Reviewerly/exHarmony huggingface dataset:
Toward Equitable Participatory Design

CSCW, like many other academic communities, is reckoning with its roles, responsibilities, and practices amidst 2020's multiple pandemics of COVID-19, anti-Black racism, and a global economic crisis. Reviewing our work with data and communities demands we address harms from overexposure caused by surveillance or algorithmic bias and from underexposure caused by design that is insufficiently participatory and equitable. This workshop will elicit narratives of good and bad design and data work with communities, apply the lenses of equitable participatory design and data feminism to current CSCW projects and our global context, and develop practical outputs for supporting academics and practitioners in pursuit of democratic and just partnerships.
Entanglement–Coherence and Discord–Coherence analytical relations for X states

In this work, we derive analytical relations between Entanglement and Coherence as well as between Discord and Coherence, for Bell-diagonal states and for X states, evolving under the action of several noise channels: Bit Flip, Phase Damping and Depolarizing. We demonstrate that for these families, Coherence is the fundamental correlation, that is, Coherence is necessary for the presence of Entanglement and Discord.
Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data

The promises of advanced quantum computing technology have driven research in the simulation of quantum computers on classical hardware, where the feasibility of quantum algorithms for real-world problems can be investigated. In domains such as High Energy Physics (HEP) and Remote Sensing Hyperspectral Imagery, classical computing systems are held back by enormous readouts of high-resolution data. Due to the multi-dimensionality of the readout data, processing and performing pattern recognition operations for this enormous data are both computationally intensive and time-consuming. In this article, we propose a methodology that utilizes Quantum Haar Transform (QHT) and a modified Grover's search algorithm for time-efficient dimension reduction and dynamic pattern recognition in data sets that are characterized by high spatial resolution and high dimensionality. QHT is performed on the data to reduce its dimensionality at preserved spatial locality, while the modified Grover's search algorithm is used to search for dynamically changing multiple patterns in the reduced data set. By performing search operations on the reduced data set, processing overheads are minimized. Moreover, quantum techniques produce results in less time than classical dimension reduction and search methods. The feasibility of the proposed methodology is verified by emulating the quantum algorithms on classical hardware based on field programmable gate arrays (FPGAs). We present designs of the quantum circuits for multi-dimensional QHT and multi-pattern Grover's search. We also present two emulation techniques and the corresponding hardware architectures for this methodology. A high performance reconfigurable computer (HPRC) was used for the experimental evaluation, and high-resolution images were used as the input data set. Analysis of the methods and implications of the experimental results are discussed.
A Generalization of Otsu’s Method and Minimum Error Thresholding

We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.
Relative Pose from Deep Learned Depth and a Single Affine Correspondence

We propose a new approach for combining deep-learned non-metric monocular depth with affine correspondences (ACs) to estimate the relative pose of two calibrated cameras from a single correspondence. Considering the depth information and affine features, two new constraints on the camera pose are derived. The proposed solver is usable within 1-point RANSAC approaches. Thus, the processing time of the robust estimation is linear in the number of correspondences and, therefore, orders of magnitude faster than by using traditional approaches. The proposed 1AC+D (Source code: Open image in new window https://github.com/eivan/one-ac-pose) solver is tested both on synthetic data and on 110395 publicly available real image pairs where we used an off-the-shelf monocular depth network to provide up-to-scale depth per pixel. The proposed 1AC+D leads to similar accuracy as traditional approaches while being significantly faster. When solving large-scale problems, e.g. pose-graph initialization for Structure-from-Motion (SfM) pipelines, the overhead of obtaining ACs and monocular depth is negligible compared to the speed-up gained in the pairwise geometric verification, i.e., relative pose estimation. This is demonstrated on scenes from the 1DSfM dataset using a state-of-the-art global SfM algorithm.
Sarcasm detection in natural language processing

