Reviewerly/exHarmony huggingface dataset:
Image Valence Modulates the Processing of Low-Resolution Affective Natural Scenes

In natural vision, noisy and distorted visual inputs often change our perceptual strategy in scene perception. However, it is unclear the extent to which the affective meaning embedded in the degraded natural scenes modulates our scene understanding and associated eye movements. In this eye-tracking experiment by presenting natural scene images with different categories and levels of emotional valence (high-positive, medium-positive, neutral/low-positive, medium-negative, and high-negative), we systematically investigated human participants’ perceptual sensitivity (image valence categorization and arousal rating) and image-viewing gaze behaviour to the changes of image resolution. Our analysis revealed that reducing image resolution led to decreased valence recognition and arousal rating, decreased number of fixations in image-viewing but increased individual fixation duration, and stronger central fixation bias. Furthermore, these distortion effects were modulated by the scene valence with less deterioration impact on the valence categorization of negatively valenced scenes and on the gaze behaviour in viewing of high emotionally charged (high-positive and high-negative) scenes. It seems that our visual system shows a valence-modulated susceptibility to the image distortions in scene perception.
HDAG-explorer

Hierarchical directed acyclic graph (HDAG) is an essential graph model to represent terminology relationships in a hierarchy, such as Disease Ontology, Gene Ontology, and Wikipedia. However, due to massive terminologies and complex structures in a HDAG, an end user might feel difficult to explore and summarize the whole graph, which is practically useful but less studied in the literature. In this demo, we develop an interactive system of HDAG-Explorer to help users summarize HDAG with highly important and diverse vertices. Our HDAG-Explorer system exhibits several useful features including summarized visualization, interactive exploration, and structural statistics report. All these features facilitate in-depth understanding of the HDAG data. We showcase the usability of the HDAG-Explorer through two real-world applications of summarized topic recommendation and visual data exploration.
Developing an AI project

Artificial intelligence applications can very powerful in areas of speech recognition, image processing and identification, medical diagnosis and clustering to name a few. There is a perception that developing your own artificial intelligence (AI) application can be a daunting task, requiring in-depth knowledge and programming skills. This is not entirely true since many desktop and laptop systems have computing power that can accommodate machine- and deeplearning development, the available options for code development and a broad support base. A generic guide in developing a platform for AI project development is presented.
Weighted co-association rate-based Laplacian regularized label description for semi-supervised regression

Smoothness regularization derives the optimal regression function by minimizing the squared loss combined with a smoothness regularizer that restricts the variation of the function within a neighboring region. Thus, the regression function can effectively accommodate intrinsic data structures, and prediction performance can be improved when the label information is insufficient. In this study, we propose a weighted co-association rate-based Laplacian regularized label description algorithm. In the proposed algorithm, we define a regression function by combining weighted co-association rates and a label descriptive function. We use the weighted co-association rate, computed by summarizing various clustering solutions, to depict the data structure. The label descriptive function identifies a latent label distribution, and hence helps the regression function to accurately involve as much true label information as possible. To derive the optimal label descriptive function, we apply the smoothness regularizer to label descriptive function. Experiments were conducted on various benchmark datasets to examine the properties of the proposed algorithms, and the results were compared with those of the existing methods. The experimental results confirm that the proposed algorithm outperforms the previous methods.
DCT-phase statistics for forged IMEI numbers and air ticket detection

New tools have been developing with the intention of having more flexibility and greater user-friendliness for editing the images and documents in digital technologies, but, unfortunately, they are also being used for manipulating and tampering information. Examples of such crimes include creating forged International Mobile Equipment Identity (IMEI) numbers which are embedded on mobile packages and inside smart mobile cases for illicit activities. Another example of such crimes is altering the name or date on air tickets for breaching security at the airport. This paper presents a new expert system for detecting forged IMEI numbers as well as altered air ticket images. The proposed method derives the phase spectrum using the Discrete Cosine Transform (DCT) to highlight the suspicious regions; it is unlike the phase spectrum from a Fourier transform, which is ineffective due to power spectrum noise. From the phase spectrum, our method extracts phase statistics to study the effect of distortions introduced by forgery operations. This results in feature vectors, which are fed to a Support Vector Machine (SVM) classifier for detection of forged IMEI numbers and air ticket images. Experimental results on our dataset of forged IMEI numbers (which is created by us for this work), on altered air tickets, on benchmark datasets of video caption text (which is tampered text), and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method is robust across different datasets. Furthermore, comparative studies of the proposed method with the existing methods on the same datasets show that the proposed method outperforms the existing methods. The dataset created will be available freely on request to the authors.
Further results on 2-uniform states arising from irredundant orthogonal arrays

