Reviewerly/exHarmony huggingface dataset:
IsoTrotter: Visually Guided Empirical Modelling of Atmospheric Convection

Empirical models, fitted to data from observations, are often used in natural sciences to describe physical behaviour and support discoveries. However, with more complex models, the regression of parameters quickly becomes insufficient, requiring a visual parameter space analysis to understand and optimize the models. In this work, we present a design study for building a model describing atmospheric convection. We present a mixed-initiative approach to visually guided modelling, integrating an interactive visual parameter space analysis with partial automatic parameter optimization. Our approach includes a new, semi-automatic technique called IsoTrotting, where we optimize the procedure by navigating along isocontours of the model. We evaluate the model with unique observational data of atmospheric convection based on flight trajectories of paragliders.
A ResNet Based End-to-End Wireless Communication System under Rayleigh Fading and Bursty Noise Channels

Deep learning has been applied recently in the wireless communication area such as modulation classification, channel estimation and signal detection. Many of the wireless communication problems can be modeled as classification problems. Residual learning has proven to have a crucial role in image recognition for providing fascinating classification accuracy. This paper proposes a residual learning-based autoencoder model that can jointly optimize the transmitter and the receiver while communicating over Rayleigh flat fading and bursty noise channels. Depending on the number of bits per symbol at the transmitter, the proposed system can automatically learn the constellation mapping and reconstruct the transmitted bits with very low loss metrics. Simulation studies show that the block error rate performance of the proposed model is superior to the convolutional layer based autoencoder system as well as the conventional modulation system under Rayleigh flat fading and bursty noise channels.
Airline tweets sentimental analysis using Adaptive rider optimization based support vector neural network

Nowadays, peoples are posting their personal opinion about different brands on social media. Brands such as airlines, cars, sports teams, entertainment, finance, retail, and the food industry are popular on Twitter. Among them, airlines are the most used brand in business people. In airline companies, building customer loyalty is a major challenge. In the past, traveller's reviews are manually read, analyze, and categorize, which required a lot of effort and time. To avoid the problem, an efficient automatic sentimental analysis method is proposed. The main aim of this paper is to fining best and work airline from tweets. The proposed method consists of three stages namely, pre-processing, feature extraction, and sentiment classification. Initially, the collected reviews are pre-processed to reduce the complexity of classification process. After the pre-processing, word2vec conversion is performed using a skip-gram model. Then this output vector is given to the Adaptive Rider optimization based support vector neural network (AR-SVNN) classifier to classify a review as positive, negative, or neutral. Here, the SVNN classifier is enhanced using adaptive rider optimization (ARO) algorithm. The performance of the proposed methodology is analyzed in terms of different metrics namely, accuracy, precision, recall and F-Measure. The experimental results show that proposed method outperformed the other methods.
Positive Active Power Outlier Detection based on One-Class SVM

The smart grid creates a large-scale intelligent energy delivery network. It requires a much more complex and highdimensional data processing system. Thus, smart grid has become one of the most potential fields for machine learning application. This paper adopts an anomaly detection algorithm based on one-class SVM to realize anomaly detection task based on large-scale electrical energy data. One-class SVM model uses training data to train a hypersphere and calculate the distance bebveen data points and center. The algorithm is able to process a large number of data quickly and has high reliability. In this paper, large-scale positive active power data is used as training and testing datasets. As the experiment shows, the algorithm has high detection efficiency and high accuracy.
Integrating research and teaching for data curation in <scp>iSchools</scp>

Abstract The quickly changing nature of information science and technology creates unique and remarkable challenges in terms of developing curriculum focused on building data competencies. Faculties responsible for teaching current developments in information studies have the unique burden of needing to continuously update our curricula without sacrificing our broader teaching goals. This panel features diverse perspectives on teaching data curation skills in five US‐based schools of information at the undergraduate and graduate levels. Panelists will present their unique perspectives on pedagogical approaches in courses dedicated to data curation, digital preservation, description and access standards, as well as data access and interchange. Topics introduced will range from flipped classroom techniques, finding messy datasets, common pitfalls, hands‐on labs, cloud based tools, data carpentry labs, and sequencing learning objectives to match stages of the data life cycle. This panel will give ASIST conference participants an opportunity to see a range of junior faculty, each with IMLS funded research projects related to data curation, share their experiences of teaching data competencies in the classroom.
Research on Entity Relation Extraction in Education Field Based on Multi-feature Deep Learning

