Reviewerly/exHarmony huggingface dataset:
Old tradition, new technologies: comprehension and retention using augmented reality

Traditional media, especially printed media, such as newspapers, books, and posters, is static. The increased usage of smart devices has trigged the use of mobile augmented reality (AR) in commercial print media. This technology allows consumers to overlay supplemental multimodal content on the real-world environment through the use of an AR app on a smartphone or tablet. The addition of mobile AR provides an extra dimension through which to absorb or dismiss an advertisement’s content. The main objective of this study is to determine if the use of mobile augmented reality in print affects the customer’s ability to understand and remember the message of an advertisement. Assessing augmented reality’s affect on printed media will help determine the value of investing in this technology and its future potential in the advertising landscape. This experiment was designed to test whether participants in two groups, one that used Layar, an augmented reality application, and one that did not, were still able to understand and remember the messages and content of a Nissan car advertisement featured in the Toronto Star. Through interviews and questionnaires subjects were tested on their recall ability and message comprehension given the passage of time. The results indicate that AR poses no significant advantage or disadvantage when compared to traditional print media. It appears that regardless of the mode of communication the advertisement’s messages are successfully conveyed. The results have shown that over time the viewer is likely to remember what interests them independently of whether augmented reality is used or not.
Using Clustering Techniques to Optimize Panel Grouping for Large PV Arrays with Non-uniform Orientation and Shading Obstructions.

This paper demonstrates a clustering method for grouping PVs of arbitrary orientation affected by nonuniform local shading.For a project with 44,000 PV panels cladding doubly curved roof surfaces, every panel has a unique orientation.In addition, clerestory windows cause non-uniform near-field shading.The combination of curvature and shading causes every panel to receive a different amount of irradiance at any time.The PV panel with lowest output (lowest irradiance) in a MPPT string limits the output of the whole string.The method for grouping 44,000 PV panels into 296 MPPT strings affects the total annual energy producing.The PV array grouped with clustering generated 7.7% more energy annually compared to the same array grouped in a simple grid.
Sistem Pakar Menggunakan Metode Certainty Factor dalam Akurasi Identifikasi Penyakit pada Paru

Lungs are a very importand part of the human organ, which functions as a place for oxygen exchange. This organ that is located under the ribs has a very heavy task, as well as the pollution of the air we breathe everyday which will cause various diseases in the lungs. Lung disease is a disease that is common to everyone, and there are still many who are less concemed with lung healty, so that is causes many indications of lung diseas. Expert system is a system that uses human knowledge recorded in a computer to solve a problem. The purpose og this study was to datermine the accuracy of disease identification in the lungs using the Certainty Factor method. The date obtained is datae about the symptoms that prove wherher a person has lung disease or not and conduct an analysis of the date, so that later conclusions can be abtained from the facts found using an expert system of the Certianty Factor method. The date obtained is date about the sympyoms thet prove whethera person has lung as a problem solving metric which is a parameter value to show the amount of trust. The result of the research from an expert system on pulmonary disease with pulmonary tuberkolosis (TBC) with a certainty level og 68%. Expert system on lung disease using the Certainty Factor method can make it easien for sufferes to know and handle prevention and handling
Similarity-based Learning Approaches to Arabic Sentiment Analysis

This paper investigates opinion detection and sentiment analysis (SA) to automatic classifying Arabic comments and placing text documents into proper category positive or negative based on their contents.In this paper K-Nearest Neighbor (K-NN) classifier is used to determine the polarity of the document and is studied the performance of K-NN classification when applying the different distance measures.The K-NN algorithm is based on learning by comparing a given test documents with training documents.Measuring the similarity between documents is presented also in the paper; the three distance metrics (Euclidean distance, Cosine similarity and jaccard distance) are presented.The effect of these three measures is performed on three available resources (dataset) composed of Arabic language.The Evaluation parameters such as accuracy, recall and precision are measured the experimental results give that the performance of the jaccard measure with k =15 is better than other measures for Dataset.
Novel Alphabet Deduction Using MATLAB by Neural Networks and Comparison with the Fuzzy Classifier

Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 &lt; P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.
Fusion of Image Feature Descriptors for Person Re-identification

