Reviewerly/exHarmony huggingface dataset:
Sparse Multi-Bending Snakes

Active contour models are one of the most emblematic algorithms of computer vision. Their strong theoretical foundations and high user interoperability turned them into a reference approach for object segmentation and tracking tasks. A high number of modifications have already been proposed in order to overcome the known problems of traditional snakes, such as initialization dependence and poor convergence to concavities. In this paper, we address the scenario where the user wants to segment an object that has multiple dynamic regions but some of them do not correspond to the true object boundary. We propose a novel parametric active contour model, the Sparse Multi-Bending snake, which is capable of dividing the contour into a set of contiguous regions with different bending properties. We derive a new energy function that induces such behavior and presents a group optimization strategy that can be used to find the optimal bending resistance parameter for each point of the contour. We show the flexibility of our model in a set of synthetic images. In addition, we consider two real applications, lung segmentation in Computerized Tomography data and hand segmentation in depth images. We show how the proposed method is able to improve the segmentations obtained in both applications, when compared with other active contour models.
Interactive Border Contour with Automatic Tracking Algorithm Selection for Medical Images

Contour tracking methods such as Live Wire and Riverbed have been widely used for several medical imaging applications such as tissue and tumor segmentation. Several variations of these methods have been proposed, but none of them is better than the others for all kind of tasks and image modalities. In this paper, we propose an interactive framework for medical image segmentation with four different contour tracking methods with an intuitive interface for visualizing and choosing the best option for each contour segment. The framework also includes a classifier which indicates the most appropriated method in both automatically and semi-automatically fashions. Our experiments employ a robot user which simulates the human behavior and was able to indicate the correct method for 67% of the segments.
Ghazal: Toward Truly Authoritative Web Certificates Using Ethereum

Recently, a number of projects (both from academia and industry) have examined decentralized public key infrastructures (PKI) based on blockchain technology. These projects vary in scope from fullfledged domain name systems accompanied by a PKI to simpler transparency systems that augment the current HTTPS PKI. In this paper, we start by articulating, in a way we have not seen before, why this approach is more than a complementary composition of technologies, but actually a new and useful paradigm for thinking about who is actually authoritative over PKI information in the web certificate model. We then consider what smart contracts could add to the web certificate model, if we move beyond using a blockchain as passive, immutable (subject to consensus) store of data—as is the approach taken by projects like Blockstack. To illustrate the potential, we develop and experiment with an Ethereum-based web certificate model we call Ghazal, discuss different design decisions, and analyze deployment costs.
Wind disturbance control for V-tail Y-shape quadcopter

The flight control with wind disturbance predictive model for the University of Glasgow Singapore (UGS) Lynxmotion Hunter Y-shape V-Tail 500 are presented. The V-Tail has the capability of high directional manoeuvre by the tilted (angled) hind rotors arms. However, the drawbacks of this design are that it causes instability during strong crosswind and structural instability at the rear. As such, the development of a new flight control system with wind correction is required to assist in making this platform stable. The previous phase of this work was to understand the needs and objectives of this project, source for the right configuration and constructing of the V-Tail for the investigation of its flight behaviour. The subsequent phase was to recreate a flight control firmware using Simulink with analysis and discuss the simulation results. The current phase is to integrate the wind correction model. With the new developed wind disturbance control, the model can be flown on course despite strong wind. This can be used for future research into tiltrotor initiatives or even use it to recreate a robust location correction in auto mode without the use of GPS localization.
Development of SEED (solar energy estimator for Daejeon) using chollian satellite imagery

Daejeon Metropolitan City and the Korea Institute of Energy Research (KIER) have developed a platform with the Solar Energy Estimator for Daejeon (SEED) for Daejeon citizens and solar power generation companies according to the government plan to expand new and renewable energy. Developed through four phases, the platform can calculate the solar irradiance by reflecting the shading variation of urban buildings and compute the power generation considering the module performance. The estimated power generation by the platform exhibited high accuracy of R2 = 0.89 compared with the ground power generation amount. The SEED was developed to satisfy both accuracy and convenience requirements, and it is expected to meet the purpose of solar energy expansion and promotion.
Viewer-Tailored Advertising for Video on Demand Platforms

