Reviewerly/exHarmony huggingface dataset:
Automated Model-Based Test Case Generation for Web User Interfaces (WUI) From Interaction Flow Modeling Language (IFML) Models

Since the emergence of web 2.0, the architecture of web applications has been transformed significantly and its complexity has grown enormously. In such web applications, the user interface (UI) is an important ingredient and with the increased complexity, its testing is getting increasingly complex and cost/time-consuming process. Recently introduced, interaction flow modeling language (IFML) is an object management group (OMG) standard. IFML is gaining popularity for developing web applications, primarily, because of its excellent features for modeling UI elements, their content, and their interaction capturing capabilities. However, despite its superior UI modeling features, its UI testing is accomplished through traditional time-consuming techniques, which are employed after implementing the UI code. Hence, to overcome these limitations, this paper introduces a novel model-based testing approach for IFML UI elements. The proposed approach provides complete navigation testing using formal models. Moreover, the approach transforms the IFML models to all necessary UI testing artifacts by generating state transition matrix plus detailed UI test case document. As a part of a research, model-based user interface test case (MBUITC) generator tool is implemented to automatically generate navigation model for formal verification, test case document, and transition matrices from IFML models. The applicability of the proposed approach is validated through two benchmark case studies. The results have shown that the proposed approach provides test cases at the early stages of development, i.e., specification and analysis, which eventually helps in building a right product at the right time at a comparatively lower cost.
Learning to Describe Unknown Phrases with Local and Global Contexts

Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Locally optimum image watermark decoder by modeling NSCT domain difference coefficients with vector based Cauchy distribution

Improving the ability of imperceptibility, watermark capacity, and robustness at the same time still remains a challenge within the digital image watermarking community. By modeling the robust nonsubsampled Contourlet transform (NSCT) difference coefficients with vector based Cauchy distribution and employing locally most powerful (LMP) test, we propose a locally optimum image watermark decoder in NSCT domain. We first compute the difference coefficients according to the inter-scale dependency between NSCT coefficients, and investigate the robustness of the NSCT difference coefficients by subjective visual error and objective mean squared error (MSE) terms. We then embed the digital watermark into the significant NSCT difference subband with highest energy by modifying the robust NSCT difference coefficients. At the receiver, by combining the vector based Cauchy probability distribution and LMP test, we propose a locally optimum blind watermark decoder in the NSCT domain. Here, robust NSCT difference coefficients are firstly modeled by employing the vector based Cauchy probability density function (PDF), where the Cauchy marginal statistics and various strong dependencies of NSCT coefficients are incorporated. Then the statistical model parameters of vector based Cauchy PDF are estimated using second-kind statistics approach. And finally a blind image watermark decoder is developed using vector based Cauchy PDF and LMP decision rule. We conduct extensive experiments to evaluate the performance of the proposed blind watermark decoder, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature.
A Steganography Algorithm Based on CycleGAN for Covert Communication in the Internet of Things

With the wide application of the Internet of Things (IoT), the risk of data leakage and theft in IoT is gradually increasing since communication channel is public in data transmission.Thus, the IoT security has become a major problem in information security.Steganography is one of the key methods to solve the problems of personal privacy disclosure and covert communication.In order to make sure secure communications, this paper proposes a novel steganography algorithm based on image-to-image translation by adding steganography module and steganalysis module to CycleGAN, adapting to the covert communication and privacy preserving of the IoT.Steganalysis network is used to improve the anti-detection ability of stego image.Moreover, cycle consistent in CycleGAN can guarantee the quality of the generated image.Through the proposed scheme, the stego image can resist steganalysis by monitors to some extent and remain intact.The experimental results show that this method has a better performance than the stateof-the-art approach.
Quantification of quantum dynamics with input-output games

