Efficient resource allocation for delay-sensitive traffic, such as telephony and video streaming, in Orthogonal Frequency Division Multiple Access (OFDMA) networks is needed to increase system performance. In our system, users try to achieve a low queuing delay and buffer space usage by competing for transmission over the subchannels. We formulate this problem as a bargaining game and use the Nash Bargaining Solution (NBS) to realize a fair and efficient subchannel allocation for the users. Simulation results show performance improvements, with regard to packet dropping and delay distribution, over other algorithms.

This Letter explores the adaptive hybrid automatic repeat request (HARQ) using rate-compatible polar codes constructed with a common information set. The rate adaptation problem is formulated using Markov decision process and solved by a dynamic programming framework in a low-complexity way. Simulation verifies the throughput efficiency of the proposed adaptive HARQ.

The generalized Hamming weight played an important role in coding theory. In the study of the wiretap channel of type II, the generalized Hamming weight was extended to a two-code format. Two equivalent concepts of the generalized Hamming weight hierarchy and its two-code format, are the inverse dimension/length profile (IDLP) and the inverse relative dimension/length profile (IRDLP), respectively. In this paper, the Singleton upper bound on the IRDLP is improved by using a quotient subcode set and a subset with respect to a generator matrix, respectively. If these new upper bounds on the IRDLP are achieved, in the corresponding coordinated two-party wire-tap channel of type II, the adversary cannot learn more from the illegitimate party.

In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.

Recognizing human action in complex scenes is a challenging problem in computer vision. Some action-unrelated concepts, such as camera position features, could significantly affect the appearance of local spatio-temporal features, and therefore the performance of low-level features based methods degrades. In this letter, we define the action-unrelated concept: the position of camera as high-level features. We observe that they can serve as a prior to local spatio-temporal features for human action recognition. We encode this prior by modeling interactions between spatio-temporal features and camera position features. We infer camera position features from local spatio-temporal features via these interactions. The parameters of this model are estimated by a new max-margin algorithm. We evaluate the proposed method on KTH, IXMAS and Youtube actions datasets. Experimental results show the effectiveness of the proposed method.

In this letter, a novel physical layer signaling transmission scheme is proposed, where the signaling information is conveyed by a pair of training sequences located in the odd and even subcarriers of an orthogonal frequency division multiplexing (OFDM) training symbol. At the receiver side, only a single correlator is required to detect the signaling information. Computer simulations verify the proposed signaling could outperform the S1 signaling and achieve similar robustness as the S2 signaling of the DVB-T2 standard.

The user authorization query (UAQ) problem determines whether there exists an optimum set of roles to be activated to provide a set of permissions requested by a user. It has been deemed as a key issue for efficiently handling user's access requests in role-based access control (RBAC). Unfortunately, the weight is a value attached to a permission/role representing its importance, should be introduced to UAQ, has been ignored. In this paper, we propose a comprehensive definition of the weighted UAQ (WUAQ) problem with the role-weighted-cardinality and permission-weighted-cardinality constraints. Moreover, we study the computational complexity of different subcases of WUAQ, and show that many instances in each subcase are intractable. In particular, inspired by the idea of the genetic algorithm, we propose an algorithm to approximate solve an intractable subcase of the WUAQ problem. An important observation is that this algorithm can be efficiently modified to handle the other subcases of the WUAQ problem. The experimental results show the advantage of the proposed algorithm, which is especially fit for the case that the computational overhead is even more important than the accuracy in a large-scale RBAC system.

Previous research illustrates that LRU replacement policy is not efficient when applications exhibit a distant re-reference interval. Recently RRIP policy is proposed to improve the performance for such kind of workloads. However, the lack of access recency information in RRIP confuses the replacement policy to make the accurate prediction. To enhance the robustness of RRIP for recency-friendly workloads, we propose an Dynamic Adaptive Insertion and Re-reference Prediction (DAI-RRP) policy which evicts data based on both re-reference prediction value and the access recency information. DAI-RRP makes adaptive adjustment on insertion position and prediction value for different access patterns, which makes the policy robust across different workloads and different phases. Simulation results show that DAI-RRP outperforms LRU and RRIP. For a single-core processor with a 1 MB 16-way set last-level cache (LLC), DAI-RRP reduces CPI over LRU and Dynamic RRIP by an average of 8.1% and 2.7% respectively. Evaluations on quad-core CMP with a 4 MB shared LLC show that DAI-RRP outperforms LRU and Dynamic RRIP (DRRIP) on the weighted speedup metric by an average of 8.1% and 15.7% respectively. Furthermore, compared to LRU, DAI-RRP consumes the similar hardware for 16-way cache, or even less hardware for high-associativity cache. In summary, the proposed policy is practical and can be easily integrated into existing hardware approximations of LRU.

