Reviewerly/exHarmony huggingface dataset:
A Domain-Independent Ontology for Capturing Scientific Experiments

Semantic web technologies have proved their usefulness in facilitating the documentation, annotation and to a certain extent the reuse and reproducibility of scientific experiments in laboratories. While useful, existing solutions suffer from some limitations when it comes to supporting scientists. Indeed, it is up to him/her to identify which ontologies to use, which fragments of those ontologies are useful for his experiments and how to combine them. Furthermore, the behavior and constraints of the domain of interest to the scientist, e.g., constraints and business rules, are not captured and as such are decoupled from the ontologies. To overcome the above limitations, we propose in this paper a methodology and underlying ontologies and solutions with the view to facilitate for the scientist the task of creating an ontology that captures the specificities of the domain of interest by combining existing well-known ontologies. Moreover, we provide the scientist with the means of specifying behavioral constraints, such as integrity constraints and business rules, with the ontology specified. We showcase our solution using a real-world case study from the field of agronomy.
Performance Evaluation of Lossless Compression Algorithms for Medical Images

With the spread of telemedicine networks, the demand for compression algorithms that provide the efficient transmission of medical data has increased. Although lossy algorithms provide significantly higher compression ratios than lossless methods, lossless techniques are more preferred in the medical field because any loss of information may result in irretrievable results. In recent years, a significant performance improvement as well has been observed in the lossless techniques specialized for medical data. In this study, the performance of lossless compression algorithms with 16-bit support which can be used in telemedicine networks are examined. Various algorithms have been implemented on a wide range of medical data and performance evaluation are presented in quantitative criteria such as processing time and compression ratio.
Limited Receptive Field Network for Real-Time Driving Scene Semantic Segmentation

Most existing real time semantic segmentation models focus on leveraging global context information and large receptive field. However, these undoubtedly introduce more computational cost and limit the inference speed. Inspired by the mechanism of human eyes, we propose a novel Limited Receptive Field Network (LRFNet) which achieves a good balance between the segmentation speed and accuracy. Specifically, we design two sub-encoders: the fine encoder which encodes sufficient context information, and the coarse encoder which supplements spatial information. In order to recover high-resolution accurate outputs, we fuse the features from the two sub-encoders followed by a lightweight decoder. Extensive comparative evaluations demonstrate the advantages of our LRFNet model for real-time driving scene semantic segmentation task over many state-of-the-art methods on two standard benchmarks (Cityscapes, CamVid).
Machine Learning for Quantum Metrology

Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach such bound when only a small number of probes is employed. Here, we discuss an asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limited number of photons is employed.
Performance Degradation Prediction of Industrial Equipment using Machine Learning

Unexpected facility failures of heavy industry derive large cost so that it is important to prevent it beforehand by estimating machine health status, predicting remaining useful life (RUL), and determining proper time schedule of facility for inspection and replacement. Accordingly, active researches have been proceeding recently, such as prognostics and health management (PHM), condition based maintenance (CBM) method for failure prediction, and so on. The PHM and CBM are technologies that collect operating and environment data related to the failure/lifetime of the equipment and predict failure in advance before failure. In accordance with this purpose, this study proposes a method to predict remaining useful life by collected operating and environment data. The methodology proposed in this study includes signal processing techniques to remove noise signal from sensor data, and clustering, support vector machine, and deep learning, which are used to calculate and predicting reduction rate according to usage environment.
OCR based Image Processing with Audio Output for Visually Challenged People

Image Processing is a technique which is used to find the text in a captured image. Visually challenged people face many problems in day to day life. One of the important problems is reading the text. A digital speech synthesizer is used for doing the same. But most of the printed works doesn't have audio version. So the technology of Optical Character Recognition (OCR) and the technology of speech synthesis (TTS) is used. TTS will convert the text of the captured image into spoken form of that text which is easy to understand. MSER algorithm is further used for better accuracy and the image is processed to get an audio output. It can be listened through headset. Tesseract is an open source OCR engine which is useful in text detection. Open CV (Open Source Computer Vision) is a library of programming functions through which the required algorithm is selected. Application of this project is virtual reading environment is possible for visually challenged people. This can also help out the older people with their partial eye-sight or people with diseases like Bleitz where they can see things hazy. This provides a virtual environment for reading text in front of them and is outputted through a headphone.
Language Politics in Pakistan and Its Sociolinguistic Impacts on Sindh: A Critique

