Reviewerly/exHarmony huggingface dataset:
Web System Prototype based on speech recognition to construct medical reports in Brazilian Portuguese

The overall purpose of automatic speech recognition systems is to make possible the interaction between humans and electronic devices through speech. For example, the content captured from user's speech using a microphone can be transcribed into text. In general, such systems should be able to overcome adversities such as noise, communication channel variability, speaker's age and accent, speech speed, concurrent speeches from other speakers and spontaneous speech. Despite this challenging scenario, this study aims to develop a Web System Prototype to generate medical reports through automatic speech recognition in the Brazilian Portuguese language. The prototype was developed by applying a Software Engineering technique named Delivery in Stage. During the conduction of this technique, we integrated the Google Web Speech API and Microsoft Bing Speech API into the prototype to increase the number of compatible platforms. These automatic speech recognition systems were individually evaluated in the task of transcribing the dictation of a medical area text by 30 volunteers. The recognition performance was evaluated according to the Word Error Rate measure. The Google system achieved an error rate of 12.30%, which was statistically significantly better (p-value <0.0001) than the Microsoft one: 17.68%. Conducting this work allowed us to conclude that these automatic speech recognition systems are compatible with the prototype and can be used in the medical field. The findings also suggest that, besides supporting medical reports construction, the Web System Prototype can be useful for purposes such as recording physicians' notes during a clinical procedure.
Design and Prototyping of an Interactive Virtual Environment to Foster Citizen Participation and Creativity in Urban Design

Public Participation encounters great challenges in the domain of urban design concerning decision making and citizens’ appropriation of a future place. Many tools and methods have been proposed to ease the participation process. In this paper we are targeting artefacts used in face-to-face workshops, in which citizens are asked to make design proposals for a public space. We claim that current state of the art can be improved (i) by better articulating digital artefacts with participatory processes and (ii) by providing interfaces that enhance citizen’s spatial awareness and comprehension as well as collective creativity in urban design projects. We present the design and prototyping of an interactive virtual environment that follow the design-science research guidelines.
COMPACT: Biometric Dataset of Face Images Acquired in Uncontrolled Indoor Environment

Biometric databases are important components that help to improve state-of-the-art recognition performance. The availability of more and more difficult data attracts the researchers' attention, who systematically develop novel recognition algorithms and increase identification accuracy. Surprisingly, most of the popular face datasets, like LFW or IJBA are not fully unconstrained. The majority of the available images were not acquired on-the-move, which reduces the amount of blur caused by motion or incorrect focusing. Therefore, in this paper, the COMPACT database for studying less-cooperative face recognition is introduced. The dataset consists of high-resolution images of 108 subjects acquired in a fully automated manner as people go through the recognition gate. This ensures that the collected data contains the real world degradation factors: different distances, expressions, occlusions, pose variations and motion blur. Additionally, the authors conducted a series of experiments that verify face recognition performance on the collected data.
A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship

Objective: Investigators implemented the Rural Information Connection (RIC) project, a library-initiated deployment of iPad Mini 3s for third-year medical students who were enrolled in a seven-month rural longitudinal integrated clerkship (LIC) rotation. The research aims were to determine if devices preloaded with high-quality mobile health apps enhanced the experience and increased access to and awareness of mobile health information resources for the enrolled project participants.Methods: Nine participants enrolled in this mixed methods research project. Pre- and post-survey and structured learning journals (SLJs) were used for data collection on device and app use. Descriptive statistics and thematic coding analysis included data from seven pre-surveys, nine post-surveys, and sixty-four SLJ prompts. The validated Technology Acceptance Model instrument was also incorporated to gauge the devices’ integration into the participants’ workflow.Results: The investigation indicated that the iPad Mini 3 and resources were utilized and integrated at varying levels in the participants’ workflow. Reported use of health information apps suggests a preference for broad-based information sources rather than specific or specialized information resources. Participants performed several tasks on the device, including seeking background information, educating patients, and managing rotation schedules. Participant reflections indicated positive experiences utilizing the device and health information resources, which enhanced their rural LIC rotations.Conclusions: The research analysis demonstrates the information-seeking behavior of medical students immersed in a rural environment and indicates an acceptance of mobile technology into the workflow of participants in this project. Mobile device deployments offer great opportunities for librarians to design innovative programming in medical education.
Catheter segmentation in three-dimensional ultrasound images by feature fusion and model fitting

