This paper introduces a novel method for matching visible light and infrared images, termed the Visible Light and Infrared Image Matching Network (VLIMNet). In the image encoding stage, we incorporate a generative architecture-based modality transformation network after the SuperPoint encoder, enabling the local geometric features extracted from infrared images to more closely resemble those of visible light images. This reduces the impact of modality differences. Simultaneously, we utilize the image encoder of the Segment Anything Model to obtain global semantic descriptors. During the decoding stage, we fuse the global semantic descriptors with the local geometric descriptors and perform joint decoding to obtain keypoints and new feature vectors. The matching process is computed by solving a differentiable optimal transport problem based on the LightGlue network, a graph neural network with an attention mechanism. Compared to other matching models, our approach demonstrates improvements across various metrics in the domain of infrared and visible light image matching, particularly excelling in matching images with significant pose differences. Specifically, our method achieves approximately a 5% improvement in matching accuracy compared to the highest accuracy matching methods.

In recent years, gesture recognition based on data gloves has attracted increasing attention as a human-computer interaction (HCI) method that is natural, convenient, stable, robust, easy to recognize, and applicable to various usage environments. This research first proposes an advanced smart data glove that integrates cutting-edge flexible capacitive sensors on the fingertips and a 6-axis IMU on the back of the hand to recognize gestures. Secondly, this study proposes a personalized continuous gesture segmentation (PCGS) model that can adaptively calculate the most appropriate gesture segmenting threshold based on the current user and introduces the multi-sliding window theory and kinematic knowledge to perform personalized gesture segmentation. The accuracy of gesture segmentation can reach 94.3%. The result shows that our PCGS model achieves an average segmentation accuracy of 94.3% and outperforms the state-of-the-art pproaches by 11.2% to 18.5%.

Person re-identification (Re-ID) systems in edgecloud continuum face critical trade-offs between latency sensitivity and energy efficiency due to the resource-constrained edge environment. This paper proposes Re-IDFaaS, a serverless ReID system that dynamically optimizes energy consumption and computational performance across the edge-cloud continuum. Leveraging serverless architectures, our system implements three key improvements: (1) An event-driven workflow triggered by motion detection, eliminating idle GPU resource consumption during inactive periods. (2) A hardware-aware dynamic scheduler that allocates tasks based on real-time energy states and container availability, achieving balanced resource utilization across heterogeneous nodes. (3) An adaptive batching mechanism that reduces cold-start frequency through latency-constrained request grouping while maintaining the efficiency of GPU memory. Experiments demonstrate a 23.3% improvement in edge node availability and 55% reduction in memory usage compared to existing methods. The system design provides practical insights for building AI services in hybrid computing environments requiring cross-framework compatibility and adaptive resource orchestration, achieving 53% higher throughput than traditional architectures. These innovations address the challenges of dynamic workload scheduling and runtime optimization in hardware-diverse scenarios, ensuring sustainable operation under bursty surveillance workloads.

Stream partitioning has a fundamental effect on the efficiency of data parallelism in distributed stream processing systems. The skewed and time-varying nature of streaming data makes it challenging to achieve load balancing while minimizing the cost incurred. The requirement of adaptivity further complicates the problem, that the partitioning mechanism should not only be able to capture the changes in workload and adjust itself but also be quite tolerant of the changes because of the lag in statistics. Existing approaches use one-choice or multiple-choice schemes to make tradeoffs between these factors, but they tend to treat them as opposites, which either fails to achieve good load balancing or incurs excessive cost. There is a lack of deeper insight into how partitioning behavior affects load balancing, cost, and adaptivity when the keys have a different number of candidate choices. Also, it requires a flexible partitioning scheme to allow different trade-offs among the three factors for various scenarios.

Stream join is a fundamental operation in stream processing and has attracted extensive research due to its large resource consumption and serious impact on system performance. As the theoretical basis of stream join systems, the stream join model greatly affects system performance. State-of-the-art stream join models either consume too much computing resources or too much storage resources, thus resulting in lower throughput or higher latency. In this paper, we propose a new stream join model for processing arbitrary join predicates, called CoModel, which offers a flexible trade-off between memory and computing resource consumption. More importantly, CoModel can achieve the minimum sum of the number of store operations and join operations among all existing join models, and thus can achieve the lowest latency and highest throughput when the overheads associated with the local stream join for each input tuple are approximately constant. We give a trade-off strategy for CoModel and theoretically prove its performance advantages based on queuing theory. Furthermore, we design and implement an adaptive distributed stream join system, CoStream, based on CoModel. CoStream can adaptively adjust its structure according to resource constraints and statistics of input data. We conduct extensive experiments for CoStream to evaluate its performance and adaptivity, and the results show that CoStream has the lowest latency and highest throughput in various scenarios.

