Reviewerly/exHarmony huggingface dataset:
Point-of-Interest Recommendation With Global and Local Context

The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation. Specifically, we propose two novel methods to incorporate geographical information in AUC-MF. Different from most existing models where the contextual information are incorporated into the objective function, the incorporation of contextual information in AUC-MF is a refinement of the model and a sampling strategy. The sampling strategy could speedup convergence and the refining of recommendations is independent of training of the model. This mechanism also enables AUC-MF to be able produce recommendations refined towards different contextual information, with minimum computational cost. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.
Biomedical Named Entity Recognition at Scale

Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc. In the medical domain, NER plays a crucial role by extracting meaningful chunks from clinical notes and reports, which are then fed to downstream tasks like assertion status detection, entity resolution, relation extraction, and de-identification. Reimplementing a Bi-LSTM-CNN-Char deep learning architecture on top of Apache Spark, we present a single trainable NER model that obtains new state-of-the-art results on seven public biomedical benchmarks without using heavy contextual embeddings like BERT. This includes improving BC4CHEMD to 93.72% (4.1% gain), Species800 to 80.91% (4.6% gain), and JNLPBA to 81.29% (5.2% gain). In addition, this model is freely available within a production-grade code base as part of the open-source Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java; and can be extended to support other human languages with no code changes.
Variational Inference with a Quantum Computer

Inference is the task of drawing conclusions about unobserved variables given observations of related variables. Applications range from identifying diseases from symptoms to classifying economic regimes from price movements. Unfortunately, performing exact inference is intractable in general. One alternative is variational inference, where a candidate probability distribution is optimized to approximate the posterior distribution over unobserved variables. For good approximations, a flexible and highly expressive candidate distribution is desirable. In this work, we use quantum Born machines as variational distributions over discrete variables. We apply the framework of operator variational inference to achieve this goal. In particular, we adopt two specific realizations: one with an adversarial objective and one based on the kernelized Stein discrepancy. We demonstrate the approach numerically using examples of Bayesian networks, and implement an experiment on an IBM quantum computer. Our techniques enable efficient variational inference with distributions beyond those that are efficiently representable on a classical computer.
Adversarial perturbation in remote sensing image recognition

Recent works have demonstrated that current deep neural networks suffer from small but intentional perturbation during the testing phase of the model. Such perturbations aiming at misclassifying the model’s inputs produce adversarial examples. To explain the origin of adversarial examples, the threat they pose towards deep learning networks, and their transferability property across models learning on the same data distribution, many works have been proposed without reaching any consensus. On the other hand, the study of adversarial examples in remote sensing imagery has gained popularity thanks to the availability of very high spatial resolution images, and the rapid development of deep learning algorithms in image recognition. Although many constructions aiming at mitigating adversarial examples in remote sensing imagery have been proposed, not all of them are realizable for they are based on unrealistic assumptions, or incur a lot of computation overhead. In this paper, we revisit adversarial examples in remote sensing imagery. First, we discuss the origin of adversarial perturbations, the various methods to generate them, as well as propose a framework to construct them under real-world constraints in remote sensing imagery. Second, we investigate the property of adversarial transferability or cross-model generalization, and detail its origin. Third, we revisit the various defense mechanisms to achieve certified robustness against adversarial perturbations in remote sensing imagery. Fourth, we discuss some important trade-offs regarding adversarial examples. Finally, we present the direction for future research aiming at more secure machine learning models against adversarial examples in remote sensing imagery.
A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images