Ultrasound (US) has been increasingly used during interventions, such as cardiac catheterization. To accurately identify the catheter inside US images, extra training for physicians and sonographers is needed. As a consequence, automated segmentation of the catheter in US images and optimized presentation viewing to the physician can be beneficial to accelerate the efficiency and safety of interventions and improve their outcome. For cardiac catheterization, a three-dimensional (3-D) US image is potentially attractive because of no radiation modality and richer spatial information. However, due to a limited spatial resolution of 3-D cardiac US and complex anatomical structures inside the heart, image-based catheter segmentation is challenging. We propose a cardiac catheter segmentation method in 3-D US data through image processing techniques. Our method first applies a voxel-based classification through newly designed multiscale and multidefinition features, which provide a robust catheter voxel segmentation in 3-D US. Second, a modified catheter model fitting is applied to segment the curved catheter in 3-D US images. The proposed method is validated with extensive experiments, using different in-vitro, ex-vivo, and in-vivo datasets. The proposed method can segment the catheter within an average tip-point error that is smaller than the catheter diameter (1.9 mm) in the volumetric images. Based on automated catheter segmentation and combined with optimal viewing, physicians do not have to interpret US images and can focus on the procedure itself to improve the quality of cardiac intervention.
Overcoming Multi-model Forgetting

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search. Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.
Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming

360 ∘ panorama video displayed through Virtual Reality (VR) glasses or large screens offers immersive user experiences, but as such technology becomes commonplace, the need for efficient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360 ∘ panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the different trade-offs in order to save bandwidth while preserving the video quality in the user’s field-of-view (FoV). Using 360 ∘ VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top-box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection configurations. In this paper, we present an efficient end-to-end design and real-world implementation of such a 360 ∘ streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user’s FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.
An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications

Object tracking is an efficient technique adopted in video surveillance applications for monitoring a particular object in a zone. This paper presents a novel efficient tracking and counting approach for Pedestrians in video sequences. For object detection, a Gaussian Mixture Model (GMM) is used to obtain binary masks. In the Speeded Up Robust Feature feature recognition, only the features of the object are retained. This significantly improves the precision of the Speeded Up Robust Feature method. For clustering the features, an enhanced grouping algorithm Density based Spatial Clustering of Application with Noise is proposed in which the motion features are grouped and the remaining features are excluded. The features are tracked based on optical flow method. For counting the number of objects, the Pedestrian eigen vectors are created based on the Speeded Up Robust Features and the eigen vectors are trained with a SVM (support vector regression machine). The proposed work combines the object detection, feature extraction, and objects counting. The experimental results validate that the proposed pedestrian tracking and counting method is efficient than the existing approaches.
Improved Arabic–Chinese Machine Translation with Linguistic Input Features

This study presents linguistically augmented models of phrase-based statistical machine translation (PBSMT) using different linguistic features (factors) on the top of the source surface form. The architecture addresses two major problems occurring in machine translation, namely the poor performance of direct translation from a highly-inflected and morphologically complex language into morphologically poor languages, and the data sparseness issue, which becomes a significant challenge under low-resource conditions. We use three factors (lemma, part-of-speech tags, and morphological features) to enrich the input side with additional information to improve the quality of direct translation from Arabic to Chinese, considering the importance and global presence of this language pair as well as the limitation of work on machine translation between these two languages. In an effort to deal with the issue of the out of vocabulary (OOV) words and missing words, we propose the best combination of factors and models based on alternative paths. The proposed models were compared with the standard PBSMT model which represents the baseline of this work, and two enhanced approaches tokenized by a state-of-the-art external tool that has been proven to be useful for Arabic as a morphologically rich and complex language. The experiment was performed with a Moses decoder on freely available data extracted from a multilingual corpus from United Nation documents (MultiUN). Results of a preliminary evaluation in terms of BLEU scores show that the use of linguistic features on the Arabic side considerably outperforms baseline and tokenized approaches, the system can consistently reduce the OOV rate as well.
Learning Descriptors With Cube Loss for View-Based 3-D Object Retrieval

