Reviewerly/exHarmony huggingface dataset:
DEPREM EĞİTİMİNİN İSTANBUL'DA ORTAÖĞRETİM ÖĞRENCİLERİNİN DEPREM BİLİNCİNİN ARTTIRILMASINA ETKİSİ

In this study, it was aimed to determine how the earthquake consciousness of secondary school students and their teachers was affected in Istanbul after education focusing on how individuals behave before, during and after the earthquake. A survey has been conducted to measure the knowledge, attitudes and behaviors of earthquake survivors residing in the regions with high risk of earthquakes. In addition, earthquake consciousness of teachers and students has been measured. One month later, the students and teachers were given earthquake training and the questionnaire was applied again. Findings showed that; earthquake awareness education has a positive effect on creating earthquake consciousness on students and teachers. Life-long earthquake consciousness is directly related to earthquake-oriented planning and maintenance of education and training activities. For this reason, local authorities, teachers and school administrators need to consider how to provide effective earthquake awareness.
Towards a Software Tool for Planning IHE-Compliant Information Systems.

As hospital information systems are complex and the requirements for interoperability grow with the increasing networking in healthcare, careful planning becomes more and more necessary. The use of standards as described in IHE profiles, for example, are an important prerequisite for enabling interoperability. Enterprise Architecture Planning (EAP) methods should support this, but none of the currently available EAP methods offers the option of using IHE profiles. The 3LGM2IHE project wants to close this gap and implement the support of IHE profiles in the 3LGM2 tool. This paper describes how requirements for this tool were determined and presents the results.
Generalised Mermin-type non-locality arguments

We broadly generalise Mermin-type arguments on GHZ states, and we provideexact group-theoretic conditions for non-locality to be achieved. Our resultsare of interest in quantum foundations, where they yield a new hierarchy ofquantum-realisable All-vs-Nothing arguments. They are also of interest toquantum protocols, where they find immediate application to a non-trivialextension of the hybrid quantum-classical secret sharing scheme of Hillery,Bu\v{z}ek and Berthiaume (HBB). Our proofs are carried out in the graphicallanguage of string diagrams for dagger compact categories, and their validityextends beyond quantum theory to any theory featuring the relevant algebraicstructures.
Replication Redux: The Reproducibility Crisis and the Case of Deworming

Abstract In 2004, a landmark study showed that an inexpensive medication to treat parasitic worms could improve health and school attendance for millions of children in many developing countries. Eleven years later, a headline in The Guardian reported that this treatment, deworming, had been “debunked.” The pronouncement followed an effort to replicate and re-analyze the original study, as well as an update to a systematic review of the effects of deworming. This story made waves amidst discussion of a reproducibility crisis in some of the social sciences. In this paper, I explore what it means to “replicate” and “reanalyze” a study, both in general and in the specific case of deworming. I review the broader replication efforts in economics, then examine the key findings of the original deworming paper in light of the “replication,” “reanalysis,” and “systematic review.” I also discuss the nature of the link between this single paper's findings, other papers’ findings, and any policy recommendations about deworming. Through this example, I provide a perspective on the ways replication and reanalysis work, the strengths and weaknesses of systematic reviews, and whether there is, in fact, a reproducibility crisis in economics.
Novel Approach for Automatic Region of Interest and Seed Point Detection in CT Images based on Temporal and Spatial Data

Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) i... | Find, read and cite all the research you need on Tech Science Press
Effect of Relational Coordination on Employee Turnover Intentions through Job Satisfaction: The use of Structural Equation Modeling and Monte Carlo Simulation

Purpose: This study sought to examine the mediating effect of job satisfaction on the relationship between relational coordination and turnover intentions. Methodology: Partial least squares structural equation modeling (PLS-SEM) was used to analyze data a cross-sectional survey of 262 employees banking sector. The capacity of this study&rsquo;s theoretical model was demonstrated in a Monte Carlo simulation that created a simulated data set the underlying true effects were established. Results: The findings of this study suggest that relational coordination negatively affects turnover intentions, and job satisfaction mediates this relationship. The simulation results indicate fluctuation in path coefficients for different correlations and sample sizes. The values of path coefficients smooth out for all levels of correlations, as sample size increases. Limitations: This research used cross-sectional design which limits its ability to determine the true causal relationships. The external viability of results may be limited due to the sample selection banking sector. Although the empirical results support our theoretical model, other descriptions of these results need to be tested. Practical implications: Managers can reduce employees&rsquo; turnover intentions by promoting relational coordination among their employees.Social implications: Reduced turnover intentions may result in reduced actual turnover, which provides stability to organizations and to the lives of individual employees. It may reduce social discontent and instability. Originality/value: No prior research has empirically tested the relationship between relational coordination and turnover intentions. Moreover, this research is first to test mediation between above mentioned relationship. It also provides a greater generalizability of theoretical model by using Monte Carlo simulation.
Fully convolutional adaptive tracker with real time performance

