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To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts. In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers’ data are computed and transmitted without complicated encryption. To further reduce each worker's overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers’ perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads. [ABSTRACT FROM AUTHOR]

Contribution: This article features a systematic literature review with the objective of presenting a study that reflects the current scenario of research on the cognitive load theory (CLT) in the domain of teaching and learning computer programming. Background: Computer programming is a highly cognitive skill, requiring mastering multiple competencies, and recognized as being difficult to learn, for this reason, the cognitive load (CL) in the learner’s working memory emerged as an influential concept, making CLT one of the most common theories in computing education research. Research Questions: What are the approaches that relate CLT to teaching and learning computer programming? What CLT-related concepts are covered? What evidence is reported with respect to this context? Methodology: Following a formal protocol, a survey was performed for papers linking CLT to teaching and learning programming published between 2010 and 2020. The selection of papers was based on a set of criteria established to drive the selection process, including alignment with the research questions and paper quality assessment. Findings: The approaches applied in the papers are based on measuring the CL; through instructional design based on the development or use of resources or tools, a range of different pedagogy strategies and the CLT concepts. With respect to the concepts, the subjective measurement technique and worked example effect are the most commonly deployed. As far as the evidence reported, the better part is related to the worked example effect and measuring CLs. [ABSTRACT FROM AUTHOR]

This paper presents a non-orthogonal multiple access (NOMA)-assisted full-duplex (FD) cooperative spectrum sharing protocol to achieve full-rate at a cell-edge primary user, where the secondary receiver helps the primary network relay the information to the cell-edge primary user. Unlike other FD cooperative schemes, the FD relay in the proposed system is not transmitting while receiving symbols from the primary transmitter. Hence, the proposed scheme can achieve full-rate at a primary user without degrading its outage performance. Further, it guarantees every alternate time-slots to the secondary network for accessing the licensed spectrum by employing a secondary receiver as a cooperative relay. In this paper, we derive the analytical expression for the outage probability and ergodic capacity. Moreover, we validate the derived expression through simulation and asymptotic results. The numerical and simulation results substantiate that the proposed scheme significantly outperforms other existing protocols. [ABSTRACT FROM AUTHOR]

This paper provides a summary of media distribution in the 3GPP 5G System. We provide a status overview on the basics of 5G and in particular the Release 16 technologies on 5G Media Streaming and 5G Broadcast. Particular focus in this document are the recent Release-17 activities including extensions to 5G Media Streaming and 5G Broadcast. On the former, for example the integration of edge computing into media streaming is a major focus. On the latter, 5G Broadcast is extended to support co-existence of the technology with other broadcast technologies in the UHF band. A new set of functionalities is developed under the 5G Multicast–Broadcast Services (5MBS) umbrella work item. This includes new user service and system architecture functions to support broadcast and multicast throughout the 5G system. Particular focus in this paper are the radio layer and physical layer extensions to support point-to-multipoint delivery on top of New Radio (NR). We also touch briefly on the relevance of the 5G media distribution for third party ecosystems and industry with special focus on collaboration of media verticals and the 5G ecosystem. [ABSTRACT FROM AUTHOR]

This paper presents a method for controlling the voltage of inverter-based Microgrids by proposing a new scale-free distributed cooperative controller. The main contribution of this paper is that the proposed distributed cooperative controller is scale-free where is independent of any information about the communication system and the number of distributed generators, as such it works for any Microgrids with any size. Moreover, the communication network is modeled by a general time-varying graph which enhances the resilience of the proposed protocol against communication link failure, data packet loss, and arbitrarily fast plug and play operation in the presence of arbitrarily finite communication delays as the protocol does not require the knowledge of the upper bound on the delay. The stability analysis of the proposed protocol is provided. The proposed method is simulated on the CIGRE medium voltage Microgrid test system. The simulation results demonstrate the feasibility of the proposed scale-free distributed nonlinear protocol for regulating voltage of Microgrids in the presence of communication failures, data packet loss, noise, and degradation. [ABSTRACT FROM AUTHOR]

IEC61850 is the mainstream of the development for substation automation. This paper presents a practical consideration and analysis for implementing a secure sampled measured value (SeSV) message in substation automation system. Due to the lack of security features in the standard, IEC Working Group 15 of Technical Committee 57 published IEC62351 on security for IEC61850 profiles. However, the use of authentication methods for SV based on IEC62351 standards are still not integrated, and computational capabilities and performance are not validated and tested with commercial grade equipment. Hence, this paper shows the performance of security feature enabled SeSV packets transmitted between protection and control devices by appending a message authentication code (MAC) to the extended IEC61850 packets. A prototype implementation on a low cost commodity embedded system has proved that the MAC-enabled SV message can fully secure the process bus communication in the digital substation with negligible time delay. [ABSTRACT FROM AUTHOR]

