Reviewerly/exHarmony huggingface dataset:
Usability Evaluation of User Requirement–Based Teleconsultation Robots: A Preliminary Report from South Korea

Abstract Background Telepresence robots used to deliver a point-of-care (POC) consultation system that may provide value to enable effective decision making by healthcare providers at care sites. Objectives This study aimed to evaluate usability of teleconsultation robots, based on endusers' needs, that can improve acceptance in future robot applications. Methods This is a single group postdesign study using mixed methods to assess the usability of teleconsultation robots using scenarios. To collect opinions from various departments, 15 nurses or physicians currently working at medical institutions in Korea were selected using purposive sampling. The usability evaluation was conducted on healthcare providers twice at the simulation center; the think-aloud method was used and surveys and interviews were conducted to identify problems or improvements that may arise from the use of robots in hospital settings. Results The results showed that perceived usefulness, perceived ease of use, and satisfaction level each scored 4 points or higher out of 7 points, showing usability of midhigh level. Camera angle control and robot driving functions were the most difficult. Other basic robot user interface was shown to be relatively easy. There was no difference in usability depending on the characteristics of the evaluator. Some functions including user interface were modified based on the usability test. Conclusion Using robots in health care institutions may support effective communication among healthcare providers, thus contributing to health care improvement.
Ontology-Based Context Modeling in Physical Asset Integrity Management

Asset management is concerned with the management practices, technologies and tools necessary to maximise the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximised too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modelling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, twofold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications.
A Deep Paraphrase Identification Model Interacting Semantics with Syntax

Paraphrase identification is central to many natural language applications. Based on the insight that a successful paraphrase identification model needs to adequately capture the semantics of the language objects as well as their interactions, we present a deep paraphrase identification model interacting semantics with syntax (DPIM-ISS) for paraphrase identification. DPIM-ISS introduces the linguistic features manifested in syntactic features to produce more explicit structures and encodes the semantic representation of sentence on different syntactic structures by means of interacting semantics with syntax. Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. Experiments are conducted on the corpus of Microsoft Research Paraphrase (MSRP), PAN 2010 corpus, and PAN 2012 corpus for paraphrase plagiarism detection. The experimental results demonstrate that DPIM-ISS outperforms the classical word-matching approaches, the syntax-similarity approaches, the convolution neural network-based models, and some deep paraphrase identification models.
Android Application for Presence Recognition based on Face and Geofencing

The Attendance system, especially in companies is needed to help assess the attendance and discipline of employees. Some attendance systems that have been made based on the detection of biometrics, barcodes, and QR Codes have not been able to simplify the attendance process where employees still have to queue in front of the attendance machine. This paper aims to design an attendance system that flexible which can simplify and speed up the process by using a mobile application based on geofencing and face recognition so the company does not need to expend the extra cost to buy dedicated machine. The system is using a mobile application as a device to presence. Each of the employees has their own geofencing area which worked as a location virtual boundary. The employee face images are sent to the server from mobile application for the attendance process which includes a recognition process using k-Nearest Neighbours (k-NN) and Principal Component Analysis (PCA). The results obtained are using face recognition k-NN and PCA obtained a 90% accuracy rate with a processing time of 1.5 seconds. The fastest time to do a complete presence is 3.4s which include a geofencing authentication and face recognition process.
Target

Virtual Reality (VR) games and applications require accurate spatial positioning of the head-mounted display (HMD) within the virtual environment. Inside-out tracking technologies such as 'Oculus Insight' derive the HMD position by tracking 'landmarks' in the user's environment. Because these technologies face specific limitations in deriving the HMD position and rotation, players can experience noticeable spatial drift[1]. To develop a better understanding of these limitations and improve tracking accuracy, we tested 'Oculus Insight' under several environmental and user conditions. We used Unreal Engine to develop a calibration program which measures the discrepancy between the HMD's real-world and its in-game positions, in order to calculate the relative spatial drift. We replicated this program in several environments, duration and distance travelled by a user. Preliminary findings suggest methods for improving spatial accuracy over time; most notably providing a white grid pattern against dark carpet, which improved tracking by a factor of 4.3. This study helps researchers and practitioners minimize spatial drift in VR optical tracking systems such as Oculus Insight.
Chinese Speech Synthesis System Based on End to End

