Progress of quantum computing technology seriously threaten the industrial information security based on traditional public-key cryptosystem. Thus, the cryptosystem with anti-quantum attack characteristics is gradually becoming a significant research in the security field. In this article, a flexible and high-performance secure coprocessor is designed for security in industrial processes, which can execute the post-quantum cryptographic algorithm Saber efficiently. Custom instruction set and arithmetic accelerators are proposed to effectively optimize the flexibility of system architecture, and improve the performance of calculation. The hardware implementation results show that the maximum operating frequency of the coprocessor can reach 345 MHz. Compared with related state-of-the-art works, it achieves the highest operating frequency on the same Xilinx UltraScale+ FPGA platform, performing the encryption and decryption operations within 13.5 and 15.4μs, respectively. Meanwhile, this article achieves 1.7/3.1/5.9× area-time product improvements in look-up table flip-flop block memory storage with good flexibility.

To ensure an efficient and conflict-free task allocation, this paper presents an option-based multi-agent reinforcement learning (OMARL) method for a cooperative multi-station multi-robot system in aircraft painting application. The problem can be described as a fully-connected graph, whose nodes and edges represent stations and trajectories between stations, respectively. The aim is to minimize the maximum execution time of conflict-free aircraft painting with multiple robots. Traditional methods cannot handle mutual collision avoidance well when robots are in large size. Therefore, this work solved the planning problem with multi-agent reinforcement learning algorithm based on option framework. The moving trajectories were pre-planned by a simplified 2D A* algorithm, which is fast and reduces rotations. The agents would finish the whole task with less time, and potential collisions were punished at every time step. Hierarchical option-based model with shared network parameters greatly reduced the complexity of training for painting planning scenarios. A fuzzy clustering method was introduced, and the clustering scores of stations would contribute to greedy agents. Comparisons were made on static and dynamic situations among several planning methods. OMARL can give optimal schedules as well as, or better than the most efficient traditional method does. Furthermore, OMARL can provide satisfactory adjustments in real time when practical process is different from plan or there is one broken robot. In contrast, traditional methods take lots of time to recalculate the plan. The results indicate that OMARL can achieve better performance in multi-station multi-robot task allocation when the collision between robots cannot be ignored.

Kyber is a promising lattice-based post-quantum cryptography (PQC) for key encapsulation mechanisms. Since number theoretic transform (NTT) is the most computationally expensive operation in Kyber, this paper focuses on novel optimization techniques for efficient hardware implementation of NTT for Kyber. Benefiting from the proposed fast modular multiplication method and a doubled bandwidth ping-pong memory access scheme, our NTT architecture can complete an NTT operation in Kyber in 490 cycles using only 609 LUTs, 640 FFs, 2 DSPs on a Xlinx Artix-7 FPGA. The proposed NTT architecture is 3.95 times faster than the state-of-the-art design for Kyber while achieves an improvement of more than 1.5 times in the area time product (ATP). Compared with the state-of-the- art NTT designs for other algorithms, our NTT architecture reduces 24% FFs and 50% DSPs and ranks second smallest in ATPs, which can also confirm the high efficiency of our design.

Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods.

Understanding social tie development among users is crucial for user engagement in social networking services. In this paper, we analyze the social interactions, both online and offline, of users and investigate the development of their social ties using data trail of “how social ties grow” left in mobile and social networking services. To the best of our knowledge, this is the first research attempt on studying social tie development by considering both online and offline interactions in a heterogeneous yet realistic relationship. In this study, we aim to answer three key questions: 1) is there a correlation between online and offline interactions? 2) how is the social tie developed via heterogeneous interaction channels? 3) would the development of social tie between two users be affected by their common friends? To achieve our goal, we develop a Social-aware Hidden Markov Model (SaHMM) that explicitly takes into account the factor of common friends in measure of the social tie development. Our experiments show that, comparing with results obtained using HMM and other heuristic methods, the social tie development captured by our SaHMM is significantly more consistent to lifetime profiles of users.

