Reviewerly/exHarmony huggingface dataset:
Potential Use of AREL in English Song Analysis to Improve Argumentation Skills and English Proficiency

Potential Use of AREL in English Song Analysis to Improve Argumentation Skills and English Proficiency
Towards Intelligent Decision Making in Emotion-aware Applications

In this paper, we propose an intelligent emotion-aware system (IES), which aims to provide a systematic approach that can make use of the online technology to improve the intelligence of different emotion-aware mobile applications.IES is constructed to provide multi-dimensional online social community data collection and processing approaches for decision making, so as to recommend intelligent services for emotion-aware mobile applications.Furthermore, we present a flow of intelligent decision making process designed on IES, and highlight the implementation and orchestration of several key technologies and schemes applied in this system for different emotion-aware mobile applications in run-time.We demonstrate the feasibility of the proposed IES by presenting a novel emotion-aware mobile application -iSmile, and evaluate the system performance based on this application.
Farsi document image recognition system using word layout signature

In this paper, a new representation of Farsi words is proposed to present the keyword spotting problems in Farsi document image retrieval. In this regard, we define a signature for each Farsi word based on the word connected component layout. The mentioned signature is shown as boxes, and then, by sketching vertical and horizontal lines, we construct a grid of each word to provide a new descriptor. One of the advantages of this method is that it can be used for both handwritten and machine-printed texts. Finally, to evaluate the performance of our system in comparison to other methods, a database that contains 19,582 printed Farsi words is examined, and after applying this approach, a recall rate of 98.1% and a precision rate of 94.3% are obtained.
Annular subaperture interferometry for high-departure aspheres using paraboloidal parameterization

A low-cost technique is presented for constructing stitched Fizeau interferometric measurements of high-spherical-departure concave aspheres of arbitrary conic constant without the use of null optics. The optical test configuration assembles the surface figure of the asphere using subapertures parameterized as variances from best-fit paraboloids. Subtracting the optical path difference between each idealized paraboloid and the corresponding annulus of measured data removes the annular wavefront aberrations without the need to fit Zernike polynomials. The proof-of-concept measurement and reconstruction of a 250&#x00A0;mm diameter diamond-turned ellipsoidal mirror with more than 3000 waves of spherical departure are reported. The presented technique is an inexpensive addition to the array of tools used to measure large-aperture aspheres and high-departure freeform optics.
Pemanfaatan Aplikasi Wattpad Dalam Memotivasi Siswa Untuk Menulis Cerita

Social media is a online media, with the users can easily participate, share, and created the blog, covering social network, wiki, forum and virtual world. Now, many one who uses social media especially teenagers. Teenager in indonesia often takes social media to channel their creativity. One of them is from writing. Inordinate social media that can be used to channel from writing. Research methodology used the research is research methodology qualitative, interpret how the role the application wattpad in motivate students for writing a story is a data collection. he result of research that is done in minutely student average by researchers , in general have motivation and desire to write wattpad applications .Although some of them have not dare do it with reason lacking in confidence .But , there are face the its position not only as the reader , will also active as a writer to keep writing motivated and be able to .
Detecting fake reviews via dynamic multimode network

Online product reviews can greatly affect the consumer's shopping decision. Thus, a larger number of unscrupulous merchants post fake or unfair reviews to mislead consumers for their profit and fame. The common approaches to find these spam reviews are analysing the text similarity or rating pattern. With these common approaches we can easily identify ordinary spammers, but we cannot find the unusual ones who manipulate their behaviour to act just like genuine reviewers. In this paper, we propose a novel method to recognise these unusual ones by using relations among reviewers, reviews, commodities and stores. Firstly, we present four fundamental concepts, which are the quality of the merchandise, the honesty of the review, the trustworthiness of the reviewer and the reliability of the store, thus enabling us to identify the spam reviewers more efficiently. Secondly, we propose our multimode network model for identifying suspicious reviews and then give three corresponding algorithms. Eventually, we find that the multi-view spam detection based on the multimode network can detect more subtle false reviews according to our experiments.
Dynamic Texture Recognition Using 3D Random Features

In this paper, we present a novel, simple but effective approach for dynamic texture recognition using 3D random features. Compared with the existing dynamic texture recognition approaches using carefully designed features for high performance, our method use only a few 3D random filters to extract spatio-temporal features from local dynamic texture blocks, which are further encoded into a low-dimensional feature vector. To explore the representative power of the 3D random features, we use two different encoding schemes, the learning-based Fisher vector encoding and the learning-free binary encoding. The proposed method is tested on the UCLA and DynTex databases with various evaluation protocols. Experimental results demonstrate the high performance of our method for dynamic texture recognition.
Differences in Visual Search Strategies Based on Multiple Object Tracking Skill Level of Soccer Athletes

