Reviewerly/exHarmony huggingface dataset:
Twitter sentiment analysis of the relocation of Indonesia's capital city

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.
An accurate and efficient web service QoS prediction model with wide-range awareness

With the wide spread and deepening of service-oriented computing, more and more enterprises and organizations are constructing their applications by integrating third-party Web services in the cloud nowadays. Building high-quality applications has long been a critical research issue. Quality of service (QoS) prediction provides valuable information for making optimal Web service selection from a set of functionally equivalent candidate services. Commonly, collaborative filtering technique like matrix factorization (MF) is implemented for predicting unknown QoS values, and most of them are built via modeling user-service interaction based on QoS data directly or take side information such as geographical location, network autonomous region into account. Due to the overlook of the implicit but important wide-range characteristic of QoS data, existing MF methods might incur high users’ and services’ biases, and their prediction accuracy will not be good enough if we are faced with such wide-range of QoS data. In this work, we first investigate the wide-range characteristic among users and services via real-world Web service QoS dataset, and argue that such observed finding is an essential factor for accurate QoS prediction. We then propose a novel prediction model named Wide-Range Aware Matrix Factorization (WRAMF), which tackles the wide-range influence via bias information combination and an active function mapping explicitly. The proposed WRAMF model is advantageous to existing MF-based models, which optimizes the model by an adaptive learning rate strategy that guides WRAMF to approach the optimal solution accurately, and trains the model by a well-designed parallel stochastic gradient descent algorithm efficiently. Comprehensive experiments are conducted by employing real-world QoS dataset and empirical results show that our WRAMF significantly outperforms the state-of-the-art methods in terms of accuracy and efficiency.
A Weighted Topic Model Learned From Local Semantic Space for Automatic Image Annotation

Automatic image annotation plays a significant role in image understanding, retrieval, classification, and indexing.Today, it is becoming increasingly important in order to annotate large-scale social media images from content-sharing websites and social networks.These social images are usually annotated by user-provided low-quality tags.The topic model is considered as a promising method to describe these weak-labeling images by learning latent representations of training samples.The recent annotation methods based on topic models have two shortcomings.First, they are difficult to scale to a large-scale image dataset.Second, they can not be used to online image repository because of continuous addition of new images and new tags.In this paper, we propose a novel annotation method based on topic model, namely local learning-based probabilistic latent semantic analysis (LL-PLSA), to solve the above problems.The key idea is to train a weighted topic model for a given test image on its semantic neighborhood consisting of a fixed number of semantically and visually similar images.This method can scale to a large-scale image database, as training samples involved in modeling are a few nearest neighbors rather than the entire database.Moreover, this proposed topic model, online customized for the test image, naturally addresses the issue of continuous addition of new images and new tags in a database.Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art especially in terms of overall metrics.
Detection of Phishing Attacks with Machine Learning Techniques in Cognitive Security Architecture

The number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.
Human Parsing With Pyramidical Gather-Excite Context

Human parsing, especially in the wild, has attracted a lot of attention due to its great potential in many real-world applications. The Pyramid Spatial Parsing (PSP) module has shown superior performances in scene and human parsing tasks. However, the basic AvgPool operation in PSP equally aggregates spatial clues of a local region, and thus mixes up influences of different human parts presented in this region. It results in failures in capturing useful contexts relevant to parsing different parts. To address this problem, a suitable mechanism to collect spatial clues aligning with different human parts is proposed in this paper. We employ a Gather-Excite (GE) operation, a replacement of the AvgPool-Upsample operation in a pyramidical structure, to accurately reflect relevant human parts of various scales. The GE operation contains two steps: the gather operation that adaptively aggregates spatial clues to relevant human parts, and the excite operation that generates new feature maps with the gathered contextual information. This results in a novel Pyramidical Gather-Excite Context (PGEC) module to solve the multi-scale problem and parse person at various scales. The PGEC module is composed of multiple GE operations with different spatial extents and aggregates local and global spatial clues for better modeling multi-scale contextual information in parallel. Moreover, we integrate the PGEC module with fine-grained details, edge preserving module and deep supervision to formulate a novel PGEC Network (PGECNet) for human parsing. The proposed PGECNet has achieved state-of-the-art performance on four single-person human parsing datasets (i.e., LIP, PPSS, ATR and Fashion Clothing) and two multi-person human parsing datasets (i.e., PASCAL-Person-Part and CIHP). The experimental results show that the proposed PGEC is superior to the PSP and ASPP modules especially in single-human parsing task. The source code is publicly available at https://github.com/31sy/PGECNet.
Reconfigurable Photon Sources Based on Quantum Plexcitonic Systems

