For formulating Quadratic Knapsack Problems (QKPs) into the form of Quadratic Unconstrained Binary Optimization (QUBO), it is necessary to introduce an integer variable, which converts and incorporates the knapsack capacity constraint into the overall energy function. In QUBO, this integer variable is encoded with auxiliary binary variables, and the encoding method used for it affects the behavior of Simulated Annealing (SA) significantly. For improving the efficiency of SA for QKP instances, this paper first visualized and analyzed their annealing processes encoded by conventional binary and unary encoding methods. Based on this analysis, we proposed a novel hybrid encoding (HE), getting the best of both worlds. The proposed HE obtained feasible solutions in the evaluation, outperforming the others in small- and medium-scale models.

This paper presents a novel frequency-controlled beam steering scheme for a phased-array antenna system (PAS). The proposed scheme employs phase-controlled carrier signals to form the PAS beam. Two local oscillators (LOs) and delay lines are used to generate the carrier signals. The carrier of one LO is divided into branches, and then the divided carriers passing through the corresponding delay lines have the desired phase relationship, which depends on the oscillation frequency of the LO. To confirm the feasibility of the scheme, four-branch PAS transmitters are configured and tested in a 10-GHz frequency band. The results verify that the formed beam is successfully steered in a wide range, i.e., the 3-dB beamwidth of approximately 100 degrees, using LO frequency control.

This letter provides a direct construction of binary even-length Z-complementary pairs. To date, the maximum zero correlation zone ratio of Type-I Z-complementary pairs has reached 6/7, but no direct construction of Z-complementary pairs can achieve the zero correlation zone ratio of 6/7. In this letter, based on Boolean function, we give a direct construction of binary even-length Z-complementary pairs with zero correlation zone ratio 6/7. The length of constructed Z-complementary pairs is 2m+3 + 2m + 2+2m+1 and the width of zero correlation zone is 2m+3 + 2m+2.

This study presents a selective data-compression interconnection network to boost its performance. Data compression virtually increases the effective network bandwidth. One drawback of data compression is a long latency to perform (de-)compression operation at a compute node. In terms of the communication latency, we explore the trade-off between the compression latency overhead and the reduced injection latency by shortening the packet length by compression algorithms. As a result, we present to selectively apply a compression technique to a packet. We perform a compression operation to long packets and it is also taken when network congestion is detected at a source compute node. Through a cycle-accurate network simulation, the selective compression method using the above compression algorithms improves by up to 39% the network throughput with a moderate increase in the communication latency of short packets.

Channel state information (CSI) acquisition at the transmitter side is a major challenge in massive MIMO systems for enabling high-efficiency transmissions. To address this issue, various CSI feedback schemes have been proposed, including limited feedback schemes with codebook-based vector quantization and explicit channel matrix feedback. Owing to the limitations of feedback channel capacity, a common issue in these schemes is the efficient representation of the CSI with a limited number of bits at the receiver side, and its accurate reconstruction based on the feedback bits from the receiver at the transmitter side. Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6%-26% compared with DL-based receivers and 33%-58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.

Edge computing, which has been gaining attention in recent years, has many advantages, such as reducing the load on the cloud, not being affected by the communication environment, and providing excellent security. Therefore, many researchers have attempted to implement neural networks, which are representative of machine learning in edge computing. Neural networks can be divided into inference and learning parts; however, there has been little research on implementing the learning component in edge computing in contrast to the inference part. This is because learning requires more memory and computation than inference, easily exceeding the limit of resources available for edge computing. To overcome this problem, this research focuses on the optimizer, which is the heart of learning. In this paper, we introduce our new optimizer, hardware-oriented logarithmic momentum estimation (Holmes), which incorporates new perspectives not found in existing optimizers in terms of characteristics and strengths of hardware. The performance of Holmes was evaluated by comparing it with other optimizers with respect to learning progress and convergence speed. Important aspects of hardware implementation, such as memory and operation requirements are also discussed. The results show that Holmes is a good match for edge computing with relatively low resource requirements and fast learning convergence. Holmes will help create an era in which advanced machine learning can be realized on edge computing.

