Abstract Keyword search, which identifies transactions associated with specified keywords across historical blocks, is a critical query type in blockchain analytics. However, existing approaches, such as on-chain indexing and off-chain synchronization, may lead to significant space overhead or challenges in maintaining data freshness. To address these challenges, we propose BlockSketch, a novel probabilistic data structure (PDS) that adopts a differentiated encoding strategy, aimed at resolving the trade-off between query performance and storage overhead in blockchain indexing. BlockSketch features a hierarchical filtering architecture that combines Bloom filters and Sketches within a binary tree framework, enabling dynamic structural maintenance. Keywords are categorized as “hot” or “cold” based on their on-chain frequency and encoded into the most suitable component to achieve resource-efficient storage and accurate querying. In addition, BlockSketch integrates two distinct query rules, namely “level-down” and “jump,” to balance query accuracy and efficiency when processing keywords with varying frequencies. Furthermore, we enhance the query efficiency of BlockSketch by merging inefficient lower-level nodes into more compact ones and pruning redundant node checks during query execution. Extensive experiments on a real-world dataset demonstrate that BlockSketch delivers up to 73% faster query processing, achieves 44.56% of the average false positive rate of baselines at low multiplicity and as low as 1.52% at high multiplicity, and saves 79% in storage compared to state-of-the-art methods.

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques.

Existing peer-to-peer data-sharing methods suffer from low data delivery efficiency and scalability due to the naive data request/response procedure and the high redundant data transmission rate. It becomes even worse in large-scale mobile networks considering the limited resources of mobile devices. To address this issue, this paper presents MobiShare, an efficient decentralized data-sharing approach for mobile devices, which allows users to not only share the data but also the data generation methods. To achieve MobiShare, we introduce a function block encoding method and a data request method to enhance sharing efficiency, minimizing costs for decentralized data sharing. We propose a credit payment mechanism where congested devices can send data vouchers instead of actual data, containing the expected transmission time. Based on the load and bandwidth of devices, we build the optimized dissemination tree with data vouchers in a decentralized way to improve scalability. Evaluation results show that MobiShare avoids redundant transmission. It greatly shortens transmission completion time and lowers energy consumption with limited network resources.

Performance analysis is of great importance for management and optimization of space-terrestrial integrated networks (STINs). Traditional approaches to network performance analysis are often based on idealized assumptions that are deviated from the real network environment. This leads to the fact that these models are usually inefficient and restricted in real-world STINs with complicated behavior and even dynamic capacity. In this paper, we propose a network performance analysis approach EAGLE based on heterogeneous graph neural networks. Firstly, we propose a powerful computer network representation model that can preserve all of the information in computer networks. It represents different components of computer networks as a set of heterogeneous nodes and edges, and finally constructs a heterogeneous graph. Then, we obtain the topological representation for the routers in the network through a bandwidth-aware network embedding model. Based on this heterogeneous graph, we propose a heterogeneous GNN model to accurately predict network KPIs because it can completely capture the rich topological and attribute information of computer networks. Experimental results demonstrate that EAGLE can accurately model different networks, and outperforms both traditional methods and the latest neural network-based methods.

Although the leader-based consensus protocols, such as PBFT, are widely used in permissioned blockchains, the leader node may become the bottleneck of the system when the number of blockchain nodes or the system's throughput demand increases. On the contrary, the leaderless protocols deal with this problem by running multiple leader-based protocol instances concurrently. However, most of such works ignore the fact that deploying more instances is not always the best choice on some situations. When the system's workload is low, deploying more instances not only wastes resources, but may also compromises the ability of sluggish tolerance of the system (increasing latency). In this study, we propose Stretch-BFT, which dynamically adjusts the number of instances according to the system workload. Stretch-BFT includes three sub-protocols: 1) BFT workload sensing, 2) adaptive instances reconfiguration, and 3) failed instances recovery. The experimental results show that Stretch-BFT can exhibit high throughput as the existing leaderless protocol when the workload is relatively heavy, and improve the sluggish tolerance ability when the workload is relatively light.

