Reviewerly/exHarmony huggingface dataset:
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation

Although showing promising values to downstream applications, generating question and answer together is under-explored.In this paper, we introduce a novel task that targets question-answer pair generation from visual images.It requires not only generating diverse question-answer pairs but also keeping the consistency of them.We study different generation paradigms for this task and propose three models: the pipeline model, the joint model, and the sequential model.We integrate variational inference into these models to achieve diversity and consistency.We also propose region representation scaling and attention alignment to improve the consistency further.We finally devise an evaluator as a quantitative metric for consistency.We validate our approach on two benchmarks, VQA2.0 and Visual-7w, by automatically and manually evaluating diversity and consistency.Experimental results show the effectiveness of our models: they can generate diverse or consistent pairs.Moreover, this task can be used to improve visual question generation and visual question answering.
Experimental verification of the relationship between first-order coherence and linear steerability

Coherence and steerability are two essential characteristics of quantum systems. For a two-qubit state, the first-order coherence and the maximal violation of linear steering inequality are used to operationally measure the degree of coherence and steerability, respectively. Recently, a complementary relation between first-order coherence and linear steerability has been proposed. In this paper, we report an experimental verification of the complementary relation by preparing biphoton polarization entangled states in an all-optical setup. We propose an operable method for experimental measurement of the first-order coherence and linear steerability and calculate the purity of the initial states by reconstructing the density matrices of them. The experimental results coincide with the theoretical predictions very well, which provides a valuable reference for the application of optical quantum technology.
Knowledge Representation Learning with Contrastive Completion Coding

Knowledge representation learning (KRL) has been used in plenty of knowledge-driven tasks.Despite fruitfully progress, existing methods still suffer from the immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training, both of which would hinder the performance of KRL.In this paper, we propose Contrastive Completion Coding (C 3 ), a novel KRL framework that is composed of two functional components: 1. Hierarchical Architecture, which integrates both low-level standalone features and high-level topology-aware features to yield robust embedding for each entity/relation.2. Normalized Contrasitive Training, which conducts normalized one-tomany contrasitive learning to emphasize different negatives with different weights, delivering better convergence compared to conventional training losses.Extensive experiments on several benchmarks verify the efficacy of the two proposed techniques and combing them together generally achieves superior performance against state-of-the-art approaches.
Instance-adaptive training with noise-robust losses against noisy labels

In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data.However, model training with such datasets poses a challenge because popular optimization objectives are not robust to label noise induced in the annotation generation process.Several noise-robust losses have been proposed and evaluated on tasks in computer vision, but they generally use a single dataset-wise hyperparamter to control the strength of noise resistance.This work proposes novel instance-adaptive training frameworks to change dataset-wise hyperparameters of noise resistance in such losses to be instance-specific.Such instance-specific noise resistance hyperparameters are predicted by special instance-level label quality predictors, which are trained along with the main models.Experiments on noisy and corrupted NLP datasets show that proposed instance-adaptive training frameworks help increase the noiserobustness provided by such losses, promoting the use of the frameworks and associated losses in training NLP models with noisy data.
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling

Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks.However, previous works commonly have difficulty dealing with large-scale dynamic lexicons which often cause excessive matching noise and problems of frequent updates.In this paper, we propose DyLex, a plug-in lexicon incorporation approach for BERT based sequence labeling tasks.Instead of leveraging embeddings of words in the lexicon as in conventional methods, we adopt word-agnostic tag embeddings to avoid re-training the representation while updating the lexicon.Moreover, we employ an effective supervised lexical knowledge denoising method to smooth out matching noise.Finally, we introduce a col-wise attention based knowledge fusion mechanism to guarantee the pluggability of the proposed framework.Experiments on ten datasets of three tasks show that the proposed framework achieves new SOTA, even with very large scale lexicons 1 .
Think Before You Speak: Learning to Generate Implicit Knowledge for Response Generation by Self-Talk

Pei Zhou, Behnam Hedayatnia, Karthik Gopalakrishnan, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur. Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI. 2021.
Self-Supervised Quality Estimation for Machine Translation

Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT.Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and laborintensive to obtain.To address the issue of the absence of annotated training data, previous studies attempt to develop unsupervised QE methods.However, very few of them can be applied to both sentence-and word-level QE tasks, and they may suffer from noises in the synthetic data.To reduce the negative impact of noises, we propose a self-supervised method for both sentence-and word-level QE, which performs quality estimation by recovering the masked target words.Experimental results show that our method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.
Robust low-rank tensor reconstruction using high-order t-SVD

