Reviewerly/exHarmony huggingface dataset:
Effective Computer-assisted Terminology Management Through SDL MultiTerm

Abstract Long term changes have been brought about by information and computer technology to translation activity in the information age. Meanwhile, technical translation service demand is increasing, so new strategies and methods are needed to adapt to the change in translation activity. Computer-assisted terminology management tool plays an increasing important role in translation industry, attracting more and more research focus. The paper expounds the forms and significance of terminology management, with special emphasis on SDL MultiTerm. The paper argues that terminology work, also a form of collaborative team work interlinked with the whole translation process, will be further facilitated by computer and information technology to ensure quality translation service.
Paraphrase and parallel treebank for the comparison of French and Chinese syntax

Abstract This paper proposes to study the contrastive syntax of French and Chinese through the lens of syntactic mismatches, and by making use of parallel treebanks. A syntactic mismatch is the non-similarity between the syntactic structures of one linguistic unit and its translation. Syntactic mismatches are formalized using the notion of paraphrase from the Meaning-Text Theory, which allows for capturing mismatches at different levels of the linguistic description (e.g. Semantic, Deep-Syntactic, and Surface-Syntactic). In this paper, we report in details on the types of paraphrases found in the seed corpus used, demonstrating that the Deep-Syntactic paraphrases constitute the best starting point for our study. Then, we show how, starting from the seed corpus, we semi-automatically constructed a multi-layer parallel treebank with the alignment and annotation of paraphrases.
Sports Field Recognition Using Deep Multi-task Learning

In this paper we propose a novel approach to build a single shot regressor, called SFLNet, that directly predicts a parameter set relating a sports field seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports fields and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports fields, which boosts both regression and segmentation performances. SFLNet can be trained with a dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the first attempt to solve this sports field localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.
An Incremental Feature Extraction Method without Estimating Image Covariance Matrix

To solve the problems that Two-Dimensional Principal Component Analysis (2DPCA) can not implement the on-line feature extraction and can not represent the complete structure information, an Incremental 2DPCA (I2DPCA) without estimating covariance matrices is presented by an iterative estimation method, not to deal with the image covariance matrices by the eigenvalue decomposition or the singular value decomposition. The complexity will be greatly reduced and the on-line feature extraction speed can be improved. The proposed I2DPCA can only extract the horizontal features, and thus another Incremental Row-Column 2DPCA (IRC2DPCA) is proposed to incrementally extract the longitudinal ones from the feature matrices of the I2DPCA. The IRC2DPCA can preserve the horizontal and longitudinal features and implement the dimensionality reduction in both row and column directions. Finally, a series of experiments are carried out with the self-built block dataset, ORL and Yale face datasets, respectively. The results show that the proposed algorithms have significantly improved the performances of the convergence rate, the classification rate and the complexity. The convergence rate is over 99%, the classification rate can reach 97.6% and the average processing speed is about 29 frames per second, and it can meet the on-line feature extraction requirements for incremental learning.
Citizen Readiness to Adopt the New Emerging Technologies in Dubai Smart Government Services

The objective of this paper is to examine the level of readiness among Dubai citizens to adopt Dubai Smart Government services using the new emerging technologies. This study adopts the Technology Readiness theory as the underlying factor predicting the citizen's intention to adopt the e-government services. An online survey was conducted and 225 respondents replied to it. The data were analyzed using a structural equation modeling technique. The results show that a citizen's intention to adopt e-government services using the new technologies are strongly influenced by their level of the service's innovativeness and security. Meanwhile, the feeling of discomfort negatively influenced their intention to adopt e-government services. This paper ends by discussing the theoretical and practical contributions of this study.
Glimpses are forever in RC4 amidst the spectre of biases

