Reviewerly/exHarmony huggingface dataset:
Offline events and online hate

Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.
Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles

Rare genetic disorders affect more than 6% of the global population. Reaching a diagnosis is challenging because rare disorders are very diverse. Many disorders have recognizable facial features that are hints for clinicians to diagnose patients. Previous work, such as GestaltMatcher, utilized representation vectors produced by a DCNN similar to AlexNet to match patients in high-dimensional feature space to support "unseen" ultra-rare disorders. However, the architecture and dataset used for transfer learning in GestaltMatcher have become outdated. Moreover, a way to train the model for generating better representation vectors for unseen ultra-rare disorders has not yet been studied. Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them. Therefore, we first analyzed the influence of replacing GestaltMatcher DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Additionally, we experimented with different face recognition datasets for transfer learning. Furthermore, we proposed test-time augmentation, and model ensembles that mix general face verification models and models specific for verifying disorders to improve the disorder verification accuracy of unseen ultra-rare disorders. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.
Quantum chemistry simulation of ground- and excited-state properties of the sulfonium cation on a superconducting quantum processor

We study the photodissociation of the sulfonium cation using quantum computing algorithms on IBM's hardware. We combine and extend methodologies to compute molecular excited states, photodissociation spectra, and molecular dissociation pathways.
Learning to predict RNA sequence expressions from whole slide images with applications for search and classification

Abstract Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models have also been used to extract molecular features from whole slide images. Although molecular tests carry rich information, they are often expensive, time-consuming, and require additional tissue to sample. In this paper, we propose tRNAsformer, an attention-based topology that can learn both to predict the bulk RNA-seq from an image and represent the whole slide image of a glass slide simultaneously. The tRNAsformer uses multiple instance learning to solve a weakly supervised problem while the pixel-level annotation is not available for an image. We conducted several experiments and achieved better performance and faster convergence in comparison to the state-of-the-art algorithms. The proposed tRNAsformer can assist as a computational pathology tool to facilitate a new generation of search and classification methods by combining the tissue morphology and the molecular fingerprint of the biopsy samples.
Detecting Outliers with Foreign Patch Interpolation

In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model.&lt;br&gt;Our code is available at &lt;a href='https://github.com/jemtan/FPI'&gt;https://github.com/jemtan/FPI&lt;/a&gt;
Neuro-heuristic analysis of surveillance video in a centralized IoT system

Security systems are based on the monitoring of specific areas of the facility. The cameras record the selected place for the whole day. Unfortunately, it is difficult to automatically analyze the recorded situations mainly through manual analysis. In this paper, we propose an innovative automatic monitoring data analysis system. To minimize the amount of processed data, a heuristic-based method is proposed to analyze frames. Heuristic algorithm is adapted to image analysis. If the algorithm detects significant changes in pixel values, the frame is sent further to the convolutional neural network. The proposed solution is based on centralized federated learning that allows training a shared model on a local dataset. This guarantees surveillance recordings’ privacy with a shared model. The proposal is a hybrid solution that was presented as a mathematical model, tested and compared with other known solutions. Based on conducted experiments, the proposed image processing system minimizes the number of calculations by the hybrid approach that can be valuable for IoT applications. The proposed solution is more effective than the existing solution due to the use of classifiers for the analysis of single frames.
Virtual reality in tourism: adoption scepticism and resistance

Purpose Since its inception, virtual reality (VR) technologies have been widely applied in the tourism industry, given their competitive advantages in offering users a novel experience that makes an artificial environment or scene feel like the real world. As a result, VR has been reported as a highly sought-after technology, as it can offer a new way to engage tourists and enhance their travel experience. However, the adoption of VR by senior tourists (who are one of the potential groups of tourists) is still relatively low in the tourism context. Against this backdrop, this study aims to respond to the deficiency in the literature by examining the impact of barriers that contribute to senior tourists’ resistance and scepticism towards VR in tourism. Design/methodology/approach Using a cross-sectional approach, the data for this study was collected from 600 eligible senior tourists through survey questionnaires. To obtain valid and reliable responses, a purposive sampling technique with screening criteria was undertaken in this study. Upon data cleaning, the data analysis was performed on 546 samples using statistical software packages such as SPSS and the AMOS covariance-based structural equation modelling technique. Findings Grounded on the innovation resistance theory, the outcome of this study put forward the significance of technical and psychological barriers in explaining senior tourists’ resistance and scepticism towards the use of VR in tourism. In particular, perceived risk, perceived incompatibility, technology anxiety, inertia and a lack of human interaction were found to be associated with senior tourists’ scepticism and resistance towards VR usage in tourism. However, perceived complexity was not found to have a significant relationship with resistance. Moreover, both scepticism and resistance were reported to influence non-adoption intentions on VR. Originality/value This study is one of the few studies that examined the less explored territory of the tourism literature concerning the non-adoption of contemporary innovation (i.e. VR technology) for virtual tourism purposes. Moreover, this study also focuses on the perspective of senior tourists, which represents a promising market segment in the tourism sector. This study successfully contributed to the existing literature by offering empirical insights and highlighting the barriers that lead to users’ scepticism and resistance towards using VR for tourism, which could directly influence their non-adoption intention and the success of virtual tourism.
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection

Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S- T) framework has proved to be effective in solving this chal-lenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multilevel information. In this study, we propose an improved model called DeS TSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we intro-duce a denoising procedure that allows the student net-work to learn more robust representations. From synthet-ically corrupted normal images, we train the student net-work to match the teacher network feature of the same images without corruption. Second, to fuse the multi-level S-T features adaptively, we train a segmentation network with rich supervision from synthetic anomaly masks, achieving a substantial performance improvement. Experiments on the industrial inspection benchmark dataset demonstrate that our method achieves state-of-the-art performance, 98.6% on image-level AUC, 75.8% on pixel-level average precision, and 76.4% on instance-level average precision.
Banking in the metaverse: a new frontier for financial institutions

Purpose Technological advancements have catalyzed disruption in the banking sector. The impact of the metaverse on the banking sector is no exception. In view of this, the current paper aims to provide valuable insights into four key areas (i.e. corporate banking, retail banking, banking employees and public policy) that the metaverse could significantly disrupt. Design/methodology/approach Insights into four key areas of the banking sector that the metaverse could significantly impact were gathered from various invited contributors. Findings The invited contributors first introduce the association between their respective key areas with the metaverse. Subsequently, the opportunities and challenges relevant to the key areas were identified. Finally, future research agendas were proposed for the attention of all relevant stakeholders. Originality/value The metaverse's impact on key areas of the banking sector is discussed in this paper. Following the metaverse's potentially wide application in the banking sector, insights from the invited contributions offer great value to the relevant stakeholders.
The Independent Sign Bias: Gaining Insight from Multiple Linear Regression

As electronic data becomes widely available, the need for tools that help people gain insight from data has arisen. A variety of techniques from statistics, machine learning, and neural networks have been applied to databases in the hopes of mining knowledge from data. Multiple regression is one such method for modeling the relationship between a set of explanatory variables and a dependent variable by fitting a linear equation to observed data. Here, we investigate and discuss some factors that influence whether the resulting regression equation is a credible model of the data.
The Impact of Participation in VEX Robotics Competition on Middle and High School Students’ Interest in Pursuing STEM Studies and STEM-related Careers

Mathematics, and Computing (CEISMC) at Georgia Institute of Technology.Alemdar has experience evaluating programs that fall under the umbrella of educational evaluation, including K-12 educational curricula, after-school programs, and comprehensive school reform initiatives.Across these evaluations, she has used a variety of evaluation methods, ranging from multi-level evaluation plan designed to assess program impact to methods such as program monitoring designed to facilitate program improvement.Her leadership evaluation work includes serving as a lead evaluator on NASA's electronic professional development network (ePDN), a new initiative dedicated to preparing teachers to engage their students in STEM (science, technology, engineering and mathematics) through the use of NASA-developed learning materials and resources.She also serves a the lead evaluator on several NSF-funded Noyce Scholarship programs.She has direct experience leading or contributing to evaluations of leadership, and STEM-related innovations.
Tablet Pc Applications In A Large Engineering Program

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Tablet PC Applications in a Large Engineering Program Abstract A Tablet PC computing initiative was announced by Virginia Tech in summer 2006. Use of Tablet PC-based instruction was implemented in a freshman engineering course with about 1200 students in fall 2006. Examples of instructional activities included electronic note taking, use of inking features to review homework solutions and completing skeleton PowerPoint slides designed to increase the engagement level of students in a large classroom, and setting up online collaboration sessions to do group design project and problem solving activities. In addition, the Tablets helped students maintain an electronic log of their individual and group efforts in completing a 6-week long sustainable energy design project. A summary of assessment data from in-class clicker-based questions and end of semester course exit survey is presented to discuss the effectiveness of Tablet based instruction. Students liked electronic note taking features and quickly became proficient at setting up collaborative sessions using Tablet PC for design and problem solving activities and thought the Tablet PC was a good tool for that purpose. 1. Introduction The College of Engineering (COE) at Virginia Tech announced a new Tablet PC computing initiative in summer 2006 for incoming freshmen. This initiative made it mandatory for all engineering freshmen (~1200 each year) to own a Tablet PC starting fall 2006 for use in engineering instruction. With this decision, the COE became the largest and first public college of engineering to require the Tablet PCs for the engineering freshmen. The college only recommended the specifications of the Tablet and students bought their machines from a number of manufacturers. Figure 1 shows the response to an exit survey question (~220 respondents) showing ownership of Tablets by brand. It may be mentioned that the Dell computers in this figure were laptops owned by students who were repeating the course. In 1984, the COE was the first public institution in the U.S. to require its entering engineering freshmen to own a personal computer. In 2002, the college moved to a laptop requirement and many of its academic buildings were outfitted to offer wireless communication capabilities[1]. Tablet PC Ownership By Brand 2% 1% 2% Fujitsu Toshiba 17% Gateway 39% HP Asus Dell 39% Figure 1: Tablet PC Ownership by Engineering Freshmen at a Large Engineering Program. Note: Dell computers represent laptops.
Shaping graph pattern mining for financial risk