This study aimed to clarify the differences in visual search strategies based on Multiple Object Tracking (MOT) skill level using an MOT subject newly developed for soccer. Twenty-nine university soccer athletes participated in the experiment. First, in order to confirm the validity of the developed MOT subject for soccer, the participants executed both a general and the developed MOT subject for soccer. On calculating the correlation coefficient for individual subject results, we observed a significant positive correlation among them (r = .767, p < 0.001). We then divided the participants into two groups (n = 7, high MOT skill group ; n = 8, low MOT skill group) based on their scores on the MOT subject for soccer and investigated the visual search strategy that was underway as the MOT subject for soccer. We compared the migration length of the visual line in both groups and found that the length for the high MOT skill group tended to be longer than that for the low MOT skill group (t [13] = 1.30, n.s. ; d = 0.68). Moreover, we divided the soccer field into six areas and analyzed the rate of viewing time. We found that both groups may have used a visual search strategy that involved emphasizing ball movement and the chase-targeted athletes’ motion. Finally, we precisely investigated the time-series visual line data of the MOT subjects for soccer, and the results indicated that the high MOT skill group may have been moving the visual line in anticipation of the next playing scene. We thus developed the general MOT subject specifically for use in soccer. Moreover, these results indicate that MOT skill may have correlated with the prediction accuracy of the play at the scene, along with situation judgment, owing to MOT skill.
Exploring the Privacy Bound for Diﬀerential Privacy: From Theory to Practice

Data privacy has attracted significant interests in both database theory and security communities in the past few decades.Differential privacy has emerged as a new paradigm for rigorous privacy protection regardless of adversaries prior knowledge.However, the meaning of privacy bound and how to select an appropriate may still be unclear to the general data owners.More recently, some approaches have been proposed to derive the upper bounds of for specified privacy risks.Unfortunately, these upper bounds suffer from some deficiencies (e.g., the bound relies on the data size, or might be too large), which greatly limits their applicability.To remedy this problem, we propose a novel approach that converts the privacy bound in differential privacy to privacy risks understandable to generic users, and present an in-depth theoretical analysis for it.Finally, we have conducted experiments to demonstrate the effectiveness of our model.
WEIGHT PRODUCT DALAM IMPLEMENTASI SISTEM PENDUKUNG KEPUTUSAN BANTUAN BEDAH RUMAH

North Lampung is a district in Lampung province. The government issued an aid program, he program is that every region is given the opportunity to manage aid funds, one of which is in the form of home surgery , This good government program is thirsty supported by the level of data accuracy, efficiency of the time of the recipient's management. Therefore in order to get good results. Then in this study using the weight product method in the implementation of decision support systems for home surgery recipients.
Weighted sequence loss based recurrent model for repurchase recommendation

Next basket recommendation becomes an increasing concern. Repurchase recommendation, i.e., predicting which products a user will buy again in a user's next order, is a key subproblem. However, most conventional models are not able to extract the whole important features to describe the customer's repurchase process: context information and sequential information. In our work, we firstly utilize the causal dilated convolutions and recurrent neural network to capture context information and sequential information in different ways. Furthermore, the information extracted by causal dilated convolutions and recurrent neural network is combined at each time step for recommendation. More importantly, to effectively adapt the repurchase recommendation, we introduce a weighted sequence loss, which is able to ignore invalid logloss at special time steps to guide the RNN combined with causal dilated convolutions(RCCNN) training. A deep experimentation shows that RCCNN is able to explain the customer repurchase behaviors, and provide reasonable recommendation.
Performance Enhancement of Machine Translation Evaluation Systems for English – Hindi Language Pair

Machine Translation (MT) is a programmed conversion in which computer software is utilized to convert manuscripts from one Natural Language (like English) to a different Language (such as Hindi).To process any such conversion, through human or through automatic means, the conversion must be established such that it reinstate the complete sense of a manuscript from its base (source) linguistic into the target language.In this paper, the study of prevailing evaluation systems along with assessing their performance is achieved through the similarity metrics.Moreover, the authors have also presented an improved technique of translation employing features of Natural Language Processing and consequently, to acquire an enhanced and more accurate assessing Machine Translation system, a corpus is selected and the outcomes are compared with the prevailing methods.Besides this, two well-known systems such as Google and Bing decoders are selected to inquire and to assess the study of metrics called similarity metrics through Assessment of Text Essential Characteristics score.This is found to provide more accuracy than prevailing methods.Furthermore, evaluations are tested under various metrics systems like Jaccard similarity metrics, cosine similarity metrics, and sine metrics to deliver enhanced accuracy than prevailing methods.
Using Reputation as a Coin to Bet on Information Items Distributed in a Smart City

