Large language models (LLMs) have transformed code generation.However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity language, the predominant programming language for Ethereum smart contracts.Due to the lack of adequate benchmarks for Solidity, LLMs' ability to generate secure, costeffective smart contracts remains unexplored.To fill this gap, we construct SolEval, the first repository-level benchmark designed for Solidity smart contract generation, to evaluate the performance of LLMs on Solidity.Sol-Eval consists of 1,507 samples from 28 different repositories, covering 6 popular domains, providing LLMs with a comprehensive evaluation benchmark.Unlike the existing Solidity benchmark, SolEval not only includes complex function calls but also reflects the real-world complexity of the Ethereum ecosystem by incorporating Gas@k and [email protected] evaluate 16 LLMs on SolEval, and our results show that the best-performing LLM achieves only 26.29% Pass@10, highlighting substantial room for improvement in Solidity code generation by LLMs.Additionally, we conduct supervised fine-tuning (SFT) on Qwen-7B using SolEval, resulting in a significant performance improvement, with Pass@5 increasing from 16.67% to 58.33%, demonstrating the effectiveness of fine-tuning LLMs on our benchmark.We release our data and code at https: //github.com/pzy2000/SolEval.

Domain generalization (DG) strives to address distribution shifts across diverse environments to enhance model’s generalizability. Current DG approaches are confined to acquiring robust representations with continuous features, specifically training at the pixel level. However, this DG paradigm may struggle to mitigate distribution gaps in dealing with a large space of continuous features, rendering it susceptible to pixel details that exhibit spurious correlations or noise. In this paper, we first theoretically demonstrate that the domain gaps in continuous representation learning can be reduced by the discretization process. Based on this inspiring finding, we introduce a novel learning paradigm for DG, termed Discrete Domain Generalization (DDG). DDG proposes to use a codebook to quantize the feature map into discrete codewords, aligning semantic-equivalent information in a shared discrete representation space that prioritizes semantic-level information over pixel-level intricacies. By learning at the semantic level, DDG diminishes the number of latent features, optimizing the utilization of the representation space and alleviating the risks associated with the wide-ranging space of continuous features. Extensive experiments across widely employed benchmarks in DG demonstrate DDG’s superior performance compared to state-of-the-art approaches, underscoring its potential to reduce the distribution gaps and enhance the model’s generalizability.

Minimum storage regenerating (MSR) codes are extensively studied in the literature to reduce the network bandwidth consumed during node repair. In this paper, we focus on repairing multiple node failures and construct binary cooperative MSR codes with optimal access bandwidth. Specifically, we present explicit constructions of the codes by designing the parity-check matrices over a special polynomial ring ${\mathcal{R}} = {{\mathbb{F}}_2}[x]/\left({1 + x + \cdots + {x^{p - 1}}}\right)$ where p is a prime. The obtained codes with length n and dimension k achieve the lower bound on repair bandwidth under cooperative repair model with optimal access property for any 2 ≤ h ≤ r and k+1 ≤ d ≤ n – h where h and d are the number of failure nodes and helper nodes, respectively. Moreover, the computation operations involved in encoding, decoding and nodes repair are only exclusive ORs and cyclic shifts, resulting in less CPU overhead compared with the complex multiplication operations over finite fields.

While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing gas-efficient and secure code, which are critical requirements for real-world smart contract deployment. Although recent advances leverage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for code preference alignment, existing approaches treat functional correctness, gas optimization, and security as independent objectives, resulting in contracts that may achieve operational soundness but suffer from prohibitive execution costs or dangerous vulnerabilities. To address these limitations, we propose PrefGen, a novel framework that extends standard DPO beyond human preferences to incorporate quantifiable blockchain-specific metrics, enabling holistic multi-objective optimization specifically tailored for smart contract generation. Our framework introduces a comprehensive evaluation methodology with four complementary metrics: Pass@k (functional correctness), Compile@k (syntactic correctness), Gas@k (gas efficiency), and Secure@k (security assessment), providing rigorous multi-dimensional contract evaluation. Through extensive experimentation, we demonstrate that PrefGen significantly outperforms existing approaches across all critical dimensions, achieving 66.7% Pass@5, 58.9% Gas@5, and 62.5% Secure@5, while generating production-ready smart contracts that are functionally correct, cost-efficient, and secure.

Graph Neural Networks (GNNs) achieve strong performance in node classification tasks but exhibit substantial performance degradation under label noise. Despite recent advances in noise-robust learning, a principled approach that exploits the node-neighbor interdependencies inherent in graph data for label noise detection remains underexplored. To address this gap, we propose GD$^2$, a noise-aware \underline{G}raph learning framework that detects label noise by leveraging \underline{D}ual-view prediction \underline{D}iscrepancies. The framework contrasts the \textit{ego-view}, constructed from node-specific features, with the \textit{structure-view}, derived through the aggregation of neighboring representations. The resulting discrepancy captures disruptions in semantic coherence between individual node representations and the structural context, enabling effective identification of mislabeled nodes. Building upon this insight, we further introduce a view-specific training strategy that enhances noise detection by amplifying prediction divergence through differentiated view-specific supervision. Extensive experiments on multiple datasets and noise settings demonstrate that \name~achieves superior performance over state-of-the-art baselines.

With the development of quantum blockchain, the quantum consensus protocols have garnered increasing attention, which play a crucial role in driving the implementation of quantum blockchains. However, existing protocols, derived from the classical consensus algorithms, face practical application challenges due to current quantum technology limitations. The first challenge is the bottleneck in generating large-scale entangled quantum states. The second challenge arises from the generation of malicious quantum states. The final challenge involves privacy concerns. To address these challenges, we propose a practical iterative QUantum consensus protocol with sharding construction, namely, Q-Union. In fact, Q-Union employs an iterative consensus algorithm where participating nodes are divided into multiple smaller shards, with the consensus process occurring within the current shard, and new shards are involved only if consensus is not achieved. Leveraging Greenberger-Horne- Zeilinge states and Aharonov states, Q-Union harnesses the advantages of quantum mechanics to achieve anonymous consensus, protecting the private information of participating nodes. Additionally, by integrating state verification, Q-Union ensures the correctness of the consensus procedure in the presence of malicious nodes generating adversarial quantum states. Finally, it is proven that Q-Union can also defend against Byzantine attacks from adversarial nodes, maintaining the same security level as traditional non-sharded consensus protocols. Specifically, it consistently outputs the correct consensus when the fraction of adversaries among participating nodes is less than 1/2 with synchronous communication. Both the theoretical analysis and performance illustration demonstrate the superior performance of the proposed Q-Union compared to state-of-the-art protocols.

Privacy-preserving federated learning (PPFL) is a promising secure distributed learning paradigm, which enables collaborative training of a global machine learning model through sharing encrypted local models instead of sensitive raw data. PPFL, however, is vulnerable to model poisoning attacks. Most existing Byzantine-robust PPFL solutions typically employ two non-colluding servers to achieve secure model detection and aggregation by executing interactive security protocols, which incur considerable computation and communication overheads. To tackle this issue, we propose an efficient and secure federated learning (ESFL) technique to accelerate the detection of poisonous models in PPFL. First, to improve computational efficiency, we construct a lightweight non-interactive efficient decryption functional encryption (NED-FE) scheme to protect the data privacy of local models. Then, to ensure high communication performance, we elaborately design a non-interactive privacy-preserving robust aggregation strategy, which efficiently detects the blind poisonous models and aggregates benign models. Finally, we implement ESFL and conduct extensive theoretical analysis and experiments. The numerical results demonstrate that ESFL not only achieves the confidentiality and robustness design goals but also maintains high efficiency. Compared with the baseline, ESFL effectively reduces the aggregation latency by up to 88%.