A single photon in a strongly nonlinear cavity is able to block the transmission of a second photon, thereby converting incident coherent light into antibunched light, which is known as the photon blockade effect. Photon antipairing, where only the entry of two photons is blocked and the emission of bunches of three or more photons is allowed, is based on an unconventional photon blockade mechanism due to destructive interference of two distinct excitation pathways. We propose quantum plexcitonic systems with moderate nonlinearity to generate both antibunched and antipaired photons. The proposed plexcitonic systems benefit from subwavelength field localizations that make quantum emitters spatially distinguishable, thus enabling a reconfigurable photon source between antibunched and antipaired states via tailoring the energy bands. For a realistic nanoprism plexcitonic system, chemical and optical schemes of reconfiguration are demonstrated. These results pave the way to realize reconfigurable nonclassical photon sources in a simple quantum plexcitonic platform.
Data-driven three-dimensional super-resolution imaging of a turbulent jet flame using a generative adversarial network

Three-dimensional (3D) computed tomography (CT) is becoming a well-established tool for turbulent combustion diagnostics. However, the 3D CT technique suffers from contradictory demands of spatial resolution and domain size. This work therefore reports a data-driven 3D super-resolution approach to enhance the spatial resolution by two times along each spatial direction. The approach, named 3D super-resolution generative adversarial network (3D-SR-GAN), builds a generator and a discriminator network to learn the topographic information and infer high-resolution 3D turbulent flame structure with a given low-resolution counterpart. This work uses numerically simulated 3D turbulent jet flame structures as training data to update model parameters of the GAN network. Extensive performance evaluations are then conducted to show the superiority of the proposed 3D-SR-GAN network, compared with other direct interpolation methods. The results show that a convincing super-resolution (SR) operation with the overall error of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>∼<!-- ∼ --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>4</mml:mn> </mml:mrow> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> and the peak signal-to-noise ratio of 37 dB can be reached with an upscaling factor of 2, representing an eight times enhancement of the total voxel number. Moreover, the trained network can predict the SR structure of the jet flame with a different Reynolds number without retraining the network parameters.
Cloud Application Security using Hybrid Encryption

The massive growth of sensitive information on cloud has made it more vulnerable.Thus, undeniably, the vulnerability shoots from the increasing number of users whose intentions are malicious.Since, the cloud is managed by a third party, ensuring cloud security services are of utmost importance, and coupled with the fact that cloud data and services resident in data centers are ubiquitous.The enlarged user surfing on the cloud for numerous purposes, therefore, necessitate a vastly safe and secured data.Consequent upon the highlighted points and providing a secure environment, this paper proposes a hybrid encryption algorithm comprising symmetric and asymmetric cryptography schemes suitable as state-of-the-art safeguarding users' privacy and security in the cloud.We propose a privacy model using Advanced Encryption Standard (AES) as a first level data encryption scheme prior cloud application data storage, while Elliptic Curve Cryptography (ECC) is the subsequent encryption scheme with AES key to achieve our aim of data confidentiality as well as is security in the cloud.
Digital Image Correlation for Cement-based Materials and Structural Concrete Testing

This paper presents the application of a low-cost digital image correlation (DIC) system for automated crack mapping. The system employed makes use of a conventional digital camera, a remote image recording controller, and an open-source digital image correlation MATLAB software Ncorr. To showcase the potential of the proposed system, two case studies involving general structural and material testing were undertaken. In the first series, the DIC system was used to study the fracture processes in a reinforced concrete beam and to investigate the influence of key input parameters on the spatial quality of strain maps obtained from the DIC analysis. In the second series, the application of the DIC method was expanded to assist in the study of complex multiple micro-crack formations in ductile cement composite testing. It is shown that the strain maps obtained from the DIC technique have a close resemblance to the actual crack patterns
Comparing and Combining Interaction Data and Eye-tracking Data for the Real-time Prediction of User Cognitive Abilities in Visualization Tasks

Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye-tracking data. Performing this type of user modeling is important for devising visualizations that can detect a user's abilities and adapt accordingly during the interaction. In this article, we extend previous user modeling work by investigating for the first time interaction data as an alternative source to predict cognitive abilities during visualization processing when it is not feasible to collect eye-tracking data. We present an extensive comparison of user models based solely on eye-tracking data, on interaction data, as well as on a combination of the two. Although we found that eye-tracking data generate the most accurate predictions, results show that interaction data can still outperform a majority-class baseline, meaning that adaptation for interactive visualizations could be enabled even when it is not feasible to perform eye tracking, using solely interaction data. Furthermore, we found that interaction data can predict several cognitive abilities with better accuracy at the very beginning of the task than eye-tracking data, which are valuable for delivering adaptation early in the task. We also extend previous work by examining the value of multimodal classifiers combining interaction data and eye-tracking data, with promising results for some of our target user cognitive abilities. Next, we contribute to previous work by extending the type of visualizations considered and the set of cognitive abilities that can be predicted from either eye-tracking data and interaction data. Finally, we evaluate how noise in gaze data impacts prediction accuracy and find that retaining rather noisy gaze datapoints can yield equal or even better predictions than discarding them, a novel and important contribution for devising adaptive visualizations in real settings where eye-tracking data are typically noisier than in laboratory settings.
Proof-of-principle demonstration of compiled Shor’s algorithm using a quantum dot single-photon source

We report a proof-of-principle demonstration of Shor's algorithm with photons generated by an on-demand semiconductor quantum dot single-photon source for the first time. A fully compiled version of Shor's algorithm for factoring 15 has been accomplished with a significantly reduced resource requirement that employs the four-photon cluster state. Genuine multiparticle entanglement properties are confirmed to reveal the quantum character of the algorithm and circuit. The implementation realizes the Shor's algorithm with deterministic photonic qubits, which opens new applications for cluster state beyond one-way quantum computing.
The TechQA Dataset

We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain.The TECHQA corpus highlights two real-world issues from the automated customer support domain.First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task.Second, it has a real-world size -600 training, 310 dev, and 490 evaluation question/answer pairs -thus reflecting the cost of creating large labeled datasets with actual data.Hence, TECHQA is meant to stimulate research in domain adaptation rather than as a resource to build QA systems from scratch.TECHQA was obtained by crawling the IB-MDeveloper and DeveloperWorks forums for questions with accepted answers provided in an IBM Technote-a technical document that addresses a specific technical issue.We also release a collection of the 801,998 Technotes available on the web as of April 4, 2019 as a companion resource that can be used to learn representations of the IT domain language.
TAG : Type Auxiliary Guiding for Code Comment Generation

Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc.However, introducing the type information into the existing framework is non-trivial due to the hierarchical dependence among the type information.In order to address the issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for the code comment generation task which considers the source code as an N-ary tree with type information associated with each node.Specifically, our framework is featured with a Typeassociated Encoder and a Type-restricted Decoder which enables adaptive summarization of the source code.We further propose a hierarchical reinforcement learning method to resolve the training difficulties of our proposed framework.Extensive evaluations demonstrate the state-of-the-art performance of our framework with both the auto-evaluated metrics and case studies.
Text-based emotion prediction system using machine learning approach