Many remote sensing applications, such as forest fire monitoring, need to send a large volume of data to the ground with low delay. Therefore, the cooperation transmission, which relies on cooperation among satellites to achieve continuous transmission, emerges as an indispensable technique. Most existing work cannot minimize the delay through dynamic cooperation transmission. In this paper, we investigate how to minimize the delay in remote sensing satellite networks based on cooperation transmission, where cooperation hotspots refer to the satellites with ground-satellite links to the Earth Stations (ESs). First, we propose the cooperation capability model to quantify capabilities of cooperation hotspots. Then, we formulate the satellite cooperation transmission problem and prove its NP-hardness. To solve the problem, we propose the delay-minimized cooperation transmission scheme. Both CCT and DCT algorithms adapt well to the dynamic topology and time-varying available resources. Finally, we formally analyze the approximation ratios and the time complexities of both algorithms. We also prove that the DCT always setups loop-free paths. NS2-based simulation results demonstrate that our schemes have good scalability, and both CCT and DCT algorithms reduce the end-to-end delay on average by more than 21.77%, and significantly improve throughput, packet loss rate and flow completion time.

How to provide long-term care for the elderly is one significant challenge for the current aging society. A multiform service platforms and a large number of non-professional caregivers might influence the accuracy and appropriateness of the care programs. In CSCW and related fields, the intelligent methods brought by the development of information technology and pervasive end equipment have attracted wide attention. However, most researches are conducted in a laboratory environment, which makes it difficult to meet the highly complex realities of the elderly. Based on a series of field studies, we have an in-depth understanding of practical effectiveness of directly using intelligence algorithms into the generating process of caring programs. We identify the existing challenges, and proposes a data-driven and human-engaged health care services recommendation algorithm for seniors. We implement our proposed algorithm in a prototype system, and conduct a primary study to validate the effectiveness of the framework.

Smartphones are ubiquitous and becoming increasingly sophisticated, with ever-growing sensing powers. Recent years, more and more applications of activity recognition based on sensors are developed for routine behavior monitoring and helping the users form a healthy habit. In this field, finding an efficient method of recognizing the physical activities (e.g., sitting, walking, jogging, etc) becomes the pivotal, core and urgent issue. In this study, we construct a Convolutional Neural Network (CNN) to identify human activities using the data collected from the three-axis accelerometer integrated in users' smartphones. The daily human activities that are chosen to be recognized include walking, jogging, sitting, standing, upstairs and downstairs. The three-dimensional (3D) raw accelerometer data is directly used as the input for training the CNN without any complex pretreatment. The performance of our CNN-based method for multi human activity recognition showed 91.97% accuracy, which outperformed the Support Vector Machine (SVM) approach of 82.27% trained and tested with six kinds of features extracted from the 3D raw accelerometer data. Therefore, our proposed approach achieved high recognition accuracy with low computational cost.

Satellite networks are capable of contenting a variety of data transmission needs of users in geographically diverse locations throughout the world. Multi‐layered satellite networks (MLSNs) can construct efficient communications networks due to their extensive coverage and high network capacity. However, throughput degradation and severe end‐to‐end delay could occur in MLSNs because of the traffic congestion. To resolve these problems, the authors first propose a novel MEO/LEO satellite network architecture that construct effective inter‐satellite links. Then the authors present a network coding‐based multi‐path routing algorithm to deliver traffic through the hybrid satellite network. The analysis of characteristics of the proposed scheme are addressed by performance evaluations in simulation.