Person re-identification has gained a lot of research interest in recent years. Extracting and matching features play an important role in this scenario. Past studies of image feature detectors and descriptors are more generic in nature. Different types of detectors and descriptors are used for person re-identification over the last few years. Most of these descriptors are a combination of two or more variants of descriptors. This research paper will focus on the comparative analysis and evaluation of various features detectors and descriptors used for image matching with relevance to person re-identification. We also explore how the combination of local and global descriptors can improve the re-identification rate. VIPeR dataset is used for the evaluation of descriptors.
An Optimized Feature Regularization in Boosted Decision Tree

We put forward a tree regularization, which empowers numerous tree models to do feature collection effectively. The type thought of the regularization system be to punish choosing another feature intended for split when its gain is like the features utilized in past splits. This paper utilized standard data set as unique discrete test data, and the entropy and information gain of each trait of the data was determined to actualize the classification of data. Boosted DT are between the most prominent learning systems being used nowadays. Likewise, this paper accomplished an optimized structure of the decision tree, which is streamlined for improving the efficiency of the algorithm on the reason of guaranteeing low error rate which was at a similar dimension as other classification algorithms
Intelligent Water Drop Algorithm based Relevant Image Fetching using Histogram and Annotation Features

Social media network increase trend of image collection at various platforms. Hence getting an relevant image as per query image or text is depend on retrieval algorithms. Number of researcher has proposed algorithms for fetching relevant images, but relevancy of those still need improvement. Hence proposed paper has utilized the Intelligent water Drop algorithm for initial clustering of images as per feature values. Clustering or relevancy of an image depends on visual feature histogram and annotation similarity. Here property of moving a water drop from one node to another in a water drop graph has increase the clustering accuracy of the work. Experiment was done on real dataset having five different group of image set with annotation. Result shows that proposed work has increase the retrieval relevancy accuracy as well as reduce the fetching of the images. This reduction of time was obtained by using the clustering structure of image dataset.
On pronominal locative and temporal adverbs in Dabida (Bantu)

The paper describes linguistic properties of the Dabida pronominal demonstrative adverbs of space and time formed from the demonstrative pronouns in noun class 16 (locative) agreement through the conversion. The language data has been obtained from two Kenyan informants who were native speakers of the Mbololo dialect of Dabida studying in Moscow.
ANALISIS PEMELIHARAAN SARANA DAN PRASARANA SEKOLAH DI SEKOLAH MENENGAH KEJURUAN (SMK) NEGERI 3 PADANG

This study purposing to determine the process of maintenance of school facilities and infrastructure in Sekolah Menengah Kejuruan (SMK) Negeri 3 Padang.This research is categorized as qualitative descriptive with data sources coming from interviews, direct observation to the object of research and documentation. The validity of the data was tested using Source Triangulation. The results showed that overall maintenance activities had not been carried out optimally. This is caused by: 1) difficulty in disbursing funds used for maintenance activities, 2) inadequate library space, 3) lack of bookshelves in the library room, 4) the school does not use control cards as proof of liability answer. Maintenance activities are carried out by means of periodic, continuous and preventive maintenance. Meanwhile, the solutive action taken is to repair itself if there are damaged facilities or infrastructures, striving to create and increase the number of bookshelves in the library room. By carrying out optimal maintenance activities, it can extend the economic value and maximize the utilization of the facilities and infrastructure in the school. Keywords: Analysis, Maintenance, Facilities and Infrastructure.
A customizable web service selection framework using MCDM approaches

With the tremendous increase of web services published online, the problem of selecting the best service offers becomes more challenging. Users need to make their decisions on multiple and conflicting non-functional requirements. It is a natural fit to apply the Multi-Criteria Decision Making (MCDM) theory to the service selection and ranking process. In our proposed QoS-based service selection system, we take the user-centric standpoint to design the system. We improve the original MCDM models so that the user requirements on the QoS criteria are included in the rank calculation process. Our proposed QoS weighting method considers the well-defined ANP method combined with the user-defined weights. We compared the improved selection methods and we found that the Constraint Programming method is the best in terms of its sensitivity to the changes made to the QoS weights. Consequently, the results produced from this comparison would be presented to the user.
Geospatial and Multivariate Statistical Interpretations for Relationships between Physico-Chemical Properties and Heavy Metals in Some Agricultural Soils of Kalar and Quratoo Sub-Districts, East Iraq