&lt;p style='text-indent:20px;'&gt;The notion of an &lt;i&gt;irredundant orthogonal array&lt;/i&gt; (IrOA) was introduced by Goyeneche and &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \dot{Z} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;yczkowski who showed an IrOA&lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ _{\lambda}(t, k, v) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; corresponds to a &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-uniform state of &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; subsystems with local dimension &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ v $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; (Physical Review A. 90 (2014), 022316). In this paper, we construct some kinds of 2-uniform states by establishing the existence of IrOA&lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ _{\lambda}(2, 5, v) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; for any integer &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ v\geq 4 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ v\neq 6 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;; IrOA&lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ _{\lambda}(2, 6, v) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; for any integer &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ v\geq 2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;; IrOA&lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ _{\lambda}(2, q, q) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and IrOA&lt;inline-formula&gt;&lt;tex-math id="M12"&gt;\begin{document}$ _{\lambda}(2, q+1, q) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; for any prime power &lt;inline-formula&gt;&lt;tex-math id="M13"&gt;\begin{document}$ q &amp;gt;3 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Personalised Meta-Learning for Human Activity Recognition with Few-Data

State-of-the-art methods of Human Activity Recognition (HAR) rely on a considerable amount of labelled data to train deep architectures. This becomes prohibitive when tasked with creating models that are sensitive to personal nuances in human movement, explicitly present when performing exercises and when it is infeasible to collect training data to cover the whole target population. Accordingly, learning personalised models with few data remains an open challenge in HAR research. We present a meta-learning methodology for learning-to-learn personalised models for HAR; with the expectation that the end-user only need to provide a few labelled data. These personalised HAR models benefit from the rapid adaptation of a generic meta-model using provided few end-user data. We implement the personalised meta-learning methodology with two algorithms, Personalised MAML and Personalised Relation Networks. A comparative study shows significant performance improvements against state-of-the-art deep learning algorithms and other personalisation algorithms in multiple HAR domains. Also, we show how personalisation improved meta-model training, to learn a generic meta-model suited for a wider population while using a shallow parametric model.
Transparent many‐core partitioning for high‐performance big data I/O

Summary As the number of cores equipped in a single computing node is rapidly increasing, utilizing many cores for contemporary applications in an efficient manner is a challenging issue. We need to consider both parallelization and locality to fully exploit many cores for multifarious operations of emerging applications. In particular, big data applications perform computation and I/O intensive operations alternately. For instance, Apache Hadoop MapReduce assumes local persistent storage for each computing node. Thus, unlike traditional parallel programming models, the MapReduce framework performs not only networking but also storage I/O. In this study, we aim to improve the locality of network and storage I/O operations on many‐core systems by partitioning cores for I/O system calls and event handlers. In order to implement fine‐grained many‐core partitioning, we decouple the system call context from the user‐level process by suggesting message‐based system calls. The suggested design provides user‐level transparency and does not require any kernel‐level modifications. In addition, we propose a scheme that dynamically decides the core affinity of system calls and event handlers by considering locality, run‐time loads, and hardware architectures. The experimental results show that the proposed many‐core partitioning can improve the locality of network and storage I/O operations in an integrated manner for MapReduce applications.
User experiences using FLAME: A Case study modelling conflict in large enterprise system implementations

