Reviewerly/exHarmony huggingface dataset:
Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification

Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress.Based on results from previous work, the performance bottleneck of current models lies in the stage of content planning (selecting and ordering salient content from the input).That is, performance drops drastically when an oracle content plan is replaced by a model-inferred one during surface realization.In this paper, we propose to enhance neural content planning by (1) understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning; (2) verifying the importance and ordering of the selected sequence of records with policy gradient.We evaluated our model on ROTOWIRE and MLB, two datasets on this task, and results show that our model outperforms existing systems with respect to content planning metrics. NCP+CC (NCP) GoldThe Memphis Grizzlies defeated the Brooklyn Nets , 109 -122 , at FedEx Forum on Friday .The Grizzlies ( 36 -28 ) came into this game as a huge favorite and they left no doubt with this result .In fact , there were 10 ties and 11 lead changes in total , with no team leading by more than 12 points .Three-point shooting was the difference in the game , with the Grizzlies going 53 percent from the field and 47 percent from three -point range , while the Grizzlies went 53 percent from the field and 47 percent from three -point range .The Grizzlies also dominated the assist -to -turnover ratio , recording 10 more assists than Brooklyn , while committing five less turnovers .Sean Kilpatrick led the Nets in scoring , as he tallied 23 points , five rebounds and three assists .Brook Lopez finished with just eight points , four rebounds and two assists .Sean Kilpatrick was huge off the bench , providing 23 points , five rebounds and three assists .Mike Conley was the player of the game , as he collected 32 points , three rebounds and six assists .Marc Gasol collected 18 points , six rebounds , four assists and two blocks .Zach Randolph accrued 10 points , seven rebounds and three assists .On deck for the Nets is a home matchup against the Portland Trail Blazers on Friday .Memphis looked poised for an easy win when seeing this matchup on the schedule , but Brooklyn had other ideas .The Nets took care of business when it mattered most , winning the fourth quarter , 34 -19 .Shooting was key , as Brooklyn shot 53 percent from the field , while holding Memphis to just 44 percent shooting .Sean Kilpatrick led the way for Brooklyn , as he provided 23 points , five rebounds and three assists off the bench .Jeremy Lin led the starters , as he accrued 18 points and four rebounds .The Nets won despite getting just eight points and four rebounds from Brook Lopez .Randy Foye and Caris LeVert scored 14 points apiece .Mike Conley carried the load for Memphis , as he collected 32 points , three rebounds and six assists .Marc Gasol contributed 18 points , six rebounds , four assists and two blocks .
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning

Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe attacks on multiple perceptual ad-blocking techniques, and unveil a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks. We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's detection pipeline, that enable publishers or ad networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. On one hand, we show that perceptual ad-blocking must visually classify rendered web content to escape an arms race centered on obfuscation of page markup. On the other, we present a concrete set of attacks on visual ad-blockers by constructing adversarial examples in a real web page context. For seven ad-detectors, we create perturbed ads, ad-disclosure logos, and native web content that misleads perceptual ad-blocking with 100% success rates. In one of our attacks, we demonstrate how a malicious user can upload adversarial content, such as a perturbed image in a Facebook post, that fools the ad-blocker into removing another users' non-ad content. Moving beyond the Web and visual domain, we also build adversarial examples for AdblockRadio, an open source radio client that uses machine learning to detects ads in raw audio streams.
Chapter Captor: Text Segmentation in Novels

Books are typically segmented into chapters and sections, representing coherent subnarratives and topics.We investigate the task of predicting chapter boundaries, as a proxy for the general task of segmenting long texts.We build a Project Gutenberg chapter segmentation data set of 9,126 English novels, using a hybrid approach combining neural inference and rule matching to recognize chapter title headers in books, achieving an F1-score of 0.77 on this task.Using this annotated data as ground truth after removing structural cues, we present cut-based and neural methods for chapter segmentation, achieving an F1-score of 0.453 on the challenging task of exact break prediction over book-length documents.Finally, we reveal interesting historical trends in the chapter structure of novels.
Hierarchical Poset Decoding for Compositional Generalization in Language