The skyline query, aiming at identifying a set of skyline tuples that are not dominated by any other tuple, is particularly useful for multicriteria data analysis and decision making. For uncertain databases, a probabilistic skyline query, called P-Skyline, has been developed to return skyline tuples by specifying a probability threshold. However, the answer obtained via a P-Skyline query usually includes skyline tuples undesirably dominating each other when a small threshold is specified; or it may contain much fewer skyline tuples if a larger threshold is employed. To address this concern, we propose a new uncertain skyline query, called U-Skyline query, in this paper. Instead of setting a probabilistic threshold to qualify each skyline tuple independently, the U-Skyline query searches for a set of tuples that has the highest probability (aggregated from all possible scenarios) as the skyline answer. In order to answer U-Skyline queries efficiently, we propose a number of optimization techniques for query processing, including 1) computational simplification of U-Skyline probability, 2) pruning of unqualified candidate skylines and early termination of query processing, 3) reduction of the input data set, and 4) partition and conquest of the reduced data set. We perform a comprehensive performance evaluation on our algorithm and an alternative approach that formulates the U-Skyline processing problem by integer programming. Experimental results demonstrate that our algorithm is 10-100 times faster than using CPLEX, a parallel integer programming solver, to answer the U-Skyline query.

In this paper, we introduce the notion of sufficient set and necessary set for distributed processing of probabilistic top-k queries in cluster-based wireless sensor networks. These two concepts have very nice properties that can facilitate localized data pruning in clusters. Accordingly, we develop a suite of algorithms, namely, sufficient set-based (SSB), necessary set-based (NSB), and boundary-based (BB), for intercluster query processing with bounded rounds of communications. Moreover, in responding to dynamic changes of data distribution in the network, we develop an adaptive algorithm that dynamically switches among the three proposed algorithms to minimize the transmission cost. We show the applicability of sufficient set and necessary set to wireless sensor networks with both two-tier hierarchical and tree-structured network topologies. Experimental results show that the proposed algorithms reduce data transmissions significantly and incur only small constant rounds of data communications. The experimental results also demonstrate the superiority of the adaptive algorithm, which achieves a near-optimal performance under various conditions.

Group activities are essential ingredients of people's social life. The rapid growth of online social networking services has greatly boosted group activities by providing convenient platform for users to organize and participate in such activities. Therefore, recommender systems, as a critical component in social networking services, now face new challenges in supporting group activities. In this paper, we study the group recommendation problem, i.e., making recommendations to a group of people in social networking services. We analyze the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations. The PIT model effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members. Moreover, we further enhance the discovery of personal impact with social network information to obtain an extended personal impact topic (E-PIT) model. We have conducted comprehensive data analysis and evaluations on three real datasets. The results show that our proposed group recommendation techniques outperform baseline approaches.

Newly emerged event-based online social services, such as Meetup and Plancast, have experienced increased popularity and rapid growth. From these services, we observed a new type of social network - event-based social network (EBSN). An EBSN does not only contain online social interactions as in other conventional online social networks, but also includes valuable offline social interactions captured in offline activities. By analyzing real data collected from Meetup, we investigated EBSN properties and discovered many unique and interesting characteristics, such as heavy-tailed degree distributions and strong locality of social interactions.

Social friendship has been shown beneficial for item recommendation for years. However, existing approaches mostly incorporate social friendship into recommender systems by heuristics. In this paper, we argue that social influence between friends can be captured quantitatively and propose a probabilistic generative model, called social influenced selection(SIS), to model the decision making of item selection (e.g., what book to buy or where to dine). Based on SIS, we mine the social influence between linked friends and the personal preferences of users through statistical inference. To address the challenges arising from multiple layers of hidden factors in SIS, we develop a new parameter learning algorithm based on expectation maximization (EM). Moreover, we show that the mined social influence and user preferences are valuable for group recommendation and viral marketing. Finally, we conduct a comprehensive performance evaluation using real datasets crawled from last.fm and whrrl.com to validate our proposal. Experimental results show that social influence captured based on our SIS model is effective for enhancing both item recommendation and group recommendation, essential for viral marketing, and useful for various user analysis.

