Reviewerly/exHarmony huggingface dataset:
The Simulation Model of the Resistance Factors Affecting the Adoption of Healthcare Robot Technology in Tertiary Care Center of Thailand

Abstract Healthcare robot technology has become the important tool and it has extremely important role in the healthcare field. In spite of it great potential, gaps exist in our grasp of how users will assess change related to the healthcare robot. Previously research shows that when the new technology is used, the user may make decision to accept or resist technology. According to barrier theories this study develops a modify resistance model to describe the factors which resist the medical staffs’ intention to use healthcare robot. The participants were selected from medical staffs (physician, nurse, and staffs) in Tertiary care center located in Thailand. We collected data using questionnaire to test a research model. The research model is analyzed by confirmatory factor analysis (CFA). The empirical results confirm that technological barrier, time barrier and change barrier have negative effect on medicals’ behavioral intention to use healthcare robots. This study is discussed several practical and theoretical implications.
Deep Representation Learning With Feature Augmentation for Face Recognition

Deep Convolutional Neural Networks (DCNN) significantly improve the performance of many computer vision tasks, such as classification, detection, and semantic segmentation. The ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under open-set protocol, but the current algorithm still has the open problem of implementing the criterion. In this paper, we present a feature augmentation network for the IARPA Janus Benchmark C (IJB-C) on a small CNN. The proposed feature enhancement method is used to approximate the identity features, and the original features are augmented by a small automatic encoder-decoder which can be quickly run in an embedded system with limited resources and obtains similar accuracy to a large backbone CNN network.
A Consideration of Evaluation Method of Sentiment Analysis on Social Listening

In recent years, companies around the world are using media information, blogs, word-of-mouth information, etc., and there is an increasing need to utilize them in corporate activities as customer voices. A sentiment analysis engine has been developed to extract sentiment from customer voices based on these needs. The sentiment analysis engine can give a good deal of sentiment information from the document by using customer voices as text data. In order to improve the accuracy, consideration of context and meaning in text data analysis is important with maintenance of a sentiment dictionary. Especially, since blog and word-of-mouth information is often described in a colloquial manner, maintenance must be performed regularly. An appropriate evaluation of sentiment analysis results is necessary to carry out appropriate maintenance. In case of considering the global strategy of the sentiment analysis engine, it is important to eliminate the language characteristics as much as possible in order to efficiently carry out the evaluation and improvement of the sentiment analysis engine. On the other hand, an evaluation that considers the national character and customs of each country is also required. This paper describes the correction method as well as the evaluation method of the sentiment dictionary which is important in sentiment analysis engine development.
Application and Development of Smart Mine in China

The development of coal mining from ancient times to today has experienced the artificial stage, the mechanized mining stage, and the automated mining stage. Now it is developing towards a safer, greener and more efficient unmanned and smart mine. This article suggests that the development of smart mine is a process of continuous improvement. The overall goal of the smart mine is divided into three development stages, from the smart working face and driving face (1.0 era) to the smart of the whole mine (2.0 era), and finally to the subversion of smart mining technology (3.0 era), the state and coal enterprises have given great attention. At present, there are more than 40 mining areas to carry out smart mine construction plans, but there are still many unresolved problems; Finally, it gives a prospect for the development and realization of Smart Mine.
Effects of Prediction-Length on Accuracy in Automatic Assamese word prediction

Word prediction is a technique which increases the typing speed of user by suggesting the relevant word that the user is intended to write. In this research n-gram based language models are used to find the prediction accuracy for different size of prediction-list in Assamese language and the testing is done for both in-domain and mixed-domain text-data. Accuracy of prediction is calculated for a prediction-list length from 1 to 10 and the maximum accuracies obtained are 81.39% and 63.81% with prediction-list length 10 for in-domain and mixed-domain text-data respectively.
Template Based Clustering of Web Documents Using Locality Sensitive Hashing (LSH)

