Reviewerly/exHarmony huggingface dataset:
Development of Privacy Features on Anecdata.org, a Free Citizen Science Platform for Collecting Datasets for Climate Change and Related Projects

The Anecdata website and its corresponding mobile app provide unique features to meet the needs of a wide variety of diverse citizen science projects from across the world. The platform has been developed with the help of continuous feedback from community partners, project leaders, and website users and currently hosts more than 200 projects. Over 8,000 registered users have contributed more than 30,000 images and over 50,000 observations since the platform became open to the public in 2014. From its inception, one of the core tenets of Anecdata's mission has been to make data from citizen science projects freely accessible to project participants and the general public, and in the platform's first few years, it followed a completely open data access model. As the platform has grown, hosting ever more projects, we have found that this model does not meet all project needs, especially where endangered species, property access rights, participant safety in the field, and personal privacy are concerned. We first introduced features for data and user privacy as part of "All About Arsenic," a National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) Science Education Partnership Award (SEPA)-funded project at MDI Biological Laboratory, which engages middle and high school teachers and students from schools across Maine and New Hampshire in sampling their home well water for analysis of arsenic and other heavy metals. In order to host this project on Anecdata, we developed features for spatial privacy or "geoprivacy" to conceal the coordinates of samplers' homes, partial data redaction tools we call "private fields" to withhold certain sample registration questions from public datasets, and "participant anonymity" to conceal which user account uploaded an observation. We describe the impetus for the creation of these features, challenges we encountered, and our technical approach. While these features were originally developed for the purposes of a public health and science literacy project, they are now available to all project leaders setting up projects on Anecdata.org and have been adopted by a number of projects, including Mass Audubon's Eastern Meadowlark Survey, South Carolina Aquarium's SeaRise, and Coastal Signs of the Seasons (SOS) Monitoring projects.
Object Detection with Deep Learning for Underwater Environment

In this research we have investigated the usage of deep learning algorithms for object detection in underwater environment and specifically we have employed YOLOv3 algorithm in our study. Details of the algorithm and experimental results are presented. We used available underwater database for training and investigated the method by experimenting to detect and identify the type of the fish in an aquarium in the lab. The results are also explained in this paper.
Computer Graphics and Animation at The Ohio State University

The research and production of computer graphics imagery and animation at The Ohio State University started with the artistic work of Prof. Charles Csuri. He developed the Computer Graphics Research Group in response to the award of a National Science Foundation Grant in 1974, and the group transferred its technology to a commercial production effort, Cranston/Csuri Productions, Inc., in 1981. CGRG evolved into the Advanced Computing Center for the Arts and Design in 1987. This article provides an historical review of the significant activities of these groups.
Startup Qilimanjaro—towards a European full-stack coherent quantum annealer platform

Abstract Qilimanjaro Quantum Tech is the full-stack quantum spin-off of three research institutions, the Barcelona Supercomputing Center (BSC), the Institute for High Energy Physics (IFAE) and the University of Barcelona (UB). The company addresses the emerging quantum readiness demand from industry and academia, by providing both algorithmic development services as well as access to a new coherent quantum annealer platform, a special purpose quantum computer. Qilimanjaro is a member of the AVaQus European Commission’s H2020 FET-Open consortium for coherent quantum annealing development, which is led by one of Qilimanjaro’s founders at IFAE. A special feature of Qilimanjaro are its funding sources being exclusively international client contracts.
Geological and geochemical characterization of the Nanlia and Makorongo gold prospects, Mozambique Belt, northeastern Mozambique