This critique is based on the critical analysis of the language and education policies in Pakistan and their impacts on the sociolinguistic situation in the region. The article is poised on the descriptive approach. To evaluate the policy making decision the historical, socio-political and linguistic scenario of Pakistan is analyzed in detail. It also shades light on the motives behind the decision to announce Urdu and English, the two foreign languages, as the official languages of the state, ignoring the indigenous regional languages of the newly created state. Focusing on the political decisions, which cause the socio-linguistic unrest in Pakistan, specially, in Sindh, the second largest province of Pakistan; the article highlights the dents caused to the ethnic and sociolinguistic stability due to the language politics.
DFKI-NMT Submission to the WMT19 News Translation Task

This paper describes the DFKI-NMT submission to the WMT19 News translation task.We participated in both English-to-German and German-to-English directions.We trained standard Transformer models and adopted various techniques for effectively training our models, including data selection, backtranslation, in-domain fine-tuning and model ensemble.We show that these training techniques improved the performance of our Transformer models up to 5 BLEU points.We give a detailed analysis of the performance of our system.
Query-focused Sentence Compression in Linear Time

Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface.This work introduces a new transition-based sentence compression technique developed for such settings.Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence.This theoretically efficient approach achieves an 11x empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings.Such speedups help query-focused applications, because users are measurably hindered by interface lags.Additionally, our technique does not require an ILP solver or a GPU.
Feature Points-Based Object Tracking Using Infrared Sensors in Augmented Reality Environments

Infrared (IR) images obtained from IR sensors are usually less noisy and more robust to illumination changes than RGB images. In this paper, we propose an accurate and robust method for feature points-based object tracking using IR images in augmented reality (AR) environments. Using a stereo camera consisting of an RGB camera and an IR camera, the method captures RGB and IR images of an IR marker composed of a small number of IR LED points randomly placed on a flat square. The method adopts a multi-layer thresholding algorithm devised for robustly detecting the centers of IR LED dots in each IR image. In addition, it makes good use of locally likely arrangement hashing (LLAH) in order to describe and recognize the IR LED centers as feature points, which are used to compute the transformation between the IR camera and the IR marker frames. We have implemented the proposed method and conducted various experiments to show its quality and advantages over previous methods based on marker or feature tracking in RGB images. We expect that it can be very useful where RGB imagebased tracking methods do not work well due to image noises and light conditions.
Informal Learning in STEM Education

Research has shown that exposure to a variety of STEM opportunities will have a long-term effect on individuals and the overall STEM education community (Wai, Lubinski, Benbow, & Steiger, 2010). While many opportunities for informal learning have historically existed within the science and history disciplines, such as museums, nature centers, libraries, and national parks (Fenichel & Schweingruber, 2010), there has been a lot of growth in comparable informal learning environments within STEM education (e.g., engineering, integrated STEM). Participation in informal learning environments allows K-12 grade students to delve deeper into STEM concepts, complementing STEM learning in their traditional school setting. In this chapter, we will review the literature on informal STEM learning and examine the effects these authentic learning experiences in the STEM disciplines will have on their interest and motivation towards STEM careers.
Particle Swarm Optimization based Nonlocal Means for Denoising ECG Signal

An electrocardiogram signal is a recording of electrical activity at the heart. It is one of the most important signals and is widely used to diagnose abnormalities in the heart. However, it is weak and has a small amplitude, making it vulnerable to noise. One method used to reduce noise is to use the Nonlocal means method. This method has been proven to effectively reduce the amount of noise in an electrocardiogram signal. But the performance of the Nonlocal means method is strongly influenced by three parameters, namely bandwidth $\lambda$ , patch half-width $\pmb{P}$ , and neighborhood half-width $\pmb{M}$ . In determining the three parameters, it is done manually so the results are not optimum. The analysis was carried out to determine the significance of the three parameters. Based on the results of the analysis it is known that parameter $\lambda$ has a significant influence on the performance of the Nonlocal means method. If the $\lambda$ value is too large, dissimilar patches will look similar which result in loss of information. Meanwhile, a small $\lambda$ will produce a fluctuating signal which contains a lot of noise. Therefore, in this study, we propose an automatic determination of $\lambda$ parameter to optimize the performance of the Nonlocal means method using the particle swarm optimization method. To measure the performance of the proposed method, we compare it to the state of the art methods such as Wavelet soft thresholding, Empirical Mode Decomposition soft thresholding, Nonlocal means, and modified Empirical Mode Decomposition and Nonlocal means. The results of our experiment showed that our proposed method achieved a better performance in the Mean Squared Error, Percent Distortion, and Signal to Noise Ratio Improvement.
The Effect of Transformational Leadership Style on Employees` Job Satisfaction of Syrian Private Universities

