Reviewerly/exHarmony huggingface dataset:
Selective-Supervised Contrastive Learning with Noisy Labels

Deep networks have strong capacities of embedding data into latent representations and finishing following tasks. However, the capacities largely come from high-quality annotated labels, which are expensive to collect. Noisy labels are more affordable, but result in corrupted representations, leading to poor generalization performance. To learn robust representations and handle noisy labels, we propose selective-supervised contrastive learning (Sel-CL) in this paper. Specifically, Sel-CL extend supervised contrastive learning (Sup-CL), which is powerful in representation learning, but is degraded when there are noisy labels. Sel-CL tackles the direct cause of the problem of Sup-CL. That is, as Sup-CL works in a pair-wise manner, noisy pairs built by noisy labels mislead representation learning. To alleviate the issue, we select confident pairs out of noisy ones for Sup-CL without knowing noise rates. In the selection process, by measuring the agreement between learned representations and given labels, we first identify confident examples that are exploited to build confident pairs. Then, the representation similarity distribution in the built confident pairs is exploited to identify more confident pairs out of noisy pairs. All obtained confident pairs are finally used for Sup-CL to enhance representations. Experiments on multiple noisy datasets demonstrate the robustness of the learned representations by our method, following the state-of-the-art performance. Source codes are available at https://github.com/ShikunLi/Sel-Cl.
Soft Tissue Feature Tracking Based on Deep Matching Network

Research in the field of medical image is an important part of the medical robot to operate human organs.A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results.In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed.This method is described based on the triangular matching algorithm.First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training, the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix, the inter-frame feature matching results can be obtained.In this way, the inter-frame speckle tracking is completed.On this basis, the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular, the accuracy of the MNIST handwritten data set has reached more than 90%.
Color Image Encryption Through Chaos and KAA Map

The unprecedented growth in production and exchange of multimedia over unsecured channels is overwhelming mathematicians, scientists and engineers to realize secure and efficient cryptographic algorithms. In this paper, a color image encryption algorithm combining the KAA map with multiple chaotic maps is proposed. The proposed algorithm makes full use of Shannon’s ideas of security, such that image encryption is carried out through bit confusion and diffusion. Confusion is carried out through employing 2 encryption keys. The first key is generated from the 2D Logistic Sine map and a Linear Congruential Generator, while the second key is generated from the Tent map and the Bernoulli map. Diffusion is attained through the use of the KAA map. An elaborate mathematical analysis is carried out to showcase the robustness and efficiency of the proposed algorithm, as well as its resistance to visual, statistical, differential and brute-force attacks. Moreover, the proposed image encryption algorithm is also shown to successfully pass all the tests of the NIST SP 800 suite.
Cross-view enhancement network for underwater images

Single underwater image enhancement remains a challenging ill-posed problem, even with advanced deep learning methods, due to the significant information degeneration and various irrelevant contents. Current deep learning-based underwater image enhancement methods only consider using a single clear image as a positive feature for guiding the training of the enhancement network. However, the limited amount of helpful information constrains the network performance, and irrelevant contents consume many bits. Therefore, it is crucial to efficiently utilize cross-view neighboring features and provide corresponding relevant information for underwater enhancement. To address the challenges of degraded underwater images, we propose a novel cross-domain enhancement network (CVE-Net) that uses high-efficiency feature alignment to utilize neighboring features better. We employ a self-built database to optimize the helpful information and develop a feature alignment module (FAM) to adapt the temporal features. The dual-branch attention block is designed to handle different types of information and give more weight to essential features. Experiments demonstrate that CVE-Net outperforms state-of-the-art (SOTA) underwater vision enhancement methods in terms of both qualitatively and quantitatively results, significantly boosts the performance on underwater image quality, achieving a PSNR of 28.28 dB, which is 25% higher than Ucolor on the multi-view dataset. CVE-Net improves image quality while maintaining a good complexity-performance trade-off.
Parameter-efficient fine-tuning of large-scale pre-trained language models

Abstract With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and eventually becomes practically infeasible. This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, which optimizes a small portion of the model parameters while keeping the rest fixed, drastically cutting down computation and storage costs. In general, it demonstrates that large-scale models could be effectively stimulated by the optimization of a few parameters. Despite the various designs, here we discuss and analyse the approaches under a more consistent and accessible term ‘delta-tuning’, where ‘delta’ a mathematical notation often used to denote changes, is borrowed to refer to the portion of parameters that are ‘changed’ during training. We formally describe the problem and propose a unified categorization criterion for existing delta-tuning methods to explore their correlations and differences. We also discuss the theoretical principles underlying the effectiveness of delta-tuning and interpret them from the perspectives of optimization and optimal control. Furthermore, we provide a holistic empirical study on over 100 natural language processing tasks and investigate various aspects of delta-tuning. With comprehensive study and analysis, our research demonstrates the theoretical and practical properties of delta-tuning in the adaptation of PLMs.
APMSA: Adversarial Perturbation Against Model Stealing Attacks

