Reviewerly/exHarmony huggingface dataset:
Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network

Phrases play a key role in Machine Translation (MT).In this paper, we apply a Long Short-Term Memory (LSTM) model over conventional Phrase-Based Statistical MT (PBSMT).The core idea is to use an LSTM encoder-decoder to score the phrase table generated by the PBSMT decoder.Given a source sequence, the encoder and decoder are jointly trained in order to maximize the conditional probability of a target sequence.Analytically, the performance of a PBSMT system is enhanced by using the conditional probabilities of phrase pairs computed by an LSTM encoder-decoder as an additional feature in the existing log-linear model.We compare the performance of the phrase tables in the PBSMT to the performance of the proposed LSTM and observe its positive impact on translation quality.We construct a PBSMT model using the Moses decoder and enrich the Language Model (LM) utilizing an external dataset.We then rank the phrase tables using an LSTM-based encoder-decoder.This method produces a gain of up to 3.14 BLEU score on the test set.
PanoFlex: Adaptive Panoramic Vision to Accommodate 360° Field-of-View for Humans

We propose PanoFlex, an adaptive method for projecting panoramic vision using a dynamic distortion method based on eye gaze. We stream real-time video from a 360° camera and project the view on a plane to the user. The user controls the distortion of this equirectangular projection using eye gaze. For our first user study, we compare our method with conventional equirectangular projection considering the impact on spatial perception. For our second study, we perform a simulator sickness evaluation when the user performs regular daily activities. We found that PanoFlex did not carry any significant negative impact towards the user’s spatial perception, perceived task load, and simulator sickness compared to the more conventional equirectangular view.
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health

In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different aspects of the entity in question.However, training supervised models for segment-level classification requires segment labels, which may be more difficult or expensive to obtain than review labels.In this paper, we employ Multiple Instance Learning (MIL) and use only weak supervision in the form of a single label per review.First, we show that when inappropriate MIL aggregation functions are used, then MIL-based networks are outperformed by simpler baselines.Second, we propose a new aggregation function based on the sigmoid attention mechanism and show that our proposed model outperforms the state-of-theart models for segment-level sentiment classification (by up to 9.8% in F1).Finally, we highlight the importance of fine-grained predictions in an important public-health application: finding actionable reports of foodborne illness.We show that our model achieves 48.6% higher recall compared to previous models, thus increasing the chance of identifying previously unknown foodborne outbreaks.
Trajectory Protection Scheme Based on Fog Computing and K-anonymity in IoT

With the development of cloud computing technology in the Internet of Things (IoT), the trajectory privacy in location-based services (LBSs) has attracted much attention. Most of the existing work adopts point-to-point and centralized models, which will bring a heavy burden to the user and cause performance bottlenecks. Moreover, previous schemes did not consider both online and offline trajectory protection and ignored some hidden background information. Therefore, in this paper, we design a trajectory protection scheme based on fog computing and k-anonymity for real-time trajectory privacy protection in continuous queries and offline trajectory data protection in trajectory publication. Fog computing provides the user with local storage and mobility to ensure physical control, and k-anonymity constructs the cloaking region for each snapshot in terms of time-dependent query probability and transition probability. In this way, two k-anonymity-based dummy generation algorithms are proposed, which achieve the maximum entropy of online and offline trajectory protection. Security analysis and simulation results indicate that our scheme can realize trajectory protection effectively and efficiently.
Online Signature Verification Based on a Single Template via Elastic Curve Matching

Person verification using online handwritten signatures is one of the most widely researched behavior-biometrics. Many signature verification systems typically require five, ten, or even more signatures for an enrolled user to provide an accurate verification of the claimed identity. To mitigate this drawback, this paper proposes a new elastic curve matching using only one reference signature, which we have named the curve similarity model (CSM). In the CSM, we give a new definition of curve similarity and its calculation method. We use evolutionary computation (EC) to search for the optimal matching between two curves under different similarity transformations, so as to obtain the similarity distance between two curves. Referring to the geometric similarity property, curve similarity can realize translation, stretching and rotation transformation between curves, thus adapting to the inconsistency of signature size, position and rotation angle in signature curves. In the matching process of signature curves, we design a sectional optimal matching algorithm. On this basis, for each section, we develop a new consistent and discriminative fusion feature extraction for identifying the similarity of signature curves. The experimental results show that our system achieves the same performance with five samples assessed with multiple state-of-the-art automatic signature verifiers and multiple datasets. Furthermore, it suggests that our system, with a single reference signature, is capable of achieving a similar performance to other systems with up to five signatures trained.
Factorization and Normalization, Essentially