In recent years, there has been growing interest in heterogeneous distributed storage systems (DSSs), such as clustered DSSs, which are widely used in practice. However, research regarding information-theoretic security in heterogeneous DSSs remains limited. Furthermore, unlike traditional DSSs, the heterogeneous DSSs face eavesdropper with diverse operating patterns, complicating the secrecy models. In this paper, we aim to investigate the secrecy capacity and code constructions for clustered DSSs (CDSSs), a type of heterogeneous DSSs in which the system is divided into clusters with an equal number of nodes and different repair bandwidths for intra-cluster and cross-cluster against two types of eavesdroppers: the occupying-type eavesdropper and the osmotic-type eavesdropper. We construct two CDSS secrecy models tailored to these aforementioned eavesdroppers, derive the upper bounds on adjustable secrecy capacities, and explore the relationships between the upper bounds of perfect secrecy capacities and the number of compromised nodes. Notably, the upper bounds obtained in this paper generalize those of the traditional DSS model. Additionally, we propose three repair-by-transfer code constructions that achieve the secrecy capacity under both eavesdropper scenarios. These codes are based on nested MDS code and represent a generalized form of the minimum bandwidth regenerating (MBR) codes in traditional DSSs.

Generalization under distribution shifts has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization (DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we first present a theoretical and empirical analysis on the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk, and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS. The source code will be available at https://github.com/longshaocong/DTCS.

Distribution-preserving integrated sensing and communication is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribution and the original state distribution to force the system to preserve the statistical property of the channel states. An inner bound of the distribution-preserving capacity-distortion region is provided with some capacity region results under special cases. Furthermore, we consider the case where the system aims to keep the reconstructed sequence secret from an eavesdropper who also observes the channel output and receives rate-limited side information about the estimator. An inner bound of the tradeoff region and a capacity-achieving special case are presented. In addition, we provide some numerical examples to illustrate the tradeoff between the communication rate, distortion, and the preservation of the distribution.

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.

As the adoption of electric vehicles continues to grow, the demand for extensive charging infrastructure in urban areas is concurrently rising. In response to the evolving charging infrastructure shortage, private charging piles have emerged as crucial supplementary energy sources, especially in areas lacking public charging infrastructure. The sharing of private charging piles, however, introduces several challenges. Notably, the variable availability time and extremely limited usage space of private charging piles pose scheduling complexities for charging pile owners. Furthermore, the completely peer-to-peer operation of private charging piles may lead to suboptimal solutions for fulfilling overall charging demand. To comprehensively address these challenges, we explore the potential for cooperation among geographically proximate charging piles. We introduce a novel online booking mechanism paired with specialized scheduling algorithms designed for scenarios involving both multiple private charging piles and single private charging piles. Our objective is to maximize the attained revenue of charging pile owners under fully dynamic conditions on both the supply and demand sides. Through meticulous theoretical proofs, we show that our mechanism achieves advantageous competitive ratios for both scenarios when compared to the offline optimal solutions. Numerous experiments, conducted with real charging sessions, consistently demonstrate that the proposed mechanism achieves the highest revenue, providing substantial evidence for its superior performance.

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connectsupplyanddemand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On amacroscopiclevel, it impacts the power grid's peak load and reliability, whilelocally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2.

Harnessing the benefits of blockchain, such as decentralization, immutability, and transparency, to bolster the credibility and security attributes of federated learning (FL) has garnered increasing attention. However, blockchain-enabled FL (BFL) still faces several challenges. The primary and most significant issue arises from its essential but slow validation procedure, which selects high-quality local models by recruiting distributed validators. The second issue stems from its incentive mechanism under the transparent nature of blockchain, increasing the risk of privacy breaches regarding workers’ cost information. The final challenge involves data eavesdropping from shared local models. To address these significant obstacles, this paper proposes a Blockchain-enabled Incentivized and Secure Federated Learning (BIT-FL) framework. BIT-FL leverages a novel loop-based sharded consensus algorithm to accelerate the validation procedure, ensuring the same security as non-sharded consensus protocols. It consistently outputs the correct local model selection when the fraction of adversaries among validators is less than$1/2$with synchronous communication. Furthermore, BIT-FL integrates a randomized incentive procedure, attracting more participants while guaranteeing the privacy of their cost information through meticulous worker selection probability design. Finally, by adding artificial Gaussian noise to local models, it ensures the privacy of trainers’ local models. With the careful design of Gaussian noise, the excess empirical risk of BIT-FL is upper-bounded by$\mathcal {O}(\frac{\ln n_{\min}}{ n_{\min}^{3/2}}+\frac{\ln n}{n})$, where$n$represents the size of the union dataset, and$n_{{\min}}$represents the size of the smallest dataset. Our extensive experiments demonstrate that BIT-FL exhibits efficiency, robustness, and high accuracy for both classification and regression tasks.

The source localization is a process to determine the location of a signal source based on measurements from sensor nodes or receivers that are spatially distributed in the area around the source. Localizing intruders and mobile users are some of the utilizations of wireless sensor networks (WSNs) based source localization problem. However, sensor faults and channel noise are the important issues that degrade the source localization performances of the systems. In this article, we significantly study and improve our recent results; the representative fault tolerant hitting set and feature selection approaches for localizing a source in a decentralized WSN using the faulty sensor nodes and channel noise. First, we propose a technique to place some active sensor nodes in the sleep state such that the source localization performances of the hitting set and feature selection approaches do not significantly degrade. The proposed technique takes advantage of spatial correlations among sensor nodes, where nearby sensor nodes provide same decisions to the fusion center with a high probability. We observe that the performance gap for the results with and without sleeping sensor nodes decreases by increasing total sensor nodes. In addition, we improve our recent results by proposing a technique based on spatial correlations among sensor nodes to minimize the impact of channels’ noises, especially for a higher fault probability of sensor nodes. Finally, extensive simulations on synthetic and real-world datasets validate our theoretical results.

Due to the progress in computer hardware and network technologies, federated learning (FL), a decentralized training method in machine learning, has garnered widespread attention. In this approach, individuals share local model parameters rather than raw training data to protect their privacy. However, the inherent heterogeneity of practical computing devices poses challenges to the efficiency and performance of FL. In this paper, we explore the landscape of heterogeneous FL frameworks and introduce ChronusFed, a reinforCement-based adaptive partial training method for heterogeneous Federated learning. ChronusFed employs a dynamic epoch adjustment mechanism (DEA) and a customizable partial training framework (CPT) to optimize model training efficiency. By integrating DEA and CPT, ChronusFed effectively tackles the straggler issues that arise from limited hardware resources, while simultaneously enhancing the model performance. More specifically, DEA leverages deep reinforcement learning (DRL) to model the current state of the global model and determine optimal local training epochs, while CPT utilizes our proposed maximum coverage algorithm to handle device heterogeneity and accelerate model convergence. Theoretical analysis of training convergence validates the effectiveness of ChronusFed, and comprehensive experimental evaluations demonstrate that ChronusFed outperforms state-of-the-art methods across various learning tasks, showcasing its robustness and superiority in heterogeneous FL scenarios.

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractional repetition codes that incur zero skip cost.

Distribution-preserving integrated sensing and communication with secure reconstruction is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribution and the original state distribution to force the system to preserve the statistical property of the channel states. An inner bound of the distribution-preserving capacity-distortion region is provided with some capacity region results under special cases. A numerical example demonstrates the tradeoff between the communication rate, reconstruction distortion and distribution preservation. Furthermore, we consider the case that the reconstructed sequence should be kept secret from an eavesdropper who also observes the channel output. An inner bound of the tradeoff region and a capacity-achieving special case are presented.

