Reviewerly/exHarmony huggingface dataset:
Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling

Transformer is important for text modeling.However, it has difficulty in handling long documents due to the quadratic complexity with input text length.In order to handle this problem, we propose a hierarchical interactive Transformer (Hi-Transformer) for efficient and effective long document modeling.Hi-Transformer models documents in a hierarchical way, i.e., first learns sentence representations and then learns document representations.It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence.More specifically, we first use a sentence Transformer to learn the representations of each sentence.Then we use a document Transformer to model the global document context from these sentence representations.Next, we use another sentence Transformer to enhance sentence modeling using the global document context.Finally, we use hierarchical pooling method to obtain document embedding.Extensive experiments on three benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling.
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions

This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals.We summarise the key concepts behind text anonymisation and provide a review of current approaches.Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing.Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation.We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.
PVGNet: A Bottom-Up One-Stage 3D Object Detector with Integrated Multi-Level Features

Quantization-based methods are widely used in LiDAR points 3D object detection for its efficiency in extracting context information. Unlike image where the context information is distributed evenly over the object, most LiDAR points are distributed along the object boundary, which means the boundary features are more critical in LiDAR points 3D detection. However, quantization inevitably introduces ambiguity during both the training and inference stages. To alleviate this problem, we propose a one-stage and voting-based 3D detector, named Point-Voxel-Grid Network (PVGNet). In particular, PVGNet extracts point, voxel and grid-level features in a unified backbone architecture and produces point-wise fusion features. It segments Li-DAR points into foreground and background, predicts a 3D bounding box for each foreground point, and performs group voting to get the final detection results. Moreover, we observe that instance-level point imbalance due to occlusion and observation distance also degrades the detection performance. A novel instance-aware focal loss is proposed to alleviate this problem and further improve the detection ability. We conduct experiments on the KITTI and Waymo datasets. Our proposed PVGNet outperforms previous state-of-the-art methods and ranks at the top of KITTI 3D/BEV detection leaderboards.
Cross-Domain Person Re-Identification Using Heterogeneous Convolutional Network

Person re-identification (Re-ID) is a challenging task due to variations in pedestrian images, especially in cross-domain scenarios. The existing cross-domain person Re-ID approaches extract the feature from single pedestrian image, but they ignore the correlations among pedestrian images. In this paper, we propose Heterogeneous Convolutional Network (HCN) for cross-domain person Re-ID, which learns the appearance information of pedestrian images and the correlations among pedestrian images simultaneously. To this end, we first utilize Convolutional Neural Network (CNN) to extract the appearance features for pedestrian images. Then we construct a graph in the target dataset where the appearance features are treated as the nodes and the similarity represents the linkage between the nodes. Afterwards, we propose Dual Graph Convolution (DGConv) to explicitly learn the correlation information from the similar and dissimilar samples, which could avoid the over-smoothing caused by the fully connected graph. Furthermore, we design HCN as a multi-branch structure to mine the structural information of pedestrians. We conduct extensive evaluations for HCN on three datasets, i.e. Market-1501, DukeMTMC-reID and MSMT17, and the results demonstrate that HCN is superior to the state-of-the-art methods.
Explanations for CommonsenseQA: New Dataset and Models

CommonsenseQA (CQA) (Talmor et al., 2019) dataset was recently released to advance the research on common-sense question answering (QA) task.Whereas the prior work has mostly focused on proposing QA models for this dataset, our aim is to retrieve as well as generate explanation for a given (question, correct answer choice, incorrect answer choices) tuple from this dataset.Our explanation definition is based on certain desiderata, and translates an explanation into a set of positive and negative common-sense properties (aka facts) which not only explain the correct answer choice but also refute the incorrect ones.We human-annotate a first-ofits-kind dataset (called ECQA) of positive and negative properties, as well as free-flow explanations, for 11K QA pairs taken from the CQA dataset.We propose a latent representation based property retrieval model as well as a GPT-2 based property generation model with a novel two step fine-tuning procedure.We also propose a free-flow explanation generation model.Extensive experiments show that our retrieval model beats BM25 baseline by a relative gain of 100% in F 1 score, property generation model achieves a respectable F 1 score of 36.4,and free-flow generation model achieves a similarity score of 61.9, where last two scores are based on a human correlated semantic similarity metric.
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network

Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels. While GAN generated levels are stylistically similar to human-authored examples, human designers often want to explore the generative design space of GANs to extract interesting levels. However, human designers find latent vectors opaque and would rather explore along dimensions the designer specifies, such as number of enemies or obstacles. We propose using state-of-the-art quality diversity algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to efficiently explore the latent space of a GAN to extract levels that vary across a set of specified gameplay measures. In the benchmark domain of Super Mario Bros, we demonstrate how designers may specify gameplay measures to our system and extract high-quality (playable) levels with a diverse range of level mechanics, while still maintaining stylistic similarity to human authored examples. An online user study shows how the different mechanics of the automatically generated levels affect subjective ratings of their perceived difficulty and appearance.
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding

Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.Current methods have explored and utilized the graph structure, the entity names, and attributes, but ignore the ontology (or ontological schema) which contains critical meta information such as classes and their membership relationships with entities.In this paper, we propose an ontology-guided entity alignment method named OntoEA, where both KGs and their ontologies are jointly embedded, and the class hierarchy and the class disjointness are utilized to avoid false mappings.Extensive experiments on seven public and industrial benchmarks have demonstrated the state-ofthe-art performance of OntoEA and the effectiveness of the ontologies.
Dynamic Connected Networks for Chinese Spelling Check

Chinese spelling check (CSC) is a task to detect and correct spelling errors in Chinese text.Most state-of-the-art works on the CSC task adopt a BERT-based non-autoregressive language model, which relies on the output independence assumption.The inappropriate independence assumption prevents BERTbased models from learning the dependencies among target tokens, resulting in an incoherent problem.To address the above issue, we propose a novel architecture named Dynamic Connected Networks (DCN), which generates the candidate Chinese characters via a Pinyin Enhanced Candidate Generator and then utilizes an attention-based network to model the dependencies between two adjacent Chinese characters.The experimental results show that our proposed method achieves a new state-ofthe-art performance on three human-annotated datasets.
Unleash GPT-2 Power for Event Detection

Event Detection (ED) aims to recognize mentions of events (i.e., event triggers) and their types in text.Recently, several ED datasets in various domains have been proposed.However, the major limitation of these resources is the lack of enough training data for individual event types which hinders the efficient training of data-hungry deep learning models.To overcome this issue, we propose to exploit the powerful pre-trained language model GPT-2 to generate training samples for ED.To prevent the noises inevitable in automatically generated data from hampering training process, we propose to exploit a teacher-student architecture in which the teacher is supposed to learn anchor knowledge from the original data.The student is then trained on combination of the original and GPT-generated data while being led by the anchor knowledge from the teacher.Optimal transport is introduced to facilitate the anchor knowledge-based guidance between the two networks.We evaluate the proposed model on multiple ED benchmark datasets, gaining consistent improvement and establishing state-of-the-art results for ED.
Category Dictionary Guided Unsupervised Domain Adaptation for Object Detection

Unsupervised domain adaption (UDA) is a promising solution to enhance the generalization ability of a model from a source domain to a target domain without manually annotating labels for target data. Recent works in cross-domain object detection mostly resort to adversarial feature adaptation to match the marginal distributions of two domains. However, perfect feature alignment is hard to achieve and is likely to cause negative transfer due to the high complexity of object detection. In this paper, we propose a category dictionary guided (CDG) UDA model for cross-domain object detection, which learns category-specific dictionaries from the source domain to represent the candidate boxes in target domain. The representation residual can be used for not only pseudo label assignment but also quality (e.g., IoU) estimation of the candidate box. A residual weighted self-training paradigm is then developed to implicitly align source and target domains for detection model training. Compared with decision boundary based classifiers such as softmax, the proposed CDG scheme can select more informative and reliable pseudo-boxes. Experimental results on benchmark datasets show that the proposed CDG significantly exceeds the state-of-the-arts in cross-domain object detection.
Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search

