Reviewerly/exHarmony huggingface dataset:
Quranic verse finder: a tool for speech preparation using quranic verses

&lt;span&gt;This paper presents a mobile application called the Quranic Verse Finder. The main idea of this study is to develop a mobile application to help preachers in preparing their speech text. Based on preliminary investigation, the minimum time for a preacher to prepare a speech for a given topic is 3 hours. This process is very time consuming since the speech needs to refer to different source of reference such as the Quran and Hadith. The existing Quran search on the website or mobile application are from unknown source of reference. Therefore, to solve the problems mentioned above, this application offers an efficient method to search and identify any Quran verses for reference. With this application, the user can identify the juz number, the name of the Surah, which number of verses in the Surah and the meaning of the verses of a specific verse. The Quranic Verse Finder is different from other existing Quran Search applications due to its bilanguage feature. This application provides Malay and English translation. It also has other special features such as Bookmark that allows specific Quran verse to be saved for later reference. Moreover, due to the current trends, the Quranic Verse Finder allows user to share it using popular social media sites such as Facebook and Twitter.&lt;/span&gt;
A Novel Small Target Detection Method for Marine Radar

This paper proposes a sea-surface small target detection method for marine radar. Six features (the relative amplitude, the relative Doppler peak height ， the relative entropy of the Doppler amplitude spectrum and three time-frequency features) extracted from the radar returns are combined into 6-D feature vectors. Then considering that getting a labeled set of instances of all possible type of targets is unrealistic, a PCA-based anomaly detector with adjustable false alarm rate is constructed to discriminate the target bins with the sea clutter bins in the feature space. Adaptive weighted reconstruction errors of feature vectors are calculated and ranked and then test results are given. Experiments based on the measured data of a high-resolution X-band radar show that the proposed method attains higher detection probability than several existing detectors.
Understanding the Mood of Social Media Messages

Social media is a valuable source of information when seeking to understand community opinion and sentiment about issues of public interest.Such analysis is usually based on sentiment or emotion processing using machine learning techniques or references a curated lexicon of words to measure the emotive intensity being expressed.The lexicon approach can be limited by the sparsity problem, where the lexicon words are not present in the text being processed, and context issues, where the lexicon words have different meanings in the domain under investigation.We have developed a novel technique based on word embeddings to mitigate these issues and present a case study showing its application, where the mood expressed by the community on social media about the Centenary of Armistice in Australia was determined in near real-time.
Upaya Meningkatkan Kemampuan Menulis Tegak Bersambung Melalui Contoh di Buku Halus dan Drill pada Peserta Didik Kelas I SDN Jajartunggal III Surabaya

This study aims to improve students' writing skills through the application of sampling and drill techniques to student participants. This technique is one form of developing skills learning found in four aspects of Indonesian language learning by combining letter by letter, thus forming a word and sentence. The type of research conducted is Class Action Research. The subjects of this study were first grade students of Jajartunggal III Surabaya Elementary School. This research was preceded by preliminary observations, followed by planning learning, implementation, observation per cycle and ending with evaluation. The activity is carried out with 2 cycles, with the hope that students' upright writing skills will increase. The results of upright writing learning through the model of giving examples in notebooks and drills to class I students are characterized by an increase in the average value of student. The average value of students in pre-action activities with an increase of 60. The condition experienced an increase in the average value of students in cycle 1 which is equal to 75 and the percentage of completeness by 55%. However, this increase has not yet reached the target set previously. Then after proceeding to 2nd cycle the average value in recurring writing learning increased by 81.2 with a percentage of completeness of 84%. This shows that the previously set target has been reached so that the research is stopped in 2nd cycle.
Design of Japanese-Assisted Translation Teaching System Based on Cloud Computing and Intelligent System

In order to improve the ability of Japanese-assisted instruction, a design method of Japanese-aided instruction system based on the theory of second language acquisition is proposed. The system consists of resource information management module, database module and communication module of upper computer. Network transmission control module, bus transmission module and application loading module, etc. The module connection and function control of Japanese-aided instruction system are carried out under the VME bus architecture, and the data storage layer of Japanese-aided instruction is constructed with Eclipse as the development environment. Under the guidance of second language acquisition theory, the software development and design of Japanese-aided instruction system are carried out, and the big data management and resource scheduling of Japanese-assisted instruction are carried out by using embedded database design method. The simulation results show that the Japanese-aided instruction system designed by this method has better artificial intelligence, stronger teaching ability and better ability of Japanese-aided teaching and resource management.
A Deep Learning Approach to Tumour Identification in Fresh Frozen Tissues

