Reviewerly/exHarmony huggingface dataset:
Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction

Predicting anomalies (e.g., blocked driveway and vehicle collisions) in urban space plays an important role in assisting governments and communities for building smart city applications, ranging from intelligent transportation to public safety. However, predicting urban anomalies is not trivial due to the following two factors: i) The sequential transition regularities of anomaly occurrences is complex, which exhibit with high-order and dynamic correlations. ii) The Interactions between region, time and anomaly category is multi-dimensional in real-world urban anomaly forecasting scenario. How to fuse multiple relations from spatial, temporal and categorical dimensions in the predictive framework remains a significant challenge. To address these two challenges, we propose a Cross-Interaction Hierarchical Attention network model (CHAT) which uncovers the dynamic occurrence patterns of time-stamped urban anomaly data. Our CHAT framework could automatically capture the relevance of past anomaly occurrences across different time steps, and discriminates which types of cross-modal interactions are more important for making future predictions. Experiment results demonstrate the superiority of CHAT framework over state-of-the-art baselines.
Screenplay Summarization Using Latent Narrative Structure

Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.As a result, such models are biased by position and often perform a smart selection of sentences from the beginning of the document.When summarizing long narratives, which have complex structure and present information piecemeal, simple position heuristics are not sufficient.In this paper, we propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models.We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays (i.e., extract an optimal sequence of scenes).Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode and improve summarization performance over general extractive algorithms, leading to more complete and diverse summaries.Victim: Mike Kimble, found in a Body Farm.Died 6 hours ago, unknown cause of death. CSI discover cow tissue in Mike's body.Cross-contamination is suggested.Probable cause of death: Mike's house has been set on fire.CSI finds blood: Mike was murdered, fire was a cover up.First suspects: Mike's fiance, Jane and her ex-husband, Russ.CSI finds photos in Mike's house of Jane's daughter, Jodie, posing naked.Mike is now a suspect of abusing Jodie.Russ allows CSI to examine his gun.CSI discovers that the bullet that killed Mike was made of frozen beef that melt inside him.They also find beef in Russ' gun.Russ confesses that he knew that Mike was abusing Jody, so he confronted and killed him.CSI discovers that the naked photos were taken on a boat, which belongs to Russ.CSI discovers that it was Russ who was abusing his daughter based on fluids found in his sleeping bag and later killed Mike who tried to help Jodie.Russ is given bail, since no jury would convict a protective father.
Hyperbolic Capsule Networks for Multi-Label Classification

Although deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g.pooling).Such operations limit the performance.For instance, a multi-label document may contain several concepts.In this case, one vector can not sufficiently capture its salient and discriminative content.Thus, we propose Hyperbolic Capsule Networks (HYPERCAPS) for Multi-Label Classification (MLC), which have two merits.First, hyperbolic capsules are designed to capture fine-grained document information for each label, which has the ability to characterize complicated structures among labels and documents.Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks.To efficiently handle large-scale MLC datasets, we additionally present a new routing method to adaptively adjust the capsule number during routing.Extensive experiments are conducted on four benchmark datasets.Compared with the state-of-the-art methods, HY-PERCAPS significantly improves the performance of MLC especially on tail labels.
Joint distribution matching embedding for unsupervised domain adaptation

When the distributions between the source (training) and target (test) datasets are different, the performance of classical statistical learning methods degrades significantly. Domain adaptation (DA) aims at correcting this distribution mismatch and narrowing down the distribution discrepancy. Existing methods mostly focus on correcting the mismatch between the marginal distributions and/or the class-conditional distributions. In this paper, we assume that the distribution mismatch in domain adaptation is the joint distribution mismatch, and propose an Extended Maximum Mean Discrepancy (EMMD) metric to measure the distance between joint distributions. Based on this metric, we propose the Joint Distribution Matching Embedding (JDME) approach, which finds a mapping matrix to project the samples into a latent space, where the EMMD between the source and target joint distributions is minimized. The resultant orthogonal-constrained optimization problem can be solved in the form of an unconstrained problem on the Grassmann manifold. After the joint distribution matching, we can expect the classical statistical learning methods to perform well on the target dataset. Experiments on object recognition, face recognition, and spam filtering demonstrate that our method statistically outperforms the state-of-the-art shallow methods and is also on par with the deep learning methods.
Action-Guided Attention Mining and Relation Reasoning Network for Human-Object Interaction Detection

