Because the aerospace-ground Internet no longer relies on deploying infrastructure such as base stations,it has the advantage of all-weather full coverage services that traditional terrestrial networks do not ***,the ...
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Because the aerospace-ground Internet no longer relies on deploying infrastructure such as base stations,it has the advantage of all-weather full coverage services that traditional terrestrial networks do not ***,the traditional global navigation satellite system does not support communication *** newly developing aerospace network system is still in the construction stage,and there is no applicable solution *** communication technology is an important method to solve the contradiction between the low battery capacity of the Internet of things(IoT)node and the high energy consumption of *** is the development trend of the ***,the current passive technology based on Wi-Fi and other signals cannot achieve arbitrary communication due to the excitation signal acquisition *** solve the above two major problems,this paper proposes a passive system design for aerospace-ground IoT *** system can use the global navigation signal as excitation signal for backscatter *** the global navigation signal has the characteristics of all-weather and full coverage,this design solves the carrier acquisition problem in previous *** addition,this paper also proposes a low-power signal detection technology that can detect navigation signals with high precision on passive *** evaluate system performance through simulation *** experimental results show that the backscatter system based on global navigation satellite signals can realize efficient communication of IoT nodes.
Simulating the total ionizing dose(TID)of an electrical system using transistor-level models can be difficult and expensive,particularly for digital-integrated circuits(ICs).In this study,a method for modeling TID eff...
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Simulating the total ionizing dose(TID)of an electrical system using transistor-level models can be difficult and expensive,particularly for digital-integrated circuits(ICs).In this study,a method for modeling TID effects in complementary metaloxide semiconductor(CMOS)digital ICs based on the input/output buffer information specification(IBIS)was *** digital IC was first divided into three parts based on its internal structure:the input buffer,output buffer,and functional *** of these three parts was separately *** the IBIS model,the transistor V-I characteristic curves of the buffers were processed,and the physical parameters were extracted and modeled using *** the functional area,logic functions were modeled in VHDL according to the data sheet.A golden digital IC model was developed by combining the input buffer,output buffer,and functional area ***,the golden ratio was reconstructed based on TID experimental data,enabling the assessment of TID effects on the threshold voltage,carrier mobility,and time series of the digital *** experiments were conducted using a CMOS non-inverting multiplexer,NC7SZ157,and the results were compared with the simulation results,which showed that the relative errors were less than 2%at each dose *** confirms the practicality and accuracy of the proposed modeling *** TID effect model for digital ICs developed using this modeling technique includes both the logical function of the IC and changes in electrical properties and functional degradation impacted by TID,which has potential applications in the design of radiation-hardening tolerance in digital ICs.
The discourse analysis task,which focuses on understanding the semantics of long text spans,has received increasing attention in recent *** a critical component of discourse analysis,discourse relation recognition aim...
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The discourse analysis task,which focuses on understanding the semantics of long text spans,has received increasing attention in recent *** a critical component of discourse analysis,discourse relation recognition aims to identify the rhetorical relations between adjacent discourse units(e.g.,clauses,sentences,and sentence groups),called arguments,in a *** works focused on capturing the semantic interactions between arguments to recognize their discourse relations,ignoring important textual information in the surrounding ***,in many cases,more than capturing semantic interactions from the texts of the two arguments are needed to identify their rhetorical relations,requiring mining more contextual *** this paper,we propose a method to convert the RST-style discourse trees in the training set into dependency-based trees and train a contextual evidence selector on these transformed *** this way,the selector can learn the ability to automatically pick critical textual information from the context(i.e.,as evidence)for arguments to assist in discriminating their *** we encode the arguments concatenated with corresponding evidence to obtain the enhanced argument ***,we combine original and enhanced argument representations to recognize their *** addition,we introduce auxiliary tasks to guide the training of the evidence selector to strengthen its selection *** experimental results on the Chinese CDTB dataset show that our method outperforms several state-of-the-art baselines in both micro and macro F1 scores.
Nowadays, the proliferation of open Internet of Things (IoT) devices has made IoT systems increasingly vulnerable to cyber attacks. It is of great practical significance to solve the security issues of IoT systems. Dr...
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Models based on MLP-Mixer architecture are becoming popular,but they still sufer from adversarial *** it has been shown that MLP-Mixer is more robust to adversarial attacks compared to convolutional neural networks(CN...
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Models based on MLP-Mixer architecture are becoming popular,but they still sufer from adversarial *** it has been shown that MLP-Mixer is more robust to adversarial attacks compared to convolutional neural networks(CNNs),there has been no research on adversarial attacks tailored to its *** this paper,we fll this *** propose a dedicated attack framework called Maxwell’s demon Attack(MA).Specifcally,we break the chan‑nel-mixing and token-mixing mechanisms of the MLP-Mixer by perturbing inputs of each Mixer layer to achieve high *** demonstrate that disrupting the MLP-Mixer’s capture of the main information of images by mask‑ing its inputs can generate adversarial examples with cross-architectural *** evaluations show the efectiveness and superior performance of *** generated based on masked inputs obtain a higher success rate of black-box attacks than existing transfer ***,our approach can be easily combined with existing methods to improve the transferability both within MLP-Mixer based models and to models with difer‑ent *** achieve up to 55.9%attack performance *** work exploits the true generaliza‑tion potential of the MLP-Mixer adversarial space and helps make it more robust for future deployments.
Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic *** tackle the AND problem,existing studies have propo...