Northern Mozambique hosts numerous occurrences of gold mineralization in metamorphic terranes, which are little known because of the insufficient studies. In this work, we describe the main geological and geochemical characteristics of the Nanlia and Makorongo gold prospects located in the Namuno district, northeastern Mozambique. We report the mode of occurrence of gold, petrography and mineralogy of the host rocks and ores, fluid inclusion characteristics and stable isotope data, to understand the genesis of gold mineralization in the study area. The gold mineralization in the Nanlia and Makorongo prospects is hosted by the Neoproterozoic Xixano Metamorphic Complex in the Mozambique Belt, which is composed of mafic granulite, amphibolite, paragneiss and marble. The host rock amphibolite consists mainly of amphibole, plagioclase, biotite and minor quartz, titanite and sulfides. The gold mineralization is associated with E-W to ENE-WSW trending quartz veins, oriented parallel to low angle to the foliation of the host rock amphibolite. The ore mineralogy consists of pyrite, pyrrhotite, chalcopyrite, magnetite, galena and sphalerite, with minor tellurides, xilingolite, electrum and native gold. Mineralized quartz veins in the Nanlia and Makorongo prospects host two types of fluid inclusions which locally coexist in the same assemblage: type 1, one-phase CO2 inclusions with final melting temperature between −58 and −52 °C; and type 2, two-phase H2O + NaCl (±CO2 ± N2 ± CH4) inclusions with homogenization temperature ranging from 246 to 370 °C and 239 to 382 °C in the Nanlia and Makorongo prospects, respectively, and salinity of 12–21 wt% NaCl eq. Pressure and temperature (P-T) conditions of ore-formation were estimated from fluid inclusions and sphalerite geobarometry at 420–620 °C and 160–280 MPa, and 330–440 °C and 150–200 MPa in the Nanlia and Makorongo prospects, respectively. The Nanlia and Makorongo gold prospects are classified as orogenic-type gold deposits. We suggest that gold mineralization was caused by an aqueous-carbonic fluid with low-moderate salinity originated by devolatilization during prograde metamorphism of underlying rocks. The gold mineralization post-dates the peak of metamorphism of the host rocks, thus formed during the retrograde metamorphism of the host rocks. The precipitation of gold in the Nanlia and Makorongo prospects was caused by fluid phase separation induced by pressure drop as the mineralizing fluid migrated to shallower depths.
Patrons, Vendors, and Delivery: Print Demand Driven Acquisitions at the University of Kansas

In 2010 the University of Kansas Libraries (KUL) began a print purchase on demand acquisition (PDDA) program. This new method's adoption was to both help improve patron discovery and compensate for an approval plan reduction necessitated by a tight budget. Today, purchase on demand programs are not uncommon. However, there is little research or scholarship concerning PDDA programs because most focus on unmediated electronic programs. This article highlights the authors' quest to better understand the actual materials purchased through KUL's PPDA program and how they were obtained. By analyzing three years of PDDA acquired titles the authors determined basic bibliographic characteristics of selected titles, including, which vendors supplied materials, the speed with which the titles became available to patrons, why items were delayed, and other factors that affect getting books to patrons.
Towards harmonized regional style transfer and manipulation for facial images

Regional facial image synthesis conditioned on semantic mask has achieved great success using generative adversarial networks. However, the appearance of different regions may be inconsistent with each other when conducting regional image editing. In this paper, we focus on the problem of harmonized regional style transfer and manipulation for facial images. The proposed approach supports regional style transfer and manipulation at the same time. A multi-scale encoder and style mapping networks are proposed in our work. The encoder is responsible for extracting regional styles of real faces. Style mapping networks generate styles from random samples for all facial regions. As the key part of our work, we propose a multi-region style attention module to adapt the multiple regional style embeddings from a reference image to a target image for generating harmonious and plausible results. Furthermore, we propose a new metric "harmony score" and conduct experiments in a challenging setting: three widely used face datasets are involved and we test the model by transferring the regional facial appearance between datasets. Images in different datasets are usually quite different, which makes the inconsistency between target and reference regions more obvious. Results show that our model can generate reliable style transfer and multi-modal manipulation results compared with SOTAs. Furthermore, we show two face editing applications using the proposed approach.
Electronic culinary reservations based on Android with the Scrum methodology and Firebase database

Abstract Culinary preservation activities in the process of ordering food or drinks in restaurants are the initial process in restaurant or cafe activities, the problem that occurs is the buildup of orders on the order note, and sometimes customer order errors or changes to orders by customers. The purpose of this study is to make electronic android-based culinary reservations using firebase as a realtime integrated database between tables, kitchens, and cashiers. The research method used is Agile Scrum with the product backlog, sprint, daily scrum, and sprint review stages with modeling using Unified Modeling Language diagrams. This study took a sample in one restaurant in Garut Regency. This research resulted in a prototype of a reservation application that can be used on Android smartphones for customer tables, kitchens section, and cashiers. This culinary reservation application can help improve service to customers by increasing order times faster, reducing the occurrence of booking errors, and reducing paper usage.
Waveglove: Transformer-Based Hand Gesture Recognition Using Multiple Inertial Sensors