Web template exhibits a typical structure to enable easy and fast access of the web contents. Rich web documents are created using templates by content management system (CMS). CMS populates data from database into web templates to generate web documents. Shopping websites for comparison use web templates to extract and integrate products related data from various e-commerce sites. The problem of template-based clustering of web documents is to partition web documents based on the templates they are generated from. A good amount of research has been done in template-based clustering of web documents but it is limited to homogeneous web documents which are the part of same websites. The proposed work addresses the problem of template-based clustering of heterogeneous web documents (TBCHWD) which are part of different websites. This paper discusses implementation of a Min-Hash-based locality-sensitive hashing (LSH) technique (TBCHWD-LSH). The results obtained are compared with the results obtained using traditional Jaccard (TBCHWD-Jaccard) and Cosine (TBCHWD-Cosine)-based web documents similarity measuring techniques. The experimental results of more than 100 large-sized web documents collected from 10 different domains prove the effectiveness of TBCHWD-LSH in terms of accuracy over TBCHWD-Jaccard and efficiency in terms of execution time and scalability over TBCHWD-Jaccard and TBCHWD-Cosine techniques.
An Advanced Attendance Marking system using facial Recognition

Abstract With advancement in computing and telecommunications technologies, digital images and videos are playing main role in the current information era. Human face is an important bio-metric object in image and video database of surveillance systems. Detecting and location of human faces and facial features in an image sequence is an important task in dynamic environments, such as videos where noise conditions, illuminations, locations of subjects and pose can vary significantly from frame to frame. An automated attendance system is used for marking human face recognition in real time for college to mark the attendance for their staff and students. Smart automated attendance marking using real time face recognition is a real world solution which comes with day to day activities of handling employees. Here multiple user faces are detected and recognized with the data base trained multiple texture based notability.
A structured perspective of volumes on active learning

We approximate the version space which covers all feasible classification hypotheses into a structured geometric hypersphere against agnostic distribution. We present a structured perspective that divides the available active learning (AL) sampling approaches into two kinds of strategies: Outer Volume Sampling and Inner Volume Sampling. For the outer volume, it is represented by a circumscribed hypersphere which would exclude any outlier (non-promising) hypothesis from the version space globally. While for the inner volume, it is represented by many inscribed hyperspheres, which cover most of hypotheses within the outer volume. To enhance the performance of AL, we aggregate the two kinds of volumes to eliminate their sampling biases via finding the optimal inscribed hyperspheres in the enclosing space of outer volume. We then propose a Volume-based Model for the AL sampling without any distribution assumption. To generalize our theoretical model, in a non-linear feature space, spanned by kernel, we use sequential optimization to globally optimize the original space to a sparse space by halving the size of the kernel space. Then, the expectation maximization (EM) model which returns the local center helps us to find a local representation. To describe this process, we propose an easy-to-implement algorithm called Volume-based AL (VAL). Empirical evaluation on a various set of structured clustering and unstructured handwritten digit data sets have demonstrated that, employing our proposed model can accelerate the decline of the prediction error rate with fewer sampling number compared with the other algorithms.
Extracting issues significant to valuing electricity from small photovoltaic systems using quantitative content analysis

The feed-in tariff (FIT) scheme for small photovoltaic (PV) systems is fast approaching its closure or expiration in many countries. One of the issues after the closure of the FIT will be to determine the selling price of the surplus PV generation, for which valuing the surplus PV generation is essential. This study extracted issues significant to valuing electricity from PV systems by using quantitative content analysis in four states in Australia, where the local economic regulator conventionally estimates the value. Through quantitative content analysis, the region-specific issues relevant to each state were extracted. For example, forecast of wholesale price is the most significant issue in Victoria, where the methodology was not clear and the avoided wholesale cost tends to be overestimated. Similarly, in New South Wales and Queensland, merit order effect was the most significant issue among the stakeholders, where PV installation was higher than that in the other states. Thus, the proposed analysis method can be used to extract the issues relevant to valuing the electricity from PV generation.
Imbalanced Learning of Weighted Extreme Learning Machines Ensemble Algorithm in Wastewater Treatment Plant Fault Diagnosis

Fault diagnosis by machine learning techniques is especially crucial to maintain the stability of the water processing flow in wastewater treatment plants (WWTPs). A key factor influencing the accuracy of fault diagnosis lies in the imbalance data distribution between majority classes (i.e., normal situations) and minority classes (i.e., faulty situations), which may cause misjudgments of faults and lead to failure in practical use. This study proposes an ensemble classification method called AdaWELM ensemble algorithm for fault diagnosis in wastewater treatment which builds up individual classifiers by using weighted extreme learning machine (WELM) and combines them with Adaboost ensemble algorithm. The weight matrix in WELM can be updated adaptively along with the iteration of Adaboost learning. The simulations based on 25 benchmark datasets from KEEL repository are arranged at first and the results show the proposed classifier outperforms the other comparative classifiers for most of the evaluated datasets. And then, a practical fault diagnosis model in wastewater treatment plant based on AdaWELM ensemble algorithm is built and the simulations verifies that, comparing with several classifiers, this fault diagnosis model can efficiently improve the performance of WWTP, especially improve the accuracy of faulty class and G-mean value. So we think the proposed AdaWELM ensemble method could be a promising solution for imbalanced data classification in WWTPs.Through experiments, we find that the performance of G-mean value and accuracy of minority class in a case study of WWTPs are improved by comparing with several other related classification algorithms.
Tradeoff-based architecting of the software system for autonomous robotized open pit mining