We present a Fully Convolutional Adaptive Tracker (FCAT) based on a Siamese architecture that operates in real-time and is well suited for tracking from aerial platforms. Real time performance is achieved by using a fully convolutional network to generate a densely sampled response map in a single pass. The network is fined-tuned on the tracked target with an adaptation approach that is similar to the procedure used to train Discriminative Correlation Filters. A key difference between FCAT and Discriminative Correlation Filters is that FCAT fine-tunes the template feature directly using Stochastic Gradient Descent while DCF regresses a correlation filter. The effectiveness of the proposed method was illustrated on surveillance style videos, where FCAT performs competitively with state-of-the-art visual trackers while maintaining real-time tracking speeds of over 30 frames per second.
A Training-Free Approach for Generic Object Detection

We present an approach for generic object detection using single query image for finding and locating visually similar objects from target images. The key challenge here is describing an object class using only one query without any training. Our approach is based on computation of Local Self-Similarity descriptors which captures local internal geometric layout within an image and is good representative of object class. We propose to use only predefined landmark points from query image which significantly improves performance of detection. We also present few novel ideas for selection of informative descriptors from the set of all descriptors of the test image to reduce computational expense in feature matching. The algorithm yields Hough-style similarity surface indicating likelihood of presence of the query object at every location. Presence and location of objects are finalized by employing two significance tests followed by non-maxima suppression. We evaluate results of the proposed approach on UIUC car dataset and achieve higher accuracy in comparison with earlier training-free approaches. Results are also analysed on ETHZ shape classes dataset which accounts for large intra-class variations, scale variations, and clutter. Performance of detection on this challenging dataset having diverse contexts demonstrates robustness and efficacy of the algorithm.
#Default #Interactiveart #Audiencexperience

The last decades have shown us a growing interest in different fields of how interactive art transforms the position of the viewer into a participant and how audiences engage and relate to interactive artwork. This article presents a visual analysis of the content shared on Instagram by the audience of Default –an interactive art installation presented in Santiago, Chile in 2017. The analysis shows that people reacted and engaged differently with various aspects of the installation, as shown by the strategies they used to share it. We argue that the analysis of the visual content of Instagram posts opens avenues to understanding the relationship between installation and audience, giving clues on the audience experience and, therefore, providing feedback for developers, who could use them in the design process of future installations.
Bisection searching based reference frame update strategy for digital image correlation

The strategy to update the reference frames in the digital image correlation analysis is an essential but often overlooked problem. A good reference frame update strategy should be able to adjust the frame step in respect of varying practical circumstances including the different loading rate, speckle pattern, plastic deformation, imaging system, lighting condition, etc. In this work, a simple but effective bisection searching (BS) strategy is presented to solve this problem. The frame step is reduced into one half for the unconverged locations, and the intermediate frame is utilized to assist the correlating process. This process is iteratively conducted to adjust the frame step in different regions automatically. The performance of the BS strategy is evaluated against the constant step (CS) update strategy on simulated experiments. The results indicate that the BS strategy can automatically adjust the frame step for changing speckle pattern and loading rate. The accuracy and robustness of the BS strategy are better than the CS strategy with the same pixel level and subpixel level searching algorithms. The BS strategy also successfully tracked all POIs and adjusted the frame step in the real world experiment with changing loading rate and large plastic deformation (over 70% engineering strain).
Using Reinforcement Learning to Optimize the Policies of an Intelligent Tutoring System for Interpersonal Skills Training

Reinforcement Learning (RL) has been applied successfully to Intelligent Tutoring Systems (ITSs) in a limited set of well-defined domains such as mathematics and physics. This work is unique in using a large state space and for applying RL to tutoring interpersonal skills. Interpersonal skills are increasingly recognized as critical to both social and economic development. In particular, this work enhances an ITS designed to teach basic counseling skills that can be applied to challenging issues such as sexual harassment and workplace conflict. An initial data collection was used to train RL policies for the ITS, and an evaluation with human participants compared a hand-crafted ITS which had been used for years with students (control) versus the new ITS guided by RL policies. The RL condition differed from the control condition most notably in the strikingly large quantity of guidance it provided to learners. Both systems were effective and there was an overall significant increase from pre- to post-test scores. Although learning gains did not differ significantly between conditions, learners had a significantly higher self-rating of confidence in the RL condition. Confidence and learning gains were both part of the reward function used to train the RL policies, and it could be the case that there was the most room for improvement in confidence, an important learner emotion. Thus, RL was successful in improving an ITS for teaching interpersonal skills without the need to prune the state space (as previously done).
A novel modified KASUMI block chiper for global system for mobile communications

