Reviewerly/exHarmony huggingface dataset:
Global-Local Label Correlation for Partial Multi-Label Learning

Partial Multi-label Learning (PML) addresses the scenario where each instance is assigned with multiple candidate labels, while only a subset of the labels are relevant. This task is very challenging because the training procedure can be misguided by the noisy (irrelevant) labels. Exploiting label correlations is useful for partial multi-label learning. However, the existing PML methods often ignore to explicitly and sufficiently leverage the label correlation information for handling the noisy labels. To this end, in this paper, we propose a novel <i>Global-Local Label Correlation (GLC) approach for partial multi-label learning</i>. On one hand, we introduce a label coefficient matrix to explicitly exploit the global structure information of labels from multiple subspaces. On the other hand, we present a new label manifold regularizer to capture the local label correlations to further improve the performance of our method. By jointly taking advantage of the global and local label correlations, our proposed approach achieves superior performance on both the synthetic and real-world data sets from diverse domains.
Few-Shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition

Image modality recognition is essential for efficient imaging workflows in current clinical environments, where multiple imaging modalities are used to better comprehend complex diseases. Emerging biomarkers from novel, rare modalities are being developed to aid in such understanding, however the availability of these images is often limited. This scenario raises the necessity of recognising new imaging modalities without them being collected and annotated in large amounts. In this work, we present a few-shot learning model for limited training examples based on Deep Triplet Networks. We show that the proposed model is more accurate in distinguishing different modalities than a traditional Convolutional Neural Network classifier when limited samples are available. Furthermore, we evaluate the performance of both classifiers when presented with noisy samples and provide an initial inspection of how the proposed model can incorporate measures of uncertainty to be more robust against out-of-sample examples.
Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism

The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes.Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task.However, existing works either ignore the long-tail of code frequency or the noisy clinical notes.To address the above issues, we propose an Interactive Shared Representation Network with Self-Distillation mechanism.Specifically, an interactive shared representation network targets building connections among codes while modeling the cooccurrence, consequently alleviating the longtail problem.Moreover, to cope with the noisy text issue, we encourage the model to focus on the clinical note's noteworthy part and extract valuable information through a self-distillation learning mechanism.Experimental results on two MIMIC datasets demonstrate the effectiveness of our method.
Using machine‐learning methods to support health‐care professionals in making admission decisions

Summary Background Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health‐care professional ranking patients by analyzing registration information. Objective The purpose of this study was to develop a machine‐learning approach to screening, using historical data and the experience of health‐care professionals to develop a set of screening rules to help health‐care professionals prioritize patient needs automatically. Methods We used five machine‐learning methods to sequence and predict elective patients: logistic regression (LR), random forest (RF), gradient‐boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and an ensemble model of the four models. Results The results indicate that all of the five models showed a good prioritization performance with high predictive values. In particular, XGBoost had the best predictive performance compared with others in terms of the area under the receiver operating characteristic curve (AUC), with the AUC values of LR, RF, GBDT, XGBoost, and the ensemble model being 0.881, 0.816, 0.820, 0.901, and 0.897, respectively. Conclusion The results reported here indicate that machine‐learning techniques can be valuable for automating the screening process. Our model can assist health‐care professionals in automatically evaluating less complex cases by identifying important factors affecting patient admission.
Group based Personalized Search by Integrating Search Behaviour and Friend Network

The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were proposed to incorporate the profiles of similar users when re-ranking the results. However, similar users are mostly found based on simple lexical or topical similarity in search behaviours. In this paper, we propose a neural network enhanced method to highlight similar users in semantic space. Furthermore, we argue that the behaviour-based similar users are still insufficient to understand a new query when user's historical activities are limited. To tackle this issue, we introduce the friend network into personalized search to determine the closeness between users in another way. Since the friendship is often formed based on similar background or interest, there are plenty of personalized signals hidden in the friend network naturally. Specifically, we propose a friend network enhanced personalized search model, which groups the user into multiple friend circles based on search behaviours and friend relations respectively. These two types of friend circles are complementary to construct a more comprehensive group profile for refining the personalization. Experimental results show the significant improvement of our model over existing personalized search models.
Multi-Tier Stack of Block Chain with Proxy Re-Encryption Method Scheme on the Internet of Things Platform

Block chain provides an innovative solution to information storage, transaction execution, security, and trust building in an open environment. The block chain is technological progress for cyber security and cryptography, with efficiency-related cases varying in smart grids, smart contracts, over the IoT, etc. The movement to exchange data on a server has massively increased with the introduction of the Internet of Things. Hence, in this research, Splitting of proxy re-encryption method (Split-PRE) has been suggested based on the IoT to improve security and privacy in a private block chain. This study proposes a block chain-based proxy re-encryption program to resolve both the trust and scalability problems and to simplify the transactions. After encryption, the system saves the Internet of Things data in a distributed cloud. The framework offers dynamic, smart contracts between the sensor and the device user without the intervention of a trustworthy third party to exchange the captured IoT data. It uses an efficient proxy re-encryption system, which provides the owner and the person existing in the smart contract to see the data. The experimental outcomes show that the proposed approach enhances the efficiency, security, privacy, and feasibility of the system when compared to other existing methods.
Integrating Learning and Reasoning with Deep Logic Models

Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning.
Virtual Reality Based Maintenance Training Effectiveness Measures – a Novel Approach for Rail Industry

This paper investigates engineering education and technical training optimization techniques based on augmented and virtual reality (AVR) technologies. Different learning methods are discussed in detail, with respect to identified enhanced learning outcomes such as higher engagement level, knowledge retention, and applied practical competency. Integration of AVR technology has a proven potential to elevate engagement and memory retention capacity according to the distinguished literature discussed in this paper. The primary objective of this research is establishing an evaluation framework that effectively measure the learning experience. Two experimental groups of novice trainee students were exposed to a traditional, class room based training program and virtual reality (VR) based training program. Both programs deliver a maintenance training activity of a chosen mechanism (sub-system), which will be used as preliminary results. An evaluation procedure was developed to assess each trainee to gather important data such as time consumption, accuracy, aptitude, performance level and intuition. A mathematical model was formulated to assess and index the individuals from the collected data, to establish their individual cognitive and psychomotor skill index (CPSI). This numerical rating allows assessment of workers over various tasks, and it can be used as an indicator of the competencies of trainees. Through integrated VR training, individuals were shown to have higher engagement during the training process, as their understanding of the scenario improved. This allowed them to work faster, more efficiently and more accurately with a shorter overall training time.
Binary convolutional neural network acceleration framework for rapid system prototyping

The huge model size and high computational complexity make emerging convolutional neural network (CNN) models unsuitable to deploy on current embedded or edge computing devices. Recently the binary neural network (BNN) is explored to help reduce network model size and avoid complex multiplication. In this paper, a binary network acceleration framework for rapid system prototyping is proposed to promote the deployment of CNNs on embedded devices. Firstly trainable scaling factors are adopted in binary network training to improve network accuracy performance. The hardware/software co-design framework supports various compact network structures such as residual block, 1 × 1 squeeze convolution layer, and depthwise separable convolution. With flexible network binarization and efficient hardware architecture optimization, the acceleration system is able to achieve over 2 TOPS throughput performance comparable to modern desktop GPU with much higher power efficiency.
A Semantics for the Impure Logic of Ground

This paper establishes a sound and complete semantics for the impure logic of ground. Fine (Review of Symbolic Logic, 5(1), 1–25, 2012a) sets out a system for the pure logic of ground, one in which the formulas between which ground-theoretic claims hold have no internal logical complexity; and it provides a sound and complete semantics for the system. Fine (2012b) [§§6-8] sets out a system for an impure logic of ground, one that extends the rules of the original pure system with rules for the truth-functional connectives, the first-order quantifiers, and λ-abstraction. However, no semantics has yet been provided for this system. The present paper partly fills this lacuna by providing a sound and complete semantics for a system GG containing the truth-functional operators that is closely related to the truth-functional part of the system of Fine (2012b).
Self-supervised semi-supervised nonnegative matrix factorization for data clustering

Semi-supervised nonnegative matrix factorization exploits the strengths of matrix factorization in successfully learning part-based representation and is also able to achieve high learning performance when facing a scarcity of labeled data and a large amount of unlabeled data. Its major challenge lies in how to learn more discriminative representations from limited labeled data. Furthermore, self-supervised learning has been proven very effective at learning representations from unlabeled data in various learning tasks. Recent research works focus on utilizing the capacity of self-supervised learning to enhance semi-supervised learning. In this paper, we design an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S4NMF) in a semi-supervised clustering setting. The S4NMF directly extracts a consensus result from ensembled NMFs with similarity and dissimilarity regularizations. In an iterative process, this self-supervisory information will be fed back to the proposed model to boost semi-supervised learning and form more distinct clusters. The proposed iterative algorithm is used to solve the given problem, which is defined as an optimization problem with a well-formulated objective function. In addition, the theoretical and empirical analyses investigate the convergence of the proposed optimization algorithm. To demonstrate the effectiveness of the proposed model in semi-supervised clustering, we conduct extensive experiments on standard benchmark datasets. The source code for reproducing our results can be found at https://github.com/ChavoshiNejad/S4NMF.
Decentralized Mechanism for Hiring the Smart Autonomous Vehicles Using Blockchain

