Reviewerly/exHarmony huggingface dataset:
The Impact of Corruption on Human Development in Egypt

The objective of this study is to investigate the impact of corruption on human development in Egypt, both in the short and long run, for the period from 1995 to 2018 using the Autoregressive Distributed Lag Model (ARDL). There is a broad amount of the literature that examines the effects of corruption on economic development, but less of the literature, especially empirical, focuses on corruption's effect on human development. Some studies investigate its influences on poverty, others examine those influences on education and/or health but this study assesses its impact on human development expressed by the Human Development Index with its three dimensions. This study contributes to the literature on investigating the determining factors of human development by adding corruption as one of the institutional variables influencing human development besides economic factors. The empirical analysis shows that corruption has a negative and significant effect on human development in both the short and long run, meaning that increased corruption weakens human development. As for GDP per capita, it has a significant and positive effect on human development in both the short and long run. While the government expenditure's effect is insignificant in the short run, it becomes significantly positive in the long run. With regard to urbanization, it affects human development negatively in the short run, and its effect becomes positive -but insignificant- in the long run. The study concludes that policymakers can combat corruption by focusing on causes not on effects.
Flexibility of Indonesian text pre-processing library

&lt;span lang="EN-US"&gt;This study aimed to achieve and measure flexibility as a software quality factor of text pre-processing libraries with Indonesian text from social media. Library was built based on a review of some text mining applications that did not yet have a special pre-process for Indonesian text. Text pre-processing libraries were designed and built using an object-oriented approach that was modular to achieve flexibility. Flexibility was measured by the Mc Call Cyclomatic Complexity (CC) metric. Flexibility of library was tested by implementing the library into text mining applications. The results of experiment showed that text pre-processing libraries could be flexible and easy to use without much configuration in text mining applications. It was proved by the value of CC of 2.51 which meant the library or software was not too complex, simple enough, and also flexible to us&lt;/span&gt;e&lt;span lang="EN-US"&gt;.&lt;/span&gt;
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation

Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes. In this paper, three patterns (cross-skip, skip-1 and skip-2) of distributed dense connections (DDCs) are proposed to enhance feature reuse and propagation of CNNs by constructing tunnels between key layers of the network. For better detecting and segmenting brain tumors from multi-modal 3D MR images, CNN-based models embedded with DDCs (DDU-Nets) are trained efficiently from pixel to pixel with a limited number of parameters. Postprocessing is then applied to refine the segmentation results by reducing the false-positive samples. The proposed method is evaluated on the BraTS 2019 dataset with results demonstrating the effectiveness of the DDU-Nets while requiring less computational cost.
A Novel Random Split Point Procedure Using Extremely Randomized (Extra) Trees Ensemble Method for Human Activity Recognition

INTRODUCTION: Automatic detection and recognition of various human physical movements while performing daily life activities such as walking, jogging, running, sitting, standing etc. are usually considered as Activity Recognition (AR).AR is a prominent research area in many applications, such as elderly care, security and surveillance, smart homes, health and fitness.Extremely Randomized Trees Classifier (ET Classifier) is a type of ensemble learning technique used in Activity Recognition, which clusters several different decision trees into a forest from a single learning set and gives the classification result.But it suffers from high variance and over-fitting problem due to high inter-dependency among hyperparameters during model building.OBJECTIVES: The primary objective of this paper is to propose a novel Random_Split_Point procedure for Extra tree classifier to make the existing approach more robust, less variance, less computational time in obtaining optimal split points and faster in model building.This approach generates K random split points from all the candidate features of the dataset and selects the best split point based on the maximum score obtained by information gain measure.METHODS: In the proposed method to improve the randomization and accuracy of AR system, a novel random split-point procedure for ET classifier is proposed.This approach reduces the bias-variance problem induced due to the three hyperparameters such as K, nmin and M used in split-point procedure of existing ET classifier (K : number of randomly selected attributes at each node, nmin : minimum sample size for splitting a node, M : number of decision trees for ensemble).This approach generates K random split points from all the candidate features of the dataset and selects the best split point based on the maximum score obtained by information gain measure.RESULTS: The proposed approach is experimented with two public AR datasets HAR and HAPT (UCI Machine Learning Repository) containing 6 and 12 activities respectively.In HAR dataset, smartphone sensed sensor signals of 3 static and 3 dynamic human daily activities are there, where as in HAPT dataset apart from these 6 daily activities, 6 postural transitions data is available.Experimental results and comparative analysis show that the proposed method outperforms over other existing techniques with an accuracy of 94.16% for HAR dataset and 92.63% for HAPT dataset.It also takes less computational time in finding optimal split-points and less model building time.CONCLUSION: AR systems can be used as an intelligent system in healthcare to monitor the behaviour of healthy people by recognizing their daily activities.These systems also help in early detection of some chronic diseases and improve the quality of life.In this paper, an attempt is made to improve the accuracy of Activity Recognition over some existing methods.
Hydrologic Time Series Anomaly Detection Based on Flink

