Reviewerly/exHarmony huggingface dataset:
Online UAV Trajectory Planning for Covert Video Surveillance of Mobile Targets

This article considers the use of an unmanned aerial vehicle (UAV) for covert video surveillance of a mobile target on the ground and presents a new online UAV trajectory planning technique with a balanced consideration of the energy efficiency, covertness, and aeronautic maneuverability of the UAV. Specifically, a new metric is designed to quantify the covertness of the UAV, based on which a multiobjective UAV trajectory planning problem is formulated to maximize the disguising performance and minimize the trajectory length of the UAV. A forward dynamic programming method is put forth to solve the problem online and plan the trajectory for the foreseeable future. In addition, the kinematic model of the UAV is considered in the planning process so that it can be tracked without any later adjustment. Extensive computer simulations are conducted to demonstrate the effectiveness of the proposed technique. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The “Follow Me” flight mode is available in many unmanned aerial vehicle (UAV) products, and this technique enables a UAV to automatically follow a target. However, this flight mode may make the UAV noticeable to the target and compromise the video surveillance missions of the UAV. Inspired by some security surveillance applications where UAV surveillance is conducted so that a target would not take actions to avoid being monitored, we propose an efficient method to construct the trajectory for the UAV. The proposed method considers the visual covertness and the battery capacity limitation of the UAV, and it can produce a trajectory online for the UAV. The proposed method and scenario can potentially extend the “Follow Me” flight mode and generate new applications and market for UAVs.
Effects of augmented reality glasses on the cognitive load of assembly operators in the automotive industry

Recently, new digital technologies are frequently used in the industry within the scope of Industry 4.0. Augmented reality (AR) is one of these emerging technologies; it assists users with the help of virtual images on the real display of the work environment. Although AR benefits employees in terms of productivity and quality, it is not precisely known if it has a cognitive effect on the employees. In this study, the effect of AR glasses on the cognitive loads of the employees was investigated. The experiments were carried out on the assembly line of an automobile company. Electroencephalography (EEG) and NASA Task Load Index (NASA-TLX) were used to measure cognitive load. The current and AR-based work procedures were examined comparatively. Also, the difference between the cognitive load on the first and last days of experiments was compared to determine the adaptation status of the employees. The average of the area under the EEG graph as a measure of cognitive load, were 18.96 and 5.24 μV*ms in employees working with and without AR glasses, respectively. NASA-TLX scores of the employees decreased by 10% when they work with AR glasses. So, there was a less cognitive load on employees in the case of using AR glasses. And there was no significant difference between the first and last days of device usage. Hence, AR glasses did not constitute an extra cognitive load from the first day of usage.
Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM

Benefitting from the development of deep generative networks, modern fake news generation methods called Deepfake rapidly go viral over the Internet, calling for efficient detection methods. Existing Deepfake detection methods basically use binary classification networks trained on frame-level inputs and lack leveraging temporal information in videos. Besides, the accuracy of these methods will rapidly decrease when processing low-quality data. In this work, we propose a two-stream network to detect Deepfake in video level with the capability of handling low-quality data. The proposed architecture firstly divides the input video into segments and then feeds selected frames of each segment into two streams: The first stream takes RGB information as input and tries to learn the semantic inconsistency. The second stream parallelly leverages noise features extracted by spatial rich model (SRM) filters. Additionally, our experiments found that traditional SRM filters with fixed weights contribute insignificant improvement, we thus design novel learnable SRM filters, which can better fit the noise inconsistency in tampered regions. Segmental fusion and stream fusion are conducted at last to combine the information from segments and streams. We evaluate our algorithm on the existing largest Deepfake dataset FaceForensics++ and the experimental results show that we obtain state-of-the-art performance.
Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively.
Introducing a differentiable measure of pointwise shared information