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Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic *** tackle the AND problem,existing studies have proposed various approaches based on different types of information,such as raw document features(e.g.,co-authors,titles,and keywords),the fusion feature(e.g.,a hybrid publication embedding based on multiple raw document features),the local structural information(e.g.,a publication's neighborhood information on a graph),and the global structural information(e.g.,interactive information between a node and others on a graph).However,there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so *** fill the gap,we propose a novel framework named EAND(Towards Effective Author Name Disambiguation by Hybrid Attention).Specifically,we design a novel feature extraction model,which consists of three hybrid attention mechanism layers,to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients,raw document features,and the fusion *** hybrid attention mechanism layer contains three key modules:a local structural perception,a global structural perception,and a feature ***,the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector *** results on two real-world datasets demonstrate that EAND achieves superior performance,outperforming state-of-the-art methods by at least+2.74%in terms of the micro-F1 score and+3.31%in terms of the macro-F1 score.
Point cloud completion aims to infer complete point clouds based on partial 3D point cloud *** previous methods apply coarseto-fine strategy networks for generating complete point ***,such methods are not only relativ...
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Point cloud completion aims to infer complete point clouds based on partial 3D point cloud *** previous methods apply coarseto-fine strategy networks for generating complete point ***,such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial *** this paper,a novel feature alignment fast point cloud completion network(FACNet)is proposed to directly and efficiently generate the detailed shapes of *** aligns high-dimensional feature distributions of both partial and complete point clouds to maintain global information about the complete *** its decoding process,the local features from the partial point cloud are incorporated along with the maintained global information to ensure complete and time-saving generation of the complete point *** results show that FACNet outperforms the state-of-theart on PCN,Completion3D,and MVP datasets,and achieves competitive performance on ShapeNet-55 and KITTI ***,FACNet and a simplified version,FACNet-slight,achieve a significant speedup of 3–10 times over other state-of-the-art methods.
Image bitmaps,i.e.,data containing pixels and visual perception,have been widely used in emerging applica-tions for pixel operations while consuming lots of memory space and *** with legacy DRAM(dynamic ran-dom access...
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Image bitmaps,i.e.,data containing pixels and visual perception,have been widely used in emerging applica-tions for pixel operations while consuming lots of memory space and *** with legacy DRAM(dynamic ran-dom access memory),non-volatile memories(NVMs)are suitable for bitmap storage due to the salient features of high density and intrinsic ***,writing NVMs suffers from higher energy consumption and latency compared with read *** precise or approximate compression schemes in NVM controllers show limited performance for bitmaps due to the irregular data patterns and variance in *** observe the pixel-level similarity when writing bitmaps due to the analogous contents in adjacent *** exploiting the pixel-level similarity,we propose SimCom,an approximate similarity-aware compression scheme in the NVM module controller,to efficiently compress data for each write access *** idea behind SimCom is to compress continuous similar words into the pairs of base words with *** storage costs for small runs are further mitigated by reusing the least significant bits of base *** adaptively selects an appropriate compression mode for various bitmap formats,thus achieving an efficient trade-off be-tween quality and memory *** implement SimCom on GEM5/zsim with NVMain and evaluate the perfor-mance with real-world image/video *** results demonstrate the efficacy and efficiency of our SimCom with an efficient quality-performance trade-off.
Adversarial examples(AEs) are an additive amalgamation of clean examples and artificially malicious perturbations. Attackers often leverage random noise and multiple random restarts to initialize perturbation starting...
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Adversarial examples(AEs) are an additive amalgamation of clean examples and artificially malicious perturbations. Attackers often leverage random noise and multiple random restarts to initialize perturbation starting points, thereby increasing the diversity of AEs. Given the non-convex nature of the loss function, employing randomness to augment the attack's success rate may lead to considerable computational overhead. To overcome this challenge,we introduce the one-hot mean square error loss to guide the initialization. This loss is combined with the strongest first-order attack, the projected gradient descent, alongside a dynamic attack step size adjustment strategy to form a comprehensive attack process. Through experimental validation, we demonstrate that our method outperforms baseline attacks in constrained attack budget scenarios and regular experimental settings. This establishes it as a reliable measure for assessing the robustness of deep learning models. We explore the broader application of this initialization strategy in enhancing the defense impact of few-shot classification models. We aspire to provide valuable insights for the community in designing attack and defense mechanisms.
An abundance of data have been generated from various embedded devices, applications, and systems, and require cost-efficient storage services. Data deduplication removes duplicate chunks and becomes an important tech...
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An abundance of data have been generated from various embedded devices, applications, and systems, and require cost-efficient storage services. Data deduplication removes duplicate chunks and becomes an important technique for storage systems to improve space efficiency. However, stored unique chunks are heavily fragmented, decreasing restore performance and incurs high overheads for garbage collection. Existing schemes fail to achieve an efficient trade-off among deduplication, restore and garbage collection performance, due to failing to explore and exploit the physical locality of different chunks. In this paper, we trace the storage patterns of the fragmented chunks in backup systems, and propose a high-performance deduplication system, called HiDeStore. The main insight is to enhance the physical-locality for the new backup versions during the deduplication phase, which identifies and stores hot chunks in the active containers. The chunks not appearing in new backups become cold and are gathered together in the archival containers. Moreover, we remove the expired data with an isolated container deletion scheme, avoiding the high overheads for expired data detection. Compared with state-of-the-art schemes, HiDeStore improves the deduplication and restore performance by up to 1.4x and 1.6x, respectively, without decreasing the deduplication ratios and incurring high garbage collection overheads.
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