In this paper, we introduce two types of probabilistic aggregation queries, namely, Probabilistic Minimum Value Queries (PMVQ)s and Probabilistic Minimum Node Queries (PMNQ)s. A PMVQ determines possible minimum values among all imprecise sensed data, while a PMNQ identifies sensor nodes that possibly provide minimum values. However, centralized approaches incur a lot of energy from battery-powered sensor nodes and well-studied in-network aggregation techniques that presume precise sensed data are not practical to inherently imprecise sensed data. Thus, to answer PMVQs and PMNQs energy-efficiently, we devised suites of in-network algorithms. For PMVQs, our in-network minimum value screening algorithm (MVS) filters candidate minimum values; and our in-network minimum value aggregation algorithm (MVA) conducts in-network probability calculation. PMNQs requires possible minimum values to be determined a prior, inevitably consuming more energy to evaluate than PMVQs. Accordingly, our one-phase and two-phase in-network algorithms are devised. We also extend the algorithms to answer PMNQ variants. We evaluate all our proposed approaches through cost analysis and simulations.

Online social networking platforms have become a popular channel of communications among people. However, most people can only keep in touch with a limited number of friends. This phenomenon results in a low-connectivity social network in terms of communications, which is inefficient for information propagation and social engagement. In this paper, we introduce a new recommendation service, called link revival, that suggests users to re-connect with their old friends, such that the resulted connection will improve the social network connectivity. To achieve high connectivity improvement under the dynamic social network evolvement, we propose a graph prediction-based recommendation strategy, which selects proper candidates based on the prediction of their future behaviors. We then develop an effective model that exploits non-homogeneous Poisson process and second-order self-similarity in prediction. Through comprehensive experimental studies on two real datasets (Phone Call Network and Facebook Wall-posts), we demonstrate that our proposed approach can significantly increase the social network connectivity, and that the approach outperforms other baseline solutions. The results also show that our solution is more suitable for online social networks like Facebook, partially due to the stronger long range dependency and lower communication costs in the interactions.

In this paper, we propose the notion of sufficient set for distributed processing of probabilistic Top-k queries in cluster-based wireless sensor networks. Through the derivation of sufficient boundary, we show that data items ranked lower than sufficient boundary are not required for answering the probabilistic top-k queries, thus are subject to local pruning. Accordingly, we develop the sufficient set-based (SSB) algorithm for inter-cluster query processing. Experimental results show that the proposed algorithm reduces data transmissions significantly.

Energy conservation is a key issue for algorithm designs in wireless sensor networks. In this paper, we explore in-network aggregation techniques for answering top-k queries in wireless sensor networks. A top-k query retrieves the k data objects with the highest scores evaluated by a scoring function on interested features of sensor readings. Our study shows that existing techniques for processing top-k query, e.g., Tiny AGgregation Service (TAG), are not energy efficient due to deficiencies in their routing structures and data aggregation mechanisms. To address these deficiencies, we propose to develop a new cross pruning (XP) aggregation framework for top-k data collection in wireless sensor networks. The XP framework incorporates several novel ideas to facilitate efficient in-network aggregation and filtering, including (1) building a cluster-tree routing structure to aggregate more objects locally; (2) adopting a broadcast-then-filter approach for efficiently suppressing redundant data transmissions; and (3) providing a cross pruning technique to enhance in-network filtering effectiveness. An extensive set of experiments based on simulation has been conducted to evaluate the performance of TAG and the proposed XP framework. The experimental results validate our proposals and show that XP significantly outperforms TAG in energy cost.

The Dly-ACK scheme is adopted in IEEE 802.15.3 high-rate WPANs to reduce the overhead of the ACK. Although Dly-ACK improves channel utilization, it also causes additional packet delays if not properly used, which is not good for delay sensitive video-dominating scenarios. In this paper, we first investigate the problems of applying Imm-ACK and fixed burst size Dly-ACK schemes for video streaming over IEEE 802.15.3 WPANs. It is shown that the performance of Imm-ACK degrades first and more quickly as the workload increases due to ACK overhead when the channel is good. N-Dly-ACK works quite well in favorable channel conditions, but it almost can not work when the channel quality is poor. Upon the analysis above, We propose an application-aware Dly-ACK algorithm for video streaming over 802.15.3 WPANs, which includes a frame based dynamic Dly-ACK burst size adjusting algorithm and a min-seq sending up algorithm. In order to evaluate the potential of our mechanism, extensive simulations are carried out. The results show that the proposed algorithm outperforms both Imm-ACK and fixed burst size Dly-ACK consistently over all simulated load levels and channel conditions in terms of fraction of decodable video frames