Reviewerly/exHarmony huggingface dataset:
M-Shape PV Arrangement for Improving Solar Power Generation Efficiency

This paper presents a novel design scheme to reshape the solar panel configuration and hence improve power generation efficiency via changing the traditional PVpanel arrangement. Compared to the standard PV arrangement, which is the S-shape, the proposed M-shape PV arrangement shows better performance advantages. The sky isotropic model was used to calculate the annual solar radiation of each azimuth and tilt angle for the six regions which have different latitudes in Asia—Thailand (Bangkok), China (Hong Kong), Japan (Naha), Korea (Jeju), China (Shenyang), and Mongolia (Darkhan). The optimal angle of the two types of design was found. It emerged that the optimal tilt angle of the M-shape tends to 0. The two types of design efficiencies were compared using Naha’s geographical location and sunshine conditions. Through economic analyses, both the photovoltaic base cost and the electricity sales revenue were calculated, and the results showed that the M-shape has better economic benefits compared to the S-shape design. The proposed method can save resources and improve economic efficiency as well.
Dependency-aware software release planning through mining user preferences

Software vendors aim to find, for a release of the software, an optimal subset of features that gives the highest value while respecting the resource limitations. The value of a feature subset, however, is determined by the values of the individual features within that subset—which are specified by the preferences of users. But user preferences for some features may change in the presence or absence of others. As such, the values of certain software features may be influenced, either positively or negatively, by other features. Such influences are widely recognized and referred to in the literature as value-related dependencies among software features. Value-related dependencies impact the overall value of a software product and, therefore, need to be considered in software release planning. To achieve this, we have proposed identifying value-related dependencies by mining user preferences for software features. We integrate these dependencies into an integer programming model, that finds an optimal subset of the features for a release of a software product. We have demonstrated the practicality of our proposed approach by studying a real-world software project and simulations.
Efficient Content Adaptive Plenoptic Video Coding

In this paper, a content adaptive coding method for plenoptic video is proposed to reduce both of the spatial and temporal redundancy.Based on the spatial correlations among subapertures, the plenoptic video is divided into two subaperture groups: the central view videos and the residual videos.Coding methods, multiview coding method based on spatial correlation(S-MVC) and multiview coding method based on temporal correlation(T-MVC), are adopted for residual videos according to correlation and residual energy analysis, while the central view videos are always encoded by S-MVC.Correlation analysis is performed first, and residual energy analysis is further performed if necessary.Coding the central view videos and the residual videos separately reduces bitstream, choosing different encoding methods for residual videos makes full use of the characteristics of different video content.Both the central view videos and the residual videos are compressed by MV-HEVC.The experimental results demonstrate that the proposed method outperforms a pseudo-sequence-based MVC method for plenoptic video by an average of 25.06% bitrate saving.
CLIMB: A Pervasive Gameful Platform Promoting Child Independent Mobility

Child independent mobility (CIM) refers to the freedom and capability of children to move about their local neighborhoods without constant direct adult supervision. Our climb project combats an observed decline in CIM, offering a pervasive gameful platform for home–school mobility composed of three primary components: the first two using technology to support different levels of child independence and the third providing an element of continuous motivation for positive behavior change. This paper describes these three novel technologies: PedibusSmart, SafePath, and KidsGoGreen, and reports on four years of success with more than 1800 elementary age children, their teachers, and families. We further show how (i), disappearing, pervasive technology contributes to successful adoption, (ii), properly balancing trust and tracking leads to useful, noninvasive technological support, and (iii), in-classroom, gameful technology engages and motivates participation, with behavior changes persisting over time.
AR Tips: Augmented First-Person View Task Instruction Videos

This research investigates applying Augmented Reality (AR) visualisation of spatial cues in first-person view task instruction videos. Instructional videos are becoming popular, and are not only used in formal education and training, but even in everyday life as more people seek for how-to videos when they need help with instructions. However, video clips are 2D visualisation of the task space, sometimes making it hard for the viewer to follow and match the objects in the video to those in the real-world task space. We propose augmenting task instruction videos with 3D visualisation of spatial cues to overcome this problem, focusing on creating and viewing first-person view instruction videos. As a proof of concept, we designed and implemented a prototype system, called AR Tips, which allows users to capture and watch first-person view instructional videos on a wearable AR device, augmented with 3D visual cues shown in-situ at the task environment. Initial feedback from potential end users indicate that the prototype system is very easy to use and could be applied to various scenarios.
Neighbor similarity and soft-label adaptation for unsupervised cross-dataset person re-identification