Co-simulation of heterogeneous systems allows for in-depth analysis of various aspects of power systems’ operation while staying within the environments of the simulation tools that are best fit to represent their respective domains. Equipped with a proprietary co-simulation platform, the paper focuses on the issue of power-conjugate coupling between parts of power grids modeled in transient stability and electromagnetic transient simulation tools. The problems of co-simulation stability and precision in presence of delays are tackled by means of designing a proper coupling interface. It is shown that two established interface methods – the V-I method and the Transmission Line Interface – are special cases of a generalized interface framework proposed in the paper. Moreover, a new interface algorithm is described by parametrizing the generalized framework. Analytical tools are also formulated to aid in the analysis of interface stability and precision via the concepts of passivity and transparency. Simulation results of benchmark systems of various complexity demonstrate the application of the developed power coupling interface. [ABSTRACT FROM AUTHOR]

A private proximity retrieval (PPR) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance $r$ from the user’s record $x$. The user’s privacy at each server is given by the fraction of the record $x$ that is kept private. In this paper, this research is initiated and protocols that offer trade-offs between privacy, computational complexity, and storage are studied. In particular, we assume that each server stores a copy of the database and study the required minimum number of servers by our protocol which provides a given privacy level. Each server receives a query in the protocol and the set of queries forms a code. The main focus in the paper is dedicated to studying the family of codes generated by the set of queries. These codes will be shown to satisfy a specific covering property and will be called private proximity retrieval intersection covering codes. In particular, since the query every server receives is a codeword, the goal is to minimize the number of codewords in such a code which is the minimum number of servers required by the protocol. These codes are closely related to a family of codes known as covering designs. We introduce several lower bounds on the sizes of such codes as well as several constructions. This work focuses on the case when the records are binary vectors together with the Hamming distance. Other metrics such as the Johnson metric are also investigated. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that the success of deep learning is built upon large-scale training data and tremendous computing power. However, the data and computing power are not always available for many real-world applications. In this paper, we address the machine learning problem where it lacks training data and limits computing power. Specifically, we investigate domain adaptation which is able to transfer knowledge from one labeled source domain to an unlabeled target domain, so that we do not need much training data from the target domain. At the same time, we consider the situation that the running environment is confined, e.g., in edge computing the end device has very limited running resources. Technically, we present the Faster Domain Adaptation (FDA) protocol and further report two paradigms of FDA: early stopping and amid skipping. The former accelerates domain adaptation by multiple early exit points. The latter speeds up the adaptation by wisely skip several amid neural network blocks. Extensive experiments on standard benchmarks verify that our method is able to achieve the comparable and even better accuracy but employ much less computing resources. To the best of our knowledge, there are very few works which investigated accelerating knowledge adaptation in the community. This work is expected to inspire the topic for more discussion. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain. [ABSTRACT FROM AUTHOR]

Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design and train the shared-weight backbone network, a.k.a. the super-net. Since heuristics substantially vary across different methods and have not been carefully studied, it is unclear to which extent they impact super-net training and hence the weight-sharing NAS algorithms. In this paper, we disentangle super-net training from the search algorithm, isolate 14 frequently-used training heuristics, and evaluate them over three benchmark search spaces. Our analysis uncovers that several commonly-used heuristics negatively impact the correlation between super-net and stand-alone performance, whereas simple, but often overlooked factors, such as proper hyper-parameter settings, are key to achieve strong performance. Equipped with this knowledge, we show that simple random search achieves competitive performance to complex state-of-the-art NAS algorithms when the super-net is properly trained. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of $\mathcal {O}(M)$ is achieved, with $M$ being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively. [ABSTRACT FROM AUTHOR]

The existing relay-assisted terahertz (THz) wireless system is limited to dual-hop transmission with pointing errors and short-term fading without considering the shadowing effect. This paper analyzes the performance of a multihop-assisted backhaul communication mixed with an access link under the shadowed fading with antenna misalignment errors. We derive novel probability density and distribution functions of the signal-to-noise ratio (SNR) of the multihop link employing channel-assisted (CA) and fixed-gain (FG) amplify-and-forward (AF) relaying for each hop by considering independent but not identically distributed (i.ni.d) $\alpha $ - $\mu $ fading channel with pointing errors. We present analytical expressions for the outage probability and average bit-error-rate (ABER) and develop asymptotic analysis in high SNR regime to derive the diversity order for both CA-multihop and FG-multihop backhaul links. We use the derived results of the multihop systems to analyze the statistical performance of the integrated backhaul-access relaying for both uplink and downlink transmission considering the generalized- $K$ shadowed fading model in the access link. We also present exact and asymptotic expressions of the outage probability for the considered mixed system. We perform computer simulations to provide design and deployment aspects of employing multiple relays to enhance THz wireless transmissions. [ABSTRACT FROM AUTHOR]