Software-based distributed shared memory (DSM) allows multiple processes to access shared data without the need for specialized hardware. However, this flexibility comes at a significant cost due to the need for data synchronization. One approach to mitigate these costs is to relax the consistency model, which can lead to delayed updates to the shared data. This approach typically requires the use of explicit synchronization primitives to regulate access to the shared memory and determine the timing of data synchronization. To circumvent the need for explicit synchronization, an alternative approach is to manage shared memory transparently using the underlying system. While this can simplify programming, it often imposes a fixed granularity for data sharing, which can limit the expansion of the coherence domain and increase the synchronization requirements. To overcome this limitation, we propose an abstraction called the elastic coherence domain, which dynamically adjusts the scope of data synchronization and is supported by the underlying system for transparent management of shared memory. The experimental results show that this approach can improve the efficiency of memory sharing in distributed environments.

Recent developments of Internet technologies have accelerated the growth of Web services (e.g., open APIs). As many services provide similar functionality, service recommendation systems use the Quality of Service (QoS) to help users find optimal services. Space partition attracts significant attention in service recommendation since it improves the diversity of recommendations and accelerates skyline services query. However, existing partition-based service recommendation systems are all implemented on complete QoS. They are not sufficient when some services’ QoS values are missing or invalid. To this end, we develop a new partition-based service recommendation method on incomplete QoS (named IQSrec) that combines probabilistic skyline query and space partition. The probabilistic skyline query measures top-$k$skyline services on incomplete QoS. A dimension-based partition is specially designed for splitting the incomplete QoS service space into$d$-dimensional partitions with the most representative services. The candidate skyline services are chosen from each partition and merged together for probabilistic skyline computation. IQSrec selects the highest skyline probability services in each partition as recommendations. The experiments on the synthetic and real-world datasets show IQSrec can efficiently recommend skyline services on incomplete QoS. IQSrec has higher accuracy and diversity compared to the state-of-the-art service recommendation approaches.

In the field of real-time analytics, stream joins are the basis for complex queries and greatly affect system performance. In order to satisfy the real-time requirements of streaming applications, the system imposes high requirements on the latency and throughput of the stream join operator. In this paper, we model the latency and throughput of distributed stream join systems based on queuing theory. Based on the analysis of this model, we demonstrate the impact of indexing-related overhead on the latency and throughput of stream join systems and propose a new distributed stream join system, SepJoin, which is oriented to the hash join problem. SepJoin reduces the number of tuples stored in each processing unit belonging to each input stream by designing a novel partitioning scheme that uses as many processing units as possible to store tuples belonging to each input stream, thereby reducing the index-related overhead of each processing unit when performing join operations and ultimately achieving performance benefits in terms of latency and throughput. We provide both theoretical analysis and extensive experimental evaluations to evaluate the processing latency and max throughput of SepJoin.

Dynamic binary translation (DBT) is the cornerstone of many important applications. In practice, however, it is quite difficult to maintain the performance efficiency of a DBT system due to its inherent complexity. Although performance regression testing is an effective approach to detect potential performance regression issues, it is not easy to apply performance regression testing to DBT systems, because of the natural differences between DBT systems and common software systems and the limited availability of effective test programs. In this paper, we present FADATest, which devises several novel techniques to address these challenges. Specifically, FADATest automatically generates adaptable test programs from existing real benchmark programs of DBT systems according to the runtime characteristics of the benchmarks. The test programs can then be used to achieve highly efficient and adaptive performance regression testing of DBT systems. We have implemented a prototype of FADATest. Experimental results show that FADATest can successfully uncover the same performance regression issues across the evaluated versions of two popular DBT systems, QEMU and Valgrind, as the original benchmark programs. Moreover, the testing efficiency is improved significantly on two different hardware platforms powered by x86-64 and AArch64, respectively.

