Caching is a critical method for enhancing the efficiency and the security of the Domain Name System (DNS). Initially, only successful domain name resolution results were cached. To mitigate failures in DNS transactions (e.g. NXDomain), the IETF proposed standards, further developed into RFC 9520 as of December 2023. In addition to the basic implementation, RFC 9520 standardizes more sophisticated forms of negative caching. This new standard aims to reduce redundant query retries in DNS traffic and protect resolvers from Denial of Service (DoS) attacks.In this study, we present a comprehensive examination of the specific implementations of Negative Caching in resolvers. We designed and validated a method for measuring negative caching and conducted experiments on 44 public resolvers, including their Do53, DoH, and DoT interfaces. Our findings indicate that while public resolvers generally implement various types of negative caching, some exhibit unexpected cache handling behaviors when encountering specific negative responses. Additionally, we discovered that most public resolvers modify the TTL value of negative responses before returning them to clients. Despite the lack of explicit TTL values for newly specified negative responses, we devised a method to approximate the default TTL values used by public resolvers.

The recently proposed learned bloom filter (LBF) opens a new perspective on how to reconstruct bloom filters with machine learning. However, the LBF has a massive time cost and does not apply to multidimensional spatial data. In this paper, we propose a prefix‐based and adaptive learned bloom filter (PA‐LBF) for spatial data, which efficiently supports the insertion and deletion. The proposed PA‐LBF is divided into three parts: (1) the prefix‐based classification. The Z‐order space‐filling curve is used to extract data, prefix it, and classify it. (2) The adaptive learning process. The multiple independent adaptive sub‐LBFs are designed to train the suffixes of data, combined with part 1, to reduce the false positive rate (FPR), query, and learning process time consumption. (3) The backup filter uses CBF. Two kinds of backup CBF are constructed to meet the situation of different insertion and deletion frequencies. Experimental results prove the validity of the theory and show that the PA‐LBF reduces the FPR by 84.87%, 79.53%, and 43.01% with the same memory usage compared with the LBF on three real‐world spatial datasets. Moreover, the time consumption of PA‐LBF can be reduced to 5× and 2.05× that of the LBF on the query and learning process, respectively.

Document-level relation extraction intends to extract relation facts among different entity pairs in the entire document. Previously proposed methods make numerous efforts for this task, however, these researches neglect to treat the two entities in an entity pair as an organic unity for relation extraction, which leads to the lack of valuable information about the entity pairs and insufficient information interaction. To tackle the above problem, we propose a framework, named Entity-level Attention Pooling and Information Gating (EAPIG), for document-level relation extraction. Specifically, we first utilize an encoder module to capture the long-distance dependencies of entities in the document, and then we propose two modules: the Entity-level Attention Pooling module obtains the local information of entity pairs, and the Information Gating module introduces the global information of entity pairs and promotes sufficient interaction between the local information and the global information. Experimental results on the benchmark dataset DocRED show that our approach can efficiently capture and combine the abundant information from the entity pairs to achieve better performance than the previous baselines.

This paper presents a novel clustering method, named Laplacian regularized deep subspace clustering (LRDSC), for unsupervised hyperspectral image (HSI) classification. We introduce the Laplacian regularization into the subspace clustering to consider the manifold structure reflecting geometric information. To enable the subspace clustering, which works in linear space, to deal with the complicated HSI data with nonlinear characteristics, we combine the subspace clustering as a self-expressive layer with deep convolutional auto-encoder. Furthermore, the 3-D convolutions and deconvolutions with skip connections are utilized to make full extraction of the spectral-spatial information and full use of the historical feature maps produced by the network. We compare the results of the proposed method with six existing cluster methods on four real hyperspectral data sets, showing that the proposed method is able to achieve state-of-the-art performance.

Hyperspectral image (HSI) consists of hundreds of continuous narrow bands with high redundancy, resulting in the curse of dimensionality and an increased computation complexity in HSI classification. Many clustering-based band selection approaches have been proposed to deal with such a problem. However, a few of them consider the spectral and spatial relationship simultaneously. In this letter, we proposed a novel clustering-based band selection approach using deep subspace clustering (DSC). The proposed approach combines the subspace clustering task into a convolutional autoencoder by treating it as a self-expressive layer, enabling it to be trained end to end. The resulting network can fully extract the interaction of spectral bands based on using spatial information and nonlinear feature transformation. We compared the results of the proposed method with existing band selection methods for three widely used HSI data sets, showing that the proposed method is able to accurately select an informative band subset with remarkable classification accuracy.