Currently, robust low-rank tensor reconstruction based on tensor singular value decomposition (t-SVD) has made remarkable achievements in the fields of computer vision, image processing, etc. However, existing works mainly concentrate on third-order tensors while order-d (d ≥ 4) tensors are commonly encountered in practical applications, such as fourth-order color videos, fourth-order facial images, fifth-order light-field images and sixth-order bidirectional texture functions. Aiming at this critical issue, we establish a robust order-d tensor reconstruction framework including the model, algorithm and theory by developing a novel algebraic foundation of order-d t-SVD with any invertible linear transforms. Equipped with the newly developed framework, any order-d low-rank tensors can be accurately reconstructed from a sample of its entries corrupted by arbitrary outliers. The superiority and effectiveness of the proposed framework are substantiated in comparison with state-of-the-art alternatives by conducting experiments on both synthetic and real-world tensor data.
A Novel Method for Suitable Hyperparameter Selection in an Accurate Convolutional Neural Network Architecture

The deep convolutional neural network (CNN) models are of great use in many areas and applications such as image processing and computer vision. The hyperparameter optimization in the CNN architectures is essential for an efficient implementation of model on software or hardware or “software-hardware co-design” platform to achieve better characteristics. In this paper, we have proposed CNN architecture models trained using MNIST dataset that explores the selection of various hyperparameters and their impact on the accuracy to achieve the hyperparameter optimization. The work presents thorough evaluation of various hyperparameters which offers a higher accuracy and keeps the architecture simple as compared with other published results.
Verifying QUIC implementations using Ivy

QUIC is a new transport protocol combining the reliability and congestion control features of TCP with the security features of TLS. One of the main challenges with QUIC is to guarantee that any of its implementation follows the IETF specification. This challenge is particularly appealing as the specification is written in textual language, and hence may contain ambiguities. In a recent work, McMillan and Zuck proposed a formal representation of part of draft-18 of the IETF specification. They also showed that this representation made it possible to efficiently generate tests to stress four implementations of QUIC. Our first contribution is to complete and extend the formal representation from draft-18 to draft-29. Our second contribution is to test seven implementations of both QUIC client and server. Our last contribution is to show that our tool can highlight ambiguities in the QUIC specification, for which we suggest paths to corrections.
Lay Evaluations of Restraints and Prohibitions During the 1st Wave of the COVID-19 Pandemic in Lithuania

The article analyzes how, during the 1st wave of the coronavirus pandemic, participants of a study (n = 331) assessed the government-imposed restrictions, prohibitions, liabilities and penalties for non-compliance with said restrictions, and the perceived fairness of the behavior of other people during the pandemic. The article also studies the relationships among these assessments, including the related well-being and sociodemographic characteristics.
End to End Unsupervised Rigid Medical Image Registration by Using Convolutional Neural Networks

In this paper, we focus on the issue of rigid medical image registration using deep learning. Under ultrasound, the moving of some organs, e.g., liver and kidney, can be modeled as rigid motion. Therefore, when the ultrasound probe keeps stationary, the registration between frames can be modeled as rigid registration. We propose an unsupervised method with Convolutional Neural Networks. The network estimates from the input image pair the transform parameters first then the moving image is wrapped using the parameters. The loss is calculated between the registered image and the fixed image. Experiments on ultrasound data of kidney and liver verified that the method is capable of achieve higher accuracy compared with traditional methods and is much faster.
Disease Modeling with a Forest Deep Neural Network Utilizing NLP and a Virtualized Clinical Semantic Network

We present a novel classifier, the Forest Deep Neural Network (fDNN), and apply it to disease modeling. The fDNN architecture leverages a supervised forest for feature detection, enabling the algorithm to build complex neural networks with a large number of independent features from sparse feature representations while limiting overfitting. We apply the fDNN to model influenza—more specifically, to predict the occurrence of influenza in a cohort of anonymized real-world patients, aided by Natural Language Processing (NLP) and a virtualized Clinical Semantic Network (vCSN). We report accuracy rates in the 90% range, which is extremely high for the problems of disease modeling and applications of NLP.
Applications of Machine Learning in Quantum Key Distribution Networks

