Reviewerly/exHarmony huggingface dataset:
Interactive Mechanism of Characteristic Town Construction and Industrial Upgrading in Coastal Areas

Tang, H.; Wei, Z., and Xiao, H., 2020. Interactive mechanism of characteristic town construction and industrial upgrading in coastal areas. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 47–51. Coconut Creek (Florida), ISSN 0749-0208.This paper studies the interactive mechanism between the characteristic town of Zhejiang Province and the industrial upgrading. We examined how aspects of characteristic town influenced industrial upgrading, such as industry merit-based and synergistic mechanism, mechanism of technological innovation and mechanism of brand effect--high consumption. In a longitudinal, multiple-informant study of characteristic towns in Zhejiang Province, we found that industry merit-based and synergistic mechanism, mechanism of technological innovation and mechanism of brand effect--high consumption and their interrelationships selectively influenced planning and development of a characteristic town. As anticipated, industry merit-based and synergistic mechanism and mechanism of technological innovation positively influenced characteristic town, they can promote industrial transformation and upgrading. Counter to our expectations, however, mechanism of brand effect—high consumption by itself was negatively associated with characteristic town. Interestingly, the three mechanisms played a significant role in characteristic town, as it positively influenced industrial transformation and upgrading.
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions.

A plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models. Exact inference is often prohibitively expensive, as it may require evaluating the (unnormalized) target density on its entire domain. Here we consider the setting where only a limited budget of calls to the unnormalized density oracle is available, raising the challenge of where in the domain to allocate these function calls in order to construct a good approximate solution. We formulate this problem as an instance of sequential decision-making under uncertainty and leverage methods from reinforcement learning for probabilistic inference with budget constraints. In particular, we propose the TreeSample algorithm, an adaptation of Monte Carlo Tree Search to approximate inference. This algorithm caches all previous queries to the density oracle in an explicit search tree, and dynamically allocates new queries based on a best-first heuristic for exploration, using existing upper confidence bound methods. Our non-parametric inference method can be effectively combined with neural networks that compile approximate conditionals of the target, which are then used to guide the inference search and enable generalization across multiple target distributions. We show empirically that TreeSample outperforms standard approximate inference methods on synthetic factor graphs.
Augmenting Named Entity Recognition with Commonsense Knowledge

Commonsense can be vital in some applications like Natural Language Understanding (NLU), where it is often required to resolve ambiguity arising from implicit knowledge and underspecification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research, we take advantage of the availability of the open multilingual knowledge graph ConceptNet, by using it as an additional external resource in Named Entity Recognition (NER). Our proposed architecture involves BiLSTM layers combined with a CRF layer that was augmented with some features such as pre-trained word embedding layers and dropout layers. Moreover, apart from using word representations, we used also character-based representation to capture the morphological and the orthographic information. Our experiments and evaluations showed an improvement in the overall performance with +2.86 in the F1-measure. Commonsense reasonnig has been employed in other studies and NLP tasks but to the best of our knowledge, there is no study relating the integration of a commonsense knowledge base in NER.
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records

Deep learning models have demonstrated high-quality performance in areas such as image classification and speech processing. However, creating a deep learning model using electronic health record (EHR) data, requires addressing particular privacy challenges that are unique to researchers in this domain. This matter focuses attention on generating realistic synthetic data while ensuring privacy. In this paper, we propose a novel framework called correlation-capturing Generative Adversarial Network (corGAN), to generate synthetic healthcare records. In corGAN we utilize Convolutional Neural Networks to capture the correlations between adjacent medical features in the data representation space by combining Convolutional Generative Adversarial Networks and Convolutional Autoencoders. To demonstrate the model fidelity, we show that corGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction. We also give a privacy assessment and report on statistical analysis regarding realistic characteristics of the synthetic data.
Emoji Usage Across Platforms: A Case Study for the Charlottesville Event

We study emoji usage patterns across two social media platforms, one of them considered a fringe community called Gab, and the other Twitter. We find that Gab tends to comparatively use more emotionally charged emoji, but also seems more apathetic towards the violence during the event, while Twitter takes a more empathetic approach to the event.
Proposal and Performance Evaluation of an Order-Specified Aggregate Authority-Transfer Signature

