Reviewerly/exHarmony huggingface dataset:
Enabling Fast and Universal Audio Adversarial Attack Using Generative  Model

Recently, the vulnerability of DNN-based audio systems to adversarial attacks has obtained the increasing attention. However, the existing audio adversarial attacks allow the adversary to possess the entire user's audio input as well as granting sufficient time budget to generate the adversarial perturbations. These idealized assumptions, however, makes the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e.g., playing unnoticeable adversarial perturbations along with user's streaming input). To overcome these limitations, in this paper we propose fast audio adversarial perturbation generator (FAPG), which uses generative model to generate adversarial perturbations for the audio input in a single forward pass, thereby drastically improving the perturbation generation speed. Built on the top of FAPG, we further propose universal audio adversarial perturbation generator (UAPG), a scheme crafting universal adversarial perturbation that can be imposed on arbitrary benign audio input to cause misclassification. Extensive experiments show that our proposed FAPG can achieve up to 167X speedup over the state-of-the-art audio adversarial attack methods. Also our proposed UAPG can generate universal adversarial perturbation that achieves much better attack performance than the state-of-the-art solutions.
OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D Lidar, to explicitly predict the velocities and positions of surrounding obstacles through optical flow estimation, object detection, and sensor fusion. A key aspect of our work is coupling the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm using learning-based perception and model-based planning methods offers better performance than prior algorithms in terms of navigation time and success rate of collision avoidance. Our method also provides bounds on the probabilistic collision avoidance algorithm. We highlight the realtime performance of OF-VO on a Turtlebot navigating among pedestrians in both simulated and real-world scenes. A demo video is available at https://gamma.umd.edu/ofvo/
Assisted Learning: A Framework for Multi-Organization Learning

In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organization-specific mission. However, to avoid leaking useful and possibly proprietary information, organizations typically enforce stringent security constraints on sharing modeling algorithms and data, which significantly limits collaborations. In this work, we introduce the Assisted Learning framework for organizations to assist each other in supervised learning tasks without revealing any organization's algorithm, data, or even task. An organization seeks assistance by broadcasting task-specific but nonsensitive statistics and incorporating others' feedback in one or more iterations to eventually improve its predictive performance. Theoretical and experimental studies, including real-world medical benchmarks, show that Assisted Learning can often achieve near-oracle learning performance as if data and training processes were centralized.
Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data

We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new learning based architecture designed such that it can make use of all the sources of available hand training data: image data with either 2D or 3D annotations, as well as stand-alone 3D animations without corresponding image data. It features a 3D hand joint detection module and an inverse kinematics module which regresses not only 3D joint positions but also maps them to joint rotations in a single feed-forward pass. This output makes the method more directly usable for applications in computer vision and graphics compared to only regressing 3D joint positions. We demonstrate that our architectural design leads to a significant quantitative and qualitative improvement over the state of the art on several challenging benchmarks. Our model is publicly available for future research.
SOIC: Semantic Online Initialization and Calibration for LiDAR and  Camera

This paper presents a novel semantic-based online extrinsic calibration approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera sensors. Previous online calibration methods usually need prior knowledge of rough initial values for optimization. The proposed approach removes this limitation by converting the initialization problem to a Perspective-n-Point (PnP) problem with the introduction of semantic centroids (SCs). The closed-form solution of this PnP problem has been well researched and can be found with existing PnP methods. Since the semantic centroid of the point cloud usually does not accurately match with that of the corresponding image, the accuracy of parameters are not improved even after a nonlinear refinement process. Thus, a cost function based on the constraint of the correspondence between semantic elements from both point cloud and image data is formulated. Subsequently, optimal extrinsic parameters are estimated by minimizing the cost function. We evaluate the proposed method either with GT or predicted semantics on KITTI dataset. Experimental results and comparisons with the baseline method verify the feasibility of the initialization strategy and the accuracy of the calibration approach. In addition, we release the source code at https://github.com/--/SOIC.
TEDL: A Text Encryption Method Based on Deep Learning

Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel text encryption method based on deep learning called TEDL, where the secret key includes hyperparameters in deep learning model and the core step of encryption is transforming input data into weights trained under hyperparameters. Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters. Then, a self-update codebook is constructed on the word vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing and inverted indexing on the codebook, respectively, thus achieving the transformation between plaintext and ciphertext. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks while not degrade the communication efficiency.
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of  Variable Importance

As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predictions with minimal a priori model specifications. Tools like random forests have an established track record of off-the-shelf success and even offer various strategies for analyzing the underlying relationships among variables. Here, motivated by recent insights into random forest behavior, we introduce the simple idea of augmented bagging (AugBagg), a procedure that operates in an identical fashion to classical bagging and random forests, but which operates on a larger, augmented space containing additional randomly generated noise features. Surprisingly, we demonstrate that this simple act of including extra noise variables in the model can lead to dramatic improvements in out-of-sample predictive accuracy, sometimes outperforming even an optimally tuned traditional random forest. As a result, intuitive notions of variable importance based on improved model accuracy may be deeply flawed, as even purely random noise can routinely register as statistically significant. Numerous demonstrations on both real and synthetic data are provided along with a proposed solution.
An Analysis of Strategies for Improving Intercultural Communicative Competence in English Teaching in Secondary Vocational Schools

English in secondary vocational schools emphasizes the cultivation of students’ core qualities such as language ability, cultural awareness, thinking quality and learning ability. The development of cross-cultural communication skills not only provides students with more opportunities for personal development, but also helps them establish a sense of community with a shared future for mankind and a sense of multiculturalism, form an open and inclusive attitude, deepen their understanding of the culture of the motherland, enhance their patriotism, and strengthen their cultural confidence. Therefore, teachers should actively explore the countermeasures of cultivating students’ intercultural communication ability in vocational English teaching.
HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications

This paper describes the system for the identifying Plausible Clarifications of Implicit and Underspecified Phrases.This task was set up as an English cloze task, in which clarifications are presented as possible fillers and systems have to score how well each filler plausibly fits in a given context.For this shared task, we propose our own solutions, including supervised approaches, unsupervised approaches with pretrained models, and then we use these models to build an ensemble model.Finally we get the 2nd best result in the subtask1 which is a classification task, and the 3rd best result in the subtask2 which is a regression task.
Challenging America: Modeling language in longer time scales

The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i.e. pre-train and fine-tune large Transformer models.This paper introduces an ML challenge, named Challenging America (Chal-lAm), based on OCR-ed excerpts from historical newspapers collected from the Chronicling America portal.ChallAm provides a dataset of clippings, labeled with metadata on their origin, and paired with their textual contents retrieved by an OCR tool.Three, publicly available, ML tasks are defined in the challenge: to determine the article date, to detect the location of the issue, and to deduce a word in a text gap (cloze test).Strong baselines are provided for all three ChallAm tasks.In particular, we pretrained a RoBERTa model from scratch from the historical texts.We also discuss the issues of discrimination and hate-speech present in the historical American texts.
PuzzleNet: Scene Text Detection by Segment Context Graph Learning

Recently, a series of decomposition-based scene text detection methods has achieved impressive progress by decomposing challenging text regions into pieces and linking them in a bottom-up manner. However, most of them merely focus on linking independent text pieces while the context information is underestimated. In the puzzle game, the solver often put pieces together in a logical way according to the contextual information of each piece, in order to arrive at the correct solution. Inspired by it, we propose a novel decomposition-based method, termed Puzzle Networks (PuzzleNet), to address the challenging scene text detection task in this work. PuzzleNet consists of the Segment Proposal Network (SPN) that predicts the candidate text segments fitting arbitrary shape of text region, and the two-branch Multiple-Similarity Graph Convolutional Network (MSGCN) that models both appearance and geometry correlations between each segment to its contextual ones. By building segments as context graphs, MSGCN effectively employs segment context to predict combinations of segments. Final detections of polygon shape are produced by merging segments according to the predicted combinations. Evaluations on three benchmark datasets, ICDAR15, MSRA-TD500 and SCUT-CTW1500, have demonstrated that our method can achieve better or comparable performance than current state-of-the-arts, which is beneficial from the exploitation of segment context graph.
Ensuring Privacy in Location-Based Services: A Model-based Approach

