Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for Go-Jek on Google Play Store

Go-Jek is one of the most popular providers of online transportation services in Indonesia that has now grown to become the on-demand mobile platform and the leading application that provides a full range of services ranging from transportation, logistics, payments, food delivery services, and various other services. Compared to similar services from its competitors, Go-Jek is still ranked first. In order to maintain the best service to consumers, Go-Jek must be able to improve service in various aspects continuously. One of them is the GoJek application on the Google Play Store. This study aims to measure the extent of the sentiment analysis results given by customers to Go-Jek through the comments column on the Play Store. Customer opinion is taken to get positive, negative or neutral comments.
Tracking People and Objects with an Autonomous Unmanned Aerial Vehicle using Face and Color Detection

We propose a people and object tracking algorithm for an autonomous unmanned aerial vehicle (UAV). It uses as a surveillance camera and can move anywhere. The camera from UAV is not fixed l ocation a s c losed-circuit t elevision. T he face detection and objection detection are applied to support our proposed. In this research, the UAV model for this paper was AR-Drone 2.0. It has a constraint on the front camera because it has fixed the position of view and cannot change the view during flight. W e d esigned t wo e xperiments. F irst, t he f ace detection using images and applied to the popularity of the face detection, is a Haar-cascade classifier and max-margin object detection with convolutional neural network based features because they have high precision in analysis. Second, color detection system, which only focuses on the color of objects which can developed as an obstacle detection system. The results of the experiment can be accepted to adapt to tracking people and objects in the smart-city.
A Study of Sentiment Analysis: Concepts, Techniques, and Challenges

Sentiment analysis (SA) is a process of extensive exploration of data stored on the Web to identify and categorize the views expressed in a part of the text. The intended outcome of this process is to assess the author attitude toward a particular topic, movie, product, etc. The result is positive, negative, or neutral. These study illustrated different techniques in SA approach for extracting and analytics sentiments associated with the polarity of positive, negative, or neutral on the topic selected. Social networks SA can be a useful source of information and data. SA acquires important in many areas of business, politics, and thought. So, this study contains a comprehensive overview of the most important studies in this field from the past to the recent studies till 2017. The main aim of this study is to provide full concept about SA techniques and its classification and methods used it. Also, we give a brief overview of big data techniques and its relation and use in SA field. Because the recent period has witnessed a remarkable development in the use of Big Data (Hadoop) in the process collection of data and reviews from social networks for analysis.
Effect of a global warming model on the energetic performance of a typical solar photovoltaic system

This study provides insight into how the energetic performance of a typical photovoltaic solar system (PV) will change in a warmer world with increased levels of greenhouse gases. It was considered a photovoltaic system disconnected from the distribution grid, similar to those available in the market. The methodology covers systems without batteries and with the use of batteries with finite electric charge storage. The climatic change is accessed from a scenario predicted from the HadCM3(Hadley Centre Coupled Model, version 3) major global circulation model reported in the International Panel for Climatic Change (IPCC). The outputs from a future weather scenario were used as inputs for the long-term performance simulations for the typical PV solar system. The results encompass predictions for the decades of 2020, 2050 and 2080, and are presented in terms of monthly average daily energy output, fraction of the load supplied for a system with and without battery, and energetic efficiency. Seven cities in different climatic zones were evaluated. The cities and regions presented distinct behaviors, such that in some cases the PV system generated less energy, while in others an increase in photovoltaic generation was observed. However, regarding the energetic efficiency of the system, all scenarios presented increasing losses throughout the decades.
Cache-Timing Attacks Still Threaten IoT Devices

Deployed widely and embedding sensitive data, The security of IoT devices depend on the reliability of cryptographic libraries to protect user information. However when implemented on real systems, cryptographic algorithms are vulnerable to side-channel attacks based on their execution behavior, which can be revealed by measurements of physical quantities such as timing or power consumption. Some countermeasures can be implemented in order to prevent those attacks. However those countermeasures are generally designed at high level description, and when implemented, some residual leakage may persist. In this article we propose a methodology to assess the robustness of the MbedTLS library against timing and cache-timing attacks. This comprehensive study of side-channel security allows us to identify the most frequent weaknesses in software cryptographic code and how those might be fixed. This methodology checks the whole source code, from the top level routines to low level primitives, that are used for the final application. We retrieve hundreds of lines of code that leak sensitive information.
Face Recognition using Transferred Deep Learning for Feature Extraction