Gradient coding is a distributed computing technique aiming to provide robustness against slow or non-responsive computing nodes, known as stragglers, while balancing the computational load for responsive computing nodes. Among existing gradient codes, a construction based on combinatorial designs, called BIBD gradient code, achieves the best trade-off between robustness and computational load in the worst-case adversarial straggler setting. However, the range of system parameters for which BIBD gradient codes exist is limited. In this paper, we overcome this limitation and propose a new probabilistic gradient code, termed Sparse Gaussian (SG) gradient code. The encoding matrix of the proposed SG gradient code is generated from a carefully chosen correlated multivariate Gaussian distribution, masked by Bernoulli random variables to reduce computational load. With high probability, the proposed gradient code achieves a similar worst-case error performance compared to the BIBD gradient code (when such a code of the same parameters exists) and outperforms several other existing gradient codes, including Fractional Repetition gradient codes and Bernoulli gradient codes. Moreover, it further extends the range of system parameters over existing BIBD and soft BIBD gradient codes, making it a promising solution for distributed computing tasks.

Erasure codes are widely implemented in distributed storage systems to provide high fault tolerance with small storage overhead. Maximum distance separable codes are an common choice as they achieve the optimal tradeoff between fault tolerance and storage overhead. In this paper, we focus on the repair of multiple erasures of binary MDS array codes. Specifically, we present constructions of binary MDS array codes with optimal cooperative repair bandwidth by stacking multiple Blaum-Roth code instances whose “evaluation points” are judiciously designed. The constructed array codes with length$n$and dimension$k$can achieve the optimal cooperative repair bandwidth for$2\leq h\leq n-k$and$k+1\leq d\leq n-h$where$h$and$d$are the numbers of failed nodes and helper nodes, respectively. As the codes are constructed on a special polynomial ring over binary field, computation operations involved in nodes repair and file reconstruction for these codes are only XORs and cyclic shifts. Moreover, due to the inherent parallel structure of the codes, both the encoding and decoding procedures can be finished in parallel, speeding up the computing process.

Domain generalization (DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers (ViTs), which suffer from limited receptive fields or quadratic complexity issues. Mamba, as an emerging state space model (SSM), possesses superior linear complexity and global receptive fields. Despite this, it can hardly be applied to DG to address distribution shifts, due to the hidden state issues and inappropriate scan mechanisms. In this paper, we propose a novel framework for DG, named DGMamba, that excels in strong generalizability toward unseen domains and meanwhile has the advantages of global receptive fields, and efficient linear complexity. Our DGMamba compromises two core components: Hidden State Suppressing (HSS) and Semantic-aware Patch Refining (SPR). In particular, HSS is introduced to mitigate the influence of hidden states associated with domain-specific features during output prediction. SPR strives to encourage the model to concentrate more on objects rather than context, consisting of two designs: Prior-Free Scanning (PFS), and Domain Context Interchange (DCI). Concretely, PFS aims to shuffle the non-semantic patches within images, creating more flexible and effective sequences from images, and DCI is designed to regularize Mamba with the combination of mismatched non-semantic and semantic information by fusing patches among domains. Extensive experiments on four commonly used DG benchmarks demonstrate that the proposed DGMamba achieves remarkably superior results to state-of-the-art models. The code will be made publicly available at https://github.com/longshaocong/DGMamba.

Nowadays, unmanned aerial vehicles (UAVs) are widely applied in crowd sensing. For UAV-enabled crowd sensing (UAVCS) systems, the sensing outcome and charging cost are two primary concerns. To achieve a satisfactory sensing outcome under the charging budget, we exploit joint moving, sensing, and charging scheduling of UAVs, as they all have critical impacts on such two objectives. However, the dynamically generated sensing targets and the variety of sensing tasks a UAVCS system may face make farsighted scheduling of UAVs rather challenging. To this end, we propose a novel multi-task constrained multi-agent reinforcement learning (MARL) method to help UAVs make distributed moving, sensing, and charging decisions. Specifically, we design a multi-task MARL framework to learn a single generic policy for a large collection of tasks, and propose a primal-dual training algorithm that alternates between improving the overall sensing outcome and reducing each task's constraint violation. Theoretically, we show that our algorithm provably converges, and analyze the optimality gap and constraint violation of the trained policy on unseen tasks. Extensive experiments on an incident dataset in New York City demonstrate that our method outperforms strong baselines in sensing outcome maximization and budget satisfaction, and also generalize well to unseen tasks.

Maximum distance separable (MDS) codes are commonly deployed in distributed storage systems as they provide the maximum failure tolerance for some given redundancy. The repair problem of MDS codes has drawn much attention and various constructions of MDS array codes with optimal repair bandwidth have been proposed in the last decade. However, few of the existing codes are constructed over the binary field. In this paper, we propose new constructions of binary MDS array codes with optimal repair (or access) bandwidth for single-node failure. Specifically, by stacking multiple Blaum-Roth code instances of which the parity-check matrices are judiciously designed, we obtain three families of binary MDS array codes with optimal repair bandwidth; using the permutation matrices as building blocks, we also construct two families of binary MDS array codes with optimal access bandwidth. Moreover, error-resilient capability while achieving the lower bound on repair (or access) bandwidth is obtained when the number of helper nodes d < n - 1. All the codes in this paper are constructed over a particular ring of binary polynomials. Consequently, computation operations involved in the encoding, decoding and node repair procedures for these codes are only XORs and cyclic shifts, avoiding complex multiplications and divisions over large finite fields.

Domain generalization (DG) endeavours to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques tend to improve the feature generalizability by learning domain-invariant representations, inadvertently overlooking the feature discriminability. On the one hand, the simultaneous attainment of generalizability and discriminability of features presents a complex challenge, often entailing inherent contradictions. This challenge becomes particularly pronounced when domain-invariant features manifest reduced discriminability owing to the inclusion of unstable factors,i.e., spurious correlations. On the other hand, prevailing domain-invariant methods can be categorized as category-level alignment, susceptible to discarding indispensable features possessing substantial generalizability and narrowing intra-class variations. To surmount these obstacles, we rethink DG from a new perspective that concurrently imbues features with formidable discriminability and robust generalizability, and present a novel framework, namely, Discriminative Microscopic Distribution Alignment (DMDA). DMDA incorporates two core components: Selective Channel Pruning (SCP) and Micro-level Distribution Alignment (MDA). Concretely, SCP attempts to curtail redundancy within neural networks, prioritizing stable attributes conducive to accurate classification. This approach alleviates the adverse effect of spurious domain-invariance and amplifies the feature discriminability. Besides, MDA accentuates micro-level alignment within each class, going beyond mere category-level alignment. This strategy accommodates sufficient generalizable features and facilitates within-class variations. Extensive experiments on four benchmark datasets corroborate that DMDA achieves comparable results to state-of-the-art methods in DG, underscoring the efficacy of our method. The source code will be available at https://github.com/longshaocong/DMDA.

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents an incentive mechanism HERALD*, that takes into account the uncertainty and hidden bids of tasks without real-time constraints. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

Edge computing is a highly promising technology that empowers mobile devices to offload video streaming tasks to edge servers, thereby improving the video stream analysis performance. However, most existing research on edge video streaming has failed to give adequate attention to the joint optimization of video streaming tasks with respect to dynamics, redundancy, and long-term energy constraints. To address this limitation, we propose a novel method based on a multi-agent reinforcement learning algorithm, which significantly enhances the performance of edge video stream analysis under long-term energy constraints. Specifically, our proposed method conducts video compression and offloading under long-term energy constraints to maximize the long-term rewards of video task processing. Experimental evaluations have demonstrated the convergence of the proposed method, which outperforms the baseline solutions, achieving higher long-term rewards.

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states and the channel state information (CSI) is revealed to the encoders in a causal manner. An inner bound of the capacity is provided. To establish the inner bound, we investigate coding schemes incorporating wiretap coding and secret key agreement between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are studied. The first one uses backward decoding and Wyner-Ziv coding and the secret key is constructed from a lossy reproduction of the CSI. The other one is an extended version of the existing coding scheme for point-to-point wiretap channels with causal CSI. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.

