Deep neural networks (DNNs) perform well for image recognition, speech recognition, and pattern analysis. However, such neural networks are vulnerable to adversarial examples. An adversarial example is a data sample created by adding a small amount of noise to an original sample in such a way that it is difficult for humans to identify but that will cause the sample to be misclassified by a target model. In a military environment, adversarial examples that are correctly classified by a friendly model while deceiving an enemy model may be useful. In this paper, we propose a method for generating a selective adversarial example that is correctly classified by a friendly gait recognition system and misclassified by an enemy gait recognition system. The proposed scheme generates the selective adversarial example by combining the loss for correct classification by the friendly gait recognition system with the loss for misclassification by the enemy gait recognition system. In our experiments, we used the CASIA Gait Database as the dataset and TensorFlow as the machine learning library. The results show that the proposed method can generate selective adversarial examples that have a 98.5% attack success rate against an enemy gait recognition system and are classified with 87.3% accuracy by a friendly gait recognition system.

Intelligent reconfigurable surfaces (IRS) have attracted much attention from both industry and academia due to their performance improving capability and low complexity for 6G wireless communication systems. In this letter, we introduce an IRS-assisted space-time line code (STLC) technique. The STLC was introduced as a promising technique to acquire the optimal diversity gain in 1×2 single-input multiple-output (SIMO) channel without channel state information at receiver (CSIR). Using the cosine similarity theorem, we propose a novel phase-steering technique for the proposed IRS-assisted STLC technique. We also mathematically characterize the proposed IRS-assisted STLC technique in terms of outage probability and bit-error rate (BER). Based on computer simulations, it is shown that the results of analysis shows well match with the computer simulation results for various communication scenarios.

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

In this letter, we propose an energy beamforming empowered relaying scheme for a batteryless IoT network, where wireless-powered relays are deployed between the hybrid access point (HAP) and batteryless IoT devices to assist the uplink information transmission from the devices to the HAP. In particular, the HAP first exploits energy beamforming to efficiently transmit radio frequency (RF) signals to transfer energy to the relays and as the incident signals to enable the information backscattering of batteryless IoT devices. Then, each relay uses the harvested energy to forward the decoded signals from its corresponding batteryless IoT device to the HAP, where the maximum-ratio combing is used for further performance improvement. To maximize the network sum-rate, the joint optimization of energy beamforming vectors at the HAP, network time scheduling, power allocation at the relays, and relection coefficient at the users is investigated. As the formulated problem is non-convex, we propose an alternating optimization algorithm with the variable substitution and semi-definite relaxation (SDR) techniques to solve it efficiently. Specifically, we prove that the obtained energy beamforming matrices are always rank-one. Numerical results show that compared to the benchmark schemes, the proposed scheme can achieve a significant sum-rate gain.

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.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

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.

With the development of network technology, next-generation networks must satisfy many new requirements for network functions and performance. The processing of overlong packet fields is one of the requirements and is also the basis for ID-based routing and content lookup, and packet field addition/deletion mechanisms. The current SDN switches do not provide good support for the processing of overlong fields. In this paper, we propose a series of optimization mechanisms for protocol-oblivious instructions, in which we address the problem of insufficient support for overlong data in existing SDN switches by extending the bit width of instructions and accelerating them using SIMD instruction sets. We also provide an intermediate representation of the protocol-oblivious instruction set to improve the efficiency of storing and reading instruction blocks, and further reduce the execution time of instruction blocks by preprocessing them. The experiments show that our approach improves the performance of overlong data processing by 56%. For instructions involving packet field addition and deletion, the improvement in performance reaches 455%. In normal forwarding scenarios, our solution reduces the packet forwarding latency by around 30%.