Training a Deep Learning (DL) model requires proprietary data and computing-intensive resources. To recoup their training costs, a model provider can monetize DL models through Machine Learning as a Service (MLaaS). Generally, the model is deployed at the cloud, while providing a publicly accessible Application Programming Interface (API) for paid queries to obtain benefits. However, model stealing attacks have posed security threats to this model monetizing scheme as they steal the model without paying for future extensive queries. Specifically, an adversary queries a targeted model to obtain input-output pairs and thus infer the model’s internal working mechanism by reverse-engineering a substitute model, which has deprived model owner’s business advantage and leaked the privacy of the model. In this work, we observe that the confidence vector or the top-1 confidence returned from the model under attack (MUA) varies in a relative large degree given different queried inputs. Therefore, rich internal information of the MUA is leaked to the attacker that facilities her reconstruction of a substitute model. We thus propose to leverage adversarial confidence perturbation to hide such varied confidence distribution given different queries, consequentially against model stealing attacks (dubbed as APMSA). In other words, the confidence vectors returned now is similar for queries from a specific category, considerably reducing information leakage of the MUA. To achieve this objective, through automated optimization, we constructively add delicate noise into per input query to make its confidence close to the decision boundary of the MUA. Generally, this process is achieved in a similar means of crafting adversarial examples but with a distinction that the hard label is preserved to be the same as the queried input. This retains the inference utility (i.e., without sacrificing the inference accuracy) for normal users but bounded the leaked confidence information to the attacker in a small constrained area (i.e., close to decision boundary). The later renders greatly deteriorated accuracy of the attacker’s substitute model. As the APMSA serves as a plug-in front-end and requires no change to the MUA, it is thus generic and easy to deploy. The high efficacy of APMSA is validated through experiments on datasets of CIFAR10 and GTSRB. Given a MUA model of ResNet-18 on the CIFAR10, our defense can degrade the accuracy of the stolen model by up to 15% (rendering the stolen model useless to a large extent) with 0% accuracy drop for normal user’s hard-label inference request.
AI chatbots not yet ready for clinical use

As large language models (LLMs) expand and become more advanced, so do the natural language processing capabilities of conversational AI, or "chatbots". OpenAI's recent release, ChatGPT, uses a transformer-based model to enable human-like text generation and question-answering on general domain knowledge, while a healthcare-specific Large Language Model (LLM) such as GatorTron has focused on the real-world healthcare domain knowledge. As LLMs advance to achieve near human-level performances on medical question and answering benchmarks, it is probable that Conversational AI will soon be developed for use in healthcare. In this article we discuss the potential and compare the performance of two different approaches to generative pretrained transformers-ChatGPT, the most widely used general conversational LLM, and Foresight, a GPT (generative pretrained transformer) based model focused on modelling patients and disorders. The comparison is conducted on the task of forecasting relevant diagnoses based on clinical vignettes. We also discuss important considerations and limitations of transformer-based chatbots for clinical use.
A Scenario-Generic Neural Machine Translation Data Augmentation Method

Amid the rapid advancement of neural machine translation, the challenge of data sparsity has been a major obstacle. To address this issue, this study proposes a general data augmentation technique for various scenarios. It examines the predicament of parallel corpora diversity and high quality in both rich- and low-resource settings, and integrates the low-frequency word substitution method and reverse translation approach for complementary benefits. Additionally, this method improves the pseudo-parallel corpus generated by the reverse translation method by substituting low-frequency words and includes a grammar error correction module to reduce grammatical errors in low-resource scenarios. The experimental data are partitioned into rich- and low-resource scenarios at a 10:1 ratio. It verifies the necessity of grammatical error correction for pseudo-corpus in low-resource scenarios. Models and methods are chosen from the backbone network and related literature for comparative experiments. The experimental findings demonstrate that the data augmentation approach proposed in this study is suitable for both rich- and low-resource scenarios and is effective in enhancing the training corpus to improve the performance of translation tasks.
Investigating innovation capability and organizational performance in service firms

Abstract In the service firm, innovation capability influences both financial and nonfinancial performance. It is crucial for businesses to address key challenges anticipated by the changes in socioeconomic and environmental issues. Innovation capability is largely seen as a vital source for generating sustainable competitive advantage. This article investigates the determinants of innovation capability and their relationship with organizational performance in the Jordanian banking sector.
Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control.
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification

Jiawei Wu, Wenhan Xiong, William Yang Wang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Robust Unscented Kalman Filtering With Measurement Error Detection for Tightly Coupled INS/GNSS Integration in Hypersonic Vehicle Navigation