The striking property of satire is that it makes it difficult to bridge and bridge the gap between its literal and intended meaning. Identifying sarcastic behavior in the field of online social networks such as Facebook, Twitter, Instagram, surveys, etc. has turned into a fundamental task as they affect social and personal relationships. SARCASM detection is an important processing problem in natural language processing (NLP), which is needed for better understanding to serve as an interface for mutual communication between machines and humans. To understand this is to underline the basic problem behind it - being able to detect the contradiction. For this, a need arose to define contextual understanding and emotion. To accomplish this we need to do two things - gather a stack of target words that display sentiment shifts (sarcastic words) based on context; And with an objective word given an expression, how to naturally identify whether the objective word is used in an exact or sarcastic sense. Collecting information is done by the use of an information retrieval system, for example tweepee. For the latter, some distributed semantic methods are used to convert data into useful information and are then demonstrated using multiple classifier results that is, satire is identified.
Keeping secrets

Cybersecurity - and in particular secure communication - is a topic that is, to varying degrees, a part of many modern computer science (CS) curricula. Also, students encounter it as a phenomenon of their everyday lives, e.g. when using messengers that advertise encrypted communication. They will thus in most cases have preconceptions about what encrypted communication means that they will bring into the classroom. As in many cases, we expect these conceptions to be incomplete or even inappropriate.
Reading Experiences and Interest in Reading-Assistance Tools Among Deaf and Hard-of-Hearing Computing Professionals

Automatic Text Simplification (ATS) software replaces text with simpler alternatives. While some prior research has explored its use as a reading assistance technology, including some empirical findings suggesting benefits for deploying this technology among particular groups of users, relatively little work has investigated the interest and requirements of specific groups of users of this technology. In this study, we investigated the interests of Deaf and Hard-of-Hearing (DHH) individuals in the computing industry in ATS-based reading assistance tools, motivated by prior work establishing that computing professionals often need to read about new technologies in order to stay current in their profession. Through a survey and follow-up interviews, we investigate these DHH individuals’ reading practices, current techniques for overcoming complicated text, and their interest in reading assistance tools for their work. Our results suggest that these users read relatively often, especially in support of their work, and they were interested in tools to assist them with complicated texts. This empirical contribution provides motivation for further research into ATS-based reading assistance tools for these users, prioritizing which reading activities users are most interested in seeing application of this technology, as well as some insights into design considerations for such tools.
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking

The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows \(\approx \)8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.
Language and Obfuscation Oblivious Source Code Authorship Attribution

with patterns learned from known authors' source codes.In this paper, we present an efficient approach to learn a novel representation using deep metric learning.The existing state of the art approaches tokenize the source code and work on the keyword level, limiting the elements of style they can consider.Our approach uses the raw character stream of source code.It can examine keywords and different stylistic features such as variable naming conventions or using tabs vs. spaces, enabling us to learn a richer representation than other keyword-based approaches.Our approach uses a character-level Convolutional Neural Network (CNN).We train the CNN to map the input character stream to a dense vector, mapping the source codes authored by the same author close to each other.In contrast, source codes written by different programmers are mapped farther apart in the embedding space.We then feed these source code vectors into the K-nearest neighbor (KNN) classifier that uses Manhattan-distance to perform authorship attribution.We validated our approach on Google Code Jam (GCJ) dataset across three different programming languages.We prepare our large-scale dataset in such a way that it does not induce type-I error.Our approach is more scalable and efficient than existing methods.We were able to achieve an accuracy of 84.94% across 20,458 authors, which is more than twice the scale of any previous study under a much more challenging setting.
Application of Facial Symmetrical Characteristic to Transfer Learning

Most face detection and recognition tasks are based on the training of intact facial images and corresponding labels. Both the three‐dimensional (3D) structure and two‐dimensional (2D) appearance from the frontal view of human faces are bilaterally symmetrical in general. However, sometimes, illumination on the left and right halves of faces is uneven. In such cases, the symmetrical characteristic of human faces can facilitate expressing distinct identity information. This is because even if one side of the facial image is corrupted by noise, the opposite side can still be used for feature extraction. This paper proposes an automatic selection of the better half of the face using only a half‐face for identity recognition. Unlike the MegaFace challenge of recognizing millions of identities in the wild, this paper focuses on building recognition systems for a small number of people with fewer training images; the recognition system can, for example, build access control systems for research laboratory members or family members. This paper proposes an artificial face image construction method and a half‐face training strategy for transfer learning of pretrained conventional neural network models. Extensive experimental results show that the proposed method improves the performance of state‐of‐the‐art models by utilizing the symmetrical characteristics of human faces. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Reachability Analysis Based Tracking: Applications to Non-cooperative Space Object Tracking

