Blockchain sharding parallelises processing to boost throughput. Cross-shard transactions’ low transmission efficiency and security risks limit system scalability. We propose a secure cross-shard high-performance processing queuing model. First, we model hybrid multi-distribution batch arrival-processing and accurately depict transaction arrival and processing dynamics. Second, we construct a cross-shard transaction processing queuing model based on M/M/1/N queuing, along with a metric system for key performance indicators. Modifying the queue capacity to regulate batch control of cross-shard transactions directed at the target shard, thereby improving robustness and scalability. Third, we design a dynamic adaptive malicious transaction analysis bound, which derives an upper bound on the real-time tail probability via Chernoff’s inequality and Hoeffding’s inequality, and prove that the analysis bound can converge at an exponential rate under any shard size, thus effectively limiting the impact of malicious behaviours on the security of the shard system. Experimental results show that the proposed queuing model can reach a maximum throughput of about 8.0 × 10 4 TPS and achieve load balancing in high concurrency scenarios. The queuing waiting time is less than 0.5 ms, with the overload probability and the system failure probability converging to 0%, which verifies that the model has adequate security While ensuring high processing efficiency.

Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data. These benchmarks also fall short in assessing how LLM performance changes over time, as they consist of a static set of questions without a temporal dimension. To address these limitations, we propose using future event prediction as a continuous evaluation method to assess LLMs' temporal generalization and forecasting abilities. Our benchmark, Daily Oracle, automatically generates question-answer (QA) pairs from daily news, challenging LLMs to predict "future" event outcomes. Our findings reveal that as pre-training data becomes outdated, LLM performance degrades over time. While Retrieval Augmented Generation (RAG) has the potential to enhance prediction accuracy, the performance degradation pattern persists, highlighting the need for continuous model updates. Code and data are available at https://agenticlearning.ai/daily-oracle.

Orthogonal frequency division multiplexing (OFDM) is regarded as one of the key technologies in wireless communications, particularly in the integration of space and ground networks. Nevertheless, the performance of OFDM communication systems will degrade significantly in complex scenarios, which brings severe challenge to reliable information recovery at the receiver. To address this issue, we propose an intelligent receiving method for OFDM communication based on dual-channel convolutional neural network (DCNet) from the perspective of combining knowledge and data-driven, which introduces the domain knowledge of channel estimation to assist the stability of OFDM signal recovery. The experimental results under various simulation conditions demonstrate that the proposed method can effectively enhance the performance of information recovery in OFDM communication systems.

In order to reduce computational complexity and hardware cost for reconfigurable intelligent surface (RIS)-aided multiple-input–multiple-output (MIMO) systems, in this article, the subarray partition algorithm designs at RIS are investigated. Without instantaneous channel state information (CSI) of the RIS-related links, the subarray partition algorithms aim at minimizing the number of subarrays while keeping a minimum sum rate requirement. In nature, the subarray partition optimization problem is a combinatorial optimization and NP-hard because of many discrete optimization variables. Three kinds of subarray partition algorithms are proposed. The first one is named as a fixed pattern subarray partition algorithm, in which subarray is arranged in a predefined manner. This algorithm is easy to implement but its performance is far from optimal. To reap the benefits of RIS as much as possible, two dynamic pattern subarray partition algorithms are given as well. The first dynamic pattern algorithm is the greedy dynamic pattern subarray partition algorithm that is more complicated than the fixed pattern one but benefits much better performance. To reduce complexity, the relaxation-based dynamic pattern algorithm is given, which has almost the same performance as the greedy dynamic algorithm but has a much lower complexity. At the end of the whole work, numerical results are given to access the performance of the proposed algorithms.

