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Given a graph $G$ of degree $k$ over $n$ vertices, we consider the problem of computing a near maximum cut or a near minimum bisection in polynomial time. For graphs of girth $L$, we develop a local message passing algorithm whose complexity is $O(nkL)$, and that achieves near optimal cut values among all $L$-local algorithms. Focusing on max-cut, the algorithm constructs a cut of value $nk/4+ n\mathsf{P}_\star\sqrt{k/4}+\mathsf{err}(n,k,L)$, where $\mathsf{P}_\star\approx 0.763166$ is the value of the Parisi formula from spin glass theory, and $\mathsf{err}(n,k,L)=o_n(n)+no_k(\sqrt{k})+n \sqrt{k} o_L(1)$ (subscripts indicate the asymptotic variables). Our result generalizes to locally treelike graphs, i.e., graphs whose girth becomes $L$ after removing a small fraction of vertices. Earlier work established that, for random $k$-regular graphs, the typical max-cut value is $nk/4+ n\mathsf{P}_\star\sqrt{k/4}+o_n(n)+no_k(\sqrt{k})$. Therefore our algorithm is nearly optimal on such graphs. An immediate corollary of this result is that random regular graphs have nearly minimum max-cut, and nearly maximum min-bisection among all regular locally treelike graphs. This can be viewed as a combinatorial version of the near-Ramanujan property of random regular graphs.

Self-energy recycling (sER), which allows transmit energy re-utilization, has emerged as a viable option for improving the energy efficiency (EE) in low-power Internet of Things networks. In this work, we investigate its benefits also in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multi-antenna techniques in a communication system. We analyze the trade-offs when considering not only the energy spent on transmission but also the circuitry power consumption, thus making the analysis of much more practical interest. In addition to the well known spectral efficiency improvements, results show that FD also outperforms HD in terms of reliability. We show that sER introduces not only benefits in EE matters but also some modifications on how to achieve maximum reliability fairness between uplink and downlink transmissions, which is the main goal in this work. In order to achieve this objective, we propose the use of a dynamic FD scheme where the small base station (SBS) determines the optimal allocation of antennas for transmission and reception. We show the significant improvement gains of this strategy for the system outage probability when compared to the simple HD and FD schemes.

The underlay cognitive radio-based hybrid radio frequency / free-space optical (RF / FSO) systems have been emerged as a promising technology due to its ability to eliminate spectrum scarcity and spectrum under-utilization problems. Consequently, this work analyzes the physical layer security aspects of a cognitive RF / FSO hybrid network that includes a primary user, a secondary source, a secondary receiver, and an eavesdropper where the secret communication takes place between two legitimate secondary peers over the RF and FSO links simultaneously, and the eavesdropper can overhear the RF link only. In particular, the maximum transmit power limitation at the secondary user as well as the permissible interference power restriction at the primary user are also taken into consideration. All the RF links are modeled with $\alpha$-$\mu$ fading whereas the FSO link undergoes M\'alaga (M) turbulence with link blockage and pointing error impairments. At the receiver, the selection combining diversity technique is utilized to select the signal with the best electrical signal-to-ratio (SNR). Moreover, the closed-form expressions for the secrecy outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput are derived to analyze the secrecy performance. Besides, the impacts of fading, primary-secondary interference, detection techniques, link blockage probability, atmospheric turbulence, and pointing error are examined. Finally, Monte-Carlo simulations are performed to corroborate the derived expressions.