Most of the existing person re-identification algorithms rely on supervised model learning from a large number of labeled training data per-camera-pair. However, the manual annotations often require expensive human labor, which limits the application of supervised methods for large-scale real-world deployments. To address this problem, we formulate a Neighbor Similarity and Soft-label Adaptation (NSSA) algorithm to transfer the supervised information from source domain to a new unlabeled target dataset. Specifically, we introduce a distance metric on the target domain, which incorporates inner-domain neighbor similarity and inter-domain soft-label adapted from source domain. The unlabeled samples which are close in this metric are considered to share the same pseudo-id and further selected to fine-tune the model. The training is performed iteratively to incorporate more credible sample pairs and incrementally improve the model. Extensive experimental results validate the superiority of our proposed NESSA algorithm, which significantly outperforms the state-of-the-art unsupervised and domain adaptation re-identification methods.
Computational Fact Validation from Knowledge Graph using Structured and Unstructured Information

In today's world, data or information is increasing at an exponential rate, and so is the fake news. Traditional fact-checking methods like fake news detection by experts, analysts, or some organizations do not match with the volume of information available. This is where the problem of computational fact-checking or validation becomes relevant. Given a Knowledge Graph, a knowledge corpus, and a fact (triple statement), the goal of fact-checking is to decide whether the fact or knowledge is correct or not. Existing approaches extensively used several structural features of the input Knowledge Graph to address the mentioned problem. In this work, our primary focus would be to leverage the unstructured information along with the structured ones. Our approach considers finding evidence from Wikipedia and structured information from Wikidata, which helps in determining the validity of the input facts. As features from the structured domain, we have used TransE embedding considering components of the input fact. The similarity of input fact with elements of relevant Wikipedia pages has been used as unstructured features. The experiments with a dataset consisting of nine relations of Wikidata has established the advantage of combining unstructured features with structured features for the given task.
Prediction of default payment of credit card clients using Data Mining Techniques

Recent studies showed that poor people had trouble with their financial decisions. In order to prevent these financial complications including decisions which are easier and more frequent in the unintentional failure for paying monthly credit card balances, we proposed a data mining-based failure prevention system from the view of risk management. This research realized on customers' default payments for the accurate prediction of the probability of default payment. Because the class of default dataset is imbalanced dataset, this study presents the Synthetic Minority Over-Sampling Technique (SMOTE) to deal with the imbalanced dataset. By utilizing SMOTE method, the predicting model produced by Random Forest has the best accuracy and performance with low error rate. Consequently, among the seven data mining algorithms, Random Forest is a good alternative to precisely predict the default payment. The proposed Random Forest model classifies Default of Credit Card Clients in the test data by predicting the target variable 89.01% correctly. This effect also resulted in an improvement of ROC area (AUC=0.947), and F-measure (0.89) of Random Forest, which was higher than other classifiers.
A Hyper-parameter Optimization for Deep Neural Network using an Island-based Genetic Algorithm

We present a method using an island-based genetic algorithm to automatically optimize the hyper-parameters of deep neural networks (DNNs). The method consists of two steps: hyper-parameter search and complete DNNs training. To demonstrate our method, we use deep convolutional neural networks (DCNNs). We encode structure and hyper-parameters of a DCNN as a chromosome and then apply an island-based genetic algorithm to optimize the structure and hyper-parameters. To evaluate the proposed method, we implemented an image classification task using CIFAR-10 dataset. The experimental results show that the proposed method can efficiently search for the hyper-parameters of DCNNs.
Development of real-time motion autonomous surface vehicle controlling for coral reef conservation and fisheries

Abstract Start Coral reefs are typical ecosystems with underwater beauty. In addition to beauty, there is a function to reduce abrasion, marine biota residence and as well as educational facilities. The current state of coral reef ecosystems in Indonesia is quite alarming, the condition of coral reefs that are in very good condition is 6.39%, good condition is 23.40%, sufficient condition is 35.06% and bad condition is 35.15%. So there needs to be special handling to deal with these damage by using technological developments. Based on these conditions many researches emerged for observation and conservation. One of them is research on the development of conservation aids, the floating of these tools is accompanied by the development of increasingly natural ways of user interaction and tools. Natural interaction is characterized by interactions that transform human movements into direct digital action. Motion Tracking (MT) is one of the implementations of markerless/natural interaction, in which to identify projects is no longer based on certain symbols. MT reads body parts then converted into digital movements. This study aims to develop Real-Time Motion Autonomous Surface Vehicle (ASV) Controlling for Coral Reef Conservation and Fisheries using Fuzzy Algorithms. The tools produced by the study are controlled naturally, so it is expected to facilitate users in operating ASV. This tool is expected to facilitate monitoring and observation for the conservation of marine ecosystems (coral reefs andfisheries).
A Virtual Sign Language Translator on Smartphones