Face recognition systems are a challenging field in computer vision. An important process and key to success is the feature extraction which requires a lot of data and time for learning. Deep learning has proven to be an outstanding method for extracting relevant features of image classification when a huge amount of data is available. However, it is not an easy task for face recognition, which consists of a small number of images per person considered as classes. This paper applies the idea of transferred learning for feature extraction to a face recognition application. The feature extraction part of the trained deep learning model from a different domain is transferred for extracting face features. Then, the multilayer perceptron neural network is used for model evaluation. Experimental results on public face databases show that the proposed method is highly efficient.
Confidence‐aware deep learning forecasting system for daily solar irradiance

For better deep learning forecasting systems for photovoltaic systems, confidence information about a point forecast is necessary in practical cases where uncertainties are unavoidable. In this study, using Bayesian deep learning, the authors introduce a confidence-aware deep learning forecasting system that provides confidence information as well as a point forecast. Through the experiments using the real-world data, they first solve three main issues caused by when Bayesian deep learning is applied to the forecasting of daily solar irradiance using weather forecast: selection of neural network model, selection of validation data to be used for estimating the confidence information, and ways for estimating the confidence information. Then, they examine the feasibility of the confidence-aware deep learning forecasting system in estimating the confidence information. From the experiments, classifying the forecast outputs into confident outputs and non-confident outputs using the confidence information, they show that maximum absolute percentage error of confident forecast outputs and non-confident forecast outputs are 5 and 22.8% at a specific classification threshold, respectively. This result shows that their confidence-aware deep learning forecasting system is good to estimate meaningful confidence information that is closely related to the forecast accuracy.
Evaluation of Nuance Forensics 9.2 and 11.1 under conditions reflecting those of a real forensic voice comparison case (forensic_eval_01)

Two automatic speaker recognition systems, Nuance Forensics 9.2 and 11.1, were tested within the setting of the Speech Communication virtual special issue “Multi-laboratory evaluation of forensic voice comparison systems under conditions reflecting those of a real forensic case (forensic_eval_01).” Nuance Forensics 9.2 is an i-vector PLDA system and Nuance Forensics 11.1 combines i-vector PLDA technology with some Deep Neural Networks functionalities. Both systems were tested in three variants. The difference between the first and second variant lies in the size of the “Reference Population” (42 vs. 105 speakers) and the difference between the first two and the third variant lies in the use of the “Background Model”, either working with a system default (first two variants) or a dedicated model drawn from the forensic_eval_01 training data (third variant). The Reference Population is used for the purpose of calibration (arriving at calibrated likelihood ratios from voice comparison scores); the Background Model is used for normalising the scores (Adaptive S-norm). Comparing the three variants, it was shown across the two systems that the inclusion of a Background Model that is dedicated to the conditions of the case leads to improved performance over the use of a system default. The difference in the size of the Reference Population however did not matter. Comparing the two systems, it was found that the system that includes Deep Neural Network technology leads to improved results over the use of a pure i-vector PLDA system.
Learning Chinese Word Segmentation Based on Bidirectional GRU-CRF and CNN Network Model

Chinese word segmentation is the basis of the Chinese natural language processing (NLP). With the development of the deep learning, various neural network models are applied to the Chinese word segmentation. However, current neural network models have the characteristics of artificial feature extraction, nonstandard word-weight, inability to effectively use long-distance information and long training time of models in Chinese word segmentation. To solve a series of problems, this article presents a CNN-Bidirectional GRU-CRF neural network model (CNN Bidirectional GRU CRF Network, CBiGCN), which breaks through the limit of conventional method window, truly realizes end-to-end processing and applies to the neural network model by the five-Tag set method, bias-variable-weight greedy strategy and supplements by Goldstein-Armijo guidelines. Besides, this model, with simple structure, is easy to be operated. And it can automatically learn features, reduces large amounts of tasks on specific knowledge in the form of handcrafted features and data pre-processing, makes use of context information effectively. The authors set an experiment with two data corpuses for Chinese word segmentation to evaluate their system. The experiment verified their new model can obtain better Chinese word segmentation results and greatly reduce training time.
Deep Learning Based Sentiment Classification on User-Generated Big Data