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namely translating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods. [ABSTRACT FROM AUTHOR]

Gossip protocols are popular methods for average consensus problems in distributed computing. We prove new convergence guarantees for a variety of such protocols, including path, clique, and synchronous pairwise gossip. These arise by exploiting the connection between these protocols and the block randomized Kaczmarz method for solving linear systems. Moreover, we extend existing convergence results for block randomized Kaczmarz to allow for a more general choice of blocks, rank-deficient systems, and provide a tighter convergence rate guarantee. Previous results for block randomized Kaczmarz assumed blocks formed a paving of the systems; our analysis, contrary to the title of our paper, generalizes to a broader class of blocks that we call coverings. We furthermore apply this analysis to inconsistent consensus models and obtain similar guarantees. An extensive empirical analysis of these methods is provided for a variety of synthetic and real networks. [ABSTRACT FROM AUTHOR]

In this paper, we establish an interesting duality between two different quantum information-processing tasks, namely, classical source coding with quantum side information, and channel coding over classical-quantum channels. The duality relates the optimal error exponents of these two tasks, generalizing the classical results of Ahlswede and Dueck [IEEE Trans. Inf. Theory, 28(3):430–443, 1982]. We establish duality both at the operational level and at the level of the entropic quantities characterizing these exponents. For the latter, the duality is given by an exact relation, whereas for the former, duality manifests itself in the following sense: an optimal coding strategy for one task can be used to construct an optimal coding strategy for the other task. Along the way, we derive a bound on the error exponent for classical-quantum channel coding with constant composition codes which might be of independent interest. Finally, we consider the task of variable-length classical compression with quantum side information, and a duality relation between this task and classical-quantum channel coding can also be established correspondingly. Furthermore, we study the strong converse of this task, and show that the strong converse property does not hold even in the i.i.d. scenario. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of relaying a single bit of information across a series of binary symmetric channels, and the associated trade-off between the number of hops $m$ , the transmission time $n$ , and the error probability. We introduce a simple, efficient, and deterministic protocol that attains positive information velocity (i.e., a non-vanishing ratio $\frac {m}{n}$ and small error probability) and is significantly simpler than existing protocols that do so. In addition, we characterize the optimal low-noise and high-noise scaling laws of the information velocity, and we adapt our 1-bit protocol to transmit $k$ bits over $m$ hops with $\mathcal {O}(m+k)$ transmission time. [ABSTRACT FROM AUTHOR]

Covert communications is critical in many application scenarios for ensuring transmission security and privacy. Two-way protocols are widely adopted in intelligent reflecting surface (IRS) assisted relaying networks to enable covert communications and prevent wireless signals from being overheard. To quantify the performance of covert communications in this special scenario, we derive the closed-form expression of outage probability and its asymptote in this paper. Considering the worst-case of covert communications, the optimal normalized power threshold of warden's detector is analyzed under a complex Gaussian distribution approximation. To meet the requirement of the detection error probability, the ratio of the transmission power between the covert nodes is investigated. Simulation results are provided to validate the theoretical expressions as well as the asymptotic analysis with a small number of reflecting elements. [ABSTRACT FROM AUTHOR]

As millimeter wave (mmWave) technology becomes more integrated into today's communication infrastructure, its application to mobile scenarios has attracted considerable interests. In particular, mmWave technology is one of the leading candidates in Vehicle-to-Everything (V2X) community to address the ever-rising data exchange demand from vehicles. However, the inherent directionality of the technology poses challenges to peer discovery and transmission scheduling in typical V2X scenarios, causing significant loss of network capacity. To mitigate this issue, we propose a novel sub-6 GHz V2X-assisted synchronous mmWave communication scheduler in this paper. The sub-6 GHz channel serves as control channel for mmWave scheduling. The scheduler carefully considers data importance and freshness in determining communication schedule, such that network utility across mmWave links is maximized. We formulate the scheduling problem into an integer linear programming (ILP) problem. However, ILP problem is NP-hard and has exponential computational complexity for the worst cases. We then develop a scheduling algorithm, maximum weight matching based iterative algorithm (MWMIA), that operates in polynomial time with performance guarantees for the NP-hard ILP, by leveraging structural properties of link conflicts. Comparative simulation studies verify the effectiveness of our scheduler against other solutions. [ABSTRACT FROM AUTHOR]