Abstract Text-based input becomes a common channel for humans in sharing their opinions/emotions to the product or service through online social media, shopping platform etc. Humans are easy to make errors in interpreting emotions, especially the emotion that derived from text based. The main aim of this study is to develop text-based emotion recognition and prediction system. Several market challenges facing in the advancement of emotion analysis with accuracy being the main issue. Therefore, four supervised machine learning classification algorithms such as Multinomial Naïve Bayes, Support Vector Machine, Decision Trees, and k- Nearest Neighbors were investigated. The model was developed based on Ekman’s six basic emotions which are anger, fear, disgust, joy, guilt and sadness. Data pre-processing techniques such as stemming, stop-words, digits and punctuation marks removal, spelling correction, and tokenization were implemented. A benchmark of ISEAR (International Survey on Emotion Antecedents and Reactions) dataset was used to test all models. Multinomial Naïve Bayes classifier resulted the best performance with an average accuracy of 64.08%. Finally, the best model was integrated to graphical user interface using Python Tkinter library to complete the whole system development. Besides, the detailed performance of the best model such as tf-idf and count vectorizer, confusion matrix, precision-recall rate, as well as ROC (Receiver Operating Characteristic) score were also discussed. Text-based emotion prediction system to interpret and understand human emotions was successfully developed.
Learning to execute instructions in a Minecraft dialogue

The Minecraft Collaborative Building Task is a two-player game in which an Architect A instructs a Builder B to construct a target structure out of 3D blocks.We consider the task of predicting B's action sequences (block placements and removals) in a given game context, and show that capturing B's past actions as well as B's perspective leads to a significant improvement in performance on this challenging language understanding problem.
Adaptive Attention-Aware Network for unsupervised person re-identification

Person re-identification (Re-ID) has attracted more attention in computer vision tasks recently and achieved high accuracy in some public available datasets in a supervised manner. The performance drops significantly when datasets are unlabeled, which limits the scalability of Re-ID algorithms in practical applications. Despite some unsupervised methods are proposed to address the scalability problem of Re-ID, it's hard to learn discriminative feature representations due to the lack of pairwise labels in different camera views. To overcome this problem, we propose an end-to-end network named Adaptive Attention-Aware Network for unsupervised person re-identification. Specifically, we propose a novel adaptive attention-aware module that could be easily embedded into Re-ID architecture. The proposed module focuses on learning strong expressive relationship among channels of feature maps, and alleviating the key problems of Re-ID, e.g., occlusion and local deformation. In addition, we extract the camera-invariant features by adopting camera-style transfer feature learning since matching pairs in Re-ID suffers from appearance changes under different camera views. Besides, unsupervised hard negative mining is introduced to learn large intra-person appearance variance and discriminate high inter-person appearance similarity in an unlabeled target dataset with an auxiliary labeled dataset. Comprehensive experiments on three public available Re-ID datasets demonstrate that our method can achieve the state-of-the-art results of unsupervised Re-ID and is competitive with supervised learning.
Learning and Evaluating Emotion Lexicons for 91 Languages

Emotion lexicons describe the affective meaning of words and thus constitute a centerpiece for advanced sentiment and emotion analysis.Yet, manually curated lexicons are only available for a handful of languages, leaving most languages of the world without such a precious resource for downstream applications.Even worse, their coverage is often limited both in terms of the lexical units they contain and the emotional variables they feature.In order to break this bottleneck, we here introduce a methodology for creating almost arbitrarily large emotion lexicons for any target language.Our approach requires nothing but a source language emotion lexicon, a bilingual word translation model, and a target language embedding model.Fulfilling these requirements for 91 languages, we are able to generate representationally rich high-coverage lexicons comprising eight emotional variables with more than 100k lexical entries each.We evaluated the automatically generated lexicons against human judgment from 26 datasets, spanning 12 typologically diverse languages, and found that our approach produces results in line with state-of-the-art monolingual approaches to lexicon creation and even surpasses human reliability for some languages and variables.Code and data are available at github.
Fundamentals of graphic design—essential tools for effective visual science communication

Guidance on improving the visual aspects of science communication range from “recipe”-style instructions to hyper-focused aspects of data visualization. Currently lacking in the peer-reviewed literature is a primer in graphic design tailored to a high-level overview of basic design principles and associated jargon related to layout, imagery, typeface, and colour. We illustrate why these aspects are important to effective communication. Further, we provide considerations on when to solicit professional assistance and what to expect when working with graphic designers. Having the fundamental principles of good design in your toolbox facilitates the production of effective visual communication related to your research and fruitful scientist–designer collaborations.