We formalize human language understanding as a structured prediction task where the output is a partially ordered set (Poset). Current encoder-decoder architectures do not take the Poset structure of semantics into account properly, thus suffering from poor compositional generalization ability. In this paper, we propose a novel hierarchical Poset decoding paradigm for compositional generalization in language. Intuitively: (1) the proposed paradigm enforces partial permutation invariance in semantics, thus avoiding overfitting to bias ordering information; (2) the hierarchical mechanism allows to capture high-level structures of Posets. We evaluate our proposed decoder on Compositional Freebase Questions (CFQ), a large and realistic natural language question answering dataset that is specifically designed to measure compositional generalization. Results show that it outperforms current decoders.
CoinDICE: Off-Policy Confidence Interval Estimation

We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given only access to a static experience dataset collected by unknown behavior policies. Starting from a function space embedding of the linear program formulation of the $Q$-function, we obtain an optimization problem with generalized estimating equation constraints. By applying the generalized empirical likelihood method to the resulting Lagrangian, we propose CoinDICE, a novel and efficient algorithm for computing confidence intervals. Theoretically, we prove the obtained confidence intervals are valid, in both asymptotic and finite-sample regimes. Empirically, we show in a variety of benchmarks that the confidence interval estimates are tighter and more accurate than existing methods.
Online Back-Parsing for AMR-to-Text Generation

AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph.Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on standard language modeling being used to generate outputs.We propose a decoder that back predicts projected AMR graphs on the target sentence during text generation.As the result, our outputs can better preserve the input meaning than standard decoders.Experiments on two AMR benchmarks show the superiority of our model over the previous state-of-the-art system based on graph Transformer.
On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning Problems

The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives in dimension reduction, preservation of variance and of pairwise relative distances. Investigations of their asymptotic correlation as well as numerical experiments show that a projection does usually not satisfy both objectives at once. In a standard classification problem we determine projections on the input data that balance the objectives and compare subsequent results. Next, we extend our application of orthogonal projections to deep learning tasks and introduce a general framework of augmented target loss functions. These loss functions integrate additional information via transformations and projections of the target data. In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy.
Revisiting Representation Degeneration Problem in Language Modeling

Weight tying is now a common setting in many language generation tasks such as language modeling and machine translation. However, a recent study reveals that there is a potential flaw in weight tying. They find that the learned word embeddings are likely to degenerate and lie in a narrow cone when training a language model. They call it the representation degeneration problem and propose a cosine regularization to solve it. Nevertheless, we prove that the cosine regularization is insufficient to solve the problem, as the degeneration is still likely to happen under certain conditions. In this paper, we revisit the representation degeneration problem and theoretically analyze the limitations of the previously proposed solution. Afterward, we propose an alternative regularization method called Laplacian regularization to tackle the problem. Experiments on language modeling demonstrate the effectiveness of the proposed Laplacian regularization.
Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

Graphs play a key role in data analytics. Graphs and the software systems used to work with them are highly diverse. Algorithms interact with hardware in different ways and which graph solution works best on a given platform changes with the structure of the graph. This makes it difficult to decide which graph programming framework is the best for a given situation. In this paper, we try to make sense of this diverse landscape. We evaluate five different frameworks for graph analytics: SuiteS-parse GraphBLAS, Galois, the NWGraph library, the Graph Kernel Collection, and GraphIt. We use the GAP Benchmark Suite to evaluate each framework. GAP consists of 30 tests: six graph algorithms (breadth-first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) on five graphs. The GAP Benchmark Suite includes high-performance reference implementations to provide a performance baseline for comparison. Our results show the relative strengths of each framework, but also serve as a case study for the challenges of establishing objective measures for comparing graph frameworks.
Multi Scale Temporal Graph Networks for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) can effectively capture the features of related nodes and improve the performance of model. More attention is paid to employing GCN in Skeleton-Based action recognition. But existing methods based on GCNs have two problems. First, the consistency of temporal and spatial features is ignored for extracting features node by node and frame by frame. To obtain spatiotemporal features simultaneously, we design a generic representation of skeleton sequences for action recognition and propose a novel model called Temporal Graph Networks (TGN). Secondly, the adjacency matrix of graph describing the relation of joints are mostly depended on the physical connection between joints. To appropriate describe the relations between joints in skeleton graph, we propose a multi-scale graph strategy, adopting a full-scale graph, part-scale graph and core-scale graph to capture the local features of each joint and the contour features of important joints. Experiments were carried out on two large datasets and results show that TGN with our graph strategy outperforms state-of-the-art methods.
Point-to-Set Similarity Based Deep Metric Learning for Offline Signature Verification

