Reviewerly/exHarmony huggingface dataset:
Multi-frame stereo matching with edges, planes, and superpixels

We present a multi-frame narrow-baseline stereo matching algorithm based on extracting and matching edges across multiple frames. Edge matching allows us to focus on the important features at the very beginning, and deal with occlusion boundaries as well as untextured regions. Given the initial sparse matches, we fit overlapping local planes to form a coarse, over-complete representation of the scene. After breaking up the reference image in our sequence into superpixels, we perform a Markov random field optimization to assign each superpixel to one of the plane hypotheses. Finally, we refine our continuous depth map estimate using a piecewise-continuous variational optimization. Our approach successfully deals with depth discontinuities, occlusions, and large textureless regions, while also producing detailed and accurate depth maps. We show that our method out-performs competing methods on high-resolution multi-frame stereo benchmarks and is well-suited for view interpolation applications.
Graph Based Skeleton Modeling for Human Activity Analysis

Understanding human activity based on sensor information is required in many applications and has been an active research area. With the advancement of depth sensors and tracking algorithms, systems for human motion activity analysis can be built by combining off-the-shelf motion tracking systems with application-dependent learning tools to extract higher semantic level information. Many of these motion tracking systems provide raw motion data registered to the skeletal joints in the human body. In this paper, we propose novel representations for human motion data using the skeleton-based graph structure along with techniques in graph signal processing. Methods for graph construction and their corresponding basis functions are discussed. The proposed representations can achieve comparable classification performance in action recognition tasks while additionally being more robust to noise and missing data.
A Watermarking Technique to Secure Printed Matrix Barcode—Application for Anti-Counterfeit Packaging

Counterfeiting of consumer goods is a critical problem which causes various negative impacts for consumers, enterprises and the whole economic ecosystem.Anti-counterfeit labels and packaging is one of the main solutions to help enterprises protect their brand against this concern.In this paper, we introduced a novel watermarking technique, which embeds a particular random micro-texture into a matrix barcode and transforms this one into a security layer for making packaging anti-counterfeited.The secured matrix barcode is hard to be reproduced by counterfeiters.In fact, any degradations caused by the counterfeiting process will change the statistical behaviors of the embedded micro-texture.Statistical detectors based on the hypothesis testing framework are also introduced to classify authentic and counterfeited printed barcodes.Experimental results confirm the usability and the effectiveness of the proposed anti-counterfeiting solution.
Unsupervised feature selection for large data sets

The last decade saw a considerable increase in the availability of data. Unfortunately, this increase was overshadowed by various technical difficulties that arise when analysing large data sets. These include long processing times, large requirements for data storage, and other technical issues related to the analysis of high-dimensional data sets. By consequence, reducing the cardinality of data sets (with minimum information loss) has become of interest to virtually any data scientist. Many feature selection algorithms have been introduced in the literature, however, there are two main issues with these. First, the vast majority of such algorithms require labelled samples to learn from. One should note it is often too expensive to label a meaningful amount of data, particularly when dealing with large data sets. Second, these algorithms were not designed to deal with the volume of data we have nowadays. This paper introduces a novel unsupervised feature selection algorithm designed specifically to deal with large data sets. Our experiments demonstrate the superiority of our method.
Spotify Tailoring for Promoting Effectiveness in Cross-Functional Autonomous Squads

Organisations tend to tailor agile methods to scale employed practices to have cross-functional autonomous teams while promoting sustainable creative and productive development at a constant pace. Thus, it is important to investigate how organisations tailor agile practices to get the balance right between teams' autonomy and alignment. Spotify model is originally introduced to facilitate the development of music streaming services in a very large-scale project with a Business-to-Consumer (B2C) model. However, developing a large-scale mission-critical project with a Business-to-Business (B2B) model is not essentially supported by the Spotify model. Thus, embracing Spotify model for such projects should be concerned about the question of how Spotify practices are adjusted to promote effectiveness of cross-functional autonomous squads in a mission-critical project with B2B model? In this paper, we conduct a longitudinal embedded case study, which lasted 21 months during which 14 semi-structured interviews were conducted. The Grounded Theory (GT) is adopted to analyse the collected data. As a result, we identify practices and processes that promote effectiveness in cross-functional autonomous squads, which have never been discussed in terms of Spotify model before. We also present "Spotify Tailoring" by highlighting modified and newly introduced practices by the organisation in which the case study was conducted.
Challenges and Opportunities using MultiChain for Real Estate