$$\lambda $$-calculi come with no fixed evaluation strategy. Different strategies may then be considered, and it is important that they satisfy some abstract rewriting property, such as factorization or normalization theorems. In this paper we provide simple proof techniques for these theorems. Our starting point is a revisitation of Takahashi’s technique to prove factorization for head reduction. Our technique is both simpler and more powerful, as it works in cases where Takahashi’s does not. We then pair factorization with two other abstract properties, defining essential systems, and show that normalization follows. Concretely, we apply the technique to four case studies, two classic ones, head and the leftmost-outermost reductions, and two less classic ones, non-deterministic weak call-by-value and least-level reductions.
AN EMPIRICAL STUDY OF THE RELATIONSHIPS BETWEEN THE FLEXIBLE WORK SYSTEMS (FWS), ORGANIZATIONAL COMMITMENT (OC), WORK LIFE BALANCE (WLB) AND JOB

The research study examines the relationships between the flexible work systems (FWS), organizational commitment (OC), work life balance (WLB) and job satisfaction (JS) for the teaching staff in the United Arab Emirates (UAE).The respondents are chosen from the accredited colleges in the United Arab Emirates (UAE) by convenience sampling method from the cities of Sharjah, Dubai, Abudhabhi and Ajman.They differed in terms of gender, qualifications, tenure of work, department and also the nature of work.A suitable conceptual framework was developed and the proposed model illustrates that flexible work system (FWS) is thought to have an influence on both organizational commitment (OC) and work life balance (WLB).It has been proposed that WLB significantly mediates the relationship between flexible work system and job satisfaction.It is also anticipated that WLB significantly mediates the relationship between organizational commitment (OC) and job satisfaction (JS).A sample size of 224 was chosen and a structured questionnaire was used for collecting the data from the respondents in UAE.The questionnaire consists of 34 statements and they are rated on a likert scale of 1 to 5. For this study, statistical tools like confirmatory factor analysis (CFA) and structural modeling equation (SME) methods have been used through SPSS (version-25) and for assessing the nature of relationships that existed between the studied variables (34 statements in the questionnaire).Prior to testing through structural equation modeling (SEM), the most fundamental criteria tests such as normality and confirmatory factor analysis (CFA) were conducted.As normality was established, the factor loadings were extracted by the procedure of CFA.The results from the data analysis indicated that all the hypotheses are supported and it facilitated for the acceptance of the proposed model.Thus, flexible work system (FWS) had direct effect on organizational commitment (OC) and also had direct effect on work life balance (WLB).Similarly, organizational it commitment (OC) is positively related to work-life balance (WLB).Consequently, the work life balance (WLB) displayed positive relationship towards job satisfaction (JS).The results of mediation analysis revealed the indirect effect of flexible work system An Empirical Study of The Relationships between The Flexible Work Systems (FWS), Organizational Commitment (OC), Work Life Balance (WLB) and Job Satisfaction (JS) For The Teaching Staff In The United Arab Emirates (UAE) http://iaeme.com/Home/journal/IJM12 editor
Yapay Zeka Olgusunun Güncel Sanat Çalışmalarındaki Açılımları

Each individual in the society, which is in an inevitable relationship with technology, is involved in the use and development of artificial intelligence in a large part of their daily activities in a conscious or unconscious manner.The artificial intelligence, which is implemented in many fields of activities such as driverless vehicle design, financial analysis, special education applications, marketing analyses, music classification applications, also diverge in various application areas.As a natural consequence of this situation, artificial intelligence's implementations inthe art are also presented in a variety of recentartworks.It is a matter of wonder to what extent the artificial intelligence that is relying on mathematical grounds with rational symbol systems and the art that has been based on the concept of creativity, yet which is very difficult to define, are integrated into a well-balanced configuration.The findings and research results obtained by artificial intelligence are usually fueled by the basic questionings of human existence.This article discusses the development of artificial intelligence and the various scientific definitions that are being widely appreciated.Moreover, the relationship of creativity with artificial intelligence is examined by exploring the development of information technologies and its effects on contemporary artworks are investigated.
Acoustic Pornography Recognition using Fused Pitch and Mel-Frequency Cepstrum Coefficients