Hand Gesture Recognition (HGR) based on inertial data has grown considerably in recent years, with the state-of-the-art approaches utilizing a single handheld sensor and a vocabulary comprised of simple gestures. In this work we explore the benefits of using multiple inertial sensors. Using WAVE-GLOVE, a custom hardware prototype in the form of a glove with five inertial sensors, we acquire two datasets consisting of over 11000 samples. To make them comparable with prior work, they are normalized along with 9 other publicly available datasets, and subsequently used to evaluate a range of Machine Learning approaches for gesture recognition, including a newly proposed Transformer-based architecture. Our results show that even complex gestures involving different fingers can be recognized with high accuracy. An ablation study performed on the acquired datasets demonstrates the importance of multiple sensors, with an increase in performance when using up to three sensors and no significant improvements beyond that.
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks

Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality. High-dimensional data are usually in a coherent structure and make the data in relatively small true degrees of freedom. There are global and local dimensionality reduction methods to alleviate the problem. Most of existing methods for local dimensionality reduction obtain an embedding with the eigenvalue or singular value decomposition, where the computational complexities are very high for a large amount of data. Here we propose a novel local nonlinear approach named Vec2vec for general purpose dimensionality reduction, which generalizes recent advancements in embedding representation learning of words to dimensionality reduction of matrices. It obtains the nonlinear embedding using a neural network with only one hidden layer to reduce the computational complexity. To train the neural network, we build the neighborhood similarity graph of a matrix and define the context of data points by exploiting the random walk properties. Experiments demenstrate that Vec2vec is more efficient than several state-of-the-art local dimensionality reduction methods in a large number of high-dimensional data. Extensive experiments of data classification and clustering on eight real datasets show that Vec2vec is better than several classical dimensionality reduction methods in the statistical hypothesis test, and it is competitive with recently developed state-of-the-art UMAP.
Lost in Co-curation

Online tools enable users to co-create artifacts remotely. However, creative collaborations can also occur for the social process of collaboration itself, for which measures of success and engagement expectations can be more ambiguous, and individuals' dedication and social dynamics more important. Co-curating music in collaborative playlists (CPs) is one example of creative collaboration that encompasses both roles, and can therefore have more subtleties within its interactions. We conducted two studies using online surveys to understand perceived comfort with and hesitation toward the social dynamics embedded in CPs. Differences in collaborators' ownership perceptions toward CPs and their comfort in interacting with these CPs emerged. We also found a varying desire for situated communication, dependent upon the action taken and perceived ownership (of both a CP and the songs contained), with more users expecting greater comfort when a communication channel exists. From these results, we present four design considerations for more positive and engaging experiences in creative online co-curation.
How Ethical Are Persuasive Design Practices? A Proposal for Assessment of Ethics in HCI Design

Ever since bounds on human rationality and cognitive biases in decision contexts have been reported, designers have exploited these weaknesses to yield conversion by creating persuasive HCI designs. Such design practices have been widely reported to be effective in influencing user decision making. However, the exploitation of a cognitive bias compromises the cognitive autonomy of an individual. This paper argues for the need of ethical assessment of persuasive design practices which undermine a user’s cognitive autonomy. The paper proposes a model for persuasive information design in human–computer interaction (HCI PID model) and derives from it a framework to assess the ethics of persuasive design practices. In this framework, five design parameters and their twelve subcomponents have been proposed as measures of an HCI system’s conduciveness to autonomous decision making without unduly influencing a user. The paper proposes a scoring methodology to assess design features of HCI systems on the proposed parameters. The proposed assessment framework was used by 20 participants to evaluate five mobile applications on features that are relevant to autonomous decision making. It was observed that the proposed framework has effectively helped the assessors to identify unethically persuasive design features.
Generative repair: everyday infrastructuring between DIY citizen initiatives and institutional arrangements

DIY citizen initiatives exploring everyday-life practices such as collective making or repair are on the rise. These are characterised by the resources, local knowledge, and volunteer labour they mobilise but also by fragility of long-term sustainment. To understand what is at stake in sustaining them, the authors conducted a case study of a longstanding DIY citizen initiative in Berlin, cross-checking the findings by interviewing employees of two supporting institutional arrangements and organisers of three further DIY initiatives nearby. The study revealed how the DIY initiatives tie together roles and resources to provide concrete everyday infrastructure for citizens while dependent on resources such as space, insurance, legitimacy, and knowledge to navigate surrounding bureaucracy, provided largely via various institutional arrangements. Conversely, these established institutional arrangements benefit from DIY initiatives' local knowledge, authenticity, and expertise. Finally, both sides wish to gain fuller mutual understanding and dialogue-related competencies. The findings highlight the generative repair, performed by both citizen initiatives and the institutional arrangements, that makes their change agendas relevant and lasting. They also point to potential value from considering some of these attempts as forms of infrastructuring with relevance for contemporary participatory design practices.
ARank: Toward specific model pruning via advantage rank for multiple salient objects detection