Multiple notch filters are used to suppress narrow-band or sinusoidal interferences in digital signals. In this paper, we propose a novel optimization design technique of an infinite impulse response (IIR) multiple notch filter. It is based on the Nelder-Mead simplex method. Firstly, the system function of the desired notch filter is constructed to form the objective function of the optimization technique. Secondly, the design parameters of the desired notch filter are optimized by Nelder-Mead simplex method. A weight function is also introduced to improve amplitude response of the notch filter. Thirdly, the convergence and amplitude response of the proposed technique are compared with other Nelder-Mead based design methods and the cascade-based design method. Finally, the practicability of the proposed notch filter design technique is demonstrated by some practical applications.

The objective of critical nodes problem is to minimize pair-wise connectivity as a result of removing a specific number of nodes in the residual graph. From a mathematical modeling perspective, it comes the truth that the more the number of fragmented components and the evenly distributed of disconnected sub-graphs, the better the quality of the solution. Basing on this conclusion, we proposed a new Cluster Expansion Method for Critical Node Problem (CEMCNP), which on the one hand exploits a contraction mechanism to greedy simplify the complexity of sparse graph model, and on the other hand adopts an incremental cluster expansion approach in order to maintain the size of formed component within reasonable limitation. The proposed algorithm also relies heavily on the idea of multi-start iterative local search algorithm, whereas brings in a diversified late acceptance local search strategy to keep the balance between interleaving diversification and intensification in the process of neighborhood search. Extensive evaluations show that CEMCNP running on 35 of total 42 benchmark instances are superior to the outcome of KBV, while holding 3 previous best results out of the challenging instances. In addition, CEMCNP also demonstrates equivalent performance in comparison with the existing MANCNP and VPMS algorithms over 22 of total 42 graph models with fewer number of node exchange operations.

We present a novel scheme for digital steganography of point-sampled geometry in the spatial domain. Our algorithm is inspired by the concepts proposed by Cayre and Macq for 3D polygonal models. It employs a principal component analysis (PCA), resulting in a blind approach. We validate our scheme with various model complexities in terms of capacity, complexity, visibility, and security. This scheme is robust against translation, rotation, and scaling operations. It is fast and can achieve high data capacity with insignificant visual distortion in the stego models.

This paper proposes a novel scheme to reduce the complexity of existing transmit diversity solutions to time domain synchronous OFDM (TDS-OFDM). The space shifted constant amplitude zero autocorrelation (CAZAC) sequence based preamble is proposed for channel estimation. Two flexible frame structures are proposed for adaptive system design as well as cyclicity reconstruction of the received inverse discrete Fourier transform (IDFT) block. With regard to channel estimation and cyclicity reconstruction, the complexity of the proposed scheme is only around 7.20% of that of the conventional solutions. Simulation results demonstrate that better bit error rate (BER) performance can be achieved over doubly selective channels.

Blockchain is one of the prominent rapidly used technology in the last decade in various applications. In recent years, many researchers explored the capabilities of blockchain in smart IoT to address various security challenges. Integration of IoT and blockchain solves the security problems but scalability still remains a huge challenge. To address this, various AI techniques can be applied in the blockchain IoT framework, thus providing an efficient information system. In this survey, various works pertaining to the domains which integrate AI, IoT and Blockchain has been explored. Also, this article discusses potential industrial use cases on fusion of blockchain, AI and IoT applications and its challenges.

Like many processors, GPGPU suffers from memory wall. The traditional solution for this issue is to use efficient schedulers to hide long memory access latency or use data prefetch mech-anism to reduce the latency caused by data transfer. In this paper, we study the instruction fetch stage of GPU's pipeline and analyze the relationship between the capacity of GPU kernel and instruction miss rate. We improve the next line prefetch mechanism to fit the SIMT model of GPU and determine the optimal parameters of prefetch mechanism on GPU through experiments. The experimental result shows that the prefetch mechanism can achieve 12.17% performance improvement on average. Compared with the solution of enlarging I-Cache, prefetch mechanism has the advantages of more beneficiaries and lower cost.

A novel error detection method based on coded block pattern (CBP) information verification is proposed for error concealment of inter-coded video frames transmitted in wireless channel. This method firstly modifies the original video stream structure by the aggregation of certain important information, and then inserts some error verification bits into the video stream for each encoded macro block (MB), these bits can be used as reference information to determine whether each encoded MB is corrupted. Experimental results on additive Gauss white noise simulation wireless channel and H.263+ baseline codec show that the proposed method can outperform other reference approaches on error detection performance. In addition, it can preserve the original video quality with a small coding overhead increase.

