Reviewerly/exHarmony huggingface dataset:
Dense Stereo Matching Strategy for Oblique Images That Considers the Plane Directions in Urban Areas

The perspective distortion of oblique images has a substantial impact on dense matching, i.e., it reduces the matching precision. In this article, a strategy of dense matching in which the object plane direction is considered is proposed. According to many regular planes in urban areas, epipolar rectification with minimum distortions relative to the selected reference planes can be generated. The matching results of epipolar images relative to various reference planes are weighted and fused into a single depth map, which is a better matching result. The experimental results demonstrate that the perspective distortion has a substantial influence on the dense matching performance. The root-mean-square error (RMSE) of the flatness for horizontal objects is increased by approximately 30%, and the RMSE of the flatness for façades is increased by approximately 40%. Hence, the proposed matching strategy, in which the object plane is considered, can effectively improve the matching results.
3D-Kernel Foveated Rendering for Light Fields

Light fields capture both the spatial and angular rays, thus enabling free-viewpoint rendering and custom selection of the focal plane. Scientists can interactively explore pre-recorded microscopic light fields of organs, microbes, and neurons using virtual reality headsets. However, rendering high-resolution light fields at interactive frame rates requires a very high rate of texture sampling, which is challenging as the resolutions of light fields and displays continue to increase. In this article, we present an efficient algorithm to visualize 4D light fields with 3D-kernel foveated rendering (3D-KFR). The 3D-KFR scheme coupled with eye-tracking has the potential to accelerate the rendering of 4D depth-cued light fields dramatically. We have developed a perceptual model for foveated light fields by extending the KFR for the rendering of 3D meshes. On datasets of high-resolution microscopic light fields, we observe 3.47×-7.28× speedup in light field rendering with minimal perceptual loss of detail. We envision that 3D-KFR will reconcile the mutually conflicting goals of visual fidelity and rendering speed for interactive visualization of light fields.
Real‐time running detection system for UAV imagery based on optical flow and deep convolutional networks

A fast-running human detection system for the unmanned aerial vehicle (UAV) based on optical flow and deep convolution networks is proposed in this study. In the system, running humans can be detected in real-time at the speed of 15 frames per second (fps) with an 81.1% detection accuracy. To fast locate the candidate targets, optical flow representing the motion information is calculated with every two successive frames. A series of prior-processing operations, including spatial average filtering, morphological expansion and outer contour extraction, are performed to extract the regions of interest. A classification model based on small-kernel convolution networks is proposed to achieve the accurate recognition of the running people in various backgrounds. In the model, small convolutional filters are adopted to accelerate the speed of the data representation. Moreover, a total of 60,000 samples are collected to enhance the robustness of the model to adapt to the complex outdoor UAV scenes. The proposed method is compared with other deep learning frameworks for object detection. Field experiments on UAV videos are performed to verify that the proposed system can effectively detect the running people targets in real-time.
Character-Aware Attention-Based End-to-End Speech Recognition

Predicting words and subword units (WSUs) as the output has shown to be effective for the attention-based encoder-decoder (AED) model in end-to-end speech recognition. However, as one input to the decoder recurrent neural network (RNN), each WSU embedding is learned independently through context and acoustic information in a purely data-driven fashion. Little effort has been made to explicitly model the morphological relationships among WSUs. In this work, we propose a novel character-aware (CA) AED model in which each WSU embedding is computed by summarizing the embeddings of its constituent characters using a CA-RNN. This WSU-independent CA-RNN is jointly trained with the encoder, the decoder and the attention network of a conventional AED to predict WSUs. With CA-AED, the embeddings of morphologically similar WSUs are naturally and directly correlated through the CA-RNN in addition to the semantic and acoustic relations modeled by a traditional AED. Moreover, CA-AED significantly reduces the model parameters in a traditional AED by replacing the large pool of WSU embeddings with a much smaller set of character embeddings. On a 3400 hours Microsoft Cortana dataset, CA-AED achieves up to 11.9% relative WER improvement over a strong AED baseline with 27.1% fewer model parameters.
Development of Voice Spoofing Detection Systems for 2019 Edition of Automatic Speaker Verification and Countermeasures Challenge