In this study, ninety agricultural soil samples were collected from Kalar and Quratoo subdistricts east of Iraq.Where soil samples have been analyzed to quantify concentrations of nine soil physicochemical properties and sixteen heavy metals by using ICP-OES with other analyzing tools.Analysis results showed that significant variations exist in concentrations of the investigated parameters among sampling sites.Multivariate statistics have been conducted to identify relationships between heavy metals and soil properties.Multiple correspondence analysis MCA showed strong relationships for total nitrogen T.N. with Cu, Ni, and Hg.Agglomerative hierarchical clustering (AHC) and factorial analysis (FA) divided soil property relationships with heavy metals into three main categories in terms of heavy metals nature; naturally occurred, generated from anthropogenic sources, and of weak relationships.Spatial variation map showed that soil properties in middle parts of the study area are more impacted by heavy metals that came from anthropogenic sources.This study aids to improve the environmental protection and monitoring of agricultural lands in Iraq.
Effect on Human Body of the Magnetic Field Induced by the High Voltage Transmission Line

The quality of electrical energy and the fight against energy losses are a crucial issue for electricity companies. The use of high voltage lines can limit energy losses for the transmission of electricity over long distances. But this solution has the disadvantage of the propagation of the electromagnetic wave which has a great influence on human health. The work presented in this study mainly deals with modelling problems that may be encountered by the community working in the field of low frequency electromagnetic fields. In order to model a power line, we are based on geometric data as well as phase and earth conductor data.
Formula Poems for Enriching Writing in the English Classroom

The main objective of the study was to determine the effectiveness of formula poems for enriching writing in the English classroom at the University of Cebu- Main Campus. A quasi-experimental design was used. The experimental group was treated with the use of creative formulae, while the control group was taught to use freewriting. The experts utilized the researcher-made rubric to measure the students' writing performance in both groups. The writing performances of the free writing group had fair rating and there were students got ratings that were also poor. The writing performances of the formula writing group had a very good rating and good ratings which shows that they were better in poetry writing compared to the free writing group. There is a significant difference in the performances of the free writing and formula writing groups. The engagements of the subjects with the formula poems have proved that it is much effective compared to freewriting in assessing the outputs of the subjects. The study concluded that the writing strategy of the students of the formula writing group using formula poems is effective. Furthermore, student writing guided by poetic formulas contributes to more engaged learning and provides a strong framework for creative thinking.
Error annotation in the COPLE2 corpus

We present the general architecture of the error annotation system applied to the COPLE2 corpus, a learner corpus of Portuguese implemented on the TEITOK platform. We give a general overview of the corpus and of the TEITOK functionalities and describe how the error annotation is structured in a two-level system: first, a fully manual token-based and coarse-grained annotation is applied and produces a rough classification of the errors in three categories, paired with multi-level information for POS and lemma; second, a multi-word and fine-grained annotation in standoff is then semi-automatically produced based on the first level of annotation. The token-based level has been applied to 47% of the total corpus. We compare our system with other proposals of error annotation, and discuss the fine-grained tag set and the experiments to validate its applicability. An inter-annotator (IAA) experiment was performed on the two stages of our system using Cohen’s kappa and it achieved good results on both levels. We explore the possibilities offered by the tokenlevel error annotation, POS and lemma to automatically generate the fine-grained error tags by applying conversion scripts. The model is planned in such a way as to reduce manual effort and rapidly increase the coverage of the error annotation over the full corpus. As the first learner corpus of Portuguese with error annotation, we expect COPLE2 to support new research in different fields connected with Portuguese as second/foreign language, like Second Language Acquisition/Teaching or Computer Assisted Learning.
On providing on-the-fly resizing of the elasticity grain when executing HPC applications in the cloud

Today, we observe that cloud infrastructures are gaining more and more space to execute high-performance computing (HPC) applications. Unlike clusters and grids, the cloud offers elasticity, which refers to the ability to increase or reduce the number of resources (and consequently, processes) to support, as closely as possible, the needs at a particular moment of the execution. To the best of our knowledge, current initiatives explore the elasticity for HPC applications by always handling the same number of resources at each scaling-in or scaling-out operation. This fixed elasticity grain commonly results in a stair-shaped behaviour, where successive elasticity operations take place to address the load curve. In this context, this article presents GrainElastic, an elasticity model for executing HPC applications with the capacity to adapt the elasticity grain to the requirements of each elasticity operation. Its contribution involves a mathematical formalism that uses historical execution traces and the ARIMA time-series model to predict the required number of resources (in our case, VMs) to address a reconfiguration point. Based on the proposed model, we developed a prototype that was compared with two other scenarios: 1) non-elastic application; 2) elastic middleware with a fixed grain. The results presented gains of up to 30% using GrainElastic, which demonstrates the relevance of adapting the elasticity grain to enhance system reactivity and performance.
Effects of Parameter Change on Neural Network and Deep Neural Network

