Reviewerly/exHarmony huggingface dataset:
Class-Similarity Based Label Smoothing for Confidence Calibration

Generating confidence calibrated outputs is of utmost importance for the applications of deep neural networks in safety-critical decision-making systems. The output of a neural network is a probability distribution where the scores are estimated confidences of the input belonging to the corresponding classes, and hence they represent a complete estimate of the output likelihood relative to all classes. In this paper, we propose a novel form of label smoothing to improve confidence calibration. Since different classes are of different intrinsic similarities, more similar classes should result in closer probability values in the final output. This motivates the development of a new smooth label where the label values are based on similarities with the reference class. We adopt different similarity measurements, including those that capture feature-based similarities or semantic similarity. We demonstrate through extensive experiments, on various datasets and network architectures, that our approach consistently outperforms state-of-the-art calibration techniques including uniform label smoothing.
OGFNet: Original Resolution Subnetwork and GRU Based Feature Fusion Network for Image Denoising

Many denoising models have a good performance on images containing spatially invariant noise, especially those based on Convolutional Neural Networks (CNN). Nevertheless, the result of these methods is unreliable in preserving spatial image details. In our paper, we put forward a new design that achieves the goal of image denoising as well as preserve image details. In recent years, attention mechanisms is more and more popular in computer vision. We have done some researches on attention mechanisms in image denoising. Firstly, a combination of residual network and channel attention mechanisms and Gated Recurring Unit (GRU) is proposed to boost the denoising performance. Significantly, the GRU extracts features from different layers and selects more refined image features. Secondly, Original Resolution Subnetwork (ORSNet) is proposed to preserve the structural and textural image details. Few methods use a subnetwork for detail preservation. Extensive experiments on public denoising datasets attest to the validity of our proposed model.
Improving Abstractive Summarization With Segment-Augmented And Position-Awareness

Detecting the relationship between phrases to then derive the salient information is always an art of abstractive summarizing text. In this work, we present an improved version of the extractor-abstractor system called SEGMENT, in which the extractor identifies words and phrases included in the target summary and the abstractor leverages these features into generating a fluent summary. We provide a segment embedding layer to enrich information for the abstractor that increases the cohesion among phrases. In the extractor, we combined the filtering mechanism and the position awareness to improve the quality of information selectivity. Our method demonstrates potential improvements in CNN/DM dataset and exceeds state-of-the-art 5.1% in ROUGE-1 and 5% in ROUGE-2.
Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning

Collecting large-scale medical datasets with fine-grained annotations is time-consuming and requires experts. For this reason, weakly supervised learning aims at optimising machine learning models using weaker forms of annotations, such as scribbles, which are easier and faster to collect. Unfortunately, training with weak labels is challenging and needs regularisation. Herein, we introduce a novel self-supervised multi-scale consistency loss, which, coupled with an attention mechanism, encourages the segmentor to learn multi-scale relationships between objects and improves performance. We show state-of-the-art performance on several medical and non-medical datasets. The code used for the experiments is available at https://vios-s.github.io/multiscale-pyag.
A New Zeroing Neural Network with Tolerance to Harmonic Noise for Solving Time-Variant Linear Equation

Recently, a typical recurrent neural network termed the zeroing neural network (ZNN) with prominent properties (e.g., finite-time convergence and noise suppression) has been studied to solve the time-variant linear equation. However, these ZNN models may not perform well when being affected by harmonic noise. In this paper, for completeness, a new ZNN model that is tolerant to harmonic noise is proposed and investigated for solving the time-variant linear equation. Such a model is then theoretically analyzed to converge the computational error to zero, thereby ensuring the model convergence property. Simulation results with three examples are illustrated to further validate the efficacy of the proposed harmonic-noise-tolerance ZNN model.
Dynamic Response Prediction of Bi-State Emission of Quantum Dot Lasers Based on Extreme Learning Machine

Dual-state emission is an phenomenon which takes place in Quantum Dot Lasers at different temperature and operating conditions. In this study, we investigate that issue from a nonlinear regression model based on Extreme Learning Machine, which revealed to be able to predict the spectrally resolved transient response of InAs/InGaAs quantum dot laser with error performance as low as 0.54%.
Left Recursion by Recursive Ascent

Recursive-descent parsers can not handle left recursionLeft recursion, and several solutions to this problem have been suggested. This paper presents yet another solution. The idea is to modify recursive-descent parser so that it reconstructs left-recursive portions of syntax tree bottom-up, by “recursive ascent”.
Knowledge Representation Learning Based on GAN and Pre-training