The main objective of this paper is pornography recognition using audio features.Unlike most of the previous attempts, which have concentrated on the visual content of pornography images or videos, we propose to take advantage of sounds.Using sounds is particularly important in cases in which the visual features are not adequately informative of the contents (e.g., cluttered scenes, dark scenes, scenes with a covered body).To this end, our hypothesis is grounded in the assumption that scenes with pornographic content encompass audios with features specific to those scenes; these sounds can be in the form of speech or voice.More specifically, we propose to extract two types of features, (I) pitch and (II) mel-frequency cepstrum coefficients (MFCC), in order to train five different variations of the k-nearest neighbor (KNN) supervised classification models based on the fusion of these features.Later, the correctness of our hypothesis was investigated by conducting a set of evaluations based on a porno-sound dataset created based on an existing pornography video dataset.The experimental results confirm the feasibility of the proposed acoustic-driven approach by demonstrating an accuracy of 88.40%, an F-score of 85.20%, and an area under the curve (AUC) of 95% in the task of pornography recognition.
K-Nearest Neighbor for Classification of Tomato Maturity Level Based on Hue, Saturation, and Value Colors

The selection of tomatoes can use several indicators. One of the indicators is the fruit color. In digital image processing, one of the color information that could be used in Hue, Saturation, and Value (HSV). In this research, HSV is proposed as a color model feature for information on the ripeness of tomatoes. The total data of tomato images used in this research were 400 images from four sides. The maturity level of tomatoes uses five levels, namely green, turning, pink, light red, and red. The process of divide data uses K-Fold Cross Validation with ten folds. The method used for classification is k-Nearest Neighbor (kNN). The scenario of the test performed is to combine the image size with the parameter value of the neighbor (k). The image sizes tested are 100x100 pixels, 300x300 pixels, 600x600 pixels and 1000x1000 pixels. The “k” values tested were 1, 3, 5, 7, 9, 11, and 13. The highest accuracy reached 92.5% in the image size 1000x1000 pixels with a parameter “k” is 3. The result of the experiment showed that the image size has a significant influence of accuracy, but the parameter value of neighbor (k) has an influence that is not too significant.
Paracremonium moubasheri, a new species from an alkaline sediment of Lake Hamra in Wadi-El-Natron, Egypt with a key to the accepted species

During surveys of extremophilic fungi in hypersaline, alkaline lakes of Wadi-El-Natron, Egypt, an interesting fungus was recovered from a mud sample collected from Lake Hamra in Wadi-El-Natron, Egypt.Maximum likelihood analysis of Internal Transcribed Spacer (ITS) gene along with morphological comparisons of related taxa revealed a novel taxon, Paracremonium moubasheri which is described and illustrated in the current study.Paracremonium moubasheri can be distinguished from the known species of the genus by its large conidia, in addition to the presence of chlamydospores.
Hierarchical Multi-label Classification to Identify Hate Speech and Abusive Language on Indonesian Twitter

Hate speech is one type of speech whose spread is banned in public spaces such as social media. Twitter is one of the social media used by some people to broadcast hate speech. The hate speech can be specified based on the target, category, and level. This paper discusses multi-label text classification using a hierarchical approach to identify targets, groups, and levels of speech hate on Indonesian-language Twitter. Identification is completed using classification algorithms such as the Random Forest Decision Tree (RFDT), Nave Bayes (NB), and Support Vector Machine (SVM). The feature extraction used for classification is the term frequency feature such as word n-gram and character n-gram. This research conducted five scenarios with different label hierarchy to find the highest accuracy that can possibly be reached by hierarchical classification. The experimental results show that the hierarchical approach with the SVM algorithm and word uni-gram feature has an accuracy of 68.43%. It proved that the hierarchical algorithm can increase data transformation or flat approach.
Evaluating the Impact of User Stories Quality on the Ability to Understand and Structure Requirements

Scrum is driven by user stories (US). The development team indeed uses, to fill the project’s and the sprints’ backlog, sentences describing the user expectations with respect to the software. US are often written “on the fly” in structured natural language so their quality and the set’s consistency are not ensured. The Quality User Story (QUS) framework intends to evaluate and improve the quality of a given US set. Other independent research has built a unified model for tagging the elements of the WHO, WHAT and WHY dimensions of a US; each tag representing a concept with an inherent nature and granularity. Once tagged, the US elements can be graphically represented through an icon and the modeler can link them when inter-dependencies are identified to build one or more Rationale Trees (RT). This paper presents the result of an experiment conducted with novice modelers aimed to evaluate how well they are able to build a RT out of (i) a raw real-life US set (group 1) and (ii) a new version of the US set improved in quality using QUS (group 2).The experiment requires test subjects to identify the nature of US elements and to graphically represent and link them. The QUS-compliant US set improved the ability of the test subjects to make this identification and linking. We cannot conclude that the use of the QUS framework improved the understanding of the problem/solution domain but when a QUS-compliant US set is used to build a RT, it increases the ability of modelers to identify Epic US. Building a RT thus has a positive impact on identifying the structure of a US set’s functional elements.
Inforex—a Collaborative System for Text Corpora Annotation and Analysis Goes Open

