Miracle-dz/UniFAR-FTUs huggingface dataset:
A Computing Platform for Video Crowdprocessing Using Deep Learning

Mobile devices such as smartphones are enabling users to generate and share videos with increasing rates. In some cases, these videos may contain valuable information, which can be exploited for a variety of purposes. However, instead of centrally collecting and processing videos for information retrieval, we consider crowdprocessing videos, where each mobile device locally processes stored videos. While the computational capability of mobile devices continues to improve, processing videos using deep learning, i.e., convolutional neural networks, is still a demanding task for mobile devices. To this end, we design and build CrowdVision, a computing platform that enables mobile devices to crowdprocess videos using deep learning in a distributed and energy-efficient manner leveraging cloud offload. CrowdVision can quickly and efficiently process videos with offload under various settings and different network connections and greatly outperform the existing computation offload framework (e.g., with a 2× speed-up). In doing so CrowdVision tackles several challenges: (i) how to exploit the characteristics of the computing of deep learning for video processing; (ii) how to parallelize processing and offloading for acceleration; and (iii) how to optimize both time and energy at runtime by just determining the right moments to offload.
LongBee: Enabling Long-Range Cross-Technology Communication

Cross-Technology Communication (CTC) supports direct message exchange among heterogeneous wireless technologies (e.g., Wi-Fi, ZigBee, and BlueTooth) under the same ISM band, enabling explicit cross-technology control and coordination. For instance, a Wi-Fi AP can directly control ZigBee-enabled smart light bulbs without an expensive dual-radio gateway. Such CTC capability can be further amplified if we can extend the communication range of CTC to support long-range wide-area IoT applications such as environmental monitoring, smart metering, and precision agriculture. Our work, named LongBee, is the first to extend the range of Cross-Technology Communication. At the transmitter side, LongBee concentrates the effective TX power through down-clocked operations, and at the receiver side, LongBee improves the RX sensitivity with an innovative transition coding to ensure reliable preamble detection and payload reception. All these are achieved without modifying hardware and without introducing extra Wi-Fi RF energy cost. We implemented the LongBee on the USRP platform and commodity ZigBee devices. Our comprehensive evaluation reveals that LongBee with concentrated TX power and higher RX sensitivity achieves reliably over 10x range extension over native ZigBee communication and 2x range extension than the longest distance achieved by existing CTC schemes so far.
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

This paper is about acoustic event detection and sound source localisation in urban scenarios. Specifically, we are interested in detecting and localising horns and sirens of emergency vehicles. Urban scenarios, though, can be characterised by copious, unstructured and unpredictable traffic noise, which can severely compromise the performance and effectiveness of traditional filtering techniques. By analysing the spectrograms of incoming stereo signals as images, we can leverage image processing techniques and obtain a demonstrably robust system. Indeed, image processing methods, such as convolutional neural networks, which do not operate locally, offer interesting mechanisms for background foreground separation. When applied to spectrograms, those mechanisms allow using the entire context of the soundscape to discover and learn correlations both in the time and frequency domains, de facto implementing noise detection through semantic segmentation. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the ground plane, by regressing the direction of arrival of the sound. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5° when operating on audio frames of 0.5 s, and of 2.5° when operating on frames of 2.5 s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.
Jamming Strategy Optimization for Dual-Channel ALOHA Network with Long Time Delay

The dual-channel ALOHA network with long time delay consisting of many cognitive radio terminals has particular dynamic characteristics. Its total throughput changes with the channel selection of terminals, because two channels have different attraction probabilities under the condition that the total packet arrival rate remains constant. We establish a group of differential equations for each channel’s network-adjusted arrival rate, and obtain the function relationship of total throughput with the total arrival rate. The influence of the different allocation ratio about jamming resources on each channel is analyzed under the condition that the jamming resources are kept constant. The optimal allocation ratio of jamming resources is obtained, and the total throughput is minimized for this network. The above theoretic analysis is verified by simulations. It not only puts forward a new way for effective jamming on dual-channel ALOHA network with long time delay, but also provides an important reference for the dynamic characteristics research for such networks.
Receding horizon control strategy for an electric vehicle with dual-motor coupling system in consideration of stochastic vehicle mass