Proxy cryptosystems are classified into proxy decryption systems and proxy re-encryption systems on the basis of a proxy's role. In this paper, we propose an ID-based proxy cryptosystem with revocability and hierarchical confidentialities. In our scheme, on receiving a ciphertext, the proxy has the rights to perform the following three tasks according to the message confidentiality levels of the sender's intention: (1) to decrypt the ciphertext on behalf of the original decryptor; (2) to re-encrypt the ciphertext such that another user who is designated by the original decryptor can learn the message; (3) to do nothing except for forwarding the ciphertext to the original decryptor. Our scheme supports revocability in the sense that it allows proxy's decryption and re-encryption rights to be revoked even during the valid period of the proxy key without changing the original decryptor's public information. We prove that our proposal is indistinguishable against chosen identity and plaintext attacks in the standard model. We also show how to convert it into a system against chosen identity and ciphertext attacks by using the Fujisaki-Okamoto transformation.

Based on the observation that foveation analysis can be used to find most critical content in terms of human visual perception in video and image, one effective error resilience method is proposed for robust transmission of H.264 intra-coded frame in wireless channel. It firstly exploits the results of foveation analysis to find foveated area in picture, and then considers the results of pre-error concealment effect analysis to search for the center of foveation macro-blocks (MB) in foveated area, finally new error resilient alignment order of MB and new coding order of MB are proposed that are used in video encoder. Extensive experimental results on different portrait video sequences over random bit error wireless channel demonstrate that this proposed method can achieve better subjective and objective effect than original JM 8.2 H.264 video codec with little effect on coding rate and image quality.

Border Gateway Protocol (BGP), with its advantages in routing isolation support and mature application, is a promising candidate to integrate satellite systems into the terrestrial IP network. However, with more and more ground stations accessing satellites by BGP, a significant amount of routing overhead can be produced on limited satellite links, especially for geostationary satellite networks with thousands of accessing terminals in extremely large areas. To solve this challenge, multicast transport of BGP was proposed, which takes advantage of the inherent broadcast property of wireless channels. However, its performance can be seriously degraded when interfered with the environment. In this paper, NCSR (Network Coding for Satellite network BGP Routing transport) [1] is explored in depth. Unlike existing counterparts, NCSR pays more attention to the lossy space links and can achieve reliability with more bandwidth savings. A greedy based coding algorithm is proposed to realize the network coding operation. To demonstrate the efficiency of NCSR, we conduct theoretical analyses and extensive simulations in typical scenarios of satellite systems. Simulation results show that NCSR can greatly reduce the bandwidth usage while achieving comparable latency. Discussions on practical considerations when applying network coding method for reliability assurance are also presented in detail.

The MinRank problem is investigated as a problem related to rank attacks in multivariate cryptography and the decoding of rank codes in coding theory. The Kipnis-Shamir method is one of the methods to solve the problem, and recently, significant progress has been made in its complexity estimation by Verbel et al. As this method reduces the problem to an MQ problem, which asks for a solution to a system of quadratic equations, its complexity depends on the solving degree of a quadratic system deduced from the method. A theoretical value introduced by Verbel et al. approximates the minimal solving degree of the quadratic systems in the method although their value is defined under a certain limit for the system considered. A quadratic system outside their limitation often has a larger solving degree, but the solving complexity is not always higher because it has a smaller number of variables and equations. Thus, in order to discuss the best complexity of the Kipnis-Shamir method, a theoretical value is needed to approximate the solving degree of each quadratic system deduced from the method. A quadratic system deduced from the Kipnis-Shamir method always has a multi-degree, and the solving complexity is influenced by this property. In this study, we introduce a theoretical value defined by such a multi-degree and show that it approximates the solving degree of each quadratic system. Thus, the systems deduced from the method are compared, and the best complexity is discussed. As an application, for the MinRank attack using the Kipnis-Shamir method against the multivariate signature scheme Rainbow, we show a case in which a deduced quadratic system outside Verbel et al.'s limitation is the best. In particular, the complexity estimation of the MinRank attack using the KS method against the Rainbow parameter sets I, III and V is reduced by about 172, 140 and 212 bits, respectively, from Verbel et al.'s estimation.

As a key technology of 5G, NFV has attracted much attention. In addition, monitoring plays an important role, and can be widely used for virtual network function placement and resource optimisation. The existing monitoring methods focus on the monitoring load without considering they own resources needed. This raises a unique challenge: jointly optimising the NFV monitoring systems and minimising their monitoring load at runtime. The objective is to enhance the gain in real-time monitoring metrics at minimum monitoring costs. In this context, we propose a novel NFV monitoring solution, namely, iMon (Monitoring by inferring), that jointly optimises the monitoring process and reduces resource consumption. We formalise the monitoring process into a multitarget regression problem and propose three regression models. These models are implemented by a deep neural network, and an experimental platform is built to prove their availability and effectiveness. Finally, experiments also show that monitoring resource requirements are reduced, and the monitoring load is just 0.6% of that of the monitoring tool cAdvisor on our dataset.