Reviewerly/exHarmony huggingface dataset:
Fe-oxides in jasperoids from two gold districts in Nevada: Characterization, geochemistry, and (U-Th)/He dating

Abstract This study examines the whole-rock geochemistry, Fe-oxide texture and crystal morphology, and Fe-oxide (U-Th)/He date variation of jasperoids, as well as the relationships between these characteristics, in order to evaluate jasperoids as an indicator for gold mineralization. Jasperoid samples with a range of textures, appearances, and expected gold concentrations were collected from two gold districts in Nevada: the northern Carlin trend (Gold Quarry Mine), and the Battle Mountain district (Marigold Mine; Battle Mountain prospect). In our jasperoid samples, whole-rock concentrations of Au are positively correlated with As, Ag, Pb, Sb, U, W, and Tl. The following Fe-oxide textures are associated with anomalous gold concentrations: disseminated, submicron crystals; feathery and acicular crystal morphologies; concentric zonation from hematite to goethite; and botryoidal textures. Calculated closure temperatures for the (U-Th)/He system in observed jasperoid Fe-oxide morphologies within our dataset range from 26 to 136 °C, indicating variable susceptibility to He diffusion at near-surface conditions. Fe-oxide (U-Th)/He dates range from 29.4 ± 0.5 Ma to 0.11 ± 0.01 Ma, displaying intra- and intersample variation. We attribute the date variation to multiple factors including He implantation, parent isotope exchange, and He loss through closure temperature sensitivity and alpha-ejection. The intra- and intersample variation within the Fe-oxide (U-Th)/He dates limits our ability to make geologic interpretations. Our case study identifies important limitations of the (U-Th)/He method in jasperoids and provides insight on how He date variation might be avoided in future studies.
A Comparative Study on Solar Power Forecasting using Ensemble Learning

Intensifying requests for energy is preceding towards renewable solar energy integration with nonrenewable energy resources. Unlike other nonrenewable energy resources, solar energy is recurrent. Effectual utilization of spontaneously available energy accurate solar power forecasting is essential. This study aims to predict solar power through deep neural networks (DNNs) and various machine learning (ML) techniques on a solar dataset, namely linear regression, support vector regression, random forest, etc. The dataset that is used contains solar power energy extracted every five minutes. Moreover, a comparative study is carried out between DNNs and ML techniques, which helps in crafting suitable decisions to select appropriate forecasting and prediction techniques.
HCI Research Challenges for the Next Generation of Conversational Systems

As we move past from the deployment of the first conversational systems, a new generation is shaping up with interaction patterns beyond the question and answer paradigms of today. The next wave is likely to include systems with clearly defined personalities, nuanced and emotional speech, and contexts with multiple bots and users. We propose here that a new set of design and interface challenges will rise in the context of those upcoming systems. Among them, we address five challenges which we believe are going to become relevant for the next generation of conversation systems: handling human-machine pidgins, managing language precision, creating and conveying personality, knowing when to speak, and creating the illusion of a mind.
Inferring Cognitive Style from Eye Gaze Behavior During Information Visualization Usage

Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, Information Visualizations have traditionally been designed in a manner that does not consider such individual user differences. Recent research has started to address this issue, by identifying individual user characteristics that influence individual users' interactions with Information Visualizations, as well as developing novel Information Visualization systems that provide more personalized support. This paper presents a set of experiments aimed towards building such User-Adaptive Information Visualization systems, by studying the extent to which a user's cognitive style can be inferred from a user's interaction with an Information Visualization system. Results show that a user's eye gaze data can be used to infer a user's cognitive style during information visualization usage with up to 86% accuracy, and that the most informative features relate to a user's saccade angles and fixation durations.
Research on an advanced intelligence implementation system for engineering process in industrial field under big data