Maximum distance separable codes are commonly used in large-scale distributed storage systems since they achieve the maximum fault tolerance for some given redundancy. In this paper, we focus on the repair problem of MDS codes. By stacking multiple Blaum-Roth code instances of which the parity-check matrices are judiciously designed, we construct three families of binary MDS array codes with optimal repair bandwidth for single-node failure. Specifically, codes in the first family achieve the lower bound on repair bandwidth where the number of helper nodes d is n − 1; The second family of codes are optimal-repair for any fixed d and the third are for multiple values of d simultaneously. Moreover, the last two also possess error-resilient capability and can achieve the corresponding optimal repair bandwidth. All the codes in this paper are constructed on a particular polynomial ring over binary field. Consequently, computation operations involved in node repair and file reconstruction for these codes are only XORs and cyclic shifts, avoiding complex multiplications and divisions over large finite fields.

As smart devices are becoming increasingly common in people’s daily lives, privacy and security concerns make data collection expensive and limited, which further hinder the development of data-driven tasks. This paper studies how to better conduct private data trading via a novel generator method rather than direct trading of raw data. This new method facilitates more convenient data transactions by generator, protects the privacy of data owners and is satisfactory in terms of privacy compensation and query pricing. In detail, we propose RARIEA, a market framework for tRading privAte data geneRators based on GAN under rényI diffErential privAcy, which involves data owners, a data broker, and data consumers. To start, the broker employs the GAN training generator to augment the data to relieve the data shortage, introducing noise into its training process to preserve the owners’ privacy. After that, the broker uses rényi differential privacy to quantify the privacy loss at the data item level during the GAN training process and compensates each owner according to their respective privacy policies. Finally, the data broker charges each of the data consumers for their queries, where the price is lower bounded by the total privacy compensation. We then evaluate the performance of RARIEA on classic data sets: MNIST, Fashion-MNIST, and CelebA. The analysis and simulation results reveal that the generator provided by RARIEA can not only meet the data consumers’ demand for quantity and quality but also protect the owners’ privacy. In addition, RARIEA not only allows finer control over data owner compensation, but also excels at controlling the data broker’s revenue to improve market efficiency while ensuring fairness, balance, and monotonicity of pricing.

The strong secrecy transmission problem of the arbitrarily varying wiretap channel (AVWC) with input and state constraints is investigated in this paper. First, a stochastic-encoder code lower bound of the strong secrecy capacity is established by applying the type argument and Csiszár’s almost independent coloring lemma. Then, a superposition stochastic-encoder code lower bound of the secrecy capacity is provided. The superposition stochastic-encoder code lower bound can be larger than the ordinary stochastic-encoder code lower bound. Random code lower and upper bounds of the secrecy capacity of the AVWC with constraints are further provided. Based on these results, we further consider a special case of the model, namely severely less noisy AVWC, and give the stochastic-encoder code and random code capacities. It is proved that the stochastic-encoder code capacity of the AVWC with constraints is either equal to or strictly smaller than the corresponding random code capacity, which is consistent with the property of the ordinary AVC. Finally, some numerical examples are presented to better illustrate our capacity results. Compared to the soft covering lemma that requires the codewords to be generated i.i.d., our method has more relaxed requirements regarding codebooks. It is proved that the good codebooks for secure transmission can be generated by choosing codewords randomly from a given type set, which is critical when considering the AVWC with constraints.

Locally Repairable codes (LRCs) have gained significant interest due to applications in distributed storage systems since they enable systems to recover a failed node by accessing few other active nodes. In particular, LRCs with large availability are highly desirable for parallel reading of hot data. In addition to the above applications in distributed storage systems, it was shown by Fazeli et al. that an LRC with large availability can produce a good private information retrieval (PIR) code which allows to reduce the storage overhead of a PIR protocol. Roughly speaking, one can obtain a good PIR code as long as there exists an LRC with large availability. One of the main tasks in studying PIR codes is to design a$t$-server PIR code with small length for the given dimension. In particular, the construction of binary PIR codes is of great interest. In this paper, we consider a construction of binary LRCs from polynomial evaluations. As a result, a new class of binary LRCs with large availability are obtained. Applying such LRCs to PIR codes, we obtain a new class of binary PIR codes. On one hand, the binary LRCs constructed are new in the sense that the parameter regime is not covered by the known LRCs. On the other hand, the parameters of PIR codes derived from our LRCs outperform the known results in certain parameter regimes and achieve the lower bound given by Fazeli et al. up to an absolute constant.

Sidelnikov sequences, a kind of cyclotomic sequences with many desired properties such as low correlation and variable alphabet sizes, can be employed to construct a polyphase sequence family that has many applications in high-speed data communications. Recently, cyclotomic numbers have been used to investigate the linear complexity of Sidelnikov sequences, mainly about binary ones, although the limitation on the orders of the available cyclotomic numbers makes it difficult. This paper continues to study the linear complexity over$\mathbb {F}_{M}$of$M$-ary Sidelnikov sequence of period$q-1$using Hasse derivative, which implies$q=p^{m}$,$m\geq 1$and$M|(q-1)$. The$t$th Hasse derivative formulas are presented in terms of cyclotomic numbers, and some upper bounds on the linear complexity for$M=2$and 3 are obtained only with some additional restrictions on$q$. Furthermore, concrete illustrations for several families of these sequences, such as$q\equiv 1\pmod {2}$and$q\equiv 1\pmod {3}$, show these upper bounds are tight and reachable; especially for$q=2\times 3^{\lambda }+1 (1\leq \lambda \leq 20)$, the exact linear complexities over$\mathbb {F}_{3}$of the ternary Sidelnikov sequences are determined; and it turns out that all the linear complexities of the sequences considered are very close to their periods.

The strong secrecy transmission problem of arbitrarily varying wiretap channel (AVWC) with input and state constraints is investigated in this paper. A stochastic code lower bound of the secrecy capacity is established by applying the type argument and Csiszár's almost independent coloring lemma, which includes the result of the ordinary AVWC as special case. Our codebook generation without the i.i.d. assumption of the soft covering lemma is to ensure the reliable communication over the AVWC with constraints. It is proved that the 0good codebooks for the strong secrecy transmission can be generated by choosing codewords randomly from a given type set, which is critical in this model.

This paper investigates the strong secrecy capacity of the arbitrarily varying multiple access channel (AVMAC). First, Csiszár's almost independent coloring lemma is generalized to establish an achievable secrecy rate region of the AVMAC with an eavesdropper, which includes existing results on both the arbitrarily varying wiretap channel and the multiple access wiretap channel. We then determine the capacity for a special case named semi-noiseless wiretap channel. In addition, a multi-letter outer bound is also presented. Finally, the results of this paper are further explained via a binary example with a numerical inner bound.

It is well known that finite blocklength analysis plays an important role in evaluating performances of communication systems in practical settings. This paper considers the achievability and converse bounds on the maximal channel coding rate (throughput) at a given blocklength and error probability in covert communication over AWGN channels. The covert constraint is given in terms of an upper bound on total variation distance (TVD) between the distributions of eavesdropped signals at an adversary with and without presence of active and legitimate communication, respectively. For the achievability, Gaussian random coding scheme is adopted for convenience in the analysis of TVD. The classical results of finite blocklength regime are not applicable in this case. By exploiting and extending canonical approaches, we first present new and more general achievability bounds for random coding schemes under maximal or average probability of error requirements. The general bounds are then applied to covert communication in AWGN channels where codewords are generated from Gaussian distribution while meeting the maximal power constraint. We further show an interesting connection between attaining tight achievability and converse bounds and solving two total variation distance based minimax and maxmin problems. The TVD constraint is analyzed under the given random coding scheme, which induces bounds on the transmission power through divergence inequalities. Further comparison is made between the new achievability bounds and existing ones derived under deterministic codebooks. Our thorough analysis thus leads us to a comprehensive characterization of the attainable throughput in covert communication over AWGN channels.