Recent developments surrounding resource theories have shown that any quantum state or measurement resource, with respect to a convex (and compact) set of resourceless objects, provides an advantage in a tailored subchannel or state discrimination task, respectively. Here we show that an analogous, more general result is also true in the case of dynamical quantum resources, i.e., channels and instruments. In the scenario we consider, the tasks associated to a resource are input-output games. The advantage a resource provides in these games is naturally quantified by a generalized robustness measure. We illustrate our approach by applying it to a broad collection of examples, including classical and measure-and-prepare channels, measurement and channel incompatibility, local operations assisted by classical communication, and steering, as well as discussing its applicability to other resources in, e.g., quantum thermodynamics. We finish by showing that our approach generalizes to higher-order dynamics where it can be used, for example, to witness causal properties of supermaps.
An improved quantum principal component analysis algorithm based on the quantum singular threshold method

Quantum principal component analysis (qPCA) is a dimensionality reduction algorithm for getting the eigenvectors corresponding to top several eigenvalues of the data matrix and then reconstructing. However, qPCA can only construct the quantum state contains all the eigenvectors and eigenvalues. In this paper, we present an improved quantum principal component analysis (Improved qPCA) algorithm with a fixed threshold. We can reduce the singular value less than the threshold to 0 and obtain a target quantum state which can be used to get an output similar to qPCA after phase estimation. Compared with qPCA, our algorithm has only the target eigenvalues and the probability that we get each target eigenvalue is greater. Furthermore, our algorithm can serve as an additional regularization method and a subroutine for subsequent data processing.
Two Stage Shot Boundary Detection via Feature Fusion and Spatial-Temporal Convolutional Neural Networks

Shot boundary detection is essentially to detect the position of frames where the shot changes. It has been actively studied in video analysis and management for convenience, which becomes a key technique with the rapid proliferation of rich and diverse videos. With respect to the complex characteristics of different shots in varying length and content variation property, in this paper we present a two stage method for shot boundary detection (TSSBD) which distinguishes abrupt shot by fusing color histogram and deep features, and locate gradual shot changes with C3D-based deep analysis. Abrupt shot changes are detected firstly as it occurs between two frames, which divides the complete video into segments containing gradual transitions; Over these video segments, gradual shot change detection is implemented using 3D-convolutional neural network, which classifies clips into specific gradual shot change types; Finally, an effective merging strategy is proposed to locate positions of gradual shot transitions. The experimental analysis illustrates that the proposed progressive method is capable of detecting both abrupt shot transitions and gradual shot transitions accurately.
Detecting Subevents using Discourse and Narrative Features

Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding.We present a supervised model for automatically identifying when one event is a subevent of another.Building on prior work, we introduce several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance.Error analysis further demonstrates the utility of these features.We evaluate our model on the only two annotated corpora with event hierarchies: HiEve and the Intelligence Community corpus.No prior system has been evaluated on both corpora.Our model outperforms previous systems on both corpora, achieving 0.74 BLANC F 1 on the Intelligence Community corpus and 0.70 F 1 on the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models.
Analisis Deskriptif Hasil Belajar Pembelajaran Tatap Muka dan Pembelajaran Online Menurut Gaya Belajar Mahasiswa

Each higher education institution has a choice of learning models to be held, namely face-to-face learning, online learning, and blended learning. Face-to-face and online learning models have advantages and disadvantages in supporting student learning styles. Student learning styles not only represent what students like in learning, but also affect student learning outcomes. So it is not surprising, until now there is still a crossing of opinion in the achievement of learning, face-to-face learning or online learning that provides better results. This study provides an answer solution to learning English, whether face-to-face learning or online learning that is superior in cognitive learning outcomes according to student learning styles. The results of the descriptive analysis in this study concluded that students with auditory and visual learning styles taught with online learning models had higher average learning outcomes compared to students taught with face-to-face learning models; there is no difference in the average value of kinesthetic student learning outcomes between students who are taught by face-to-face learning with students who are taught by online learning; and in general the results of descriptive analysis show that online learning students are more successful in achieving learning outcomes compared to face-to-face learning students in learning English.
All Versus One: An Empirical Comparison on Retrained and Incremental Machine Learning for Modeling Performance of Adaptable Software