In this paper we exploit elementary combinatorial techniques to settle different cryptanalytic observations on RC4 that remained unproved for more than two decades. At the same time, we present new observations with theoretical proofs. We first prove the biases (non-randomness) presented by Fluhrer and McGrew (FSE 2000) two decades ago. It is surprising that though the biases have been published long back, and there are many applications of them in cryptanalysis till recent days as well, the proofs have never been presented. In this paper, we complete that task and also show that any such bias immediately provides a glimpse of hidden variables in RC4. Further, we take up the biases of two non-consecutive key-stream bytes skipping one byte in between. We show the incompleteness of such a result presented by SenGupta et al. (JoC, 2013) and provide new observations and proofs in this direction relating the key-stream bytes and glimpses. Similarly, we streamline certain missed observation in the famous Glimpse theorem presented by Jenkins in 1996. Our results point out how biases of RC4 key-stream and the Glimpses of the RC4 hidden variables are related. It is evident from our results that the biases and glimpses are everywhere in RC4 and it needs further investigation as we provide very high magnitude of glimpses that were not known earlier.
Salient Object Detection Using Water Flow Approach and Image Boundary Contrast Map

A salient object in an image or video is the object which looks attractive to the human beings. A salient object has more prominent feature as compared to its background. Interpretation of a salient object is an easier task for human being as compared to systems. Thus, we need different salient object detection algorithms, which are feed into the computer to segment the salient object from the image or video. Detecting a salient object is a crucial task for post processing applications such as object detection, object classification, image compression and so on. Global feature elucidates that salient object is obtained by identifying the element covering major part of larger area. Furthermore, the differentiating boundary between the salient object and background is inseparable due to radiometric intensity which is represented as a local feature. In the research work, a novel salient object detection method has been proposed as Water Flow Driven using Minimum Barrier Distance and Image Boundary Contrast Map. The experimental results and analysis state that Precision-Recall, Mean Absolute and the F-measure has been significantly compared and improved with the existing work.
ModelGenGUIs – High-level Interaction Design with Discourse Models for Automated GUI Generation

Since manual creation of user interfaces is hard and expensive, automated generation may become more and more important in the future. Instead of generating UIs from simple abstractions, transforming them from high-level models should be more attractive. In particular, we let an interaction designer model discourses in the sense of dialogues (supported by a tool), inspired by human-human communication. This tutorial informs about our approach, both about its advantages and its challenges (e.g., in terms of usability of generated UIs). In particular, our unique approach to optimization for a given device (e.g., a Smartphone) that applies Artificial Intelligence (AI) techniques will be high-lighted, as well as the techniques based on ontologies for automated GUI generation and customization. We also address low-vision accessibility of Web-pages, by combining automated design-time generation of Web-pages with responsive design for improving accessibility.
Creating Domain Dependent Turkish WordNet and SentiNet

A WordNet is a thesaurus that has a structured list of words organized depending on their meanings. WordNet represents word senses, all meanings a single lemma may have, the relations between these senses, and their definitions. Another study within the domain of Natural Language Processing is sentiment analysis. With sentiment analysis, data sets can be scored according to the emotion they contain. In the sentiment analysis we did with the data we received on the Tourism WordNet, we performed a domain-specific sentiment analysis study by annotating the data. In this paper, we propose a method to facilitate Natural Language Processing tasks such as sentiment analysis performed in specific domains via creating a specific-domain subset of an original Turkish dictionary. As the preliminary study, we have created a WordNet for the tourism domain with 14,000 words and validated it on simple tasks.
A Project-Oriented Course on Guidance and Control of Autonomous Aerial Vehicles

In this paper, a project oriented course on guidance and control of autonomous aerial vehicles (UAVs) is presented. The paper describes the different modules of the course and how they are addressed using the project oriented approach. The project uses a quadrotor UAV that is used as the case study during the course. In this course, the students learn how to mathematically model quadrotor UAV flight characteristics, develop feedback control algorithms to enable stable flight control and fuse sensor measurements using Kalman filter techniques to estimate the UAV position and orientation. Students develop these concepts through both simulation and interaction with UAV real measurements. Throughout the course, students build a full 6-degree-of-freedom simulation of controlled quadrotor UAV flight using MATLAB and Simulink.
Color laparoscopic image region segmentation after contrast enhancement including SRCNN by image regions

As one of image pre-processing method to detect, recognize, and estimate lesion or characteristic region in medical image processing, there are many studies improved performance and precision of processing by contrast enhancement or super-resolution. However, it is not clarified how condition is better to apply these methods. Therefore, we experimented and discussed on affect for color laparoscopic image quality by the difference of contrast enhancement method. As a result, we obtained knowledge of high similarity among patterns of adaptive histogram equalization in three methods. However, under these conditions, in the case of considering the region segmentation, it is not clarified how processing precision is better. In this paper, first we processed the contrast enhancement for the color laparoscopic frame image cut from surgery video under laparoscopy. Next, we processed super-resolution for generated image. Finally, we compared and discussed by Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM), and texture features for contrast.
Salient object detection based on Two-stream edge attention guidance