Employee job satisfaction is influenced by the internal organizational environment, which includes organizational climate, leadership types and personnel relationships.The leaders in the universities should be aware that transformational leadership styles play an important role in maintaining successful Job satisfaction..The aim of this study was to examine the effect transformational leadership on job satisfaction of the employees in Syrian private universities.The quantitative method was used in data collection.A two-stage cluster sampling technique was conducted which comprised of 351 faculty members and staff from seven private universities in Syria.A total of three different sets of instruments were used, namely Multifactor Leadership (MLQ), Communicative Competence Scale (CCS) and the Minnesota Satisfaction Questionnaire (MSQ).PLS-SEM technique was used to analyze the relationship between the variables in this study.The study results pointed out that there was a significant relationship between transformational leadership and job satisfaction.
Automated Kidney Segmentation in Magnetic Resonance Imaging using U-Net

Manual analysis of medical images such as magnetic resonance imaging (MRI) requires a trained professional, is time-consuming and results may vary between experts. We propose an automated method fo ...
MY-AKKHARA: A Romanization-based Burmese (Myanmar) Input Method

MY-AKKHARA is a method used to input Burmese texts encoded in the Unicode standard, based on commonly accepted Latin transcription.By using this method, arbitrary Burmese strings can be accurately inputted with 26 lowercase Latin letters.Meanwhile, the 26 uppercase Latin letters are designed as shortcuts of lowercase letter sequences.The frequency of Burmese characters is considered in MY-AKKHARA to realize an efficient keystroke distribution on a QWERTY keyboard.Given that the Unicode standard has not been extensively used in digitization of Burmese, we hope that MY-AKKHARA can contribute to the widespread use of Unicode in Myanmar and can provide a platform for smart input methods for Burmese in the future.An implementation of MY-AKKHARA running in Windows is released at http: //www2.nict.go.
Offline Handwritten Signature Verification using Distance based Classifier

An offline handwritten signature verification system reduces number of occurrences of fraud events.In different document materials like credit cards, passport validation, banking transactions and different financial transactions, signatures are verified.Whether a signature is genuine or forgery is detected by comparing the training and testing data sets.This paper describes an offline handwritten signature verification system using global features.Here, signature verification is done by Euclidean distance which results False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) as 6.66%, 26.66% and 93.3% respectively.
Cyber-Physical Systems Oriented Redundant Network Node

Paradigms shifts form centralized to distributed systems are impacting Enterprise Systems Architectures. The most relevant paradigms are "Internet of Things" and Cyber-Physical Systems". State of the art Enterprise Systems are designed to seamlessly integrate specific components, technologies, sensors and actuators, locally or geographically dispersed. Systems Modelling approaches are facilitating the development of such systems. The focus of this paper define and design a redundant network, seen as a complex network in the context of Cyber-Physical Systems. Such nodes allow for the redundant connection of heterogenous distributed devices. Complex networks can be integrated within different enterprise systems or they can be connected to other complex networks such as Internet of Things based networks. A case study, detailing the preliminary design of a communication device is presented.
Visualizing Trends of Key Roles in News Articles

There are tons of news articles generated every day reflecting the activities of key roles such as people, organizations and political parties.Analyzing these key roles allows us to understand the trends in news.In this paper, we present a demonstration system that visualizes the trend of key roles in news articles based on natural language processing techniques.Specifically, we apply a semantic role labeler and the dynamic word embedding technique to understand relationships between key roles in the news across different time periods and visualize the trends of key role and news topics change over time.
TEXT MINING APPLICATIONS TO FACILITATE ECONOMIC AND FOOD SAFETY LAW ENFORCEMENT

Economic and Food Safety Authority receives on a daily basis reports and complaints regarding infractions, delicts and possible food and economic crimes.These reports and complaints can be in different forms, such as e-mails, online forms, letters, phone calls and complaint books present in every establishment.This paper aims to apply text mining and classification algorithms to textual data extracted from these reports and complains in order to help identify if the responsible entity to analyze the content is, in fact, the Economic and Food Safety Authority.The paper describes text preprocessing and feature extraction procedures applied to Portuguese text data.Supervised multi-class classification methods such as Naïve Bayes and Support Vector Machine Classifiers are employed in the task.We show that a non-semantical text mining approach can achieve good results, scoring around 70% of accuracy.
Where You Look During Automation Influences Where You Steer After Take-Over