3-D object retrieval has been a hot research topic in recent years. Within such a field, view-based approaches are attracting increasing attention because of the flexibility of data representation as well as the reported state-of-the-art performance. One of the most important issues related to view-based 3-D object retrieval is how to learn embedding features that are discriminative across classes while being compactly distributed within each class. In this paper, we analyze the difference between the two tasks of classification and retrieval, and propose a novel way to learn a view-pooling feature via a triplet network. In addition, we propose a new loss, named cube loss, which is able to sample a number of triplets equal to the cube of the samples in a batch. With the new loss, both hard-negative and hard-positive pairs can be effectively investigated. The experimental results on the ModelNet benchmark demonstrate that the proposed method achieves superior performance compared to state-of-the-art approaches.
Multiprocessing Stemming: A Case Study of Indonesian Stemming

Research in the field of Natural Language Processing (NLP) is currently increasing especially with the arrival of a new term that is "big data".The needs of the programming library that ready-touse becomes very important to speed up the phases of research.Some libraries that have already been mature is available but generally for English language and its dependently.So, it can't be used for other languages.Stemming is one of the basic processes that exist in NLP.Indonesian stemming algorithm that often used is ECS (Enhanced Confix-Stripping).One of the libraries that already implemented the algorithm is Sastrawi 1 .Results from the experiment show that the time of stemming processing by Sastrawi is still slow.Therefore, this research will optimize the speed of stemming processing using multiprocessing (MP).The data test are used in this research has manually taken from Wikipedia 2 .The experiment results show that the MP technique can decrease the average time of stemming processing about 98.45%.
Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors

The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameters or some specific behaviors. However, few research works focus on detecting the implicit irregularity involving the combination of diverse behaviors, which can assess the cognitive and physical wellbeing of elders but cannot be directly identified based on sensor data. This paper proposes an Implicit IRregularity Detection (IIRD) mechanism that aims to detect the implicit irregularity by developing the unsupervised learning algorithm based on daily behaviors. The proposed IIRD mechanism identifies the distance and similarity between daily behaviors, which are important features to distinguish the regular and irregular daily behaviors and detect the implicit irregularity of elderly health condition. Performance results show that the proposed IIRD outperforms the existing unsupervised machine-learning mechanisms in terms of the detection accuracy and irregularity recall.
A Lecturer’s and Students’ Perspective toward Ethnic Snake Game in Speaking Class at Universitas Muhammadiyah Malang

In the 21st century era, technology has influenced on many aspects of life includes education. Thus, lecturers are demanded to apply it in teaching and learning process because it can help lecturers deliver the material in attractive ways to trigger the students’ enthusiasm. There were still fewer technology usage in teaching and learning in speaking class as the researchers did a preliminary study (questionnaire) in Universitas Muhammadiyah Malang (UMM). The preliminary study showed that 63% students said their lecturer never uses multimedia-based medium in teaching speaking, yet 89% students thought the usage in classroom was important. Answering the students’ need, the researchers want to use a multimedia-based medium, Ethnic Snake Game, in speaking class for Electrical Engineering students in UMM. Thus, this research belongs to descriptive research. In using this multimedia-based medium, the lecturer and students showed positive responds.
Track-Before-Detect Framework-Based Vehicle Monocular Vision Sensors

This paper proposes a Track-before-Detect framework for a multibody motion segmentation (named TbD-SfM). Our contribution relies on a tightly coupled tracking before detection strategy intended to reduce the complexity of existing Multibody Structure from Motion approaches. Efforts were done towards an algorithm variant closer and aimed to a further embedded implementation for dynamic scene analysis while enhancing processing time performances. This generic motion segmentation approach can be transposed to several transportation sensor systems since no constraints are considered on segmented motions (6-DOF model). The tracking scheme is analyzed and its performance is evaluated under thorough experimental conditions including full-scale driving scenarios from known and available datasets. Results on challenging scenarios including the presence of multiple and simultaneous moving objects observed from a moving camera are reported and discussed.
Normality, Non-contamination and Logical Depth in Classical Natural Deduction