Deep neural networks have been successfully applied to many data mining problems in recent works. The training of deep neural networks relies heavily upon gradient descent methods, however, which may lead to the failure of training due to the vanishing gradient (or exploding gradient) and local optima problems. In this paper, we present SEvoAE method based on using Evolutionary Multiobjective optimization (EMO) algorithm to train single layer auto-encoder, and sequentially learning deeper representation in a stacking way. SEvoAE is able to achieve accurate feature representation with good sparseness by globally simultaneously optimizing two conflicting objective functions and allows users to flexibly design objective functions and evolutionary optimizers. We compare results of the proposed method with existing architectures for seven classification problems, showing that the proposed method is able to outperform existing methods with a reduced risk of overfitting the training data.

In wireless networks, communication links may be subject to random fatal impacts: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably the information can be collected over attacked links. For a single channel subject to random attacks, named as a dying channel, we model it as a block-fading (BF) channel with a finite and random channel length. For this channel, we first study the outage capacity and the outage probability when the data frame length is fixed and uniform power allocation is assumed. Furthermore, we discuss the optimization over the frame length and/or the power allocation over the constituting data blocks to minimize the outage probability. In addition, we extend the results from the single dying channel to the parallel multi-channel case where each sub-channel is a dying channel, and investigate the asymptotic behavior of the overall outage probability as the number of sub-channels goes to infinity with two different attack models: the independent-attack case and the m-dependent-attack case. It is shown that the asymptotic outage probability diminishes to zero for both cases as the number of sub-channels increases if the rate per unit cost is less than a certain threshold. The outage exponents are also studied to reveal how fast the outage probability improves with the number of sub-channels.

In this paper, we consider a two-hop interference network, which consists of two source-destination pairs and two relay nodes connected with signal-to-noise ratio (SNR) limited out-of-band conferencing links. Assuming that the amplify-and-forward (AF) relaying scheme is adopted, this network is shown to be equivalent to a two-user interference channel (IC). By deploying two IC decoding schemes, i.e., single-user decoding and joint decoding, respectively, we characterize the achievable rate regions with a two-stage iterative optimization method: First, we fix the source power pair and maximize the sum rate over the relay combining vector; second, we fix the relay combining vector and optimize the source power pair. Specifically, for single-user decoding, we design a new routine to compute the optimal solution for the first subproblem, which is more efficient than the existing scheme; and for the second subproblem, we develop an iterative algorithm, with the closed-form solution for each iteration. Furthermore, it is revealed that the AF scheme with relay conferencing achieves the full degree-of-freedom (DoF), which outperforms the case without relay conferencing. Finally, simulation results show that relay conferencing can significantly improve the system performance under certain channel conditions.

This paper presents the design of a robust beamforming scheme for a two-way relay network, composed of one multi-antenna relay and two single-antenna terminals, with the consideration of channel estimation errors. Given the assumption that the channel estimation error is within a certain range, we aim to minimize the transmit power at the multi-antenna relay and guarantee that the signal to interference and noise ratios (SINRs) at the two terminals are larger than a predefined value. Such a robust beamforming matrix design problem is formulated as a non-convex optimization problem, which is then converted into a semi-definite programming (SDP) problem by the S-procedure and rank one relaxation. The robust beamforming matrix is then derived from a principle eigenvector based rank-one reconstruction algorithm. We further propose a hybrid approach based on the best-effort principle to improve the outage probability performance, which is defined as the probability that one of two resulting terminal SINRs is less than the predefined value. Simulation results are presented to show that the robust design leads to better outage performance than the traditional non-robust approaches.

In this paper, we study a practical coding scheme for the dying binary erasure channel (DBEC), which is a binary erasure channel (BEC) subject to a random fatal failure. We consider the rateless codes and optimize the degree distribution to maximize the average recovery probability. In particular, we first study the upper bound of the average recovery probability, based on which we define the objective function as the gap between the upper bound and the average recovery probability achieved by a particular degree distribution. We then seek the optimal degree distribution by minimizing the objective function. A simple and heuristic approach is also proposed to provide a suboptimal but good degree distribution. Simulation results are presented to show the significant performance gain over the conventional LT codes.