In the paper we present the latest changes introduce to Inforex -a web-based system for qualitative and collaborative text corpora annotation and analysis.One of the most important news is the release of source codes.Now the system is available on the GitHub repository (https://github.com/CLARIN-PL/Inforex) as an open source project.The system can be easily setup and run in a Docker container what simplifies the installation process.The major improvements include: semiautomatic text annotation, multilingual text preprocessing using CLARIN-PL web services, morphological tagging of XML documents, improved editor for annotation attribute, batch annotation attribute editor, morphological disambiguation, extended word sense annotation.This paper contains a brief description of the mentioned improvements.We also present two use cases in which various Inforex features were used and tested in real-life projects.
Multilevel Self-Attention Model and its Use on Medical Risk Prediction

Various deep learning models have been developed for different healthcare predictive tasks using Electronic Health Records and have shown promising performance. In these models, medical codes are often aggregated into visit representation without considering their heterogeneity, e.g., the same diagnosis might imply different healthcare concerns with different procedures or medications. Then the visits are often fed into deep learning models, such as recurrent neural networks, sequentially without considering the irregular temporal information and dependencies among visits. To address these limitations, we developed a Multilevel Self-Attention Model (MSAM) that can capture the underlying relationships between medical codes and between medical visits. We compared MSAM with various baseline models on two predictive tasks, i.e., future disease prediction and future medical cost prediction, with two large datasets, i.e., MIMIC-3 and PFK. In the experiments, MSAM consistently outperformed baseline models. Additionally, for future medical cost prediction, we used disease prediction as an auxiliary task, which not only guides the model to achieve a stronger and more stable financial prediction, but also allows managed care organizations to provide a better care coordination.
Toward automated identification and quantification of meso-scale damage modes in plain weave glass/epoxy composite laminates

We investigated three different automated methods for identification and quantification of meso-scale damage on single-layer composite laminates subjected to ballistic impact by a 5.5 mm (0.22 caliber) right circular cylindrical steel projectile. These methods were based on the combined use of high-resolution optical imaging, fluorescence microscopy, and image detection software. High-resolution images of impacted composite samples were processed using either ImageJ alone (method 1, for sample size of 20 × 20 cm 2 ) or ImageJ plus MATLAB (method 2, for sample size of 20 × 20 cm 2 ), or fluorescence images were processed using ImageJ plus MATLAB (method 3, for sample size of 6 × 5 cm 2 ). These methods were used for identification and grouping of horizontal and vertical transverse tow cracks, and 45° matrix cracks. The identified and grouped damage modes were quantified based on spatial pixel count related to damage modes, and quantified damage modes were used to generate a digital damage map using a separate MATLAB code for all three methods. Finally, the advantages and shortcomings of each of these three automated methods for identification and quantification of meso-scale damage modes were evaluated via comparison to a baseline manual method for identifying and quantifying the damage modes on the same samples.
A Laser-Based On-Machine Measuring System for Profile Accuracy of Double-Headed Screw Rotor

Great length, large weight and other factors may cause difficulty in measuring the profile accuracy of the double-headed screw rotor. To solve this problem, an on-machine measuring system based on a laser-displacement sensor (LDS) was designed and implemented in this paper by taking an LXK100 four-axis whirlwind milling machine as the carrier. To improve the measurement accuracy of the system, the generalized variable-structural-element morphological method, polynomial interpolation algorithm and ellipse fitting method were first combined to realize the rapid subpixel centroid extraction from a noise-containing spot image, thus improving the data acquisition accuracy of the LDS, and then the hybrid method was experimentally verified. Next, a wavelet threshold function with high-order differentiability and adaptive wavelet coefficient contractility was constructed based on the hyperbolic tangent function, so as to inhibit the disturbance from random errors and preserve real profile information, and this method was simulated and verified. Subsequently, a smoothing algorithm for point cloud data was proposed based on the Lagrange multiplier method to avoid the defect of the piecewise curve-fitting method, that is, function continuity and differentiability could not be satisfied at piecewise points. Finally, the profile accuracy was calculated in real time according to the data reconstruction result and the machining quality was judged. The measurement experiment of the double-headed screw rotor indicates that the proposed on-machine measuring system can complete the profile accuracy measurement for a screw pitch within 39.7 s with measurement accuracy reaching ±8 μm, and the measurement uncertainties of the major axis, minor axis and screw pitch are 0.72 μm, 0.69 μm and 1.24 μm, respectively. Therefore, the measurement accuracy and efficiency are both remarkably improved.