Reviewerly/exHarmony huggingface dataset:
AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data.
Multilingual prediction of

Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
COMPARING INNOVATIVE XR SYSTEMS IN CULTURAL HERITAGE. A CASE STUDY

Abstract. The technological advances made in the recent years in the field of Information and Communication Technologies (ICT) have led to a revolution in many adjacent areas. One of these is Cultural Heritage (CH), as the new array of XR technologies (a concept integrating Augmented, Mixed and Virtual Reality) is offering new interaction possibilities. The purpose of this paper is to compare several such technologies at application level, and to classify them based on common features such as Interaction, Manipulability, Ease of Use and others. The study presented in this paper was carried out during the H2020 project eHERITAGE (‘Expanding the Research and Innovation Capacity in Cultural Heritage Virtual Reality Applications’). The action had as output multiple XR systems/applications. We compare 4 of them: a smartphone-based AR application, a digital book with 3D elements deployed on a large touchscreen, a large holographic display and a bow simulator built using a VR headset and a mechatronic system. We evaluate these innovative systems in the context of the 2 educational events, when over 4000 participants interacted with our team. Results show that systems with greater interaction and immersion features were preferred over the others. Furthermore, Mixed Reality was preferred over Virtual and Augmented Reality.
Racism and hate speech – A critique of Scanlon’s Contractual Theory

The First Amendment is an important value in American liberal polity. Under this value, racism, hate speech and offensive speech are protected speech. This article scrutinizes one of the clear representatives of the American liberal polity – Thomas Scanlon. The paper tracks the developments in his theory over the years. It is argued that Scanlon’s arguments downplay tangible harm that speech might inflict on its target victim audience. Scanlon’s distinction between participant interests, audience interests, and the interests of bystanders is put under close scrutiny. The article criticizes viewpoint neutrality and suggests a balancing approach, further arguing that democracy is required to develop protective mechanisms against harm-facilitating speech as well as profound offences. Both should be taken most seriously.
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization

Tobias Falke, Iryna Gurevych. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Coarse-to-Fine Image DeHashing Using Deep Pyramidal Residual Learning

Image dehashing refers to the process of inferring images by inverting image hashes. Recently, image dehashing from real-valued image retrieval hashes is shown feasible using deep convolutional neural networks. However, the perceptual quality of dehashed images is challenged when real-valued hashes are quantized to less bits. Besides, the scalability to larger or color image dehashing is limited in the previous dehashing network. To this end, we propose a pyramidal long-range residual-learning network (PyLRR-Net). PyLRR-Net is a pyramidal image reconstruction network to dehash images in a progressive manner. At each image scale, we design and insert a long-range residual block to refine the coarse image reconstruction leveraging deep residual learning. Experiments on both grayscale and color image datasets show that the proposed PyLRR-Net outperforms previous work in terms of image dehashing quality, scalability, and flexibility for large and color image dehashing problems.
Average reward reinforcement learning with unknown mixing times.

We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria. Existing algorithms explicitly require an upper bound on the mixing time. In contrast, we build on ideas from Markov chain theory and derive sampling algorithms that do not require such an upper bound. For these algorithms, we provide theoretical bounds on their sample-complexity and running time.
Investing in brain-based memory leads to decreased use of technology-based memory.

Humans frequently use external (environment-based) strategies to supplement their internal (brain-based) thought. In the memory domain, whether to solve a problem using external or internal retrieval depends on the accessibility of external information, judgment of mnemonic ability, and on the problem's visual features. It likely also depends on the accessibility of internal information. Here, we asked whether internal accessibility contributes to strategy choice even when visual features bear no information on internal accessibility. Specifically, 114 participants were to validate alphanumerical equations (e.g., A + 2 = C) whose visual appearance (Addends 2, 3, or 4) signified different difficulty levels. First, some equations were presented more frequently than others, allowing participants to establish efficient internal access to the correct solution via memory retrieval rather than counting up the alphabet. Second, participants viewed the equations again but could access the correct solution externally using a computer mouse. We hypothesized that external strategy use should selectively decrease for frequently learned equations and irrespectively of the task's visual features. Results mostly confirm our hypothesis. Exploratory analyses further suggest that participants partially used a sequential try-internal-retrieval-first mechanism to establish the adaptive behavior. Implications for intervention methods aimed at improving interactive cognition are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
PCA-driven Hybrid network design for enabling Intelligence at the Edge.