Partial information decomposition of the multivariate mutual information describes the distinct ways in which a set of source variables contains information about a target variable. The groundbreaking work of Williams and Beer has shown that this decomposition cannot be determined from classic information theory without making additional assumptions, and several candidate measures have been proposed, often drawing on principles from related fields such as decision theory. None of these measures is differentiable with respect to the underlying probability mass function. We here present a measure that satisfies this property, emerges solely from information-theoretic principles, and has the form of a local mutual information. We show how the measure can be understood from the perspective of exclusions of probability mass, a principle that is foundational to the original definition of mutual information by Fano. Since our measure is well defined for individual realizations of random variables it lends itself, for example, to local learning in artificial neural networks. We also show that it has a meaningful M\"obius inversion on a redundancy lattice and obeys a target chain rule. We give an operational interpretation of the measure based on the decisions that an agent should take if given only the shared information.
Cognitive Technologies

Cognitive science is the most important emerging discipline at the turn of the century. Since its birth, cognitive science has integrated many important branches of science, including but not limited to neuroscience, psychology, computer science, systems science, information science, and management. With the development of these related fields, cognitive science has achieved recognition and respect. However, up to date, the understanding of cognition itself remains limited. The science of meditation and mindfulness is adding new light to this field. It has been revealed that through the integration of subjective cognitive training, it may be possible to gain further understanding of cognition per se, therefore promoting new developments in cognitive technology.
Resource-Efficient Topological Fault-Tolerant Quantum Computation with Hybrid Entanglement of Light

We propose an all-linear-optical scheme to ballistically generate a cluster state for measurement-based topological fault-tolerant quantum computation using hybrid photonic qubits entangled in a continuous-discrete domain. Availability of near-deterministic Bell-state measurements on hybrid qubits is exploited for this purpose. In the presence of photon losses, we show that our scheme leads to a significant enhancement in both tolerable photon-loss rate and resource overheads. More specifically, we report a photon-loss threshold of ∼3.3×10^{-3}, which is higher than those of known optical schemes under a reasonable error model. Furthermore, resource overheads to achieve logical error rate of 10^{-6}(10^{-15}) is estimated to be ∼8.5×10^{5}(1.7×10^{7}), which is significantly less by multiple orders of magnitude compared to other reported values in the literature.
Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenes

This paper studies the task of inpainting man-made scenes. It is very challenging due to the difficulty in preserving the visual patterns of images, such as edges, lines, and junctions. Especially, most previous works are failed to restore the object/building structures for images of man-made scenes. To this end, this paper proposes learning a Sketch Tensor (ST) space for inpainting man-made scenes. Such a space is learned to restore the edges, lines, and junctions in images, and thus makes reliable predictions of the holistic image structures. To facilitate the structure refinement, we propose a Multi-scale Sketch Tensor inpainting (MST) network, with a novel encoder-decoder structure. The encoder extracts lines and edges from the input images to project them into an ST space. From this space, the decoder is learned to restore the input images. Extensive experiments validate the efficacy of our model. Furthermore, our model can also achieve competitive performance in inpainting general nature images over the competitors.
Supercompact Photonic Quantum Logic Gate on a Silicon Chip

To build universal quantum computers, an essential step is to realize the so-called controlled-NOT (CNOT) gate. Quantum photonic integrated circuits are well recognized as an attractive technology offering great promise for achieving large-scale quantum information processing, due to the potential for high fidelity, high efficiency, and compact footprints. Here, we demonstrate a supercompact integrated quantum CNOT gate on silicon by using the concept of symmetry breaking of a six-channel waveguide superlattice. The present path-encoded quantum CNOT gate is implemented with a footprint of $4.8\ifmmode\times\else\texttimes\fi{}4.45\text{ }\text{ }\ensuremath{\mu}{\mathrm{m}}^{2}$ ($\ensuremath{\sim}3\ensuremath{\lambda}\ifmmode\times\else\texttimes\fi{}3\ensuremath{\lambda}$) as well as a high-process fidelity of $\ensuremath{\sim}0.925$ and a low excess loss of $<0.2\text{ }\text{ }\mathrm{dB}$. The footprint is shrunk significantly by $\ensuremath{\sim}10\text{ }000$ times compared to those previous results based on dielectric waveguides. This offers the possibility of realizing practical large-scale quantum information processes and paving the way to the applications across fundamental science and quantum technologies.
Multimedia augmented reality game for learning math