Additional degrees of freedom existed in dual-motor coupling system bring considerable challenge to the optimal control of electric vehicles. Moreover, the stochastic characteristic of vehicle mass can further increase this challenge. A receding horizon control (RHC) strategy in consideration of stochastic vehicle mass is proposed in this study to respond to this challenge. Aiming at an electric vehicle with dual-motor coupling, a Markov chain is firstly deployed to predict future driving conditions by a formulated state transition probability matrix, based on historical driving cycles in real-world. Then, future required power is predicted by the predicted driving conditions, stochastic vehicle mass and road gradient, where the stochastic vehicle mass is formulated as stochastic variables in different bus stops. Finally, dynamic programming is employed to calculate the optimal vector of the vehicle within the defined prediction horizon, and only the first control values extracted from the optimal control vector are used to execute real-time power distribution control. The simulation results show that the proposed strategy is reasonable and can at least reduce electric consumption by 4.64%, compared with rule-based strategy.
Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation

Coprime arrays can achieve an increased number of degrees of freedom by deriving the equivalent signals of a virtual array. However, most existing methods fail to utilize all information received by the coprime array due to the non-uniformity of the derived virtual array, resulting in an inevitable estimation performance loss. To address this issue, we propose a novel virtual array interpolation-based algorithm for coprime array direction-of-arrival (DOA) estimation in this paper. The idea of array interpolation is employed to construct a virtual uniform linear array such that all virtual sensors in the non-uniform virtual array can be utilized, based on which the atomic norm of the second-order virtual array signals is defined. By investigating the properties of virtual domain atomic norm, it is proved that the covariance matrix of the interpolated virtual array is related to the virtual measurements under the Hermitian positive semi-definite Toeplitz condition. Accordingly, an atomic norm minimization problem with respect to the equivalent virtual measurement vector is formulated to reconstruct the interpolated virtual array covariance matrix in a gridless manner, where the reconstructed covariance matrix enables off-grid DOA estimation. Simulation results demonstrate the performance advantages of the proposed DOA estimation algorithm for coprime arrays.
Fast and Reliable Computational Rephotography on Mobile Device

Rephotography, aiming to take a well-aligned image from the same pose of a reference image, is a very useful technique to study history, monitor environment and detect minute changes. Previous works either rely on human eye judgement that is very challenging and tedious or depend on high-precision robot platform that is non-portable and inapplicable to wild scenes. In this paper, we propose a fast and reliable computational rephotography approach that works well on mobile devices. Through well designed fast matching, our approach can generate faithful visual navigation rectangles and provide reliable navigation information in nearly real-time, thus is able to easily guide the rephotography process. To improve robustness and speed, we present a series of techniques including effective keyframe decision, feature substitution’ flow-based fast matching and adaptive matching mode switching. As a result, our approach can be well applied on wild environments and robust to large illumination and target changes. Extensive experiments verify its accuracy, effectiveness and generality on both planar and 3D scenes.
A Method to Generate Ghost-Free HDR Images in 360 Degree Cameras with Dual Fish-Eye Lens

360 degree cameras with fish-eye lenses recently observed increase in popularity due to increasing demand of 360 degree content. However, limitation of conventional lenses to capture entire dynamic range of scene restricts these cameras to produce an image with a high dynamic range (HDR). This is a major problem for ultra-wide angle lenses such as a fish-eye lens because larger field of view tends to have large brightness range. This paper discusses the challenges that occur when existing HDR methods are used as it is on fish-eye images, and proposes a novel method to generate a 360 degree HDR image which is fast and efficient. Secondly, this paper proposes a solution to mitigate the brightness artifact that occurs in the overlapped region of two fish-eye views. We prove the efficacy of the algorithm by comparing the generated HDR output with standard normal HDR and 360 degree HDR datasets created by us.
Joint VNF Placement and CPU Allocation in 5G

Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding near-optimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum.
Image super-resolution reconstruction using the high-order derivative interpolation associated with fractional filter functions

In this stydy, the authors present a single image super-resolution (SISR) reconstruction based on high-order derivative interpolation (HDI) in the fractional Fourier transform (FRFT) domain. First, the HDI formula is derived using a simple technique, which is based on the relationship between the fractional band-limited signal and the traditional band-limited signal. This interpolation formula contains the derivative information of the image and the FRFT domain filter functions (FDFF). Moreover, the advantages of the FDFF are also analysed. Second, the new SISR reconstruction is presented via the HDI. The main advantage is that the presented method involves the derivatives of an image in the resizing process. Moreover, the authors take advantage of the FDFF to resize the image. Furthermore, three evaluation criteria and some simulations are presented to validate the effectiveness of the proposed method. Last, the proposed method is applied to colour image processing. For a colour image case, the RGB colour space is chosen for super-resolution reconstruction. In addition to peak signal-to-noise ratio, the authors have also used the correlation to assess the quality of the reconstruction. Compared with many methods, extensive experimental results validate that the proposed method can obtain the better-edge characteristic, less blur and less aliasing.
Dual-enhanced Word Representations Based on Knowledge Base