In this article, we investigate the training optimization for multiple-input–multiple-output (MIMO)-aided Internet of Things (IoTs) systems that employ subarray-based intelligent reflecting surface (IRS). In order to overcome the nonlinear relationship between two cascaded channel matrices, the IRS can be divided into a series of subarrays, for which only an equivalent cascaded channel matrix should be estimated in each subarray. Correspondingly, the training sequence should be divided into multiple segments. By sufficiently utilizing the available statistical channel state information (CSI), either mean-square error (MSE) minimization or mutual information (MUI) maximization can be taken as the performance metric for optimizing the training sequence. A variety of fairnesses among different subarray channel estimations has been taken into account. Furthermore, in order to reduce the hardware cost of the power amplifier, we propose a two-stage training sequence structure, including a fully digital filter and a constant modulus sequence. To further reduce computational complexity, various low-complexity water-filling solutions are proposed. Numerical results demonstrate the accuracy and efficiency of the proposed solutions.

James Mullenbach, Yada Pruksachatkun, Sean Adler, Jennifer Seale, Jordan Swartz, Greg McKelvey, Hui Dai, Yi Yang, David Sontag. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

To design a highly automatic and practical discrimination method for high-resolution synthetic aperture radar (SAR) images in complex scenes, a novel target discrimination framework based on weakly supervised learning (WSL) of the mid-level features is proposed in this article. First, we extract the dense SAR scale-invariant feature transform (SAR-SIFT) features of the candidate regions obtained from the detected SAR images. Then, the dense SAR-SIFT descriptors are transformed into richer mid-level features by coding and pooling. Finally, the mid-level features are input into a WSL-based target discrimination method, where the training set is initially selected by the unsupervised latent Dirichlet allocation (LDA) and iteratively updated by the linear support vector machine (SVM) discriminator. In the proposed method, only the image-level annotations (weak labels), which indicate whether the images containing the targets of interest or not, are required. By introducing WSL, the manual annotations of target regions from SAR images can be avoided, which is generally expensive in complex scenes and may tend to be less accurate and unreliable for the occluded or camouflaged targets. The comprehensive and specific experiments on the measured SAR data have demonstrated the effectiveness of the proposed method in benchmarking with the supervised learning-based linear SVM and linear support vector data description (SVDD) discriminators.

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability.SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models.We demonstrate the effectiveness of SRU on multiple NLP tasks.SRU achieves 5-9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models.We also obtain an average of 0.7 BLEU improvement over the Transformer model (Vaswani et al., 2017) on translation by incorporating SRU into the architecture.1

Target detection for synthetic aperture radar (SAR) images has great influence on the successive discrimination based on the target regions. However, as a pixel-based method, the traditional constant false alarm rate (CFAR) detection could not work well for the ship target detection problem of multiple ship targets with different sizes in a SAR image, which is referred to as the multiscale situation. Moreover, it needs to use the clustering method on the pixel-level detection results to obtain the accurate target regions, which may merge two or more different targets into a target region. In this letter, a modified CFAR based on object proposals is proposed. We use the object proposal generator to generate a small set of object proposals with different sizes, and then use the proposal-based CFAR detector, where the extracted object proposals are regarded as the guard windows instead of setting fixed guard window, to detect the true positive object proposals. By introducing the object proposals as the variable guard windows in the CFAR detector, the proposed algorithm could gain good detection performance in the multiscale situation, since the missed detection resulting from the big differences between the sizes of the fixed guard window and ship targets can be avoided. Meanwhile, the proposed method can directly obtain the accurate target regions. The effectiveness of the proposed algorithm is verified using the measured SAR data.

This letter presents a new unsupervised distribution-free change detection method for synthetic aperture radar (SAR) images based on scale-invariant feature transform (SIFT) keypoints and region information. Since the SIFT can detect blob-like structures in an image and be insensitive to noise, we first extract noise-robust SIFT keypoints in the log-ratio image to reduce the detection range. Then, in order to obtain accurate changed regions, rather than directly obtaining the change-detection map from the difference image as in some traditional change detection methods, we make segmentation around the extracted keypoints in the two original multitemporal SAR images, where the edges of detection regions are much clearer than those in the difference image, and further compare the two segmentations to generate the change-detection map. This method utilizes the blob-like structure information offered by SIFT keypoints and the region information extracted via image segmentation. Experiments on real SAR images demonstrate the effectiveness of the proposed method.