In recent years, crowdsensing has received extensive attention both in academia and industry. However, most of the prior works are based on centralized framework, where the users upload the sensor data to a platform alone. In this paper, we propose a decentralized data aggregation algorithm for crowdsensing, in which, participants negotiate the average of their sensor data in a pure distributed manner while enjoying differential privacy guarantee. Taking participants' privacy into consideration, we first redesign a distributed averaging algorithm by artificially adding random noise to accommodate the mobile crowdsensing scenario, where the sensor data may be sensible. We prove that, though random noises are involved, our algorithm almost surely yields an unbiased estimate of the exact average. We further theoretically formulate the trade-offs between differential privacy and the accuracy of the algorithm. Finally, we give the optimal choice for the additive noise with respect to the variance minimization of the estimate. The extensive simulations and realword test demonstrate the effectiveness of our algorithm.

This paper studies the privacy protection of individuals in contact tracing using social network for the viral disease outbreaks such as novel coronavirus (COVID-19). Contact tracing has been proved effective in controlling the diseases outbreaks such as Ebola virus disease in Africa. We propose the privacy protection mechanism which can be used by social networks under the assumption that the external knowledge (friendship network) is publicly available. We theoretically and experimentally analyze the proposed mechanism and show that the algorithm improves the privacy of individuals. The synthetic and real data sets are used for experimental evaluations.

Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein's general theory of relativity. However, it remains challenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity. In this work, the input data is pre-processed to form a 2D spectrum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. Then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. The simulation of our above ideas works on a generated data set with uniformly varying SNR (2-17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance on average compared to that of 1D CNNs, especially in the cases of lower SNR (4-9).

Mobile crowdsening (MCS) recently has been regarded as a newly-emerged sensing paradigm, which consists of a centralized cloud-based platform, some data demanders and some workers. However, since the platform needs to process tons of sensory data which is requested by the demanders and submitted by a large number of workers, this traditional MCS system may cause a heavy latency and serious network congestion, and thus can not be employed to some real-time and large-scale applications. Therefore, a new MCS system has been proposed recently by combining the edge computing, where some edge nodes (e.g., the base stations, laptops, smartphones) are added between the platform and workers to offload some operations from the platform to the network edges. Similar to the traditional MCS system, since participating in such edge-assisted MCS system is costly, it is important to attract more participation. However, this is more difficult since the platform and workers do not have direct communications with each other, which makes the edge nodes arbitrarily manipulate the information they transfer. Therefore, unlike the prior arts, we propose a novel incentive mechanism, namely, CHASTE, for such edge-assisted MCS system consisting of multiple demanders, some edge nodes and a crowd of workers where all of them behave strategically to maximize their own utility. Specifically, CHASTE is able to stimulate the participation, and satisfies the three-party truthfulness, three-party individual rationality, budget balance, as well as high social welfare. The desirable properties of CHASTE are validated through both theoretical analysis and extensive simulations.

Exploiting the computing capability of mobile devices with specialized engines (e.g., Neural Engine in iPhone), an attractive paradigm of federated learning that combines the mobile crowdsensing (MCS) has been deeply investigated recently (e.g., Google AI and Nvidia), where the training task is offloaded to the mobile crowd. However, this new paradigm still has numerous problems. Since executing the training task is costly for individual workers, the first problem is how to attract more participants. Following the incentive requirement, the second is how to preserve the workers' bid privacy since the reported costs are usually sensitive. Finally, the third problem is to guarantee the privacy protection on locally training models in the federated learning which involve the private information of local data. In this paper, we propose an incentivized federated learning with differential privacy in MCS system, namely, SHIELD, to solve the three significant problems. In fact, SHIELD satisfies the truthfulness and individual rationality while preserving the differential privacy of workers' bids and locally training models. Furthermore, for accuracy, the excess empirical risk of 1 SHIELD is proved to be upper bounded by O((ln(Knmin))1/2/Knmin+ ln(Knmin)/K2nmin2), where a special case for totally distributed scenario leads to a much sharper bound O( log(n)/n2) than the latest result O(ln(mnmin)/m2nmin2). Finally, comparing with the state-of-art apmin proaches, SHIELD illustrates superior performance by numerous experiments in classification and regression tasks.

In this article, a general framework for enhancing the physical layer security (PLS) in the Internet of Things (IoT) systems via channel feedback is established. To be specific, first, we study the compound wiretap channel (WTC) with feedback, which can be viewed as an ideal model for enhancing the PLS in the downlink transmission of IoT systems via feedback. A novel feedback strategy is proposed and a corresponding lower bound on the secrecy capacity is constructed for this ideal model. Next, we generalize the ideal model (i.e., the compound WTC with feedback) by considering channel states and feedback delay, and this generalized model is called the finite state compound WTC with delayed feedback. The lower bounds on the secrecy capacities of this generalized model with or without delayed channel output feedback are provided, and they are constructed according to variations of the previously proposed feedback scheme for the ideal model. Finally, from a Gaussian fading example, we show that the delayed channel output feedback enhances the achievable secrecy rate of the finite state compound WTC with only delayed state feedback, which implies that feedback helps to enhance the PLS in the downlink transmission of the IoT systems.

Min Zeng, Yisen Wang, Yuan Luo. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes). Intuitively, CE requires an extra term to facilitate learning of hard classes, and more importantly, this term should be noise tolerant, so as to avoid overfitting to noisy labels. Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels. We provide a theoretical analysis of SL and also empirically show, on a range of benchmark and real-world datasets, that SL outperforms state-of-the-art methods. We also show that SL can be easily incorporated into existing methods in order to further enhance their performance.

Despite recent breakthroughs in applications of deep neural networks, learning from a limited number of examples remains a key challenge in academia and industry. A prototypical example is one-shot learning, in which we must make predictions when only given one sample of each unseen class. Most previous works either categorize instances by the pair similarities or use Long-Short Term Memory (LSTM) structure based method, however, they usually suffer from inter-and intra-class variation problem or high computation cost. In this paper, we propose self-attentive networks to address the above issues in one-shot image recognition problem. Deep neural features and attention mechanism especially self-attention are employed in the proposed self-attentive networks. Experiments on Omniglot and MiniImagenet datasets show that our proposed self-attentive networks achieve state-of-the-art accuracy on one-shot image recognition. In addition, owing to low time complexity and parallelizable architecture, self-attentive networks require significantly less time to train and infer.

The linear complexity is a measure for the unpredictability of a sequence over a finite field. Sequences with good pseudo-random properties and large linear complexity are widely used in the CDMA spread spectrum communication and cryptography. In recent years, many researchers have focused on the linear complexity of cyclotomic sequences such as Sidel'nikov sequence. This paper studies the FM-linear complexity of M-ary Sidel'nikov sequence of period p-1 using the Hasse derivative of its generating function, where M|(p-1). The tth Hasse derivative formulas are generalized in terms of cyclotomic numbers, and then the exact F3-linear complexities of the ternary Sidel'nikov sequences are determined for p = 2·3λ+1(1 ≤ λ ≤ 20). It turns out that all of the linear complexities of the considered sequences are very close to their periods.

The model of wiretap channel (WTC) is important as it constitutes the essence of physical layer security. Recently, it has been shown that a noiseless feedback channel from the legitimate receiver to the transmitter helps to increase the secrecy capacity of the WTC. However, the present feedback strategy focuses only on generating the key from the feedback signal and using this key to protect part of the transmitted message. This secret key-based feedback strategy has been proved to be optimal only for some degraded cases. In this paper, we propose a new feedback strategy for the WTC, where the feedback signal is not only used to generate the key but also used to generate cooperative information helping the legitimate parties to improve their encoding and decoding performance. We show that the proposed new strategy performs better than the already existing one, and the results are further explained via a binary example.