When driving a vehicle, gaze direction (where the driver is looking) is tightly coupled with steering actions. For example, previous research has shown that gaze direction directly influences steering behavior. In the context of transitions of control from automated to manual driving, a new question arises: Does gaze direction before a transition influence the manual steering after it? Here we addressed this question in a simplified simulated driving scenario, for maximum experimental control. Participants (N=26) were driven around a constant curvature bend by an automated vehicle, which gradually drifted toward the outside of the bend. An auditory tone cued manual take-over of steering control and participants were required to correct the drift and return to the lane center. Gaze direction was controlled using an onscreen fixation point with a position that varied from trial to trial horizontally and/or vertically. The results showed that steering during manual control was systematically biased by gaze direction during the automated period, but notably in the opposite direction to what might have been expected based on previous research. Whilst further research is needed to understand the causal mechanisms, these findings do suggest that where a driver looks during the seconds preceding a transition to manual control may be critical in determining whether the subsequent steering actions are successful.
Saliency Computational Model for Foggy Images by Fusing Frequency and Spatial Cues

A key challenge of saliency computation in foggy images is how to effectively detect salient objects which are less visible. The primary cause may lie in the fact that the light scattering through fog particles reduces image contrast. In this paper, we propose a frequency-spatial fusion saliency computational model based on discrete stationary wavelet transform (DSWT). The input image is firstly transformed into HSV color space, and the amplitude spectrum of each color channel is adjusted to generate the frequency domain saliency map. Then, the local-global superpixel contrast is measured to obtain the spatial domain saliency map. The DSWT is finally utilized to fuse the frequency-spatial cues. Experimental results indicate that the proposed model can efficiently reduce the influence of light scattering through fog particles, and can achieve the best performance in foggy images comparing to 16 state-of-the-art saliency models.
A Study on the Improvement of College Students’ English Cultural Accomplishment under the Concept of General Education

Besides imparting knowledge, college English course is also a language carrier for students to understand western culture and a way to improve their comprehensive cultural literacy. Therefore, under the guidance of General Education theory, this paper attempts to integrate the appreciation of English literary translations into College English teaching, so that non-English majors can improve their English language ability, at the same time, they can improve their critical appreciation ability and cultural accomplishment.
Binoboost: Boosting Self-Supervised Monocular Depth Prediction with Binocular Guidance

In this paper, we study the problem of self-supervised monocular depth prediction. Owing to the fact that a vast quantity of expensive ground-truth depth data is required in supervised deep learning methods, self-supervised deep learning methods are what we apply to and are more approachable as well. Specifically, we base our model on a monocular disparity network that generates disparity images by training with an image reconstruction loss. Because binocular images implicitly provide epipolar geometry constraints, we find that binocular depth estimators always perform better than monocular ones. Therefore, we propose a novel module (i.e., BinoBoost) to boost our monocular disparity network with binocular guidance during training. In particular, the binocular disparity network is trained in a similar way to the base model to generate proxy ground truth disparity. We drive the outputs of the base model to be the same as relatively better outputs produced by the binocular disparity network. By training our base model and BinoBoost in an end-to-end fashion, we improve the performance on our base model and achieve the state-of-the-art results on the KITTI dataset.
Modeling MWEs in BTB-WN

The paper presents the characteristics of the predominant types of MultiWord expressions (MWEs) in the BulTreeBank WordNet -BTB-WN.Their distribution in BTB-WN is discussed with respect to the overall hierarchical organization of the lexical resource.Also, a catena-based modeling is proposed for handling the issues of lexical semantics of MWEs.
INTERNAL AND EXTERNAL STAKEHOLDERS’ IMPACT ON PRODUCT DEVELOPMENT CURRICULUM DESIGN

The paper examines internal and external stakeholders and their influence on curriculum design work from higher education teachers´ point of view.The teachers do not develop curriculum independently of other stakeholders.Instead, the development activity is seen here as a joint effort of both internal and external stakeholders having a common goal of improving curriculum design and practice.In this case, internal stakeholders include the faculty, the students, and the higher education teachers' own community while external stakeholders refer to the higher education policy and the labour market.The aim of the paper is twofold: Firstly, to analyse and make visible what kind of influence various stakeholders have on curriculum design work in product development education, and secondly, to discuss the ways in which higher education curriculum can be improved as a collaborative process between internal and external stakeholders.