To improve the efficiency of QoS-centric service selection, skyline query is often used to get small candidates from a large number of services. Recently, interval-valued skyline service selection attracts a lot of attention due to the QoS value fluctuation during execution. To simplify skyline computation, existing interval-valued skyline service selection methods assume the probability density function (PDF) of QoS intervals follows general mathematical distribution, such as the Uniform distribution or the Gaussian distribution, which leads to the inaccurate dominant relationship between QoS intervals. In addition to the impractical assumption of intervals, another problem of existing interval-valued skyline service selection methods is that they are all implemented for complete QoS and are not sufficient when some services’ QoS values are missing or invalid. To this end, we develop a new skyline service selection method on incomplete QoS, named ISkySel, which combines probabilistic skyline query and missing QoS prediction. ISkySel uses valid QoS values to build the PDF of QoS intervals and employs the early termination and sorting techniques to accelerate the probabilistic skyline computation. The experiments on the synthetic and real-world datasets show ISkySel has higher accuracy and efficiency compared to the state-of-the-art skyline service selection.

System virtualization is a fundamental technology that enables many important applications. However, existing virtualization techniques suffer from a critical limitation due to their limited exploitation of host SIMD hardware resources, especially when a guest application does not have inherently fine-grained data-level parallelism. To bridge this utilization gap and unleash the full potential of host SIMD resources, this paper proposes an effective and unconventional SIMD exploitation technique. The proposed exploitation takes advantage of ample host SIMD registers and powerful host SIMD instructions to generate more efficient host binary code for guest applications even without any fine-grained data-level parallelism. It also mitigates the shortage of general-purpose registers on the host platform, as well as improves the efficiency of accessing guest registers. We have implemented the exploitation in an extensively-used virtualization platform, QEMU. Experimental results on a comprehensive list of benchmarks from PARSEC, SPEC-CPU2017, and Google Octane JavaScript benchmark suite show that an average of 2.2X performance speedup can be achieved for AArch64 binaries on an x86-64 host machine. We believe the proposed technique will provide a new perspective for our community to rethink the exploitation of SIMD hardware resources.

Using fake audio to spoof the audio devices in the Internet of Things has become an important problem in modern network security. Aiming at the problem of lack of robust features in fake audio detection, an audio streams’ hidden feature extraction method based on a heuristic mask for empirical mode decomposition (HM-EMD) is proposed in this paper. First, using HM-EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). Then, on the basis of IMFs, basic features and hidden information features HCFs of audio streams are constructed, respectively. Finally, a machine learning method is used to classify audio streams based on these features. The experimental results show that hidden information features of audio streams based on HM-EMD can effectively supplement the nonlinear and nonstationary information that traditional features such as mel cepstrum features cannot express and can better realize the representation of hidden acoustic events, which provide a new research idea for fake audio detection.

Dynamic binary translation (DBT) has been adopted in many important applications. Due to the large scale and complexity of a DBT system, a minor code change may lead to unexpected impact on the performance. Therefore, it is necessary to conduct performance regression testing for DBT systems. However, existing benchmark suites are not suitable for daily performance testing due to the extremely-long testing time. To address this challenge, we propose PerffiBT, which employs a novel approach to automatically generate test programs from existing long-running benchmarks. The execution times of the generated test programs are much shorter, which allows them to be used for daily performance regression testing of a DBT system. Experimental results demonstrate that the test programs generated by PerfDBT can achieve an average of 71X testing efficiency compared to original benchmarks. Furthermore, it can also deliver similar testing results to original benchmarks.

Shared memory is widely used for inter-process communication. The shared memory abstraction allows computation to be decoupled from communication, which offers benefits, including portability and ease of programming. To enable shared memory access by processes that are on different machines, distributed shared memory (DSM) can be employed. However, DSM systems can suffer from thrashing: while different processes update certain hot data items, the largest amount of effort is spent on data synchronization, and little progress is made by each process. To avoid interference between processes during data updating while providing shared memory at page granularity, more time is reserved for a writer to hold a page in a traditional manner. In this paper, we report on complex thrashing, which can explain why extending the time of holding a page might not be sufficient to control thrashing. To increase the throughput, we propose a thrashing control mechanism that allows each process to update a set of pages during a period of time, where the pages compose a logical area. Because of the isolation of areas, updates on different areas can be performed concurrently. To allow the areas to be fairly well used, each process is assigned with a random priority for thrashing control. The thrashing control mechanism is implemented on a Linux-based DSM system. Performance results show that the execution time of the applications that are apt to cause system thrashing can be significantly reduced by our approach.