Like classical block codes, a locally repairable code also obeys the Singleton-type bound (we call a locally repairable code optimal if it achieves the Singleton-type bound). In the breakthrough work of Tamo and Barg, several classes of optimal locally repairable codes were constructed via subcodes of Reed-Solomon codes. Thus, the lengths of the codes given by Tamo and Barg are upper bounded by the code alphabet size q. Recently, it was proved through the extension of construction by Tamo and Barg that the length of q-ary optimal locally repairable codes can be q +1 by Jin et al. Surprisingly, Barg et al. presented a few examples of q-ary optimal locally repairable codes of small distance and locality with code length achieving roughly q2. Very recently, it was further shown in the work of Li et al. that there exist q-ary optimal locally repairable codes with the length bigger than q+1 and the distance proportional to n. Thus, it becomes an interesting and challenging problem to construct new families of q-ary optimal locally repairable codes of length bigger than q+1. In this paper, we construct a class of optimal locally repairable codes of distances 3 and 4 with unbounded length (i.e., length of the codes is independent of the code alphabet size). Our technique is through cyclic codes with particular generator and parity-check polynomials that are carefully chosen.

The model of wiretap channel (WTC) is important as it constitutes the essence of physical layer security (PLS). Wiretap channel with noiseless feedback (WTC-NF) is especially interesting as it shows what can be done when a private feedback is available. The already existing secret key based feedback coding scheme focuses on generating key from the feedback and using this key to protect part of the transmitted message. It has been shown that this secret key based feedback coding scheme is only optimal for the degraded WTC-NF, and finding an optimal feedback scheme for the general WTC-NF motivates us to exploit other uses of the feedback. In this paper, a new feedback coding scheme for the general WTC-NF is proposed, where the feedback is not only used to generate key, but also used to generate help information which helps the legitimate parties to improve the communication between them. We show that the proposed new feedback scheme performs better than the already existing one, and a binary example is given to further explain the results of this paper.

Minimum storage regenerating codes have minimum storage of data in each node and therefore are maximal distance separable (for short) codes. Thus, the number of nodes is upper-bounded by 2b, where ú is the bits of data stored in each node. From both theoretical and practical points of view (see the details in Section 1), it is natural to consider regenerating codes that nearly have minimum storage of data, and meanwhile, the number of nodes is unbounded. One of the candidates for such regenerating codes is an algebraic geometry code. In this paper, we generalize the repairing algorithm of Reed-Solomon codes given by Guruswami and Wotters to algebraic geometry codes and present a repairing algorithm for arbitrary one-point algebraic geometry codes. By applying our repairing algorithm to the one-point algebraic geometry codes based on the Garcia- Stichtenoth tower, one can repair a code of rate 1 - e and length n over Fqwith bandwidth (n - 1)(1 - τ) log q for any e = 2(τ-1/2)logq with a real τ ∈ (0, 1/2). In addition, storage in each node for an algebraic geometry code is close to the minimum storage. Due to nice structures of Hermitian curves, repairing of Hermitian codes is also investigated. As a result, we are able to show that algebraic geometry codes are regenerating codes with good parameters.

As a new multiple access technique, sparse code multiple access (SCMA) combines quadrature amplitude modulation mapper and spreading together and thus significantly improves spectral efficiency. However, the computation complexity of most existing detection algorithms increases exponentially with df (the degree of the resource nodes). The parameter dfmust be designed to be very small, which largely limits the choice of codebooks. Even if the codebooks are designed to have low density, the detection still takes considerable time. In this paper, a new detection algorithm is proposed by discretizing the probability density functions (PDFs) in the layer nodes. Actually, the PDFs are updated according to the constraints in the factor graph after discretization. Compared with the conventional message passing algorithm (MPA) detector, the proposed detector only takes polynomial time to update one message in resource nodes instead of exponential time with negligible performance loss. In particular, it only needs near-linear time, if the real part is independent with the imaginary part in SCMA system. Furthermore, this paper presents a new design for codebooks by greedy strategy and exhaustive search in each step. The search is feasible with the help of discretization, and the resulting codebooks have good performance in the proposed detection algorithm, as well as in the conventional MPA.

A Gaussian mixture is a weighted sum of several Gaussian densities. The probability density function (PDF) of the output of an additive white Gaussian noise (AWGN) channel with discrete input can be regarded as a Gaussian mixture. By constructing a sequence of functions to approach the Gaussian mixture, this paper presents a new approximation for the differential entropy of Gaussian mixtures. Comparing with the previous works, the proposed approximation has a much simpler form and lower computation complexity while tends to the real differential entropy. As an application, by using the approximation, we optimize the discrete input for some AWGN channels and obtain larger input-output mutual information than the previous works.

The finite-state Markov channel (FSMC), where the channel transition probability is controlled by a state undergoing a Markov process, is a useful model for the mobile wireless communication channel. In this paper, we investigate the security issue in the mobile wireless communication systems by considering the FSMC with an eavesdropper, which we call the finite-state Markov wiretap channel (FSM-WC). We assume that the state is perfectly known by the legitimate receiver and the eavesdropper, and through a noiseless feedback channel, the legitimate receiver sends his received channel output and the state back to the transmitter after some time delay. Inner and outer bounds on the capacity-equivocation regions of the FSM-WC with delayed state feedback and with or without delayed channel output feedback are provided in this paper, and we show that these bounds meet if the eavesdropper's received symbol is a degraded version of the legitimate receiver's. The above-mentioned results are further explained via a degraded Gaussian fading example.

Complexity of sequences plays an important role in pseudorandom sequences and cryptography. In this paper, we present a construction of sequences with high nonlinear complexity from function fields. The main idea is to make use of function fields with many rational places as well as an automorphism of large order. We illustrate our construction through rational function fields and cyclotomic function fields in which there exist some automorphisms of large order. It turns out that we are able to: 1) slightly increase the length of the inversive sequence without losing nonlinear complexity and 2) obtain sequences with much larger nonlinear complexity than random sequences.

Accessing to timely and accurate road condition information, especially about dangerous potholes is of great importance to the public and the government. In this paper, we propose a novel scheme, called P3, which utilizes smartphones placed in normal vehicles to sense and estimate the profiles of potholes on urban surface roads. In particular, a P3-enabled smartphone can actively learn the knowledge about the suspension system of the host vehicle without any human intervention and adopts a one degree-offreedom (DOF) vibration model to infer the depth and length of pothole while the vehicle is hitting the pothole. Furthermore, P3 shows the potential to derive more accurate results by aggregating individual estimates. In essence, P3 is light-weighted and robust to various conditions such as poor light, bad weather, and different vehicle types. We have implemented a prototype system to prove the practical feasibility of P3. The results of extensive experiments based on real trace demonstrate the efficacy of the P3 design. On average, P3 can achieve low depth and length estimation error rates of 13 and 16 percent, respectively.

This paper considers an extension of wiretap channel II, where the source message W is transmitted to the legitimate receiver via a discrete memoryless main channel (DMC). Receiving YN, the receiver needs to recover W with small error probability. Meanwhile, an eavesdropper is able to observe arbitrary subsequence of YNwith size μ = Nα, where 0 <; α <; 1 is a constant real number. The encoding-decoding scheme is designed to satisfy a strong secrecy criterion, i.e. the information of each block (instead of each bit) exposed to the eavesdropper is negligible or arbitrarily close to 0 when N is sufficiently large. We focus on the secrecy capacity of this model.