In this paper, we propose an approach for enhancing word representations twice based on large-scale knowledge bases. In the ﬁrst layer of enhancement, we use the knowledge base as another contextual form corresponding to the corpus and add it to the training of distributed semantics including neural network based and matrix-based. In the second layer, we utilize local features of the knowledge base to enhance the word representations by mutual reinforcement between the keyword and the strongly associated words. We evaluate our approach not only on the well-known datasets but also on a brand-new dataset, IQ-Synonym-323. The results show that our approach compares favorably to other word representations.
Selecting Patients for Lobar Lung Volume Reduction Therapy: What Quantitative Computed Tomography Parameters Matter?

Abstract Purpose Evaluation of emphysema distribution with quantitative computed tomography (qCT) prior to endoscopic lung volume reduction (ELVR) is recommended. The aim of this study was to determine which of the commonly assessed qCT parameters prior to endoscopic lung volume reduction (ELVR) best predicts outcome of treatment. Materials and Methods 50 patients who underwent technically successful ELVR at our institution were retrospectively analyzed. We performed quantitative analysis of the CT scans obtained prior to ELVR and carried out Mann-Whitney U-tests and a logistic regression analysis to identify the qCT parameters that predict successful outcome of ELVR in terms of improved forced expiratory volume in 1 second (FEV1). Results In the Mann-Whitney U-test, the interlobar emphysema heterogeneity index (p = 0.008) and the pulmonary emphysema score (p = 0.022) showed a statistically significant difference between responders and non-responders. In multiple logistic regression analysis only the interlobar emphysema heterogeneity index (p = 0.008) showed a statistically significant impact on the outcome of ELVR, while targeted lobe volume, total lung volume, targeted lobe emphysema score and total lung emphysema score did not. Conclusion Of all commonly assessed quantitative CT parameters, only the heterogeneity index definitely allows prediction of ELVR outcome in patients with advanced chronic obstructive pulmonary disease (COPD). Key Points Quantitative CT is recommended prior to ELVR. The relevance of the obtained parameters from quantitative CT remains controversial. This study confirms that only the emphysema heterogeneity index has a definite impact. Citation Format Theilig DC, Huebner R, Neumann K et al. Selecting Patients for Lobar Lung Volume Reduction Therapy: What Quantitative Computed Tomography Parameters Matter? Fortschr Röntgenstr 2019; 191: 40 – 47
Mechanical Deformation Detection of Building Structures Using Microstrip Patch Antennas as Sensors

A miniaturized quarter-wavelength microstrip patch antenna is developed to monitor the mechanical deformation of building structures. In order to minimize the physical dimension as well as improve the sensitivity of resonant frequency of the patch antenna, via holes are added to the antenna and the operational resonant frequency of the antenna is designed to be 2.4 GHz. Practical measurement for the resonant frequency is implemented to figure out the relationship between mechanical deformation and resonant frequency. Additional tensile tests are employed by increasing strain increments per loading step so that a larger shift caused by a larger deformation can be reached. It is revealed that the transmission efficiency of mechanical deformation is about 70% and the proposed antenna can sensitively monitor the mechanical deformation of building structures. Furthermore, different substrates in the antenna are investigated and the measurement results show that the RT5870 substrate has much better transmission efficiency than FR4 substrate. The proposed different antennas are designed to meet the specific needs of practical applications.
Intellectual method for predicting electrical loads based on analysis of big data

A method for predicting electrical loads is proposed, oriented to the big data analysis. The method is based: on decomposition of a time series into a trend and a random component using a fuzzy partition; on a subsequent separate analysis of the trend and the random component using neuro-fuzzy models such as ANFIS (Adaptive Network-based Fuzzy Inference Systems); on combining the forecasting results of the trend and the random component.
Towards Compact Visual Descriptor via Deep Fisher Network with Binary Embedding

Fisher Vector (FV) has been widely used to aggregate the local descriptors of an image into a global representation in large-scale image retrieval. However, FV has limited learning capability and its parameters are mostly fixed after constructing the codebook, which is inflexible and cannot be trained jointly with deep networks. Moreover, the high dimension of FV makes it difficult to be applied in scenarios compact descriptors are needed. In this paper, we propose a novel compact image description scheme based on Fisher network with binary embedding to solve the large-scale image retrieval problem, which consists of two components: a Fisher encoder component and a binary embedding component. Concretely, the Fisher encoder is a trainable neural network functions as the traditional FV, which aggregates the local descriptors into a global representation. And the binary encoder embeds the high-dimensional FV to a binary vector, which outputs the compact global binary descriptor. To learn such a descriptor, we further introduce a novel and effective loss function, in which maximum margin criterion is exploited to minimize the distances of positive pairs, as well as maximizing the distances of negative pairs. Extensive experiments performed on MPEG-7 CDVS benchmarks and ILSVR2010 demonstrate that the proposed framework can achieve very superior performance over the state-of-the-art methods.
Efficient Integration of Octree Based Maps in Multi-Robot System

