Reviewerly/exHarmony huggingface dataset:
A Model-based Approach to Realize Privacy and Data Protection by Design

Telecommunications and data are pervasive in almost each aspect of our every-day life and new concerns progressively arise as a result of stakes related to privacy and data protection [1]. Indeed, systems development becomes data-centric leading to an ecosystem where a variety of players intervene (citizens, industry, regulators) and where the policies regarding data usage and utilization are far from consensual. The new General Data Protection Regulation (GDPR) enacted by the European Commission in 2018 has introduced new provisions including principles for lawfulness, fairness, transparency, etc. thus endorsing data subjects with new rights in regards to their personal data. In this context, a growing need for approaches that conceptualize and help engineers to integrate GDPR and privacy provisions at design time becomes paramount. This paper presents a comprehensive approach to support different phases of the design process with special attention to the integration of privacy and data protection principles. Among others, it is a generic model-based approach that can be specialized according to the specifics of different application domains.
Exploring Learning Approaches for Ancient Greek Character Recognition with Citizen Science Data

The central dogma of handwritten character recognition remains inextricably linked to optical character recognition methods for print media. Alongside their reliance on proprietary data and lack of open-access software, the applicability of these optical character recognition methods to handwritten characters from low-quality documents (e.g., that are damaged) remains unknown. In this paper, we compare and contrast the performance of state-of-the-art optical character recognition tools for print and learning models engineered with state-of-the-art machine learning toolkits trained on handwritten inputs. Using Tesseract OCR as a baseline, we build, optimize, and evaluate three types of convolutional neural networks that are trained on the AL-ALLand AL-PUBdatasets, a collection of images of handwritten ancient Greek characters that were labeled by volunteers through the Ancient Lives online citizen science project. We find our best-performing machine learning model to be 92.57% accurate compared to Tesseract OCR’s 11.15%. Following our analysis, we present a brief examination of our models’ shortcomings, introduce the publicly-available AL-PUBdataset, and, describe Theia, a web-based tool that democratizes our machine learning models for public use. We conclude by discussing the promise of our findings for advancing research at the intersection of machine learning, manuscript transcription, and the digital humanities.
GAM

We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers improved visual explanations, when compared to existing alternatives. The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.
Automated Calculation of Height and Area of Human Body for Estimating Body Weight Using a Matlab-based Kinect Camera

Body size is an important parameter in radiology examinations. Measurements of body size may be carried out remotely using a Kinect camera. We aim to develop a Matlab-based software that can be used to automatically measure the height and area in order to estimate the weight of a human body without direct contact. The size and depth of the image were calibrated for various object-Kinect distances. The depth image was segmented and normalized into a binary image, from which the height and area were calculated. The height and area of the body were correlated with the body weight. We analyzed the images obtained from 147 participants aged 5–70 years old and weighing 13.7–89.9 kg. The height measured using the Kinect camera had a high correlation with a ruler with R2 = 0.96 (p = 0.0096), and error percentage of 1.04%. It is also found that the height and area of the participants are well correlated with the weight of the participants with R2 of 0.453 and 0.658, respectively. A graphical user interface for automatically calculating the height and area of the human body has been successfully developed.
Age Inference Using A Hierarchical Attention Neural Network

While demographic attributes, such as age, gender, and location, have been extensively studied, most previous studies usually combine different sources of data, such as the user's biography, pictures, posts, and the user's network to obtain reasonable inference accuracies. However, it is not always practical to collect all those different forms of data. Therefore, in this paper, we consider methods for inferring age that only use Twitter posts (tweet text and emojis). We propose a hierarchical attention neural model that integrates independent linguistic knowledge gained from text and emojis when making a prediction. This hierarchical model is able to capture the intra-post relationship between these different post components, as well as the inter-post relationships of a user's posts. Our empirical evaluation using a data set generated from Wikidata demonstrates that our model achieves better performance than the state-of-the-art models, and still performs well when the number of posts per user is reduced in the training data set.
An Application for Aesthetic Quality Assessment in Photography with Interpretability Features