In recent years graph pattern mining took a prominent role in knowledge discovery in many scientific fields. From Web advertising to biology and finance, graph data is ubiquitous making pattern-based graph tools increasingly important. When it comes to financial settings, data is very complex and although many successful approaches have been proposed often they neglect the intertwined economic risk factors, which seriously affects the goodness of predictions. In this paper, we posit that financial risk analysis can be leveraged if structure can be taken into account by discovering financial motifs. We look at this problem from a graph-based perspective in two ways, by considering the structure in the inputs, the graphs themselves, and by taking into account the graph embedded structure of the data. In the first, we use gBoost combined with a substructure mining algorithm. In the second, we take a subspace learning graph embedded approach. In our experiments two datasets are used: a qualitative bankruptcy data benchmark and a real-world French database of corporate companies. Furthermore, we propose a graph construction algorithm to extract graph structure from feature vector data. Finally, we empirically show that in both graph-based approaches the financial motifs are crucial for the classification, thereby enhancing the prediction results.
Occlusion-Aware Method for Temporally Consistent Superpixels

A wide variety of computer vision applications rely on superpixel or supervoxel algorithms as a preprocessing step. This underlines the overall importance that these approaches have gained in recent years. However, most methods show a lack of temporal consistency or fail in producing temporally stable superpixels. In this paper, we present an approach to generate temporally consistent superpixels for video content. Our method is formulated as a contour-evolving expectation-maximization framework, which utilizes an efficient label propagation scheme to encourage the preservation of superpixel shapes and their relative positioning over time. By explicitly detecting the occlusion of superpixels and the disocclusion of new image regions, our framework is able to terminate and create superpixels whose corresponding image region becomes hidden or newly appears. Additionally, the occluded parts of superpixels are incorporated in the further optimization. This increases the compliance of the superpixel flow with the optical flow present in the scene. Using established benchmark suites, we show the performance of our approach in comparison to state-of-the-art supervoxel and superpixel algorithms for video content.
Cross-lingual word analogies using linear transformations between semantic spaces

Abstract The ability to represent the meaning of words is one of the core parts of natural language understanding (NLU), with applications ranging across machine translation, summarization, question answering, information retrieval, etc. The need for reasoning in multilingual contexts and transferring knowledge in cross-lingual systems has given rise to cross-lingual semantic spaces, which learn representations of words across different languages. With growing attention to cross-lingual representations, it has became crucial to investigate proper evaluation schemes. The word-analogy-based evaluation has been one of the most common tools to evaluate linguistic relationships (such as male-female relationships or verb tenses) encoded in monolingual meaning representations. In this paper, we go beyond monolingual representations and generalize the word analogy task across languages to provide a new intrinsic evaluation tool for cross-lingual semantic spaces. Our approach allows examining cross-lingual projections and their impact on different aspects of meaning. It helps to discover potential weaknesses or advantages of cross-lingual methods before they are incorporated into different intelligent systems. We experiment with six languages within different language families, including English, German, Spanish, Italian, Czech, and Croatian. State-of-the-art monolingual semantic spaces are transformed into a shared space using dictionaries of word translations. We compare several linear transformations and rank them for experiments with monolingual (no transformation), bilingual (one semantic space is transformed to another), and multilingual (all semantic spaces are transformed onto English space) versions of semantic spaces. We show that tested linear transformations preserve relationships between words (word analogies) and lead to impressive results. We achieve average accuracy of 51.1%, 43.1%, and 38.2% for monolingual, bilingual, and multilingual semantic spaces, respectively.
Flexible co-manipulation and transportation with mobile multi-robot system

Purpose The purpose of this paper is to address optimal positioning of a group of mobile robots for a successful manipulation and transportation of payloads of any shape. Design/methodology/approach The chosen methodology to achieve optimal positioning of the robots around the payload to lift it and to transport it while maintaining a geometric multi-robot formation is presented. This appropriate configuration of the set of robots is obtained by combining constraints ensuring stable and safe lifting and transport of the payload. A suitable control law is then used to track a virtual structure in which each elementary robot has to keep its desired position with respect to the payload. Findings An optimal positioning of mobile robots around a payload to ensure stable co-manipulation and transportation task according to stability multi-criteria constraints. Simulation and experimental results validate the proposed control architecture and strategy for a successful transportation task based on virtual structure navigation approach. Originality/value This paper presents a new strategy for co-manipulation and co-transportation task based on a virtual structure navigation approach. An algorithm for optimal positioning of mobile robots around a payload of any mass and shape is proposed while ensuring stability during the whole process by respecting multi-criteria task stability constraints.
Semantic Bottleneck for Computer Vision Tasks

This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point in which the representation of the image is entirely expressed with natural language, while retaining the efficiency of numerical representations. We show that our approach is able to generate semantic representations that give state-of-the-art results on semantic content-based image retrieval and also perform very well on image classification tasks. Intelligibility is evaluated through user centered experiments for failure detection.