Reviewerly/exHarmony huggingface dataset:
On the Global Convergence Rates of Softmax Policy Gradient Methods

We make three contributions toward better understanding policy gradient methods in the tabular setting. First, we show that with the true gradient, policy gradient with a softmax parametrization converges at a $O(1/t)$ rate, with constants depending on the problem and initialization. This result significantly expands the recent asymptotic convergence results. The analysis relies on two findings: that the softmax policy gradient satisfies a \L{}ojasiewicz inequality, and the minimum probability of an optimal action during optimization can be bounded in terms of its initial value. Second, we analyze entropy regularized policy gradient and show that it enjoys a significantly faster linear convergence rate $O(e^{-c \cdot t})$ toward softmax optimal policy $(c > 0)$. This result resolves an open question in the recent literature. Finally, combining the above two results and additional new $\Omega(1/t)$ lower bound results, we explain how entropy regularization improves policy optimization, even with the true gradient, from the perspective of convergence rate. The separation of rates is further explained using the notion of non-uniform \L{}ojasiewicz degree. These results provide a theoretical understanding of the impact of entropy and corroborate existing empirical studies.
Comprehensive Analysis of ZNN Models for Computing Complex-Valued Time-Dependent Matrix Inverse

Computing complex-valued time-dependent matrix inverse is an important procedure in statistics and control theory.Zeroing neural network (ZNN) becomes an effective method for solving timedependent issues.This paper proposes a complex-valued synthetical ZNN (CVSZNN) model for computing complex-valued time-dependent matrix inverse within different noise environments.Two important property is synthetically dissected in the construction process of CVSZNN model.First, the finite-time convergence, to ensure the efficiency of CVSZNN model for solving complex-valued time-dependent issue.Secondly, the anti-noise property, to guarantee the robustness of CVSZNN model in presence of noises.Two important aspects above have been validated by theoretical analysis and numerical experiments.Furthermore, the impact of parameters values is investigated based on the experimental results.As compared with the existing ZNN models for computing complex-valued time-dependent matrix inverse, the superiority of the proposed CVSZNN model is displayed fully.
Understanding the Level of Students' Reading Comprehension Ability

Reading comprehension of texts is a basic competency in the Indonesian Subject Curriculum.The observation results show that reading comprehension is still low, especially reading comprehension of the meaning of the text and the context of a text.From these problems, it is necessary to conduct research on the level of reading comprehension of grade VII students of SMP Negeri 06, Rejang Lebong.This research aimed at describing the level of reading comprehension ability and the causes of students' misunderstanding in grade VII of SMP Negeri 06 Rejang Lebong on reading text material.This research belongs to mixed method design.The subjects of this study were 28 students of class VII from SMP Negeri 06, Rejang Lebong.Data were collected using tests, observation, interviews, and documentation.Data were analyzed qualitatively and quantitatively.Data validity test is done by member checking, which is assisted by language validator and triangulation.The results showed that students were not accustomed to reading texts and there were still those who were not fluent in reading.
Validation of Text Data Preprocessing Using a Neural Network Model

Many artificial intelligence studies focus on designing new neural network models or optimizing hyperparameters to improve model accuracy. To develop a reliable model, appropriate data are required, and data preprocessing is an essential part of acquiring the data. Although various studies regard data preprocessing as part of the data exploration process, those studies lack awareness about the need for separate technologies and solutions for preprocessing. Therefore, this study evaluated combinations of preprocessing types in a text-processing neural network model. Better performance was observed when two preprocessing types were used than when three or more preprocessing types were used for data purification. More specifically, using lemmatization and punctuation splitting together, lemmatization and lowering together, and lowering and punctuation splitting together showed positive effects on accuracy. This study is significant because the results allow better decisions to be made about the selection of the preprocessing types in various research fields, including neural network research.
Intention-Related Natural Language Grounding via Object Affordance Detection and Intention Semantic Extraction