Viewers consider tailored advertising more interesting and less irritating than traditional commercials, but to produce this type of advertising it is necessary to have information about the viewer and about the context where to place the advertisement. This knowledge is quite easy to get within the Web scenario, but it is not in VoD platforms. In this paper, we propose a system to produce viewer-tailored advertising in common VoD systems by using information about viewers, commercials and programs. By using closed-captions, we show that it is possible to develop a system able to provide highly personalized advertising without requiring heavy changes to current VoD systems. An evaluation of the system shows that viewers appreciate the proposal. Therefore, VoD platforms might use the proposed approach to provide viewer-tailored advertising.
Skeleton Extraction of Dance Sequences from 3D Points Using Convolutional Neural Networks Based on a New Developed C3D Visualization Interface

A combined approach, involving 3D spatial datasets, noise removal prepossessing and deep learning regression approaches for the estimation of rough skeleton data, is presented in this paper. The application scenario involved data sequences from Greek traditional dances. In particular, a visualization application interface was developed allowing the user to load the C3D sequences, edit the data and remove possible noise. The interface was developed using the OpenGL language and is able to parse aby C3D format file. The interface is supported by several functionalities such as a pre-processing of the 3D point data and noise removal of 3D points that fall apart from the human skeleton. The main research innovation of this paper is the use of a deep machine learning framework through which human skeleton can be extracted. The points are selected on the use of a Convolutional Neural Network (CNN) model. Experimental results on real-life dances being captured by the Vicon motion capturing system are presented to show the great performance of the proposed scheme.
Toward adaptive BDCT feature representation based image splicing measurement in smart cities

In smart cities, digital image splicing measurement is very important to ensure the security and safety of city monitoring, environment data fusion, cognitive decisions, etc. However, due to images obtained from various environments of cities usually face malevolence splicing, it is hard to perform the authenticity of a legitimate image from smart cities. In this paper, a novel block Discrete Cosine Transform (BDCT) coefficients feature distribution based statistical approach is proposed to discover image forgeries for image splicing measurement. In the proposed feature, all the BDCT neighbouring modes are categorized into a number of groups following the maximum likelihood (ML) criterion to ensure the modes in the same group having similar distributions. For each group, the transition probability matrix (TPM) or the joint probability matrix (JPM) is extracted from the BDCT coefficient pairs in the image. Moreover, the proposed scheme is constructed by concatenating all the TPM/JPM features for each group. Experimental results demonstrate that our feature outperforms two state-of-the-art approaches when taking both the measurement accuracy and feature dimension into consideration.
User Friendly Tab Management in Web Browsers

Modern web browsers such as Google Chrome or Mozilla Firefox have a number of tabs to open different URLs, which makes it unwieldy and inconvenient for the user to keep track of all open tabs and navigate to the desired tabs. Often, the open tabs can be divided into related groups by identifying the user`s browsing context and intention. In this paper we present methods to identify sub browsing contexts for grouping related tabs. The method for identifying a particular sub browsing context involves tracking the tab navigation sequence of the user, clustering tabs by matching the semantic topics, and loading a new contextual tab by the user using a component called the contextual tab manager. We describe a few alternative ways for implementing the system for the Chrome browser: using the TabStripModel (for desktop), using the Chrome.tabs extension API (for desktop) and modifying the TabManager class to incorporate the context (for mobile browsers). We implement the system on the default browser of a Samsung smartphone using the third method and perform a user study to evaluate the ease of use of the system for a selection of users.
A novel integrity measurement method based on copy-on-write for region in virtual machine

More and more enterprises are migrating services and data into virtual machine (VM) using base and increment, and the files in the VM may contain critical data. Therefore, it is necessary to build a trusted environment to enhance the security of the files in VM by the integrity measurement methods (IMMs). In order to simplify the management of the files in integrity measurement, the security manager needs to put these files into one region and then to measures the integrity of the region. If the region is integrity, the files in that region are integrity too. Currently, the traditional IMMs are all based on the message digest algorithms that use too much amount of data, spend much time, and make the performance of VMs degraded. Aiming to address those problems, we propose a novel IMM based on copy-on-write for the region, called RIMM. The method puts the region into the base image, gets the integrity information of the region from the base image by the structures of image and region, and periodically measures the integrity of the region in the increment image by the structure of image and copy-on-write. It is transparent for VM and can eliminate the semantic gap. The evaluation shows that the RIMM can significantly reduce the amount of data and time in the process of integrity measurement. For example, (1) when the region size is 100MB, the amount of data used by RIMM is about 400 times smaller than that used by IMMs based on MD5; the time spent by RIMM is about 600 times less than that used by IMMs based on MD5. (2) when the region size is 9GB, the amount of data used by RIMM is about 29000 times smaller than that used by IMMs based on MD5; the time spent by RIMM is about 3864 times less than that used by IMMs based on MD5.
A Fuzzy-Based Discounts Recommender System for Public Tax Payment