Relation extraction is the core task of information extraction and natural language processing tasks. Most of the existing methods stop at extracting the context characteristics of the entity in the sentence, and sometimes ignore the global context characteristics of the entity in the whole corpus. In order to fully consider the local and global information of text, a Chinese entity relation extraction model (Cooc_Att_Bilstm) based on the combination of multi-feature attention Bidirectional Long Short-term Memory (Bilstm) network and entity co-occurrence network is proposed. First, the attention-based Bilstm is utilized to extract sentence-level context features for entity pairs and combine the lexical, syntactic, semantic and positional features. Then, construct an entity co-occurrence network to extract global corpus-level features for each entity. Finally, the combination of sentence-level context features and global corpus-level features is used to extract entity relation extraction. The experimental results show that this method has good performance in the manual labeling data set.
Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models

Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue but also increases false positives, resulting in unnecessary biopsies. Our study investigated the use of Google AutoML Vision (Mountain View, California), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound. Approach: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model, were evaluated, while also investigating training strategies to enhance model performances. Pathology from the patient's biopsy was used as a reference standard. Results: The image classification models trained under different conditions demonstrated areas under the precision-recall curve (AUC) ranging from 0.85 to 0.96 during internal validation. Once deployed, the model with highest internal performance demonstrated a sensitivity of 100% [95% confidence interval (CI) of 73.5% to 100%], specificity of 83.3% (CI=51.6% to 97.9%), positive predictive value (PPV) of 85.7% (CI=62.9% to 95.5%), and negative predictive value (NPV) of 100% (CI non-evaluable) in an independent dataset. The object detection model demonstrated lower performance internally during development (AUC=0.67) and during prediction in the independent dataset [sensitivity=75% (CI=42.8 to 94.5), specificity=80% (CI=51.9 to 95.7), PPV=75% (CI=50.8 to 90.0), and NPV=80% (CI=59.3% to 91.7%)], but was able to demonstrate the location of the lesion within the image. Conclusions: Two models appear to be useful tools for identifying and classifying suspicious areas on B-mode images of indeterminate breast lesions.
Runtime verification for dynamic architectures

The architecture of a system captures important design decisions for the system. Over time, changes in a system's implementation may lead to violations of specific design decisions. This problem is common in industry and known as architectural erosion. Since it may have severe consequences on the quality of a system, research has focused on the development of tools and techniques to address the presented problem. As of today, most of the approaches to detect architectural erosion employ static analysis techniques. While these techniques are well-suited for the analysis of static architectures, they reach their limit when it comes to dynamic architectures. Thus, in this paper, we propose an alternative approach based on runtime verification: We describe techniques to formally specify constraints for dynamic architectures and algorithms to translate such specifications to instrumentation code and corresponding monitors. The approach is implemented in Eclipse/EMF, demonstrated through a running example, and evaluated using two case studies.
Augmented Reality Storytelling Teachers and Preschool Children Experience

This study aims to use AR technology to develop an AR-based learning of a digital storybook for preschool children aged 6 years old as to motivate their reading. This application will show digital storytelling based on book titled “The Three Bears” by Emma Bailey focusing on pages 4 and 5. User testing with both teachers and preschool children was conducted to examine the potential of the AR approach in motivating them learning by using both qualitative and quantitative interview in order to measure their learning experience. The findings indicate that the AR-based storybook approach does affect their enjoyment, engagement and motivation. A suggestion for further research is to embed elements of surprise to prevent boredom in an AR-based storybook to preschool children to increase their engagement in reading.
How to stop crashing more than twice: A Clean-Slate Governance Approach to IT Security

"Moving fast, and breaking things", instead of "being safe and secure", is the credo of the IT industry. However, if we look at the wide societal impact of IT security incidents in the past years, it seems like it is no longer sustainable. Just like in the case of Equifax, people simply forget updates, just like in the case of Maersk, companies do not use sufficient network segmentation. Security certification does not seem to help with this issue. After all, Equifax was IS027001 compliant.In this paper, we take a look at how we handle and (do not) learn from security incidents in IT security. We do this by comparing IT security incidents to early and later aviation safety. We find interesting parallels to early aviation safety, and outline the governance levers that could make the world of IT more secure, which were already successful in making flying the most secure way of transportation.
Comparison Study for Spinal Cord Segmentation Methods aiming to detect SC Atrophy in MRI images: case of Multiple Sclerosis

