Reviewerly/exHarmony huggingface dataset:
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter

Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still constrained to solve the problem of reading cropped line text images and serves as a subtask of optical character recognition (OCR) systems. As a result, the final text recognition result is limited by the performance of the text detector. In this paper, we propose a simple, elegant and effective paradigm called Implicit Feature Alignment (IFA), which can be easily integrated into current text recognizers, resulting in a novel inference mechanism called IFA- inference. This enables an ordinary text recognizer to process multi-line text such that text detection can be completely freed. Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore, the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to suppress negative predictions to assist in training ADP and ExCTC. We experimentally demonstrate that IFA achieves state-of-the-art performance on end-to-end document recognition tasks while maintaining the fastest speed, and ADP and ExCTC complement each other on the perspective of different application scenarios. Code will be available at https://github.com/Wang-Tianwei/Implicit-feature-alignment.
Librarians Publishing in Partnership with Other Researchers: Roles, Motivations, Benefits, and Challenges

Building on previous research on the contribution of librarians to scholarly journals in fields outside library and information science (LIS), this study uses a qualitative approach to gain a richer understanding of the nature of research collaborations between librarians and faculty. It explores librarians’ motivations for becoming involved, the benefits believed to accrue from such partnerships, and the challenges faced. Sixty librarians who had coauthored a research paper with scholars in fields other than LIS replied to a qualitative online survey. Results show that librarians become involved in the whole range of roles throughout the research process, with contributing to the writing of papers (particularly reviewing and editing the final version) being the most common. Coauthorship often results from a long-term working relationship between the librarians and researchers involved. Although librarians are seldom funded as part of the research project, coauthorship may offer benefits—it improves job satisfaction and enhances the reputation of the individual and the library as a whole. Challenges faced relate mainly to time pressures, although the participants acknowledge the need to develop relevant skills. They also must feel confident in the role they fulfill in the research process and the professional skills they can contribute.
Special Session: CAD for Hardware Security - Automation is Key to Adoption of Solutions

Although hardware security has received significant attention in the past decade or so, security design and validation engineers and researchers in industry, academia, and government have not still been equipped with a mature security-aware toolset to automatically and effectively analyze designs for various types of security vulnerabilities at different to detect and fix the security issues or build security in designs efficiently and easily. Despite such a demand, currently, there is not an ecosystem of security-aware Electronic Design Automation (EDA) or Computer-Aided Design (CAD) tools whereas the commercial design for security and validation tools are still in their infancy. However, there exist many research works that try to come up with security analysis engines and provide solutions to address different classes of security issues such as data leakage, access control violation, side-channel leakage, hardware Trojans and malicious changes, and vulnerabilities to physical attacks, fault-injection attacks, reverse engineering attacks, and chip counterfeiting or overproduction attacks. This paper presents the foundation established by several academic and industry researchers who have been supporting the realization of an ecosystem of security-aware CAD tools with their focus on hardware security coverage and fault-injection assessment for SoC designs, and security assurance standardization for electronic design integration.
Coupling Denoising to Detection for SAR Imagery

Detecting objects in synthetic aperture radar (SAR) imagery has received much attention in recent years since SAR can operate in all-weather and day-and-night conditions. Due to the prosperity and development of convolutional neural networks (CNNs), many previous methodologies have been proposed for SAR object detection. In spite of the advance, existing detection networks still have limitations in boosting detection performance because of inherently noisy characteristics in SAR imagery; hence, separate preprocessing step such as denoising (despeckling) is required before utilizing the SAR images for deep learning. However, inappropriate denoising techniques might cause detailed information loss and even proper denoising methods does not always guarantee performance improvement. In this paper, we therefore propose a novel object detection framework that combines unsupervised denoising network into traditional two-stage detection network and leverages a strategy for fusing region proposals extracted from both raw SAR image and synthetically denoised SAR image. Extensive experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from TerraSAR-X and COSMO-SkyMed satellites. Extensive experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from TerraSAR-X and COSMO-SkyMed satellites. The proposed framework shows better performances when we compared the model with using only noisy SAR images and only denoised SAR images after despeckling under multiple backbone networks.
Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning

With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency.
Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models

Pre-trained language models have achieved huge success on a wide range of NLP tasks.However, contextual representations from pretrained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive useful semantic sentence embeddings for some tasks.Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax.In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models.ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax.In this way, ParaBART learns disentangled semantic and syntactic representations from their respective inputs with separate encoders.Experiments in English show that ParaBART outperforms state-of-theart sentence embedding models on unsupervised semantic similarity tasks.Additionally, we show that our approach can effectively remove syntactic information from semantic sentence embeddings, leading to better robustness against syntactic variation on downstream semantic tasks.
Automated Extraction of Labels from Large-Scale Historical Maps