Similar to specific natural language instructions, intention-related natural language queries also play an essential role in our daily life communication. Inspired by the psychology term ``affordance" and its applications in Human-Robot interaction, we propose an object affordance-based natural language visual grounding architecture to ground intention-related natural language queries. Formally, we first present an attention-based multi-visual features fusion network to detect object affordances from RGB images. While fusing deep visual features extracted from a pretrained CNN model with deep texture features encoded by a deep texture encoding network, the presented object affordance detection network takes into account the interaction of the multi-visual features, and reserves the complementary nature of the different features by integrating attention weights learned from sparse representations of the multi-visual features. We train and validate the attention-based object affordance recognition network on a self-built dataset in which a large number of images originate from MSCOCO and ImageNet. Moreover, we introduce an intention semantic extraction module to extract intention semantics from intention-related natural language queries. Finally, we ground intention-related natural language queries by integrating the detected object affordances with the extracted intention semantics. We conduct extensive experiments to validate the performance of the object affordance detection network and the intention-related natural language queries grounding architecture.
Graph Structure Learning for Robust Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, called adversarial attacks. Adversarial attacks can easily fool GNNs in making predictions for downstream tasks. The vulnerability to adversarial attacks has raised increasing concerns for applying GNNs in safety-critical applications. Therefore, developing robust algorithms to defend adversarial attacks is of great significance. A natural idea to defend adversarial attacks is to clean the perturbed graph. It is evident that real-world graphs share some intrinsic properties. For example, many real-world graphs are low-rank and sparse, and the features of two adjacent nodes tend to be similar. In fact, we find that adversarial attacks are likely to violate these graph properties. Therefore, in this paper, we explore these properties to defend adversarial attacks on graphs. In particular, we propose a general framework Pro-GNN, which can jointly learn a structural graph and a robust graph neural network model from the perturbed graph guided by these properties. Extensive experiments on real-world graphs demonstrate that the proposed framework achieves significantly better performance compared with the state-of-the-art defense methods, even when the graph is heavily perturbed. We release the implementation of Pro-GNN to our DeepRobust repository for adversarial attacks and defenses (footnote: https://github.com/DSE-MSU/DeepRobust). The specific experimental settings to reproduce our results can be found in https://github.com/ChandlerBang/Pro-GNN.
Matching Large Scale Ontologies Based on Filter and Verification

Ontology matching is an effective method to realize intercommunication and interoperability between heterogeneous systems. The essence of ontology matching is to discover the similar entity pairs between source ontology and target ontology, which is a process calculating the similarity between entities in ontologies. The similarity can be calculated utilizing various features between entity pairs, such as string similarity, structural similarity, and semantic similarity. The larger the ontology scale, the lower the efficiency and accuracy rate of ontology matching. As the ontology scale increases, the amount of entities in ontologies will be larger and the ontologies will become more heterogeneous. This paper proposes an innovative method of matching large scale ontologies based on filter and verification, which firstly reduces the heterogeneous of large scale ontologies in the filter phase and then matches the reduced ontologies in the verification phase. Large scale ontologies will be partitioned into several subontologies to get a proper scale before matching. The benchmark of Anatomy and Food in OAEI is adopted to evaluate the proposed method, and the experimental result illuminates that the recall rate is improved in the situation of retaining efficiency and accuracy rate using the proposed method.
Raw fusion of camera and sparse LiDAR for detecting distant objects

Abstract Environment perception plays a significant role in autonomous driving since all traffic participants in the vehicle’s surroundings must be reliably recognized and localized in order to take any subsequent action. The main goal of this paper is to present a neural network approach for fusing camera images and LiDAR point clouds in order to detect traffic participants in the vehicle’s surroundings more reliably. Our approach primarily addresses the problem of sparse LiDAR data (point clouds of distant objects), where due to sparsity the point cloud based detection might become ambiguous. In the proposed model each 3D point in the LiDAR point cloud is augmented by semantically strong image features allowing us to inject additional information for the network to learn from. Experimental results show that our method increases the number of correctly detected 3D bounding boxes in sparse point clouds by at least 13–21 % and thus raw sensor fusion is validated as a viable approach for enhancing autonomous driving safety in difficult sensory conditions.
An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of Visual Sensors

Text recognition in natural scene images has always been a hot topic in the field of document-image related visual sensors. The previous literature mostly solved the problem of horizontal text recognition, but the text in the natural scene is usually inclined and irregular, and there are many unsolved problems. For this reason, we propose a scene text recognition algorithm based on a text position correction (TPC) module and an encoder-decoder network (EDN) module. Firstly, the slanted text is modified into horizontal text through the TPC module, and then the content of horizontal text is accurately identified through the EDN module. Experiments on the standard data set show that the algorithm can recognize many kinds of irregular text and get better results. Ablation studies show that the proposed two network modules can enhance the accuracy of irregular scene text recognition.
Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images