The goal of person search is to localize and match query persons from scene images. For high efficiency, one-step methods have been developed to jointly handle the pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. One is the mutual interference between the optimization objectives of multiple sub-tasks. The other is the sub-optimal identification feature learning caused by small batch size when end-to-end training. To overcome these problems, we propose a decoupled and memory-reinforced network (DMRNet). Specifically, to reconcile the conflicts of multiple objectives, we simplify the standard tightly coupled pipelines and establish a deeply decoupled multi-task learning framework. Further, we build a memory-reinforced mechanism to boost the identification feature learning. By queuing the identification features of recently accessed instances into a memory bank, the mechanism augments the similarity pair construction for pairwise metric learning. For better encoding consistency of the stored features, a slow-moving average of the network is applied for extracting these features. In this way, the dual networks reinforce each other and converge to robust solution states. Experimentally, the proposed method obtains 93.2% and 46.9% mAP on CUHK-SYSU and PRW datasets, which exceeds all the existing one-step methods.
Attack as defense: characterizing adversarial examples using robustness

As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to adversarial attacks. Though various defense mechanisms have been proposed to improve robustness of deep learning software, many of them are ineffective against adaptive attacks. In this work, we propose a novel characterization to distinguish adversarial examples from benign ones based on the observation that adversarial examples are significantly less robust than benign ones. As existing robustness measurement does not scale to large networks, we propose a novel defense framework, named attack as defense (A2D), to detect adversarial examples by effectively evaluating an example’s robustness. A2D uses the cost of attacking an input for robustness evaluation and identifies those less robust examples as adversarial since less robust examples are easier to attack. Extensive experiment results on MNIST, CIFAR10 and ImageNet show that A2D is more effective than recent promising approaches. We also evaluate our defense against potential adaptive attacks and show that A2D is effective in defending carefully designed adaptive attacks, e.g., the attack success rate drops to 0% on CIFAR10.
Vision based solar tracking system for efficient energy harvesting

&lt;p&gt;Electricity is a major source of energy for fast growing population and the use of nonrenewable source is harmful for our environment. This reason belongs to devastating of environment, so it is required to take immediate action to solve these problems which result the solar energy development. Production of a solar energy can be maximizing if we use solar follower. The major part of solar panels is microcontroller with arrangement of LDR sensor is used to follow the sun, where the sensors is less efficient to track the sun because of the low sensitivity of LDR. We are proposing a method to track sun more effetely with the help of both LDR sensors and image processing. This type of mechanism can track sun with the help of image processing software which combines both result of sensors and processed sun image to control the solar panel. The combination of both software and hardware can control thousands of solar panels in solar power plants.&lt;/p&gt;
Bayesian Quantum Multiphase Estimation Algorithm

Quantum phase estimation (QPE) is the key subroutine of several quantum computing algorithms as well as a central ingredient in quantum computational chemistry and quantum simulation. While QPE strategies have focused on the estimation of a single phase, applications to the simultaneous estimation of several phases may bring substantial advantages; for instance, in the presence of spatial or temporal constraints. In this work, we study a Bayesian algorithm for the parallel (simultaneous) estimation of multiple arbitrary phases. The protocol gives access to correlations in the Bayesian multi-phase distribution resulting in covariance matrix elements scaling inversely proportional to the square of the total number of quantum resources. The parallel estimation allows to surpass the sensitivity of sequential single-phase estimation strategies for optimal linear combinations of phases. Furthermore, the algorithm proves a certain noise resilience and can be implemented using single photons and standard optical elements in currently accessible experiments.
Chaotic Image Encryption Algorithm Based on Zigzag Transform With Bidirectional Crossover From Random Position

This paper proposes a Zigzag transform that starts at random positions and crosses bidirectionally (ZZBCRP) to scramble the image. The number of sequence types of this new Zigzag transform is the number of pixels in the plaintext image, which is more and more complicated than other Zigzag transform methods, and Arrangement in a bidirectional crossover method can better disrupt the pixel correlation of the plaintext image. After multiple rounds of Zigzag transformation, the Logistic system is used to generate a sorted sequence to scramble the image. Then the chaotic sequence generated by the hyperchaotic Chen system is used. Perform a reversible diffusion operation on the image, and finally analyze the security of the algorithm.
The Impact of Teacher Training Using Mobile Augmented Reality Games on Their Professional Development