The traditional method for learning the multiplication tables is a repetitive and boring task. Teachers try to find new methods to motivate children in this tedious duty, and one of the lines to consider is to integrate en- tertainment into educational processes. This work presents a new multimedia interaction approach in order to allow children to practice these math opera- tions and have fun. The learning process has been gamified by means of two mini-games designed for mobile platforms, based on meromictic or repetitive learning. The genre of these mini-games have been selected according to chil- dren preferences: one turn-based fighting and other throwing-objects game. A series of proposed multiplications have to be solved during the play to per- form the player actions. Moreover, in order to support learning engagement, both have been visualized through Augmented Reality, combining real and virtual reality. This paper discusses the good results of mixing entertainment with some learning tasks, due to the engagement of the children to the mobile based games. A pilot study has been performed in order to evaluate the learn- ing effectiveness and usability of the proposal. Results support that playing the video games makes this tedious multiplication practice more enjoyable and attractive for children so they improve their math skills.
A comprehensive review of the imbalance classification of protein post-translational modifications

Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.
Driver behavior detection via adaptive spatial attention mechanism

Drivers still play an important role in driving safety despite the presence of driverless vehicles. Over the last few years, millions of deaths are due to traffic accidents, and more than half of these accidents worldwide are caused by distracted driving. Therefore, driver behavior detection during driving is crucial. A novel driver behavior detection system based on the adaptive spatial attention mechanism is proposed in this study. This system realizes the extraction of adaptive discriminative spatial regions of driver images by cascading multiple attention-based convolution neural networks. Feature representation in each subnetwork is extracted from the output layer, and the discriminative region of the input image is cropped using class activation maps. The obtained region is then fed into the next subnetwork to highlight important region for improving the system performance. The model starts from full images and iteratively crops the region adaptively from coarse to fine to extract the feature representation at multiscales. Finally, the k-nearest neighbor classifier is applied to classify the cascaded multiscale features and obtain the category of driver behavior. The systems are evaluated on a driver behavior recognition database captured in actual driving environments. Experimental results indicate that our systems can achieve superior recognition performance to other state-of-the-art methods and can run in real-time with simplified structure and model in our platform.
Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images

Background and Objective: The primary causes of kidney failure are chronic and polycystic kidney diseases. Cyst, stone, and tumor development lead to chronic kidney diseases that commonly impair kidney functions. The kidney diseases are asymptomatic and do not show any significant symptoms at its initial stage. Therefore, diagnosing the kidney diseases at their earlier stage is required to prevent the loss of kidney function and kidney failure. Methods: This paper proposes a computer-aided diagnosis (CAD) system for detecting multi-class kidney abnormalities from ultrasound images. The presented CAD system uses a pre-trained ResNet-101 model for extracting the features and support vector machine (SVM) classifier for the classification purpose. Ultrasound images usually gets affected by speckle noise that degrades the image quality and performance of the CAD system. Hence, it is necessary to remove speckle noise from the ultrasound images. Therefore, a CAD based system is proposed with the despeckling module using a deep residual learning network (RLN) to reduce speckle noise. Pre-processing of ultrasound images using deep RLN helps to drastically improve the classification performance of the CAD system. The proposed CAD system achieved better prediction results when compared to the existing state-of-the-art methods. Results: To validate the proposed CAD system performance, the experiments have been carried out in the noisy kidney ultrasound images. The designed system framework achieved the maximum classification accuracy when compared to the existing approaches. The SVM classifier is selected for the CAD system based on performance comparison with various classifiers like K-nearest neighbour, tree, discriminant, Naive Bayes, and linear. Conclusions: The proposed CAD system outperforms in classifying the noisy kidney ultrasound images precisely as compared to the existing state-of-the-art methods. Further, the CAD system is evaluated in terms of selectivity and sensitivity scores. The presented CAD system with the pre-processing module would serve as a real-time supporting tool for diagnosing multi-class kidney abnormalities from the ultrasound images.
A Blockchain-Based Incremental Update Supported Data Storage System for Intelligent Vehicles