KASUMI and SNOW-3G are encryption algorithms that are used to provide security in GSM, 3G mobile networks. Many security attacks like rectangle-attack, sandwich-attack, single-key attack, etc. breach the security of the traditional KASUMI block chiper. In order to enhance the security in the traditional KASUMI block chiper, we have proposed the modifications in traditional KASUMI by introducing Rijndaels’s S-box which is present in the SNOW-3G block chiper. Also, we propose to remove the FI function completely from the traditional KASUMI block chiper and suggest the modification in the FO function. In the proposed KASUMI block cipher, two FO functions are introduced that are FO(a) and FO(b). In FO(a), a 16-bit S-box is added, where in FO(b), cyclic shift operation is added. The simulation results show the performance improvement of the proposed work over conventional work in terms of total encryption time and speed.
Press freedom and political instability in the Arab world: An empirical investigation

The purpose of this article is to empirically test the effect of press freedom on political instability in a sample of fifteen Arab countries for the 2006–15 period. Statistical analysis offers a significant effect of press freedom on political instability. However, when democracy, GDP annual growth and GDP per capita annual growth are taken into account, a fragile mixed positive and negative effect exists. Yet, the findings emphasize a significant interaction effect of press freedom and freedom on political instability. An interesting result is that Arab Spring has not positively affected the relationship between press freedom and political instability.
Modeling of Shape Attributes of the BI-RADS Lexicon for Breast Lesions Based on Multi-class Classification

In this paper, it is proposed the modeling of shape attributes of the BI-RADS lexicon for distinguishing between oval, round, lobulate, and irregular lesions. Two multi-class decomposition schemes are evaluated: OVO (one-versus-one) and OVA (one-versus-all), where the linear discriminant analysis is the base classifier. Moreover, both approaches are trained with 17 morphological features invariant to geometric transformations. The experiments considers an image dataset with 1434 mammographies: 820 benign and 614 malignant lesions. Also, the accuracy index (ACC) is used to evaluate the classification performance, where the OVO scheme performed better than the OVA scheme, with $${\text{ACC}} = 0.897$$ and $${\text{A}}CC = 0.818$$ , respectively. Besides, the t-Student test $$(\alpha = 0.05)$$ reveals that both approaches are statistically significantly different $$(p < 0.001)$$ . The results suggest that modeling the shape attributes is feasible, where the OVO scheme is recommended for this task. Moreover, the proposed approach can be extended to model other terms of the BI-RADS lexicon such as margin and density.
An adaptive cost aggregation method based on bilateral filter and canny edge detector with segmented area for stereo matching

In traditional stereo matching, global approach is more accurate but time consuming, also have high accuracy in occlusion area. On the contrary, local approach is usually fast but have bad performance, and easily influenced by noise. This paper proposed a novel method to compute disparity between two images. It is based on local approach, but our new cost function aggregated the cost in global way. This aggregation is processed by a weight map which created by the bilateral filter. Every pixel transfers its own cost information to all pixels on the same object, but this information would be restricted by the weight map. After finishing preliminary depth map, we use L-R check to find occlusion and mismatch pixels to refined our depth map. These refinement mechanics fix occlusion areas by the smallest disparity nearby. At last, we use bilateral filter clean up whole depth map. All of above computing process can be parallelized on GPU or cloud sever. Although this algorithm is designed for lowlevel machine, it still exerts high performance in high-level hardware.
A Novel Weighted Frequent Pattern-Based Outlier Detection Method Applied to Data Stream