A robust speaker verification system is expected to provide high recognition accuracy not only in adverse environments but also in the presence of spoofing attacks, which renders voice spoofing detection as crucial to prevent automatic speaker verification systems from a security breach. In this work, we present anti-spoofing systems developed for tackling spoofing attacks introduced for the ASVspoof 2019 challenge. We employ frame-level descriptors such as discrete Fourier transform, as well as constant Q transform-based spectral and cepstral features as countermeasures. These descriptors are both used on their own with a spoofing detection classifier to detect spoofing attacks, or in tandem with deep bottleneck features, i.e. approximate posteriors parametrized by a neural network designed to discriminate between bonafide and spoof signals. Fisher vector encoding and i-vector representations are further learned from the frame-level descriptors of the signals. For modeling, we employ two classification strategies. We finally build an end-to-end anti-spoofing system by making use of modified versions of light convolution neural networks as well as well-known ResNets. Our primary system for the logical access task and a single end-to-end system for the case of physical access we attain significant improvements over two baseline systems.
Perceptual objective quality assessment of stereoscopic stitched images

Large view stereoscopic images can provide users with immersive depth experience. Image stitching techniques aim to obtain large view stitched images, and there have been various image stitching algorithms proposed recently. However, there is still no effective objective quality assessment for stereoscopic stitched images. In this paper, we propose a new perceptual objective stereoscopic stitched image quality assessment (S-SIQA) method by considering different distortion types in the existing stitching methods, including color distortion, ghost distortion, structure distortion(shape distortion, information loss), and disparity distortion. The quality evaluation methods for these distortion types are designed by using the color difference coefficient, points distance, matched line inclination degree, information loss, and disparity difference. Then we fuse these measures in the proposed S-SIQA model by an optimally weighted linear combination. In addition, to evaluate the performance of the proposed S-SIQA, we build a subjective quality assessment database for stereoscopic stitched images. Experimental results have confirmed the proposed method can effectively measure the perceptual quality of stereoscopic stitched images.
Underwater Image Enhancement Using White Balance, USM and CLHE

Enhancement of the underwater images is essential because of the poor illumination, dispersion and scattering losses of the environment. This paper proposes a luminosity conserving and contrast enhancing method for enhancement of the underwater images. In the proposed method, initially, the images are subjected to white balance in order to remove the unwanted colour cast. A modified approach adopted from Gray World algorithm is used for colour correction. The processed image is subsequently subjected to unsharp masking and contrast limited histogram equalization to ensure the enhancement of edges and contrast respectively. The experimental results demonstrate that the proposed method can enhance the images.
Breaking the Eyes: How Do Users Get Started with a Coordinated and Multiple View Geovisualization Tool?

Maps are frequently combined with data displays in the form of coordinated and multiple views (CMV). Although CMV are valuable geovisualization tools, novice users may find them complex and thus require explanation. However, no tutorial guidelines have been developed that indicate what is helpful in understanding CMV geovisualization tools. We therefore conducted a study on the learnability of a CMV tool, informed with eye-tracking data, talk-aloud and interaction logs. We have investigated how untrained users work with a CMV geovisualization tool. The study revealed that: (1) despite their initial confusion, users found the tested tool pleasant to play with while getting to grips with how dynamic brushing works, (2) when examining the tool’s interface, participants mainly looked freely at explanatory elements, such as labels and the legend, but they explored interactive techniques only to a limited degree. We conclude with tips about tutorial design and layout design for CMV tools.
IoT Device for Disabled People

Sign language is a medium used for communicating feelings and emotions with normal people using expression and gestures. The focus of this research work is to create an IoT device that connects the real world and the people with disability. Physically challenged still prefer using sign language and our aim is to create a bridge that removes the communication gap between the disabled and normal people. The proposed design makes use of hand gloves mounted with flex sensors which recognize the characters and commands [4]. The gestures recognized will be displayed as audio and visual output through LCD and Bluetooth speaker. The Optical Character Recognition is used for the blind people in order to recognize the text-based images for audio and LCD display [16]. This system consists of live tracking as one of the modules for tracking the physically challenged people. The purpose is to enhance and improve the system for detecting sign language. The device not only converts sign languages to speech but also have incorporated modules like Optical Character Recognition (OCR) and live tracking[I].
Nom document digitalization by deep convolution neural networks