Recent research showcased several cyber-attacks against unmodified licensed vehicles, demonstrating the vulnerability of their internal networks. Many solutions have already been proposed by industry and academia, aiming to detect and prevent cyber-attacks targeting in-vehicle networks. The majority of these proposals borrow security algorithms and techniques from the classical ICT domain, and in many cases they do not consider the inherent limitations of legacy automotive protocols and resource-constrained microcontrollers. This paper proposes DAGA, an anomaly detection algorithm for in-vehicle networks exploiting $n-$ gram analysis. DAGA only uses sequences of CAN message IDs for the definition of the $n-$ grams used in the detection process, without requiring the content of the payload or other CAN message fields. The DAGA framework allows the creation of detection models characterized by different memory footprints, allowing their deployment on microcontrollers with different hardware constraints. Experimental results based on three prototype implementations of DAGA showcase the trade off between hardware requirements and detection performance. DAGA outperforms the state-of-the-art detectors on the most performing microcontrollers, and can execute with lower performance on simple microcontrollers that cannot support the vast majority of IDS approaches proposed in literature. As additional contributions, we publicly release the full dataset and our reference DAGA implementations. [ABSTRACT FROM AUTHOR]

A human contact network (HCN) consists of individuals moving around and interacting with each other. In HCN, it is essential to detect malicious users who break the data delivery through terminating the data delivery or tampering with the data. Since malicious users will pay more but gain less when breaking the data delivery of opportunistic contacts, we focus on the non-opportunistic contacts that occur more frequently and stably. It is observed that people contact with each other more frequently if they have more social features in common. In this paper, we build up topology structure for HCN based on social features, and propose a graph theoretical comparison detection model to perform malicious users detection. Then we present an adaptive detection scheme based on Hamiltonian cycle decomposition. Also, we define comparison-0-string and comparison-1-string to improve the detection efficiency. Moreover, we perform scenario simulations on real data to realize the detected process of malicious users. Experiments show that, when the number of malicious users is bounded by the dimension of HCN, our scheme has a detection rate of 100% with both false positive rate and false negative rate being 0%, and the running cost is also very low when compared to baseline approaches. When the number of malicious users exceeds the bound, the detection rate of our scheme decreases slowly, while the false positive rate and false negative rate increase slowly, but they are still better than the baseline approaches. [ABSTRACT FROM AUTHOR]

XOR-AND Graphs (XAGs) are an enrichment of the classical AND-Inverter Graphs (AIGs) with XOR nodes. In particular, XAGs are networks composed by ANDs, XORs, and inverters. Besides several emerging technologies applications, XAGs are often exploited in cryptography-related applications based on the multiplicative complexity of a Boolean function. The multiplicative complexity of a function is the minimum number of AND gates (i.e., multiplications) that are sufficient to represent the function over the basis {AND, XOR, NOT}. In fact, the minimization of the number of AND gates is important for high-level cryptography protocols such as secure multiparty computation, where processing AND gates is more expensive than processing XOR gates. Moreover, it is an indicator of the degree of vulnerability of the circuit, as a small number of AND gates corresponds to a high vulnerability to algebraic attacks. In this paper we study the multiplicative complexity of Boolean functions characterized by two particular regularities, called autosymmetry and D-reducibility. Moreover, we exploit these regularities for decreasing the number of AND nodes in XAGs. The experimental results validate the proposed approaches. [ABSTRACT FROM AUTHOR]

Clock synchronization is crucial for applications in wireless sensor networks, such as event scheduling and data fusion. In practical wireless networks, environmental changes cause the oscillator to be imperfect. Thus, it is necessary to track the nonlinear varying clock dynamically. In this paper, we propose a timestamp-free clock skew (frequency difference) and offset (time difference) joint tracking algorithm based on extended Kalman filter (EKF), which can be embedded into the general network data flow to achieve long-term synchronization without additional communication overhead. To further improve energy efficiency of clock synchronization, this paper develops an EKF clock skew tracking algorithm for silent nodes in receiver-only synchronization. For the situation that silent node overhears synchronization information from multiple active nodes, we present multi-information timestamp-free synchronization and multi-observation clock skew fusion scheme for silent nodes. Simulation results show the effectiveness of the proposed tracking algorithms. [ABSTRACT FROM AUTHOR]