Parameters in neural network such as number of neurons and layers have a direct effect on efficiency and accuracy of the network. On the other hand, it is observed that structure of a network is dependent on data complexity. It is perceived that choosing a complex network for a simple data (e.g. linearly separable data) may not only result in a weak learner but also an increase in computation time. Here, accuracy of the network has been challenged by adding Gaussian Noise to these data to investigate the effect of noise. This additive noise flags overcomplexity thus, decision boundary is generalized which it directs the learner to have better classification as if the number of parameters have been decreased. Throughout these comparisons, other constrains have been minimized in MATLAB to solely observe the effect of each change.
A chi assomiglia Elena Ferrante? Un profilo stilometrico aggior-nato

Based on a corpus including 150 novels by 40 authors, a stylometric survey was conducted to assess which modern authors were similar to Elena Ferrante, the pen name used for eight novels, including My Brilliant Friend (Tuzzi & Cortelazzo 2018a and2018b).The survey proved that Elena Ferrante's writing style is remarkably different from that of the other main contemporary Italian novelists
Analysis Of The Effect Of Slope Of The Scratch Side Angle Through The Post Tool On Surface Level Surface At The Working Tool Stermination ST 37

The purpose of this research is to see the effect of slope of incision angle chisel through Tool Post to roughness of surface of object. The method used is a lathe ST 37, with 0.5 mm cutting depth, with side angle cutting angle by tilting the Post Tool, ie: 10˚ tilted to the left, 10˚ tilted right, 0˚ (perpendicular), 5˚ tilted right, and 5˚ tilted left. Feeding (constant feeding) is: 0.281 mm / rotation, main cutting corner of chisel 80˚ and cutting speed = 740 rpm. Then the workpiece measured the surface roughness by using Surface Tester Mitutoyo SJ-201P. The result showed that, the smallest surface roughness rate achieved was (ΣRap) = 7, 42 μm with N9 surface roughness class with the Post Tool angle 5 ° to the left. While the largest surface roughness rate achieved is (ΣRap) = 13, 76 μm with the surface roughness class N11 with the tool angle post 10 ° to the right. The value of the roughness level achieved is between N9 - N11.
Cost-Effective Vision based Obstacle Avoidance System integrated Multi Array Ultrasonic sensor for Smart Wheelchair

A smart wheelchair (SW) is a power wheelchair (PW) consist of microcontroller, actuators, sensor, and implement the assistive technology in system architecture. Users with severe motor impairment may realize the difficulty to operate a wheelchair when they are in a tight space such as passing a doorway or when avoiding obstacles since, they are unable to control the wheelchair. This project embarked on an obstacle avoidance system of a wheelchair. This project developed a cost-effective system that alarmed the user to avoid obstacle on its path. The prototype includes a Kinect camera and ultrasonic sensors. Kinect camera was placed at the right side of the wheelchair for real time video obstacle detection. Four of the ultrasonic sensors was used to detect obstacle at the front and one ultrasonic sensor for rear obstacle detection. Any obstacle detected by the ultrasonic sensors triggered the buzzer. Obstacle detected by Kinect camera was displayed with specific command for the user in attempt for obstacle avoidance. The performance of the obstacle avoidance system was tested indoor to detect obstacles in a controlled environment. The accuracy of the ultrasonic system was tested at a specific distance of 20mm to 200mm at 20mm intervals. Real time video received from the Kinect Camera was used to analyse the depth of the environment and the location of the object.
Survey on CNN based super resolution methods

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.
Kompetencije knjižničara i informatologa: izazovi u vremenu društvenih i kulturnih promjena

In this paper we discuss the competencies of librarians and informatologists, terminological disagreements as well as attempts to investigate the various paths of influence on academic education in the field of Information sciences.We focus on changes in European educational arena and stimulus that encouraged the start-up and funding of EINFOSE project.Special attention is paid towards competencies of librarians and informatologists from the perspective of EINFOSE project.