Offline signature verification is a challenging task, where the scarcity of the signature data per writer makes it a few-shot problem. We found that previous deep metric learning based methods, whether in pairs or triplets, are unaware of intra-writer variations and have low training efficiency because only point-to-point (P2P) distances are considered. To address this issue, we present a novel point-to-set (P2S) metric for offline signature verification in this paper. By dividing a training batch into a support set and a query set, our optimization goal is to pull each query to its belonging support set. To further strengthen the P2S metric, a hard mining scheme and a margin strategy are introduced. Experiments conducted on three datasets show the effectiveness of our proposed method.
How to lose a hand: Sensory updating drives disembodiment

Abstract Body representations are readily expanded based on sensorimotor experience. A dynamic view of body representations, however, holds that these representations cannot only be expanded but that they can also be narrowed down by disembodying elements of the body representation that are no longer warranted. Here we induced illusory ownership in terms of a moving rubber hand illusion and studied the maintenance of this illusion across different conditions. We observed ownership experience to decrease gradually unless participants continued to receive confirmatory multisensory input. Moreover, a single instance of multisensory mismatch – a hammer striking the rubber hand but not the real hand – triggered substantial and immediate disembodiment. Together, these findings support and extend previous theoretical efforts to model body representations through basic mechanisms of multisensory integration. They further support an updating model suggesting that embodied entities fade from the body representation if they are not refreshed continuously.
SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers

Keyphrases in a research paper succinctly capture the primary content of the paper and also assist in indexing the paper at a concept level. Given the huge rate at which scientific papers are published today, it is important to have effective ways of automatically extracting keyphrases from a research paper. In this paper, we present a novel method, Syntax and Semantics Aware Keyphrase Extraction (SaSAKE), to extract keyphrases from research papers. It uses a transformer architecture, stacking up sentence encoders to incorporate sequential information, and graph encoders to incorporate syntactic and semantic dependency graph information. Incorporation of these dependency graphs helps to alleviate long-range dependency problems and identify the boundaries of multi-word keyphrases effectively. Experimental results on three benchmark datasets show that our proposed method SaSAKE achieves state-of-the-art performance in keyphrase extraction from scientific papers.
Improved Faster RCNN for Traffic Sign Detection

With the increasing prevalence of autonomous driving, research on traffic sign detection (TSD) draws substantial attention recently. Existing studies usually adopt a twostep framework by first enhancing features of small Region of Interests and then for image analytic. However, the training process of this approach can be in-stable due to the lack of context information, which may restrict the quality of superresolution features. In this paper, we propose an efficient one-step learning-based solution to deal with the TSD problem. We first develop an improved Faster RCNN to detect small objects in traffic images. Then we introduce a new sampling method to optimize the proposed network by selecting highquality proposals. We also present a post-processing scheme to resample the hard false samples having significant contributions to network optimization. Moreover, we adopt Res2net as the backbone of the proposed network in order to obtain more discriminative features. We conduct extensive experiments on the Tsinghua-Tencent 100k dataset, and the results show that our method outperforms other algorithms in terms of accuracy and recall.
Primula