Due to institutional and privacy issues, medical imaging researches are confronted with serious data scarcity. Image synthesis using generative adversarial networks provides a generic solution to the lack of medical imaging data. We synthesize high-quality brain tumor-segmented MR images, which consists of two tasks: synthesis and segmentation. We performed experiments with two different generative networks, the first using the ResNet model, which has significant advantages of style transfer, and the second, the U-Net model, one of the most powerful models for segmentation. We compare the performance of each model and propose a more robust model for synthesizing brain tumor-segmented MR images. Although ResNet produced better-quality images than did U-Net for the same samples, it used a great deal of memory and took much longer to train. U-Net, meanwhile, segmented the brain tumors more accurately than did ResNet.
CSNE: Conditional Signed Network Embedding

Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing network embedding methods for sign prediction, however, generally enforce different notions of status or balance theories in their optimization function. These theories, are often inaccurate or incomplete which negatively impacts method performance.
Breast Cancer Prediction using ML Techniques

Breast cancer is one amongst the foremost dangerous kinds of cancer exists among ladies.The breast cancer is diagnosed using microscopic anatomy pictures .The aim of this paper is to classify different kinds of breast cancer using histology images.The classification of histology images can be effectively done by image process techniques.Among totally different image process algorithms, deep learning offers the most effective performance for image classification applications.There are different convolutional neural network(CNN) architectures used for classification purpose like AlexNet, Inception-Net, ResNet etc.Since conventional convolutional neural network.
Authorship Studies and the Dark Side of Social Media Analytics

The computational analysis of documents to learn about their authorship (also known as authorship attribution and/or authorship profiling) is an increasingly important area of research and application of technology. This paper discusses the technology, focusing on its application to social media in a variety of disciplines. It includes a brief survey of the history as well as three tutorial case studies, and discusses several significant applications and societal benefits that authorship analysis has brought about. It further argues, though, that while the benefits of this technology have been great, it has created serious risks to society that have not been sufficiently considered, addressed, or mitigated.
Queries with Arithmetic on Incomplete Databases

The standard notion of query answering over incomplete database is that of certain answers, guaranteeing correctness regardless of how incomplete data is interpreted. In majority of real-life databases, relations have numerical columns and queries use arithmetic and comparisons. Even though the notion of certain answers still applies, we explain that it becomes much more problematic in situations when missing data occurs in numerical columns.
A Simple Method for Inducing Class Taxonomies in Knowledge Graphs

The rise of knowledge graphs as a medium for storing and organizing large amounts of data has spurred research interest in automated methods for reasoning with and extracting information from this representation of data. One area which seems to receive less attention is that of inducing a class taxonomy from such graphs. Ontologies, which provide the axiomatic foundation on which knowledge graphs are built, are often governed by a set of class subsumption axioms. These class subsumptions form a class taxonomy which hierarchically organizes the type classes present in the knowledge graph. Manually creating and curating these class taxonomies oftentimes requires expert knowledge and is time costly, especially in large-scale knowledge graphs. Thus, methods capable of inducing the class taxonomy from the knowledge graph data automatically are an appealing solution to the problem. In this paper, we propose a simple method for inducing class taxonomies from knowledge graphs that is scalable to large datasets. Our method borrows ideas from tag hierarchy induction methods, relying on class frequencies and co-occurrences, such that it requires no information outside the knowledge graph's triple representation. We demonstrate the use of our method on three real-world datasets and compare our results with existing tag hierarchy induction methods. We show that our proposed method outperforms existing tag hierarchy induction methods, although both perform well when applied to knowledge graphs.
Local Constitutive Behavior of Undermatched Welded Joints in Pipeline Steel Using Digital Image Correlation Technology

Abstract Although the identification of local constitutive behaviors is possible based on digital image correlation (DIC), few studies have been reported that characterize the properties of the girth-welded joints of pipeline steel. The DIC technique was used to measure the strain fields of undermatched girth-welded joints of X80 pipeline steel under uniaxial tension in this paper. First, microstructure optical observations and micrometer hardness measurements were used to test the size and hardness of the subregions in the specimens. Second, the local strain data in different regions of the girth-welded joint were obtained via DIC technology, and the stress data were obtained via uniaxial tensile tests. Then, the stress–strain relationships of the weld metal (WM), base metal (BM), and subregions of the heat-affected zone (HAZ) of the girth-welded joints of pipeline steel were obtained. Finally, the constitutive parameters of the Ramberg–Osgood model in the different regions were determined by curve fitting of the strain and stress data. The local yield strength, elastic modulus, and hardening exponent of the welded joints were obtained. The yield stresses of the different subregions of the welded steel joint followed the sequence BM &amp;gt; WM &amp;gt; HAZ, which was consistent with the results of the hardness measurements. The hardening exponents of the different subregions of the welded steel joint followed the sequence HAZ &amp;gt; WM&amp;gt; BM.
Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images