This paper presents the architectural solution for a mission-critical, software intensive, large-scale software product intended for autonomous robotized open pit mining. The idea is to establish a stable architecture as early as possible. Therewith, the core concern is providing resilient communication between the client and the developer parties. The key issue is aligning functional requirements with the constraints of both technical and business nature. Although software engineering provides a number of frameworks, a few are really suitable for such an alignment and resilience. For this reason, we suggest a carefully selected blend of tradeoff-centered analysis and optimization methods, including ATAM and ACDM from CMU/SEI and our own enterprise architecture matrix-based approach. Based on the approach suggested, we identify and prioritize the mission-critical quality attributes required for the software product to be built, and describe the architecture in terms of components and connectors as prescribed by the SWEBOK software engineering standard. In addition, based on the product scale and scope, we recommend the high-level architecture patterns for the future system, such as layers, pipelines, and microservices.
LiDAR and Camera Sensor Fusion for 2D and 3D Object Detection

Perception of the world around is key for autonomous driving applications. To allow better perception in many different scenarios vehicles can rely on camera and LiDAR sensors. Both LiDAR and camera provide different information about the world. However, they provide information about the same features. In this research two feature based fusion methods are proposed to combine camera and LiDAR information to improve what we know about the world around, and increase our confidence in what we detect. The two methods work by proposing a region of interest (ROI) and inferring the properties of the object in that ROI. The output of the system contains fused sensor data alongside extra inferred properties of the objects based on the fused sensor data.
An Attention-Based Approach for Mongolian News Named Entity Recognition

In the field of Natural Language Processing (NLP) of Mongolian, Named Entity Recognition (NER) has great significance. The traditional model is to use the Conditional Random Field (CRF) and Long-Short Term Model (LSTM) method. According to the characteristics of Mongolian, a named entity recognition method based on attention mechanism is proposed in this paper. According to the characteristic of the word-building of the Mongolian language, the suffix of the partial word is divided into morphemes. Based on morphemes, character vectors are trained by LSTM. After that, the word vector is sent to another LSTM to get its context representation. Then the attention mechanism is used to obtain the representation of the full text range of the character vector. Finally, the label sequence of the article is obtained by using CRF. The experimental results show that the Mongolian Named Entity Recognition of attention mechanism is superior to the traditional Bi-LSTM-CRF joint model.
Performance Appraisal of an Educational Institute Using Data Mining Techniques

The proper implementation of a performance appraisal system in an educational institute could enhance the growth and development of its faculty members, which will positively reflect on the institution and education system as a whole. Improving the efficiency of institutes depends mainly on encouraging the skills and motivating to improve and upgrade the performance of teaching faculties. The main objective of this work is to improve the faculty performance so as to strengthen the department and the institute. Data are collected from the educational institutes about the faculty’s performance. A data set includes multiple sets of questionnaires for assessing the quality of faculty. Formative and Summative Assessment is taken into consideration along with grades assigned by the management for overall performance. Feedback is collected in the form of questionnaires for analyzing professional, research, and other traits of faculties. All the three parameters are given some weightages for analysis and yielding results. The proposed paper recommends performance appraisal of educational institutes using Data Mining Techniques.
A Novel Skeleton Spatial Pyramid Model for Skeleton-based Action Recognition

With the development of computer science and the rise of deep learning technologies, the skeleton-based action recognition dataset has become larger and larger, which has pushed experts and scholars in the field of action recognition to seek more efficient and accurate algorithms. Considering the most critical factors in the task of action recognition are the intra-frame representation of the joints of a skeleton and the inter-frame representation of the skeleton sequence, we propose a novel skeleton spatial pyramid model (S-SPM). The spatial information of different levels is gradually weighted and aggregated, which effectively models the spatial features of the skeleton sequence. Then the spatio-temporal feature representation based on the skeleton spatial pyramid model is proposed to model the temporal information to obtain deep spatio-temporal feature. Finally, the spatio-temporal feature is fed into the convolutional neural network (CNN) to effectively recognize the actions. The experimental results of the proposed algorithm in the NTU RGB+D dataset show that the S-SPM can improve the accuracies for skeleton-based action recognition.
Cross-Ratio Based Gaze Estimation for Multiple Displays using a Polarization Camera