The recent advent of IOT has increased the demand for enabling AI-based edgecomputing in several applications including healthcare monitoring systems,autonomous vehicles etc. This has necessitated the search for efficientimplementations of neural networks in terms of both computation and storage.Although extreme quantization has proven to be a powerful tool to achievesignificant compression over full-precision networks, it can result insignificant degradation in performance for complex image classification tasks.In this work, we propose a Principal Component Analysis (PCA) drivenmethodology to design mixed-precision, hybrid networks. Unlike standardpractices of using PCA for dimensionality reduction, we leverage PCA toidentify significant layers in a binary network which contribute relevanttransformations on the input data by increasing the number of significantdimensions. Subsequently, we propose Hybrid-Net, a network with increasedbit-precision of the weights and activations of the significant layers in abinary network. We show that the proposed Hybrid-Net achieves over 10%improvement in classification accuracy over binary networks such as XNOR-Netfor ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while stillachieving upto 94% of the energy-efficiency of XNOR-Nets. The proposed designmethodology allows us to move closer to the accuracy of standard full-precisionnetworks by keeping more than half of the network binary. This workdemonstrates an effective, one-shot methodology for designing hybrid,mixed-precision networks which significantly improve the classificationperformance of binary networks while attaining remarkable compression. Theproposed hybrid networks further the feasibility of using highly compressedneural networks for energy-efficient neural computing in IOT-based edgedevices.
COMPARATIVE STUDY OF THE DIFFERENT VERSIONS OF THE GENERAL IMAGE QUALITY EQUATION

Abstract. The General Image Quality Equation (GIQE) is an analytical tool derived by regression modelling that is routinely employed to gauge the interpretability of raw and processed images, computing the most popular quantitative metric to evaluate image quality; the National Image Interpretability Rating Scale (NIIRS). There are three known versions of this equation; GIQE 3, GIQE 4 and GIQE 5, but the last one is scarcely known. The variety of versions, their subtleties, discontinuities and incongruences, generate confusion and problems among users. The first objective of this paper is to identify typical sources of confusion in the use of the GIQE, suggesting novel solutions to the main problems found in its application and presenting the derivation of a continuous form of GIQE 4, denominated GIQE 4C, that provides better correlation with GIQE 3 and GIQE 5. The second objective of this paper is to compare the predictions of GIQE 4C and GIQE 5, regarding the maximum image quality rating that can be achieved by image processing techniques. It is concluded that the transition from GIQE 4 to GIQE 5 is a major paradigm shift in image quality metrics, because it reduces the benefit of image processing techniques and enhances the importance of the raw image and its signal to noise ratio.
A Hybrid Model for Paraphrase Detection Combines pros of Text Similarity with Deep Learning

Paraphrase detection (PD) is a very essential and important task in Natural language processing.The goal of paraphrase detection is to check whether two statements written in natural language have the identical semantic or not.Its importance appears in many fields like plagiarism detection, question answering, document clustering and information retrieval, etc.This paper proposes a hybrid model that combines the text similarity approach with deep learning approach in order to improve paraphrase detection.This model verified results with Microsoft Research Paraphrase Corpus (MSPR) dataset, shows that accuracy measure is about 76.6% and F-measure is about 83.5%.
Driver Identification Based on Hidden Feature Extraction by Using Deep Learning

The rapid development of intelligent transportation and Internet of Vehicles technology provides a technical means for obtaining massive, real-time, and multi-dimensional driving behavior data. It can be used to evaluate the driving habits, we can even distinguish drivers by analyzing driving behavior, which can be used in vehicle anti-theft systems. Existing driver identification models use complicated artificial feature extraction, and it is difficult to achieve good performance. We propose a data-driven, end-to-end driver identification model based on improved convolutional neural network. The cross-validation results from the naturalistic driving dataset indicate the superiority of our model.
Adaptive Weighted CNN Features Integration for Correlation Filter Tracking

Visual object tracking is an active and challenging research topic in computer vision, as objects often undergo significant appearance changes caused by occlusion, deformation, and background clutter. Although convolutional neural network (CNN)-based trackers have achieved competitive results, there are still some limitations. Most existing CNN-based trackers track the object by leveraging high-level semantic features of the highest convolutional layer, which may lead to low-spatial resolution feature maps and degrade the localization precision of tracking. Furthermore, these trackers hardly benefit from end-to-end training since the extraction of features and the learning of classifier are separated. To deal with the above-mentioned issues, we design an adaptive weighted CNN features-based Siamese network for tracking. To capture spatial and semantic information of the object, we design a feature extraction network that derives feature maps by concatenating features of all convolutional layers. To make the features representation more discriminative, we propose a feature integration network. In the feature integration network, we propose a holistic-part network to capture strong visual cues and learn the semantic relations between the holistic object and its parts and combine the holistic-part network with spatial and channel attention mechanisms to adaptively assign weights to each region and channel of the feature maps. In addition, the designed Siamese network can be trained offline end-to-end. The experimental results on the benchmark datasets OTB50 and OTB100 demonstrate that the proposed tracker achieves favorable performance against several state-of-the-art trackers while running at an average speed of 20.5 frames/s.
Analyzing Incorporation of Emotion in Emoji Prediction

In this work, we investigate the impact of incorporating emotion classes on the task of predicting emojis from Twitter texts. More specifically, we first show that there is a correlation between the emotion expressed in the text and the emoji choice of Twitter users. Based on this insight we propose a few simple methods to incorporate emotion information in traditional classifiers. Through automatic metrics, human evaluation, and error analysis, we show that the improvement obtained by incorporating emotion is significant and correlate better with human preferences compared to the baseline models. Through the human ratings that we obtained, we also argue for preference metric to better evaluate the usefulness of an emoji prediction system.
Construct Validity of Mathematical Creativity Instrument: First-Order and Second-Order Confirmatory Factor Analysis

