Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

A new method for hiding information in digital images is proposed. Our method differs from existing techniques in that the information is hidden in a mixture of colors carefully tuned on a specific device according to the device's signal-to-luminance (gamma) characteristics. Because these reproduction characteristics differ in general from device to device and even from model to model, the hidden information appears when the cover image is viewed on a different device, and hence the hiding property is device-dependent. To realize this, we modulated a cover image using two identically-looking checkerboard patterns and switched them locally depending on the hidden information. Reproducing these two patterns equally on a different device is difficult. A possible application of our method would be secure printing where an image is allowed to be viewed only on a screen but a warning message appears when it is printed.

Multiple wireless communication systems are often operated together in the same area in such manufacturing sites as factories where wideband noise may be emitted from industrial equipment over channels for wireless communication systems. To perform highly reliable wireless communication in such environments, radio wave environments must be monitored that are specific to each manufacturing site to find channels and timing that enable stable communication. The authors studied technologies using machine learning to efficiently analyze a large amount of monitoring data, including signals whose spectrum shape is undefined, such as electromagnetic noise over a wideband. In this paper, we generated common supervised data for multiple sensors by conjointly clustering features after normalizing those calculated in each sensor to recognize the signal reception timing from identical sources and eliminate the complexity of supervised data management. We confirmed our method's effectiveness through signal models and actual data sampled by sensors that we developed.

This study focuses on creating arithmetical stories as a sub-task of problem posing and proposes a game named “Tri-prop scrabble” as a learning environment based on a fusion method of learning and game. The problem-posing ability has a positive relationship with mathematics achievement and understanding the mathematical structure of problems. In the proposed game, learners are expected to experience creating and concatenating various arithmetical stories by integrating simple sentences. The result of a preliminary feasibility study shows that the participants were able to pose and concatenate a variety of types of arithmetic stories and accept this game is helpful for learning arithmetic word problems.

Broadcast services for wireless local area networks (WLANs) are being standardized in the IEEE 802.11 task group bc. Envisaging the upcoming coexistence of broadcast access points (APs) with densely-deployed legacy APs, this paper addresses a learning-based spatial reuse with only partial receiver-awareness. This partial awareness means that the broadcast APs can leverage few acknowledgment frames (ACKs) from recipient stations (STAs). This is in view of the specific concerns of broadcast communications. In broadcast communications for a very large number of STAs, ACK implosions occur unless some STAs are stopped from responding with ACKs. Given this, the main contribution of this paper is to demonstrate the feasibility to improve the robustness of learning-based spatial reuse to hidden interferers only with the partial receiver-awareness while discarding any re-training of broadcast APs. The core idea is to leverage robust adversarial reinforcement learning (RARL), where before a hidden interferer is installed, a broadcast AP learns a rate adaptation policy in a competition with a proxy interferer that provides jamming signals intelligently. Therein, the recipient STAs experience interference and the partial STAs provide a feedback overestimating the effect of interference, allowing the broadcast AP to select a data rate to avoid frame losses in a broad range of recipient STAs. Simulations demonstrate the suppression of the throughput degradation under a sudden installation of a hidden interferer, indicating the feasibility of acquiring robustness to the hidden interferer.

In this paper, we report the recent progress in visible light positioning and communication systems using light-emitting diodes (LEDs). Due to the wide deployment of LEDs for indoor illumination, visible light positioning (VLP) and visible light communication (VLC) using existing LEDs fixtures have attracted great attention in recent years. Here, we review our recent works on visible light positioning and communication, including image sensor-based VLP, photodetector-based VLP, integrated VLC and VLP (VLCP) systems, and heterogeneous radio frequency (RF) and VLC (RF/VLC) systems.

This paper proposes multi-input physical layer network coding (multi-input PLNC) for high speed wireless communication in two-dimensional wireless multihop networks. In the proposed PLNC, all the terminals send their packets simultaneously for the neighboring relays to maximize the network throughput in the first slot, and all the relays also do the same to the neighboring terminals in the second slot. Those simultaneous signal transmissions cause multiple signals to be received at the relays and the terminals. Signal reception in the multi-input PLNC uses multichannel filtering to mitigate the difficulties caused by the multiple signal reception, which enables the two-input PLNC to be applied. In addition, a non-linear precoding is proposed to reduce the computational complexity of the signal detection at the relays and the terminals. The proposed multi-input PLNC makes all the terminals exchange their packets with the neighboring terminals in only two time slots. The performance of the proposed multi-input PLNC is confirmed by computer simulation. The proposed multi-input physical layer network coding achieves much higher network throughput than conventional techniques in a two-dimensional multihop wireless network with 7 terminals. The proposed multi-input physical layer network coding attains superior transmission performance in wireless hexagonal multihop networks, as long as more than 6 antennas are placed on the terminals and the relays.