Knowledge representation learning can realize the representation of entity and relation semantic information by projecting the triple entity or relation in knowledge graph into vector space, and can effectively calculate entity, relation and their complex semantic association. However, most of the existing models only use the structure information of triples, ignoring the semantic rich entity description. In addition, knowledge representation learning methods usually use negative sampling method. Through random sampling, the negative samples generated by this method are easy to be recognized by the model, and with the iteration of training, the contribution to the improvement of the model performance becomes smaller and smaller. Therefore, we propose a Knowledge Representation learning method based on Generative adversarial network and Pre-training language model (KRGP). Specifically, it uses the idea of generative adversarial network, semantic matching model and translation distance model to generate high-quality negative samples. We combine descriptions of head and tail entities and relations corresponding to negative sample triples into text sequences, and then use next sentence prediciton task of BERT to transform text sequences into a binary classification problem. Experimental results show that this method can improve the performance of link prediction and triple classification on multiple data sets.
On the Estimation of POD and LOD of Qualitative Microbiological Assays from a Multi-Laboratory Validation Study

Jarvis et al. in 2019 (J. AOAC Int. 102: 1617-1623) estimated the mean laboratory effect (µ), standard deviation of laboratory effects (σ), probability of detection (POD), and level of detection (LOD) from a multi-laboratory validation study of qualitative microbiological assays using a random intercept complementary log-log model. Their approach estimated σ based on a Laplace approximation to the likelihood function of the model, but estimated µ from a fixed effectmodel due to a limitation in the MS Excel spreadsheet which was used by the authors to develop a calculation tool.We compared the estimates of µ and σ from three approaches (the Laplace approximation that estimates µ and σ simultaneously from the random intercept model, adaptive Gauss-Hermite quadrature (AGHQ), and the method of Jarvis et al.) and introduced an R Shiny app to implement the AGHQ using the widely used "lme4" R package.We conducted a simulation study to compare the accuracy of the estimates of µ and σ from the three approaches and compared the estimates of µ, σ, LOD, etc. between the R Shiny app and the spreadsheet calculation tool developed by Jarvis et al. for an example dataset.Our simulation study shows that, while the three approaches produce similar estimates of σ, the AGHQ has generally the best performance for estimating µ (and hence mean POD and LOD). The differences in the estimates between the R Shiny app and the spreadsheet were demonstrated using the example dataset.The AGHQ is the best method for estimating µ, POD, and LOD.The user-friendly R Shiny app provides a better alternative to the spreadsheet.
Neural Machine Reading Comprehension on COVID Dataset using Bi-directional Encoder Representations from Transformers

Global impact of COVID-19 disrupted normal life, during the pandemic enormous amount of unreliable information about COVID-19 is creating and disseminating every minute, which leads to information flooding. So people cannot extract reliable information from these unreliable sources. Machine Reading Comprehension can alleviate this problem. Machine Reading Comprehension (MRC) is a core process in question answering systems and that efficiently extracts the answer from relevant resources automatically for the questions posed by humans. Machine reading comprehension brings attention to a textual understanding with answering questions. This article introduces a new dataset, COVIDATA for the span extraction task of MRC in the domain of COVID-19 and exploits the advancements of the Transformer-BERT fine-tuned model. COVIDATA dataset generated from the COVID-19 news and information from the World Health Organization. Fine-tuned BERT, SciBERT, BioBERT, ClinicalBERT, and Bio+Clinical BERT models are used for the experiment and evaluation of the COVIDATA dataset.
A Survey on Accurate Breast Cancer Detection and Classification using Machine Learning Approach

This survey paper is used to discuss about the detection of breast cancer tissues using different machine learning algorithms. Identification of cancers using scanned images are very important for correct diagnosis. Many algorithms are present for detection of cancer using image processing techniques, all these algorithms have the main goal of detecting those cell tissues. Each algorithm has their own assumptions and advantages, here is a review of some of those algorithms for breast cancer detection. This paper highlights the algorithms and their assumptions of the prior published papers.
Self-Calibration and Optimization of Binocular Camera Extrinsic Parameters

To the calibration of the extrinsic parameters for binocular camera in the unknown environment, we propose a novel method to estimate the rotation matrix and the translation vector based on the essential matrix. By extracting SIFT feature point pairs from stereo images which taken by binocular camera, and matching the feature point pairs with the FLANN algorithm, the essential matrix can be estimated, and the extrinsic parameters can be initially derived. On this basis, the objective function is constructed by adopting the physical distance between two spatial points of the calibrator whose size is known in 3D space, and the rotation matrix is further optimized. The experimental results present that the algorithm has a high accuracy of depth estimation, and the error is smaller than the un-optimized algorithm. This method can be used as a feasible scheme for the estimation of extrinsic parameters of binocular camera.
Stefano De Franchi e il dialetto genovese rusticale