In this paper, we consider a two-hop interference network, which consists of two source-destination pairs and two relay nodes connected with signal-to-noise ratio (SNR) limited out-of-band conferencing links. Assuming that the amplify-and-forward (AF) relaying scheme is adopted, this network is shown to be equivalent to a two-user interference channel (IC). By deploying two IC decoding schemes, i.e., single-user decoding and joint decoding, respectively, we characterize the achievable rate regions with a two-stage iterative optimization method. The associated convergence issue is also studied. Furthermore, we compare the rates in the high SNR regime. Finally, simulation results show that relay conferencing can significantly improve the system performance under certain channel conditions.

In this paper, we investigate the optimal transmission schemes for dying channels, which were introduced in (M. Zeng et al., 2008). The dying channels are resulted in wireless networks subject to random fatal impacts, e.g., sensor networks under sudden physical attacks or cognitive radio networks with unpredictable primary user occupancy. Due to the non-ergodic and delay- limited nature of a dying channel, the outage capacity is adopted as the performance metric. Firstly, we show that the optimal power allocation profile is non-increasing when fading gains are independently and identically distributed (i.i.d.). Secondly, when the fading gains over the blocks are the same, we prove that the optimal number of blocks over which a codeword should be spanned is K = 1. At last, we consider the case where uniform power allocation is utilized and fading gains are i.i.d. In this case, we derive the upper and lower bounds for the outage probability. Moreover, for the high signal-to-noise ratio (SNR) case with Rayleigh fading , we derive analytical results on the optimal number of coding blocks K. For the low SNR case, we show that repetition transmissions are approximately optimal.

In this paper, we investigate a new type of channels named as "dying" channels, which are resulted in wireless networks subject to random fatal impacts, e.g., sensor networks under sudden physical attacks or cognitive radio networks with unpredictable primary user occupancy. Under such circumstances, it is critical to quantify how fast and reliable information can be collected over "dying" links. In this paper, we focus on a simple point-to-point communication setting and model the "dying" channel by the traditional if-block block-fading (BF) model subject to a fatal attack that may happen randomly in any of the K blocks. The resultant channel is non-ergodic and delay-limited in nature, and thus its information-theoretic limit can be measured by adopting the conventional outage capacity concept. An outage event in a "dying" channel could be caused by two mechanisms: fading over finite K blocks; and random attack within K blocks. In this paper, we present the general problem formulation to determine the outage capacity of the "dying" channel, as well as the corresponding optimal transmit power allocation over the K blocks, based upon the known probability distributions of both the fading channel and the attack time. The optimal power allocation turns out to be in general nonuniform with a decreasing profile over transmission blocks. In addition, there exists an optimal number of blocks over which the codeword is spanned. Some other interesting observations are also made pertinent to the optimal transmission over a "dying" channel.

While most current researches on opportunistic beamforming assume that users are uniformly distributed around BS, no attention has been paid to address more pratical scenarios where spatial user density is heterogeneous. In this paper, we propose a novel opportunistic beamforming scheme to better exploit the multiuser diversity in case of heterogeneous user density. The key idea is to grant the highest service priority to users in the area with highest spatial user density. It is implemented by an improved selection of the weight vector at each time slot without explicit knowledge of the spatial user density. To address the fairness issue, the joint area-user proportional fair scheduling (JAUPFS) is proposed, which simultaneously guarantees the fairness between areas with different spatial user densities and different users in the same area. Simulations are conducted to demonstrate that our proposed scheme has a better rate performance than that of the conventional opportunistic beamforming.

In opportunistic beamforming, the throughput can be improved by broadcasting multiple weight vectors in one time slot and selecting the best one. However, broadcasting too many weight vectors will consume the time for data transmission and thus bring down the throughput. In this paper, we investigate two multiple weight vector(MWV) opportunistic beamforming schemes tailored for fast fading and slow fading scenarios respectively and we derive tight upper bounds of the data rates for both schemes. To maximize the upper bounds, we obtain the optimal numbers of weight vectors to be broadcast. Simulation results demonstrate the validity of our theoretical analysis. Based on these results, we obtain some basic guidelines for MWV design problem. For the MWV scheme of fast Rayleigh fading scenario, we claim that (1) the faster the fading is, the less weight vectors are desired; (2) the more users there are, the less weight vectors are desired. For the MWV scheme of slow Rayleigh fading scenario, we have the conclusion that a small number of weight vectors are good enough to achieve a desirable performance.