Sign languages are natural languages used primarily by the deaf communities. Unfortunately, it is difficult for people outside of the communities to understand the languages. As smartphones are now ubiquitous, it will be very convenient for people from different communities to communicate if smartphones can help translate the languages. In this paper, a smartphone application called Sign Language Translator (SLT) is introduced, which records the person signing and translates the hand gestures into texts. SLT integrates two pre-trained models proposed by previous works and a classification model developed in this study. Given a frame showing the person making the sign, SLT first gets the body skeleton of the person, from which the positions of the hands are identified and the fingers' skeleton is then detected. The classification model is next used to extract features from the skeleton to map to the specific words that are expressed. In the experiments, we chose three specific words in American Sign languages for evaluation. Both training and testing datasets are the videos with the corresponding signs. The accuracy of translation into right words in the testing dataset is more than 80 percent. It shows that SLT can effectively help people to communicate with deaf people.
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Constructions of permutation polynomials over finite fields have attracted much interests in recent years, especially those with few terms, such as trinomials, due to their simple form and additional properties. In this paper, we construct several classes of permutation trinomials over Fp2k with Niho exponents of the form f(x)=x+λ1xs(pk−1)+1+λ2xt(pk−1)+1; some necessary and sufficient conditions for the polynomial f(x) to permute Fp2k are provided. Specifically, for p=5, new permutation trinomials are presented. We also give recursive constructions of permutation polynomials using self-reciprocal polynomials.
Recognition of Isolated Handwritten Arabic Characters

The challenges that face the handwritten Arabic recognition are overwhelming such as different varieties of handwriting and few public databases available.Also, teaching the non-Arabic speaker at the young age is very difficult due to the unfamiliarity of the words and meanings.So, this project is focused on building a model of a deep learning architecture with convolutional neural network (CNN) and multilayer perceptron (MLP) neural network by using python programming language.This project analyzes the performance of a public database which is Arabic Handwritten Characters Dataset (AHCD).However, training this database with CNN model has achieved a test accuracy of 95.27% while training it with MLP model achieved 72.08%.Therefore, the CNN model is suitable to be used in the application device.
Co-Presence in a Shared Virtual Environment (SVE): A Case Study of Highway Work Zone Construction

Given the high rate of motor vehicle fatalities in work zones in the U.S., this study proposes the use of a virtual reality (VR) as a tool for studying safety in construction work zones. This testing environment makes possible to oversee the critical safety situations and attenuate the necessary future design adjustments. Besides, VR application in this area of study can revolutionize the traditional training techniques. Remarkably, with the recent technological advances, there is an opportunity to create rich and high quality shared virtual environments (SVEs) in which users can actively interact with one another. This study reports on an experiment in that a virtual highway construction zone has been used as a platform for investigating the real-time interaction between two participants: a driver and a construction worker. The study aims at 1) investigating the impact of experiment design and technology on participants’ SVE experience; 2) suggesting a framework for designing SVEs with higher levels of presence and co-presence; 3) assessing the viability of SVE’s as a research tool in construction and safety domain.
Hepatocellular Carcinoma Recognition in Ultrasound Images Using Textural Descriptors and Classical Machine Learning

The classification of the liver tissue based on the analysis of the ultrasound images is an important task in the field of computer aided diagnosis, the ultrasonography providing a non-invasive, low-cost, imaging solution. An early, accurate diagnosis of the malignant tumours, based on those images, would be very useful for both patients and doctors. Within ultrasound images, the malignant tumours can be hardly distinguished by the human eye, the golden standard for diagnosis being the biopsy, an invasive, dangerous method. In our research, we develop computerized, non-invasive methods, based on advanced image analysis and recognition techniques, for performing tumour detection within ultrasound images. This paper proposes a method for hepatocellular carcinoma recognition in ultrasound images, by employing textural features obtained from the Gray Level Co-occurence Matrix (GLCM), combined with Local Binary Patterns (LBP). At the end, a true positive rate of about 72% was obtained for HCC, using ensembles of Adaptive Boosted classifiers.
Toward A Real-Time Social Recommendation System

