In this paper, we explore the potential of snapshot compressive imaging (SCI) for dynamic 3D scene reconstruction from a single temporal compressed image. SCI is a low-cost imaging technique that captures high-dimensional information-such as temporal data-using$2 D$sensors and coded masks, significantly reducing data bandwidth while offering inherent privacy advantages. While recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have enabled 3D reconstruction from SCI measurements, these methods are fundamentally limited to static scenes and fail to generalize to dynamic content. To address this, we propose SCIGaussian-D, a novel framework that enables dynamic 3D reconstruction from a single SCI image. Our method represents the scene with 3D Gaussians defined in a canonical space and models motion using learnable deformation fields. By incorporating the SCI imaging model into the training loop, SCIGaussian-D directly reconstructs the dynamic 3D scene and recovers the corresponding camera motion from a single SCI. We evaluate our method on both synthetic and real SCI datasets, demonstrating significant improvements in reconstruction quality over existing baselines. Our results establish a new state of the art for dynamic scene reconstruction within the SCI framework, paving the way for practical applications in high-speed imaging and real-time scene rendering.

Searchable symmetric encryption (SSE) enables the storage and retrieval of encrypted data on untrusted cloud servers, while dynamic searchable symmetric encryption (DSSE) further supports updating encrypted data. To date, in multi-user environments, most DSSE schemes cannot achieve simultaneous access control for both keyword retrieval and data updates. To address this issue, we propose a new DSSE scheme with efficient and complete(keyword retrieval and update)access control for multi-user environments, named EFCAM. Our work has simultaneously achieved efficient, flexible, and fine-grained access control for keyword retrieval and updating, this is extremely rare in existing research. For update operations, we combine file index encoding and homomorphic encryption (HE) technology, so that EFCAM optimizes the calculation; to achieve flexible access control, we adopt an equality test scheme that can supports three types of update authorization. For retrieval operations, users do not need to share keys. By executing a single query, the users can effectively retrieve all the data that they have permission to access. To enhance system security and operational efficiency, we have extended EFCAM with a dynamic policy update mechanism for flexible and real-time adjustment of access control policies. We formally analyze the security of EFCAM to prove that our scheme has forward security (FS) and backward security (BS). Experimental results show that, EFCAM maintains outstanding efficiency in encrypted data retrieval and update operations within multi-user environments, while also exhibiting strong scalability.

Fuzz testing is an dynamic program analysis technique designed for discovering vulnerabilities in IoT systems. The core goal is to deliberately feed maliciously crafted inputs into an IoT device or service, triggering vulnerabilities such as system crashes, buffer overflow exploits, and memory corruption, etc. Efficiently generating malicious inputs remains challenging, with leading methods often relying on randomly mutating existing valid inputs. In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations. Specifically, we develop a framework that leverages code LLMs to guide the mutation process to perform meaningful input mutations. We formulate the mutation process as the sequenceto-sequence modeling, where LLM receives a sequence of bytes and outputs the mutated byte sequence. FuzzCoder is fine-tuned on our created instruction dataset (FuzzInstruct), where the successful fuzzing history is collected from the heuristic fuzzing tool. FuzzCoder can predict mutation positions and strategies for input files to trigger abnormal behaviors of the program. Most importantly, the experiment reveals results that FuzzCoder achieves better fuzzing performance compared to traditional and other AFL-based fuzzers, such as AFL, AFL++, AFLSmart, etc. On average, FuzzCoder achieves an improvement in code coverage of more than 20%, along with a significant increase in the number of crashes. 1