Human-object interaction (HOI) detection is important to understand human-centric scenes and is challenging due to subtle difference between fine-grained actions, and multiple co-occurring interactions. Most approaches tackle the problems by considering the multi-stream information and even introducing extra knowledge, which suffer from a huge combination space and the non-interactive pair domination problem. In this paper, we propose an Action-Guided attention mining and Relation Reasoning (AGRR) network to solve the problems. Relation reasoning on human-object pairs is performed by exploiting contextual compatibility consistency among pairs to filter out the non-interactive combinations. To better discriminate the subtle difference between fine-grained actions, an action-aware attention based on class activation map is proposed to mine the most relevant features for recognizing HOIs. Extensive experiments on V-COCO and HICO-DET datasets demonstrate the effectiveness of the proposed model compared with the state-of-the-art approaches.
FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task. As a result, too many closed high-utility itemsets (CHUIs) will not only increase the time and memory consumption but also make it difficult for users to analyze the results. To address this problem, this paper proposes an efficient algorithm called FCHUIM, which is used to mine a concise representation called the frequent closed high-utility itemset (FCHUI). FCHUIM is based on a total summary list structure for storing and retrieving utility lists without repeatedly scanning the database. In addition, the proposed algorithm uses an effective upper bound on the utility of items to efficiently reduce the search space. Furthermore, a precheck method and a nested list structure are also introduced in this work to quickly discover FCHUIs. To test the performance of FCHUIM, we conduct extensive experiments on real-life and synthetic databases. We also select two state-of-the-art algorithms to compare with FCHUIM: CHUI-Miner and CLS-Miner. The results show that FCHUIM performs well on databases, whether the databases are dense or sparse.
Exploring Content Selection in Summarization of Novel Chapters

We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides.This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries.We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries.We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods.Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis.* Equal contribution.Work done while at Amazon. 1 We tried two abstractive models (Chen and Bansal, 2018; Liu and Lapata, 2019) but ROUGE was low and the output was poor with many repetitions and hallucinations.
SECURING TWITTER ANALYSED DATA USING CBB22 ALGORITHM

In this paper, we show that the power function of sub E-function f n (x) is sub E-function.Furthermore, we establish some new integral inequalities of Hadamard type involving sub E-functions and concave E-functions.
Capturing Contextual Factors in Sentiment Classification: An Ensemble Approach

Sentiment classification is a crucial task in sentiment analysis, and has received significant attention from researchers. Previous studies have focused on using several techniques to solve this problem. However, to the best of our knowledge, none of these works has fully investigated the exploitation and the manipulation of contextual information in the text, or taken advantage of the combined power of state-of-the-art models. In this paper, we propose an effective ensemble learning model for the sentiment classification problem. In our system, the contextual information in the text is fully captured by integrating rule-based methods and other state-of-the-art deep learning models. We found that the combination of word embedding representation and the attention mechanism, along with pre-defined rules and specific-domain sentiment dictionaries are helpful in dealing with numerous valence-shifting cases. Although the computational cost of the proposed system is higher than those of certain other algorithms, this system obtains better results than other approaches when tested on three different datasets.
Can phone apps slow the spread of the coronavirus?