This paper proposes a novel random access identifier (RAID)-linked receiver beamforming method for time division duplex (TDD)-based random access. When the number of receiver antennas at the base station is large in a massive multiple-input multiple-output (MIMO) scenario, the channel estimation accuracy per receiver antenna at the base station receiver is degraded due to the limited received signal power per antenna from the user terminal. This results in degradation in the receiver beamforming (BF) or antenna diversity combining and active RAID detection. The purpose of the proposed method is to achieve accurate active RAID detection and channel estimation with a reasonable level of computational complexity at the base station receiver. In the proposed method, a unique receiver BF vector applied at the base station is linked to each of the M RAIDs prepared by the system. The user terminal selects an appropriate pair comprising a receiver BF vector and a RAID in advance based on the channel estimation results in the downlink assuming channel reciprocity in a TDD system. Therefore, per-receiver antenna channel estimation for receiver BF is not necessary in the proposed method. Furthermore, in order to utilize fully the knowledge of the channel at the user transmitter, we propose applying transmitter filtering (TF) to the proposed method for effective channel shortening in order to increase the orthogonal preambles for active RAID detection and channel estimation prepared for each RAID. Computer simulation results show that the proposed method greatly improves the accuracy of active RAID detection and channel estimation. This results in lower error rates than that for the conventional method performing channel estimation at each antenna in a massive MIMO environment.

Industrial networks need to provide reliable communication services, usually in a redundant transmission (RT) manner. In the past few years, several device-redundancy-based, layer 2 solutions have been proposed. However, with the evolution of industrial networks to the Industrial Internet, these methods can no longer work properly in the non-redundancy, layer 3 environments. In this paper, an SDN-based reliable communication framework is proposed for the Industrial Internet. It can provide reliable communication guarantees for mission-critical applications while servicing non-critical applications in a best-effort transmission manner. Specifically, it first implements an RT-based reliable communication method using the Industrial Internet's link-redundancy feature. Next, it presents a redundant synchronization mechanism to prevent end systems from receiving duplicate data. Finally, to maximize the number of critical flows in it (an NP-hard problem), two ILP-based routing & scheduling algorithms are also put forward. These two algorithms are optimal (Scheduling with Unconstrained Routing, SUR) and suboptimal (Scheduling with Minimum length Routing, SMR). Numerous simulations are conducted to evaluate its effectiveness. The results show that it can provide reliable, duplicate-free services to end systems. Its reliable communication method performs better than the conventional best-effort transmission method in terms of packet delivery success ratio in layer 3 networks. In addition, its scheduling algorithm, SMR, performs well on the experimental topologies (with average quality of 93% when compared to SUR), and the time overhead is acceptable.

The prosperous Internet communication technologies have led to e-commerce in mobile computing and made Web of Things become popular. Electronic payment is the most important part of e-commerce, so many electronic payment schemes have been proposed. However, most of proposed schemes cannot give change. Based on proxy blind signatures, an e-cash payment system is proposed in this paper to solve this problem. This system can not only provide change divisibility through Web of Things, but also provide anonymity, verifiability, unforgeability and double-spending owner track.

It has been widely recognized that in compressed sensing, many restricted isometry property (RIP) conditions can be easily obtained by using the null space property (NSP) with its null space constant (NSC) 0<θ≤1 to construct a contradicted method for sparse signal recovery. However, the traditional NSP with θ=1 will lead to conservative RIP conditions. In this paper, we extend the NSP with 0<θ<1 to a scale NSP, which uses a factor τ to scale down all vectors belonged to the Null space of a sensing matrix. Following the popular proof procedure and using the scale NSP, we establish more relaxed RIP conditions with the scale factor τ, which guarantee the bounded approximation recovery of all sparse signals in the bounded noisy through the constrained l1 minimization. An application verifies the advantages of the scale factor in the number of measurements.