Against the problems that the weight value will seriously influence the result of outlier detection, this paper proposes a weighted frequent pattern-based outlier detection method called WFP-Outlier to discover the implicit outliers from the weighted data stream, where the WFP-Outlier method is conducted in two phases. In the weighted frequent pattern mining phase, we use the "pattern extension" operation to extend the "weighted frequent" k-patterns to (k+1)-patterns, and the maximal weight value is used in the mining process to improve the mining efficiency. In the outlier detection phase, based on the mined weighted frequent patterns, we defined two abnormal indexes named Weighted Frequent Pattern Abnormal Index (WFPAI) and Transaction Abnormal Index (TAI) to measure the abnormal degree of the detected transactions, and the transactions with small TAI values are judged as the abnormal transactions. The experimental results on a synthetic dataset show that the proposed WFP-Outlier method is suitable detecting the implicit outliers for the weighted data stream, and the detection efficiency is higher than that of FindFPOF method, OODFP method and LFP method.
Graph-Based Annotation Engineering: Towards a Gold Corpus for Role and Reference Grammar

This paper describes the application of annotation engineering techniques for the construction of a corpus for Role and Reference Grammar (RRG). RRG is a semantics-oriented formalism for natural language syntax popular in comparative linguistics and linguistic typology, and predominantly applied for the description of non-European languages which are less-resourced in terms of natural language processing. Because of its cross-linguistic applicability and its conjoint treatment of syntax and semantics, RRG also represents a promising framework for research challenges within natural language processing. At the moment, however, these have not been explored as no RRG corpus data is publicly available. While RRG annotations cannot be easily derived from any single treebank in existence, we suggest that they can be reliably inferred from the intersection of syntactic and semantic annotations as represented by, for example, the Universal Dependencies (UD) and PropBank (PB), and we demonstrate this for the English Web Treebank, a 250,000 token corpus of various genres of English internet text. The resulting corpus is a gold corpus for future experiments in natural language processing in the sense that it is built on existing annotations which have been created manually.A technical challenge in this context is to align UD and PB annotations, to integrate them in a coherent manner, and to distribute and to combine their information on RRG constituent and operator projections. For this purpose, we describe a framework for flexible and scalable annotation engineering based on flexible, unconstrained graph transformations of sentence graphs by means of SPARQL Update.
Braille text entry on smartwatches

Smartwatches are gaining popularity on market with a set of features comparable to smartphones in a wearable device. This novice technology brings new interaction paradigms and challenges for blind users, who have difficulties dealing with touchscreens. Among a variety of tasks that must be studied, text entry is analyzed, considering that current existing solutions may be unsatisfactory (as voice input) or even unfeasible (as working with tiny QWERTY keyboards) for a blind user. More specifically, this paper presents a study on possible solutions for composing a Braille cell on smart-watches. Five prototypes were developed and different feedback features were proposed. These are confronted with seven specialists on an evaluation study that results in a qualitative analysis of which strategies can be more useful for blind users in a Braille text entry.
A Greedy Algorithm for Extraction of Handwritten Strokes

This paper presents an algorithm for the extraction of handwritten strokes from a binary image. Each stroke is represented by a family of discs covering the area of the handwritten word. These discs are selected and connected to each other using heuristic (especially greedy) techniques.
A hybrid predictive technique for lossless image compression

Compression of images is of great interest in applications where efficiency with respect to data storage or transmission bandwidth is sought.The rapid growth of social media and digital networks have given rise to huge amount of image data being accessed and exchanged daily. However, the larger the image size, the longer it takes to transmit and archive. In other words, high quality images require huge amount of transmission bandwidth and storage space. Suitable image compression can help in reducing the image size and improving transmission speed. Lossless image compression is especially crucial in fields such as remote sensing healthcare network, security and military applications as the quality of images needs to be maintained to avoid any errors during analysis or diagnosis. In this paper, a hybrid prediction lossless image compression algorithm is proposed to address these issues. The algorithm is achieved by combining predictive Differential Pulse Code Modulation (DPCM) and Integer Wavelet Transform (IWT). Entropy and compression ratio calculation are used to analyze the performance of the designed coding. The analysis shows that the best hybrid predictive algorithm is the sequence of DPCM-IWT-Huffman which has bits sizes reduced by 36%, 48%, 34% and 13% for tested images of Lena, Cameraman, Pepper and Baboon, respectively.
The Research and Design of a High Efficient Ball Image Recognition System Based on Microcontroller

A calculation method of the installation height of the camera, a high efficient image processing algorithm for the ball position searching and a coordinate calculation equation are proposed firstly base on the performances of the system. Then, by analyzing the coordinates measured by the method above, the installation errors of the components cause the inaccurate during the coordinates measuring. After that, a coordinate correction method based on matrix transformation theory is proposed, then linearized, simplified and improved. At last, by some measuring tests, the ball image recognition system designed by these algorithms discussed has high accuracy and fast coordinates output rate, and meets the requirements of the Ball and Plate System as the results. These algorithms can also be used in the other object recognition application.