Currently, NDN-based VANETs protocols have several problems with packet overhead of rebroadcasting, control packet, and the accuracy of next-hop selection due to the dynamic topology. To deal with these problems in this paper, we propose a robust and lightweight forwarding protocol in Vehicular ad-hoc Named Data Networking. The concept of our forwarding protocol is adopting a packet-free approach. A vehicle collects its neighbor's visual identification by a pair of cameras (front and rear) to assign a unique visual ID for each node. Based on these IDs, we construct a hop-by-hop FIB-based forwarding strategy effectively. Furthermore, the Face duplication [1] in the wireless environment causes an all-broadcast problem. We add the visual information to Face to distinguish the incoming and outgoing Face to prevent broadcast-storm and make FIB and PIT work more accurate and efficiently. The performance evaluation results focusing on the communication overhead show that our proposal has better results in overall network traffic costs and Interest satisfaction ratio than previous works.

Sememes are the smallest semantic units of human languages, the composition of which can represent the meaning of words. Sememes have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation of a word's sememes depends on language experts, which is both time-consuming and labor-consuming, limiting the large-scale application of sememe. Researchers have proposed some sememe prediction methods to automatically predict sememes for words. However, existing sememe prediction methods focus on information of the word itself, ignoring the expert-annotated knowledge bases which indicate the relations between words and should value in sememe predication. Therefore, we aim at incorporating the expert-annotated knowledge bases into sememe prediction process. To achieve that, we propose a CilinE-guided sememe prediction model which employs an existing word knowledge base CilinE to remodel the sememe prediction from relational perspective. Experiments on HowNet, a widely used Chinese sememe knowledge base, have shown that CilinE has an obvious positive effect on sememe prediction. Furthermore, our proposed method can be integrated into existing methods and significantly improves the prediction performance. We will release the data and code to the public.

When a disaster hits a network, network service disruptions can occur even if the network facilities have survived and battery and power generators are provided. This is because in the event of a disaster, the power supply will not be restarted within the lifetime of the battery or oil transportation will not be restarted before running out of oil and power will be running out. Therefore, taking a power grid into account is important. This paper proposes a polynomial-time algorithm to identify the critical location C*D of a communications network Nc when a disaster hits. Electrical power grid Np supplies power to the nodes of Nc, and a link in Nc is disconnected when a node or a link in Nc or Np fails. Here, the disaster area is modeled as co-centric disks and the failure probability is higher in the inner disk than the outer one. The location of the center of the disaster with the greatest expected number of disconnected links in Nc is taken as the critical location C*D.

In this paper, we propose a novel radial line planar phased array in which helical antenna elements are individually rotated by their respective connected micromotors to realize dynamic beam-scanning. To our knowledge, this is the first radial line planar array (RLPA) that has antenna elements electromechanically rotated by their individual micromotors. To facilitate its fabrication, helix and its probe are directly metallized on a plastic shaft using molded interconnect device technology, and a motor shaft is press-fitted into the plastic shaft. We also present a new design methodology for RLPA, which combines the equivalent circuit theory and electromagnetic simulations of the unit cell element. The proposed procedure is practical to design an RLPA of antenna elements with arbitrary probe shape without large-scale full-wave analysis of the whole structure of the RLPA. We design, fabricate, and evaluate a 7-circle array with 168 helical antenna elements fabricated using molded interconnect device technology. The prototype antenna exhibits dynamic and accurate beam-scanning performance. Furthermore, the prototype antenna exhibits a low reflection coefficient (less than -17dB) and high antenna efficiency (above 77%), which validates the proposed design methodology.

Defect prediction approaches have been greatly contributing to software quality assurance activities such as code review or unit testing. Just-in-time defect prediction approaches are developed to predict whether a commit is a defect-inducing commit or not. Prior research has shown that commit-level prediction is not enough in terms of effort, and a defective commit may contain both defective and non-defective files. As the defect prediction community is promoting fine-grained granularity prediction approaches, we propose our novel class-level prediction, which is finer-grained than the file-level prediction, based on the files of the commits in this research. We designed our model for Python projects and tested it with ten open-source Python projects. We performed our experiment with two settings: setting with product metrics only and setting with product metrics plus commit information. Our investigation was conducted with three different classifiers and two validation strategies. We found that our model developed by random forest classifier performs the best, and commit information contributes significantly to the product metrics in 10-fold cross-validation. We also created a commit-based file-level prediction for the Python files which do not have the classes. The file-level model also showed a similar condition as the class-level model. However, the results showed a massive deviation in time-series validation for both levels and the challenge of predicting Python classes and files in a realistic scenario.

To improve the performance of underwater acoustic communications, this letter proposes a polar coding scheme with adaptive channel equalization, which can reduce the amount of feedback information. Furthermore, a hybrid automatic repeat request (HARQ) mechanism is provided to mitigate the impact of estimation errors. Simulation results show that the proposed scheme outperforms the turbo equalization in bit error rate. Computational complexity analysis is also provided for comparison.