Mathematical creativity instrument is a tool to assess students' creative thinking skills in solving mathematical problems.Mathematical creativity has a pivotal role in improving the quality of life, solving problems, making a change, and increasing the efficiency and effectiveness of a system.Unfortunately, there is a gap in the assessment of mathematical creativity in Indonesia because the instrument used is merely confirm teachers' explanation through mathematical problem which focuses on measuring students' knowledge.Therefore, it is necessary to prove the construct validity of the items in the instrument of mathematical creativity assessment.Based on the concept, there are 3 aspects used in creating mathematical creativity instruments, i.e. fluency, flexibility, and originality.This study aimed at examining the construct validity of the instrument.This study used a design and development model.The data were obtained from testing 313 junior high school students in Indonesia.The data were analyzed using Confirmatory Factor Analysis (CFA), using Lisrel 8.80 software through the first-order and the second-order stages.The results show that of all items, totally 15 items, are valid or uni-dimensional with T-Value at the value of the loading factor >1.96.This finding proves that unidimensional data parameters can measure the constructs of mathematical creativity and have a significant effect.
Determinants of Barriers to Knowledge Sharing in the Jordanian Hospitality Industry

As well as to examine the differences in attitude of respondents in regard to their demographic characteristic, the purpose of this research is to identify the main barriers to Knowledge Sharing (KS) within the Jordanian hospitality industry; a quantitative approach has been adopted in order to achieve these research objectives, the data being collected via the questionnaire. The sample of this research consisted of 273 managers and employees within ten top hotels in Jordan and the researcher implemented the Statistical Analysis Package (SPSS) so as to analyse the data, the descriptive statistics, mathematical averages, and standard deviations being used to identify the main barriers of KS within the Jordanian hospitality industry. The T-test and One-way ANOVA test were additionally used to examine the hypotheses, the study showcasing that individual and organisational barriers were the main barriers to KS within the Jordanian hospitality industry; meanwhile, technology and knowledge nature were not considered as barriers to KS. The results additionally indicated that there were no statistically significant differences between the responses of the sample members at the level of &amp;alpha;&amp;le;0.05 on the barriers to sharing knowledge from the employees at hospitality industry attributed to the following variables: gender; job; age; years of experiences; qualifications.
A Hybrid Cancer Classification Model Based Recursive Binary Gravitational Search Algorithm in Microarray Data

Nowadays, in clinical medicine diagnosticians usually use DNA microarray datasets for diagnosis and classification of cancer. However, DNA microarray datasets typically have very large number of genes and less number of samples, therefore, before diagnosis and classification of cancer it is quite requisite to select most relevant genes. In this paper, we have developed a two phase classification model in which most relevant genes are selected by integrating ReliefF with Recursive Binary Gravitational Search Algorithm (RBGSA) in the help of a classifier of Multinomial Naive Bayes. The RBGSA recursively transforms a very raw gene space to an optimized one at each iteration while not degrading the accuracy. We evaluate our model by comparing it with 6 other known methods on 6 different microarray datasets of cancer. Comparison results show that our model gets substantial improvements in accuracy over other methods.
STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters

There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.
Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

Human facial age estimation has been widely used in many computer vision applications, including security surveillance, forensics, biometrics, human-computer interaction (HCI), and so on. We propose a facial age estimation method oriented to non-ideal facial imagery. The method consists of image preprocessing, feature extraction, and age predication. First, we preprocess non-ideal input images in RGB stream, luminance modified (LM) stream, and YIQ stream. Then, we leverage the deep convolutional neural networks (DCNNs) to extract the feature of images preprocessed in each stream. To reduce the training data volume and training complexity, we adopt the transfer learning to build the DCNN structure. With the extracted feature, the weak classifier equipped at every stream is designed to obtain a weak classification prediction of the age range. Moreover, in order to generate estimation, we use the ensemble learning to fuse the three weak classifiers. We design an integrated strategy algorithm based on the combination of voting method and weighted average method. The simulation results show that our proposed algorithm can improve the an exact match (AEM) and an error of one age category (AEO) by 4.75% and 6.75% compared with the best AEM and AEO of the three weak classifiers. Furthermore, in comparison with the unweighted average method, our proposed algorithm can improve the AEM and AEO by 8.68% and 12.79%, respectively.
A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor

Pedestrian detection is a fundamental problem in various computer vision applications and is addressed by complex solutions involving complex feature extractor and classification techniques.In this paper, feature selector is used along with Histogram of Significant Gradients (HSG) descriptor and linear SVM classifier to enhance the detection accuracy and reduce the processing time.A feature selector organizes the extracted features in decreasing order of their significance.A comparative study has been done employing different classifiers and feature selectors and a system is proposed with better detection capability.The proposed scheme is found to have a 50 % reduction in feature size, 67 % reduction in processing time and 3.93 % improvement in accuracy.