This paper presents a reachability set based method for tracking maneuvering space objects in the presence of sparse measurements. The proposed approach invokes the Dynamic Data Driven Application Systems (DDDAS) paradigm by dynamically integrating model forecasts due to uncertainties in maneuver capabilities with collected sensor data to search and track for a non-cooperative satellite in a control theoretic framework. The typically large time interval between measurements from ground stations presents significant problems for tracking satellites that have maneuvered during this interval. Using reachability set propagation techniques and a particle filter update scheme, an intelligently guided search algorithm is developed. This algorithm enables the systematic reduction of likely reachable states until measurements of the target are acquired and traditional tracking techniques can be resumed. Numerical simulations of a space-based sensor tasking scenario are given, however, the method is generic and can be extended to ground-based sensors or a combination of both ground and space-based sensors.
Saline groundwater in the Buffels River catchment, Namaqualand, South Africa: A new look at an old problem

Namaqualand, South Africa, is a global biodiversity hotspot but local populations are affected by challenging economic conditions largely because of poor access to water. In this study groundwater types are characterised and sources of salts and salinisation processes are identified using hydrochemistry and δ18O, δ2H and 87Sr/86Sr data. Analysis of δ18O and δ2H data suggests that evaporation does not play a major role in salinisation of the groundwater. However, major ion chemistry and 87Sr/86Sr ratios indicate that salts present in the groundwater are linked to dry deposition of marine aerosols and ion-exchange reactions in soils in the alluvial aquifer systems. The hydrochemical variability of the groundwater in the basement aquifer system suggests that there are strong local controls linked to weathering processes in individual basement rock types. The region is also notable for the high density of heuweltjies, biophysical features associated with increased nutrient levels, associated with termite activity. Electromagnetic scanning as well as measurement of water-soluble soil electrical conductivity values on and off heuweltjies, show that heuweltjies are saline with salinity increasing with depth. The level of groundwater salinity correlates with the level of heuweltjie salinity. Precipitation records from the last 150 years provide support for the hypothesis that accumulated salts, and in particular, heuweltjie salts are flushed into the groundwater system during sporadic large volume precipitation events. Thus, heuweltjies and hence termite activity, could potentially represent a previously unrecognized contributor to groundwater salinisation across Namaqualand and in other parts of the world.
Automation of hemocompatibility analysis using image segmentation and supervised classification

The hemocompatibility of blood-contacting medical devices remains one of the major challenges in biomedical engineering and makes research in the field of new and improved materials inevitable. However, current in-vitro test and analysis methods are still lacking standardization and comparability, which impedes advances in material design. For example, the optical platelet analysis of material in-vitro hemocompatibility tests is carried out manually or semi-manually by each research group individually. As a step towards standardization, this paper proposes an automation approach for the optical platelet count and analysis. To this end, fluorescence images are segmented using Zach's convexification of the multiphase-phase piecewise constant Mumford–Shah model. The non-background components then need to be classified as platelet or no platelet. For this purpose, a supervised random forest is applied to feature vectors derived from the components using features like area, perimeter and circularity. With an overall high accuracy (> 93 %) and low error rates (≤ 5 %), the random forest achieves reliable results. This is supported by high areas under the receiver–operator characteristic curve (≥ 0.94) and the prediction–recall curve (≥ 0.77), respectively. We developed a novel method for a fast, user-independent and reproducible analysis of material hemocompatibility tests. The automatized analysis method overcomes the current obstacles in the way of standardized in-vitro material testing and is therefore a unique and powerful tool for advances in biomaterial research.
Pleasant Locomotion -- Towards Reducing Cybersickness using fNIRS during Walking Events in VR