The demand for pathology services are significantly increasing whilst the numbers of pathologists are significantly decreasing. In order to overcome these challenges, a growing interest in faster and efficient diagnostic methods such as computer-aided diagnosis (CAD) have been observed. An increase in the use of CAD systems in clinical settings has subsequently led to a growing interest in machine learning. In this paper, we show the use of machine learning algorithms in the prediction of tumour content in Fresh Frozen (FF) histological samples of head and neck. More specifically, we explore a pre-trained convolutional neural network (CNN), namely the AlexNet, to build two common machine learning classifiers. For the first classifier, the pre-trained AlexNet network is used to extract features from the activation layer and then Support Vector Machine (SVM) based classifier is trained by using these extracted features. In the second case, we replace the last three layers of the pre-trained AlexNet network and then fine tune these layers on the FF histological image samples. The results of our experiments are very promising. We have obtained percentage classification rates in the high 90s, and our results show there is little difference between SVM and transfer learning. Thus, the present study show that an AlexNet driven CNN with SVM and fine-tuned classifiers are a suitable choice for accurate discrimination between tumour and non-tumour histological samples from the head and neck.
Effective Heterogeneous Image Matching by Multiple Cues Integration

This paper proposes an effective heterogeneous image matching algorithm by multiple cues integration. Given two kinds of heterogeneous images, i.e., infrared (IR) and visible images, we calculate the negative image of the IR image at first. Then, to enhance the matching performance, both the IR image and its negative image will be matched with the visible image respectively. Subsequently, for each image in the image groups, multiple cues, called DOG-SIFT, H-SC, and LPQ are proposed to extract and describe the feature points. Based on these points, the nearest neighbor ratio method is used for initial matching, and then the random sample consensus algorithm is adopted to eliminate the false matching point pairs. At last, by fusing the matching results of different image groups, we can get the final result. Experimental results show that the proposed method can achieve good performance for heterogeneous image matching.
Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models

Two probabilistic forecasting models for the clear-sky index, based on the Markov-chain mixture distribution (MCM) and copula clear-sky index generators, are presented and evaluated. In terms of performance, these models are compared with two benchmark models: a Quantile Regression (QR) model and the Persistence Ensemble (PeEn). The models are tested on minute resolution clear-sky index data, which was estimated from irradiance data for two different climatic regions: Hawaii, USA and Norrköping, Sweden. Results show that the copula model generally outperforms the PeEn, while the MCM and QR models are superior in all tested aspects. Comparing MCM and QR reliability, the QR is superior, while the MCM is superior in mean CRPS and skill score. The MCM model is proposed as a potential benchmark for probabilistic solar forecasting. The MCM model is available in Python as SheperoMah/MCM-distribution-forecasting at GitHub.
SNOW: Subscribing to Knowledge via Channel Pooling for Transfer & Lifelong Learning of Convolutional Neural Networks

SNOW is an efficient learning method to improve training/serving throughput as well as accuracy for transfer and lifelong learning of convolutional neural networks based on knowledge subscription. SNOW selects the top-K useful intermediatefeature maps for a target task from a pre-trained and frozen source model through a novel channel pooling scheme, and utilizes them in the task-specific delta model. The source model is responsible for generating a large number of generic feature maps. Meanwhile, the delta model selectively subscribes to those feature maps and fuses them with its local ones to deliver high accuracy for the target task. Since a source model takes part in both training and serving of all target tasksin an inference-only mode, one source model can serve multiple delta models, enabling significant computation sharing. The sizes of such delta models are fractional of the source model, thus SNOW also provides model-size efficiency.Our experimental results show that SNOW offers a superior balance between accuracy and training/inference speed for various image classification tasks to the existing transfer and lifelong learning practices.
Constructing a Large-Scale Data Center Network Structure Using Regular Graphs