The tumor proliferation, which is correlated with tumor grade, is a crucial biomarker indicative of breast cancer patients' prognosis. The most commonly used method in predicting tumor proliferation speed is the counting of mitotic figures in Hematoxylin and Eosin (H&E) histological slides. Manual mitosis counting is known to suffer from reproducibility problems. This paper presents a fully automated system for tumor proliferation prediction from whole slide images via mitosis counting. First, by considering the epithelial tissue as mitosis activity regions, we build a deep-learning-based region of interest detection method to select the high mitosis activity regions from whole slide images. Second, we learned a set of deep neural networks to detect mitosis detection from selected areas. The proposed mitosis detection system is designed to effectively overcome the mitosis detection challenges by two novel deep preprocessing and two-step hard negative mining approaches. Third, we trained a Support Vector Machine (SVM) classifier to predict the final tumor proliferation score. The proposed method was evaluated on the dataset of the Tumor Proliferation Assessment Challenge (TUPAC16) and achieved a 73.81 % F-measure and 0.612 weighted kappa score, respectively, outperforming all previous approaches significantly. Experimental results demonstrate that the proposed system considerably improves the tumor proliferation prediction accuracy and provides a reliable automated tool to support health care make-decisions.
Cross-platform dynamic goods recommendation system based on reinforcement learning and social networks

Aiming at the problems of cold start, gray sheep and sparsity of the traditional collaborative filtering recommendation system, this paper proposes a cross-platform dynamic goods recommendation system based on reinforcement learning and edge computing. First of all, this system models the current friendship relationship networks and potential friendship relationship networks, it also constructs two layers preference prediction models. Then, we consider the frequent change characteristic of social networks and shopping platforms, we design a dynamic reinforcement learning method and edge computing to learn the minimized entropy loss error. Finally, we finish the validation experiments based on the real datasets, the results show the proposed system realizes better link prediction accuracy, and using our proposed system can obtain an obvious increase in the accuracy compared to the existing of collaborative filtering recommendation systems.
AIM 2020 Challenge on Image Extreme Inpainting

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods.
Generalizing quasi-ordinal knowledge spaces to polytomous items

Based on the celebrated Birkhoff theorem, Doignon and Falmagne (1985) establish a one-to-one correspondence between certain binary relations on a domain of (dichotomous) items and the quasi-ordinal knowledge spaces defined on it. It is shown that this construction can be generalized to apply to polytomous response formats, where response values are partially ordered so that they form a lattice. Polytomous knowledge states are defined for the case where these response scales are identical across items, as well as for item-specific response scales, a generalization which emerges quite naturally within the developed theoretical framework. Certain collections of polytomous knowledge states are characterized through precedence relations that form particular quasi-orders on an extended set of (virtual) items, including for each item several dichotomous instances representing all the possible response values. These kinds of polytomous knowledge structures can be built from data through applying to the extended item set procedures commonly used in knowledge structure theory. Confining consideration to finite sets of items and response values, the presented theory generalizes previous approaches in this direction. Its application is illustrated for data from the 2011 Trends in International Mathematics and Science Study (TIMSS).
Joint feature extraction and classification in a unified framework for cost-sensitive face recognition

Cost-sensitive face recognition is a challenging problem in pattern recognition. Due to the high-dimensional face features, cost-sensitive face recognition usually conducts feature extraction in advance, followed by the learning of classifier in reduced subspace. However, the pre-extracted face features are kept fixed and may suboptimal for subsequent classifier learning, which will degrade the final face recognition performance. Besides, most of face learners are cost insensitive. Even the cost-sensitive methods proposed for face recognition, they only incorporate the cost information in feature extraction or classification phase as an alternative. There is no doubt that some cost-sensitive information will be lost in their cost insensitive steps. To deal with these issues, this paper proposes to incorporate feature extraction and classification in a unified cost-sensitive framework for face recognition. The experimental results on three public face benchmarks, including Extended Yale B, CMU PIE and LFW datasets, demonstrate that the proposed method can significantly reduce the overall misclassification loss of face recognition system as well as the classification errors associated with high costs, when comparing with eleven state-of-the-art face learners and nine cost-sensitive methods.
Graph-Revised Convolutional Network

Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graphs are often incomplete and noisy, treating them as ground-truth information, which is a common practice in most GCNs, unavoidably leads to sub-optimal solutions. Existing efforts for addressing this problem either involve an over-parameterized model which is difficult to scale, or simply re-weight observed edges without dealing with the missing-edge issue. This paper proposes a novel framework called Graph-Revised Convolutional Network (GRCN), which avoids both extremes. Specifically, a GCN-based graph revision module is introduced for predicting missing edges and revising edge weights w.r.t. downstream tasks via joint optimization. A theoretical analysis reveals the connection between GRCN and previous work on multigraph belief propagation. Experiments on six benchmark datasets show that GRCN consistently outperforms strong baseline methods, especially when the original graphs are severely incomplete or the labeled instances for model training are highly sparse. (Our code is available at https://github.com/Maysir/GRCN).
Distributed degrowth technology: Challenges for blockchain beyond the green economy

This commentary considers the challenges and trade-offs in using blockchain as the facilitating digital infrastructure for degrowth projects. A blockchain is simply a distributed database. The technology is being used for a wide range of applications relevant to economic exchange and environmental sustainability. Many degrowth scholars wholly reject technical fixes for politically induced environmental crises, seeing blockchain projects as wasteful and counter to convivial social relations. Others highlight the technology's potential for facilitating redistributive and regenerative economies, but without much detail. This paper argues that if blockchain is ever to prove useful for the degrowth movement it would need to overcome challenges in three important areas: 1) building democratic and (re)distributive economies, 2) regenerating the environment without commodifying it, and 3) facilitating international alliances without imposing a particular set of values. What is certain is that technology on its own will not transcend the political struggles tackled by degrowth activists. However, under certain conditions, blockchain might make those struggles more effective.
"Is this all you can do? Harder!"

Most social robot behaviors in human-robot interaction are designed to be polite, but there is little research about how or when a robot could be impolite, and if that may ever be beneficial. We explore the potential benefits and tradeoffs of different politeness levels for human-robot interaction in an exercise context. We designed impolite and polite phrases for a robot exercise trainer and conducted a 24-person experiment where people squat in front of the robot as it uses (im)polite phrases to encourage them. We found participants exercised harder and felt competitive with the impolite robot, while the polite robot was found to be friendly, but sometimes uncompelling and disingenuous. Our work provides evidence that human-robot interaction should continue to aim for more nuanced and complex models of communication.
A Survey of Offensive Language Detection for the Arabic Language

The use of offensive language in user-generated content is a serious problem that needs to be addressed with the latest technology. The field of Natural Language Processing (NLP) can support the automatic detection of offensive language. In this survey, we review previous NLP studies that cover Arabic offensive language detection. This survey investigates the state-of-the-art in offensive language detection for the Arabic language, providing a structured overview of previous approaches, including core techniques, tools, resources, methods, and main features used. This work also discusses the limitations and gaps of the previous studies. Findings from this survey emphasize the importance of investing further effort in detecting Arabic offensive language, including the development of benchmark resources and the invention of novel preprocessing and feature extraction techniques.
An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm

The image segmentation method based on clustering analysis has the advantages of small sample space constraints and strong universality. As an unsupervised clustering algorithm, the fuzzy C-means clustering algorithm is widely used in practical engineering. However, it is still some shortcomings: the fuzzy C-means clustering algorithm is difficult to interpret the noise effectively, which makes it more sensitive to the noise, and the selection of key parameters has to be made by trial and error experiments, reducing the adaptability of the algorithm. Besides, its iteration process is heavily influenced by the initial clustering centers and easy to fall into local optimum. Therefore, an intuitionistic Fuzzy C-means clustering method, based on local-information weight, is proposed in this paper. By introducing the local-information weight, the proposed algorithm adjusts the local-information influence weight adaptively in fuzzy partition, which enhances its robustness to noisy images. Furthermore, a novel swarm intelligence algorithm, called the Gold-Panning Algorithm, is proposed to optimize the initial clustering centers and key parameters in the clustering algorithm. By utilizing the Gold-Panning Algorithm, the adaptability of the proposed clustering algorithm is further improved. In this paper, the proposed methods are explained in detail and compared with the existing methods to demonstrate its superior performance.
Stacking ensemble model of deep learning and its application to Persian/Arabic handwritten digits recognition