This paper proposes a design of high-speed interconnection between optical modules and electrical modules via bonding-wires and coplanar waveguide transmission lines on printed circuit boards for 400 Gbps 4-channel optical communication systems. In order to broaden the interconnection bandwidth, interdigitated capacitors were integrated with GSG pads on chip for the first time. Simulation results indicate the reflection coefficient is below -10 dB from DC to 53 GHz and the insertion loss is below 1 dB from DC to 45 GHz. Both indicators show that the proposed interconnection structure can effectively satisfy the communication bandwidth requirements of 100-Gbps or even higher data-rate PAM4 signals.

This letter proposes a novel intelligent dynamic channel assignment (DCA) scheme with small-cells to improve the system performance for uplink machine-type communications (MTC) based on OFDMA-FDD. Outdoor MTC devices (OMDs) have serious interference from indoor MTC devices (IMDs) served by small-cell access points (SAPs) with frequency reuse. Thus, in the proposed DCA scheme, the macro base station (MBS) first measures the received signal strength from both OMDs and IMDs after setting the transmission power. Then, the MBS dynamically assigns subchannels to each SAP with consideration of strong interference from IMDs to the MBS. Through simulation results, it is shown that the proposed DCA scheme outperforms other schemes in terms of the capacity of OMDs and IMDs.

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.

Multi-hop V2V communication is a fundamental way to realize data transmission in Vehicular Ad-hoc Networks (VANET). It has excellent potential in intelligent transportation systems and automatic vehicle driving, and positively affects the safety, reliability, and comfort of vehicles. With advantages in speed and trajectory, distribution along the route, size, etc., the urban buses have become prospective relay nodes for urban VANETs. However, it is a considerable challenge to construct stable and reliable (meeting the requirements of bandwidth, delay, and bit error rate) multi-hop routing because of the complexity of the urban road and bus line network in the communication area, as well as many unevenly distributed buses on the road, etc. Given this above, this paper proposes a new hierarchical routing algorithm based on V2V geographic topology segmentation. Urban hierarchical routing is divided into two layers. The first layer of routing is called coarse routing, which is composed of areas; the second layer of routing is called internal routing (bus routing within the area). Q-learning is used to formulate the sequence of buses that transmit information within each area. Details are as follows: Firstly, based on a city map containing road network information, the entire city is divided into small grids by physical streets. Secondly, based on an analysis of the characteristics of the adjacent grid bus lines, the grids with the same routing attributes are integrated into the same area, reducing the algorithm's computational complexity during route discovery. Then, for the calculated area set, a coarse route composed of the selected area is established by filtering out a group of areas satisfying from the source node to the destination node. Finally, the bus sequence between anchor intersections is selected within the chosen area, and a complete multi-hop route from the source node to the destination node is finally constructed. Sufficient simulations show that the proposed routing algorithm has more stable performance in terms of packet transmission rate, average end-to-end delay, routing duration, and other indicators than similar algorithms.

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.

Convolutional neural network (CNN) based vehicle re-identificatioin (ReID) inevitably has many disadvantages, such as information loss caused by downsampling operation. Therefore we propose a vision transformer (Vit) based vehicle ReID method to solve this problem. To improve the feature representation of vision transformer and make full use of additional vehicle information, the following methods are presented. (I) We propose a Quadratic Split Architecture (QSA) to learn both global and local features. More precisely, we split an image into many patches as “global part” and further split them into smaller sub-patches as “local part”. Features of both global and local part will be aggregated to enhance the representation ability. (II) The Auxiliary Information Embedding (AIE) is proposed to improve the robustness of the model by plugging a learnable camera/viewpoint embedding into Vit. Experimental results on several benchmarks indicate that our method is superior to many advanced vehicle ReID methods.

Multi-core fiber (MCF) is one of the most promising candidates for achieving ultra-wideband optical transmission in the near future. To build a network using MCF, a high-performance and reliable MCF connector is indispensable. We have developed an SC-type optical connector for MCF and confirmed its excellent optical performance, mechanical durability, and environmental reliability. To put the communication system using MCF into practical use, it is necessary to establish a procedure for measuring the initial connection characteristics. Fan-in / fan-out (FIFO) devices are indispensable for measuring the connection characteristics of MCF connectors. To measure the return loss of the MCF connector, it is necessary to remove the influence of reflection at the FIFO itself and at the connection points with the FIFO. In this paper, we compare four types of return loss measurement procedures (three usual method and a new method we proposed) and find that most stable measurement method involves using our new method, the OCWR method without FIFO. The OCWR method without FIFO is considered to be the most advantageous when used for outgoing inspection of connectors. The reason is that it eliminates the measurement uncertainty caused by the FIFO and enables speedy measurement.