Web application second-order vulnerabilities first inject malicious code into the persistent data stores of the web server and then execute it at later sensitive operations, causing severe impact. Nevertheless, the dynamic features, the complex data propagation, and the inter-state dependencies bring many challenges in discovering such vulnerabilities. To address these challenges, we propose DISOV, a web application property graph (WAPG) based method to discover second-order vulnerabilities. Specifically, DISOV first constructs WAPG to represent data propagation and inter-state dependencies of the web application, which can be further leveraged to find the potential second-order vulnerabilities paths. Then, it leverages fuzz testing to verify the potential vulnerabilities paths. To verify the effectiveness of DISOV, we tested it in 13 popular web applications in real-world and compared with Black Widow, the state-of-the-art web vulnerability scanner. DISOV discovered 43 second-order vulnerabilities, including 23 second-order XSS vulnerabilities, 3 second-order SQL injection vulnerabilities, and 17 second-order RCE vulnerabilities. While Black Widow only discovered 18 second-order XSS vulnerabilities, with none second-order SQL injection vulnerability and second-order RCE vulnerability. In addition, DISOV has found 12 0-day second-order vulnerabilities, demonstrating its effectiveness in practice.

This study examined whether distance learning in a first-year PBL courses in the first unit of instruction improves the effectiveness of subsequent group work learning over face-to-face learning. The first-year PBL consisted of three units: an input unit, a group work unit and an outcomes presentation unit. In 2017/2018, the input unit was conducted in the classroom with face-to-face learning. In 2017, a workshop was held in addition to face-to-face learning in classroom. In 2020/2021, the input unit was conducted with distance learning. In the years, approximately 100 people completed the questionnaire. A preliminary check confirmed that the average score of students' self-assessment of their own social skills were not significantly different among the four years. Analysis showed that in 2018, the perceived efficacy in the group work unit depended on learners' high social skills. Alternatively, in 2017/2020/2021, the perceived efficacy in group work was not dependent on learners' social skills. This suggests that distance learning and face-to-face learning with workshop learning, instead of full face-to-face learning for the units placed before the group work unit facilitates the learning efficacy of the group work unit, even for students with social skill concerns.

The package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While our recent study indicates that package management features of PM are related to end-user experiences, it is unclear what those issues are and what information is required to resolve them. In this paper, we have investigated PM issues faced by end-users through an empirical study of content on Stack Overflow (SO). We carried out a qualitative analysis of 1,131 questions and their accepted answer posts for three popular PMs (i.e., Maven, npm, and NuGet) to identify issue types, underlying causes, and their resolutions. Our results confirm that end-users struggle with PM tool usage (approximately 64-72%). We observe that most issues are raised by end-users due to lack of instructions and errors messages from PM tools. In terms of issue resolution, we find that external link sharing is the most common practice to resolve PM issues. Additionally, we observe that links pointing to useful resources (i.e., official documentation websites, tutorials, etc.) are most frequently shared, indicating the potential for tool support and the ability to provide relevant information for PM end-users.

In this study, we have proposed a millimeter-wave (MMW) single-pixel imaging (SPI) system with a liquid-crystal (LC) mask cell. The LC cell functions as an electrically switchable mask based on the change in absorption properties, which depend on the orientation of the LC. We investigated the influence of noise on the measured and estimated data (reconstructed image). The proposed system exhibited moderate robustness against random noise (that were added) compared to raster scan-based and Hadamard matrix-based SPI systems. Finally, the results of some demonstrative experiments were introduced to ensure the applicability of the constructed MMW-SPI system, and steps for improving the reconstructed image quality were discussed.

During the COVID-19 pandemic there was a rapid shift to emergency remote teaching practices and online tools for education have already gained further attention. While eLearning initiatives are developed and its implementation at scale are widely discussed, this research focuses on the utilization of data which can be logged in such eLearning systems. We demonstrate the need and potential of utilizing learning logs to create services supporting sustainable quality improvement of education. Learning and Evidence Analytics Framework (LEAF), is the overarching technology framework with affordances to adopt evidence-based practices for education. It aims to promote learning for all by introducing data-driven services for personalized approaches.