Digital contact tracing is growing—and facing its first real-world tests.
Dynamic Density Clustering Ant Colony Algorithm With Filtering Recommendation Backtracking Mechanism

Ant colony system has a good performance in solving Traveling Salesman Problem (TSP), but it tends to fall into local optimum and is deficient in convergence speed. To address this problem, a dynamic density clustering ant colony algorithm with a filtering recommendation backtracking mechanism is proposed (DBACS). Firstly, a dynamic density clustering strategy is proposed to accelerate the convergence speed of the algorithm and improve the quality of the solution. Under this strategy, the search radius is expanded dynamically to merge adjacent classes, so as to form the differential pheromone distribution. The splicing paths between each class are adjusted through the ant colony algorithm to achieve better performance. Secondly, a recommendation backtracking mechanism based on collaborative filtering is proposed to increase the diversity of the population, thus helping the algorithm jump out of the local optimum. With the help of the collaborative filtering algorithm, some dense data points are recommended for pheromone dynamic backtracking, which can not only help algorithm jump out of the local optimum, but also help the algorithm accelerate convergence. Simulation results show that the improved algorithm can obtain a better solution and higher stability. Especially in solving large-scale TSP, the accuracy of the solution is significantly improved.
An efficient approach for outlier detection from uncertain data streams based on maximal frequent patterns

Outlier identification is an important technology to improve the credibility of data and aims at detecting patterns that rarely appear and exhibit a significant difference from other data. However, the detection accuracy achieved by the simple deviation factors of existing pattern-based outlier detection methods is not competitive. In addition, given the large scale of uncertain data streams, the efficiency of many pattern-based outlier detection methods is not high because they use a vast number of frequent patterns to conduct the outlier detection. In this paper, to contend with the uncertain data streams, we propose a maximal-frequent-pattern-based outlier detection method, namely, MFP-OD, for identifying the outliers with a lower time cost. For further improving the detection accuracy of existing outlier detection methods, we design three deviation factors to measure the deviation degree of each transaction. The experimental results indicate that the proposed MFP-OD method can quickly and accurately identify the outliers from uncertain data streams.
PulseSatellite: A Tool Using Human-AI Feedback Loops for Satellite Image Analysis in Humanitarian Contexts

Humanitarian response to natural disasters and conflicts can be assisted by satellite image analysis. In a humanitarian context, very specific satellite image analysis tasks must be done accurately and in a timely manner to provide operational support. We present PulseSatellite, a collaborative satellite image analysis tool which leverages neural network models that can be retrained on-the fly and adapted to specific humanitarian contexts and geographies. We present two case studies, in mapping shelters and floods respectively, that illustrate the capabilities of PulseSatellite.
End-to-End Negation Resolution as Graph Parsing

We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem.Our approach allows for the straightforward inclusion of many types of graph-structured features without the need for representationspecific heuristics.In our experiments, we specifically gauge the usefulness of syntactic information for negation resolution.Despite the conceptual simplicity of our architecture, we achieve state-of-the-art results on the Conan Doyle benchmark dataset, including a new top result for our best model.
Staying True to Your Word: (How) Can Attention Become Explanation?

The attention mechanism has quickly become ubiquitous in NLP.In addition to improving performance of models, attention has been widely used as a glimpse into the inner workings of NLP models.The latter aspect has in the recent years become a common topic of discussion, most notably in work of Jain and Wallace, 2019; Wiegreffe and Pinter, 2019.With the shortcomings of using attention weights as a tool of transparency revealed, the attention mechanism has been stuck in a limbo without concrete proof when and whether it can be used as an explanation.In this paper, we provide an explanation as to why attention has seen rightful critique when used with recurrent networks in sequence classification tasks.We propose a remedy to these issues in the form of a word level objective and our findings give credibility for attention to provide faithful interpretations of recurrent models.
Subsampled Rényi Differential Privacy and Analytical Moments Accountant

We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Specifically, we provide a tight upper bound on the Renyi Differential Privacy (RDP) [Mironov, 2017] parameters for algorithms that: (1) subsample the dataset, and then (2) apply a randomized mechanism M to the subsample, in terms of the RDP parameters of M and the subsampling probability parameter.Our results generalize the moments accounting technique, developed by [Abadi et al. 2016] for the Gaussian mechanism, to any subsampled RDP mechanism.
Automated Social Text Annotation With Joint Multilabel Attention Networks