Given the ever-increasing complexity of adaptable software systems and their commonly hidden internal information (e.g., software runs in the public cloud), machine learning based performance modeling has gained momentum for evaluating, understanding and predicting software performance, which facilitates better informed self-adaptations. As performance data accumulates during the run of the software, updating the performance models becomes necessary. To this end, there are two conventional modeling methods: the retrained modeling that always discard the old model and retrain a new one using all available data; or the incremental modeling that retains the existing model and tunes it using one newly arrival data sample. Generally, literature on machine learning based performance modeling for adaptable software chooses either of those methods according to a general belief, but they provide insufficient evidences or references to justify their choice. This paper is the first to report on a comprehensive empirical study that examines both modeling methods under distinct domains of adaptable software, 5 performance indicators, 8 learning algorithms and settings, covering a total of 1,360 different conditions. Our findings challenge the general belief, which is shown to be only partially correct, and reveal some of the important, statistically significant factors that are often overlooked in existing work, providing evidence-based insights on the choice.
Feature Selection with Conditional Mutual Information Considering Feature Interaction

Feature interaction is a newly proposed feature relevance relationship, but the unintentional removal of interactive features can result in poor classification performance for this relationship. However, traditional feature selection algorithms mainly focus on detecting relevant and redundant features while interactive features are usually ignored. To deal with this problem, feature relevance, feature redundancy and feature interaction are redefined based on information theory. Then a new feature selection algorithm named CMIFSI (Conditional Mutual Information based Feature Selection considering Interaction) is proposed in this paper, which makes use of conditional mutual information to estimate feature redundancy and interaction, respectively. To verify the effectiveness of our algorithm, empirical experiments are conducted to compare it with other several representative feature selection algorithms. The results on both synthetic and benchmark datasets indicate that our algorithm achieves better results than other methods in most cases. Further, it highlights the necessity of dealing with feature interaction.
Enhancement of Ant Colony Optimization for QoS-Aware Web Service Selection

In service-oriented computing, web services composition is the process of translating user requirements into a workflow. This workflow comprises many tasks, each of which includes an abstract definition for some of the user requirements. Web services can be aggregated to handle the workflow. Many of these services are available from various providers for each task; they are referred to, in aggregate, as the candidate list. The web service selection (WSS) problem centers on selecting the best service from these candidates based on the quality of service (QoS) features. In this paper, we propose an enhancement to the ant colony optimization (ACO) algorithm based on a swap concept for the QoS-aware WSS problem. The aim of the enhancement to the ACO is to avoid the trap of local optima and reduce the search duration. We believe that the integration of many potent solutions will help the ACO algorithm yield a better solution and avoid stagnation. Several experiments were conducted to compare the proposed algorithm with the ACO and flying ACO (FACO) algorithms. Two different types of experiments using 22 datasets were done with 30 independent repetitions. The first type of experiment's results shows that the proposed algorithm is better than ACO by 12% and FACO by 11% in terms of quality of solutions. The results in the second type of experiment show that the proposed algorithm continuously outperforms both algorithms in terms of quality of solutions.
DAA: Dual LSTMs with adaptive attention for image captioning

Image captioning enables people to better understand images through fine-grained analysis. Recently the encoder-decoder architecture with attention mechanism has achieved great achievements in image captioning and visual question answering. In this paper, we propose a new captioning algorithm that integrates two separate LSTM (Long-short Term Memory) networks through an adaptive semantic attention model. Within our approach, the first LSTM network is followed by an attention model, which serves as a visual sentinel can flexibly make a trade off between the visual semantic region and textual content. Another LSTM is used as a language model, which combines the hidden state representation of the first LSTM and attention context vector, then outputs the word sequence. The proposed model has been extensively evaluated on two large-scale datasets: MSCOCO and Flickr30k. Experimental results show that the proposed method pays more attention to visual salient regions and achieves significant performance of prior state-of-the-art approaches on multiple evaluation metrics.
Attribute-Guided Sketch Generation