To develop an advanced CBR system to well adapt to the intelligence implementation of new engineering process in the big data environment, Bayesian network (BN) model is introduced to CBR system for knowledge reasoning. However, as engineering application is becoming more and more complicated, the number of parameters used to define engineering application grows larger and larger, leading to the seriously reduced efficiency as well as the accuracy of the integrated model. For the problem of reduced efficiency, this paper proposes In-External (IE) algorithm to perform the assignment of big data distribution for parallel data processing, which can fully utilize the capacity of Hadoop system and attain the best efficiency of knowledge reasoning. For the problem of reduced accuracy, in view of the fact that traditional probability learning methods are unfit for the proposed CBR system, this paper proposes Discount Exponential Coefficients of Multivariate Beta Distribution (DECMBD) algorithm to conduct the probability learning of proposed system. In DECMBD algorithm, a discount ratio is given to each exponential coefficient of multivariate Beta distribution to improve the occurrence times counting of all problem features and then gain better effect of probability learning. Finally, lots of experiments are performed to validate the effectiveness of the proposed advanced CBR system.
Cartoon-Texture Image Decomposition using Orientation Characteristics in Patch Recurrence

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 25 September 2019Accepted: 15 April 2020Published online: 16 July 2020Keywordscartoon-texture decomposition, patch recurrence, regularization methodAMS Subject Headings68T05, 68U10, 65D18Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
Conversational User Interfaces on Mobile Devices

Conversational User Interfaces (CUI) on mobile devices are the most accessible and widespread examples of voice-based interaction in the wild. This paper presents a survey of mobile conversation user interface research since the commercial deployment of Apple's Siri, the first readily available consumer CUI. We present and discuss Text Entry & Typing, Application Control, Speech Analysis, Conversational Agents, Spoken Output, & Probes as the prevalent themes of research in this area. We also discuss this body of work in relation to the domains of Health & Well-being, Education, Games, and Transportation. We conclude this paper with a discussion on Multi-modal CUIs, Conversational Repair, and the implications for CUIs of greater access to the context of use.
Deep learning assessment of child gross-motor

The acquisition of gross motoer (GM) skills during childhood is very important for physical and psychological development. Various body function measurement tests have been designed to assess a child's GM performance, but the assessment process is a laborious manual task; hence, IT automation combined with activity recognition (AR) is highly desirable. This paper focuses on GM assessment deep-learning (DL) by expanding the previous fruitful result of GM classifiction, which utilized OpenPose to detect childrens' skeletons, a specific person tracking algorithm to recover the OpenPose's drawbacks, conversion of the skeleton's time-series data into motional time-series images, and its data augmentation technieque. A procedure for building a database containing assessment information is presented, and a new CNN-based deep learning network that performs both GM classification and evaluation simultaneously is proposed. Applying these methods to actual GM assessment including 13 types GM motions including 155 combinations of GM assesment scores, the new GM-AR could classify them with a very high accuracy of 99.6%.
A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering

Deep learning-based recommender systems are nowadays defining the state-of-the-art. Unfortunately, their hard interpretability restrains their application in scenarios in which explainability is required/desirable. Many efforts have been devoted to injecting explainable information inside deep models. However, there is still a lot of work that needs to be done to fill this gap. In this paper, we take a step in this direction by providing an intuitive interpretation of the inner representation of a conditioned variational autoencoder (C-VAE) for collaborative filtering. The interpretation is visually performed by plotting the principal components of the latent space learned by the model on MovieLens. We show that in the latent space conditions on correlated genres map users in close clusters. This characteristic enables the model to be used for profiling purposes.
Proof-Theoretic Analysis of the Logics of Agency: The Deliberative STIT

Abstract A sequent calculus methodology for systems of agency based on branching-time frames with agents and choices is proposed, starting with a complete and cut-free system for multi-agent deliberative STIT; the methodology allows a transparent justification of the rules, good structural properties, analyticity, direct completeness and decidability proofs.
Think+

The main purpose of Think+ is to provide both psychiatrists and common people alike to help treat phobias without the need of complex treatment procedures. Using a virtual environment, the app intends to help provide an experience for a user to give the players a chance to get over their fears without having to go through the burden of multiple therapies. Given that people with phobias do not see a doctor or psychiatrist as often as they should, people are always feeling threatened whenever they come in contact with what caused their fear in the first place, which is sometimes inevitable. The study focus on most common phobias with the use of Virtual Reality Technology that will assist psychologists and psychiatrists in treating phobias. The developed virtual reality system is fully functional and technically assessed to meet the requirements of its intended use. Based from the Psychologists, Psychiatrist and experts' user acceptance testing done, the developed system is equipped with requirements intended for patients with phobia. The application is working properly according to its purpose and is suited to be used for patients' for phobia exposure and interaction in a virtual reality environment.
Image Inpainting Based on Multi-Patch Match with Adaptive Size

