ICT Express最新文献

Digital credentials represent digital versions of physical credentials. They are the cornerstone of digital identity on the Internet. In order to enhance privacy, different authors implement selective disclosure in digital credentials, allowing users to disclose only the claims or attributes they want. This paper gives an overview of the most influential articles for selective disclosure, a chronology of the evolution of the methods, and a list of strategies and approaches to the problem. We identify the categories of approaches and their advantages and disadvantages. In addition, we recognize research gaps and open challenges and provide potential future directions.

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.

Consider a large-scale distributed system in which each computing device is observing triggers from an external source. Distributed Trigger Counting (DTC) algorithm is used to detect the state of the system when the aggregated number of the observed triggers reaches a predefined value. In this paper, we propose a simple and efficient DTC algorithm: Cascading Thresholds (CT). We mathematically show that CT is an optimal DTC algorithm in terms of the total number of exchanged messages among the devices (message complexity). For the maximum number of received messages per device (MaxRcv), CT is sub-optimal. The average message complexity of CT is $O(Nlog(W/N))$, and MaxRcv of it is $O(klog(W/N)+N)$, where $W$ is the number of triggers to be detected, $N$ is the number of devices, and $k$ is the degree of a node in the tree-like structure. Compared to the previous optimal algorithm (TreeFill), CT is much simpler: in our implementation the code size is about 2.5 times smaller. Also, unlike TreeFill CT does not require complicated mechanisms including distributed locking. Experimental results show that CT has a lower message complexity and MaxRcv compared to the previous work (CoinRand and RingRand). Furthermore, CT and TreeFill show a similar performance. From its simplicity, CT is more practical than previous work including TreeFill, CoinRand and RingRand.

Keyphrase Prediction (KP) is essential for identifying keyphrases in a document that can summarize its content. However, recent Natural Language Processing (NLP) advances have developed more efficient KP models using deep learning techniques. The limitation of a comprehensive exploration jointly both keyphrase extraction and generation using pre-trained language models spotlights a critical gap in the literature, compelling our survey paper to bridge this deficiency and offer a unified and in-depth analysis to address limitations in previous surveys. This paper extensively examines the topic of pre-trained language models for keyphrase prediction (PLM-KP), which are trained on large text corpora via different learning (supervisor, unsupervised, semi-supervised, and self-supervised) techniques, to provide respective insights into these two types of tasks in NLP, precisely, Keyphrase Extraction (KPE) and Keyphrase Generation (KPG). We introduce appropriate taxonomies for PLM-KPE and KPG to highlight these two main tasks of NLP. Moreover, we point out some promising future directions for predicting keyphrases.

Cooperative beamforming (CB), where spatially distributed nodes synchronize their transmit phases to maximize the power of their combined signal at a desired receiver, can be used as an effective solution for hardware-limited nodes to achieve communication range extension. Thus, CB demonstrates effectiveness in providing higher reliability, lower latency, and extended transmission range for nodes in non-terrestrial networks (NTNs), which inherently face power and hardware limitations. In this paper, we consider the CB technique using UAVs in three-dimensional (3D) networks and analyze the average beampattern of the virtual antenna array constructed by the multiple UAVs. Further, because the mobile nature of the UAVs may cause carrier frequency offsets (CFOs), we analyze the impact of the CFO using the non-parametric kernel method. The simulation and analytical results show that the peak average beampattern degrades by about 3 dB with the CFO standard deviation of 1 kHz, which emphasizes the significance of frequency synchronization.

The proposed idea is to give all the agricultural stakeholders secure storage. We must automate several processes utilizing brilliant codes to reduce risks and errors. The suggested schema applies Blockchain, source codes, and IoT on a farm network to enhance the analysis of agrarian datasets and tracking products to raise the productivity of agro-based supply chains. The application’s architecture will fix the faults found in earlier research. In the suggested method, sensors give us information about the environment. The Blockchain ledger stores our data in blocks. We create special agricultural automated codes in the treatment layer to automate task decisions.

In this paper, a bi-directional sliding window decoder is proposed for spatially coupled low-density parity-check (SC-LDPC) codes, which improves the decoding complexity and performance compared to the conventional sliding window decoding (SWD) by sharing messages at the overlapped part of forward and backward decoding windows. Moreover, by using proper scaling factors that determine the weight of each message at the overlapped part of two sliding windows, good local decoding effects can be efficiently spread out to both ends of SC-LDPC code during decoding process. Such effective message updates of the proposed bi-directional overlapped sliding window decoding (BO-SWD) improve error floor performance compared to the conventional SWD. The validity of BO-SWD is verified by simulation with various SC-LDPC ensembles.

This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), for wind speed forecasting. Initially, CEEMDAN with two nested layers decomposes the time series into intrinsic mode functions (IMFs) to enhance forecasting capabilities. Subsequently, CNN extracts features from IMFs, and BiLSTM captures temporal dependencies for precise predictions. GWO further enhances the accurac by selecting optimal hyperparameters based on decomposition results. Test results on diverse wind speed datasets demonstrate the model’s superiority, with a mean absolute percentage error (MAPE) of approximately 3%.

This paper proposes a channel response generative adversarial network (CRGAN)-based turbo code interleaver that estimates a channel response and interleaver indices at a transmitter by using a sound speed profile (SSP) and the ocean environments without feedback from a receiver. The interleaver indices are designed to allocate important bits from the turbo code to subcarriers with great channel gains, which reduces them from being affected by deep fading. Computer simulations and practical ocean experiments demonstrate that the proposed method estimates the channel response with low mean squared errors (MSEs) and improves bit error rate (BER) performances compared with the conventional method.

Photovoltaic power generation forecasting is crucial for energy management, smart grid construction, and energy markets. This study proposes a hybrid quantum–classical gated recurrent unit (HQGRU)-based framework for forecasting short-term photovoltaic power generation in a time-series manner. The HQGRU model uses a classical layer followed by a quantum embedding circuit to convert classical data into quantum data. Subsequently, variational quantum circuits are used for feature extraction. To demonstrate the performance of the proposed model, we used practical data on photovoltaic power generation and the weather in Busan, Republic of Korea. The results demonstrate the high accuracy of the proposed HQGRU model.