The real estate market involves multiple untrusted actors with contrasting objectives. We propose REchain, a blockchain-based platform for real estate, which provides benefits to all actors in terms of transparency, trust, immutability, and traceability. REchain architecture handles real estate data through well-defined workflows that leverage the MultiChain platform. REchain changes the real estate market enabling direct transactions without intermediaries, and providing new mechanisms for negotiating the purchase conditions. This paper refutes the misleading idea of using blockchains for solving generic problems as blockchain platforms have peculiar features that make them suitable for specific applications. REchain circumvents the intrinsic limits of smart filters by introducing off-chain procedures. REchain is platform-agnostic, but many aspects of the actual implementation of the smart logic depend on the underlying blockchain platform. The experimental results are promising and motivate us to explore the direction of automatic behaviour in real estate further.
Learning-Based Multi-Frame Video Quality Enhancement

Convolution neural network (CNN) has shown its great success in video quality enhancement. Existing methods mainly conduct enhancement tasks in the spatial domain, exploring the pixel correlations within one frame. Taking advantage of the similarity across successive frames, this paper develops a learning-based multi-frame approach, with an aim to explore the greatest potential for video quality enhancement leveraging the temporal correlation. First, we apply a learning-based optical flow to compensate the temporal motion across neighboring frames. Afterwards, a deep CNN network, which is structured in an early-fusion manner, is designed to discover the joint spatial-temporal correlations within a video. To ensure the generality of our CNN model, we further propose a robust training strategy. One high-quality frame and one moderate-quality frame are paired to enhance the remaining low-quality frames in between, which considers a trade-off between frame distances and various frame quality. Experimental results demonstrate that our method outperforms state-of-the-art work in objective quality. The code and model of our approach are published in Github (https://github.com/IVC-Projects/LMVE).
Underwater Image Synthesis from RGB-D Images and its Application to Deep Underwater Image Restoration

This paper proposes a method to generate synthesized underwater images from clean RGB-D images taken on the ground. It is beneficial for training a deep neural network for underwater image restoration (UWIR), and also for measuring the performances among UWIR methods. The underwater images are synthesized on the modeling of an accurate degradation process with the consideration of absorption and scattering as well as ten water types. The water types result in different attenuation coefficients, i.e., different synthesized images. In the experimental results, it is validated that our method successfully synthesizes underwater images, and presents a state-of-the-art performance for UWIR by utilizing our synthesized images for the training of deep learning-based UWIR.
AV1 in-loop Filtering using a Wide-Activation Structured Residual Network

The in-loop filter, which constitutes an important part in modern video coding, improves both subjective and objective quality of reconstructed frames. Lately, Convolutional Neural Network (CNN) has demonstrated its superiority over traditional methods in addressing in-loop filtering problem. In this paper, we develop a CNN-based in-loop filter, namely Wide Activation Residual Network (WARN), for AV1 encoder. On top of the plain Residual Network (ResNet), we introduce wide activation to each residual block, making a more reasonable allocation of network parameters. When incorporating WARN into video encoder, particular to inter coding, it is intricate to obtain the global optimum performance. After simplifying this as an end-to-end trainable problem, we propose a skipping method by taking advantage of the hierarchical reference structure in AV1. Experimental results show that our WARN achieves up to 14.42% and 9.64% BD-rate reduction in intra and inter coding, respectively. All the code and model of our approach are available at https://github.com/IVC-Projects/AV1_WARN.
Smaller Object Detection for Real-Time Embedded Traffic Flow Estimation Using Fish-Eye Cameras

Real-time embedded traffic flow estimation (RETFE) systems need accurate and efficient vehicle detection models to meet limited resources in budget, dimension, memory, and computing power. In recent years, object detection became a less challenging task with latest deep CNN-based state-of-the-art models, i.e., RCNN, SSD, and YOLO; however, these models cannot provide desired performance for RETFE systems due to their complex time-consuming architecture. In addition, small object (<; 30×30 pixels) detection is still a challenging task for existing methods. Thus, we propose a shallow model named Concatenated Feature Pyramid Network (CFPN) that inspired from YOLOv3 to provide above mentioned performance for the smaller object detection. Main contribution is a proposed concatenated block (CB) which has reduced number of convolutional layers and concatenations instead of time-consuming algebraic operations. The superiority of CFPN is confirmed on the COCO and an in-house CarFlow datasets on Nvidia TX2. Thus we conclude that CFPN is useful for real-time embedded smaller object detection task.
High-dimensional quantum frequency converter