In this paper we provide a detailed proof-theoretical analysis of a natural deduction system for classical propositional logic that (i) represents classical proofs in a more natural way than standard Gentzen-style natural deduction, (ii) admits of a simple normalization procedure such that normal proofs enjoy the Weak Subformula Property, (iii) provides the means to prove a Non-contamination Property of normal proofs that is not satisfied by normal proofs in the Gentzen tradition and is useful for applications, especially in formal argumentation, (iv) naturally leads to defining a notion of depth of a proof, to the effect that, for every fixed natural k, normal k-depth deducibility is a tractable problem and converges to classical deducibility as k tends to infinity.
A Hybrid Approach for Requirements Prioritization Using LFPP and ANN

Requirements prioritization is a most important activity to rank the requirements as per their priority of order .It is a crucial phase of requirement engineering in software development process.In this research introduced a MCDM model for requirements prioritization.To select a best supplier firm of washing machine three important criteria are used.In this proposed model investigation for requirements prioritization, a case study adopted from Ozcan et al using LOG FAHP (Logarithmic fuzzy analytic hierarchy process) and ANN (Artificial Neural Network) based model to choose the best supplier firm granting the highest client satisfaction among all technical aspects.The test was conducted on MATLAB software and result evaluated on fuzzy comparison matrix with three supplier selection criteria based on FAHP and LOGANFIS that shows the decision making outcome for requirements prioritization is better than existing approaches with higher priority.
A Cross-Dichroic-Prism-Based Multi-Perspective Digital Image Correlation System

A robust three-perspective digital image correlation (DIC) system based on a cross dichroic prism and single three charge-coupled device (3CCD) color cameras is proposed in this study. Images from three different perspectives are captured by a 3CCD camera using the cross dichroic prism and two planar mirrors. These images are then separated by different CCD channels to perform correlation calculation with an existing multi-camera DIC algorithm. The proposed system is considerably more compact than the conventional multi-camera DIC system. In addition, the proposed system has no loss of spatial resolution compared with the traditional single-camera DIC system. The principle and experimental setup of the proposed system is described in detail, and a series of tests is performed to validate the system. Experimental results show that the proposed system performs well in displacement, morphology, and strain measurement.
Genetic programming with transfer learning for texture image classification

Genetic programming (GP) represents a well-known and widely used evolutionary computation technique that has shown promising results in optimisation, classification, and symbolic regression problems. However, similar to many other techniques, the performance of GP deteriorates for solving highly complex tasks. Transfer learning can improve the learning ability of GP, which can be seen from previous research on including, but not limited to, symbolic regression and Boolean problems. However, utilising transfer learning to tackle image-related, specifically, image classification, problems in GP is limited. This paper aims at proposing a new method for employing transfer learning in GP to extract and transfer knowledge in order to tackle complex texture image classification problems. To assess the improvement gained from using the extracted knowledge, the proposed method is examined and compared against the baseline GP method and a state-of-the-art method on three publicly available and commonly used texture image classification datasets. The obtained results indicate that the reuse of the extracted knowledge from an image dataset has significant impact on improving the performance in learning different rotated versions of the same dataset, as well as other related image datasets. Further, it is found that the proposed approach in the very first generation of the evolutionary process produces better classification accuracy than the final classification accuracy obtained by the baseline method after 50 generations.
Correction to “ASIC-Resistance of Multi-Hash Proof-of-Work Mechanisms for Blockchain Consensus Protocols”

In the above paper <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , the funding information should read “This work was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government [Ministry of Science, ICT and Future Planning (MSIP)] under Grant NRF-2017R1C1B5017414, and in part by the 2017 Hongik University Research Fund.”