Recent research has investigated approaches and models to produce optimal results in social recommendation systems (SRSs) particularly in text-based form. The aim is to analyze the user generated-content (UGC) to suggest appropriate recommendations to interested users. However, users are often not satisfied with the initial recommendations because some models do not elicit their preferences at the beginning of the interaction nor do they understand their actual needs. In this paper, we propose a real-time SRSs called ChatWithRec that aims to improve the accuracy of recommendations by analyzing the user's contextual conversation dynamically, detect the topic, and then match it with a suitable advertisement. We used the Latent Dirichlet Allocation topic model (LDA) to analyze the user's conversation and perceive topics. We evaluated our system by applying several metrics like coherence, and F-score to evaluate the performance of ChatWithRec recommendation system. The results are encouraging, indicating that the system is fast, satisfies users by getting exactly what they seek in their conversation flow.
Salient object detection based on backbone enhanced network

The Convolutional Neural Networks (CNNs) with encoder-decoder architecture has shown powerful ability in semantic segmentation and it has also been applied in saliency detection. In most researches, the parameters of the backbone network which have been pre-trained on the ImageNet dataset will be retrained using the new training dataset to let CNNs adapt to the new task better. But the retraining will weaken generalization of the pre-trained backbone network and result in over-fitting, especially when the scale of the new training data is not very large. To make a balance between generalization and precision, and to further improve the performance of the CNNs with encoder-decoder architecture in salient object detection, we proposed a framework with enhanced backbone network (BENet). A encoder with structure of dual backbone networks (DBNs) is adopted in BENet to extract more diverse feature maps. In addition, BENet includes a connection module based on improved Res2Net to efficiently fuse feature maps from the two backbone networks and a decoder based on weighted multi-scale feedback module (WMFM) to perform synchronous learning. Our approach is extensively evaluated on six public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods without any additional supervision.
Pangolin: An SFL-Based Toolset for Feature Localization

Pinpointing the location where a given unit of functionality-or feature-was implemented is a demanding and time-consuming task, yet prevalent in most software maintenance or evolution efforts. To that extent, we present PANGOLIN, an Eclipse plugin that helps developers identifying features among the source code. It borrows Spectrum-based Fault Localization techniques from the software diagnosis research field by framing feature localization as a diagnostic problem. PANGOLIN prompts users to label system executions based on feature involvement, and subsequently presents its spectrum-based feature localization analysis to users with the aid of a color-coded, hierarchic, and navigable visualization which was shown to be effective at conveying diagnostic information to users. Our evaluation shows that PANGOLIN accurately pinpoints feature implementations and is resilient to misclassifications by users. The tool can be downloaded at https://tqrg.github.io/pangolin/.
A Quantitative Analysis Framework for Recurrent Neural Network

Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversarial attacks and hinder their deployment in real-world applications. In this paper, we propose a quantitative analysis framework - DeepStellar - to pave the way for effective quality and security analysis of software systems powered by RNNs. DeepStellar is generic to handle various RNN architectures, including LSTM and GRU, scalable to work on industrial-grade RNN models, and extensible to develop customized analyzers and tools. We demonstrated that, with DeepStellar, users are able to design efficient test generation tools, and develop effective adversarial sample detectors. We tested the developed applications on three real RNN models, including speech recognition and image classification. DeepStellar outperforms existing approaches three hundred times in generating defect-triggering tests and achieves 97% accuracy in detecting adversarial attacks. A video demonstration which shows the main features of DeepStellar is available at: https://sites.google.com/view/deepstellar/tool-demo.
New Image Encryption Algorithm Based on Switching-type Chaotic Oscillator

Image encryption is different than text encryption due to image characteristics such as high redundancy of data and bulk data capacity. To handle the difference, new algorithms are being designed. Those image encryption algorithms evolve around shuffling and encrypting. For certain algorithms, a key as large as the image itself needs to be constructed. Chaotic systems are very sensitive to the initial condition which makes it best fit to create a random number generator which is very beneficial in image cryptography applications. In this paper, an algorithm scheme is proposed based on chaotic switching-type oscillator. The paper is divided into two main phases which are shuffling and encrypting using the chaotic key and a different number of predefined iterations. The encryption algorithm is a bitwise linear operator. The key space contains 262144 chaotic values. The initial values of the keystream are very sensitive, changing one of the inputs from 0 to 0.00000001 caused a dramatic change in the keystream and makes it impossible to decrypt the image using the same encryption scheme. The encryption process changed the correlation from 0.9730 in the original image into 0.0330 in the ciphered image.
Investigation of mineralization, REE geochemistry, and fluid inclusions studies of the Shalang vein-type polymetallic ore deposit, southwest of Kerman

Investigation of mineralization, REE geochemistry, and fluid inclusions studies of the Shalang vein-type polymetallic ore deposit, southwest of Kerman