Ongoing technology progress sustains innovative teaching approaches. Mobile devices, augmented reality (AR), and games are a few of the new resources that teachers have at their disposal to promote student learning. However, their effective integration into practices requires training, so there is a need to analyze the impact of training initiatives on teacher professional development. A case study is being conducted on the development process of mobile AR games for Science, Technology, Engineering and Mathematics (STEM) learning by 14 Portuguese in-service teachers in a 50 h workshop. This contribution refers to the analysis of this training’s impact on teacher professional development through a questionnaire filled in at the beginning and end of the workshop. This study registered a higher impact on teachers’ understanding of AR educative use, the less-known approach, compared to mobile and game-based learning. Moreover, teachers became more experienced with these approaches as learners, and reported having explored them with their students during the workshop period. Teacher ability to identify benefits and barriers in these approaches increased with the workshop, particularly the learning that could be promoted with mobile AR games. The presented set of barriers to implementation is relevant to future teacher professional-development initiatives.
Practical One-Shot Federated Learning for Cross-Silo Setting

Federated learning enables multiple parties to collaboratively learn a model without exchanging their data. While most existing federated learning algorithms need many rounds to converge, one-shot federated learning (i.e., federated learning with a single communication round) is a promising approach to make federated learning applicable in cross-silo setting in practice. However, existing one-shot algorithms only support specific models and do not provide any privacy guarantees, which significantly limit the applications in practice. In this paper, we propose a practical one-shot federated learning algorithm named FedKT. By utilizing the knowledge transfer technique, FedKT can be applied to any classification models and can flexibly achieve differential privacy guarantees. Our experiments on various tasks show that FedKT can significantly outperform the other state-of-the-art federated learning algorithms with a single communication round.
Investigating data representation for efficient and reliable Convolutional Neural Networks

Nowadays, Convolutional Neural Networks (CNNs) are widely used as prediction models in different fields, with intensive use in real-time safety-critical systems. Recent studies have demonstrated that hardware faults induced by an external perturbation or aging effects, may significantly impact the CNN inference, leading to prediction failures. Therefore, ensuring the reliability of CNN platforms is crucial, especially when deployed in critical applications. A lot of effort has been made to reduce the memory and energy footprint of CNNs, paving the way to the adoption of approximate computing techniques such as quantization, reduced precision, weight sharing, and pruning. Unfortunately, approximate computing reduces the intrinsic redundancy of CNNs making them more efficient but less resilient to hardware faults. The goal of this work is twofold. First, we assess the reliability of a CNN when reduced bit widths and two different data types (floating- and fixed-point) are used to represent the network parameters (i.e., synaptic weights). Second, we intend to investigate the best compromise between data type, bit-widths reduction, and reliability. The characterization is performed through a fault injection environment built on the darknet open-source framework and targets two CNNs: LeNet-5 and YOLO. Experimental results show that fixed-point data provide the best trade-off between memory footprint reduction and CNN resilience. In particular, for LeNet-5, we achieved a 4X memory footprint reduction at the cost of a slightly reduced reliability (0.45% of critical faults) without retraining the CNN.
Automated Breast Cancer Detection and Classification in Full Field Digital Mammograms Using Two Full and Cropped Detection Paths Approach

Breast cancer is one of the most severe diseases that threaten women's life results in increasing the death rate annually as confirmed by the World Health Organization.Breast cancer early detection is one of the main reasons behind reducing cancer severity.However, with the huge number of mammograms taken daily, the checking process conducted by radiologists becomes lengthy, tiring, and pruning to errors process.Hence, with the tremendous success achieved by utilizing CNNs in bioinformatics, the development of Computer-Aided Detection (CAD) systems has proved its necessity to solve the challenging cases for the biopsies missed by the ordinary checking leads to decreasing the false positive and negative rates.In this paper, we present a YOLOV4 based CAD system to localize lesions in full and cropped mammograms and then classify them to obtain their pathology type.The proposed method mainly consists of three phases that are applied on the full-field digital mammograms of the INbreast dataset.First, the mammograms are preprocessed to remove any extra artifacts and then cropped into small, overlapped slices.Second, masses are localized through two paths: the full mammograms and the cropped slices detection after configuring the YOLO-V4 model.Third, other feature extractors like ResNet, VGG, Inception, etc. are used to classify the localized lesions to compare their performance against YOLO.The proposed method proved using the experimental results the impact of utilizing YOLO-V4 as a detector with the 2-paths of detection of a full mammogram and the cropped slices in a trial to avoid any data loss by resizing the large-sized mammograms.Our system succeeds in detecting the masses' location with an overall accuracy of ≈98% which is more than the recently introduced breast cancer detection methods.Moreover, its ability to distinguish between benign and malignant tumors with an accuracy of ≈95%.