The paper presents an approach for integration of three-dimensional world models (octree based maps) in multi-robot systems. The process of maps integration is based on ICP (Iterative Closest Point) algorithm that has been adapted and optimized to operate on octomaps. The presented method has been verified in numerous experiments both in simulation and with publicly available datasets captured from robots. The solution can be applied to various robotic applications such as underwater robots, aerial robots or robots equipped with manipulators - wherever 3D maps are needed and a task is performed by group of mobile robots.
Multiscale Tensor Dictionary Learning Approach for Multispectral Image Denoising

Taking advantage of different sensitivities of each component/material in an object to different bands, multispectral image (MSI) is obtained by shooting the object from multiple bands individually or simultaneously. Including these different complementary information with the space-time correlation, the MSI can describe the object more clearly and comprehensively. In practice, however, it is unavoidable that MSIs are corrupted by noise. In order to solve the denoising problem of MSIs, a framework is proposed to suppress noise by learning multiscale sparse representations of MSIs with an overcomplete tensor dictionary. In our method, the tensor patches are extracted from an image tensor, and a tensor-based dictionary is trained using a special tensor decomposition, in which each atom is a rank-one tensor. The so-called multiscale learned representation is obtained based on an efficient quadtree decomposition of the trained tensor dictionary. Experimental results on numerical simulations and real MSIs demonstrate that multiscale tensor dictionary gets better indexes in terms of PSNR and SSIM compared with single-scale tensor dictionary and other related competing methods. At the same time, from the perspective of visual quality, our method restores more image details than other methods.
Unsupervised Multiple-Instance Learning for Instance Search

Traditional supervised Multiple-Instance Learning (MIL) has served as an important tool for a wide range of vision applications, for instance, image classification, object detection, and visual tracking. In this paper, we move forward one step further to tackle unsupervised computer vision problems by proposing an unsupervised multiple-instance learning algorithm, termed UnMIL. Different from classical MIL, our proposed unsupervised MIL does not require any manual annotations on neither bags nor instances. Given a collection of bags without any labels, our goal is to jointly optimize the bag label and instance label in a unified framework under the constraint of Noisy-OR model. The proposed UnMIL can be easily applied to object discovery in wild images by treating the object proposals extracted from images as instances and the according images as bags. Extensive experiments on MUSK1 & MUSK2, which is popularly used in MIL literature, on Oxford5k dataset for instance search, and on Object Discovery dataset for object co-localization, demonstrate the effectiveness of the proposed UnMIL.
Pointwise Shape-Adaptive Texture Filtering

The structure-preserving image smoothing is an essential pre-processing to high-level semantic image analysis. In this paper, we introduce a structure-preserving filtering scheme based on a novel and flexible line shift to separate prominent structures from textures. Unlike the previous works, the line shift can form arbitrarily shaped pure texture area for texture smoothing. We integrate the line shift with the prior knowledge of structures into a framework of joint bilateral filtering. This can greatly improve the efficiency of removing textures on pixels that are close to sharp corners or located in long narrow strip areas, while preserve fine structures simultaneously. We demonstrate the performance of the algorithm by testing it on many images with diverse texture patterns.
Towards concurrency race debugging: an integrated approach for constraint solving and dynamic slicing

Existing data race detectors can witness race conditions for their occurrence but few can also identify the fundamental reasons for their understanding. In this paper, we present a new debugging framework for providing a concise schedule that can help not only witness but also understand data race. The key innovation of this work is to integrate dynamic slicing into constraint solving technique. We propose to use race manifestation (a fraction of memory accesses) to characterize buggy behaviors arising from data races. By encoding race manifestation constraints, existing constraint solving system is enabled to generate the buggy-behavior-exposing schedule, which is further simplified to a minimum as root cause via a tailored data race slicing technique further proposed. Results show the effectiveness of race manifestation in exposing buggy behaviors. Evaluation on both real-world and benchmark programs shows that root cause schedules can diagnose 376 out of 382 data races detected by the state-of-the-art, with 99.7% fewer events and 99.3% fewer inter-thread context switches compared to the original schedules.