We propose a new order-specified aggregate authority-transfer signature based on the gap Diffie-Hellman group and evaluate the performance of it. In companies, in order to prevent stagnation of the work flow or suspension of operations accompanied by an accident due to concentration of authority to one or a few people, distribution and delegation of authority to multiple persons is performed. Currently, since various operations in a company are performed via a computer network, a mechanism of authority transfer to allow delegation and distribution of authority quickly and properly on this network is needed. In this paper, we propose an authority-transfer signature scheme combining an order-specified aggregate signature and a group signature. In our method, a signature scheme uses a group signature scheme to guarantee authority. Moreover, it transfers the authority owned by the manager to another member of the group. The difference from the group manager of existing group signatures is that this manager not only manages the group but also delegates authority. Regarding this signature, we implement a simulation program and evaluate the performance. As a result, we show that our proposal is practical.
Motivational Aspects and Modern Innovative Technologies in the Formation of Professional Competencies of Future Specialists in Higher Medical Educational Institutions

Background and Aim of Study: During the research the improvement ideas of the educational system were analysed. Accented attention on an advantages of the distance education, and also his roles and place in the system of higher education. As the demand to the quality of the students' knowledge has become the main criterion in employment. According to the new educational standards at the grant of educational services it is necessary to be oriented on the professional qualities of the future activity of the graduate. The aim of the study: to represent the results of theoretical analysis and empirical study of the formation of oriented educational-scientific information-computer environment on the basis of the Department of Microbiology of Danylo Halytsky Lviv National Medical University. Material and Methods: Modern methods of scientific research used in process: an analysis of the systems, biblio-semantic, statistical, empirical observations and information processing of oriented educational information and computer environment in the process of students’ educational and scientific activity. Results: According to the analysis of the obtained results, the key features of the motivational complex of students of higher educational institution was identified. The ways of organization of control and an analysis of students’ success by means of information and computer technologies according to the state standards of higher education. It is offered, new approaches to forming of content of educational discipline. Conclusions: The innovative tools and interactive applications on the basis of ICT will increase the possibilities of studying the discipline of microbiology, virology and immunology in higher medical institutions. What is appropriate in the context of integration of the new strategy of education.
Human Activity Recognition Using Tri-Axial Angular Velocity

Human Activity recognition is one of the prime applications of machine learning algorithms nowadays. It is being used in the field of healthcare, game development, smart environments, etc. Data from the sensors connected to a person can be employed to train supervised machine learning models in order to foresee the movement being done by the individual. In this paper, we will utilize data available at UCI machine learning Repository. It contains data generated from the accelerometer, gyroscope and other different sensors of Smartphone to train supervised predictive models using machine learning strategies like KNN, Logistic Regression, etc. to generate a model which can be used to predict the kind of movement being carried out by the individual which is branched into six activities like walking, walking upstairs, walking downstairs, sitting, standing and laying. We will be comparing the accuracy of our model using KNN and Logistic Regression.
Ministry of Multispecies Communications

The proposed artwork includes a participatory performative masked walk and a photographic visual essay to explore imaginative reflection and embodied play on data, communication technologies and future urban multispecies relations. These different presentation formats act as socio-material comparisons between different creative acts of more- than-human experimentation. Led by a fictional agency, the Ministry of Multispecies Communications, groups are recruited as trainees to interrogate urban spaces conducive for the well-being of different species. The visual essay and performative walk will draw together insights on situated anticipatory narratives, personalization through making and brand identity to support articulation of both meaningful and conflictual dialogue that draws attention to the sometimes uncomfortable interdependencies of being with each other in more-than-human urban worlds.
Sosyal Medyada Kullanıcı Etkileşimi ve İçerik Kategorizasyonu: Ankara’daki Halk Kütüphanelerinin Facebook Gönderilerinin Analizi

The presence of libraries in social media platforms that mostly based on interaction, sharing, and engagement can contribute to the identification of user expectations as well as service delivery and marketing.In this regard, the content produced by libraries on social media platforms and the reaction data obtained from users can be utilized for impact assessment purposes.In this study, it is aimed to describe user interaction and to detect the content categories by analyzing the Facebook public page contents of public libraries in Ankara.Accordingly, 1739 posts and the interaction data related to these posts were collected on the Facebook pages of 10 public libraries in Ankara.The collected data were firstly presented with descriptive statistics, and then the posts were analysed by using text mining applications.Additionally, topics and sub-topics are detected.The results showed that specific libraries actively use their Facebook pages.It was also revealed in the study that the post contents in the event category were mostly for children and students at primary and secondary levels.The number of posts included in the collection category was lower than the number of posts in other categories.It is recommended in the study that the analyzed public libraries should develop more content according to their target audiences.It is also emphasized that the analyzed libraries should increase the number of post interactions, and they should increase the number of posts related to collections and services.Lastly, It was emphasized that the analyzed libraries should act in the context of a social media strategy to increase user interaction and provide its sustainability.
K-Means Clustering in Monitoring Facility Reporting of HIV Indicator Data: Case of Kenya.