Pruning unnecessary regions from weight kernels and filters used in convolutional nerual networks has proven to be effective for model compression. However, prior studies have often over-optimized for generic application areas, which are not ideal for specific problems in salient object detection. In this study, a novel pruning strategy denoted advantage rank (ARank) is developed for multiple salient object detection (Mul-SOD), in which salient priority criterion (SPC) is used to evaluate parameter contributions. The proposed SPC is a measurable standard for object priority that can be quantified and calculated using ARank. Simulation experiments demonstrate that Mul-SOD can be optimized by removing interference from low-SPC parameters. ARank effectively decreased interference and distinguished high-priority salient objects from multiple background objects.
An Improved Algorithm of D-S Evidence Fusion

Abstract In the theory of evidence fusion, it is a very important task to deal with the conflicts between evidences correctly. Based on the credibility degree of evidence proposed in [10], in order to increase the credibility degree of each group of evidence, the concept of falsity is introduced, which is another concept to represent the conflict between two groups of evidence, this treatment makes up for the deficiency of normalized evidence weight coefficient when one group of evidence credibility degree is zero. Because we consider not only the credibility degree of evidence, but also the falsity of evidence, the weight coefficient of unreliable information is effectively reduced. The numerical simulation results show that the algorithm has the advantages of simple idea, fast convergence speed and strong focusing.
Taxonomy of Cyber Security in Medical Science

Recently, an incrementing number of cyber-attacks in the medical field have resulted in great losses. In the health care industry, since medical information plays an essential role in human health. To prevent from these types of cyber attacks, there are various steps are taken by Hospital Institute Management Systems (HIMS) regarding to its security which is also called cyber security as it is related to computer technology. In response, we have been discussing this misconduct and trying to increase the awareness of researchers, but it seems that there is a lack of research that presents guidelines for editors to help them protect themselves against these threats In this paper, we understand more about cyber security in medical field and a brief overview of cyber securities is presented along with building of cyber securities is explained for the better understanding. We then explain the details involved in each type of attack.
Artifact-Free Single Image Defogging

In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.
Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognition

Although deep learning has solved difficult problems in visual pattern recognition, it is mostly successful in tasks where there are lots of labeled training data available. Furthermore, the global back-propagation based training rule and the amount of employed layers represents a departure from biological inspiration. The brain is able to perform most of these tasks in a very general way from limited to no labeled data. For these reasons it is still a key research question to look into computational principles in the brain that can help guide models to unsupervisedly learn good representations which can then be used to perform tasks like classification. To that end, we start by recalling four key brain-inspired principles that relate to simple vision: modeling ”whats” and ”wheres” separately; including a time component; context dependency; and layer-wise learning. Then, we take these principles and use them to convey an a priori structure to our model that makes the learning problem easier. With that, our model is able to generate such high quality representations for the MNIST data set. We compare the obtained results with similar recent works and verify extremely competitive results.
Scaling of multi-core quantum architectures

In the quest of large-scale quantum computers, multi-core distributed architectures are considered a compelling alternative to be explored. A crucial aspect in such approach is the stringent demand on communication among cores when qubits need to interact, which conditions the scalability potential of these architectures. In this work, we address the question of how the cost of the communication among cores impacts on the viability of the quantum multi-core approach. Methodologically, we consider a design space in which architectural variables (number of cores, number of qubits per core), application variables for several quantum benchmarks (number of qubits, number of gates, percentage of two-qubit gates) and inter-core communication latency are swept along with the definition of a figure of merit. This approach yields both a qualitative understanding of trends in the design space and companion dimensioning guidelines for the architecture, including optimal points, as well as quantitative answers to the question of beyond which communication performance levels the multi-core architecture pays off. Our results allow to determine the thresholds for inter-core communication latency in order for multi-core architectures to outperform single-core quantum processors.