A key characteristic of Smart Cities is the ability to improve decision making by combining the maximum possible information provided by sensors or people. This paper presents a Blockchain-based reputation system for allowing citizens to assess the quality of information shared by other participating citizens. Citizens have the possibility to confirm or refute information posted by others, and the reputation of participants has a role in the final credibility of the information shared. Individual reputation is used as a “currency”, citizens bet with their reputation when giving an opinion, and get some reputation back when they are considered to be right. The reputation system is supervised by municipality stakeholders, which have the role of giving a final decision on the quality of the information and, consequently, in the update of the reputations associated with it. Stakeholders use Blockchain technology as a natural solution to spread their power regarding reputation management, ensuring a trustworthy dissemination of information, for instance, when reporting accidents.
Entailment and Spectral Clustering based Single and Multiple Document Summarization

Text connectedness is an important feature for content selection in text summarization methods.Recently, Textual Entailment (TE) has been successfully employed to measure sentence connectedness in order to determine sentence salience in single document text summarization.In literature, Analog Textual Entailment and Spectral Clustering (ATESC) is one such method which has used TE to compute inter-sentence connectedness scores.These scores are used to compute salience of sentences and are further utilized by Spectral Clustering algorithm to create segments of sentences.Finally, the most salient sentences are extracted from the most salient segments for inclusion in the final summary.The method has shown good performance earlier.But the authors observe that TE has never been employed for the task of multi-document summarization.Therefore, this paper has proposed ATESC based new methods for the same task.The experiments conducted on DUC 2003 and 2004 datasets reveal that the notion of Textual Entailment along with Spectral Clustering algorithm proves to be an effective duo for redundancy removal and generating informative summaries in multi-document summarization.Moreover, the proposed methods have exhibited faster execution times.
3D UNDERWATER MINE MODELLING IN THE ¡VAMOS! PROJECT

Abstract. The project Viable Alternative Mine Operating System (¡VAMOS!) develops a novel underwater mining technique for extracting inland mineral deposits in flooded open-cut mines. From a floating launch and recovery vessel a remotely-operated underwater mining vehicle with a roadheader cutting machine is deployed. The cut material is transported to the surface via a flexible riser hose. Since there is no direct intervisibility between the operator and the mining machine, the data of the sensor systems can only be perceived via a computer interface. Therefore, part of the efforts in the project focus on enhancing the situational awareness of the operator by providing a 3D model of the mine combined with representations of the mining equipment and sensor data. We present a method how a positioning and navigation system, perception system and mapping system can be used to create a replica of the physical system and mine environment in Virtual Reality (VR) in order to assist remote control. This approach is beneficial because it allows visualizing different sensor information and data in a consistent interface, and enables showing the complete context of the mining site even if only part of the mine is currently observed by surveying equipment. We demonstrate how the system is used during tele-operation and show results achieved during the field trials of the complete system in Silvermines, Ireland.
A novel bio- inspired algorithm for hunting in multi robot scenario

In a multi-robot scenario, cooperative hunting is a key issue when a group of robots are hunting for evader/evaders and when the location of the evader is continuously changing. Cooperative hunting is addressed in this paper by proposing a novel bio inspired Corner Dragging Algorithm (CDA). Corner Dragging Algorithm operates by making an alliance of robots that drag the evader towards any one of the four corners; whichever is closest to the evader. Different shapes of obstacles are avoided during this pursuit. While developing the Corner Dragging Algorithm, we analyze the shortcomings and advantages of some of the existing algorithms including dynamic alliance and formation construction algorithm and incorporate these changes in our design to achieve improved results. Performance of the algorithm is evaluated on the basis of simulation in MATLAB.
A Weight-shared Dual-branch Convolutional Neural Network for Unsupervised Dense Depth Prediction and Camera Motion Estimation

Convolutional Neural Network (CNN) can be used to indiscriminately predict dense depth and camera motion from images, however, ignoring the relationship between depth map and camera motion increases the computational burden to label the datasets and limits the accuracy of the results. In this paper, an end-to-end unsupervised dual-branch CNN is proposed to predict a pixel-wise depth map and simultaneously estimate camera pose. In particular, a weight sharing strategy for two branches is designed to increase the connection between depth map and camera motion. Besides, to reduce the impact of photometric noise, the intermediate feature maps are utilized to compute feature errors. Experimental results on the KITTI datasets demonstrate that our method achieves better performance on dense map prediction and camera pose estimation comparing with the state-of-the-art approaches.
A Study on Digital Ability Change after the Smart Worker Education of the Prime-Aged Learner