The complexity of systems now under consideration (be they biological, physical, chemical, social, etc), together with the technicalities of experimentation in the real-world and the non-linear nature of system dynamics, means that computational modelling is indispensible in the pursuit of furthering our understanding of complex systems. Agent-based modelling and simulation is rapidly increasing in its popularity, in part due to the increased appreciation of the paradigm by the non-computer science community, but also due to the increase in the usability, sophistication and number of modelling frameworks that use the approach. The Flexible Large-scale Agent-based Modelling Environment (FLAME) is a relatively recent addition to the list. FLAME was designed and developed from the outset to deal with massive simulations, and to ensure that the simulation code is portable across different scales of computing and across different operating systems. In this study, we report our experiences when using FLAME to model the development and propagation of conflict within large multi-partner enterprise system implementations, which acts as an example of a complex dynamical social system. We believe FLAME is an excellent choice for experienced modellers, who will be able to fully harness the capabilities that it has to offer, and also be competent in diagnosing and solving any limitations that are encountered. Conversely, because FLAME requires considerable development of instrumentation tools, along with development of statistical analysis scripts, we believe that it is not suitable for the novice modeller, who may be better suited to using a graphical user interface driven framework until their experience with modelling and competence in programming increases.
Digital wayfinding systems in hospitals: A qualitative evaluation based on managerial perceptions and considerations before and after implementation

• A comprehensive evaluation of digital wayfinding systems in hospitals. • Reduced task complexity and overall user stress; enhanced user control and empowerment. • Reduced delayed/missed appointments and staff time providing directions to patient. • Needs of the elderly and disabled remain unaddressed.
Classification of breast ultrasound with human-rating BI-RADS scores using mined diagnostic patterns and optimized neuro-network

Breast ultrasound (BUS) is a powerful screening tool for examination of breast lesions. Recently, research attention has been paid to combining doctor's opinions and machine learning technology to build up a better computer-aided diagnosis (CAD) system. In this paper, we propose an improved approach that uses human-rating BI-RADS scores to classify the BUS samples. A BI-RADS feature scoring scheme is firstly adopted to standardize the descriptions on breast lesions, and then the diagnostic patterns are mined by a biclustering algorithm in the collected BI-RADS feature score dataset. With an input sample, the diagnostic patterns could be activated to different degrees which represent the high-level features by calculating the distance between input sample and patterns. The high-level features of the sample are input into a multi-layer perception neural network (MLPNN) and we use a cost matrix to convert the output from probabilities to classification cost. The structure of the MLPNN and the values of elements in cost matrix are optimized by Particle Swarm Optimization, and it finally classifies the input of BUS sample. According to the comparative experiments with other CAD approaches and the experienced sonographers, the proposed approach achieved the best sensitivity, indicating that it can serve as an assistant diagnostic system in clinical practices.
Rule-based knowledge discovery of satellite imagery using evolutionary classification tree

The classification tree (CT) may be used to establish explicit classification rules for Satellite Imagery (SI). However, the accuracy of explicit classification rules attained by this method is poor. Back-propagation networks (BPN) and the support vector machine (SVM) may both be used to establish highly accurate models for predicting the classification of SI. However, neither is able to generate explicit rules. This study proposes the evolutionary classification tree (ECT) as a novel mining rule method. Composed of the particle bee algorithm (PBA) and classification tree (CT), the ECT produces self-organized rules automatically to predict the classification of SI. In ECT, CT serves as the architecture to represent explicit rules and PBA acts as the optimization mechanism to optimize CT in order to fit the experimental data. A total of 600 experimental datasets were used to compare the accuracy and complexity of four model-building techniques: CT, BPN, SVM, and ECT. The results demonstrate the ability of ECT to produce rules that are more accurate than CT and SVM but less accurate than BPN. However, because BPN is black box model, the ability of ECT to generate explicit rules makes ECT the best model for users wanting to mine the explicit rules and knowledge in practical applications.
Interdependences Between Cryptocurrencies: A Network Analysis from 2013 to 2018

Through a data-driven analysis, namely network analysis, we investigate the relationships between all existing cryptocurrencies. Starting from the analysis of cryptocurrencies in 2013, we extend our study until July 2018 to study the interdependencies between 1636 cryptocurrencies. Our study shows that, although Bitcoin is the older and the most famous cryptocurrency, it does not appear as an influential asset on the virtual currency market. Our analysis also indicates a densification of the interconnections between virtual currencies, indicating that change of a single coin will likely influence many other coins. Interestingly, we also observe that the most influential cryptocurrencies for a year appear not to be influential the following year. Finally, cryptocurrencies tend to change their influence over time suggesting a short-term interdependence between them. JEL: G11, G12
Conversations with Materials and Diagrams about some of the Intricacies of Oscillatory Motion