The data mining and calculation of time series in critical application is still worth studying. Currently, in the field of hydrological time series, most of the detection of outliers focus on improving the specificity. To efficiently detect outliers in massive hydrologic sensor data, an anomaly detection method for hydrological time series based on Flink is proposed. Firstly, the sliding window and the ARIMA model are used to forecast data stream. Then, the confidence interval is calculated for the prediction result, and the results outside the interval range are judged as alternative anomaly data. Finally, based on the historical batch data, the K -Means++ algorithm is used to cluster the batch data. The state transition probability is calculated, and the anomaly data are evaluated in quality. Taking the hydrological sensor data obtained from the Chu River as experimental data, experiments on the detection time and outlier detection performance are carried out, respectively. The results show that when calculating the tens of millions of data, the time costed by two slaves is less than that by one slave, and the maximum reduction is 17.43%. The sensitivity of the evaluation is increased from 72.91% to 92.98%. In terms of delay, the average delay of different slaves is roughly the same, which is maintained within 20 ms. It shows that, under big data platform, the proposed algorithm can effectively improve the computational efficiency of hydrologic time series detection for tens of millions of data and has a significant improvement in sensitivity.
Attention-Based Adaptive Memory Network for Recommendation With Review and Rating

Item recommendation has become a significant means to help people discover interesting items. Meanwhile, plenty of reviews and ratings in recommender system can be utilized to relieve data sparsity problem. However, existing review-based approaches ignore the influence of static preference of user and the static characteristics of item, which could reflect long-term and stationary property, and guide feature extraction from reviews. Moreover, adaptive property, i.e., the importance of the historical records to each user and item, is not fully exploited in previous works. In this paper, we propose an Attention- based Adaptive Memory Network (AAMN) model to leverage historical reviews and ratings systemically. Specifically, we propose an attention mechanism guided by the static features to learn the importance of different historical records, for modeling the adaptive features of users and items. Notably, this paper is the first to bring static features into adaptively extracting semantic information from reviews, which can not only characterize user and item from a global view, but also assist to distinguish the importance of different reviews. In addition to the attention mechanism, we propose a non-linear feature fusing layer and a deep interaction layer to combine the static features and adaptive features, which capture underlying interactions among these features. To further improve prediction accuracy and training efficiency, we propose a dynamic sampling strategy for model training. We conduct extensive experiments on 16 benchmark datasets from Amazon and Yelp. The results demonstrate that our model outperforms the state-of-the-art models.
A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys

Large numbers of seismic channels and high-density energy-efficient acquisition systems are the development trend of seismic instruments and have attracted high R&amp;D interests in recent years. The combination of remote sensing and wireless sensor network technology provides superior observation capabilities for high-density seismic exploration. However, large-scale and multi-node acquisition methods place higher requirements on time synchronization performance. Seismic data with poor time synchronization will cause considerable errors in the interpretation of seismic data and even have no practical significance. Thus, the strict time synchronization performance is the prerequisite and basis for the application of cable-less storage seismograph in high-density seismic array applications. The existing time synchronization methods have high power consumption and poor time synchronization accuracy, which is not suitable for the long-time task. In addition, these methods are affected by the number of nodes and the distance. This paper presents an energy-efficient time-sharing indexed interpolation intercept method for the seismic data synchronization. The time synchronization method uses the high-precision TCXO as the main clock and records GPS time in the SD card at intervals to achieve the high-precision time-stamp for the seismic data. Then the seismic data is intercepted intermittently based on precise time stamps, which achieves the strict seismic data synchronization. Performance analysis shows that the time synchronization accuracy of the proposed method is 0.6 μs and saves 73% energy of the time-sync periods compared to the common GPS timing method. The field measurement results indicate that the time synchronization accuracy is not associated with the working time and the distance between nodes so that the proposed synchronization method is suitable for the high-density seismic survey.
Intelligent Tutoring Systems for Psychomotor Training – A Systematic Literature Review