American fuzzy lop (AFL), as a representative tool for fuzzing, is capable of uncovering security vulnerabilities in industrial systems. It suffers from consuming a large amount of computational resources during the mutation. To improve the performance of AFL, researchers adopt algorithms, such as particle swarm optimization and long short-term memory, to optimize mutation operator selection. However, challenges persist in these approaches integrated with AFL, including optimization model complexity, insufficient accuracy, and poor generalization scalability. To address these issues, the article proposes a new fuzzer calledDPRFuzzto optimize AFL’s mutation phases. First, in the deterministic mutation strategy mutation phase, deep Q network and trust region policy optimization are leveraged to precisely generate effective mutated samples through perceiving mutation process in a relatively short time. Then, to boost the efficiency of the Havoc random mutation phase, we improve the Thompson sampling algorithm based on a multiagent strategy to generate an overall optimal mutation strategy chain. Finally, the approach is tested on eight programs, such asreadelf,tcpdump,andnm, and the advantages ofDPRFuzzare analyzed. Most importantly, the experiment reveals results thatDPRFuzzachieves better fuzzing performance compared to the traditional and other AFL-based fuzzers, such as AFL, AFL++, AFLSmart, etc. On average,DPRFuzzachieves an improvement in code coverage of over 10%, along with a significant increase in the number of crashes.

Artificial intelligence (AI) server systems, including AI servers and AI server clusters, are widely utilized in AI applications. The performance of an AI server system determines the performance of the performance an AI application, which has garnered significant attentions and investments from industries and users. However, performance is influenced not only by the AI computing accelerating chips but also by the other configurations of an AI server system, such as architecture, memory, bus, central processing unit (CPU), interconnect equipment, software, etc. As these components are often provided by different vendors, the myriad combinations thereof present challenges in assessing performance in an architecture-neural, reproducible, fairness-enhanced, and performance bottleneck identification-oriented manner. In response to this need, this paper introduces AISBench, a performance benchmark for AI server systems. AISBench comprises standardized rules and a test toolkit that has been agreed upon by over 20 AI server system and server component manufacturers. Compared to other AI performance benchmarks, AISBench provides a more comprehensive metrics system that enables performance benchmarking as well as identification of performance bottlenecks for optimization. Experimental data indicate that our benchmark testing approach offers sufficient comprehensiveness, effectiveness, and stability.

Outcrop records contain very rich geological historical information, and the study of fractures in outcrop areas is an important part of geological exploration work. The accurate fracture information can provide useful technical support for the development and exploration of subsurface oil and gas. The outcrop images usually include unclear boundaries, complex structure and inconspicuous features, which make fracture detection from outcrop images a difficult task. To tackle these challenges, an improved U-Net algorithm based on the ResNeXt module is proposed in this paper to segment the fractures from the outcrop images. Experiments are conducted on the outcrop images from Yijianfang area in the Tarim Basin in China, and the results show that the proposed algorithm has improved the accuracy and IoU in fracture segmentation from the outcrop images.

Objective: To illustrate the need for wider implementation of the CancelRx message by quantifying and characterizing the inappropriate usage of new electronic prescription (NewRx) messages for communicating discontinuation instructions to pharmacies. Materials and Methods: A retrospective analysis on a nationally representative random sample of 1 400 000 NewRx messages transmitted over 7 days to identify e-prescriptions containing medication discontinuation instructions in NewRx text fields. A vocabulary of search terms signifying cancellation instructions was formulated and then iteratively refined. True-positives were subsequently identified programmatically and through manual reviews. Two independent reviewers identified incidences in which these instructions were associated with high-alert or look-alike-sound-like (LASA) medications. Results: We identified 9735 (0.7% of the total) NewRx messages containing prescription cancellation instructions with 78.5% observed in the Notes field; 35.3% of identified NewRxs were associated with high-alert or LASA medications. The most prevalent cancellation instruction types were medication strength or dosage changes (39.3%) and alternative therapy replacement orders (39.0%). Discussion: While the incidence of prescribers using the NewRx to transmit cancellation instructions was low, their transmission in NewRx fields not intended to accommodate such information can produce significant potential patient safety concerns, such as duplicate or inaccurate therapies. These findings reveal the need for wider industry adoption of the CancelRx message by electronic health record (EHR) and pharmacy systems, along with clearer guidance and improved end-user training, particularly as states increasingly mandate electronic prescribing of controlled substances. Conclusion: Encouraging the use of CancelRx and reducing the misuse of NewRx fields would reduce workflow disruptions and unnecessary risks to patient safety.