring of the above process was performed using CHERT. The test results show that CHERT can be used to observe the migration of LNAPLs and the initial evolution of the contaminated area. Differences in leakage rate will result in variation in soil wettability and fluid distribution, which will cause changes in soil resistivity in the corresponding region. The low-saturation LNAPL-contaminated area may exhibit low-resistivity characteristics and is easily overlooked in field investigations. In addition, the degree of contamination in the saturated zone can be quantitatively evaluated by CHERT; however, the pollution range and oil saturation value determined by CHERT are underestimated. The results showed the electrical variation characteristics of the initial evolution process of the fresh pollution source area and provide data that will enable early warnings of LNAPL leakage. This shows that CHERT is a promising tool for monitoring LNAPL pollution source leakage even if further research is needed to fully understand the effect of hydrological processes on electronic signals.

We present the KENSv2 (KAIST Educational Network System) framework for network protocol implementation. The framework is event-driven to guarantee deterministic behaviour and reproducibility, which in turn delivers ease of debugging and evaluation. Our framework consists of four components: the event generator, the virtual host, the TCP driver and the IP driver. The two drivers are what students have to implement, and we offer to the students the drivers in the binary format for paired testing and debugging. We have developed a test suite that covers three categories of test cases: specification, paired, and logic tests. The framework logs packet transmissions in the PCAP format to allow use of widely available packet analysis tools to inspect logical behaviour of student solutions, such as congestion control. We have designed five step-by-step assignments and evaluated student submissions. With our automated test suite, we have cut down the number of TAs by half for the doubled class size compared to the previous semester, in total 3 TAs with 49 students. We plan to continue using KENSv2 in our undergraduate networking course and expand the test suite.

This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in‐plane head rotation and break up facial region from background. Then, spatial‐temporal motion local binary pattern (LBP) feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information of facial expressions. Finally, a one‐versus‐one strategy based multiclass support vector machine (SVM) classifier is applied to classify facial expressions. Experiments on Cohn‐Kanade (CK) + facial expression dataset illustrate that integrated framework outperforms methods using single descriptors. Compared with other state‐of‐the‐art methods on CK+, MMI, and Oulu‐CASIA VIS datasets, our proposed framework performs better.

How to fuse static and dynamic information is a key issue in event analysis. In this paper, a top-down motion guided fusing method is proposed for recognizing events in an unconstrained news video. In the method, the static information is represented as a Bag-of-SIFT-features and motion information is employed to generate event specific attention map to direct the sampling of the interest points. We build class-specific motion histograms for each event so as to give more weight on the interest points that are discriminative to the corresponding event. Experimental results on TRECVID 2005 video corpus demonstrate that the proposed method can improve the mean average accuracy of recognition.

Identifying influential nodes in complex networks: Effective distance gravity model

Today, various Internet of Things (IoT) devices and applications are being developed. Such IoT devices have different hardware (HW) and software (SW) capabilities; therefore, most applications require customization when IoT devices are changed or new applications are created. However, the applications executed on these devices are not optimized for power and performance because IoT device systems do not provide suitable static and dynamic information about fast-changing system resources and applications. Therefore, this paper proposes a light-weight and versatile monitor for a self-adaptive software framework to automatically control system resources according to the system status. The monitor helps running applications guarantee low power consumption and high performance for an optimal environment. The proposed monitor has two components: a monitoring component, which provides real-time static and dynamic information about system resources and applications, and a controlling component, which supports real-time control of system resources. For the experimental verification, we created a video transport system based on IoT devices and measured the CPU utilization by dynamic voltage and frequency scaling (DVFS) for the monitor. The results demonstrate that, for up to 50 monitored processes, the monitor shows an average CPU utilization of approximately 4% in the three DVFS modes and demonstrates maximum optimization in the Performance mode of DVFS.