In recent years, the widespread of mobile devices equipped with GPS and communication chips has led to the growing use of location-based services (LBS) in which a user receives a service based on his current location. The disclosure of user's location, however, can raise serious concerns about user privacy in general, and location privacy in particular which led to the development of various location privacy-preserving mechanisms aiming to enhance the location privacy while using LBS applications. In this paper, we propose to model the user mobility pattern and utility of the LBS as a Markov decision process (MDP), and inspired by probabilistic current state opacity notation, we introduce a new location privacy metric, namely $\epsilon-$privacy, that quantifies the adversary belief over the user's current location. We exploit this dynamic model to design a LPPM that while it ensures the utility of service is being fully utilized, independent of the adversary prior knowledge about the user, it can guarantee a user-specified privacy level can be achieved for an infinite time horizon. The overall privacy-preserving framework, including the construction of the user mobility model as a MDP, and design of the proposed LPPM, are demonstrated and validated with real-world experimental data.
HeteroCorpus: A Corpus for Heteronormative Language Detection

In recent years, plenty of work has been done by the NLP community regarding gender bias detection and mitigation in language systems.Yet, to our knowledge, no one has focused on the difficult task of heteronormative language detection and mitigation.We consider this an urgent issue, since language technologies are growing increasingly present in the world and, as it has been proven by various studies, NLP systems with biases can create real-life adverse consequences for women, gender minorities and racial minorities and queer people.For these reasons, we propose and evaluate Het-eroCorpus; a corpus created specifically for studying heterononormative language in English.Additionally, we propose a baseline set of classification experiments on our corpus, in order to show the performance of our corpus in classification tasks.
Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi and Nepali

Word embeddings are growing to be a crucial resource in the field of NLP for any language.This work introduces a novel technique for static subword embeddings transfer for Indic languages from a relatively higher resource language to a genealogically related low resource language.We primarily work with Hindi-Marathi, simulating a low-resource scenario for Marathi, and confirm observed trends on Nepali.We demonstrate the consistent benefits of unsupervised morphemic segmentation on both source and target sides over the treatment performed by fastText.Our best-performing approach uses an EM-style approach to learning bilingual subword embeddings; we also show, for the first time, that a trivial "copyand-paste" embeddings transfer based on even perfect bilingual lexicons is inadequate in capturing language-specific relationships.We find that our approach substantially outperforms the fastText baselines for both Marathi and Nepali on the Word Similarity task as well as WordNet-Based Synonymy Tests; on the former task, its performance for Marathi is close to that of pretrained fastText embeddings that use three orders of magnitude more Marathi data.
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary

This paper introduces the result of Team LingJing's experiments in SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE) 1 .This task aims at comparing two types of semantic descriptions, including the definition modeling and reverse dictionary track.Our team focuses on the reverse dictionary track and adopts the multi-task selfsupervised pre-training for multilingual reverse dictionaries.Specifically, the randomly initialized mDeBERTa-base model is used to perform multi-task pre-training on the multilingual training datasets.The pre-training step is divided into two stages, namely the MLM pretraining stage and the contrastive pre-training stage.As a result, all the experiments are performed on the pre-trained language model during fine-tuning.The experimental results show that the proposed method has achieved good performance in the reverse dictionary track, where we rank the 1-st in the Sgns targets of the EN and RU languages.All the experimental codes are open-sourced at https:
Team LEGO at SemEval-2022 Task 4: Machine Learning Methods for PCL Detection

In this paper, we present our submission to the SemEval 2022 -Task 4 on Patronizing and Condescending Language (PCL) detection.We approach this problem as a traditional text classification problem with machine learning (ML) methods.We experiment and investigate the use of various ML algorithms for detecting PCL in news articles.Our best methodology achieves an F1-Score of 0.39 for subtask1 with a rank of 63 out of 80, and F1-score of 0.082 for subtask2 with a rank of 41 out of 48 on the blind dataset provided in the shared task.
Lossless Compression of Mosaic Images with Convolutional Neural Network  Prediction