One of the challenges in recognizing handwritten texts is the individual style of writing. There is the structural similarity of the different digits to each other in writing. Along with the mentioned challenge, these similarities in the text may increase and make it difficult to correctly recognize digits and numbers. In this paper, a new model for recognizing handwritten digits is presented. The proposed model is a stacking ensemble classifier. This classifier is based on the convolutional neural network (CNN) and the bidirectional long-short term memory (BLSTM). Another innovation of the model is the use of the probability vector of images class as the input of the meta-classifier layer. One of the strengths of BLSTM is the ability to learn arrays and vectors; therefore, from a technical point of view, considering the output probability vector of the first model as the input of the meta-classifier (BLSTM) improves the accuracy of the deep learning model. The reason for using stacking ensemble classification is the sameness of the main body of some Persian/Arabic digits (e.g., “2, 3, and 4”). Also, the style the author writes makes classes that are not similar in a structure similar to each other, which causes errors incorrect recognition. This model helps to recognize the correct set of input digits by examining the structure of similarities. To achieve a reliable result in the face of this challenge, this model has been tested on a large Persian/Arabic dataset to cover a wide range of writing styles from different individuals. The dataset has a total of 102,352 data which 60,000 of them are for training data and 20,000 of them are for test data in ten classes of digits that are used in this paper. The result of using this database is to improve the recognition performance of these challenging digits. In examining the datasetpresented by the model, the accuracy rate of the training set was 99.98%. And the sample accuracy rate in the test set was 99.39%. That is, compared to experimenting with the convolutional neural network and other researches, the rate has increased. (All codes available at http://web.nit.ac.ir/ h.omranpour/.)
Residual-Based Graph Convolutional Network for Emotion Recognition in Conversation for Smart Internet of Things

Recently, emotion recognition in conversation (ERC) has become more crucial in the development of diverse Internet of Things devices, especially closely connected with users. The majority of deep learning-based methods for ERC combine the multilayer, bidirectional, recurrent feature extractor and the attention module to extract sequential features. In addition to this, the latest model utilizes speaker information and the relationship between utterances through the graph network. However, before the input is fed into the bidirectional recurrent module, detailed intrautterance features should be obtained without variation of characteristics. In this article, we propose a residual-based graph convolution network (RGCN) and a new loss function. Our RGCN contains the residual network (ResNet)-based, intrautterance feature extractor and the GCN-based, interutterance feature extractor to fully exploit the intra-inter informative features. In the intrautterance feature extractor based on ResNet, the elaborate context feature for each independent utterance can be produced. Then, the condensed feature can be obtained through an additional GCN-based, interutterance feature extractor with the neighboring associated features for a conversation. The proposed loss function reflects the edge weight to improve effectiveness. Experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods.
Extracting clinical terms from radiology reports with deep learning

Extracting clinical terms from free-text format radiology reports is a first important step toward their secondary use. However, there is no general consensus on the kind of terms to be extracted. In this paper, we propose an information model comprising three types of clinical entities: observations, clinical findings, and modifiers. Furthermore, to determine its applicability for in-house radiology reports, we extracted clinical terms with state-of-the-art deep learning models and compared the results. We trained and evaluated models using 540 in-house chest computed tomography (CT) reports annotated by multiple medical experts. Two deep learning models were compared, and the effect of pre-training was explored. To investigate the generalizability of the model, we evaluated the use of other institutional chest CT reports. The micro F1-score of our best performance model using in-house and external datasets were 95.36% and 94.62%, respectively. Our results indicated that entities defined in our information model were suitable for extracting clinical terms from radiology reports, and the model was sufficiently generalizable to be used with dataset from other institutions.