Abstract The existing U-Net structure convolutional neural network is widely used in salient object detection, but the problem of target boundary blur is caused by the convolution pooling operation. In order to keep the edge structure of salient objects clear, a two-stream edge attention guided convolutional neural network (SEANet) method is proposed to strengthen the guided learning of edge features, mainly by guiding the edge features to compare the original image features and the depth features are enhanced to improve the accuracy of salient object detection. The loss is minimized by using consistent cross entropy loss and IOU loss to maximize the coincidence rate of the true value map and the predicted map, as well as the actual boundary and the predicted boundary. At the same time, the obtained prediction map is compared with the current mainstream nine models, and good results have been achieved. Four sets of experiments are carried out in the ablation experiment, and the experimental results also confirmed that the model has a great improvement effect on the performance of salient object detection.
Natural alpha embeddings

Learning an embedding for a large collection of items is a popular approach to overcome the computational limitations associated to one-hot encodings. The aim of item embeddings is to learn a low dimensional space for the representations, able to capture with its geometry relevant features or relationships for the data at hand. This can be achieved for example by exploiting adjacencies among items in large sets of unlabelled data. In this paper we interpret in an Information Geometric framework the item embeddings obtained from conditional models. By exploiting the $$\alpha $$ -geometry of the exponential family, first introduced by Amari, we introduce a family of natural $$\alpha $$ -embeddings represented by vectors in the tangent space of the probability simplex, which includes as a special case standard approaches available in the literature. A typical example is given by word embeddings, commonly used in natural language processing, such as Word2Vec and GloVe. In our analysis, we show how the $$\alpha $$ -deformation parameter can impact on standard evaluation tasks.
A Deep Learning-based Super-resolution Model for Bistatic SAR Image

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images.
Discovering Critical Factors Affecting RDF Stores Success

Technologies for the effective and efficient handling of RDF data are one of the main success factors for a larger scale take-up of Semantic Web Technologies in real scenarios. In this regard, several software components (RDF Stores) devoted to the semantic data persistence and retrieval are available in literature. However, each of them may be appropriate and usable for some kinds of tasks and not for others, and a one-size-fits-all killer application for this type of solutions is still not (and probably will never be) available. The large number of available solutions and the lack of widely accepted benchmarks for their rigorous evaluation do not help the selection and the adoption of an appropriate RDF store compliant with the identified needs of a specific case study. In order to contribute to fill this gap, a methodological approach to evaluate and rank the relevant features of the RDF stores is presented in this paper. Such an approach can help on one hand other researchers to discover the factors affecting the success of the RDF stores and the other hand software architects to select which RDF stores best fits the requirements of a certain application scenario.
Spatial Clustered Regression Analysis of 2017 Getis Score Indonesian Malaria Prevalence Data

Abstract Malaria is a disease that is still endemic to Indonesia. In the national scale, morbidity rate related to malaria continued to decline from 2009 to 2017. Eastern areas of Indonesia (namely Papua, West Papua, and East Nusa Tenggara) still have high endemic levels of malaria, while western areas (Islands Sumatera and Java) mostly have low endemic levels or even free of malaria. This indicates that malaria has a clustering pattern. This research used the Annual Parasite Incidence (API) data as the dependent variable and Slide Positivity Rate (SPR), Annual Blood Examination Rate (ABER), number of positive cases on children aged 1-14 years, and the number of Plasmodium falciparum cases and Plasmodium vivax cases data as the independent variables. Since they contain spatial autocorrelation, Getis Ord G_i were employed in the clustered regression analysis. Finite Mixture Model was employed as the clustering method. This study resulted in the forming of 3 clusters. Cities/regencies belonging to Cluster-1 were neither hotspots nor coldspots. Elements of Cluster-2 are SPR hotspots. Elements of Cluster-3 are hotspot areas of API, ABER, number of Plasmodium falciparum cases, number of Plasmodium vivax cases, and number of cases on children aged 1-14 years. Cluster-1 consists of 360 cities/regencies located in Islands Kalimantan, Sulawesi, Sumatera, and Java, and North Maluku Province. Cluster-2 consists of 88 cities/regencies, mostly located in provinces South Sumatera, Bengkulu, Lampung, Banten, DKI Jakarta, and West Java. Cluster-3 consists of 66 cities/regencies located in the Papua Island and East Nusa Tenggara Province.
Technological Tools in Teaching the EU: A Design Thinking Proposal