With the rapid growth of the amount of data which have to be stored and processed, the scalability of data center network attracts attentions recent years. Traditional treeshape network structures are far from meeting the scalability requirements. DCell and its construction principle provided an innovated way of building large scale data center network structure. This paper borrows the iterative construction principle of DCell. Different to DCell who is built using complete graph, we use regular graphs as the basic graphs to construct a more scalable structure. The structure definition, naming method and connecting rules are given in this paper, and we also discuss the scale and diameter with different parameters.
Recurrent Neural Networks

A feedforward fully-connected neural network cannot be used successfully for modeling sequences of data. A few basic reasons are the following: they cannot handle variable-length input sequences, do not share parameters, cannot track long-term dependencies, and cannot maintain information about the order of input data. Hence there is a need for a neural architecture that can handle successfully all the previous requirements.
Research on binocular real-time ranging method in window area

In order to solve the problem of difficult target matching and low matching efficiency in binocular measurement, this paper proposes a real-time target feature matching algorithm based on Binocular Stereo Vision-absolute window error minimization (CAEW, Calculate the Absolute Error Window ) to improve the speed and accuracy of measurements. Firstly, the calibration of the camera is solved by using Zhang's calibration method, and the Bouguet algorithm is used for Binocular Stereo Vision of the final calibration data. Then, the AdaBoost iterative algorithm is used to train the target detector for target recognition. The CAEW algorithm is compared with the commonly used SURF (Speeded-Up Robust Feature) algorithm. The evaluation data of experimental results showed that the CAEW algorithm can achieve an evaluation of more than 90%. It is significantly improved compared with the SURF algorithm and meet the needs of binocular real-time target matching.
The Effectiveness Material Based Stories in Improving Students Reading Comprehension

This research was intended to explain the students reading comprehension through material based stories at the eighth grade of SMP Negeri 1 Maiwa Kabupaten Enrekang in the 2017/2018 academic year. This research used a quasi-experimental method class. It had conducted into two groups namely the experimental group and control group. The Sample in this research were 44 students. The researcher took real data from class to know the students reading comprehension. The instrument of this research was pre-test-posttest with multiple choice, T/F and read test. From these findings, there was a significant effect on the students reading comprehension through material based stories. The data was analyzed by using a t-test and the result showed that the t-test value 4.629 was higher than the t-table value 2.021. It meant that there was a significant difference between students’ reading achievement before and after the treatment through the stories. It concluded that material based stories can be used in improving the reading comprehension of the students.
Framework for Cloud Database Protection by Using Reversible Data Hiding Methods

A new framework of data security for database numeric data by using reversible data hiding method is proposed. In the framework, reversible data hiding (RDH) methods are recommended to encode the to-be-protected numeric data in databases for hiding copyright information. Via the use of the proposed framework, not only the values of leaked numeric data are unreal but the copyright information can be extracted from the leaked numeric data to assert the ownership and disclose the illegal behavior. Furthermore, the real values of the leaked numeric data can be regained by performing the reverse process of the RDH method.
Deep Learning Based Face Recognition Application with Augmented Reality Devices

Augmented reality devices such as the Microsoft HoloLens are currently in development and have potential uses in many applications. These devices allow the user to interact with holograms projected into their surroundings. Because the augmented reality devices can project into the user’s environment, one potential application for these devices is in recognizing and labeling objects and people in the user’s view. This paper describes a deep learning based method that uses a deep Convolutional Neural Network (CNN) with the HoloLens to recognize and classify individual faces in a scene.
A multiple constraints modified artificial potential field algorithm for complex dynamic environment

This paper presents an effective multi-constraint modified artificial potential to solve the deadlock and oscillation problems of vehicle routing in complex environment. We design the repulsive potential function by introducing a regulating factor to escape from local minima and eliminate distortion. Meanwhile, considering the relative velocity and direction between obstacles and vehicles, the velocity repulsion potential field is constructed to reduce the computational time and avoid collision with obstacles. Finally, the trajectory around the obstacle is smoother by using the rotation force. Simulation results show the effectiveness of the method.
Object detection for rotated and densely arranged objects in aerial images using path aggregated feature pyramid networks