Automatic aesthetic quality assessment is a computer vision problem in which we quantify the attractiveness or the appealingness of a photograph. This is especially useful in social networks, where the amount of images generated each day requires automation for processing. This work presents Aesthetic Selector, an application able to identify images of high aesthetic quality, showing also relevant information about the decisions and providing the use of the most appropriate filters to enhance a given image. We then analyzed the main proposals in the aesthetic quality field, describing their strengths and weaknesses in order to determine the filters to be included in the application Aesthetic Selector. This proposed application was tested, giving good results, in three different scenarios: image selection, image finding, and filter selection. Besides, we carried out a study of distinct visualization tools to better understand the models' behavior. These techniques also allow detecting which areas are more relevant within the images when models perform classification. The application also includes this interpretability module. Aesthetic Selector is an innovative and original program, because in the field of aesthetic quality in photography, there are no applications that identify high-quality images and also because it offers the capability of showing information about which parts of the image have affected this decision.
Learning Dual Low-Rank Representation for Multi-Label Micro-Video Classification

Currently, with the rapid development of mobile Internet, micro-video has become a prevailing format of user-generated contents (UGCs) on various social media platforms. Several studies have been conducted towards to understanding high-level micro-video semantics, such as venue categorization, memorability, and popularity. However, these approaches supported tasks with only a single output, which exhibited limitations when attempting to use them to resolve tasks with multiple outputs, especially the multi-label micro-video classification. To tackle this problem, in this paper, we propose a dual multi-modal low-rank decomposition (DMLRD) method for multi-label micro-video classification tasks. To learn more comprehensive micro-video representations, we first learn the low-rank-regularized modality-specific and modality-shared components by considering the consistency and the complementarity among modalities simultaneously. Meanwhile, the less descriptive power of each modality aroused by inherent properties can be solved to a certain extent. To obtain unseen label representations, we next construct a sparsity-regularized multi-matrix normal estimation term to jointly encode the latent relationship structures among labels and dimensions. Experiments on two datasets demonstrate the effectiveness of our proposed method over the state-of-art methods.
Now I’m Not Afraid: Reducing Fear of Missing Out in 360° Videos on a Head-Mounted Display using a Panoramic Thumbnail

Cinematic virtual reality, or 360° video, provides viewers with an immersive experience, allowing them to enjoy a video while moving their head to watch in any direction. However, there is an inevitable problem of feeling fear of missing out (FOMO) when viewing a 360° video, as only a part of the video is visible to the viewer at any given time. To solve this problem, we developed a technique to present a panoramic thumbnail of a full 360° video to users through a head-mounted display. With this technique, the user can grasp the overall view of the video as needed. We conducted an experiment to evaluate the FOMO, presence, and quality of viewing experience while using this technique compared to normal viewing without it. The results of the experiment show that the proposed technique relieved FOMO, the quality of viewing experience was improved, and there was no difference in presence. We also investigated how users interacted with this new interface based on eye tracking and head tracking data during viewing, which suggested that users used the panoramic thumbnail to actively explore outside their field of view.
Discrete Truss Optimization Using Rao Algorithms

Rao algorithms are known as simple, parameter-free metaheuristics that have shown good performance and are quite competitive in solving engineering optimization problems. Although Rao algorithms are quite new, they gain increasing attention from researchers. This paper freshly explores the applicability of Rao algorithms in solving discrete truss optimization problems. Furthermore, a novel adaptive scheme is proposed to balance the global exploration and the local exploitation of these algorithms and a composite Rao algorithm is established. Three benchmark truss structures are used to test the performance of the original and the new algorithms. The effect of the population size, the only required parameter, is investigated, and some remarks are drawn from the numerical results.
Single-node partitioned-memory for huge graph analytics

Because of cost, non-volatile memory NVDIMMs such as Intel Optane are attractive in single-node big-memory systems. We evaluate performance and cost trade-offs when using Optane as volatile memory for huge-graph analytics. We study two scalable graph applications with different work locality, access patterns, and parallelism. We evaluate single and partitioned address spaces---Memory and AppDirect modes---and compare with distributed executions on GPU-accelerated and CPU-based supercomputers.
A diffusion-based method for removing background stars from astronomical images

