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To improve detection performance, this paper proposes a non-cooperative unmanned aerial vehicle (UAV) detection strategy based on multichannel detection of remote signal with energy detector (ED). More specifically, the sampling point of remote signal on each subchannel adaptively varies with environmental signal-to-noise (SNR) within constrained scope. The decision on the presence or absence of remote signal is made at the fusion center (FC) based on the majority voting rule. Both theoretical derivation and simulation experiments validate the effectiveness of the proposed scheme.

We introduce a unified system for SCHOLAT to import information into Encyclopedia, called Semi-automatic Scholar Encyclopedia Generating System, which consists of four modules: website display page, backstage administration module, version comparison and website security module. Scholar Encyclopedia is implemented as a subsystem of Semi-automatic Scholar Encyclopedia Generating System. Scholar Encyclopedia is a scholar information website that has been independently compiled by the scholars and filled in by personal information. Scholar Encyclopedia Import Model consists of three modules: user evaluation, information import and manual review. At present, Semi-automatic Scholar Encyclopedia Generating System has been used, and the beta version of Scholar Encyclopedia has been deployed in our school network center. The online application shows that Scholar Encyclopedia Import Model has good accuracy and practicability.

The development of vocational education is inseparable from the deep integration with industrial development. However, there are many difficulties in the integration of industry and education, such as the lack of motivation and willingness of many domestic enterprises to participate in vocational education, the insufficient ability of vocational colleges to provide services for industrial development, and the different operation mechanism between the education system and the industrial system. This paper constructs the overall framework of “Industry Education Alliance + industry college + professional group” to create a new ecology of industry education integration. Under the “ecosystem” of the industry Education Alliance, relying on the industrial college, the paper constructs the model of technical skills convergence and social service of large and small enterprises. Through data analysis, we found that it can improve the social service ability of vocational colleges, and provide an effective scheme for the deep integration of vocational education and information technology industry development.

Autonomous underwater vehicle(AUV) plays an important role in modern aquaculture. For example, it can be applied to monitor water quality as well as the fish biomass. In these activities, path planning is significant for AUV. This paper proposed a path planning algorithm for underwater vehicles based on improved rapidly expanding random tree(RRT). The artificial potential field(APF) algorithm is introduced into the heuristic function of bidirectional RRT to improve the expansion efficiency as a global path. To solve the problem of dynamic obstacles, global path planning and local path planning are combined. The dynamic window method is used in local path planning to deal with dynamic obstacles. This approach takes the robot’s speed as the reference point. It sets the window and then selects a better path by the evaluation function. In this way, the path planning of underwater vehicle in a dynamic environment is realized. Finally, the algorithm was simulated in both static and dynamic environments. The results show that the proposed algorithm can provide effective paths for underwater vehicles quickly.

This work-in-progress (WIP) paper in the research track explored the muddiest point and self-efficacy for students in introductory computer science courses, starting with one course at a southern public institution. With the increasing demand in computing-related careers, the increased enrollments in undergraduate computer science courses are facing challenges to ensure the average passing rate. In this work, we applied the muddiest point to capture student’s perceptions of concepts of difficulty and used self-efficacy to better contextualize students’ perfections of self-efficacy in relation to concepts of difficulty. In this paper we present the results of one section of one introductory computing course, that revealed the perceived difficult topics as well as changes in self-efficacy throughout the semester. Furthermore, a positive correlation was found between self-efficacy and performance. Keywords: self-efficacy, muddiest points, student performance, computer science education

This research full paper describes how web scraping and natural language processing can be utilized to answer complex questions in computer science education. In this work, we apply connectivism as the theoretical framework, and demonstrate how web scraping can be useful for extrapolating large amounts of data from publicly available web pages to pool data from a wider array of sources and to further knowledge in the field. In addition, we discuss how natural language processing can be used to reliably obtain salient information from textual data, and how it can complement qualitative analysis. To illustrate these techniques in practice, we provide a specific application in which we examine the current trends in the job market for computer science students. The information gathered in this example provides additional areas for educational consideration, such as offering students Python programming language and machine learning. Also, the job postings delineate a clear need for applicants to exhibit programming and testing skills. Although programming may be taught already, testing is widely considered a knowledge deficiency, which suggests that educators should consider placing an increased emphasis on this area to ensure their students are adequately prepared for their career endeavors, and able to transfer the knowledge taught to critically assess and debug their own programs.

As chiral structures, three-dimensional (3D) nanohelixes have the circular dichroism and optical rotation under circularly polarized light. It also has a good application prospect in optical modulators with high modulation efficiency. However, it is still a challenging task to fabricate 3D nanohelixes with high precision, controllable parameters and perfect morphology. In this study, we develop FIB (focused ion beam)-SID (stress induced deformation) by using HIB (helium ion beam) instead of GIB (gallium ion beam) for nanofabrication. This general 3D nanofabrication technique allows much stronger manufacturing control, which lead to a greater programmable and accurate bidirectional folding $(-160^{\circ}-+75^{\circ})$ of various metal and dielectric thin films. Higher manufacturing accuracy of HIB makes it possible to fabricate smaller nanostructures (characteristic size is about 100 nm), which provides strong support for 3D nanostructure fabricated in visible light wave band.