This paper describes an end-to-end Chinese speech synthesis scheme, a neural network architecture for speech synthesis directly from text, which is based on the improvement of tacotron2. In order to improve the original seq2seq model, multi attention mechanism is used to replace the original position-based attention mechanism to improve the sound quality of synthetic speech. The prosodic style coding module is added to better show the prosodic and stylistic characteristics of the synthesized speech. Then an LpcNet vocoder is used to replace the original WaveNet to generate time-domain waveform, which improves the synthesis efficiency and reduces the synthesis time. The experiments show that the speech synthesis system is effective and timely. The synthesized speech is natural and has good prosodic style.
Document Re-Ranking Model for Machine-Reading and Comprehension

Recently, the performance of machine-reading and comprehension (MRC) systems has been significantly enhanced. However, MRC systems require high-performance text retrieval models because text passages containing answer phrases should be prepared in advance. To improve the performance of text retrieval models underlying MRC systems, we propose a re-ranking model, based on artificial neural networks, that is composed of a query encoder, a passage encoder, a phrase modeling layer, an attention layer, and a similarity network. The proposed model learns degrees of associations between queries and text passages through dot products between phrases that constitute questions and passages. In experiments with the MS-MARCO dataset, the proposed model demonstrated higher mean reciprocal ranks (MRRs), 0.8%p–13.2%p, than most of the previous models, except for the models based on BERT (a pre-trained language model). Although the proposed model demonstrated lower MRRs than the BERT-based models, it was approximately 8 times lighter and 3.7 times faster than the BERT-based models.
Robust Image Classification with Cognitive-Driven Color Priors

Existing image classification methods based on convolutional neural networks usually use a large number of samples to learn classification features hierarchically, causing the problems of over-fitting and error propagation layer by layer. Thus, they are vulnerable to adversarial samples generated by adding imperceptible disturbances to input samples. To address the above issue, we propose a cognitive-driven color prior model to memorize the color attributes of target samples inspired by the characteristics of human memory. At inference stage, color priors are indexed from the memory and fused with features of convolutional neural networks to achieve robust image classification. The proposed color prior model is cognitive-driven and has no training parameters, thus it has strong generalization and can effectively defend against adversarial samples. In addition, our method directly combines the features of the prior model with the classification probability of the convolutional neural network, without changing the network structure and its parameters of the existing algorithm. It can be combined with other adversarial attack defense methods, such as various preprocessing modules such as PixelDefense or adversarial training methods, to improve the robustness of image classification. Experiments on several benchmark datasets show that the proposed method improves the anti-interference ability of image classification algorithms.
2D numerical modeling of the Jeneberang River Flood due to the overflow of the Bili-Bili Dam

Abstract High rainfall at the end of January 2019 in South Sulawesi, especially in the upstream area of the Jeneberang watershed, caused an increase in the height and volume of water in the Bili-Bili dam reservoir. The elevation of the water level was recorded on January 22, 2019 at 18:00 WITA reaching +101.87 m ASL above the normal water level elevation of +99.50 m ASL. In accordance with the spillway door operating pattern, if the water level exceeds the normal water level elevation limit, the dam floodgates must be opened to prevent dam collapse. To find out the impact caused, it is necessary to numerically model the flow velocity, depth, and inundation area in the downstream area of the Jeneberang river caused by overflow of discharge from the Bili-Bili Dam spillway. The data used are the flow hydrograph data from the outflow of 10-hour dam outflow as an upstream boundary condition and the rating of the rubber dam rating curve downstream of the Jeneberang river as a downstream boundary condition. The 2D research area focuses on 2 sub-districts directly adjacent to the Jeneberang river, namely Pallangga and Somba Opu sub-districts. From the results of flood simulations using the 2D HEC-RAS numerical model, the flow velocity is between 0.57m / s to 4.84 m / s, the depth is between 1.70 m and 7.38 m, and the inundation area is 1.7 km 2 . These results were then calibrated with the results of observations and measurements in the field of the former high flood inundation in the building and the results of interviews with the affected communities which found that the simulation results correlated well at the seven points of measurement in the field using RMSE which is 0.37.
Real-Time, Real World Learning—Capitalising on Mobile Technology