The model-based steganographic algorithm MB2 modified the blockiness after secret messages are embedded, which makes the existed detection algorithm based on border artifacts invalidate. By further researching on the embedding principle of MB2, this paper analyzes the coefficients alteration results of given stego image after re-embedding with maximum messages. Based on the conclusions, this paper proposes an evaluation method for the relativity between intra-block pixels, the approximately linear relationship between the evaluated value and embedding ratio is derived by experiments. Based on these, an embedding ratio estimation method to MB2 is proposed. Experimental results show that the proposed method can estimate the embedding ratio of MB2 effectively.

In this paper, we propose a fast placer for FPGA placement on a new commercial hierarchical FPGA device. The novelty of this research lies in the application of a multilevel V-shape optimization flow including an architecture related cluster process and a constructive placement. The new placer can handle large-scale FPGA placement problem quickly. Experimental results show that the proposed placer can further reduced the wirelength average 28.3% compared with simulated annealing based tool while achieving near 5X speedup in runtime for the five largest MCNC benchmarks.

To improve spectrum efficiency, future wireless devices will use cognitive radios to dynamically access spectrum. While offering great flexibility and software-reconfigurability, unsecured cognitive radios can be easily manipulated to attack legacy and future wireless networks. In this paper, we explore the feasibility and impact of cognitive radio based jamming attacks on 802.11 networks. We show that attackers can utilize cognitive radios' fast channel switching capability to amplify their jamming impact across multiple channels using a single radio. We also examine the impact of hardware channel switching delays and jamming duration on the impact of jamming.

Today's architecture for interconnecting wireless sensor networks (WSNs) and the Internet is based on treating a WSN as a separate entity from the Internet. Our approach to unifying sensor networks with the Internet is instead to decouple the relationship between the API-enforced database view and the gateway, making the gateway more general by introducing application-level overlay networking into the gateway. In our approach, sensor network packets, rather than being stopped at the gateway, are instead encapsulated into IP packets, and then directed from the gateway to any number of interested applications and services residing remotely on the Internet.

An efficient and reliable file storage system is important to micro sensor nodes so that data can be logged for later asynchronous delivery across a multi-hop wireless sensor network. Designing and implementing such a file system for a sensor node faces various challenges. Sensor nodes are highly resource constrained in terms of limited runtime memory, limited persistent storage, and finite energy. Also, the flash storage medium on sensor nodes differs in a variety of ways from the traditional hard disk, e.g. in terms of the limited number of writes for a flash memory unit. We present the design and implementation of ELF, an efficient log-structured flash-based file system tailored for sensor nodes. ELF is adapted to achieve memory efficiency, low power operation, and tailored support for common types of sensor file operations such as appending data to a file. ELF's log-structured approach achieves wear levelling across flash memory pages with limited write lifetimes. ELF also uniquely provides garbage collection capability as well as reliability for micro sensor nodes. A performance evaluation of an implementation of ELF based on TinyOS and MICA2 sensor motes is presented.

By spreading the workload across a sensor network, load balancing reduces hot spots in the sensor network and increases the energy lifetime of the sensor network. In this paper, we design a node-centric algorithm that constructs a load-balanced tree in sensor networks of asymmetric architecture. We utilize a Chebyshev Sum metric to evaluate via simulation the balance of the routing trees produced by our algorithm. We find that our algorithm achieves routing trees that are more effectively balanced than the routing based on breadth-first search (BFS) and shortest-path obtained by Dijkstra's algorithm.

The MANTIS MultimodAl system for NeTworks of In-situ wireless Sensors provides a new multithreaded embedded operating system integrated with a general-purpose single-board hardware platform to enable flexible and rapid prototyping of wireless sensor networks.

This paper describes methods for service selection and service access for mobile, sensor-enhanced web clients such as wireless cameras or wireless PDAs with sensor devices attached. The clients announce their data-creating capabilities in "Produce" headers sent to servers; servers respond with forms that match these capabilities. Clients fill in these forms with sensor data as well as text or file data. The resultant system enables clients to access dynamically discovered services spontaneously, as their users engage in everyday nomadic activities.