While eye tracking has been typically used for achieving intuitive user interfaces, it is not sufficient when it comes to dealing with multiple-display environments. In such environments, which have become popular recently, the point-of-gaze should be estimated on multiple screens. Therefore, we propose a cross-ratio based gaze estimation using a polarization camera for multiple screens. The point-of-gaze can be estimated on each monitor by identifying the screen reflected on the corneal surface at a polarization angle. Near-infrared light emitting diodes (NIR-LEDs) attached to the display are not required. This means that standard displays can be used with high general versatility as an advantage.
Improving Collaborative Filtering’s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information

Collaborative filtering generates recommendations tailored to the users’ preferences by exploiting item ratings registered by users. Collaborative filtering algorithms firstly find people that have rated items in a similar fashion; these people are coined as “near neighbors” and their ratings on items are combined in the recommendation generation phase to predict ratings and generate recommendations. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as “Early Adopters”, who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we propose an algorithm that considers the eagerness shown by users to adopt products, so as to leverage the accuracy of rating prediction. The proposed algorithm is evaluated using seven popular datasets.
Factors Affecting a Mobile Learning System: A Case Study

The emergence of m-learning, i.e., mobile learning, has given further strength to traditional teaching and method, as it has a great advantage in social interaction, teaching/learning methods, and knowledge transformation. The m-learning cannot be an alternative to the traditional teaching methods, but a technical facet of new teaching and learning. It is alternative to the e-learning, but with many major changes. A learner plays a very critical role in any learning environment. Learner, as a factor of m-learning, also makes certain impact on m-learning environment. Learning objectives are impacted by different attributes of the student and influences the way in which learning happens. The characteristics of the learner play a vital role in determining whether or not the learning experience is meaningful. Understanding of these learner characteristics is dynamic and a complex process. This chapter will discover the factors that make an impact on m-learning system, and then it analyzes roles of a learner and their contribution to m-learning.
Face recognition on 3D point clouds

Point cloud has achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, although image-based algorithm become more accurate and faster, open world face recognition still suffers from the influences i.e. illumination, occlusion, pose, etc. 3D face recognition based on point cloud containing both shape and texture information can compensate these shortcomings. However training a network to extract discriminative 3D feature is model complex and time inefficient due to the lack of large training dataset. To address these problems, we propose a novel 3D face recognition network(FPCNet) using modified PointNet++ and a 3D augmentation technique. Face-based loss and multi-label loss are used to train the FPCNet to enhance the learned features more discriminative. Moreover, a 3D face data augmentation method is proposed to synthesize more identity-variance and expression-variance 3D faces from limited data. Our proposed method shows excellent recognition results on CASIA-3D, Bosphorus and FRGC2.0 datasets and generalizes well for other datasets.
Mathematical Foundation of Cognitive Computing Based Artificial Intelligence

Today Cognitive computing and Artificial Intelligence (AI) face the same challenges namely, simulate human thought processes and mimic the way human brain works. The main difference between Cognitive computing and AI is: (i) AI models various functions of human intelligence, where computer is one of the modelling means though often the most important one, i.e. intelligence is in the focus while (ii) Cognitive computing models human thought processes and simulates the hypothetical way human brain works as computation. Our aim is to develop a theoretically and methodologically well-founded theory of AI together with a unified computational theory, which will provide specific tools and methods for Cognitive computing. To achieve our goal we follow a methodology triangle, consisting of a conceptual-philosophical, a system theoretical and a logical-mathematical component. Computing will play a fundamental role in both system-theoretical and logical-mathematical methodological components. Hereby we concentrate on the development of the logical-mathematical foundation in detail by the use of category theory, which provides an excellent frame for defining all notions necessary for developing a universal theory for computing, specification, cognitive reasoning, information, knowledge and their various combinations. Foundation theory is by the use of the so-called constitutions, the mathematical basis for the cognitive computation. Logical foundation will be developed as a special constitution and cognitive computing processes are defined by using situations, infons and information. The main properties are discussed with some examples.