Quantum key distribution (QKD) technology can achieve information theoretical secure with one-time pad encryption. Machine learning (ML) provides systems the ability to automatically learn and improve from experience without being explicitly programmed. There are some applications of ML in telecommunication networks, such as traffic prediction and fault prediction. There is increasing interest in the field in applying ML to improve the performance of quantum communication networks. In this talk, I will introduce the applications of machine learning (ML) in quantum key distribution networks (QKDNs), including the applications in quantum layer, key management layer, and control and management layers.
SM9-based Traceable and Accountable Access Control for Secure Multi-user Cloud Storage

Access control, as one of flagship security mechanisms for cloud storage, allows authorized users' access right while repels unauthorized behaviors. State-of-the-art cryptographic access control systems are deployed on attribute-based encryption or identity-based encryption. They commonly inherit the key escrow problem, which incurs that pirate's untraceability. Meanwhile, with announcement of cryptographic industry standards, kinds of cryptographic algorithms according with these standards have better industrial applications. In this paper, we design SM9-based Traceable and Accountable Access Control (TA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C) to support pirate traceability and accountability. Built on top of identity-based broadcast encryption and SM9 specification, we present an SM9-based TA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C construction, which is provably secure in the indistinguishability and traceability security models. We also implement an SM9-based TA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C prototype system that supports 100 users and evaluation results show that it just needs about 1 second for encryption/decryption and tracing operations on a workstation with basic configuration.
Research on the Application of Computer Big Data Technology in Cloud Storage Security

In view of the continuous progress of current science and technology, cloud computing has been widely used in various fields. This paper proposes a secure data storage architecture based on cloud computing. The architecture studies the security issues of cloud computing from two aspects: data storage and data security, and proposes a data storage mode based on Cache and a data security mode based on third-party authentication, thereby improving the availability of data, from data storage to transmission. Corresponding protection measures have been established to realize effective protection of cloud data.
Architecture Scheme of DevOps for Cross Network and Multiple Environment Collaboration

In the process of traditional enterprises exploring the middle platform architecture, DevOps is the cornerstone of its digital platform's long-term iterative construction and sustainable operation and maintenance. Aiming at the problems of physical isolation of multiple environments and cross regional collaboration of R&D teams faced by high security applications, based on the systematic analysis of DevOps concept, support tools and adoption situation, and based on the DevOps standard, this paper designs a DevOps architecture scheme for internal and external collaboration within cross-network multiple environments.
Refining Privacy-Aware Data Flow Diagrams

Privacy, like security, is a non-functional property, yet most software design tools are focused on functional aspects, using for instance Data Flow Diagrams (DFDs). In previous work, a conceptual model was introduced where DFDs were extended into so-called Privacy-Aware Data Flow Diagrams (PA-DFDs) with the aim of adding specific privacy checks to existing DFDs. An implementation to add such automatic checks has also been developed. In this paper, we define the notion of refinement for both DFDs and PA-DFDs as a special type of structure-preserving map (or graph homomorphism). We also provide three algorithms to find, check and transform refinements, and we show that the standard diagram “transform\(\rightarrow \)refine/refine\(\rightarrow \)transform” commutes. We have implemented our algorithms in a proof-of-concept tool called DFD Refinery, and have applied it to realistic scenarios.
Modeling Entity Knowledge for Fact Verification

Fact verification is a challenging task of identifying the truthfulness of given claims based on the retrieval of relevant evidence texts.Many claims require understanding and reasoning over external entity information for precise verification.In this paper, we propose a novel fact verification model using entity knowledge to enhance its performance.We retrieve descriptive text from Wikipedia for each entity, and then encode these descriptions by a smaller lightweight network to be fed into the main verification model.Furthermore, we boost model performance by adopting and predicting the relatedness between the claim and each evidence as additional signals.We demonstrate experimentally on a large-scale benchmark dataset FEVER that our framework achieves competitive results with a FEVER score of 72.89% on the test set.
Image Recognition of Standard Actions in Sports Videos Based on Feature Fusion