Recently, progress has been made in the supervised training of Convolutional Object Detectors (e.g. Faster R-CNN) for threat recognition in carry-on luggage using X-ray images. This is part of the Transportation Security Administration's (TSA's) mission to protect air travelers in the United States. While more training data with threats may reliably improve performance for this class of deep algorithm, it is expensive to stage in realistic contexts. By contrast, data from the real world can be collected quickly with minimal cost. In this paper, we present a semi-supervised approach for threat recognition which we call Background Adaptive Faster R-CNN. This approach is a training method for two-stage object detectors which uses Domain Adaptation methods from the field of deep learning. The data sources described earlier make two "domains": a hand-collected data domain of images with threats, and a real-world domain of images assumed without threats. Two domain discriminators, one for discriminating object proposals and one for image features, are adversarially trained to prevent encoding domain-specific information. Without this penalty a Convolutional Neural Network (CNN) can learn to identify domains based on superficial characteristics, and minimize a supervised loss function without improving its ability to recognize objects. For the hand-collected data, only object proposals and image features from backgrounds are used. The losses for these domain-adaptive discriminators are added to the Faster R-CNN losses of images from both domains. This can reduce threat detection false alarm rates by matching the statistics of extracted features from hand-collected backgrounds to real world data. Performance improvements are demonstrated on two independently-collected datasets of labeled threats.
Words ranking and Hirsch index for identifying the core of the hapaxes in political texts

This paper deals with a quantitative analysis of the content of official political speeches. We study a set of about one thousand talks pronounced by the US Presidents, ranging from Washington to Trump. In particular, we search for the relevance of the rare words, i.e. those said only once in each speech -- the so-called hapaxes. We implement a rank-size procedure of Zipf-Mandelbrot type for discussing the hapaxes' frequencies regularity over the overall set of speeches. Starting from the obtained rank-size law, we define and detect the core of the hapaxes set by means of a procedure based on an Hirsch index variant. We discuss the resulting list of words in the light of the overall US Presidents' speeches. We further show that this core of hapaxes itself can be well fitted through a Zipf-Mandelbrot law and that contains elements producing deviations at the low ranks between scatter plots and fitted curve -- the so-called king and vice-roy effect. Some socio-political insights are derived from the obtained findings about the US Presidents messages.
Medical Knowledge-Based Deep Learning Framework for Disease Prediction on Unstructured Radiology Free-Text Reports Under Low Data Condition

There has been increasing popularity in medical text mining due to its vast applications in the field of disease prediction and clinical Recommendation systems. Radiology reports possess rich information depicting radiologists investigations on the health conditions of the patients in associated radiology images. However, radiology reports exist in a free-text unstructured format consisting of valuable information for disease prediction. This information cannot be easily retrieved and utilized for prediction without suitable text mining techniques. The medical dataset available in the current procedure is small, domain-specific and restricted to the institution. However, data is one of the critical factors to power Machine Learning (ML) and Deep Learning (DL) models. To overcome the above challenge of predicting disease in the low data condition, we present a practical Deep Learning framework that combines a Knowledge Base (KB) with the Deep Learning for accurate text mining and predicting the lung diseases from the unstructured radiology free-text reports. We adopt Glove word embeddings with the KB trained on large corpus for effective text modelling. Further, we incorporate Convolutional Neural Network-based Discriminative Dimensionality Reduction (CNN-DDR) to obtain the most discriminative feature vector. Finally, a fully connected Deep Neural Network (DNN) is leveraged as the prediction model to detect the diseases. We applied the proposed framework to predict the lung diseases on radiology reports from both publicly available Indiana University (IU) dataset [6] and data collected from the private hospital. We benchmark the performance of the proposed framework, which outperforms against the standard ML Techniques.
Segmentation Loss Odyssey

Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods. Many loss functions have been proposed in existing literature, but are studied separately or only investigated with few other losses. In this paper, we present a systematic taxonomy to sort existing loss functions into four meaningful categories. This helps to reveal links and fundamental similarities between them. Moreover, we explore the relationship between the traditional region-based and the more recent boundary-based loss functions. The PyTorch implementations of these loss functions are publicly available at \url{https://github.com/JunMa11/SegLoss}.