To aggregate opinions on aspects of entities mentioned in large-scale online reviews, it is important to automatically extract aspects of different granularities, identify associated opinions, especially aspect-specific opinions, and classify sentiment polarity. Recently, various topic models are proposed to process some of these tasks, but there is little work available to do all simultaneously. In this paper, we propose a Joint AspectBased Sentiment Topic (JABST) model to jointly extracting multi-grain aspects and opinions, which addresses all the tasks mentioned above. JABST models aspect, opinion, sentiment polarity and granularity simultaneously. To better separate opinion and aspect words, we propose JABST-ME, in which a maximum entropy (ME) classifier is applied to extend JABST. We evaluated the models on reviews of electronic devices and restaurants qualitatively and quantitatively. The experimental results show that the proposed models outperform state-of-the-art baselines.

Along with the popularization of smart mobile devices and the rapid development of wireless networks, a new class of crowdsourcing, termed with spatial crowdsourcing, is drawing much attention, which enables workers to perform spatial tasks based on their positions. In this paper, we study an important spatial crowdsourcing problem, namely information based maximum task matching (IG-MTM), in which each spatial task needs to be performed before its expiration time and workers are dynamically moving. The goal of IG-MTM problem is to maximize the number of spatial tasks that are assigned to workers while satisfying the quality requirement of collected answers. We first define this problem, and then two approximation approaches are proposed, namely greedy and extremum algorithms. Subsequently, in order to improve time efficiency, we propose an optimization methodology. Through extensive experiments on both real-world and synthetic datasets, we evaluate the performance of our proposed approaches.

Cognitive radio significantly mitigates the spectrum scarcity for various applications built on wireless communication. Current techniques on mobile cognitive ad hoc networks (MCADNs), however, cannot be directly applied to time-critical applications due to channel interference, node mobility as well as unexpected primary user activities. In multichannel multiflow MCADNs, it becomes even worse because multiple links potentially interfere with each other. In this paper, we propose a delay-minimized routing (DMR) protocol for multichannel multiflow MCADNs. First, we formulate the DMR problem with the objective of delay minimization. Next, we propose a delay prediction model based on a conflict probability. Finally, we design the minimized path delay as a routing metric, and propose a heuristic joint routing and channel assignment algorithm to solve the DMR problem. Our DMR can find out the path with a minimal end-to-end (e2e) delay for time-critical data transmission. NS2-based simulation results demonstrate that our DMR protocol significantly outperforms related proposals in terms of average e2e delay, throughput, and packet loss rate.

The benefits of network layer cooperation cognitive radio networks have been gradually recognized in recent years. In this paper we consider the network layer cooperation in cognitive radio network, whereby primary users select some secondary users to relay packets, in return for more favourable spectrum access rules for secondary users. Under this cooperation scheme, we investigate how to enlarge the throughput of the whole network, where a QoS(Quality of Service)-guaranteed adaptive cooperation scheme is developed. Our scheme can guarantee the QoS demand of primary users and update its frequency division cooperation scheme dynamically according to the status of nodes. Our algorithm requires knowledge of only instantaneous queue lengths at secondary nodes and the predictable end-to-end delay. Simulation results reveal that our proposed scheme significantly outperforms previous works in terms of throughput.

The stability of links in mobile cognitive networks (MCNets) is significantly affected by primary user activities and node mobility, which makes topology control and stable routing more challenging than that in traditional wireless networks. In multi-channel multi-hop MCNets, it will become worse. In this paper, we propose a primary user activity prediction model to reveal channel utilization patterns of primary users. Next, we put forward a novel routing metric Primary user activity Prediction based Stability Metric (PPSM) to quantitatively capture the affect of primary user activities and node mobility. Finally, we propose and implement a Primary user activity Prediction based Joint Topology Control and Stable Routing (PP-JTCSR) protocol for maximizing network throughput based on our primary user activity prediction model, which can find out the most stable and the shortest path between a source and a destination. NS2-based simulation results demonstrate that our PP-JTCSR protocol can generate stable topology through predicting link and path duration quantitatively, and outperforms related proposals in terms of path stability and average throughput.