Standard actions are crucial to sports training of athletes and daily exercise of ordinary people. There are two key issues in sports action recognition: the extraction of sports action features, and the classification of sports actions. The existing action recognition algorithms cannot work effectively on sports competitions, which feature high complexity, fine class granularity, and fast action speed. To solve the problem, this paper develops an image recognition method of standard actions in sports videos, which merges local and global features. Firstly, the authors combed through the functions and performance required for the recognition of standard actions of sports, and proposed an attention-based local feature extraction algorithm for the frames of sports match videos. Next, a sampling algorithm was developed based on time-space compression, and a standard sports action recognition algorithm was designed based on time-space feature fusion, with the aim to fuse the time-space features of the standard actions in sports match videos, and to overcome the underfitting problem of direct fusion of time-space features extracted by the attention mechanism. The workflow of these algorithms was explained in details. Experimental results confirm the effectiveness of our approach.
On Building Users’ Initial Trust in Autonomous Vehicles

With a high degree of uncertainty, taxi service companies begin incorporating autonomous vehicles in their transportation services. The authors examine the factors that influence users’ initial trust in autonomous vehicles. They extend the initial trust model and validate the model in the context of an emerging technology. The initial trust model examines the influence of trust base factors (representing cognitive, personality, calculative, and institutional categories) and subjective norms on trusting beliefs, trusting attitude, and trusting intention. Findings indicate that faith in intelligent machines, calculative cost/benefit perception, perceived societal benefits, organizational situation normality, and technology structural assurance are potential indicators of users’ trusting beliefs in this context. The paper contributes to the theory and practice by extending the initial trust model and examining trusting factors in the context of an emerging technology that requires understanding users’ mentality before launching autonomous vehicles on a wide scale.
Multi-Head Deep Metric Learning Using Global and Local Representations

Deep Metric Learning (DML) models often require strong local and global representations, however, effective integration of local and global features in DML model training is a challenge. DML models are often trained with specific loss functions, including pairwise-based and proxy-based losses. The pairwise-based loss functions leverage rich semantic relations among data points, however, they often suffer from slow convergence during DML model training. On the other hand, the proxy-based loss functions often lead to significant speedups in convergence during training, while the rich relations among data points are often not fully explored by the proxy-based losses. In this paper, we propose a novel DML approach to address these challenges. The proposed DML approach makes use of a hybrid loss by integrating the pairwise-based and the proxy-based loss functions to leverage rich data-to-data relations as well as fast convergence. Furthermore, the proposed DML approach utilizes both global and local features to obtain rich representations in DML model training. Finally, we also use the second-order attention for feature enhancement to improve accurate and efficient retrieval. In our experiments, we extensively evaluated the proposed DML approach on four public benchmarks, and the experimental results demonstrate that the proposed method achieved state-of-the-art performance on all benchmarks.
CMSuG: Competitive mechanism-based superpixel generation method for image segmentation

During the last years, object-based image segmentation (OBIA) has seen a considerable increase in the image segmentation. OBIA is generally based on superpixel methods, in which the clustering-based method plays an increasingly important role. Most clustering methods for generating superpixels suffer from inaccurate classification points with inappropriate cluster centers. To solve the problem, we propose a competitive mechanism-based superpixel generation (CMSuG) method, which both accelerates convergence and promotes robustness for noise sensitivity. Then, image segmentation results will be obtained by a region adjacent graph (RAG)-based merging algorithm after constructing an RAG. However, high segmentation accuracy is customarily accompanied by expensive time-consuming costs. To improve computational efficiency, we address a parallel CMSuG algorithm, the time of which is much less than the CMSuG method. In addition, we present a parallel RAG method to decrease the expensive time-consuming cost in serial RAG construction. By leveraging parallel techniques, the running time of the whole image segmentation method decline with the time complexity from O (N) + O (K2) to O (N/K) or O (K2), in which N is the size of an input image and K is the given number of the superpixel. In the experiments, both nature image and remote sensing image segmentation results demonstrate that our CMSuG method outperforms the state-of-the-art superpixel generation methods, and then performs well for image segmentation in turn. Compared with the serial segmentation method, our parallel techniques gain more than four times acceleration in both remote sensing image dataset and nature image dataset.
GradMask

We present GradMask, a simple adversarial example detection scheme for natural language processing (NLP) models. It uses gradient signals to detect adversarially perturbed tokens in an input sequence and occludes such tokens by a masking process. GradMask provides several advantages over existing methods including improved detection performance and an interpretation of its decision with a only moderate computational cost. Its approximated inference cost is no more than a single forward- and back-propagation through the target model without requiring any additional detection module. Extensive evaluation on widely adopted NLP benchmark datasets demonstrates the efficiency and effectiveness of GradMask. Code and models are available at https://github.com/Han8931/grad_mask_detection