With the development of autonomous driving and the Internet of Vehicles, vehicle data communication and data security become more and more important. Blockchain which has transparency, decentralization and immutability nature is treated as a promising approach to support intelligent vehicle systems. However, due to the high data update overhead, vulnerable raw data storage policy and inflexible consensus algorithm, traditional blockchain technologies are not suitable in modern vehicular systems. Hence, we propose, a blockchain data storage system that supports incremental data updating. Specifically, the system reduces the re-uploaded data size through smart contract and data partition to decrease the overhead. Besides, data replica and multi-data source addressing of index on the chain enhance the data reliability. In addition, an adaptive proof-of-work algorithm is developed, whose execution cost is dynamically adjusted based on nodes' behavior. It greatly improves the data record and updating efficiency. Comprehensive experimental results show that BUS can effectively improve the data updating efficiency with low overhead and fewer resources in intelligent vehicle scenarios.
A Location Privacy Protection Algorithm Based on Double K-Anonymity in the Social Internet of Vehicles

As an emerging complex social network, the Social Internet of Vehicles (SIoV) potentially exposes user location privacy. In this letter, we propose a location privacy protection method based on double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -anonymity that hides user locations and request information. The cloud server is introduced as a trusted third party to isolate the direct communication between users and the service provider, while correlation between identities and requests is also reduced by means of a permutation and combination method. Extensive simulations are conducted to demonstrate that the method can protect user location privacy to the greatest extent possible while still ensuring service availability.
Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) have made great progress in remote sensing image super-resolution (SR). The CNN-based methods can learn powerful feature representation from plenty of low- and high-resolution counterparts. For remote sensing images, there are many similar ground targets recurred inside the image itself, both within the same scale and across different scales. In this article, we argue that this internal recurrence can be used for learning stronger feature representation, and we propose a new hybrid-scale self-similarity exploitation network (HSENet) for remote sensing image SR. Specifically, we introduce a single-scale self-similarity exploitation module (SSEM) to compute the feature correlation within the same scale image. Moreover, we design a cross-scale connection structure (CCS) to capture the recurrences across different scales. By combining SSEM and CCS, we further develop a hybrid-scale self-similarity exploitation module (HSEM) to construct the final HSENet, which simultaneously exploits single- and cross-scale similarities. Experimental results demonstrate that HSENet can obtain superior performance over several state-of-the-art methods. Besides, the effectiveness of our method is also verified by the assistance to the remote sensing scene classification task.
Warehouse-scale video acceleration: co-design and deployment in the wild

Video sharing (e.g., YouTube, Vimeo, Facebook, TikTok) accounts for the majority of internet traffic, and video processing is also foundational to several other key workloads (video conferencing, virtual/augmented reality, cloud gaming, video in Internet-of-Things devices, etc.). The importance of these workloads motivates larger video processing infrastructures and – with the slowing of Moore’s law – specialized hardware accelerators to deliver more computing at higher efficiencies. This paper describes the design and deployment, at scale, of a new accelerator targeted at warehouse-scale video transcoding. We present our hardware design including a new accelerator building block – the video coding unit (VCU) – and discuss key design trade-offs for balanced systems at data center scale and co-designing accelerators with large-scale distributed software systems. We evaluate these accelerators “in the wild" serving live data center jobs, demonstrating 20-33x improved efficiency over our prior well-tuned non-accelerated baseline. Our design also enables effective adaptation to changing bottlenecks and improved failure management, and new workload capabilities not otherwise possible with prior systems. To the best of our knowledge, this is the first work to discuss video acceleration at scale in large warehouse-scale environments.