Abstract Parry, Richards and McAuliffe critique an active learning approach that makes use of mobile technology and augmented reality to enhance students’ real-world learning. Students are now bringing a variety of mobile technology into the classroom and the chapter discusses the challenge of maintaining students’ interest and engagement when they can be connected to the world outside the classroom via electronic devices. Although sometimes a distraction, embracing the use of Web 2.0 technologies can provide the “highly active and participatory experiences” that today’s students are searching for (Brill &amp; Park. Facilitating engaged learning in the interaction age taking a pedagogically disciplined approach to innovation with emergent technologies. International Journal of Teaching and Learning in Higher Education, 20 (1), 71. https://doi.org/10.12691/education-4-1-9 ). The chapter explores the use of Web 2.0 technologies and their use to upskill students in the technical complexities of the digital world and the use of specialised online discourses suitable for real world learning and working.
Strengthening human autonomy in the era of autonomous technology.

The aim of this workshop is to address the role that human autonomy presently receives in HCI research and how "autonomous technologies" might challenge, rather than facilitate human autonomy. We acknowledge a need for new ways of understanding HCI and interaction design as digital technologies develop in the "4th wave". In this workshop, participants are invited to a discussion where the autonomous human-being—who can act autonomously with autonomous technologies—is emphasized, beyond simple human control to a more flexible, sophisticated, subtle, and sustainable autonomy.
Blockchain technology to improve transparency in the Brazilian destatization process

In the next years, the amount of public destatization (public-private partnerships and privatizations) in Brazil should increase, according to the federal government. The Brazilian Development Bank (BNDES), a state-owned company, as a new provider of services for public sector and responsible for structuring these projects has the challenge of conduct this process in a transparent and reliable way. This paper presents a proposal to use Blockchain technology (BCT) for privatization and public-private partnerships (PPPs) agreements structured by BNDES, which is expected to increase the transparency not only on the bidding process for study providers, including payments and contracted service delivery, but also on the management of the contract during its execution.
Robust Least-Squares Support Vector Machine Using Probabilistic Inference

The least-square support vector machine (LS-SVM) has been deeply studied in the machine-learning field and widely applied on a great deal of occasions. A disadvantage is that it is less efficient in dealing with the non-Gaussian noise. In this article, a novel probabilistic LS-SVM is proposed to enhance the modeling reliability even data contaminated by the non-Gaussian noise. The stochastic effect of noise on the kernel function and the regularization parameter is first analyzed and estimated. On the basis of this, a new objective function is constructed under a probabilistic sense. A probabilistic inference method is then developed to construct the distribution of the model parameter, including distribution estimation of both the kernel function and the regularization parameter from data. Using this distribution information, a solving strategy is then developed for this new objective function. Different from the original LS-SVM that uses a deterministic scenario approach to gain the model, the proposed method builds the distribution relation between the model and noise and makes use of this distribution information in the process of modeling; thus, it is more robust for modeling of noise data. The effectiveness of the proposed probabilistic LS-SVM is demonstrated by using both artificial and real cases.
Removing Incivility from Google: What determines the number of government content take-down requests?

Requesting Internet platform providers to remove content is one of the common approaches for government worldwide to regulate online incivility. This study investigates the driving forces behind the variation in the number of uncivil content removal requests across countries. By connecting data from the 2017 Google transparency report to a host of societal level indicators, this study finds that the quantity of uncivil content removal requests is related to both social reality and political value factors. Countries with high levels of social vulnerability and low levels of freedom of speech tend to send more requests to Google for removing online incivility. However, democratic and non-democratic societies show different patterns: social reality and political values play important roles in democratic societies while in non-democratic societies, social reality is the only determining factor.
Non Linear Tensor Diffusion Based Unsharp Masking for Filtering of COVID-19 CT Images

COVID-19 is a dreadful disease caused by coronavirus and it belongs to the family of single-stranded RNA viruses. The Computed Tomography (CT) imaging was found to be a primary diagnostic tool in the screening of COVID-19. Preprocessing is the first stage in image processing operation, it improves segmentation and classification accuracy and hence it gains importance. Preprocessing techniques plays vital role in the improvement of image quality and the objective is to minimize noise, elimination of artifacts and aliasing effects. The improved contrast aids image segmentation and compression algorithms for better diagnosis by physicians. The CT images in general are corrupted by Gaussian and salt and pepper noise. The classical filtering techniques are median, Gaussian, bilateral and anisotropic diffusion. This chapter proposes a novel filtering technique, Non Linear Tensor Diffusion based Unsharp Masking for CT images. The performance validation was done by performance metrics like Just Noticeable Distortion (JND), Discrete Entropy (DE) and average mean brightness error (AMBE) for comparative analysis, classical filtering algorithms are used. The filtering algorithms are implemented in Matlab2015b and tested on real time CT images of COVID-19.
Towards a Theory of Hyperintensional Belief Change