Measuring contact friction in soft-bodies usually requires a specialised physics bench and a tedious acquisition protocol. This makes the prospect of a purely non-invasive, video-based measurement technique particularly attractive. Previous works have shown that such a video-based estimation is feasible for material parameters using deep learning, but this has never been applied to the friction estimation problem which results in even more subtle visual variations. Because acquiring a large dataset for this problem is impractical, generating it from simulation is the obvious alternative. However, this requires the use of a frictional contact simulator whose results are not only visually plausible, but physically-correct enough to match observations made at the macroscopic scale. In this paper, which is an extended version of our former work A. H. Rasheed, V. Romero, F. Bertails-Descoubes, S. Wuhrer, J.-S. Franco, and A Lazarus, “Learning to measure the static friction coefficient in cloth contact,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2020, pp. 9909–9918, we propose to our knowledge the first non-invasive measurement network and adjoining synthetic training dataset for estimating cloth friction at contact, for both cloth-hard body and cloth-cloth contacts. To this end we build a protocol for validating and calibrating a state-of-the-art frictional contact simulator, in order to produce a reliable dataset. We furthermore show that without our careful calibration procedure, the training fails to provide accurate estimation results on real data. We present extensive results on a large acquired test set of several hundred real video sequences of cloth in friction, which validates the proposed protocol and its accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image. Our model takes estimated 2D pose as the input and learns a generalized 2D-3D mapping function to leverage into 3D pose. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNNs) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a data augmentation algorithm to further improve model robustness against appearance variations and cross-view generalization ability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges. [ABSTRACT FROM AUTHOR]

In this paper, two new families of asynchronous channel-hopping (CH) sequences—Symmetric Maximum-Length CH (SML-CH) Sequences and Relaxed Difference Set (RDS) of Maximum-Length CH (RDSML-CH) Sequences—are constructed. For the first time, the maximum-length sequences (also called as m-sequences) are used to create 2-D CH matrices with a reduced number of “jump” columns. The two new constructions carry the desirable properties of maximum rendezvous diversity (MRD) and even channel use (ECU) and, more importantly, have the shortest periods in their categories of constructions. Another contribution is on the embedment of optical orthogonal codes (OOCs) to eliminate “stay” columns in the RDSML-CH matrices, thus resulting in the first and only family of the asynchronous CH sequences that can asymptotically achieve the theoretical period lower bound. To support the RDSML-CH construction, a new shortened RDS algorithm for creating the OOCs with smaller weight-to-length ratios than the existing ones is proposed. Numerical and simulation analyses show that the two new constructions have a good balance of short period, MRD, ECU, short time-to-rendezvous (TTR) mean, small TTR variance, and short maximum-TTR, thus more suitable for practical CR wireless networks than the existing constructions. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) and device-to-device (D2D) communication are two promising technologies for improving transmission reliability between transceivers in communication systems. In this paper, we consider the design of reliable communication between the access point (AP) and actuators for a downlink multiuser multiple-input single-output (MISO) system in the industrial IoT (IIoT) scenario. We propose a two-stage protocol combining IRS with D2D communication so that all actuators can successfully receive the message from AP within a given delay. The superiority of the protocol is that the communication reliability between AP and actuators is doubly augmented by the IRS-aided first-stage transmission and the second-stage D2D transmission. A joint optimization problem of active and passive beamforming is formulated, which aims to maximize the number of actuators with successful decoding. We study the joint beamforming problem for cases where the channel state information (CSI) is perfect and imperfect. For each case, we develop efficient algorithms that include convergence and complexity analysis. Simulation results demonstrate the necessity and role of IRS with a well-optimized reflection matrix, and the D2D network in promoting reliable communication. Moreover, the proposed protocol can enable reliable communication even in the presence of stringent latency requirements and CSI estimation errors. [ABSTRACT FROM AUTHOR]

In this paper we introduce Ouroboros, a new family of Key Exchange protocols based on coding theory. The protocols propose a middle ground between the cryptosystems based on $\mathsf {QC}$ - $\mathsf {MDPC}$ codes, which feature small parameter sizes, but have a security reduction to two problems: the syndrome decoding problem and the indistinguishability of the code, and the HQC protocol, which features bigger parameters but has a security reduction to the syndrome decoding problem only. Ouroboros features a reduction to the syndrome decoding problem with only a small overhead compared to the $\mathsf {QC}$ - $\mathsf {MDPC}$ based cryptosystems. The approach is based on an ideal structure and also works for the rank metric. This yields a simple, secure and efficient approach for key exchange, the Ouroboros family of protocols. For the Hamming metric we obtain the same type of parameters (and almost the same simple decoding) as for $\mathsf {MDPC}$ based cryptosystems, but with a security reduction to decoding random quasi-cyclic codes in the Random Oracle Model. This represents a reduction of up to 38% on the public key size compared to HQC, for the most secure parameters. For the rank metric, we obtain better parameters than for RQC, saving up to 31% on the public key for the most secure set of parameters, using non homogeneous errors in Ouroboros. In this full version, the protocol and decoding algorithm have been slightly improved, additional details are given in the security proof, and the protocol is fully described for the rank metric. [ABSTRACT FROM AUTHOR]