Network coding has been widely used in vehicular ad hoc networks (VANETs). However, the challenges for network coding come from resource constrains like I/O and memory. Existing network coding does not address the increasingly important problem of I/O overhead. In this paper, optimizing I/O consumption during the whole regeneration process is our main focus. Since vehicles may drive off the ad hoc network at halfway, it is necessary to regenerate the lost data in order to maintain reliability for further data reconstruction. We have come up with one recovery algorithm which is based on PM-RBT codes to combat against the high packet loss probability in VANETs. It significantly reduces the total I/O cost when taking the whole regeneration process into consideration. Simulation demonstrates that the algorithm performs better in case of high packet loss probability and large regeneration times.

Erasure codes are introduced to reduce file downloaded latency for content distribution in vehicle networks. Since node failure happens frequently, naive repair will lead to network traffic (bandwidth) congestion. In this work, we employ a mutual information based approach to improve the minimal repair bandwidth by considering the relative generalized Hamming weight. Compared with naive repair, this approach improves about 30% repair bandwidth. Although the approach is in the form of recovering one single node, it can be extended to any failed nodes. Finally, simulations are conducted to support our results.

Exploration of white spaces has been recognized as a promising way to improve the utilization of wireless spectrums. Especially, indoor white space exploration has been shown to be much more challenging than that in outdoor environment. Most of existing works on indoor white space exploration infer availabilities of TV channels based on the correlations among the channels and locations, which is learned from the training data measured beforehand. However, the process of training data collection normally requires considerable time and devices, as well as human power. In this paper, we perform a measurement of indoor white spaces and study their characteristics. Based on the in-depth understanding of the characteristics, we propose a Training-free Indoor white space exploration MEchanism (TIME). In TIME, we design an algorithm for channel state inference based on Bayesian compressive sensing, as well as an incremental method for deployment of spectrum detectors. Furthermore, we present an algorithm to determine a proper number of spectrum detectors in need. Extensive real-world experiments are conducted to evaluate TIME's performance. The evaluation results demonstrate that TIME achieves competitive performances against the state-of-the-art training-based mechanisms.

Wireless trace data play an important role in wireless network researches. However, publishing the raw WLAN traces poses potential privacy risks of network users. Therefore, it is necessary to sanitize users' sensitive information before these traces are published, and provide high data utility for wireless network researches as well. Although some existing works based on various anonymization methods have started to address the problem of sanitizing WLAN traces, the anonymization techniques cannot provide strong and provable privacy guarantees. Differential Privacy is the only framework that can provide strong and provable privacy guarantees. However, we find that existing studies on differential privacy fail to provide effective data utility on multi-dimensional and large-scale datasets. Aim at WLAN trace datasets that have unique characteristics of multi-dimensional and large-scale, this paper proposes a privacy-preserving data publishing algorithm which not only satisfies differential privacy but also realizes high data utility. Furthermore, the theoretical analysis shows the noise variance of our sanitization algorithm is O(logo(1)n/ϵ2) which indicates the algorithm can achieve a higher data utility on large-scale datasets. Moreover, from the results of extensive experiments on an large-scale WLAN trace dataset, we also show that our sanitization algorithm can provide high data utility.

Incorporating information security and error correction in network coding, which has various applications in communication theory, for example, secret key sharing through a network, is of special interest and has been widely studied. To make a more intensive analysis of secure error‐correcting network codes, we investigate how to secure k source symbols transmission in a multicast network against an adversary that can obtain k 1 source symbols as side information, eavesdrop μ channels and contaminate d channels. We introduce relative network generalised Hamming weight (RNGHW) with network error correction (NEC), or briefly NEC‐RNGHW, to measure the equivocation to the adversary. Network generalised singleton bound on NEC‐RNGHW is obtained and code constructions achieving the bound are provided. By these constructions, the maximum rate of a secure linear multicast is n − 2 d − μ − k 1 , where n is the minimum value of the maxflows from a source node to sink nodes. We also characterise the equivocation by the relative profiles of a linear code and a subcode, and tighten the singleton bound on equivocation by the generalised Griesmer bound on relative profiles.

Virtualization plays a vital role in building the infrastructure of clouds, and isolation is considered as one of its important features. However, we demonstrate with practical measurements that there exist two kinds of isolation problems in current virtualized systems, due to cache interference in a multi-core processor. That is, one virtual machine could degrade the performance or obtain the load information of another virtual machine, which running on a same physical machine. Then we present a time-sensitive contention management approach (TSAC) for allocating resources dynamically in the virtual machine monitor, in which virtual machines are controlled to share some physical resources (e.g., CPU or page color) in a dynamical manner, in order to enforce isolation between the virtual machines without sacrificing performance of the virtualized system. We have implemented a working prototype based on Xen, evaluated the implemented prototype with experiments, and experimental results show that TSAC could significantly improve isolation of virtualization. Specifically, compared to the default Xen, TSAC could improve the performance of the victim virtual machine by up to about 78 percent, and perform well in blocking its cache-based load information leakage.

An important and well-studied problem with many applications in communication systems is to find sequences with good correlation property that means auto- and cross-correlations of the sequences are all very small comparing with their periods. This paper focuses on sequences of period 2r- 1(r>> 1) with infinite third depth and shows that the difference operator really works with the interleaving technique on producing sequences with good correlation property and long period N = 22r-2r+1+1, which are constructed from 2-level autocorrelation sequences of period 2r- 1 except m-sequences. The method is lightweight since the computational complexity is O(p√N) and only the XOR logical operator is used.

Security of linear ramp secret sharing schemes can be characterized by the relative generalized Hamming weights of the involved codes [23], [22]. In this paper we elaborate on the implication of these parameters and we devise a method to estimate their value for general one-point algebraic geometric codes. As it is demonstrated, for Hermitian codes our bound is often tight. Furthermore, for these codes the relative generalized Hamming weights are often much larger than the corresponding generalized Hamming weights.

The distance profiles of linear block codes can be employed to design variational coding scheme for encoding message with variational length and getting lower decoding error probability by large minimum Hamming distance, where one example is in the design of transport format combination indicators (TFCIs) in CDMA. Considering convenience for encoding, we focus on the distance profiles with respect to cyclic subcode chains (DPCs) of cyclic codes over GF(q) with length n such that gcd(n, q) = 1. In this paper, the optimum DPCs and the corresponding optimum cyclic subcode chains are investigated on the punctured second-order Reed-Muller code RM(2, m)* for increasing message length, where two standards on the optimums are studied according to the rhythm of increase. Ignoring the dimension profile, the device will coincide with that of TFCI.

In practice, sequences with long period but lightweight generation are always welcome in the applications of communication systems; however, the generation is not easy. From a certain angle of very high probability, this paper presents a solution, which is performed using cyclic difference sequences. As an application, the generated sequences can be interleaved to obtain good correlation properties. Furthermore, synchronization with blind detection is often required but difficult to be achieved. Even harder, periodic sequences may be affected to be ultimately periodic sequences with overhead because of device switching or noise. The determinations of the ultimate periods of above sequences and corresponding distributions are also investigated in this paper.

Security of linear ramp secret sharing schemes can be characterized by the relative generalized Hamming weights of the involved codes. In this paper, we elaborate on the implication of these parameters and devise a method to estimate their value for general one-point algebraic geometric codes. As it is demonstrated, for Hermitian codes, our bound is often tight. Furthermore, for these codes, the relative generalized Hamming weights are often much larger than the corresponding generalized Hamming weights.

With the development and popularization of cloud computing, more and more services and applications are migrated to cloud for the sake of low cost, high availability and excellent performance. As the foundation of cloud computing, virtualization technology integrates and reallocates the computing capability, storage and network resource fairly among virtual machines and provides a full-featured, isolated and reliable hardware environment for various operating systems. Owe to the virtualization technology, computing capability of virtual machines has achieved fantastic performance, some even achieve near native speed. However, low I/O performance is still a bottleneck, especially in I/O intensive applications. The leading causes include redundant data copy and frequent VM exits. Focusing on network I/O optimization, we design and implement virtsocket, a new network socket library in virtualization scenario which utilizes shared memory for data transmission. A ring buffer data structure stores I/O requests of virtual machine which is triggered to issue all requests with only one hypercall according to scheduler. Data referred in the I/O requests is read directly from virtual machine memory by host machine kernel module with interfaces provided by modified hypervisor. Experimental results show that throughput is improved by hundreds of times when compared with original virtualization scenario, and the latency also achieves a remarkable reduction. Both throughput and latency performance exceed existing para-virtualization solutions.