In high-dimensional quantum communication networks, the quantum frequency converter (QFC) is indispensable as an interface in the frequency domain. For example, many QFCs have been built to link atomic memories and fiber channels. However, almost all QFCs work in a two-dimensional space. It is still a pivotal challenge to construct a high-quality QFC for some complex quantum states, e.g., a high-dimensional single-photon state that refers to a qudit. Here, we firstly propose a high-dimensional QFC for an orbital-angular-momentum qudit via sum-frequency conversion with a flat-top beam pump. As a proof-of-principle demonstration, we realize quantum frequency conversions for a qudit from infrared to visible range. Based on the qudit quantum state tomography, the fidelities of a converted state are 98.29(95.02)%, 97.42(91.74)%, and 86.75(67.04)% for a qudit without (with) accidental counts in 2, 3, and 5 dimensions, respectively. The demonstration is very promising for constructing a high-capacity quantum communication network.
Novel continuous-variable quantum secure direct communication and its security analysis

Quantum secure direct communication (QSDC) allows secret messages to be directly communicated over a quantum channel; its further development could lead to many practical applications. In this paper, we propose a novel continuous-variable (CV)-based QSDC which could be compatible with fully developed optical telecommunication technologies to minimize the implementation costs. Through a security analysis we show that the proposed scheme can effectively resist an eavesdropper's attacks on the light intensity. Furthermore, our work should inspire the improvement, development and application of CV-based QSDC.
Robust object tracking with crow search optimized multi-cue particle filter

Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of sample degeneracy and impoverishment. In order to address this, we propose a novel resampling method based upon crow search optimization to overcome low performing particles detected as the outlier. Proposed outlier detection mechanism with transductive reliability achieves faster convergence of the proposed PF tracking framework. In addition, we present an adaptive fusion model to integrate multi-cue extracted for each evaluated particle. Automatic boosting and suppression of particles using the proposed fusion model not only enhance the performance of the resampling method but also achieve optimal state estimation. Performance of the proposed tracker has been evaluated over benchmark video sequences and compared with state-of-the-art solutions. Qualitative and quantitative results reveal that the proposed tracker not only outperforms existing solutions but also efficiently handles various tracking challenges. On average of the outcome, we achieve CLE of 10.99 and F measure of 0.683.
Vehicle and Weather Detection Using Real Time Image Processing Using Optical Flow and Color Histogram

Image processing is the method that will be used in this system. A surveillance camera will be mounted at an optimal position so that it could detect the vehicles efficiently. The Foreground Detector and Blob Analysis make up the techniques that will be used to identify an object as a vehicle in a video sequence. The Mixture Model, specifically Gaussian must have an initialized Foreground Detector that has sampled a video frame before it can be used. After initializing Foreground Detector, Blob Analysis would be used to detect vehicles and place bounding box. Image processing used in detecting vehicles is used in determining the weather condition. The only difference is that histogram would also be used. Since it will operate in real-time, it is capable of detecting the density of vehicles on the lane even in various conditions like raining or changes in illumination.
Kalman Filtering of Patches for Frame-Recursive Video Denoising

A frame recursive video denoising method computes each output frame as a function of only the current noisy frame and the previous denoised output. Frame recursive methods were among the earliest approaches for video denoising. However in the last fifteen years they have been used almost exclusively for real-time applications with denoising performance far from being state-of-the-art. In this work we propose a simple frame recursive method which is fast, has a low memory complexity and achieves results competitive with more complex state-of-the-art methods that require processing several input frames for producing each output frame. Furthermore, in terms of visual quality, the proposed approach is able to recover many details that are missed by most non-recursive methods. As an additional contribution we also propose an off-line post-processing of the denoised video that boosts denoising quality and temporal consistency.
Dressing for Attention: Outfit Based Fashion Popularity Prediction

Analysis of fashion trends is crucial. However, existing predictive algorithms of fashion popularity are restricted to be feasible on the coarse style level but not a finer item level. That is, they are only predictive in the future popularity of a given type of fashion styles (e.g., Rocker), but cannot be precisely down to a particular outfit look chosen by individuals. This paper thus proposes the first solution directly aimed at predicting the fine-grained fashion popularity of an outfit look by taking social media as the learning source. Particularly, a deep temporal sequence learning framework is developed and the proposed framework is evaluated on a real dataset of 380,000 street fashion images collected from the fashion website lookbook.nu. The experimental results show that our proposed framework outperforms the state-of-the-art approaches, with a relative increase of 11.51% to 27.62% (MSE metric) and 7.02% to 32.61% (CSE metric) in the prediction accuracy.
Investigating Quality Requirements for Blockchain-Based Healthcare Systems