Mobile terminals have been widely used in the communication applications (e.g., vehicular network, e-Health, smart grid and e-Commerce) as they can obtain all kinds of services and upload data for users anytime and anywhere. With the number of users increasing dramatically and user demands diversifying, multi-server architecture was proposed to solve issues on lack of service resources that single-server architecture remains. However, multi-server architecture faces a series of challenges in applications, such as security and authentication efficiency requirements. Thus, many authenticated key agreement (AKA) protocols are presented, yet most of them require unacceptable computation and communication overhead, which is inefficient indeed. In this paper, we propose an adaptive secure handover authenticated key agreement protocol for multi-server architecture. Specifically, broadcast mechanism is utilized to assist users to select appropriate service providers with the lowest cost. Users can realize efficient handover authentication with multiple servers in the proposed scheme, that is, after completing initial authentication, users can switch to other servers and complete authentication through simple operations. Security analysis shows the proposed protocol can meet all security requirements and compared with other relevant protocols, it greatly reduces the computation and communication overhead. In addition, the above advantages grow significantly as the number of servers to be authenticated increases. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm. [ABSTRACT FROM AUTHOR]

The integration of phasor measurement units (PMUs) and phasor data concentrators (PDCs) in smart grids may be exploited by attackers to initiate new and sophisticated false data injection (FDI) attacks. Existing FDI attack mitigation approaches are generally less effective against sophisticated FDI attacks, such as collusive false data injection (CFDI) attacks launched by compromised PDCs (and PMUs) as we demonstrate in this paper. Thus, we propose a secure and resilience-enhanced scheme (SeCDM) to detect and mitigate such cyber threats in smart grids. Specifically, we design a decentralized homomorphic computation paradigm along with a hierarchical knowledge sharing algorithm to facilitate secure ciphertext calculation of state estimation residuals. Following this, a centralized FDI detector is implemented to detect FDI attacks. Findings from the security analysis demonstrate our approach achieves enhanced conventional FDI and CFDI attack resilience, and findings from our performance evaluations on the standard IEEE 14-, 24-, and 39-bus power systems also show that the communication overheads and computational complexity are reasonably “low”. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has emerged as a promising technique to enhance wireless communication performance cost-effectively. The existing literature has mainly considered IRS being deployed near user terminals to improve their performance. However, this approach may incur a high cost if IRSs need to be densely deployed in the network to cater to random user locations. To avoid such high deployment cost, in this paper we consider a new IRS aided wireless network architecture, where IRSs are deployed in the vicinity of each base station (BS) to assist in its communications with distributed users regardless of their locations. Besides significantly enhancing IRSs’ signal coverage, this scheme helps reduce the IRS-associated channel estimation overhead as compared to conventional user-side IRSs, by exploiting the nearly static BS-IRS channels over short distance. For this scheme, we propose a new two-stage transmission protocol to achieve IRS channel estimation and reflection optimization for uplink data transmission efficiently. In addition, we propose effective methods for solving the user-IRS association problem based on long-term/statistical channel knowledge and the selected user-IRS-BS cascaded channel estimation problem. Finally, all IRSs’ passive reflections are jointly optimized with the BS’s multi-antenna receive combining to maximize the minimum achievable rate among all users for data transmission. Numerical results show that the proposed co-site-IRS empowered BS scheme can achieve significant performance gains over the conventional BS without co-site IRS and existing schemes for IRS channel estimation and reflection optimization, thus enabling an appealing low-cost and high-performance BS design for future wireless networks. [ABSTRACT FROM AUTHOR]

Video facial expression recognition is useful for many applications and received much interest lately. Although some methods give good results in controlled environments (no occlusion), recognition in the presence of partial facial occlusion remains a challenging task. To handle facial occlusions, methods based on the reconstruction of the occluded part of the face have been proposed. These methods are mainly based on the texture or the geometry of the face. However, the similarity of the face movement between different persons doing the same expression seems to be a real asset for the reconstruction. In this paper we exploit this asset and propose a new method based on an auto-encoder with skip connections to reconstruct the occluded part of the face in the optical flow domain. To the best of our knowledge, this is the first work that directly reconstructs the movement for facial expression recognition. We validated our approach in the controlled CK+ datasets on which different occlusions were generated. Our experiments show that the proposed method reduces the gap in the recognition accuracy between occluded and unoccluded situations. We also compare our approach with existing state-of-the-art approaches. In order to lay the basis of a reproducible and fair comparison in the future, we also propose a new experimental protocol that includes occlusion generation and reconstruction evaluation. [ABSTRACT FROM AUTHOR]