Abstract This article relates a case study on how a conversation with materials and diagrams – the actual use of materials and diagrams to think, imagine, explain, collaborate, design and build – featured a certain kind of interplay between material and digital components. The physical components present in this setting included a water wheel, which is a wheel driven by flow of water whose rotational motion is a classic example of chaotic dynamics regulated by Lorenz equations. Digital components allowed for real-time graphical displays corresponding to the turning of the water wheel. We selected for this article a sequence of episodes from an interview with Jake, an undergraduate student majoring in engineering. Through a micro-ethnographic analysis, we reflect on how Jake combined the responsiveness of the digital displays with the tangibility of the water wheel to gain insight into some of the intricacies of oscillatory motion.
A comprehensive survey of procedural video datasets

Procedural knowledge is crucial for understanding and performing concrete real-world tasks. Yet, despite the importance of procedural knowledge, research into procedural knowledge understanding is still under-developed. In particular, videos contain rich semantics that are important for understanding procedural knowledge, but have traditionally been less explored than natural language texts for understanding procedural knowledge. Motivated by harnessing procedural knowledge from videos for task assistance (i.e., assisting people in performing procedural tasks), we present the first comprehensive survey of procedural video datasets. Through systematically surveying 23 procedural video datasets, including both instructional and non-instructional videos, in a conceptual framework for task assistance, we seek to understand the trends and gaps in existing datasets, as well as to gain insights into the future of such datasets. This survey examines the current state of procedural video datasets, in terms of their data, content and annotation characteristics, as well as processing function and evaluation. The survey also identifies and suggests a number of possible directions to bring this area to the next level.
Data Clothing and BigBarChart: Designing Physical Data Reports on Indoor Pollutants for Individuals and Communities

In response to participant preferences and new ethics guidelines, researchers are increasingly sharing data with health study participants, including data on their own household chemical exposures. Data physicalization may be a useful tool for these communications, because it is thought to be accessible to a general audience and emotionally engaged. However, there are limited studies of data physicalization in the wild with diverse communities. Our application of this method in the Green Housing Study is an early example of using data physicalization in environmental health report-back. We gathered feedback through community meetings, prototype testing, and semistructured interviews, leading to the development of data t-shirts and other garments and person-sized bar charts. We found that participants were enthusiastic about data physicalizations, it connected them to their previous experience, and they had varying desires to share their data. Our findings suggest that researchers can enhance environmental communications by further developing the human experience of physicalizations and engaging diverse communities.
How old was he? Disguises, age, and race impact upon age estimation accuracy

Summary There are many situations in which a person must accurately estimate a stranger's age. For example, a salesperson must accurately estimate the age of a stranger who wishes to purchase age‐restricted goods. Information from a stranger's eyes and hair can indicate their likely age. Here, two experiments examined whether adult participants' accuracy when estimating strangers' ages is reduced when the strangers' eyes are disguised by sunglasses and/or hair is disguised by a hat. The strangers' age and race also varied, and participants' social contact with the other age/race groups measured. At best, estimations of undisguised strangers' ages were inaccurate by an average of 5.10 years. Accuracy decreased when the strangers' eyes, but not hair, were disguised and when they came from other age/race groups. Accuracy when estimating other age/race group members' ages and social contact with their members were unrelated. The theoretical and applied implications of these findings are discussed.
A Signal-Processing Framework for Occlusion of 3D Scene to Improve the Rendering Quality of Views

Occlusions will reduce the performance of systems in many computer vision applications with discontinuous surfaces of 3D scenes. We explore a signal-processing framework of occlusions based on the light ray visibility to improve the rendering quality of views. An occlusion field (OCF) theory is derived by calculating the relationship between the occluded light rays and the nonoccluded light rays to quantify the occlusion degree (OCD). The OCF framework can describe the various in-scene information captured by the changes in the camera configuration (i.e., position and direction) through a quantitative description of the occlusion information. From a spectral analysis of the OCF, we mathematically derive analytical functions to determine the changing relationship between the scene and the camera configuration. A reconstruction filter can be designed to achieve interference cancellation and compensate for the missing information caused by the occlusions. Our measurements of different occlusions using this OCF framework included both synthetic and actual scenes. The experimental results show that the proposed OCF framework can improves the rendering quality of views and outperforms other known occlusion quantization schemes in a complex scene.