Patch-based image inpainting methods iteratively fill the missing region via searching the best sample patch from the source region. However, most of the existing approaches basically use the fixed size of patch regardless of content features nearby, which may lead to inpainting defects. Also, global match is needed for searching the best sample patch, but only to fill one target patch in each iteration, resulting in low efficiency. To handle the issues above, we first evaluate the nonuniformity in an image, by which the patch size is adaptively determined. Moreover, we divide the source region into multiple non-overlapping subregions with different nonuniformity levels, and the patch match proceeds in every subregion, respectively. This strategy not only saves the match time for single target patch, but also reduces the mismatch, and enables the simultaneous filling of multiple target patches in a single iteration. Experimental results show that in comparison to previous patch-based works, our method has achieved further improvement both in quality and efficiency. We believe our method could provide a new way for patch match with better accuracy and efficiency in image inpainting tasks.
QoESoft: QoE Management Architecture for Softwarized 5G Networks

For 5G network softwarization, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have emerged as key technologies to enable efficient network management. 5G network virtualization will allow multiple service providers to share a single physical infrastructure offering an abstraction between the user and physical resources. As we move towards softwarized 5G networks, scalability issues, network performance with respect to Quality of Service (QoS) and Quality of Experience (QoE) and QoE-aware resource management remain less studied topics. In this paper, we adopt the SDN control logic to perform an autonomic QoE-aware resource management approach in virtualized 5G networks. We propose QoESoft, a network softwarization strategy for multimedia streaming services that performs a dynamic link and switch resources management in softwarized 5G systems. With QoESoft, we aim to improve user QoE for multimedia services through efficient monitoring and adaptation of the average load of the substrate links and switches and resource costs of mapped virtual links in 5G networks. The performance of the proposed approach is analysed in testbed environments. Preliminary results indicate that, QoESoft achieves better performance than the compared baseline approach in terms of link and switch resources utilization by 78% and 70% respectively. A video reception quality ratio metric and visual inspection on the output video both confirmed significant improvements in video QoE.
A Tool Supporting Architecture Principles and Guidelines in Large-Scale Agile Development

In today's business environments, organizations are confronted with technological advancements, regulatory uncertainties, and time-to-market pressures. The ability to detect relevant changes and to react timely and effectively becomes an important determinant for business survival. As a result, enterprises apply agile methods to larger projects as a part of their digital transformation. The adoption of agile methods at scale poses new challenges such as establishing effective knowledge networks or coordinating various development activities to produce desirable enterprise-wide effects. The latter can be addressed by applying architecture principles. However, there is a lack of academic research on how architecture principles can be created and applied in large-scale agile development. Against this backdrop, we propose a prototypical web application called "Architecture Belt" that supports the establishment of architecture principles. It uses social design principles and the analogy of belts in martial arts to enforce the application of architecture principles by exerting institutional pressures on agile teams.
Graphical passwords: Behind the attainment of goals

Abstract Graphical authentication methods have emerged as an alternative to the conventional authentication methods over the past couple of decades. One of the most popular among types of graphical authentication methods is recognition‐based authentication, where the user taps on pass images from one or more challenge sets of images in order to authenticate. The study of existing graphical authentication systems shows that several of them compromise their security while making the method simpler, which could lead to perpetration of numerous attacks like guessing, hidden camera, smudge, shoulder surfing, and many other. Furthermore, a few of them sacrifice performance while targeting security alone. Yet, this paper proposes a new method that resists the aforementioned attacks with good performance, while preserving the benefits of graphical passwords such as ease of use and increased memorability.
A Disturbance-Aware Trajectory Planning Scheme Based on Model Predictive Control