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms.The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents.It can be formulated as a multilabel classification problem.We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels.The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence.To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics.The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN).We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets.In terms of F 1 , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6%and the hierarchical attention network (HAN) by around 3.9%-23.8%.The JMAN model demonstrates advantages in convergence and training speed.Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM).When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F 1 was also boosted.It is also found that dynamic update of the label semantic matrices (JMAN d ) has
Extensible Extraction of Efficient Imperative Programs with Foreign Functions, Manually Managed Memory, and Proofs

We present an original approach to sound program extraction in a proof assistant, using syntax-driven automation to derive correct-by-construction imperative programs from nondeterministic functional source code. Our approach does not require committing to a single inflexible compilation strategy and instead makes it straightforward to create domain-specific code translators. In addition to a small set of core definitions, our framework is a large, user-extensible collection of compilation rules each phrased to handle specific language constructs, code patterns, or data manipulations. By mixing and matching these pieces of logic, users can easily tailor extraction to their own domains and programs, getting maximum performance and ensuring correctness of the resulting assembly code. Using this approach, we complete the first proof-generating pipeline that goes automatically from high-level specifications to assembly code. In our main case study, the original specifications are phrased to resemble SQL-style queries, while the final assembly code does manual memory management, calls out to foreign data structures and functions, and is suitable to deploy on resource-constrained platforms. The pipeline runs entirely within the Coq proof assistant, leading to final, linked assembly code with overall full-functional-correctness proofs in separation logic.
Integrating Features Acceleration in Visual Predictive Control

This paper proposes new prediction models for Visual Predictive Control that can lead to both better motions in the feature space and shorter sensor trajectories in 3D.Contrarily to existing first-order models based only on the interaction matrix, it is proposed to integrate acceleration information provided by second-order models.This allows to better estimate the evolution of the image features, and consequently to evaluate control inputs that can properly steer the system to a desired configuration.By means of simulations, the performances of these new predictors are shown and compared to those of a classical model.Included experiments using both image point features and polar coordinates confirm the validity and generality of the approach, showing that the increased complexity of the predictors does not prevent real-time implementations.
Classification of digital pathological images of non‐Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis

Purpose Non‐Hodgkin's lymphoma (NHL) is a serious malignant disease. Delayed diagnosis will cause anemia, increased intracranial pressure, organ failure, and even lead to death. The current main trend in this area is to use deep learning (DL) for disease diagnosis. Extracting classification information from the digital pathology images by DL may realize the automated qualitative and quantitative analysis of NHL. Previously, DL has been used to classify NHL digital pathology images with some success. However, shortcomings still exist in the data preprocessing methods and feature extraction. Therefore, this paper presents a method for the classification of NHL subtypes based on the fusion of transfer learning (TL) and principal component analysis (PCA). Methods First, the NHL digital pathology images were preprocessed by image division and segmentation and then input into the transfer models for fine‐tuning and feature extraction. Second, PCA was used to map the extracted features. Finally, a neural network was used as a classifier to classify the mapped features. During the fine‐tuning of the transfer models, two methods, freezing all feature extraction layers and fine‐tuning all layers, were employed to select the optimal model with the best classification result among all the preselected transfer models. On this basis, the use of freezing the layers' location was discussed and analyzed. Results The results show that the proposed method achieved average fivefold cross‐validation accuracies of 100%, 99.73%, and 99.20% for chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL) tumor, and each category has standard deviations 0.00, 0.53, and 0.65, respectively, in the NHL reference dataset. The overall classification accuracy for fivefold cross‐validation is 98.93%, which is an increase of 1.26% compared to the latest reported methods, having a lower standard deviation (1.00). Conclusion The method proposed in this paper achieves a high classification accuracy and strong model generalization for the classification of NHL, which makes it possible to conduct intelligent classification of NHL in clinical practice. Our proposed method has definite clinical value and research significance.