Background: Sentiment analysis of big data such as Twitter primarily aids the organizations with the potential of surveying public opinions or emotions for the products and events associated with them. Objective: In this paper, we propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F-measure as key performance indicators. Methods: Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks. Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the CNN model outperforms the baseline supervised learning algorithms with an accuracy of around 87% to 88%. Conclusion: Statistical analysis validates that the proposed CNN model outperforms the existing techniques and thus can enhance the performance of sentiment classification viability and coherency.
Discriminative information-based nonparallel support vector machine

Twin support vector machine (TSVM), as a typical nonparallel support vector machine, has been demonstrated to be effective in terms of classification performance. However, the existing TSVM does not take the within-class and between-class constraint projections into account. Inspired by modified pairwise constraint trick, we propose a novel classifier termed discriminative information-based nonparallel support vector machine (DINPSVM) to improve the performance of TSVM by introducing two novel regularization terms for each hyperplane, which takes the tightness between the similar patterns and discrepancy between the dissimilar pairs into consideration. The new classifier can not only learn the prior discriminative information about each constrained pair, but also combine the discrimination metric and spatial distance measure together. Experimental results on an artificial and twenty-three UCI datasets verify the efficiency and advantage of the proposed DINPSVM.
Super-Resolution Reconstruction of Transmission Electron Microscopy Images Using Deep Learning

Deep learning techniques have shown promising outcomes in single image super-resolution (SR) reconstruction from noisy and blurry low resolution data. The SR reconstruction can cater the fundamental limitations of transmission electron microscopy (TEM) imaging to potentially attain a balance among the trade-offs like imaging-speed, spatial/temporal resolution, and dose/exposure-time, which is often difficult to achieve simultaneously otherwise. In this work, we present a convolutional neural network (CNN) model, utilizing both local and global skip connections, aiming for 4× SR reconstruction of TEM images. We used exact image pairs of a calibration grid to generate our training and independent testing datasets. The results are compared and discussed using models trained on synthetic (downsampled) and real data from the calibration grid. We also compare the variants of the proposed network with well-known classical interpolations techniques. Finally, we investigate the domain adaptation capacity of the CNN-based model by testing it on TEM images of a cilia sample, having different image characteristics as compared to the calibration-grid.
Face super-resolution via bilayer contextual representation

Learning-based face super-resolution (SR), an important application field in computational imaging technology, offers a paradigm to enhance the quality and resolution of low-resolution (LR) facial images with the prior from training samples. The core of face SR is to learn the complex relationship between LR and high-resolution (HR) face images. Although most of the existing SR methods have achieved good results, the nonlinear nature of contextual prior is often ignored by locally linear assumption. In order to explore the accurate mapping relationship between LR and HR images, we propose a novel face SR method using bilayer contextual representation. In the first layer, we use context-patch to enrich the prior of image representation and shrink contextual dictionary in solution domain to induce local linear representation model in Euclidean space, thereby improving a set of LR images. In the second layer, kernel function provides a nonlinear mapping model for these enhanced images in Hilbert space, which maintains the consistency of the underlying manifold structure to capture a more accurate mapping between LR and HR. Furthermore, contextual residual learning is used to boost the reconstruction performance. The experimental results on FEI, CAS-PEAL-R1 and LDHF face databases show that the proposed approach preserves more image details and effectively improves the quality of the reconstructed image comparing with some state-of-the-art face SR methods.
Video Extrapolation Using Neighboring Frames

With the popularity of immersive display systems that fill the viewer’s field of view (FOV) entirely, demand for wide FOV content has increased. A video extrapolation technique based on reuse of existing videos is one of the most efficient ways to produce wide FOV content. Extrapolating a video poses a great challenge, however, due to the insufficient amount of cues and information that can be leveraged for the estimation of the extended region. This article introduces a novel framework that allows the extrapolation of an input video and consequently converts a conventional content into one with wide FOV. The key idea of the proposed approach is to integrate the information from all frames in the input video into each frame. Utilizing the information from all frames is crucial because it is very difficult to achieve the goal with a two-dimensional transformation based approach when parallax caused by camera motion is apparent. Warping guided by three-dimensnional scene points matches the viewpoints between the different frames. The matched frames are blended to create extended views. Various experiments demonstrate that the results of the proposed method are more visually plausible than those produced using state-of-the-art techniques.
Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming

Computation of stereoscopic depth and disparity map extraction are dynamic research topics. A large variety of algorithms has been developed, among which we cite feature matching, moment extraction, and image representation using descriptors to determine a disparity map. This paper proposes a new method for stereo matching based on Fourier descriptors. The robustness of these descriptors under photometric and geometric transformations provides a better representation of a template or a local region in the image. In our work, we specifically use generalized Fourier descriptors to compute a robust cost function. Then, a box filter is applied for cost aggregation to enforce a smoothness constraint between neighboring pixels. Optimization and disparity calculation are done using dynamic programming, with a cost based on similarity between generalized Fourier descriptors using Euclidean distance. This local cost function is used to optimize correspondences. Our stereo matching algorithm is evaluated using the Middlebury stereo benchmark; our approach has been implemented on parallel high-performance graphics hardware using CUDA to accelerate our algorithm, giving a real-time implementation.
Research and Implementation of CNN Based on TensorFlow

TensorFlow is Google's open source machine learning and deep learning framework, which is convenient and flexible to build the current mainstream deep learning model. Convolutional neural network is a classical model of deep learning, the advantage lies in its powerful feature extraction capabilities of convolutional blocks. Based on the TensorFlow platform, a convolutional neural network model with two-convolution-layers was built. The model was trained and tested with the MNIST data set. The test accuracy rate could reach 99.15%, and compared with the rate of 98.69% with only one-convolution-layer model, which shows that the two-convolution-layers convolutional neural network model has a better ability of feature extraction and classification decision-making.
Evaluation of Open-Source IDE Plugins for Detecting Security Vulnerabilities

Securing information systems has become a high priority as our reliance on them increases. Global multi-billion dollar companies have their critical information regularly exposed, costing them money and impairing their users' privacy. To defend against security breaches, IDE-integrated plugins to detect and remove security vulnerabilities in the first place are being used more frequently. More information about these plugins is needed in order to improve the state of the art within the field. Five open-source IDE plugins which can identify and report vulnerabilities are evaluated. We evaluate and compare how many categories of vulnerabilities the plugins can detect, how well the plugins detect the vulnerabilities, and how user-friendly the output of the plugin is to the developers. Our results show that certain vulnerabilities such as injection and broken access control are vastly covered by most plugins, while others have been completely ignored. A discrepancy between the claimed and actually confirmed coverage of the plugins is discovered, underlining the importance of this research. High false positive rate and obvious limitations in usability show that more work is needed before these plugins can be widely used and relied upon in a corporate setting.
HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing

Sign language conveys information through multiple channels, such as hand shape, hand movement, and mouthing.Modeling this multichannel information is a highly challenging problem.In this paper, we elucidate the link between spoken language and sign language in terms of production phenomenon and perception phenomenon.Through this link we show that hidden Markov model-based approaches developed to model "articulatory" features for spoken language processing can be exploited to model the multichannel information inherent in sign language for sign language processing.
Query-based summarization of discussion threads

Abstract In this paper, we address query-based summarization of discussion threads. New users can profit from the information shared in the forum, Please check if the inserted city and country names in the affiliations are correct. if they can find back the previously posted information. However, discussion threads on a single topic can easily comprise dozens or hundreds of individual posts. Our aim is to summarize forum threads given real web search queries. We created a data set with search queries from a discussion forum’s search engine log and the discussion threads that were clicked by the user who entered the query. For 120 thread–query combinations, a reference summary was made by five different human raters. We compared two methods for automatic summarization of the threads: a query-independent method based on post features, and Maximum Marginal Relevance (MMR), a method that takes the query into account. We also compared four different word embeddings representations as alternative for standard word vectors in extractive summarization. We find (1) that the agreement between human summarizers does not improve when a query is provided that: (2) the query-independent post features as well as a centroid-based baseline outperform MMR by a large margin; (3) combining the post features with query similarity gives a small improvement over the use of post features alone; and (4) for the word embeddings, a match in domain appears to be more important than corpus size and dimensionality. However, the differences between the models were not reflected by differences in quality of the summaries created with help of these models. We conclude that query-based summarization with web queries is challenging because the queries are short, and a click on a result is not a direct indicator for the relevance of the result.