Facial attributes are important since they provide a detailed description and determine the visual appearance of human faces. In this paper, we aim at converting a face image to a sketch while simultaneously generating facial attributes. To this end, we propose a novel Attribute-Guided Sketch Generative Adversarial Network (ASGAN) which is an end-to-end framework and contains two pairs of generators and discriminators, one of which is used to generate faces with attributes while the other one is employed for image-to-sketch translation. The two generators form a W-shaped network (W-net) and they are trained jointly with a weight-sharing constraint. Additionally, we also propose two novel discriminators, the residual one focusing on attribute generation and the triplex one helping to generate realistic looking sketches. To validate our model, we have created a new large dataset with 8,804 images, named the Attribute Face Photo & Sketch (AFPS) dataset which is the first dataset containing attributes associated to face sketch images. The experimental results demonstrate that the proposed network (i) generates more photo-realistic faces with sharper facial attributes than baselines and (ii) has good generalization capability on different generative tasks.
Leukocyte Image Segmentation Based on Adaptive Histogram Thresholding and Contour Detection

Aims: The proposed method falls into the category of medical image processing. Background: Computer-aided automatic analysis systems for the analysis and cytometry of leukocyte (White Blood Cells, WBCs) in human blood smear images are a powerful diagnostic tool for many types of diseases, such as anemia, malaria, syphilis, heavy metal poisoning, and leukemia. Leukocyte segmentation is a basis of its automatic analysis, and the segmentation accuracy will directly influence the reliability of image-based automatic leukocyte analysis. Objective: This paper aims to present a leukocyte segmentation method, which improves segmentation accuracy under rapid and standard staining conditions. Methods: The proposed method first localizes leukocytes by color component combination and Adaptive Histogram Thresholding (AHT), and crops sub-image corresponding to each leukocyte. Then, the proposed method employs AHT to extract the nucleus of leukocyte and utilizes image color features to remove image backgrounds such as red blood cells and dyeing impurities. Finally, Canny edge detection is performed to extract the entire leukocyte. Accordingly, cytoplasm is obtained by subtracting nucleus with leukocyte. Results: Experimental results on two datasets containing 160 leukocyte images show that the proposed method obtains more accurate segmentation results than their counterparts. Conclusion: The proposed method obtains more accurate segmentation results than their counterparts under rapid and standard staining conditions.
Deep Bayesian Natural Language Processing

This introductory tutorial addresses the advances in deep Bayesian learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, “deep learning” is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The “semantic structure” in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The “distribution function” in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network, long short-term memory, sequence-to-sequence model, variational auto-encoder, generative adversarial network, attention mechanism, memory-augmented neural network, skip neural network, stochastic neural network, predictive state neural network and policy neural network. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies and domain applications are presented to tackle different issues in deep Bayesian processing, learning and understanding. At last, we will point out a number of directions and outlooks for future studies.
Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts

In this paper, we study the problem of answering questions of type "Could X cause Y?" where X and Y are general phrases without any constraints. Answering such questions will assist with various decision analysis tasks such as verifying and extending presumed causal associations used for decision making. Our goal is to analyze the ability of an AI agent built using state-of-the-art unsupervised methods in answering causal questions derived from collections of cause-effect pairs from human experts. We focus only on unsupervised and weakly supervised methods due to the difficulty of creating a large enough training set with a reasonable quality and coverage. The methods we examine rely on a large corpus of text derived from news articles, and include methods ranging from large-scale application of classic NLP techniques and statistical analysis to the use of neural network based phrase embeddings and state-of-the-art neural language models.
E2E

Conventional wisdom states that to improve quality of experience (QoE), web service providers should reduce the median or other percentiles of server-side delays. This work shows that doing so can be inefficient due to user heterogeneity in how the delays impact QoE. From the perspective of QoE, the sensitivity of a request to delays can vary greatly even among identical requests arriving at the service, because they differ in the wide-area network latency experienced prior to arriving at the service. In other words, saving 50ms of server-side delay affects different users differently.
Sensing Kirigami

This pictorial presents our material-driven inquiry into carbon-coated paper and kirigami structures. We investigated two variations of this paper and their affordances for tangible interaction; particularly their electrical, haptic, and visual aspects when shaped into three-dimensional forms through cutting, folding, and bending. Through this exploration, we uncovered distinct affordances between the two paper types for sensing folds and bends, due to differences in their material compositions. From these insights, we propose three applications that showcase the possibilities of this material for tangible interaction design. In addition, we leverage the pictorial format to expose working design schematics for others to take up their own explorations.