Moving in virtual reality without causing cybersickness is still an unsolved and difficult problem, especially if the virtual space is much larger than the real space and the virtual reality environment asks for quick movements. Many methods to reduce cybersickness are proposed but most of them also reduce immersion. In this paper, we explore the use of fNIRS as an additional modality to detect the level of cybersickness for movement events in VR. We try to mitigate the sickness an individual feels by narrowing the field of vision based on the sickness level detected via measuring increased deoxygenated hemoglobin (Hb) with fNIRS. Our overall goal is to reduce cybersickness in virtual reality applications using physiological signals and appropriate adjustments with little to no impact on immersion.
Real Time Human Face Detection and Recognition Based on Haar Features

In this research, a system has been designed which is based on the approach to measure the effectiveness of different feature types. There are eight different Haar features. This work has considered only six of them. The implemented system has been trained by using a handpicked database. Additionally, Variable illumination condition and occlusion on the face are one of the biggest hinders of facial data processing. Therefore, a database has been built by considering these factors. This research contains sample images from various persons with various poses, and lighting condition etc. The detectors that have been used in this work which has been trained by using numerous stages of classifying such as weak classifier and strong classifier. If the image can pass through all stages of the detector, then the face will be recognized. After identifying, it will compare locally stored database for comparison. If it matches with the database, then the face will be detected automatically and the real-time output will be shown.
Research on 3D Reconstruction in Binocular Stereo Vision Based on Feature Point Matching Method

Computer vision as an important branch of computer science and artificial intelligence has made rapid progress in the past thirty years. Binocular stereo vision is one of the most important parts in computer vision. Binocular stereo vision can well simulate human eyes stereoscopic perception of three-dimensional objects, and has been widely used in various fields of industrial automation production and practical life. Binocular stereo vision technology is a comprehensive technology, whose knowledge includes optics, physics, image processing, computer, artificial intelligence and electronic technology, and other fields of content. Camera calibration and feature point matching are difficult and key points in binocular stereo vision technology. In this paper, the 3D reconstruction of binocular stereo vision based on feature point matching method is discussed. The main research includes the following contents: binocular stereo vision system principle, feature matching algorithm research and 3D reconstruction system implementation. This project puts forward a set of feasible algorithms and finally can get a good three-dimensional reconstruction effect based on the analysis and research of a series of algorithms.
Evaluation of Headset-based Viewing and Desktop-based Viewing of Remote Lectures in a Social VR Platform

We study experiences of students attending classes remotely from home using a social VR platform, considering both desktop-based and headset-based viewing of remote lectures. Ratings varied widely. Headset viewing produced higher presence overall. Strong negative correlations between headset simulator sickness symptoms and overall experience ratings, and some other ratings, suggest that the headset experience was much better for comfortable users than for others. Reduced sickness symptoms, and no similar correlations, were found for desktop viewing. Desktop viewing appears to be a good alternative for students not comfortable with headsets. Future VR systems are expected to provide more stable and comfortable visuals, providing benefits to more users.
PV Energy Performance in a Sustainable Campus

The challenge of photovoltaic integration as the basis of an energy generation system has been achieved and carried out by the University Autónoma de Cali, Colombia, using an avant-garde energy technology model. This innovative sustainable campus not only fulfills its purpose as an advanced model of a renewable energy integration system, it also aims at environmental research, e-mobility, and energy efficiency. This paper describes how the university implements the technological innovation of integrating the photovoltaic system installation in a university campus, showing its relevant contribution to the electricity generation in the campus buildings by analyzing the different electrical parameters together with the system performance indicators. The implementation of technological solutions has allowed the generation of a quantity of renewable energy within the campus, supplying a sustainable energy response based on energy efficiency and carbon emissions savings. This innovation has been applied following the international standards for the evaluation of the energy performance of photovoltaic systems (IEC 61724), reaching very optimal values for this type of renewable solution. In this paper, the dynamic monitoring of several parameters has been carried out in order to analyze the energy performance, and an energy simulation has been used to achieve optimal solutions and to obtain the perfect modeling of the system. This study shows how to evaluate the performance of an integration of a photovoltaic system in a smart university campus, according to international standards. It achieves complete viability due to its economic savings, energy efficiency and reduction of carbon emission.