Health management information systems (HMISs) in low- and middle-income countries have been used to collect large amounts of data after years of implementation, especially in support of HIV care services. National-level aggregate reporting data derived from HMISs are essential for informed decision-making. However, the optimal statistical approaches and algorithms for deriving key insights from these data are yet to be fully and adequately utilized. This paper demonstrates use of the k-means clustering algorithm as an approach in supporting monitoring of facility reporting and data-informed decision-making, using the case example of Kenya HIV national reporting data. Results reveal four homogeneous cluster categories that can be used in assessing overall facility performance and rating of that performance.
NON-LINEAR HEBBIAN LEARNING ALGORITHM FOR INTUITIONISTIC FUZZY COGNITIVE MAPS IN PREDICTIVE MODEL

Adv. Math. Sci. J. Vol. 9, No. 4 Open Access NON-LINEAR HEBBIAN LEARNING ALGORITHM FOR INTUITIONISTIC FUZZY COGNITIVE MAPS IN PREDICTIVE MODEL N. Martin and R. Priya DOI: https://doi.org/10.37418/amsj.9.4.66 Full Text Abstract Abstract Fuzzy cognitive map is a tool which establishes the causal relation between the factors of decision-making problem. It is … page AMSJ9-N4-66 Read More »
Optimized Path Planning in Reinforcement Learning by Backtracking

There exist many solutions to the maze pathfinding problemfor more than three decays. The problem is referred to as mazepathfinding, maze navigation, micro mouse and indoor robotpathfinding
Real time direction estimation for pointing interactions using a depth sensor and a nine axis inertial motion unit

Pointing devices are omnipresent in human-AI interaction scenarios. Existing 3D pointing devices like virtual reality(VR) wands, joystick, force balls, and track-pads are either cumbersome or restricted to operate on tabletop like surfaces. There exist gesture-based approaches for 3D pointing that use inertial motion units(IMU) based or computer vision based techniques. Existing IMU-based techniques are suitable only for pointing on fixed surfaces like big displays. Computer vision based methods suffer from issues like incorrect hand detection due to overlap with other body parts, restricted region of operation due to the use of multiple cameras and non real-time operation. To overcome the issues faced by existing commercial devices and techniques we propose index finger based 3D pointing direction estimation(hereafter known as PDE) technique that uses a combination of nine-axis IMU and depth data from a RGB-D sensor. The proposed method works in real-time and is highly robust to variation in the orientation and the depth of the hand w.r.t. the camera. The proposed method is suitable for personal usage applications that require high accuracy like computer games, VR, home automation etc. We show that our technique can be easily incorporated in existing hardware. We provide a few examples of use cases to enable readers to apply the proposed PDE technique to their applications.
Object Detection under Rainy Conditions for Autonomous Vehicles.

Advanced automotive active-safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects such as pedestrians, traffic signs and lights, and other nearby cars, to assist the corresponding vehicles maneuver safely in their environments. However, the performance of object detection methods could degrade rather significantly under challenging weather scenarios including rainy conditions. Despite major advancements in the development of deraining approaches, the impact of rain on object detection has largely been understudied, especially in the context of autonomous driving. The main objective of this paper is to present a tutorial on state-of-the-art and emerging techniques that represent leading candidates for mitigating the influence of rainy conditions on an autonomous vehicle's ability to detect objects. Our goal includes surveying and analyzing the performance of object detection methods trained and tested using visual data captured under clear and rainy conditions. Moreover, we survey and evaluate the efficacy and limitations of leading deraining approaches, deep-learning based domain adaptation, and image translation frameworks that are being considered for addressing the problem of object detection under rainy conditions. Experimental results of a variety of the surveyed techniques are presented as part of this tutorial.
Neural hand reconstruction using an RGB Image

We present a neural hand reconstruction method for monocular 3D hand pose and shape estimation in this paper. Instead of directly representing hand with 3D data, a novel UV position map is introduced to represent hand pose and shape with 2D data, which maps 3D hand surface points to 2D image space. Furthermore, an encoder-decoder neural network is proposed to infer such UV position map from only single image. To train such network with the lack of ground truth training pairs, we propose a novel MANOReg module which employs MANO model as shape prior to constrain high-dimensional space of UV position map. Both quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.
&amp;lt;i&amp;gt;Geospatial upscaling of atrazine’s transport using electromagnetic induction across point to field scales&amp;lt;/i&amp;gt;