Among neurological diseases, Multiple Sclerosis (MS) is the leading cause of disability in young adults. Several researches have been carried out to explore this disease and detect MS lesions in Magnetic Resonance (MR) images. In fact, lesions segmentation in MR images is very important for accurate diagnosis, adequate treatment and for monitoring the patient with MS. Spinal cord (SC) atrophy occurs at the onset of MS and there is a correlation between atrophy and disability development. Generally, Magnetic Resonance Imaging (MRI) is the most sensitive method, which allows the visualization of demyelination plaques and the quantification of spinal cord atrophy. Detection and quantification of SC atrophy in MRI images are the key to the assessment of the states of MS patients. In this paper, we present a comparative study between different spinal cord segmentation methods aiming to quantify spinal cord atrophy.
A Real Time Multi-thread Underwater Images Enhancement System

Underwater images provide the most information for man-machine collaboration of underwater operations, however severe image quality degradation due to light absorption and scattering in water medium with suspended matter greatly affects efficiency to preceive the surrounding environment. In this paper, we develop a system for fast underwater enhancement to assist efficient underwater operations by giving more detailed and clear vision information. It uses auto multi-thread method to squeeze computing ability of hardware by creating a optimal number of processing threads considering both consistency and real time. And it is flexible to apply other algorithms or methods with a few changes as it is a method irrelavant framework and it can improve their efficiency.
Differential Temporal Difference Learning

Value functions derived from Markov decision processes arise as a central component of algorithms as well as performance metrics in many statistics and engineering applications of machine learning. Computation of the solution to the associated Bellman equations is challenging in most practical cases of interest. A popular class of approximation techniques, known as temporal difference (TD) learning algorithms, are an important subclass of general reinforcement learning methods. The algorithms introduced in this article are intended to resolve two well-known issues with TD-learning algorithms. Their slow convergence due to very high central limit theorem variance, and the fact that, for the problem of computing the relative value function, consistent algorithms exist only in special cases. First we show that the gradients of these value functions admit a representation that lends itself to algorithm design. Based on this result, a new class of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differential TD-learning</i> algorithms is introduced. For Markovian models on Euclidean space with smooth dynamics, the algorithms are shown to be consistent under general conditions. Numerical results show dramatic variance reduction in comparison to standard methods.
Ranking Multiple Choice Question Distractors using Semantically Informed Neural Networks

Automatically generating or ranking distractors for multiple-choice questions (MCQs) is still a challenging problem. In this work, we have focused towards automatic ranking of distractors for MCQs. Accordingly, we have proposed an semantically aware CNN-BiLSTM model. We evaluate our model with different word level embeddings as input over two different openly available datasets. Experimental results demonstrate our proposed model surpasses the performance of the existing baseline models. Furthermore, we have observed that intelligently incorporating word level semantic information along with context specific word embeddings boost up the predictive performance of distractors, which is a promising direction for further research.
Discriminative Pose Analysis for Human Action Recognition

Analysis of pose-level information is critical for human action recognition. For fast visual perception, we propose to encode human pose with discriminative information in the pose feature space, which needs low computational cost. The main idea is that the common poses that exist in different actions have low discrimination while the discriminative poses exist in few actions. Therefore, we employ the Gaussian Mixture Model to cluster the feature vectors of all poses in all classes of actions, and then quantify the discrimination of each pose as well as its contribution to each action class. Meanwhile, we calculate the correlation between the clusters and action classes as a rating matrix. The query action is inferred by simply calculating the Gaussion weights of poses and multiply the rating matrix. The framework runs without deep neural networks, which is low cost for application on low power devices. The proposed method is validated on three benchmark human action datasets, and the experimental results show good performances of the proposed method. The inferring time of each query is only 24ms on a 2.5 GHz CPU that indicats the efficiency of our method.
TiCCo

Time is a natural way to order information and can be utilized to summarize events and to construct a chronology of contents within a document collection in many application domains. Structuring the sequence of events along a timeline allows users to grasp information at-a-glance, which enables them to get familiar with a topic in only a short amount of time and can hence support the analysis of more complicated and heterogeneous textual data. The manual construction of timelines, however, is a tedious and error-prone task, leading to static timeline representations that limit users to a passive role. In this paper, TiCCo, an automated extraction pipeline from arbitrary English and German text collections, is provided and presented to the user in an interactive manner. This puts the user in an active role in which she not only absorbs knowledge, but also influences in which ways the information is presented to her. In-depth investigations of a specific point in time are augmented by utilizing time-centric co-occurrence graphs that further summarize information extracted from a document collection, and enable users to explore the chronology of events by allowing them to interact with the constructed graphs as well as the underlying documents.
LRHNE: A Latent-Relation Enhanced Embedding Method for Heterogeneous Information Networks