Metal-insulator-metal (MIM) multilayer structures as representative plasmonic systems have been widely studied. In such a system, the metal surface can sustain bound surface plasmon polaritons (SPPs) and couple the SPPs in a dielectric cavity. Herein, a novel fabrication process, based on electron beam lithography (EBL) followed by electroplating deposition of gold, has been developed to form longitudinal metal-air-metal structures. The structures consist of a suspended top film of gold supported by an array of gold pillars and a bottom ultrathin gold film. When the gap of the two gold films is comparable to or smaller than the decay length of the interface mode, the interaction between SPPs gives rise to coupled modes which can localize the light into the insulating layer for stronger field confine and light-matter interaction. This structure exhibits vibrant reflective color at normal incident light and significant color varying with different matter filling in the nano-cavity. The sensitivity can reach 269.8 nm/RIU which offers great potential for visual and real-time biochemical detection.

This paper reports an arrayed nanometer precision gap-tunable bowtie nanoantennas fabricated by focused helium ion beam (HIB) milling. Plasmonic resonance is excited and tightly localized inside the nanogaps of the bowtie nanoantennas. Both numerical simulations and cathodoluminescence (CL) measurements manifest that the plasmon coupling resonance is significantly enhanced by decreasing the nanoantennas' gap width down to sub-10 nm scale. The stronger plasmon resonance also leads to a remarkable improvement of the spectral sensitivity to the surrounding media changes, which can be used to detect streptavidin with the concentration down to 100 ng/mL by simply monitoring the spectral shift.

The particle swarm optimization (PSO) algorithm is prone to local optimal position in the late iteration when solving the reactive power optimization problem of active distribution network. Aiming at the above problems, a reactive power optimization algorithm, improved quantum particle swarm optimization (IQPSO), is proposed in this paper. Based on quantum particle swarm optimization (QPSO) algorithm, the inverse point of worst particle position in the iteration process is introduced into the update formula of particle position, which makes the particle jump out of the local optimal position constraint in the iterative process. In this paper, an optimization model is established with the aim of minimum voltage deviation and system loss in a typical 34 node system, and the effectiveness of proposed method is verified by comparing with QPSO.

Principal component analysis is an unsupervised machine learning algorithm for linear dimensionality reduction of data. The essence of principal component analysis is to solve the problem of maximum variance of projection point. Generally, the gradient rise method is used to solve the optimal parameters of the objective function, but it is easy to fall into the local maximum trap. In this paper, the principal component analysis method with Gaussian quantum particle swarm is proposed to overcome the shortcomings of gradient ascent method, which makes the algorithm independent of gradient and has stronger global optimization performance.

Virtual try-on systems under arbitrary human poses have significant application potential, yet also raise extensive challenges, such as self-occlusions, heavy misalignment among different poses, and complex clothes textures. Existing virtual try-on methods can only transfer clothes given a fixed human pose, and still show unsatisfactory performances, often failing to preserve person identity or texture details, and with limited pose diversity. This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-On Network (MG-VTON) generates a new person image after fitting the desired clothes into the person and manipulating the pose. MG-VTON is constructed with three stages: 1) a conditional human parsing network is proposed that matches both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) that warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and the desired one; 3) a refinement render network recovers the texture details of clothes and removes artifacts, based on multi-pose composition masks. Extensive experiments on commonly-used datasets and our newly-collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively, showing promising virtual try-on performances.

The AdaBoost-based real-time face detections have been widely used in current video surveillance. However, the AdaBoost-based face detection has poor performances in detecting multi-face with different scales, multiple poses, and occlusion in complex lighting environment. Recent research shows that the convolutional neural network (CNN) can improve its accuracy. In this work, a FPGA based real-time multi-face detection system for crowded area surveillance application using CNN is presented. A hardware friendly fully quantization strategy is proposed and the result is tested on WIDER FACE dataset. With acceptable loss of accuracy, the FPGA based system can achieve a frame rate of 37 FPS at $512 \times 288$ resolution with only 65 ms processing delay.

The open nature of radio propagation makes wireless transmissions exposed to unauthorized users and become vulnerable to both the jamming and eavesdropping attacks. In industrial environments, wireless transmissions are also adversely affected by the presence of large-bodied obstructing machinery, metallic friction-induced impairments, and equipment noise. This may result in the failure of wireless transmissions and thus wastes the precious energy resources for industrial wireless sensor networks (IWSNs). This paper is motivated to present a review on the challenges and solutions of improving the physical-layer security and reliability for IWSNs. We first discuss some wireless reliability enhancement techniques for mitigating the background interference, path loss, multipath fading, and link failure. Then, we provide an overview of wireless jamming and eavesdropping attacks along with their countermeasures, where a jammer attempts to emit an interfering radio signal for disrupting the desired communications between a wireless sensor and its sink, while an eavesdropper intends to tap the confidential sensor-sink transmissions. Additionally, we evaluate the tradeoff between the security and reliability, called security-reliability tradeoff , in the context of wireless sensor-sink transmissions. Finally, we discuss a range of open challenges and future trends for IWSNs, including the energy-efficient security and reliability designs, joint jamming-and-eavesdropping defense mechanisms, as well as the energy harvesting for IWSNs.