Abstract. Historical maps are frequently neither readable, searchable nor analyzable by machines due to lacking databases or ancillary information about their content. Identifying and annotating map labels is seen as a first step towards an automated legibility of those. This article investigates a universal and transferable methodology for the work with large-scale historical maps and their comparability to others while reducing manual intervention to a minimum. We present an end-to-end approach which increases the number of true positive identified labels by combining available text detection, recognition, and similarity measuring tools with own enhancements. The comparison of recognized historical with current street names produces a satisfactory accordance which can be used to assign their point-like representatives within a final rough georeferencing. The demonstrated workflow facilitates a spatial orientation within large-scale historical maps by enabling the establishment of relating databases. Assigning the identified labels to the geometries of related map features may contribute to machine-readable and analyzable historical maps.
Generation of quantum states with nonlinear squeezing by Kerr nonlinearity

In quantum optics, squeezing corresponds to the process in which fluctuations of a quadrature operator are reduced below the shot noise limit. In turn, nonlinear squeezing can be defined as reduction of fluctuations related to nonlinear combination of quadrature operators. Quantum states with nonlinear squeezing are a necessary resource for deterministic implementation of high-order quadrature phase gates that are, in turn, sufficient for advanced quantum information processing. We demonstrate that this class of states can be deterministically prepared by employing a single self-Kerr gate accompanied by suitable Gaussian processing. The required Kerr coupling depends on the energy of the initial system and can be made arbitrarily small. We also employ numerical simulations to analyze the effects of imperfections and to show to which extent can they be neglected.
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers

Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods.Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting.These methods train a model to discriminate between different transformations applied to visual data and then use the output to compute an anomaly score.We use this approach for AD in text, by introducing a novel pretext task on text sequences.We learn our DATE model end-to-end, enforcing two independent and complementary self-supervision signals, one at the token-level and one at the sequencelevel.Under this new task formulation, we show strong quantitative and qualitative results on the 20Newsgroups and AG News datasets.In the semi-supervised setting, we outperform state-of-the-art results by +13.5% and +6.9%, respectively (AUROC).In the unsupervised configuration, DATE surpasses all other methods even when 10% of its training data is contaminated with outliers (compared with 0% for the others).
On the Identification, Evaluation and Treatment of Risks in Smart Homes: A Systematic Literature Review

The emergence of smart technologies in homes comes with various services and functions for everyday life. While a smart home (SH) is associated with great potential in terms of comfort and risk treatment, it also introduces new and alters existing risks. Despite a growing number of academic studies on SH risks, research is fragmented with regard to its focus on certain disciplines and is still rather technology-focused. In this paper, we fill this gap by providing a comprehensive understanding of relevant risks through a systematic literature review. Following the guidelines of the PRISMA reporting protocol, we search 1196 academic and practitioners’ publications related to household risks or risk perceptions of SH users. A final set of 59 records results in three main themes. They include (1) a synthesis of pre-existing and emerging risks sketching the new risk landscape of SH households, (2) a discussion of the prevailing risk evaluation methods, and (3) a presentation of SH-related risk treatment options with a particular emphasis on insurance. We specify the influence of SH on risks and risk perception, and highlight the relevance of analyzing the interconnection of risks in complex systems, such as SH. Our review lays the basis for assessing SH risks and for enabling more comprehensive and effective risk management optimization.
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data

We study stochastic gradient descent (SGD) with local iterations in the presence of malicious/Byzantine clients, motivated by the federated learning. The clients, instead of communicating with the central server in every iteration, maintain their local models, which they update by taking several SGD iterations based on their own datasets and then communicate the net update with the server, thereby achieving communication-efficiency. Furthermore, only a subset of clients communicate with the server, and this subset may be different at different synchronization times. The Byzantine clients may collaborate and send arbitrary vectors to the server to disrupt the learning process. To combat the adversary, we employ an efficient high-dimensional robust mean estimation algorithm at the server to filter-out corrupt vectors; and to analyze the outlier-filtering procedure, we develop a novel matrix concentration result that may be of independent interest. We provide convergence analyses for both strongly-convex and non-convex smooth objectives in the heterogeneous data setting, where different clients may have different local datasets, and we do not make any probabilistic assumptions on data generation. We believe that ours is the first Byzantine-resilient algorithm and analysis with local iterations. We derive our convergence results under minimal assumptions of bounded variance for SGD and bounded gradient dissimilarity (which captures heterogeneity among local datasets); and we provide bounds on these quantities in the statistical heterogeneous data setting. We also extend our results to the case when clients compute full-batch gradients.
Hierarchical Transformer for Task Oriented Dialog Systems

Generative models for dialog systems have gained much interest because of the recent success of RNN and Transformer based models in tasks like question answering and summarization.Although the task of dialog response generation is generally seen as a sequence to sequence (Seq2Seq) problem, researchers in the past have found it challenging to train dialog systems using the standard Seq2Seq models.Therefore, to help the model learn meaningful utterance and conversation level features, Sordoni et al. (2015b); Serban et al. ( 2016) proposed Hierarchical RNN architecture, which was later adopted by several other RNN based dialog systems.With the transformer-based models dominating the seq2seq problems lately, the natural question to ask is the applicability of the notion of hierarchy in transformer based dialog systems.In this paper, we propose a generalized framework for Hierarchical Transformer Encoders and show how a standard transformer can be morphed into any hierarchical encoder, including HRED and HIBERT like models, by using specially designed attention masks and positional encodings.We demonstrate that Hierarchical Encoding helps achieve better natural language understanding of the contexts in transformer-based models for task-oriented dialog systems through a wide range of experiments.The code and data for all experiments in this paper has been open-sourced 1 2 .
Blockchain‐based framework of power demand response in China