Heterogeneous information networks (HINs) have been successfully applied into several fields to accomplish complex data analytics, such as bibliography, bioinformatics, NLP, etc. In the meantime, network embedding at present has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. Despite recent breakthroughs in HIN embedding methods, little research attention has been paid to exploit the relation semantics in HINs and further integrate it to improve the embedding quality. Considering the sophisticated correlations in HINs, we in this paper propose a novel HIN embedding method LRHNE to yield latent-relation enhanced embeddings for nodes. Our work mainly involves three contributions: i) we verify that the latent relation can promote the embedding quality indeed through a real-world dataset, then a novel graph inception network is proposed to extract the latent relational features under the guidance of partial prior knowledge; ii) taking into account the existing structure information and inferred latent relation knowledge, we propose a cross-aligned variational graph autoencoder to extract and further fuse both the structure and latent relational features into the embeddings; and iii) we perform extensive experiments to validate our proposed LRHNE, and experimental results show that our LRHNE can significantly outperform state-of-the-art methods. The multi-facet inspections also exhibit our method is robust and hyper-parameter insensitive, therefore, our method can serve as a radical tool to tackle the relation-sophisticated HINs.
Explaining the Behavior of Neuron Activations in Deep Neural Networks

Deep Neural Networks has shown superior performance in various applications. But it is often seen as black box in real world applications, which is challenging to explain from the viewpoint of humans. It is important to understand the behavior of deep neural networks so as to trust the decision made and improve the classification accuracy of deep neural networks. In this study, the information theoretical analysis is used to investigate the behavior of layer-wise neurons in deep neural networks. The activation patterns of individual neurons in fully connected layers can provide insights for the performance of the neural network model. The behavior of neuron activation is investigated based on state-of-art classification network model. We study and compare the layer-wise pattern of neurons activation in fully connected layers given the same image input. Experiments are conducted on various data sets. We find that in a well trained classification model, the randomness level of the neurons activation pattern is reduced with the depth of the fully connected layers. This means that the neuron activation patterns of deep layers is more stable than that of shallow layers. The results in this study can also answer the question of how many layers are needed to avoid overfitting in deep neural networks. Corresponding experiments are conducted to validate the assumptions.
Visual Hybrid Recommendation Systems Based on the Content-Based Filtering

In light of access to a huge amount of data and ever-changing trends, it is necessary to use recommendation systems to find information of interest to us. In this paper, a new approach to designing recommendation systems is proposed. It is designed to recommend images based on their content. To this end, the convolutional neural network and the Bahdanau attention mechanism are combined. In consequence, the method makes it possible to identify areas that were particularly important for a given image to be recommended. The algorithm has been tested on the publicly available Zappo50K database.
Bug! Falha! Bachi! Fallo! Défaut! 程序错误!

Background. Testing is an essential activity in the software development life cycle. Nowadays, testing activities are widely spread along the software development process, since software products are continuously tested to meet the user's expectations and to compete in global markets. In this context, internationalization testing is defined as the practice focused on determining that a software works properly in a specific language and in a particular region. Aims. This study aims to explore the particularities of internationalization testing in the software industry and discuss the importance of this practice from the point of view of professionals working in this context. Method. We developed an exploratory qualitative study and conducted interviews with professionals from an international software company, in order to understand three aspects of internationalization testing: general characteristics and importance of this practice, particularities of the process, and the role of test automation in this context. Results. An amount of 13 professionals participated in this study. Results demonstrated that internationalization testing is mostly related to aspects of graphical user interfaces. In this context, truncation and mistranslations are the main faults observed, and test automation might be difficult to implement and maintain due to amount validations that are human-dependent. Conclusion. Internationalization testing is an important practice to guarantee the quality of software products developed for global markets. However, this aspect of software testing remains unpopular or unfamiliar among professionals. This study is a step forward in the process of informing and enlightening academic researchers and practitioners in industry about this theme.