AGM’s belief revision is one of the main paradigms in the study of belief change operations. Despite its popularity and importance to the area, it is well recognized that AGM’s work relies on a strong idealisation of the agent’s capabilities and on the nature of beliefs themselves. Particularly, it is well-recognized in the area of Epistemology that Belief and Knowledge are hyperintensional notions, but to our knowledge only a few works have explicitly considered how hyperintensionality affects belief change. In this work, we investigate belief change operations based on Berto’s topic-sensitive framework and provide three different pseudo-contraction operations to account for the hyperintensional behaviour of beliefs. Our work highlights the connection of a foundational hyperintensional theory of belief with the results of AGM Belief Change. Also we propose and characterise different possible contraction-like operations in this framework.
Salient object detection via reliability‐based depth compactness and depth contrast

It can be intuitively inferred that a high-quality depth map can be used to quickly detect the salient region in stereo vision, implying that depth information plays an essential role in stereoscopic visual attention. However, existing methods generally use the depth map as an auxiliary cue to improve the saliency detection performance. In this study, the authors present an algorithm to directly detect the salient object from a high-quality depth image. The proposed algorithm utilises a depth reliability indicator to assess the confidence of a depth image. Depth compactness, a novel feature that incorporates the depth reliability of the super-pixels, is computed as a primary salient feature. Moreover, in order to enhance another salient feature (i.e. depth contrast), they develop a coarse background filtering method to suppress background interference. Experimental results demonstrate that the proposed method performs favourably against the popular depth-aware saliency detection approaches at a lower computational cost.
A Novel Approach to Rapidly Generate Document from Hand Drawn Flowcharts

Flowcharts have a variety of applications in this era. Many business planners, software developers, management, and process engineers are comfortable using pen and paper or whiteboards to draw flowcharts. To bring their hand-drawn flowcharts to the digital world, this paper proposed a methodology to generate a flowchart in a computer file format which can then be embedded in word file, draw.io, or can generate an image. The transformation is done by extracting the basic shapes of a flowchart with the help of the Otsu thresholding algorithm, various types of morphological operations such as erosion, dilation, and opening, polygon approximation using the Ramer-Douglas-Peucker algorithm. At last, a handwriting recognition model is used for texts inside the blocks of a flowchart. Because the proposed method supports editable formats of the transformed flowchart, the flowchart can be finely tuned if the system output is not fully satisfying.
Enhancing Crops Production Based on Environmental Status Using Machine Learning Techniques

The suitable crop for a particular location is necessary for agriculture to bring the most productivity. Here we have designed a model that contains prediction and recommendation with machine learning approaches that determines productivity based on the parameters humidity, rainfall, and temperature. For the prediction, we have applied k-nearest neighbor (KNN), support vector machines (SVM), random forest, naive Bayes' classifier and logistic regression, collaborative filtering, and Multi-Condition Filtering algorithms. After training the dataset and applying these algorithms, we have made a comparison of the algorithms by analyzing the precision. On the other hand, for the recommendation, we have applied collaborative filtering and Multi-Condition Filtering algorithms where these algorithms take input parameters. And then, in the collaborative filtering the input parameter is compared with the trained data that is already in the system and filters out the best 5 crops as output based on cosine similarity and Multi-Condition Filtering algorithm categorizes the crop with a different combination of the high, low and moderate ranges of the input parameter and displays the crop accordingly.
A Model for Evaluating the Performance of a Multiple Keywords Spotting System for the Transcription of Historical Handwritten Documents

This paper proposes a performance model for estimating the user time needed to transcribe small collections of handwritten documents using a keyword spotting system (KWS) that provides a number of possible transcriptions for each word image. The model assumes that only information obtained from a small training set is available, and establishes the constraints on the performance measures to achieve a reduction of the time for transcribing the content with respect to the time required by human experts. The model is complemented with a procedure for computing the parameters of the model and eventually estimating the improvement of the time to achieve a complete and error-free transcription of the documents.