The increasing amount of graph-structured data catalyzes analytics over graph databases using semantic queries. Motivated by the ubiquity of commercial cloud platforms, data owners are willing to store their graph databases remotely. However, data privacy has emerged as a widespread concern since the cloud platforms are not fully trusted. One viable solution is to encrypt sensitive data before outsourcing, which inevitably hinders data retrieval. To enable queries over encrypted data, searchable symmetric encryption (SSE) has been introduced. Yet, the most well-studied class of SSE schemes focuses on retrieving textual files given keywords, which cannot be applied to graph databases directly. This paper extends our preliminary work (FC’17) and proposes GraphShield, a structured encryption scheme for graphs. Beyond shortest distance queries, GraphShield can support other classic graph-based queries (e.g., maximum flow) and more complicated analytics (e.g., PageRank). Technically, we incorporate a suite of (efficient) cryptographic primitives and tailor some extra secure protocols for facilitating graph analytics. Our scheme also allows updates on the encrypted graph with forward privacy guaranteed. We formalize the security model and prove the adaptive security with reasonable leakage. Finally, we implement our scheme on various real-world datasets, and the experiment results demonstrate its practicality and scalability. [ABSTRACT FROM AUTHOR]

The recently proposed dual active protocol stack (DAPS) handover (HO) is one of the mobility enhancements that can effectively reduce the handover interruption time (HIT) in 5G networks. By using a DAPS solution, users can be connected to both the source cell and target cell during the HO execution phase, and thereby 0 ms of HIT becomes possible. However, the DAPS HO procedure may fail in 5G networks due to the channel characteristics of millimeter-wave (mmWave) signals. Since mmWave links are vulnerable to blockages, the received signal quality may degrade suddenly, which gives rise to an abrupt outage before DAPS HO can be completed. In this paper, a novel learning-based DAPS HO technology named intelligent DAPS (I-DAPS) HO is proposed to avoid sudden radio link failure (RLF) while providing a high data rate. The proposed I-DAPS HO uses a double deep Q-network (DDQN) deep reinforcement learning (DRL) framework, where blockage predictions are made based on past received signal data such that RLFs can be actively avoided. The performance evaluation demonstrates that the proposed I-DAPS HO scheme can effectively avoid RLF and improve the throughput performance compared to advanced 5G HO schemes. [ABSTRACT FROM AUTHOR]

Due to the deployment of 5G technology, the number of IoV (Internet of Vehicle) devices connected to the Internet will explosively grow. However, as a kind of edge network device, IoV devices also face some problems including weak password authentication, lack of security protection, and lagged firmware updating, which largely threaten the security and legitimacy of these devices. IoV device identification is important in discovering, monitoring, and protecting these devices. Although existing proactive identification methods based on device fingerprints can be used to identify the large-scale Internet-connected IoV devices, they can not meet the fine-grained requirements for security risk assessment. Due to the increase in the types and brands of IoV devices, the fingerprint granularity will be insufficient. In this paper, we proposed a retransmission-based TCP fingerprints for large-scale fine-grained proactive device identification. Firstly, a probing scheme was designed to obtain TCP retransmission packet and increase the granularity space of traditional TCP fingerprint by selecting the multi-group features of TCP retransmission messages. Then, according to the bagging strategy of ensemble learning, a combined classifier with five predominant machine learning algorithms was generated. The experimental results showed that the identification accuracy and recall of IoV devices respectively reached 96.7% and 95.2%. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV)-based streaming media transmission may become unstable when the bit rate generated by the source load exceeds the channel capacity owing to the UAV location and speed change. The change of the location can affect the network connection, leading to reduced transmission rate; the change of the flying speed can increase the video payload due to more I-frames. To improve the transmission reliability, in this paper we design a Client-Server-Ground&User (C-S-G&U) framework, and propose an algorithm of splitting-merging stream (SMS) for multi-link concurrent transmission. We also establish multiple transport links and configure the routing rules for the cross-layer design. The multi-link transmission can achieve higher throughput and significantly smaller end-to-end delay than a single-link especially in a heavy load situation. The audio and video data are packaged into the payload by the Real-time Transport Protocol (RTP) before being transmitted over the User Datagram Protocol (UDP). The forward error correction (FEC) algorithm is implemented to promote the reliability of the UDP transmission, and an encryption algorithm to enhance security. In addition, we propose a Quality of Service (QoS) strategy so that the server and the user can control the UAV to adapt its transmission mode dynamically, according to the load, delay, and packet loss. Our design has been implemented on an engineering platform, whose efficacy has been verified through comprehensive experiments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel communication framework, Acoustic-WiFi, that utilizes the acoustic channel (i.e., microphone/speaker) as a control channel to develop more efficient Wi-Fi networks. We used the Acoustic-WiFi framework to develop a Power Saving Mode (PSM) scheme; A2PSM, and a smart contention resolution scheme; Harmony. In A2PSM, we propose an acoustic channel assisted power saving scheme for the Wi-Fi interface, which addresses the inefficiency of the existing power saving schemes in smart devices. In this scheme, we leverage the low power consumption of the acoustic interfaces to reduce the wakeup events of the Wi-Fi interface when it is in power saving mode. We develop a small-scale prototype testbed on real smartphones to evaluate the proposed A2PSM scheme. Experiments show that A2PSM could save up to more than 25% more power than the existing schemes. In Harmony, we address the overhead of the Wi-Fi backoff scheme (i.e., contention window countdown, DIFS) and reduce the overall collisions among the devices. Harmony uses the acoustic channel for contention resolution in Wi-Fi networks. We also evaluate Harmony using real testbed and simulation. Experiments using the testbed show a 40% gain in throughput over the 802.11 Wi-Fi backoff scheme, while simulation results show a gain of more than 27% for dense networks. [ABSTRACT FROM AUTHOR]