Despite the development of numerous trajectory planners based on computationally fast algorithms targeting accurate motion of robots, the nowadays robotic applications requiring compliance for interaction with environment demand more comprehensive schemes to cope with unforeseen situations. This letter discusses the problem of online Cartesian trajectory planning, targeting a final state in a desired time interval, in such a way that the generated trajectories comply with the tracking abnormalities due to considerable motion disturbances. We propose a planning scheme based on Model Predictive Control. It utilises a novel strategy to monitor the tracking performance via state feedback and consequently update the trajectory. Also, it ensures the continuity of the generated reference while accounting for realistic implementation constraints, particularly due to computational capacity limits. To validate the efficacy of the proposed scheme, we examine a practical robotic manipulation scenario in which a given task is executed via a Cartesian impedance controller, while an external interaction interrupts the motion. The performance of the proposed strategy as compared to that of a state-of-the-art study is demonstrated in simulation. Finally, a set of experiments verified the effectiveness of the proposed scheme in practice.
CMG-based anthropomorphic test device for human rider behavior reproduction for two-wheeled self-balancing personal mobility

With the expansion of the self-balancing personal mobility (SBPM) market, an increasing number of accidents have been reported in recent years, while the relevant safety regulations have not been established yet. Safety and dynamic stability of the SBPM is critical for their operation, and they must be evaluated for them. SBPMs are operated by a human rider even for the evaluation tests, so the objectivity and the repeatability of the tests cannot be ensured. Also, there could even be a safety issue with the test rider. To solve this problem, we propose an anthropomorphic test device (ATD), which is a type of dummy robot. The ATD reproduces the human rider's dynamic behavior using a control moment gyroscope (CMG), so it can drive the SBPM instead of a human rider for the evaluation tests. Just like a human rider controls the SBPM by the disturbance torque made by the posture (i.e. the center of gravity) change, the CMG-based ATD reproduces the dynamic behavior of the SBPM by creating the same disturbance torque using the CMG. A detailed working principle and the design of the CMG-based ATD is introduced in this paper. For the validation of the CMG-based ATD's human rider behavior reproduction performance, the dynamic behavior of the SBPM measured from the actual human ride is compared with the data from the computer simulations.
Computer-based art in folklore illustration: Development of mixed media digital painting in education context

Abstract Computer-based art has recently played a pivotal role in the artistic process of producing digital versions of visual illustration. One of them is the digital painting trends which becomes a new phenomenon in art practice in Indonesia, a transformation between engineering, aesthetics, and culture. This study aims to develop the creative process in mixed media digital painting in the project of folklore visualization as a computer-based art practice. This study employed a research and development design which was carried out by following steps: (a) defining, (b) designing, (c) developing and (d) validating. Trial of development results (mixed media of digital painting) were carried out to art students in the visual arts department of the Faculty of Languages and Arts, Semarang State University who were working on art projects within the scope of folklore illustration. The results of the study showed that the development of mix media digital painting was conducted by using software that is applicable to combine graphic design of bitmap and vector graphics, namely Corel Draw X7 and Adobe Photoshop CS6. The combination between bitmap and vector graphics in folklore illustration project can improve shape variation and artistic effect in digital painting.
Automatic related work section generation: experiments in scientific document abstracting

Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment. However, writing a good related work section is an activity which requires considerable expertise to identify, condense/summarize, and combine relevant information from different sources. In this work we compare different automatic methods to produce “descriptive” related work sections given as input the set of papers which need to be described. The main contribution of our work is a neural sequence learning process which produces citation sentences to be included in a related work section of an article. We train the neural architecture using an available scientific data set of citation sentences and we test over a data set of related work sections; we also compare the performance to a set of baseline extractive summarizers, an abstractive summarizer and a state of the art CNNs approach. Our results indicate that our approach outperforms the simple as well as the informed baselines.
A parameter-free affinity based clustering

Several methods have been proposed to estimate the number of clusters in a dataset; the basic idea behind all of them has been to study an index that measures inter-cluster separation and intra-cluster cohesion over a range of cluster numbers and report the number which gives an optimum value of the index. In this paper, we propose a simple parameter-free approach that is more like human cognition of clusters, where closely lying points are easily identified to form a cluster and the total number of clusters is revealed. To identify closely lying points, the affinity of two points is defined as a function of distance and a threshold affinity is identified, above which two points in a dataset are likely to be in the same cluster. Well separated clusters are identified even in the presence of outliers, whereas for a not well-separated dataset, the final number of clusters is estimated from the detected clusters. And they are merged to produce the final clusters. Experiments performed with several large dimensional synthetic and real datasets show good results with robustness to noise and density variation within a dataset.