<b><sc>Abstract.</sc></b> Atrazine is an herbicide commonly used to control weeds in corn crops. When it moves offsite to surface and groundwater, Atrazine becomes an environmental concern. Understanding how Atrazine moves at the field scale is vital for developing management controls. However, it is unclear if the estimation of Atrazine movement across multiple scales and depths can contribute to mitigate its effect on ecosystem services and human health across environmental managements scales. Our goal is to conceptualize the geospatial upscaling of Atrazine‘s leaching potential from point measurements to field-scale simulations in agricultural landscapes. We hypothesize that geophysical attributes of soils across point to point across a field -estimated through the use of electromagnetic induction (EMI), vadose-zone modeling, and geospatial analytics- can be used to determine and upscale the magnitude of attrazine‘s transport and uncertainties across the point-to-field scale. The study‘s objectives are three-folded: (1) estimate and simulate Atrazine‘s transport using laboratory and EMI-based transport parameters for HYDRUS 2D; (2) estimate the differences between lab- and model-based values of Atrazine; and (3) quantify the Atrazine‘s transport using geophysical attributions of soils geospatially aggregated. EMI-based estimations of Atrazine‘s transport in a silage cornfield at the Northern High Plains in the US are mapped across a field. Results indicate that each transport parameter builts a statistical significant interdependence with the apparent electrical conductivity (ECa) measured by the EMI, which leads to a robust methodology to trace atrazine‘s movement. HYDRUS 2D esimates the atrazine‘s leaching across which is not significantly different from the lab-based estimations. Also, coupling EMI technology and HYDRUS 2D modeling and the application of a geophysically-based-aggregation algorithm lead to a gradient of Atrazine transport from Silt Loams to Clay. Upscaled estimations of Atrazine loss in a season are in average 6 mg ha<sup>-1</sup>, Silt Loams have atrazine loss of 40 mg ha<sup>-1</sup> and Clay have accumulation of atrazine of 26 mg ha<sup>-1</sup>. The errors from the upscaling process are an accumulation of +/-33%. The presented approach enables the design of improved Atrazine management practices reducing surface water and human health risks. Further studies are needed to determine the effectiveness of this approach across a wider diversity of soil types and geospatial scales.
Visualization of Features in Multivariate Gait Data: Use of a Deep Learning for the Visualization of Body Parts and Their Timing During Gait Training

In this study, we aimed to examine the usefulness of gait classification and feature visualization based on multivariate data for the development of a gait feedback training system capable of considering the physical differences among the trainees. The multivariate data considered in this study were the joint angles and the ground reaction forces. In addition, all multivariate gait data were labeled as gait “rarely associated with stumbling” or “frequently associated with stumbling”. A convolutional neural network was used to learn the gait features. Furthermore, the feature parts of the multivariate gait data used for classification were visualized on a heat map created using Grad-CAM. As the results indicate, a heatmap is able to show the feature parts of a gait frequently associated with stumbling, through which the trainee can adjust their gait.
Certifai: A Toolkit for Building Trust in AI Systems

As more companies and governments build and use machine learning models to automate decisions, there is an ever-growing need to monitor and evaluate these models' behavior once they are deployed. Our team at CognitiveScale has developed a toolkit called Cortex Certifai to answer this need. Cortex Certifai is a framework that assesses aspects of robustness, fairness, and interpretability of any classification or regression model trained on tabular data, without requiring access to its internal workings. Additionally, Cortex Certifai allows users to compare models along these different axes and only requires 1) query access to the model and 2) an “evaluation” dataset. At its foundation, Cortex Certifai generates counterfactual explanations, which are synthetic data points close to input data points but differing in terms of model prediction. The tool then harnesses characteristics of these counterfactual explanations to analyze different aspects of the supplied model and delivers evaluations relevant to a variety of different stakeholders (e.g., model developers, risk analysts, compliance officers). Cortex Certifai can be configured and executed using a command-line interface (CLI), within jupyter notebooks, or on the cloud, and the results are recorded in JSON files and can be visualized in an interactive console. Using these reports, stakeholders can understand, monitor, and build trust in their AI systems. In this paper, we provide a brief overview of a demonstration of Cortex Certifai's capabilities.