Quantum cybersecurity is the study of all facets regarding the security of communication and computation in a distributed network. Significant developments in quantum technologies have outclassed their classical counterparts, thus envisioning the realization of a quantum internet. However, such quantum resources, in the hands of an adversary, can jeopardize network security. In this paper, we study two secure network connectivity concerns, namely, privacy and anonymity, in quantum information retrieval systems. To this end, we propose a state-of-the-art single-server multi-user quantum anonymous private information retrieval (QAPIR) protocol. To actualize this, we utilize anonymous entanglement as a quantum resource. We show that the QAPIR protocol not only provides privacy but also introduces anonymity as an added layer of security in quantum networks. Furthermore, we also detail a comparative security analysis that establishes the desirable properties of our proposal. [ABSTRACT FROM AUTHOR]

The work presented in this paper describes a multi-issue negotiation protocol between active consumers and a management platform in order to establish load coordination in a highly congested network. The multi-issue negotiation protocol considers simultaneously the consumption interval, the price, and the size of the energy packages, which is the main contribution of this work. Regarding the implementation methodology, the proposed algorithms have been implemented using MATLAB classes that allow emulating the behaviour of a multi-agent system in which each vehicle is an agent that interacts with the platform, which is another agent. In the present work, and without loss of generality, the algorithm is applied to coordinate the charging of electric vehicles (EVs) in a distribution network in which building loads represent critical loads. The algorithm is tested in a realistic environment, and its stability and performance are evaluated. Furthermore, the description of the algorithm is provided in a generic form, and it could be applied to any other scenario. [ABSTRACT FROM AUTHOR]

With increasing usage of face recognition algorithms, it is well established that external artifacts and makeup accessories can be applied to different facial features such as eyes, nose, mouth, and cheek, to obfuscate one’s identity or to impersonate someone else’s identity. Recognizing faces in the presence of these artifacts comprises the problem of disguised face recognition, which is one of the most arduous covariates of face recognition. The challenge becomes exacerbated when disguised faces are captured in real-time environment, with low resolution images. To address the challenge of disguised face recognition, this paper first proposes a novel multi-objective encoder-decoder network, termed as DED-Net. DED-Net attempts to learn the class variations in the feature space generated by both disguised as well non-disguised images, using a combination of Mahalanobis and Cosine distance metrics, along with Mutual Information based supervision. The DED-Net is then extended to learn from the local and global features of both disguised and non-disguised face images for efficient face recognition, and the complete framework is termed as Disguise Resilient (D-Res) framework. The efficacy of the proposed framework has been demonstrated on two real-world benchmark datasets: Disguised Faces in the Wild (DFW) 2018 and DFW2019 competition datasets. In addition, this research also emphasizes on the importance of recognizing disguised faces in low resolution settings and proposes three experimental protocols to simulate the real-world surveillance scenario. To this effect, benchmark results have been shown on seven protocols for three low resolution settings ($32\times 32$ , $24\times 24$ , and $16\times 16$) of the two DFW benchmark datasets. The results demonstrate superior performance of the D-Res framework, in comparison with benchmark algorithms. For example, an improvement of around 3% is observed on the Overall protocol of the DFW2019 dataset, where the D-Res framework achieves 96.3%. Experiments have also been performed on benchmark face verification datasets (LFW, YTF, and IJB-B), where the D-Res framework achieves improved verification accuracy. [ABSTRACT FROM AUTHOR]