This chapter focuses on the main challenges of teaching and learning European Union (EU) issues, bearing in mind that the future of the EU is far from being granted, the shock of Brexit, and the new technological innovations. The purpose is to design a methodology for teaching EU using knowledge management and design thinking procedures. Knowledge management refers to information selection, acquisition, integration, analysis and sharing knowledge that takes place in an environment dominated by social networks in which technological links play a major role. The design thinking procedures, as a collaborative methodology, create groups of students in the classroom. Each group should represent an EU member state. Once groups are created, the teacher must give them a task that may include a problem that the EU has had to solve during its history. Then, individual groups must be asked to design a solution. A consensus among all participating groups on the proposed solution should be reached. To design a solution, based on a mind map, groups should be working inside and outside the classroom using technological tools and interacting through social media. At the end of this process, students must play a Kahoot to fix and clarify the key concepts of each lesson. This process must be repeated for all the chapters of the EU syllabus. The syllabus is made up of key issues of the EU. Students should be taken to discover how EU affects their lives and to wonder how they would be without the EU.
Switching Detection and Density Regression Network for Crowd Counting

Counting pedestrians or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem. For crowd counting methods, the detection based and the density regression methods have their advantages and disadvantages in terms of scene complexity and head scale. Therefore, using only one of these methods may not be suitable for pedestrian counting with different densities. Given this current issue, we propose a novel crowd counting framework combining both detection-based and density-based methods, which is named SDDRNet (Switching Detection and Density Regression CNN). First, we divided the entire image into several blocks. Each picture block was labeled by a switch classifier according to the crowd density. Then, for low-density image blocks, we used detection-based networks to predict the number of pedestrians, and for high-density image blocks, we used density-regression-based networks to predict the number of pedestrians. Finally, we fuse the results of crowd counting across all of the blocks to get the final output. We evaluated the proposed method on a realistic rail-way station pedestrian image dataset. The experimental results demonstrate that our proposed method outperforms several tested state-of-the-art approaches in overall performance.
A Wonderful Old World and New Factors of Development

The very complex trends in global development are now being reinterpreted. The epidemic and pandemic were accelerating current trends in world development, both obviously negative and conditionally positive. However, there is no new quality of international relations, including international foreign economic relations. Inevitably, traditional contradictions between organizations, countries, classes and social groups are exacerbated.
On Luminance Noise Removal Using Convolutional Neural Network

This paper's focus is the problem of removing a luminance noise from a raster image, more precisely - an application of artificial neural networks to luminance noise removal.A neural network is one of the newest approaches to image processing, that began to gain popularity nearly in 2010. Nowadays noise removal via neural network processing tends to replace more traditional methods, such as linear and nonlinear filtering. This paper compares two algorithms of removing luminance noise from grayscale images: passing an image into the neural network to remove noise directly, and applying a Gaussian blur filter to an image and passing the blurred one into the neural network to restore the sharpness. The result of a comparative analysis is, that a method with applying Gaussian blur to the image and subsequent image restoring shows better image quality and PSNR metric results, than direct noise removal via a neural network.
Multi-objective trajectory design for overtaking maneuvers of automated vehicles

The paper proposes a trajectory design method for overtaking maneuvers, in which several performances are incorporated. Thus, it leads to a multi-objective optimization task. It is based on a simple mathematical representation of feasible trajectories by using a potential field approach. The solution of the complex computations in the optimization is approximated by neural networks. The proposed solution is based on the idea that there are a large number of collision free feasible trajectories. The goal of the method is to find the trajectory, which is suitable for the specified objectives. The result of the paper is an integrated decision for both longitudinal and lateral directions, such as longitudinal acceleration command and the values of the defined path. The effectiveness of the method is presented through CarMaker simulation environment.