Due to the high maneuverability of a hypersonic vehicle, the measurements for tightly coupled INS/GNSS (inertial navigation system/global navigation satellite system) integration system inevitably involve errors.The typical measurement errors include outliers in pseudorange observations and non-Gaussian noise distribution.This paper focuses on the nonlinear state estimation problem in hypersonic vehicle navigation.It presents a new innovation orthogonality-based robust unscented Kalman filter (IO-RUKF) to resist the disturbance of measurement errors on navigation performance.This IO-RUKF detects measurement errors by use of the hypothesis test theory.Subsequently, it introduces a defined robust factor to inflate the covariance of predicted measurement and further rescale the Kalman gain such that the measurements in error are less weighted to ensure the filtering robustness against measurement errors.The proposed IO-RUKF can not only correct the UKF sensitivity to measurement errors, but also avoids the loss of accuracy for state estimation in the absence of measurement errors.The efficacy and superiority of the proposed IO-RUKF have been verified through simulations and comparison analysis.
SPARQA: Skeleton-Based Semantic Parsing for Complex Questions over Knowledge Bases

Semantic parsing transforms a natural language question into a formal query over a knowledge base. Many existing methods rely on syntactic parsing like dependencies. However, the accuracy of producing such expressive formalisms is not satisfying on long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing. Besides, to align the structure of a question with the structure of a knowledge base, our multi-strategy method combines sentence-level and word-level semantics. Our approach shows promising performance on several datasets.
SOM-Hunter: Video Browsing with Relevance-to-SOM Feedback Loop

This paper presents a prototype video retrieval engine focusing on a simple known-item search workflow, where users initialize the search with a query and then use an iterative approach to explore a larger candidate set. Specifically, users gradually observe a sequence of displays and provide feedback to the system. The displays are dynamically created by a self organizing map that employs the scores based on the collected feedback, in order to provide a display matching the user preferences. In addition, users can inspect various other types of specialized displays for exploitation purposes, once promising candidates are found.
Application of Blockchain to Maintaining Patient Records in Electronic Health Record for Enhanced Privacy, Scalability, and Availability

Objectives Electronic Health Record (EHR) systems are increasingly used as an effective method to share patients' records among different hospitals. However, it is still a challenge to access scattered patient data through multiple EHRs. Our goal is to build a system to access patient records easily among EHRs without relying on a centralized supervisory system. Methods We apply consortium blockchain to compose a distributed system using Hyperledger Fabric incorporating existent EHRs. Peer nodes hold the same ledger on which the address of a patient record in an EHR is written. Individual patients are identified by unique certificates issued by a local certificate authorities that collaborate with each other in a channel of the network. To protect a patient's privacy, we use a proxy re-encryption scheme when the data are transferred. We designed and implemented various chaincodes to handle business logic agreed by member organizations of the network. Results We developed a prototype system to implement our concept and tested its performance including chaincode logic. The results demonstrated that our system can be used by doctors to find patient's records and verify patient's consent on access to the data. Patients also can seamlessly receive their past records from other hospitals. The access log is stored transparently and immutably in the ledger that is used for auditing purpose. Conclusions Our system is feasible and flexible with scalability and availability in adapting to existing EHRs for strengthening security and privacy in managing patient records. Our research is expected to provide an effective method to integrate dispersed patient records among medical institutions. Keywords: Health Information Exchanges, Electronic Health Records, Patient Data Privacy, Computer Security, Decentralization
Overcoming Language Priors with Self-supervised Learning for Visual Question Answering

Most Visual Question Answering (VQA) models suffer from the language prior problem, which is caused by inherent data biases. Specifically, VQA models tend to answer questions (e.g., what color is the banana?) based on the high-frequency answers (e.g., yellow) ignoring image contents. Existing approaches tackle this problem by creating delicate models or introducing additional visual annotations to reduce question dependency and strengthen image dependency. However, they are still subject to the language prior problem since the data biases have not been fundamentally addressed. In this paper, we introduce a self-supervised learning framework to solve this problem. Concretely, we first automatically generate labeled data to balance the biased data, and then propose a self-supervised auxiliary task to utilize the balanced data to assist the VQA model to overcome language priors. Our method can compensate for the data biases by generating balanced data without introducing external annotations. Experimental results show that our method achieves state-of-the-art performance, improving the overall accuracy from 49.50% to 57.59% on the most commonly used benchmark VQA-CP v2. In other words, we can increase the performance of annotation-based methods by 16% without using external annotations. Our code is available on GitHub.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.
Low-Light Image Enhancement With Regularized Illumination Optimization and Deep Noise Suppression

Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this article, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB. The adaptive gamma correction method is then introduced to adjust the refined illumination map. Based on the assumption of Retinex theory, a guided filter-based detail boosting method is introduced to optimize the reflection map. The adjusted illumination and optimized reflection maps are finally combined to generate the enhanced maritime images. To suppress the effect of unwanted noise on imaging performance, a deep learning-based blind denoising framework is further introduced to promote the visual quality of enhanced image. In particular, this framework is composed of two sub-networks, i.e., E-Net and D-Net adopted for noise level estimation and non-blind noise reduction, respectively. The main benefit of our image enhancement method is that it takes full advantage of the regularized illumination optimization and deep blind denoising. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods. Experimental results have illustrated its superior performance in terms of both quantitative and qualitative evaluations.