Serverless computing has recently gained much attention as a feasible alternative to always-on IaaS for data processing. However, existing severless frameworks are not (yet) usable enough to reach out to a large number of users. To wit, they still require developers to specify the number of serverless functions for a simple sort job. We report our experience in designing Primula, a serverless sort operator that abstracts away users from the complexities of resource provisioning, skewed data and stragglers, yielding the most accessible sort primitive to date. Our evaluation on the IBM Cloud platform demonstrates the usability of Primula without abandoning performance (e.g., 3x faster than a serverless Spark backend and 62% slower than a hybrid serverless/IaaS solution).
Toward Understanding the Influence of Individual Clients in Federated Learning

Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guarantee of the global model, however, it is still unclear how each individual client influences the collaborative training process. In this work, we defined a new notion, called {\em Fed-Influence}, to quantify this influence over the model parameters, and proposed an effective and efficient algorithm to estimate this metric. In particular, our design satisfies several desirable properties: (1) it requires neither retraining nor retracing, adding only linear computational overhead to clients and the server; (2) it strictly maintains the tenets of federated learning, without revealing any client's local private data; and (3) it works well on both convex and non-convex loss functions, and does not require the final model to be optimal. Empirical results on a synthetic dataset and the FEMNIST dataset demonstrate that our estimation method can approximate Fed-Influence with small bias. Further, we show an application of Fed-Influence in model debugging.
Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modeling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are utilized to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modeling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies.
Automatic Segmentation of Sign Language into Subtitle-Units

We present baseline results for a new task of automatic segmentation of Sign Language video into sentence-like units. We use a corpus of natural Sign Language video with accurately aligned subtitles to train a spatio-temporal graph convolutional network with a BiLSTM on 2D skeleton data to automatically detect the temporal boundaries of subtitles. In doing so, we segment Sign Language video into subtitle-units that can be translated into phrases in a written language. We achieve a ROC-AUC statistic of 0.87 at the frame level and 92% label accuracy within a time margin of 0.6s of the true labels.
Improved Sublinear Time Algorithm for Longest Increasing Subsequence

We present a novel sublinear time algorithm for approximating LIS. If we denote the ratio of the solution size over the input size by λ, our approach yields an algorithm with an approximation factor of Ω(λ∊) for any constant ∊ > 0, and a truly sublinear runtime. This improves over for example the recent work of Rubinstein et al. [RSSS19] that approximates LIS within a factor Ω(λ3) in truly sublinear time. Our work makes use of a grid packing technique recently introduced by Mitzenmacher and Seddighin to approximate LIS in the dynamic setting [MS20], providing another application for this technique.
Transfer Learning for Humanoid Robot Appearance-Based Localization in a Visual Map

Autonomous robot visual navigation is a fundamental locomotion task based on extracting relevant features from images taken from the surrounded environment to control an independent displacement.In the navigation, the use of a known visual map helps obtain an accurate localization, but in the absence of this map, a guided or free exploration pathway must be executed to obtain the images sequence representing the visual map.This paper presents an appearance-based localization method based on a visual map and an end-to-end Convolutional Neural Network (CNN).The CNN is initialized via transfer learning (trained using the ImageNet dataset), evaluating four state-of-the-art CNN architectures: VGG16, ResNet50, InceptionV3, and Xception.A typical pipeline for transfer learning includes changing the last layer to adapt the number of neurons according to the number of custom classes.In this work, the dense layers after the convolutional and pooling layers were substituted by a Global Average Pooling (GAP) layer, which is parameter-free.Additionally, an L 2 -norm constraint was added to the GAP layer feature descriptors, restricting the features from lying on a fixed radius hypersphere.These different pre-trained configurations were analyzed and compared using two visual maps found in the CIMAT-NAO datasets consisting of 187 and 94 images, respectively.For evaluating the localization tasks, a set of 278 and 94 images were available for each visual map, respectively.The numerical results proved that by integrating the L 2 -norm constraint in the training pipeline, the appearance-based localization performance is boosted.Specifically, the pre-trained VGG16 and Xception networks achieved the best localization results, reaching a top-3 accuracy of 90.70% and 93.62% for each dataset, respectively, overcoming the referenced approaches based on hand-crafted feature extractors.