Image processing techniques for background star removal from astronomical images have important applications such as streak detection and star/galaxy differentiation in galaxy morphology classification. Previous star removal methods relied on frame differencing and so required multiple images. In this work we present a new diffusion-based method that can remove stars and noise at the same time, using only a single image. The method measures a parameter called the local asymmetry around each pixel to identify stars and remove them with varying diffusion strength according to the measured parameter. To optimise the balance between signal preservation and star removal, it further incorporates a local stopping function that can adaptively turn the diffusion process on and off by monitoring the image residues. In order to test the method, we constructed a synthetic image that mimics astronomical images of interest and compared the processed images to the ground truth through the measurement of a range of metrics such as mean average error, root mean squared error, peak signal-to-noise ratio and structural similarity index. The results showed clear improvements in all the metrics compared to previous diffusion algorithms. Finally, we applied our method to a dataset of natural images and confirmed its effectiveness at preserving a wide variety of astronomical objects.
Evaluating the Performance of Ensemble Methods and Voting Strategies for Dense 2D Pedestrian Detection in the Wild

As vehicles experience a wide variety of driving settings in the wild, 2D pedestrian detection offers a substantial barrier to autonomous vehicle navigation systems. In this work, we demonstrate the effectiveness of a lightweight ensemble architecture for pedestrian detection in the wild, which combines detectors and data augmentation techniques to improve the performance of well-established detectors. The framework uses voting strategies to increase the explainability of object detection in navigation systems while also improving the precision of bounding box predictions on the dataset. The ensemble of the best model and augmentation technique achieved 41.41 % AP in detecting pedestrians in the wild using the consensus voting strategy on the WiderPerson dataset.
Reliable and Privacy-Preserving Task Matching in Blockchain-Based Crowdsourcing

With the number of users in crowdsourcing increasing rapidly, task matching service is attracting more and more attention. However, it also causes many security concerns, one of which is the leakage of sensitive information. Privacy-preserving task matching techniques can protect the private information of task requesters and workers. Whereas existing privacy-preserving task matching schemes are constructed on a central server, and thereby they may suffer from potential wrongdoings of a malicious server. In addition, most of them only provide accurate task matching, which means that they cannot tolerate keyword spelling errors, leading to the decline of task matching accuracy. In this paper, we propose a Reliable and Privacy-preserving Task Matching scheme (RPTM) for crowdsourcing. To guarantee the reliability of task matching results, RPTM employs smart contracts to ensure that operations of RPTM are faithfully performed. However, it may still disclose the privacy of users due to the transparency of the blockchain. In order to deal with this problem, RPTM can perform task matching service without compromising the privacy of task requesters and workers by leveraging a novel integer vector encryption scheme. Moreover, RPTM supports multi-keyword fuzzy matching by exploiting locality sensitive hashing and Bloom filter, which can tolerate keyword spelling errors and different expression formats. Extensive analysis and experiments based on a test net of EOS show that RPTM is efficient and secure.
Naturalness balance contrast enhancement using adaptive gamma with cumulative histogram and median filtering

In the domain of contrast enhancement, contrast as well as detail retention are critical for human visualization and onward processing by any other computer based application. Contrast enhancement of an image with naturalness and preserved details is a challenge. In this paper, we propose a method using an edge-preserving smoothing operator approach, based on the weighted least squares (WLS) optimization. The detail and base layers are extracted using the WLS and median filters respectively. Base layer is a smooth layer of the image that contains large scale intensity changes and the detail layer captures the smaller scale details in the image. The layers are enhanced with a fine tuning between the rate of enhancement of the base and detail layers. It is vital to manage the level of enhancement in both the extracted layers as any mistune in the rate of enhancement may lead to severe distortion and addition of artifacts. Color correction is applied to the image to provide a higher-resolution improved image. The simulation results over 680 images from different datasets, show that the proposed method outperforms the existing methods in terms of qualitative and quantitative parameters.
Explainable Persuasion in Interactive Design

Persuasive technology refers to the use of digital means to influence attitude, behaviour, and decisions. While a professional design of persuasive interfaces shall consider user interests and freedom of choice as a primary requirement, principles, and methods to achieve it are yet to be introduced. In the design of persuasive interfaces, fulfilling conditions of informed consent can help establish transparency and resolve such ethical issues. This paper introduces the concept of explainable persuasion as a way to address informed consent within persuasive interfaces. We provide a definition for explainable persuasion, highlight the need for it, discuss the design approach and underline the challenges to be addressed when designing explainable persuasive interfaces.
Non-contact and fast measurement of small roll angle using digital shearography