With limited bandwidth and power constraints, ensuring low latency communication is critical for applications with real-time communication requirements such as autonomous driving. With the development of mobile edge computing and future 6G network research, drone communication is becoming an emergency communication solution for low latency applications. This work examines the green transmission design with low latency constraints, where a drone as a wireless relay can forward signals from a base station (BS). Considering the scenario where the nearby drone interferes with the downlink transmission of the desired drone, a resource optimization scheme including interference location awareness and interference avoidance is proposed. The interference prediction mechanism studied will run on the mobile edge computing entities contained in future 6G networks. The entity uses an artificial neural network (ANN) model to quickly predict the distance between the nearby drone and the desired drone. Based on the predicted results, the desired drone will adjust its own flight altitude to reduce the interference from the nearby drone. Finally, the successive convex approximation algorithm (SCA) is used to optimize the resource allocation of the drone to further control the downlink energy consumption. The simulation results show that the proposed scheme with interference avoidance has better energy efficiency than the baseline scheme.

Cross-modal retrieval has gained much attention due to the growing demand for enormous multi-modal data in recent years (i.e., image-text or text-image retrieval). In order to alleviate the problem of ignoring the existence of irrelevant information between images and texts, this paper proposes Deep Pairwise Ranking model with multi-label information for Cross-Modal retrieval (DPRCM). DPRCM directly learns a mapping from images and texts to a compact Euclidean space where distances correspond to the similarity measure of images and texts. The bi-triplet loss function in DPRCM reduces the distance between associated images and texts on the common subspace and increases the margin of independent samples. The classification loss function can better utilize the multi-label information to reduce the semantic gap between image features and text descriptions. Experiments on three widely-used datasets show that DPRCM can achieve competitive performance compared to state-of-the-art methods.

Scene text detection is one of the most challenging tasks in many computer vision applications due to the large variety of scene text appearance and the complexity of scene context. In this paper, we propose an end-to-end trainable framework RefineText for multi-oriented scene text detection, which has a strong ability to detect different-scale texts and split them precisely. High-resolution semantic features are first generated by our designed Feature Refinement Module, which refines features progressively at multiple levels of abstraction. Then text regions are densely produced on the high-level semantic features and followed by Non-Maximum Suppression(NMS) to get final detection results. Experiments on benchmark datasets including ICDAR 2015, ICDAR 2013 and MSRA TD500 demonstrate that our proposed method has competitive performance and strong robustness.

Visual and real-time plasmonic metamaterial biosensors, relying on surface plasmon resonance (SPR) on metallic subwavelength structures, have significant potential for colorimetric biochemical sensing by simple instrumentation or even naked eye. In this paper, we present a visual and real-time sensing strategy for the detection of the biotin-avidin-system (BAS), which is a powerful tool in biological sciences. The metamaterial, consisting of an array of three-dimensional all-metal nano-cavities, can confine the light into the near field and enable strong light-matter interactions there. Therefore, The metamaterial shows significant color shifts with small refraction index changes. When a monomolecular layer of thiolated biotin and streptavidin bonding to the surface of the nanostructure successively, the color of the metamaterials changes from red to violet and then loden. This sensing strategy offers new opportunities for convenient detection of proteins, nucleic acids and lipids.

The hardware impairments of transceiver nodes generate signal distortions and degrade the secrecy performance of wireless communications systems. In this paper, we investigate the effect of hardware impairments on the secrecy outage probability of hybrid satellite-terrestrial relay systems (HSTRS). The source satellite transmits its signal to the terrestrial destination with the help of decode-and-forward (DF) terrestrial relays, while the eavesdropper attempts to tap the transmission from the terrestrial relays to destination. An optimal relay selection (ORS) scheme is proposed for enhancing the physical-layer security of HSTRS against hardware impairments. The traditional round-robin scheduling (RRS) is also considered for comparison purposes. We derive closed-form expressions of the secrecy outage probability for both the RRS and ORS schemes. Numerical results show that given certain hardware impairments, the secrecy outage performance of ORS scheme is better than that of the RRS. Additionally, as the number of terrestrial relays increases, the secrecy outage probability of proposed ORS scheme decreases significantly, demonstrating the advantage of exploiting relay selection against hardware impairments for HSTRS.

In this demonstration, we present an end-to-end web-aided data imputation prototype system named WebPut. WebPut consults the Web for imputing the missing values in a local database when the traditional inferring-based imputation method has difficulties in getting the right answers. Specifically, WebPut investigates the interaction between the local inferring-based imputation methods and the web-based retrieving methods and shows that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. Besides, WebPut also incorporates a crowd intervention component that can get advice from humans in case that the web-based imputation methods may have difficulties in making the right decisions. We demonstrate, step by step, how WebPut fills an incomplete table with each of its components.