Channel interference and node mobility cause significant performance degradation to wireless networks. In multichannel multi-flow mobile cognitive ad hoc networks, it becomes even worse due to both unexpected primary user activities and potential interference among multiple flows. In this paper, we propose a Joint Routing and Channel Assignment (JRCA) approach based on delay prediction. Firstly, it formulates the JRCA problem with the objective of delay minimization. Next, a delay prediction model is proposed based on the channel collision probability. Then, a heuristic algorithm joints routing and channel assignment is designed to solve the JRCA problem. the JRCA algorithm can find out the path with minimal end- toend (e2e) delay. NS2-based simulation results demonstrate that the JRCA approach significantly outperforms related proposals in terms of average e2e delay.

It is a difficult but fundamental goal to fully utilize various resources to deliver data as efficiently as possible in wireless networks. In CRAHNs, it becomes more challenging due to uncertain primary users' activities, time- and location-varying channels, and arbitrary traffic arrivals with unpredictable rate demand. In this paper, we propose a Joint Rate, Channel and Route Selection (JRCRS) approach to optimize the network resource utility in multi-hop, multi-channel CRAHNs, with the objective of maximizing social welfare. Our JRCRS jointly optimizes the data transmission rate adaptive to network condition, assigns interference-free channels and selects a route when a new flow arrives or a primary user activates. The routing metric in our JRCRS considers the relay workload, the distance between the relay and the destination node, and co-channel interference to primary and secondary users. Simulation results demonstrate that our JRCRS outperforms the most related solutions in terms of social welfare, average throughput, network stability and end-to-end delay.

Abstract Scratch‐pad memory (SPM), a small, fast, software‐managed on‐chip SRAM (Static Random Access Memory) is widely used in embedded systems. With the ever‐widening performance gap between processors and main memory, it is very important to reduce the serious off‐chip memory access overheads caused by transferring data between SPM and off‐chip memory. In this paper, we propose a novel compiler‐assisted technique, ISOS (Iteration‐access‐pattern‐based Space Overlapping SPM management), for dynamic SPM management with DMA (Direct Memory Access). In ISOS, we combine both SPM and DMA for performance optimization by exploiting the chance to overlap SPM space so as to further utilize the limited SPM space and reduce the number of DMA operations. We implement our technique based on IMPACT and conduct experiments using a set of benchmarks from DSPstone and Mediabench on the cycle‐accurate VLIW simulator of Trimaran. The experimental results show that our technique achieves run‐time performance improvement compared with the previous work. The average improvements are 13.15, 19.05, and 25.52% when the SPM sizes are 1KB, 512 bytes, and 256 bytes, respectively. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract In this paper, we propose an effective data pipelining technique, SPDP (Scratch‐Pad Data Pipelining), for dynamic scratch‐pad memory (SPM) management with DMA (Direct Memory Access). Our basic idea is to overlap the execution of CPU instructions and DMA operations. In SPDP, based on the iteration access patterns of arrays, we group multiple iterations into a block to improve the data locality of regular array accesses. We allocate the data of multiple iterations into different portions of the SPM. In this way, when the CPU executes instructions and accesses data from one portion of the SPM, DMA operations can be performed to transfer data between the off‐chip memory and another portion of SPM simultaneously. We perform code transformation to insert DMA instructions to achieve the data pipelining. We have implemented our SPDP technique with the IMPACT compiler, and conduct experiments using a set of loop kernels from DSPstone, Mibench, and Mediabench on the cycle‐accurate VLIW simulator of Trimaran. The experimental results show that our technique achieves performance improvement compared with the previous work. Copyright © 2010 John Wiley & Sons, Ltd.

As MOSFETs (metal-oxide-semiconductor field effect transistor) dimensions enter the sub-micrometer region, reducing leakage power becomes a significant issue of VLSI industry. In this paper, we propose a novel prediction approach to predict idleness of functional units for leakage energy management. Using a state-based predictor, historical utilization information of functional units is exploited to adjust the state of the predictor so as to enhance the accuracy of prediction. We implement our approach based on SimpleScalar and conduct experiments with a suite of fourteen benchmarks from Trimaran. The experimental results show that our approach achieves the better results compared with the previous work.