Nom is an ancient script used in Vietnam until the current Latin-based Vietnamese alphabet became common, and a large number of ancient Nom documents are in existence. Due to the gradual degradation of Nom documents and a decrease in the number of scholars who can understand them, a system to digitalize Nom documents is urgently necessary. This paper presents a segmentation-based method for digitalizing Nom documents using deep convolution neural networks. Nom pages are preprocessed, segmented into isolated characters, and then recognized by a single-character OCR. The structure of the U-Net is applied to create segmentation maps and extract character regions from them. Subsequently, we propose coarse and fine combined classifiers to recognize each character pattern. The results by the best classifier are revised by a decoder using a langue model. The decoder is the same as the connectionist temporal classification decoder used in end-to-end text recognition systems. Compared with the traditional segmentation method using projection profiles and the Voronoi diagram (IoU = 81.23%), the segmentation method using the deep convolution neural network produces a better result (IoU = 92.08%) for detecting character regions. The proposed CNN models for recognizing segmented character patterns outperforms the traditional models using the modified quadratic discriminant function and the learning vector quantization with the recognition rate of 85.07%. The combination of coarse and fine classifiers, the training dataset with salt and pepper noises, and the attention layer are the key factors in the recognition rate improvement.
A Balance Adjusting Approach of Extended Belief-Rule-Based System for Imbalanced Classification Problem

The extended belief-rule-based (EBRB) system has become a widely recognized and effective rule-based system in decision-making.The system uses a data-driven method to generate the rule base by transforming each training sample into a rule.Hence, when an EBRB system is applied in an imbalanced classification dataset, the imbalance of training dataset will retain in the generated rule base.More specifically, the number of rules transformed from majority classes will be far greater than the rules transformed from minority classes.This issue usually leads to a sharp decrease in the accuracies of minority classes.This study analyses how the imbalance of training dataset exists in the generated EBRB and then proposes a Balance Adjusting (BA) approach to eliminate the influence of imbalance in the rule base.The BA approach adjusts rule activation weights of all activated rules, and further enhances the competitiveness of rules with higher activation weight during the rule aggregation process of the EBRB system.Several case studies in imbalanced benchmark classification datasets from UCI demonstrate how the use of the BA approach improves the performance of the EBRB system.This study also conducts a series of experiments to validate the improvement of the proposed approach compared with some conventional and recent existing works.The comparison results illustrate that the BA approach is feasible, effective and robust, and it performs well especially in large scale datasets.Moreover, the BA approach can also combine with various rule activation weight calculation methods, which means it might worth to be applied as a generic process before the rule aggregation process of the EBRB system.
Technology selection for photovoltaic cell from sustainability perspective: An integrated approach

Photovoltaic (PV) technology can directly convert radiation into electricity, which is an important way to construct a low-carbon society. The assessment of five commercially available PV technologies is a representative multi-criteria decision making (MCDM) issue with a large amount of ambiguous and subjective information. Although some scholars have made improvements to the evaluation method, the problem of uncertain information has not been completely solved. A novel approach is put forward in this paper based on cloud model and grey relational analysis (CMGRA), integrating the fortes of cloud model for dealing with uncertainty and the merits of grey relational analysis for overcoming the errors caused by small sample size. An illustrative example of PV technology selection is applied to validate the feasibility and effectiveness of CMGRA. The newly proposed approach provides both sustainable choices for technology selection and policy supports for governors to construct low-carbon society.
Online Feature Selection with Capricious Streaming Features: A General Framework

Online streaming feature selection has received extensive attention in the past few years. Existing approaches have a common assumption that the feature space of the fixed data instances increases dynamically without any missing entry. This assumption, however, does not always hold in many real-world applications. For example, in a credit evaluation system, we cannot collect the complete dynamic features for each person and/or enterprise. Motivated by this observation, this paper aims at conducting online feature selection from capricious streaming features, where features flow in one by one with some random missing entries while the number of data instances remains fixed. To do so, we propose a general framework named GF-CSF. The main idea of GF-CSF is to adopt latent factor analysis to preprocess capricious streaming features for completing their missing entries before conducting feature selection. Both theoretical and experimental analyses indicate that GF-CSF can efficiently improve any existing model of online streaming features selection to achieve online capricious streaming features selection.
Landau-Zener-Stückelberg interferometry in dissipative circuit quantum electrodynamics