Lifelong learning organization have been accomplishing computerization education for prime-aged learners in Korea.In early days, it started from computer literacy education, and it carried over information utilization, internet utilization, smart-phone utilization, video production, and SNS utilization.This study aimed to the ability improvement for information utilization change of the prime-aged learners after smart worker curriculum learning, the education program carry out smart worker education differentiated from the existing computerization education by prime-aged learners who faced to the smart working era.Therefore, this study chose prime-aged educational program in progress throughout one of the 1 lifelong learning organization of the Gwangju area in Korea.This curriculum was designed by the smart worker ability subjects, it included ability for documentation, smart ability for apparatus utilization, ability for collaborative work, information management ability for cloud utilization that was a basic ability of smart working.It was performed from the questionnaire survey to the statistical analysis about the factor of ability for documentation, smart ability for apparatus utilization, ability for collaborative work, information management capability for the learners who learned this class.As a result, ability improvement for information utilization appeared significantly in all factors of cloud utilization, smart apparatus utilization, documentation, collaborative work, and information management after learners completed education program of smart worker curriculum.Based on this research result, study about the relation between improvement of the ability for information utilization and change of smart working style will have to be progressed continuously in the future.
Development of a portable community video surveillance system

In 2016, a crime rate has been evidently increasing particularly in Kuala Lumpur areas, including reports on house break-ins, car thefts, motorcycle thefts and robbery. One way of deterring such cases is by installing CCTV monitoring system in premises such as houses or shops, but this usually requires expensive equipment and installation fees. In this paper a cheaper alternative of a portable community video surveillance system running on Raspberry Pi 3 utilizing OpenCV is presented. The system will detect motion based on image subtraction algorithm and immediately inform users when intruders are detected by sending a live video feed to a Telegram group chat, as well as sound the buzzer alarm on the Raspberry Pi. Additionally, any Telegram group members can request images and recorded videos from the system at any time by sending a get request in Telegram which will be handled by Telegram Bot. This system uses the Pi NoIR camera module as the image acquisition device equipped with a 36 LED infrared illuminator for night vision capability. In addition to the Python language, OpenCV, a computer vision simulation from Intel is also used for image processing tasks. The performance analysis of the completed system is also presented computational complexity while offering improved flexibility. The performance time is also presented, where the whole process is run with a noticeable 3 seconds delay in getting the final output.
Completely Blind Image Quality Assessment Using Latent Quality Factor from Image Local Structure Representation

Although opinion-unaware (OA) blind image quality assessment (BIQA) is the most difficult task, it is still very attractive because of its great potential for good generalization capability and practical usage. In this paper, a novel OA-BIQA algorithm is proposed. This algorithm is based on the construction of a ‘quality aware’ collection of statistical features based on a simple and successful local structure descriptor – generalized local binary pattern (GLBP). GLBP statistical features of images are regarded as the latent characteristics that can distinguish the visual distorted image from those of ‘natural’ or ‘pristine’ images. First, the statistical GLBP feature vectors learned from a corpus of the pristine images are divided into different subgroups. Second, a set of multivariate Gaussian (MVG) models are learned from each subgroup features fitting. Finally, the quality of a test image is evaluated by integrating the distances between its pair-wised parameters which describes each MVG models and that of each MVG models learned from a corpus of pristine images. Experimental results show that the proposed model delivers performance correlates well with human difference mean opinion scores on the LIVE IQA database.
Augmented reality application for chemical bonding based on android

&lt;p&gt;Augmented Reality can provide information that can be more easily understood by the user. Because of its advantages, Augmented Reality can be utilized to create learning applications that can support teaching and learning process. Chemical learning about chemical reactions is usually boring by students. This is because students only know the theory of the book and the explanation of teachers only. This research utilized Augmented Reality to be able to see 3D model of each chemical element and also can see animation of merging between elements. In order to bring up 3D objects of chemical elements then made a card that will be used as a marker / tracker. In addition, there can be a combination of several chemical elements based on several cards detected from the camera. The test is performed for chemical elements with periodic table such as H, Ca, Na, K, C, Cl, Br, O, S, SO2, CO2, OH. Testing the application by performing several merge elements such as HCl, NaCl, H20, CO2, MgS, SO2, O2, CaCl2, HCN, N2, and others. The results of this study will be tested on several students to see the benefits of student learning.&lt;/p&gt;