We present a CNN-based predictive lossless compression scheme for raw color mosaic images of digital cameras. This specialized application problem was previously understudied but it is now becoming increasingly important, because modern CNN methods for image restoration tasks (e.g., superresolution, low lighting enhancement, deblurring), must operate on original raw mosaic images to obtain the best possible results. The key innovation of this paper is a high-order nonlinear CNN predictor of spatial-spectral mosaic patterns. The deep learning prediction can model highly complex sample dependencies in spatial-spectral mosaic images more accurately and hence remove statistical redundancies more thoroughly than existing image predictors. Experiments show that the proposed CNN predictor achieves unprecedented lossless compression performance on camera raw images.
Insertion-Deletion Transformer

We propose the Insertion-Deletion Transformer, a novel transformer-based neural architecture and training method for sequence generation. The model consists of two phases that are executed iteratively, 1) an insertion phase and 2) a deletion phase. The insertion phase parameterizes a distribution of insertions on the current output hypothesis, while the deletion phase parameterizes a distribution of deletions over the current output hypothesis. The training method is a principled and simple algorithm, where the deletion model obtains its signal directly on-policy from the insertion model output. We demonstrate the effectiveness of our Insertion-Deletion Transformer on synthetic translation tasks, obtaining significant BLEU score improvement over an insertion-only model.
Infinite families of class groups of quadratic fields with 3-rank at least one: quantitative bounds

We improve an effective lower bound on the number of imaginary quadratic fields whose absolute discriminants are less than or equal to [Formula: see text] and whose ideal class groups have 3-rank at least one, which is [Formula: see text]. We also obtain a better bound on the number of imaginary quadratic fields with 3-rank at least two, which is [Formula: see text]; the best-known lower bound so far is [Formula: see text]. For finding these effective lower bounds, we use the Scholz criteria and the parametric families of quadratic fields [Formula: see text] and [Formula: see text] (defined as follows) with escalatory case. We find new infinite families of quadratic fields [Formula: see text] and [Formula: see text], where [Formula: see text] and [Formula: see text] are integers subject to certain conditions for [Formula: see text]. More specifically, we find a complete criterion for the 3-rank difference between [Formula: see text] and its associated quadratic field [Formula: see text] to be one; this is the escalatory case. We also obtain a sufficient condition for the family [Formula: see text] and its associated family [Formula: see text] to have escalatory case. We illustrate some selective implementation results on the 3-class group ranks of [Formula: see text] and [Formula: see text] for [Formula: see text].
Recommendation Versus Regression Neural Collaborative Filtering

Bobadilla, Jesús Alonso, Santiago Gutiérrez, Abraham González, ÁlvaroNeural Collaborative Filtering recommendations are traditionally based on regression architectures (returning continuous predictions, e.g. 2.8 stars), such as DeepMF and NCF. However, there are advantages in the use of collaborative filtering classification models. This work tested both neuronal approaches using a set of representative open datasets, baselines, and quality measures. The results show the superiority of the regular regression model compared to the regular classification model (returning discrete predictions, e.g. 1–5 stars) and the binary classification model (returning binary predictions: recommended, non-recommended). Results also show a similar performance when comparing our proposed recommendation neural approach with the state-of-the-art neural regression baseline. The key issue is the additional information the recommendation approach provides compared to the regression model: While the regression baseline only returns the recommendation values, the proposed recommendation model returns $$\langle $$ value, probability $$\rangle $$ pairs. Extra probability information can be used in the recommender systems area for different objectives: recommendation explanation, visualization of results, quality improvements, mitigate attack risks, etc.
Hiding Information in Big Data based on Deep Learning

The current approach of information hiding based on deep learning model can not directly use the original data as carriers, which means the approach can not make use of the existing data in big data to hiding information. We proposed a novel method of information hiding in big data based on deep learning. Our method uses the existing data in big data as carriers and uses deep learning models to hide and extract secret messages in big data. The data amount of big data is unlimited and thus the data amount of secret messages hided in big data can also be unlimited. Before opponents want to extract secret messages from carriers, they need to find the carriers, however finding out the carriers from big data is just like finding out a box from the sea. Deep learning models are well known as deep black boxes in which the process from the input to the output is very complex, and thus the deep learning model for information hiding is almost impossible for opponents to reconstruct. The results also show that our method can hide secret messages safely, conveniently, quickly and with no limitation on the data amount.