Recently, robust speech recognition for real-world applications has attracted much attention. This paper proposes a robust speech recognition method based on the teacher-student learning framework for domain adaptation. In particular, the student network will be trained based on a novel optimization criterion defined by the encoder outputs of both teacher and student networks rather than the final output posterior probabilities, which aims to make the noisy audio map to the same embedding space as clean audio, so that the student network is adaptive in the noise domain. Comparative experiments demonstrate that the proposed method obtained good robustness against noise.

Recently, Linux Container has been the de-facto standard for a cloud system, enabling cloud providers to create a virtual environment in a much more scaled manner. However, configuring container networks remains immature and requires automatic verification for efficient cloud management. We propose Verikube, which utilizes a novel graph structure representing policies to reduce memory consumption and accelerate verification. Moreover, unlike existing works, Verikube is compatible with the complex semantics of Cilium Policy which a cloud adopts from its advantage of performance. Our evaluation results show that Verikube performs at least seven times better for memory efficiency, at least 1.5 times faster for data structure management, and 20K times better for verification.

Distributed array radars consist of multiple sub-arrays separated by tens to hundreds of wavelengths and can match narrow beamwidths with large-aperture, high-gain antennas. The physical independence of the sub-arrays contributes to significant structure flexibility and is one of the advantages of such radars. However, a typical problem is the grating lobes in the digital beam forming (DBF) beam pattern. Unfortunately, the need to suppress the generation of grating lobes makes the design of acceptable sub-array arrangements very difficult. A sigma-delta beam former direction of arrival (DOA) estimation method is proposed in this study to solve this problem. The proposed method performs DOA estimation by acquiring the difference signals in addition to the sum signals of all sub-arrays. The difference signal is typically used for monopulse DOA estimation in the phased array radar. The sigma-delta beamformer simultaneously has both advantages of DOA estimations using a distributed array with a large aperture length and using a sub-array that is not affected by the grating lobe. The proposed method can improve the DOA estimation accuracy over the conventional method under grating lobe situations and help the distributed array radar achieve flexibility in the sub-array arrangement. Numerical simulations are presented to verify the effectiveness of the proposed DOA estimation method.

To the best of our knowledge, there have been very few work on computational algorithms for the core or its variants in MC-nets games. One exception is the work by [Hirayama, et.al., 2014], where a constraint generation algorithm has been proposed to compute a payoff vector belonging to the least core. In this paper, we generalize this algorithm into the one for finding a payoff vector belonging to ϵ-core with pre-specified bound guarantee. The underlying idea behind this algorithm is basically the same as the previous one, but one key contribution is to give a clearer view on the pricing problem leading to the development of our new general algorithm. We showed that this new algorithm was correct and never be trapped in an infinite loop. Furthermore, we empirically demonstrated that this algorithm really presented a trade-off between solution quality and computational costs on some benchmark instances.

A path loss prediction formula for IoT (Internet of Things) wireless communication close to ceiling beams in the 920MHz band is presented. In first step of our investigation, we conduct simulations using the FDTD (Finite Difference Time Domain) method and propagation tests close to a beam on the ceiling of a concrete building. In the second step, we derive a path loss prediction formula from the simulation results by using the FDTD method, by dividing into three regions of LoS (line-of-sight) situation, situation in the vicinity of the beam, and NLoS (non-line-of-sight) situation, depending on the positional relationship between the beam and transmitter (Tx) and receiver (Rx) antennas. For each condition, the prediction formula is expressed by a relatively simple form as a function of height of the antennas with respect to the beam bottom. Thus, the prediction formula is very useful for the wireless site planning for the IoT wireless devices set close to concrete beam ceiling.