An approach to non-contact, fast, and precision measurement of small roll angle using digital shearography is introduced. The roll angles are determined directly by recording the interferometric phase changes when using a dual-beam digital shearography to measure tested surfaces. Phase at a point rather than full-field phase distribution is used to calculate the roll angle, so global image processing is no longer needed and the measurement speed becomes much faster. The proposed approach is robust due to the nature of digital shearography, making it a promising method in field applications. The theoretical measurement resolution of the method is from 1.5 µrad to deep sub-microradian, which can compete with the best resolution of the existing methods.
A decentralized social network architecture

Billions of people use social networks, and they playa significant role in people's lifestyles in the current world. At the same time, due to globalization and other factors, the use of these social platforms is expanding daily, and a variety of activities take place inside these platforms. These networks are centralized, allowing social network-owned companies to track and observe the activities of their users. Therefore, this has been challenged to the privacy of the data of users. Also, these companies tend to sell them to third parties keeping huge profits without users' permission. Since data is the most valuable asset in today's and tomorrow's world, many have pointed out this issue. Even though decentralized, community-driven applications have come to playas a solution to this problem, there is still no successful application that competes with centralized social network platforms. Therefore, this study attempted to develop a decentralized social network architecture with the basic functionalities of a social media platform to assure the privacy of the users' data.
Optimal distribution of PV modules on roofs with limited space

The present work proposes a new algorithm for finding the optimal distribution of PV modules on flat roofs of urban building so that the total absorbed energy is maximised. The paper is divided in 3 sections. Section 1 shows an analytic technique that allows for annual irradiation of a PV module to be calculated for every tilt angle, consequently, this procedure can also be used to determine the optimal tilt that maximises the total irradiation of such module. In Section 2, a new packing algorithm is developed (specifically for this problem) so that total area of the PV modules of the flat roof is maximised. Studying the shadowing between modules and the needed spacing is key for this part. Lastly, in Section 3, it’s checked whether modifying the fixed tilt of the PV modules to be installed in a flat roof results in an increase of the total PV surface that compensates the loss of energy per module deriving from not using the optimal tilt angle, so that the maximum possible total absorbed energy for a given terrace is achieved.
Description and recognition of complex spatial configurations of object pairs with Force Banner 2D features

A major challenge in scene understanding is the handling of spatial relations between objects or object parts. Several descriptors dedicated to this task already exist, such as the force histogram which is a typical example of relative position descriptor. By computing the interaction between two objects for a given force in all the directions, it gives a good overview of the configuration, and it has useful properties that can make it invariant to the 2D viewpoint. Considering that using complementary forces (negative for repulsion, positive for attraction) should improve the description of complex spatial configurations, we propose to extend the force histogram to a panel of forces so as to make it a more complete descriptor. This gives a 2D descriptor that we called “(discrete) Force Banner” and which can be used as input of a classical Convolutional Neural Network (CNN), benefiting from their powerful performances, and reduced into more compact spatial features to use them in another system. As an illustration of its ability to describe spatial configurations, we used it to solve a classification problem aiming to discriminate simple spatial relations, but with variable configuration complexities. Experimental results obtained on datasets of synthetic and natural images with various shapes highlight the interest of this approach, in particular for complex spatial configurations.
Fast Superpixel-Based Clustering Algorithm for SAR Image Segmentation

In this letter, we propose a fast superpixel-based clustering algorithm (FSC) for synthetic aperture radar (SAR) image segmentation. First, the SAR image is over-segmented into superpixels by our previously proposed edge-aware superpixel generation method with one iteration merging (ESOM). Second, based on the obtained superpixels, the number of clusters is automatically selected by the density peak (DP) algorithm and knee point method instead of manual specification. Finally, the modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering with the generalized-likelihood ratio (GLR) dissimilarity is performed on the superpixels to generate the final segmentation result. Experimental results on two real SAR images show that the proposed method outperforms other state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. Moreover, our method is free of clustering parameters and achieves automatic SAR image segmentation.
Semantic Segmentation of Radio-Astronomical Images

In the context of next-generation radio-astronomical visual surveys, automated object detection and segmentation are necessary tasks to support astrophysics research from observations. Indeed, identifying manually astronomical sources (e.g., galaxies) from the daunting amount of acquired images is largely unfeasible, greatly limiting the huge potential of big data in the field. As a consequence, the astrophysics research has directed its attention, with increasing interest given the recent success in AI, to learning-based computer vision methods.