We present a mathematical formulation of a trust metric using a quality and quantity pair. Under a certain assumption, we regard trust as an additive value and define the soundness of a trust computation as not to exceed the total sum. Moreover, we point out the importance of not only soundness of each computed trust but also the stability of the trust computation procedure against changes in trust value assignment. In this setting, we define trust composition operators. We also propose a trust computation protocol and prove its soundness and stability using the operators.

Contemporary development projects benefit from code review as it improves the quality of a project. Large ecosystems of inter-dependent projects like OpenStack generate a large number of reviews, which poses new challenges for collaboration (improving patches, fixing defects). Review tools allow developers to link between patches, to indicate patch dependency, competing solutions, or provide broader context. We hypothesize that such patch linkage may also simulate cross-collaboration. With a case study of OpenStack, we take a first step to explore collaborations that occur after a patch linkage was posted between two patches (i.e., cross-patch collaboration). Our empirical results show that although patch linkage that requests collaboration is relatively less prevalent, the probability of collaboration is relatively higher. Interestingly, the results also show that collaborative contributions via patch linkage are non-trivial, i.e, contributions can affect the review outcome (such as voting) or even improve the patch (i.e., revising). This work opens up future directions to understand barriers and opportunities related to this new kind of collaboration, that assists with code review and development tasks in large ecosystems.

On an overlay network where a number of nodes work autonomously in a decentralized way, the efficiency of broadcasts has a significant impact on the performance of distributed systems built on the network. While a broadcast method using a spanning tree produces a small number of messages, the routing path lengths are prone to be relatively large. Moreover, when multiple nodes can be source nodes, inefficient broadcasts often occur because the efficient tree topology differs for each node. To address this problem, we propose a novel protocol in which a source node selects an efficient tree from multiple spanning trees when broadcasting. Our method shortens routing paths while maintaining a small number of messages. We examined path lengths and the number of messages for broadcasts on various topologies. As a result, especially for a random graph, our proposed method shortened path lengths by approximately 28% compared with a method using a spanning tree, with almost the same number of messages.

Local (private) 5G system can provide a secure and flexible network using the cellular-based technologies at their facilities (e.g., factories, agricultural lands, and buildings). We constructed a small-scale demonstration system that exhibits the remote control of a patrol and work robot with arms using a local 5G system. The constructed robot comprises a robot operating system-based unmanned ground vehicle, two laser range finders, a webcam, an omnidirectional camera, and a six-axis robot arm. To fabricate a demonstration system with open-source software, we assessed the one-way delay of video streaming by changing different CPU, camera types, drivers, applications, and video resolutions. According to the assessment findings, it was demonstrated that it is possible to realize approximately 100ms delay under the limited resolution condition, and the allowable maximum absolute delay of 300ms can be attained even for full HD (1920 × 1080) resolution of this demonstration. Furthermore, local 5G was demonstrated to reduce delay variations to the same level as wired systems. It was also clarified that the increase in delay due to the application of local 5G is relatively small (5-25% in total delay) in this demonstration. Finally, we employed the small-scale demonstration system for the online and onsite campus tours for high school students.

Digital Microfluidic Biochips (DMFBs) can execute biochemical experiments very efficiently, and thus they are drawing attention recently. In biochemical experiments on a DMFB, “sample preparation” is an important task to generate a sample droplet with the desired concentration value. We merge/split droplets in a DMFB to perform sample preparation. When we split a droplet into two droplets, the split cannot be done evenly in some cases. By some unbalanced splits, the generated concentration value may have unacceptable errors. This paper shows that we can decrease the impact of errors caused by unbalanced splits if we duplicate some mixing nodes in a given dilution graph for most cases. We then propose an efficient method to transform a dilution graph in order to decrease the impact of errors caused by unbalanced splits. We also present a preliminary experimental result to show the potential of our method.

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.