Intelligent Tutoring Systems (ITSs) were proven efficient for supporting cognitive tasks; however, their potential contribution to support the development of other human skills is less known, even though they could be of high relevance in dealing with health issues. Lack of physical activity is considered the fourth leading cause for preventable death worldwide. Thus, our current work is aimed at assessing the amount and quality of research conducted to design and develop ITSs for training psychomotor abilities. A systemic literature search was conducted on the most reputable online data sources. After methodological filtering, a short-list of these studies was analyzed in-depth, and the results of their efficacy were considered through a structured comparative grid. The developed tutors use ITS architectures or variations, and capture several sub-fields of the psychomotor domain, from medicine (e.g., surgery, radiology), to military (e.g., training marksmanship), or learning to drive.
Two-Level Progressive Attention Convolutional Network for Fine-Grained Image Recognition

The learning of discriminative features is the key for fine-grained image recognition. To better extract effective features and improve the accuracy of fine-grained image recognition, we propose a two-level progressive attention convolutional network (TPA-CNN) for fine-grained image recognition. The model includes a multi-channel attention-fusion (MCAF) module and a cross-layer element-attention (CEA) module. The MCAF module is used to find distinctive feature map channels which significantly responds to specific regions. Inspired by Hierarchical Bilinear Pooling model, The CEA module is further assign weight values to feature map elements. From the perspective of attention visualization map, MCAF module can focused on one or more positive regions, CEA module further locates the most discriminative regions by interaction between the feature map elements. The model can dynamically search the discriminative region of the image, not limited to the boost or crop a selected region. Compared to previous models basing on attention mechanism, the model can extract non-correlated part features which spread over object foreground areas, further improving the recognition accuracy. Experimental results on CUB-200-2011, FGVC-Aircraft, and Stanford Cars datasets demonstrate that the proposed TPA-CNN achieves competitive performance.
Virtual Reality Acceptance in Classrooms: A Case Study in Teaching Science

Virtual reality can be defined as "near reality" where virtual is near and reality is experience by humans.The aim of this research is to explore the benefits of VR technology in education that can engage the learning process using Virtual Reality (VR) in the classroom.Thus, the research objectives are to seek for student design and development of application, convenient, facileness and gratification of VR application in learning process.Using quantitative method, data from questionnaires and evaluations of existing Virtual Reality applications were analyzed in descriptive way to assess the viability of mobile Virtual Reality as a medium for improving learning.The developing process of an VR mobile based application is defined explicitly, and the final graphical student interface of the application is demonstrated to show overall look of the final product that is developed for this project.This includes three types of testing in this project which are performance testing, user acceptance testing and usability testing.Based on the 40 respondents results, it can be concluded that virtual reality application is a good application that can enhance and also engage students learning process in science subject as the mean value for perceived of convenient items was 3.67, followed by 3.66 for perceived of facileness items and finally mean value for perceived of gratification items was 3.61.To conclude, virtual reality application is an effective application for students' science learning process in school especially in the 21st century teaching and learning.
Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients

Purpose: The purpose of this study was to compare the diagnostic performance of breast lesions between deep learning-based computer-aided diagnosis (deep learning-based CAD) system and experienced radiologists and to compare the performance between symptomatic and asymptomatic patients. Methods: From January to December 2018, a total of 451 breast lesions in 389 consecutive patients were examined (mean age 46.86±13.03 years, range 19-84 years) by both ultrasound and deep learning-based CAD system, all of which were biopsied, and the pathological results were obtained. The lesions were diagnosed by an experienced radiologist according to the fifth edition Breast Imaging Reporting and Data System (BI-RADS). The final deep learning-based CAD assessments were dichotomized as possibly benign or possibly malignant. The diagnostic performances of the radiologist and deep learning-based CAD were calculated and compared for asymptomatic patients and symptomatic patients. Results: There were 206 asymptomatic screening patients with 235 lesions (mean age 45.06±10.90 years, range 21-73 years) and 183 symptomatic patients with 216 lesions (mean age 50.03±14.97 years, range 19-84 years). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and area under the receiver operating characteristic curve (AUC) of the deep learning-based CAD in asymptomatic patients were 93.8%, 83.9%, 75.0%, 96.3%, 87.2% and 0.89, respectively. In asymptomatic patients, the specificity (83.9% vs 66.5%, p<0.001), PPV (75.0% vs 59.4%, p=0.013), accuracy (87.2% vs 76.2%, p=0.002) and AUC (0.89 to 0.81, p=0.0013) of CAD were all significantly higher than those of the experienced radiologist. The sensitivity (93.8% vs 80.0%), specificity (83.9% vs 61.8%,), accuracy (87.2% vs 73.6%) and AUC (0.89 vs 0.71) of CAD were all higher for asymptomatic patients than for symptomatic patients. If the BI-RADS 4a lesions diagnosed by the radiologists in asymptomatic patients were downgraded to BI-RADS 3 according to the CAD, then 54.8% (23/42) of the lesions would avoid biopsy without missing the malignancy. Conclusion: The deep learning-based CAD system had better performance in asymptomatic patients than in symptomatic patients and could be a promising complementary tool to ultrasound for increasing diagnostic specificity and avoiding unnecessary biopsies in asymptomatic screening patients.
IMPLEMENTATION OF CLUSTERING USING K-MEANS METHOD TO DETERMINE NUTRITIONAL STATUS

Cluster analysis aims to classify data objects into two categories: objects that are similar in characteristics in one cluster and objects that are different in characteristics with the other objects of another cluster. K-Means is a method included in the distance-based clustering algorithm that starts by determining the number of desired clusters. Malnutrition is one of the biggest concerns in Indonesia. According to Riskesdas 2018 data, as many as 17.7% infants under 60-month-old are still having problems with nutrition intake while 3.9% are having malnutrition. This might result in higher death rate. This research was conducted to classify the nutritional status of infants under 60-month-old conducted by the C-Means Clustering method. This research is non-reactive, using secondary data in Ponkesdes Mayangrejo, Bojonegoro without direct interaction with the subject. This study concluded that the grouping of nutritional status is possible by using K-Means with 4 clusters formed which are 23 malnourished toddlers, 17 undernourished toddlers, 7 nourished toddlers, and 10 over-nourished toddlers.
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning

Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems. Sokoban is a PSPACE-complete planning task and represents one of the hardest domains for current AI planners. Even domain-specific specialized search methods fail quickly due to the exponential search complexity on hard instances. Our approach based on deep reinforcement learning augmented with a curriculum-driven method is the first one to solve hard instances within one day of training while other modern solvers cannot solve these instances within any reasonable time limit. In contrast to prior efforts, which use carefully handcrafted pruning techniques, our approach automatically uncovers domain structure. Our results reveal that deep RL provides a promising framework for solving previously unsolved AI planning problems, provided a proper training curriculum can be devised.
Modeling Code-Switch Languages Using Bilingual Parallel Corpus

Language modeling is the technique to estimate the probability of a sequence of words.A bilingual language model is expected to model the sequential dependency for words across languages, which is difficult due to the inherent lack of suitable training data as well as diverse syntactic structure across languages.We propose a bilingual attention language model (BALM) that simultaneously performs language modeling objective with a quasi-translation objective to model both the monolingual as well as the cross-lingual sequential dependency.The attention mechanism learns the bilingual context from a parallel corpus.BALM achieves state-of-theart performance on the SEAME code-switch database by reducing the perplexity of 20.5% over the best-reported result.We also apply BALM in bilingual lexicon induction, and language normalization tasks to validate the idea.
Quantum Dilation and Erosion

The dilation and erosion operations are the first fundamental step in classical image processing. They are important in many image processing algorithms to extract basic image features, such as geometric shapes; such shapes are then fed to higher level algorithms for object identification and recognition. In this paper, we present an improved quantum method to realize dilation and erosion in imaging processing. Unlike in the classical way, in the quantum version of imaging processing, all of the information is stored in quantum bits (qubits). We use qubits to code the location and other information of each pixel of the images and apply quantum operators (or quantum gates) to accomplish specific functions. Because of quantum entanglement and other nonintuitive features in quantum mechanics, qubits have many advantages over classical bits, but their nature presents challenges in designing quantum algorithms. We first built the quantum circuit theoretically, and then ran it on the IBM Quantum Experience platform to test and process real images. With this algorithm, we are looking forward to more potential applications in quantum computation.