Data sharing in the cloud, fueled by favorable cloud technology trends, has emerging as a promising pattern in regard to enabling data more accessible to users in a convenient manner. To achieve data sharing, enterprises and customers in increasing numbers keep their data stored into cloud server. In this paper, we focus on seeking a solution that allows secure and effective access to the cloud data. We propose an effective and flexible privacy-preserving data policy, P2E, utilizing ciphertext policy attribute-based encryption (CP-ABE) and combining it with technique of identity-based encryption (IBE). In addition to ensuring strong data sharing security, the policy succeeds in preserving the privacy of cloud users. Security analysis indicates that the proposed policy is security and enforces fine-grained access control and full collusion resistance simultaneously. Furthermore, our performance analysis and experimental results show that P2E is as light as possible.

Cloud storage services enable users to remotely store their data and eliminate excessive local installation of software and hardware. One critical issue is how to enable a secure data collaboration service including data access and update in cloud computing. A data collaboration service is to support the availability and consistency of the shared data among multi-users. In this paper, we propose a secure and efficient data collaboration scheme SECO. In SECO, we employ a two-level hierarchical identity based encryption (HIBE) to guarantee data confidentiality against untrusted cloud. This paper is the first attempt to explore secure cloud data collaboration service that precludes information leakage and enables a one-to-many encryption paradigm, data writing operation and fine-grained access control simultaneously. Security analysis indicates that the SECO enforces fine-grained access control and collusion resistant. Extensive performance analysis and experiment results demonstrate that SECO is highly efficient and low overhead on computation and communication.

As an important cornerstone for clouds, virtualization plays a vital role in building this emerging infrastructure. Virtual machines (VMs) with a variety of workloads may run simultaneously on a physical machine in the cloud platform. The scheduling algorithm used in Xen schedules virtual CPUs (VCPUs) of a VM asynchronously and guarantees the proportion of the CPU time allocated to the VM. This proportional sharing (PS) method is beneficial as it simplifies the implementation of CPU scheduling in the virtual machine monitor (VMM), and can deliver near-native performance for some workloads. However, when workloads in VMs are concurrent applications such as multithreaded programs with the synchronization operation, it has been demonstrated that this method in the VMM can reduce the performance, due to the negative impact of virtualization on synchronization. To address this issue, we present a hybrid scheduling framework for CPU management in the VMM to adapt to the diversity of VMs running simultaneously on a physical machine. We implement a hybrid scheduler based on Xen, and experimental results indicate that the hybrid CPU management method is feasible to mitigate the negative influence of virtualization on synchronization, and improve the performance of concurrent applications in the virtualized system, while maintaining the performance of high-throughput applications.

In this paper, we investigate the model of degraded broadcast channel with noncausal side information, confidential messages and noiseless feedback. This work is from Steinberg's work on the degraded broadcast channel with noncausal side information, and Csiszár and Körner's work on broadcast channel with confidential messages. In this new model, the transmitter sends a confidential message to the non-degraded receiver, and meanwhile sends a common message to both the degraded and non-degraded receivers. Moreover, the channel for the non-degraded receiver is controlled by channel state information (side information), and it is available to the transmitter in a noncausal manner (termed here noncausal side information). In addition, we assume that there is a noiseless feedback from the output of the channel for the non-degraded receiver to the transmitter, and it helps them share a secret key, which enlarges the degraded receiver's uncertainty about the confidential message. Measuring the uncertainty by equivocation (conditional entropy), the capacity-equivocation region composed of all achievable rates-equivocation triples is determined for this new model. Furthermore, the secrecy capacity is formulated, which provides the best transmission rate with perfect secrecy.

The non-degraded wiretap channel with noiseless feedback is first investigated by R. Ahlswede and N. Cai, where lower and upper bounds on the secrecy capacity are provided in their work. However, the capacity-equivocation region has not been determined yet. In this paper, the capacity-equivocation region is determined for the non-degraded wiretap channel with noiseless feedback. Furthermore, the secrecy capacity of this model is formulated, which provides the best transmission rate with perfect secrecy.

A well-known operator of vectors over finite field is the derivative, which is used to investigate the complexity of vectors in game theory, communication theory and cryptography. According to the operator, a corresponding complexity of the vector is called (the first) depth, which also contributes to two other definitions (the second and the third depths) by using polynomial factor and high order difference, respectively. For an n-dimensional vector over Fq(a finite field with q elements and characteristic p), the three depths are the same as its linear complexity if n = pr(r ≥ 0). In this paper, by investigation on vectors s of length n (or equivalent sequences of period n) with infinite third depth, and the cyclic-left-shift-difference operator E-1 on s, long least ultimate period sequences {(E-1)i(s)}i≥0are constructed with high probability over big alphabet using lightweight calculation. Furthermore, distributions of sequences s with period n = pr-1 (r >; 0), are described in terms of the least ultimate periods of {(E-1)i(s)}i≥0. In addition, we depict circulant matrix structure of the operator (E - 1)ifor 0i(s)}i≥0and a method to determine the least ultimate period are provided. The least ultimate period presents an adversary a sufficient condition to win the rotating-table game with rapid counteraction (RGRC).

In this paper, for some linear block codes, two kinds of optimum distance profiles (ODPs) are introduced to consider how to construct and then exclude (or include) the basis codewords one by one while keeping a distance profile as large as possible in a dictionary order (or in an inverse dictionary order, respectively). The aim is to improve fault-tolerant capability by selecting subcodes in communications and storage systems. One application is to serve a suitable code for the realization of the transport format combination indicators (TFCIs) of code-division multiple-access (CDMA) systems. Another application is in the field of address retrieval on optical media.

Load balancing plays a key role in workload scheduling policies which count in the performance improvement of parallel applications. A critical problem of load balancing is to make a reasonable definition of load index. Unfortunately, few studies provided enough scientific justifications for the choice of load indices. In this paper, a reasonable approach for defining a load index based on factor analysis theory is introduced, which is helpful to reasonable designs of workload scheduling algorithms. An example testing on an accounting log of the CM-5 parallel machine is presented to show the usage of this method.

In this paper, two kinds of optimum distance profiles of linear block codes are introduced to study how to include or exclude some basis codewords one by one while keeping the minimum distances (of the generated subcodes) as large as possible. One application is to serve a suitable code for the realization of the transport format combination indicators (TFCI) of some CDMA systems.

An inverse relative dimension/length profile (IRDLP) of a pair of codes was introduced to describe the equivocation. In this paper, the maximum distance separable (MDS) code is extended to a two-code format: relative MDS pair. For a code and a subcode, the pair is called a relative MDS pair if its inverse relative dimension-length profile (IRDLP) reaches the generalized singleton bound. We consider properties and constructions of relative MDS pairs.

This paper extends the Gaussian wiretap channel (GWC) model introduced by Leung-Yan-Cheong and Hellman (1978) to the Gaussian wiretap channel with side information (GWCSI) by adding an additive white Gaussian interference in the main channel, which is available to the encoder in advance. It can also be looked at as an extension of the dirty-paper channel (DPC) introduced by Costa (1983). We propose a coding strategy achieving perfect secrecy for the extended channel, called Gaussian wiretap channel with side information. The corresponding achievable rates with asymptotic perfect secrecy are obtained and compared to known upper and lower bounds.