To adapt to the uncertain environment smartly and timely, cyber physical systems (CPSs) have to interact with the physical world in a decentralized but rigorous, organized way. Guaranteeing the timing reliability is key to achieve consensus on the order of distributed events, as well as dependable cooperative decision processing. Based on our hierarchically decentralized compositional self-adaptive framework, we propose a formal compositional reliability-contract-based solution to guarantee the timing reliability of event observation and decision processing in a large-scale, geographically distributed CPS. As the prophetic decision may not fit the local situation well because of the uncertainties, we propose a gradual contract optimization solution to refine the dependability, timeliness, and energy consumption. Following the seven proposed composition schemes, we employ the nondominated sorting genetic algorithm II (NSGA-II) algorithm to optimize arrangement of decision. Moreover, a topology-aware time reserving solution is applied to improve the resilience of processing time and to tolerance timing failures. Both simulation results and real-world testing are introduced to evaluate the efficacy of our proposal. We believe that the formal compositional contract will be a competitive CPS solution to analyze requirements and optimize the self-adaptation decision at runtime.

Due to the increasing scale of cloud data center, the issue of energy consumption is becoming pretty significant. To tackle this problem, an extremely effective approach is increasing the utilization of resource in data center. Researchers have found that accurate load prediction can help allocator distribute resource reasonably, so as to increase the utilization. There are a lot of traditional prediction algorithms which have been applied to cloud data center, such as linear regression. However, with the development of technologies, a number of novel prediction algorithms are brought out, for example, neural network. This paper assesses and analyzes the performance of several different prediction algorithms applying on data sets from real world. We get some meaningful and interesting conclusions from comparison among these algorithms, which may offer references for system designers of cloud data center.

The dominant position of High-Performance Fault-Tolerant (HPFT) computers in security and economics has advanced the studies on the performance benchmarks on the HPFT computers in the specific field, such as bank finance and telecommunication etc. Although TPC (Transaction Processing Council) has proposed some benchmarks models for different OLTP (On-Line Transaction Processing) complex business, such as TPC-C and TPC-E, there is still a lack of the performance benchmark model dedicated to the bank intermediary business on HPFT computers. This paper proposes a Bank Intermediary Business performance benchmark (BIBbench), and gives a solution to test and evaluate this benchmark on HPFT computers for bank intermediary business. In this paper, we present the architecture of BIBbench, defining the structures and attributes of the business model, the database model and the transaction/frame model, and illuminating the workload generation mechanism of the intermediary business system as well. The BIBbench testing environment architecture is also discussed in the paper, as well as the testing solutions and tools. Currently, this BIBbench has been partly implemented on the Oracle 10g database system, and some performance testing experiences based on the BIBbench for HPFT computers have been made.

An issue pertinent to the design of checkpointing protocols is how to improve the autonomy of checkpointing and keep computation loss under control. To address the problem, a time-based multi-cycle checkpointing protocol is proposed in this paper. In this protocol, processes are allowed to take checkpoints with desired checkpoint cycles. To enable recent checkpoints to be used to form a consistent global checkpoint, a communication-based checkpoint cycle adjustment approach is also proposed. In this approach, the checkpoint cycle adjustment of each process follows a P-pattern. Simulation results show that the rollback deviation of the proposed protocol can be well controlled under a low checkpointing overhead.

In a mobile computing system, mobile hosts may move around cells, resulting in a considerable cost for locating and retrieving the recovery information, which is necessary for fault tolerance. To speed up the recovery, traditionally, recovery information is migrated according to the location of the mobile host. In this paper, a scheme for efficiently handling the recovery information is proposed. When a mobile host moves out of a certain range, only partial recovery information of the mobile host needs to be migrated to mobile support stations. It can avoid the unnecessary migration of recovery information. Moreover, the performance of the proposed scheme is evaluated and compared with the traditional movement based scheme.