Demand response is recognized as an effective solution for eliminating power fluctuations and satisfying capacity constraints in power systems. A growing customer base equipped with energy storage and intelligent power meter on the demand side has resulted in the strong interest of China's power companies in demand response. However, most exiting demand response programs in China are based on a centralized framework, which is easier for management, but cannot support a large number of scattered small-scale users' participation effectively. Therefore, taking the desired features of blockchain technology, such as decentralization, trustworthiness, trackability, and immutability into consideration, the applicability of blockchain in demand response is analyzed. On this basis, a new blockchain-based framework of China's typical demand response programs is proposed, in which consensus mechanism, encryption algorithm, and smart contract of blockchain are applied to the process of invitation, bidding, and settlement in demand response. Furthermore, the development suggestions of China's demand response based on blockchain technology are put forward from the aspects of trading products, credit management, and platform construction at the end of this work.
Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs

End-to-End task-oriented dialogue systems generate responses based on dialog history and an accompanying knowledge base (KB).Inferring those KB entities that are most relevant for an utterance is crucial for response generation.Existing state of the art scales to large KBs by softly filtering over irrelevant KB information.In this paper, we propose a novel filtering technique that consists of (1) a pairwise similarity based filter that identifies relevant information by respecting the n-ary structure in a KB record.and, (2) an auxiliary loss that helps in separating contextually unrelated KB information.We also propose a new metric -multiset entity F1 which fixes a correctness issue in the existing entity F1 metric.Experimental results on three publicly available task-oriented dialog datasets show that our proposed approach outperforms existing state-ofthe-art models.
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation

Subword segmentation algorithms have been a de facto choice when building neural machine translation systems.However, most of them need to learn a segmentation model based on some heuristics, which may produce suboptimal segmentation.This can be problematic in some scenarios when the target language has rich morphological changes or there is not enough data for learning compact composition rules.Translating at fully character level has the potential to alleviate the issue, but empirical performances of character-based models has not been fully explored.In this paper, we present an in-depth comparison between character-based and subword-based NMT systems under three settings: translating to typologically diverse languages, training with low resource, and adapting to unseen domains.Experimental results show strong competitiveness of character-based models.Further analyses show that compared to subword-based models, character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains.
Learning Comprehensive Motion Representation for Action Recognition

For action recognition learning, 2D CNN-based methods are efficient but may yield redundant features due to applying the same 2D convolution kernel to each frame. Recent efforts attempt to capture motion information by establishing inter-frame connections while still suffering the limited temporal receptive field or high latency. Moreover, the feature enhancement is often only performed by channel or space dimension in action recognition. To address these issues, we first devise a Channel-wise Motion Enhancement (CME) module to adaptively emphasize the channels related to dynamic information with a channel-wise gate vector. The channel gates generated by CME incorporate the information from all the other frames in the video. We further propose a Spatial-wise Motion Enhancement (SME) module to focus on the regions with the critical target in motion, according to the point-to-point similarity between adjacent feature maps. The intuition is that the change of background is typically slower than the motion area. Both CME and SME have clear physical meaning in capturing action clues. By integrating the two modules into the off-the-shelf 2D network, we finally obtain a Comprehensive Motion Representation (CMR) learning method for action recognition, which achieves competitive performance on Something-Something V1 &amp; V2 and Kinetics-400. On the temporal reasoning datasets Something-Something V1 and V2, our method outperforms the current state-of-the-art by 2.3% and 1.9% when using 16 frames as input, respectively.
Group Fairness by Probabilistic Modeling with Latent Fair Decisions

Machine learning systems are increasingly being used to make impactful decisions such as loan applications and criminal justice risk assessments, and as such, ensuring fairness of these systems is critical. This is often challenging as the labels in the data are biased. This paper studies learning fair probability distributions from biased data by explicitly modeling a latent variable that represents a hidden, unbiased label. In particular, we aim to achieve demographic parity by enforcing certain independencies in the learned model. We also show that group fairness guarantees are meaningful only if the distribution used to provide those guarantees indeed captures the real-world data. In order to closely model the data distribution, we employ probabilistic circuits, an expressive and tractable probabilistic model, and propose an algorithm to learn them from incomplete data. We show on real-world datasets that our approach not only is a better model of how the data was generated than existing methods but also achieves competitive accuracy. Moreover, we also evaluate our approach on a synthetic dataset in which observed labels indeed come from fair labels but with added bias, and demonstrate that the fair labels are successfully retrieved.