Governmental institutions around the world need funding to keep and raise their social and financial programs. One of the main incomes of different national and local governments is based on taxation. Increasing the income through tax payers has been and remains a challenge for government institutions. On the other hand, recommender systems (RSs) have presented evidence of successful results to improve business revenues in the field of eCommerce. This research presents a fuzzy-based recommender system model and its preliminary outcomes applied to a dataset from the Municipality of Quito to advise citizens on their payments behaviour. The proposed model shows some insights in the use of RSs to increase citizens' awareness over tax payments and therefore enhance governmental institutions' incomes. At the end of this chapter, preliminary results of the system developed are presented.
Game Theoretic Suppression of Forged Messages in Online Social Networks

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages.
Enhancing explainability of social recommendation using 2D graphs and word cloud visualizations

Explainability is an important desired property of recommendation systems. We consider a graph-based recommendation approach, which generates detailed explanations to users in the form of labelled connecting relational paths and present a visualization interface intended to convey such detailed relational information in clear and intuitive manner. As initial evaluation, we performed a user study at an academic conference, recommending participants the users may be interested to meet (using <u>rsr.cloud</u>). The feedbacks were enthusiastic, indicating that the proposed visualizations of relational explanations are engaging and useful.
Mean Field Quantum Annealing Correction

In this review, how quantum annealing correction works is described from the statistical physics point of view. Quantum annealing correction (QAC) is a programmable error correction scheme in quantum annealing. It is designed to suppress errors by imposing energy penalties. There have been various experimental tests on D-wave machines demonstrating that QAC significantly improves the success probabilities of finding ground states of Ising models. We analyze QAC in the ferromagnetic and Hopfield models by using the mean field theory. The results of our analyses show that QAC efficiently suppresses the thermal fluctuations. In addition, it can significantly increase the energy gap during annealing by changing the energy penalties. In some instances, exponentially small energy gaps turn into polynomially small energy gaps. This suggests that QAC can drastically improve the computational efficiency.
Correction to: Bitcoin and the rise of decentralized autonomous organizations

Following the publication of this article [1], it was noted that the author list was incomplete and was missing the following three authors.
An Empirical Study on Pair Performance and Perception in Distributed Pair Programming

This paper reports students’ perceptions and experiences attending an object-oriented programming course in which they developed software using the Distributed Pair Programming (DPP) technique. Pair programming (PP) is typically performed on one computer, involving two programmers working collaboratively on the same code or algorithm. DPP on the other hand is performed remotely allowing programmers to collaborate from separate locations. PP started in the software industry as a powerful way to train programmers and to improve software quality. Research has shown that PP (and DPP) is also a successful approach to teach programming in academic programming courses. The main focus of PP and DPP research was PP’s effectiveness with respect to student performance and code quality, the investigation of best team formation strategies and studies of students’ attitudes. There are still limited studies concerning relationships between performance, attitudes and other critical factors. We have selected some of the most common factors which can be found in the literature: academic performance, programming experience, student confidence, “feel-good” factor, partner compatibility and implementation time. The main goal of this study was to investigate correlations between these attributes, while DPP was used as the main programming technique.
On Nonadaptive Reductions to the Set of Random Strings and Its Dense Subsets