Reconfigurable intelligent surface (RIS) has become a promising technology to improve wireless communication in recent years. It steers the incident signals to create a favorable propagation environment by controlling the reconfigurable passive elements with less hardware cost and lower power consumption. In this paper, we consider a RIS-aided multiuser multiple-input single-output downlink communication system. We aim to maximize the weighted sum-rate of all users by joint optimizing the active beamforming at the access point and the passive beamforming vector of the RIS elements. Unlike most existing works, we consider the more practical situation with the discrete phase shifts and imperfect channel state information (CSI). Specifically, for the situation that the discrete phase shifts and perfect CSI are considered, we first develop a deep quantization neural network (DQNN) to simultaneously design the active and passive beamforming while most reported works design them alternatively. Then, we propose an improved structure (I-DQNN) based on DQNN to simplify the parameters decision process when the control bits of each RIS element are greater than 1 bit. Finally, we extend the two proposed DQNN-based algorithms to the case that the discrete phase shifts and imperfect CSI are considered simultaneously. Our simulation results show that the two DQNN-based algorithms have better performance than traditional algorithms in the perfect CSI case, and are also more robust in the imperfect CSI case.

In view of the security issues of the Internet of Things (IoT), considered better combining edge computing and blockchain with the IoT, integrating attribute-based encryption (ABE) and attribute-based access control (ABAC) models with attributes as the entry point, an attribute-based encryption and access control scheme (ABE-ACS) has been proposed. Facing Edge-Iot, which is a heterogeneous network composed of most resource-limited IoT devices and some nodes with higher computing power. For the problems of high resource consumption and difficult deployment of existing blockchain platforms, we design a lightweight blockchain (LBC) with improvement of the proof-of-work consensus. For the access control policies, the threshold tree and LSSS are used for conversion and assignment, stored in the blockchain to protect the privacy of the policy. For device and data, six smart contracts are designed to realize the ABAC and penalty mechanism, with which ABE is outsourced to edge nodes for privacy and integrity. Thus, our scheme realizing Edge-Iot privacy protection, data and device controlled access. The security analysis shows that the proposed scheme is secure and the experimental results show that our LBC has higher throughput and lower resources consumption, the cost of encryption and decryption of our scheme is desirable.

We studied power splitting-based simultaneous wireless information and power transfer (PS-SWIPT) in multiple access channels (MAC), considering the decoding cost and non-linear energy harvesting (EH) constraints at the receiving nodes to study practical limitations of an EH communication system. Under these restrictions, we formulated and analyzed the achievable rate and maximum departure regions in two well-studied scenarios, i.e., a classical PS-SWIPT MAC and a PS-SWIPT MAC with user cooperation. In the classical PS-SWIPT MAC setting, closed-form expressions for the optimal values of the PS factors are derived for two fundamental decoding schemes: simultaneous decoding and successive interference cancellation. In the PS-SWIPT MAC with user cooperation, the joint optimal power allocation for users as well as the optimal PS factor are derived. This reveals that one decoding scheme outperforms the other in the classical PS-SWIPT MAC, depending on the function type of the decoding cost. Finally, it is shown that the cooperation between users can potentially boost the performance of a PS-SWIPT MAC under decoding cost and non-linear EH constraints. Moreover, effects of the decoding cost functions, non-linear EH model and channel quality between the users are studied, and performance characteristics of the system are discussed.

In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. We focus on demonstrating how clustering of similar users enables a critical new functionality: supporting more personalized, and thus more accurate predictions for users. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset. We then discuss how improving the detection of trusted relationships in social media can assist in supporting online users in their battle against the spread of misinformation and rumours, within a social networking environment which has recently exploded in popularity. We conclude with a reflection on a particularly vulnerable user base, older adults, in order to illustrate the value of reasoning about groups of users, looking to some future directions for integrating known preferences with insights gained through data analysis.

Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the rendering algorithm using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving ('Real NVP') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with many existing rendering techniques by treating them as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.

This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalized recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain.

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations can cause proficient but narrowly-learned policies to fail at test time. In this work, we propose to learn how to quickly and effectively adapt online to new situations as well as to perturbations. To enable sample-efficient meta-learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach trains a global model such that, when combined with recent data, the model can be be rapidly adapted to the local context. Our experiments demonstrate that our approach can enable simulated agents to adapt their behavior online to novel terrains, to a crippled leg, and in highly-dynamic environments.