Object detection based on deep learning algorithms has been an important yet challenging research field in computer vision. The feature pyramid network has become a dominant network architecture in many detection applications because of its powerful feature learning ability for objects with varying scales. To address the challenges in detecting small and densely packed objects, this paper proposes an innovative object detection approach by combining the path aggregation scheme and the feature pyramid network into a unified framework. Specifically, we add a bottom-up branch with lateral connection onto the existing feature pyramid network and apply adaptive feature fusion strategy, which improves the detection performance for small and densely arranged objects in remote sensing images. Experiment results show that our proposed path aggregated feature pyramid network can improve the detection performance for diverse objects in aerial images.
A Novel Architecture to Integrate Multi-Source Data into Distributed Environment using Big Data Infrastructure

The amount of data has been increasing over the last few years due to the emergence of various end-user applications. These applications utilize cloud computing infrastructure in the data centers. Apart from the increasing volume of data, there are other factors such as variety, velocity, and veracity of the data which result in the problem of big data. Traditional database management systems are not efficient to handle big data. The use of big data platform is necessary to resolve the big data problem. Hadoop is one of the platforms which resolves the problem of big data. Hadoop uses distributed storage. HBase is one of the big data tools for storing big data in Hadoop. HBase is a column-oriented, distributed and high fault-tolerant database. It can store billions of rows at a time. However, there are some issues in HBase. When the data comes from multiple sources, it is stored in multiple tables in HBase. As a result, the performance of HBase degrades when there is a need to perform join operations. In this research, we propose a data model which stores data of multiple sources into a single HBase table. There is no issue of join performance of HBase in the proposed technique as there is no need to perform join because the data is integrated into a single HBase table. The proposed model has a unique row key and multiple column families to integrate data from multiple sources in single table. We evaluated the proposed technique using a real testbed by considering a dataset of two publishers. We compare the performance of the proposed technique by storing data in to multiple tables in Hive. Results show improved query performance of the proposed technique as compared to the traditional approach of using join operations in multiple tables in Hive.
Textual Entailment based on Semantic Similarity Using WordNet

We propose a method for automatic recognition of textual entailment based on word sense disambiguation using cosine similarity proposed by Abdalgader and Skabar. This algorithm finds semantic similarity of the sentence pairs- entailing text and entailed text. Both the hypothesis and text are converted into vectors using Jiang and Conrath similarity measure and cosine similarity is computed. Based on the cosine similarity score, a threshold is applied and the sentence pairs are classified into entailment and no entailment. The accuracy of the proposed scheme is better or comparable to many of the state of the art schemes.
Image categorization based on visual saliency and Bag-of-Words model

Large-scale image categorization is a challenging task. In this paper, we propose a new image categorization approach based on visual saliency and bag-of-words model. Firstly, a saliency map is generated by visual saliency method that exploits some coarsely localized information, i.e. the salient region shape and contour. Secondly, size of salient region is acquired by calculating maximum entropy. Thirdly, the local image descriptor-SIFT extracted in the salient region and visual saliency information are combined to build visual words. Finally, the visual word bag is categorized by Support Vector Machine. By comparing with BOW model categorization methods, experiment results show that our methods can effectively improve the accuracy of image categorization.
Evolving a Deep Neural Network Training Time Estimator

We present a procedure for the design of a Deep Neural Network (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be embedded in the scheduler of a shared GPU infrastructure, capable of providing estimated training times for a wide range of network architectures, when the user submits a training job. To this end, a very short and simple representation for a given DNN is chosen. In order to compensate for the limited degree of description of the basic network representation, a novel co-evolutionary approach is taken to fit the estimator. The training set for the estimator, i.e. DNNs, is evolved by an evolutionary algorithm that optimizes the accuracy of the estimator. In the process, the genetic algorithm evolves DNNs, generates Python-Keras programs and projects them onto the simple representation. The genetic operators are dynamic, they change with the estimator’s accuracy in order to balance accuracy with generalization. Results show that despite the low degree of information in the representation and the simple initial design for the predictor, co-evolving the training set performs better than near random generated population of DNNs.
Graph-Based Attention Networks for Aspect Level Sentiment Analysis

With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we present a deep learning model that employs graph neural networks and graph-based attention mechanisms for aspect based sentiment analysis. In our work, the given text is considered as a graph based on its syntactic structure and the target is the specific region of the graph. Structural attention model and graph attention model are used to concentrate on relations between words and certain regions of the graph. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines. Code is available in supplementary materials.