We explain our recent results [21] on the computational power of an arbitrary distinguisher for (not necessarily computable) hitting set generators. This work is motivated by the desire of showing the limits of black-box reductions to some distributional $$\mathsf {NP}$$ problem. We show that a black-box nonadaptive randomized reduction to any distinguisher for (not only polynomial-time but also) exponential-time computable hitting set generators can be simulated in $$\mathsf {AM}\cap \mathsf {co} \mathsf {AM}$$; we also show an upper bound of $$\mathsf {S}_2^\mathsf {NP}$$ even if there is no computational bound on a hitting set generator. These results provide additional evidence that the recent worst-case to average-case reductions within $$\mathsf {NP}$$ shown by Hirahara (2018, FOCS) are inherently non-black-box. (We omit all detailed arguments and proofs, which can be found in [21].)
Robust Fourier-Based Checkerboard Corner Detection for Camera Calibration

Precise localization of reference markers is crucial for the accuracy of target-based camera calibration. State-of-the art detectors, however, are sensitive to optical blur corrupting the image in many practical calibration scenarios. We propose a novel method for the sub-pixel refinement stage of common checkerboard target detectors. It uses the symmetry of checkerboard crossings and exploits the periodicity in the angular frequency domain when the origin of a polar coordinate system is centered at the crossing. The detector estimates the crossing center’s sub-pixel position by minimizing spurious frequency components that occur increasingly at ever larger distances from the crossing center. An average localization error of 0.08 px is achieved in noisy and artificially blurred synthetic images, surpassing the state of the art by 65 %. In addition, we evaluated the detector in real-world camera calibration using a public data set, achieving an reprojection error of 0.11 px compared to 0.27 px for the state of the art.
3D scanning by means of dual-projector structured light illumination

This paper introduces a dual-projector phase measuring profiler that adds a second projector to a traditional structured light illumination system to improve the overall quality of 3D scanning. With this method, two projectors are synchronized to a single camera, but each one projects structured light patterns of a unique frequency. The system performance benefits from a wider projection angle and doubled light intensity. In particular, a detailed system implementation in hardware is described. Moreover, the major difference between the phase unwrapping of our dual-projector system versus a single-projector system is discussed with a LUTbased phase unwrapping scheme proposed.
A Novel Real Time Visual Tracking Method Using Modified Flower Pollination Algorithm

This study presented a novel algorithm, namely, modified flower pollination algorithm (MFPA), for moving object tracking in real time video. Three variables, i.e., x-coordinate, y-coordinate and width, known as search window, were used to represent the features of the target object. The image within the search window was extracted, and used to calculate the Hue, Saturation, and Value (HSV) histogram. The HSV histogram was then used as a feature model of the target object. Subsequently, the MFPA will search for an optimal match of the target using Bhattacharyya distance as the fitness function. Simulation results demonstrated that the MFPA outperformed FPA in the aspect of higher accuracy, where 75.35% and 92.13% of improvements were observed in two considered case studies.
Faster repetition-aware compressed suffix trees based on Block Trees

The suffix tree is a fundamental data structure in stringology, but its space usage, though linear, is an important problem in applications like Bioinformatics. We design and implement a new compressed suffix tree (CST) targeted to highly repetitive texts, such as large genomic collections of the same species. Our first contribution is to enhance the Block Tree, a data structure that captures the repetitiveness of its input sequence, to represent the topology of trees with large repeated subtrees. Our so-called Block-Tree Compressed Topology (BT-CT) data structure augments the Block Tree nodes with data that speeds up tree navigation. Our Block-Tree CST (BT-CST), in turn, uses the BT-CT to compress the topology of the suffix tree, and also replaces the sampling of the suffix array and its inverse with grammar- and/or Block-Tree-based representations of those arrays. Our experimental results show that BT-CTs reach navigation speeds comparable to compact tree representations that are insensitive to repetitiveness, while using 2–10 times less space on the topologies of the suffix trees of repetitive collections. Our BT-CST is slightly larger than previous repetition-aware suffix trees based on grammar-compressed topologies, but outperforms them in time, often by orders of magnitude.
Enhanced Service Recommender and Ranking System Using Browsing Patterns of Users

We present an enhanced service recommender system for web services, which takes existing recommendations from services or content providers and based on topics identified by the user's recent browsing history (including past clicks and searches) re-ranks the recommended URLs for each service, giving an aggregate re-ranked recommendation list. This information can be used by third party services for giving more relevant recommendations and notifications to the user, as well as to build a user interests profile. We have implemented our system using a modified version of LDA to cluster the browsing history, and validated it using browsing data gathered from a selection of users.