Healthcare is a data-intensive domain, once a considerable amount of data is daily produced due to monitoring patients, managing identities, producing medical records and processing medical insurance claims. Hence, security, besides other quality requirements, is prominent to preserve the confidentiality and integrity of such data. Blockchain technology has received attention as a mean to support secure transactions and records, including in the healthcare domain. The main contribution of this paper is providing preliminary results of an investigation on the recent literature on how the inherent characteristics of blockchain can enhance or hinder particular quality requirements in healthcare systems. We provide a preliminary analysis of five essential quality requirements for healthcare domain, besides pointing for advances that must still be achieved.
Detecting IoT Devices and How They Put Large Heterogeneous Networks at Security Risk

The introduction of the Internet of Things (IoT), i.e., the interconnection of embedded devices over the Internet, has changed the world we live in from the way we measure, make calls, print information and even the way we get energy in our offices or homes. The convenience of IoT products, like closed circuit television (CCTV) cameras, internet protocol (IP) phones, and oscilloscopes, is overwhelming for end users. In parallel, however, security issues have emerged and it is essential for infrastructure providers to assess the associated security risks. In this paper, we propose a novel method to detect IoT devices and identify the manufacturer, device model, and the firmware version currently running on the device using the page source from the web user interface. We performed automatic scans of the large-scale network at the European Organization for Nuclear Research (CERN) to evaluate our approach. Our tools identified 233 IoT devices that fell into eleven distinct device categories and included 49 device models manufactured by 26 vendors from across the world.
Towards large scale high fidelity collaborative augmented reality

In recent years, there has been an increasing amount of Collaborative Augmented Reality (CAR) experiences, classifiable by the deployed scale and the fidelity of the experience. In this paper, we create HoloRoyale, the first large scale high fidelity (LSHF) CAR experience. We do this by first exploring the LSHF CAR design space, drawing on technical implementations and design aspects from AR and video games. We then create and implement a software architecture that improves the accuracy of synchronized poses between multiple users. Finally, we apply our target experience and technical implementation to the explored design space. A core design component of HoloRoyale is the use of visual repellers as crowd control elements to guide players away from undesired areas. To evaluate the effectiveness of the employed visual repellers in a LSHF CAR context we conducted a user study, deploying HoloRoyale in a 12.500 m2 area. The results from the user study suggest that visual repellers are effective crowd control elements that do not significantly impact the user's overall immersion. Overall our main contribution is the exploration of a design space, discussing several means to address the challenges of LSHF CAR, the creation of a system capable of LSHF CAR interactions along with an experience that has been fitted to the design space, and an indepth study that verifies a key design aspect for LSHF CAR. As such, our work is the first to explore the domain of LSHF CAR and provides insight into designing experiences in other AR domains.
Empirical Evidence and Economic Implications of Blockchain as a General Purpose Technology

Leading experts from academic, industrial and policy-making circles describe Blockchain as a disruptive and game changing technology across various sectors. However, the question, whether Blockchain does already have the properties of a General Purpose Technology (GPT) and, as such, will determine macroeconomic dynamics in the next decades, has been disregarded in the academic literature. This paper covers the research gap by systematically revealing the acknowledged features of a GPT - pervasiveness, innovation spawning effects and scope for improvement - in the newest available Blockchain-related patent data from PATSTAT. To gain insights about pervasiveness, (1) a generality index of Blockchain is compared to respective values of co-existing technologies usually considered (information and communication technology) and not considered (pharmaceutical technology) GPTs. The second feature, innovation spawning is dissected (2) analyzing variety of innovators patenting in Blockchain domain and (3) looking at firms' behavior in terms of entry and exit in line with industrial dynamics. (4) Investigation of evolution patterns of Blockchain patents provides insights about its scope for improvement. The empirical analysis advances the claim that Blockchain does already represent a GPT in the making and, therefore, has a potential to shape a technological era and cause substantial changes in the economic, social and institutional structures.
Fireworks explosion based artificial bee colony for numerical optimization

Artificial bee colony (ABC) is a swarm optimization algorithm that shows competitive performance for many optimization problems. However, ABC often suffers from poor exploitation and slow convergence. To overcome this deficiency, this paper introduces the explosion search mechanism of fireworks algorithm into ABC, and proposes a fireworks explosion based artificial bee colony (FW-ABC) framework. More specifically, the proposed FW-ABC framework consists of two search stages, namely bee search stage and fireworks explosion search stage. After three bee search operators (i.e., employed, onlooker and scout), a fireworks explosion search is implemented, which is used to exploit the promising regions for finding better solutions. The proposed FW-ABC framework is applied to six ABC algorithms, and the experimental results show that FW-ABC can significantly improve the performances of various existing ABC algorithms on CEC2014 benchmark functions. Moreover, FW-ABC algorithm also exhibits better performance compared with state-of-the-art fireworks algorithms and other meta-heuristic algorithms.