Nowadays, technologies like Autonomous Vehicles (AVs) are influencing the ways of our traveling. This paper inspects closely the development of a decentralized blockchain-based mechanism for providing secure, reliable and real-time availability of AVs for the customers who want to do the ride. The AVs have many advanced control systems and sensors to detect a number of hurdles (unsafe design of vehicles, negligence of civilians, etc.) in the environment. Blockchain is a decentralized temper proof business protocol used to facilitate the users with transparent, reliable, secure and cost-effective solutions. The consensus mechanisms are used in blockchain for validation purposes. This paper uses the Proof of Work consensus algorithm for the validation of Demand Response (DR) events. It provides the mechanism for real-time monitoring and real-time supervision to the ride of the end-user. Furthermore, it briefly specifies that the AVs working with blockchain mechanisms provides real-time traffic information to the end-user. The blockchain-based mechanism provides secure services to the end-user. It also provides the mechanism of Peer to Peer (P2P) car-sharing that removes the need for any bank or any reliable authority. The proposed system is proved in the Ethereum environment by DR events in the network. The simulations portray that our system is much cost-effective, efficient and reliable to meet the demands of customers.
Application of Multi-Object Tracking with Siamese Track-RCNN to the Human in Events Dataset

Multi-object tracking systems often consist of a combination of a detector, a short term linker, a re-identification feature extractor and a solver that takes the output from these separate components and makes a final prediction. Differently, this work aims to unify all these in a single tracking system. Towards this, we propose Siamese Track-RCNN, a two stage detect-and-track framework which consists of three functional branches: (1) the detection branch localizes object instances; (2) the Siamese-based track branch estimates the object motion and (3) the object re-identification branch re-activates the previously terminated tracks when they re-emerge. We used this design and apply it to the Human in Events dataset.
Reconstructing Training Data with Informed Adversaries

Given access to a machine learning model, can an adversary reconstruct the model’s training data? This work studies this question from the lens of a powerful informed adversary who knows all the training data points except one. By instantiating concrete attacks, we show it is feasible to reconstruct the remaining data point in this stringent threat model. For convex models (e.g. logistic regression), reconstruction attacks are simple and can be derived in closed-form. For more general models (e.g. neural networks), we propose an attack strategy based on training a reconstructor network that receives as input the weights of the model under attack and produces as output the target data point. We demonstrate the effectiveness of our attack on image classifiers trained on MNIST and CIFAR-10, and systematically investigate which factors of standard machine learning pipelines affect reconstruction success. Finally, we theoretically investigate what amount of differential privacy suffices to mitigate reconstruction attacks by informed adversaries. Our work provides an effective reconstruction attack that model developers can use to assess memorization of individual points in general settings beyond those considered in previous works (e.g. generative language models or access to training gradients); it shows that standard models have the capacity to store enough information to enable high-fidelity reconstruction of training data points; and it demonstrates that differential privacy can successfully mitigate such attacks in a parameter regime where utility degradation is minimal.
Are smart contracts too smart for Supply Chain 4.0? A blockchain framework to mitigate challenges

Purpose Smart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon. Design/methodology/approach A game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes. Findings The numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation. Research limitations/implications This work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights. Practical implications By using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis. Originality/value This work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.
Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Underwater imaging is widely used in ocean, river and lake exploration, but it is affected by properties of water and the optics. In order to solve the lower-resolution underwater image formed by the influence of water an... | Find, read and cite all the research you need on Tech Science Press
Research on Transfer Learning of Vision-based Gesture Recognition

Abstract Gesture recognition has been widely used for human-robot interaction. At present, a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains. For each new domain, it is required to collect and annotate a large amount of data, and the training of the algorithm does not benefit from prior knowledge, leading to redundant calculation workload and excessive time investment. To address this problem, the paper proposes a method that could transfer gesture data in different domains. We use a red-green-blue (RGB) Camera to collect images of the gestures, and use Leap Motion to collect the coordinates of 21 joint points of the human hand. Then, we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning. This paper compares the effects of three classification algorithms, i.e., support vector machine (SVM), broad learning system (BLS) and deep learning (DL). We also compare learning performances with and without using the joint distribution adaptation (JDA) algorithm. The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion. In addition, we found that when using DL to classify the data, excessive training on the source domain may reduce the accuracy of recognition in the target domain.
Impact of Full-Body Avatars in Immersive Multiplayer Virtual Reality Training for Police Forces

Recent advancements in virtual reality technology contributed to the development of immersive games and simulations of any kind. Simulations are a valuable tool for police forces to practice specific skills within threatening, dangerous, and stressful situations without dangerous real-world implications. Effective virtual simulations need to be realistic in terms of actions and also need to elicit emotion, engagement, and stress. To this end, we developed a multiplayer virtual reality training simulation for police forces to explore the effect of full-body avatars, especially regarding the stress level and response to threats. We analyzed the statistical significance and effect sizes of the data from 32 police officers under two conditions: either they could see an assailant with a full-body avatar or only a head and hands. The results reveal an increase in stress level and threat during the immersion with the full-body avatar. This was shown objectively by analyzing the heart rate variability and subjectively by examining a questionnaire in response to a threat to the assailant’s virtual body. These findings indicate that an assailant with a full-body avatar is beneficial during training for police forces, as it increases the feeling of a threat and contributes to a more realistic training experience.