With the development of blockchain technology, the automatic generation of smart contract has become a hot research topic. The existing smart contract automatic generation technology still has improvement spaces in complex process, third-party specialized tools required, specific the compatibility of code and running environment. In this paper, we propose an automatic smart contract generation method, which is domain-oriented and configuration-based. It is designed and implemented with the application scenarios of government service. The process of configuration, public state database definition, code generation and formal verification are included. In the Hyperledger Fabric environment, the applicability of the generated smart contract code is verified. Furthermore, its quality and security are formally verified with the help of third-party testing tools. The experimental results show that the quality and security of the generated smart contract code meet the expect standards. The automatic smart contract generation will “elegantly” be applied on the work of anti-disclosure, privacy protection, and prophecy processing in government service. To effectively enable develop “programmable government”.

CPU flush instruction-based cache side-channel attacks (cache instruction attacks) target a wide range of machines. For instance, Meltdown / Spectre combined with FLUSH+RELOAD gain read access to arbitrary data in operating system kernel and user processes, which work on cloud virtual machines, laptops, desktops, and mobile devices. Additionally, fault injection attacks use a CPU cache. For instance, Rowhammer, is a cache instruction attack that attempts to obtain write access to arbitrary data in physical memory, and affects machines that have DDR3. To protect against existing cache instruction attacks, various existing mechanisms have been proposed to modify hardware and software aspects; however, when latest cache instruction attacks are disclosed, these mechanisms cannot prevent these. Moreover, additional countermeasure requires long time for the designing and developing process. This paper proposes a novel mechanism termed FlushBlocker to protect against all types of cache instruction attacks and mitigate against cache instruction attacks employ latest side-channel vulnerability until the releasing of additional countermeasures. FlushBlocker employs an approach that restricts the issuing of cache flush instructions and the attacks that lead to failure by limiting control of the CPU cache. To demonstrate the effectiveness of this study, FlushBlocker was implemented in the latest Linux kernel, and its security and performance were evaluated. Results show that FlushBlocker successfully prevents existing cache instruction attacks (e.g., Meltdown, Spectre, and Rowhammer), the performance overhead was zero, and it was transparent in real-world applications.

In this paper, a passive multiple-input multiple-output (MIMO) radar system with widely separated antennas that estimates the positions and velocities of multiple moving targets by utilizing time delay (TD) and doppler shift (DS) measurements is proposed. Passive radar systems can detect targets by using multiple uncoordinated and un-synchronized illuminators and we assume that all the measurements including TD and DS have been known by a preprocessing method. In this study, the algorithm can be divided into three stages. First, based on location information within a certain range and utilizing the DBSCAN cluster algorithm we can obtain the initial position of each target. In the second stage according to the correlation between the TD measurements of each target in a specific receiver and the DSs, we can find the set of DS measurements for each target. Therefore, the initial speed estimated values can be obtained employing the least squares (LS) method. Finally, maximum likelihood (ML) estimation of a first-order Taylor expansion joint TD and DS is applied for a better solution. Extensive simulations show that the proposed algorithm has a good estimation performance and can achieve the Cramér-Rao lower bound (CRLB) under the condition of moderate measurement errors.

Considering the rapidly increasing number of academic papers, searching for and citing appropriate references has become a nontrivial task during manuscript composition. Recommending a handful of candidate papers to a working draft could ease the burden of the authors. Conventional approaches to citation recommendation generally consider recommending one ground-truth citation from an input manuscript for a query context. However, it is common for a given context to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modelling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4REC), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective in recommending multiple positive candidates. Second, we adopt noise distributions on the basis of historical co-citation frequencies; thus, MP-BERT4REC is not only effective in recommending high-frequency co-citation pairs, but it also significantly improves the performance of retrieving low-frequency ones. Third, the proposed dynamic context sampling strategy captures macroscopic citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allows the algorithm to further improve performance. Single and multiple positive recommendation experiments confirmed that MP-BERT4REC delivers significant improvements over current methods. It also effectively retrieves the full list of co-citations and historically low-frequency pairs better than prior works.

The hybrid implicit-explicit single-field finite-difference time-domain (HIE-SF-FDTD) method based on the wave equation of electric field is reformulated in a concise matrix-vector form. The global approximation error of the scheme is discussed theoretically. The second-order convergence of the HIE-SF-FDTD is numerically verified.