We study Landau-Zener-St\"uckelberg (LZS) interferometry in a cQED architecture under the effects of dissipation. To be specific, we consider a superconducting qubit driven by a $\mathrm{dc}+\mathrm{ac}$ signal and coupled to a transmission line resonator, but our results are valid for general qubit-resonators devices. To take the environment into account, we assume that the resonator is coupled to an Ohmic quantum bath. The Floquet-Born-Markov master equation is numerically solved to obtain the dynamics of the system for an arbitrary amplitude of the drive and different timescales. We unveil important differences in the resonant patterns between the strong coupling and ultrastrong coupling regimes in the qubit-resonator interaction, which are mainly due to the magnitude of photonic gaps in the energy spectrum of the system. We identify in the LZS patterns the contribution of the qubit gap and the photonic gaps, showing that for large driving amplitudes the patterns present a weaving structure due to the combined intercrossing of the different gaps contributions.
Evaluation of Self-Healing Systems: An Analysis of the State-of-the-Art and Required Improvements

Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions.
BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment

Highly textured video content is challenging to compress since the bit-rate to video quality trade-off is high and complex perceptual masking influences performance. Test datasets that cover a wide range of texture types are thus important for codec evaluation, but few exist. In order to study the properties of video texture, this paper introduces a Synthetic video Texture dataset (BVI-SynTex) that was generated using a Computer-Generated Imagery (CGI) environment. It contains 196 sequences clustered in three different texture types and offers the capability of being able to generate many versions of the same scene with different video parameters. It therefore provides a flexible basis for studying the influence of texture type and parameters on video compression and perceived video quality. A thorough validation and comparison of BVI-SynTex with similarly textured natural video content is performed. The comparisons show that BVI-SynTex exhibits a comparable coverage over the spatial and temporal domain and that it produces similar encoding statistics to real video datasets. A subset of the BVI-SynTex dataset was selected to perform a subjective evaluation of compression using the MPEG HEVC codec.The results show the impact of the content parameters to both the compression efficiency and the perceived quality. The publicly available BVI-SynTex dataset contains all source sequences, the objective and subjective analysis results, providing a valuable resource for the research community.
A Vital Role of Blockchain Technology Toward Internet of Vehicles

Nowadays, blockchain is one of the propitious, geniuses, safe, reliable, and innovative technologies for managing number of mobile node connectivity. Moreover, blockchain is a key to unlock the potential of any technology such as Internet of Things (IoT), artificial intelligence, as well as Industry 4.0. The rapid growth of connected vehicles or Internet of Vehicles (IoV) has faced many difficulties regarding data storage, network management, and importantly security and privacy of the network. With the introduction of blockchain technology in the IoV, it creates a bridge to overcome the above problems. In this chapter, we discuss about blockchain-enabled IoV architecture as well as illustrate the application of architecture in various domains. Moreover, how it becomes beneficial for the future research. Analysis of security and privacy issues is also discussed in this chapter.
Performance of Different Front-Opening Unified Pod (FOUP) Moisture Removal Techniques With Local Exhaust Ventilation System

A highly effective moisture removal technique for front opening unified pods (FOUPs) can significantly improve the production yield of semiconductor manufacturing industries by minimizing the number of faulty dies rejected due to humidity related defects in silicon wafers. In this study, FOUP purging using clean dry air (CDA) was applied using conventional and diffuser purge method. An innovative laminar air curtain was also used to increase the performance of the FOUP purging techniques. These moisture removal techniques were applied in an open FOUP connected to a mini-environment in which a local exhaust ventilation (LEV) was installed. A novel smoke flow visualization technique and particle image velocimetry (PIV) were employed to visualize the flow pattern inside the FOUP and to generate a velocity vector field from the flow field. In addition, digital humidity sensors were installed inside the FOUP to evaluate the performance of the techniques. The flow analysis and humidity data show that the air curtain can significantly improve the performance of the purge methods. The observations demonstrate that the application of the air curtain, in combination with diffuser purge, can significantly improve the FOUP cleanliness. The LEV system was not found to have influence on the flow field inside the FOUP.