Abstract The paper deals with the particular linguistic variety spoken by some servants in Stefano De Franchi’s comedies (1714‒1785). After briefly hinting at some fundamental aspects of his production (§1), we examine the text of his comedies (§2) and analyze the servants’ language. This variety presents numerous traits which do not belong to Genoese (§3), some others which will spread in the urban variety only between the nineteenth and twentieth centuries (§4) and many others which had been expelled from it between the fifteenth and seventeenth centuries (§5). Finally (§6), the linguistic analysis of Gerusalemme Liberata ’s Genoese translation (1755) will allow us to order chronologically the linguistic relics of §5.
Adam Smith’s Invisible Hand as a New, Powerful and Robust Control Paradigm for Collective AI Robotics

The market launch of Tesla electric cars with an intelligent autopilot capable of dealing with the chaos of street traffic, shows how dramatic the need to develop a theory of building control systems for autonomous intelligent robots is, where there is no central control or it only exists locally. However, a suitable theory exists in the area of economics, for free (quasi free) markets, called “Adam Smith’s Invisible Hand” (ASIH). The general assumption of this theory is “that there is no control system, which will do better for the welfare of the free market, than spontaneous, unconscious, distributed, self-control/optimization that (ASIH) is able to perform”. In general, this theory is the front line of a harsh dispute between advocates of interventionism and advocates of liberalism. This paper presents how the ASIH theory could be distilled to obtain a general control theory for systems of autonomous, mobile, quasi-intelligent robots, for unmanned building sites, deep mines, etc. such that collective self-control is inborn into the nature of robots and no centralized control is necessary. The first theoretical results indicate that self-control and optimization systems based on such a theory should be much more intelligent in terms of problem solving than centralized systems could be and incomparably more resistant, e.g. when parts of a robot group are destroyed. The reason for this is, that such systems better exploit the potential of individual intelligences of agents.
A Platform for Theorizing about Socio-Technical HCI Design

Human Work Interaction Design (HWID) is a socio-technical HCI design approach. This introductory chapter presents HWID as a platform with five parts: context, human work, interaction design, relations between human work and interaction design, and theory and methodology. The platform is to be used for building locally valorized theory and action in the form of IT artefacts. The contexts for a HWID project can, for example, be a small- and medium-sized manufacturing company that suffers from problems with productivity and worker satisfaction related to the use of algorithms. Current knowledge about socio-technical HCI design approaches offers solutions to such design cases. This book focus on what HWID can offer to socio-technical HCI design researchers, practitioners, and policymakers.
A Fault Data Generation Algorithm Based on GAN and Policy Gradient Mechanism

Generative adversarial networks(GAN) are widely used in various fields. However, when generating text data with contextual correlation characteristics such as fault data, GAN has many limitations. On the one hand, discrete data output makes it difficult to pass gradient updates from the discriminator to the generator; on the other hand, it is difficult for the discriminator to process incompletely generated sequences. In this paper, we propose a fault data generation algorithm based on GAN and policy gradient mechanism. Using the reinforcement learning method aims to solve the gradient update transfer problem, and the policy gradient algorithm is used to directly update the parameters of the generator; at the same time, by using the Upper Confidence Bound Apply to Tree(UCT) algorithm to simulate the incomplete sequence into a complete sequence so that the discriminator can evaluate its reward value. The simulation results show that our fault data generation algorithm based on GAN and policy gradient mechanism performs better in the fault data generation task.
Modulation Recognition of Composite Signal Based on ResNet and Frequency Domain Graph

Abstract Aiming at the problem of composite signal recognition in measurement and control system, a modulation recognition method of composite signal is proposed based on clipped internal modulation signal frequency image and residual network. Residual network has the advantages of fast convergence and high recognition accuracy, which is used to realize the recognition of different modulation modes and different bandwidth signals. The experimental results show that, compared with the traditional decision tree algorithm and the constellation based CNN recognition algorithm, the recognition performance of the proposed method is improved by more than 3dB for the variable bandwidth composite modulation signal set (BPSK-FM and QPSK-FM with internal modulation bandwidth of 128kHz, BPSK-FM and QPSK-FM with internal modulation bandwidth of 2kHz and 2FSK-FM with internal modulation bandwidth of 32kHz and 2kHz).
Artificial Intelligence and Ethics

In this article, author describes relationship between AI (Artificial Intelligence) and Ethics. First, the term technological singularity is defined. Then the future is predicted when the ability of AI is above humans. Situation in the near future will also be discussed when human’s jobs are replaced by AIs.
Multi-atlas Segmentation Combining Multi-task Local Label Learning and Semi-supervised Label Propagation

Multi-atlas based segmentation methods have achieved great success in hippocampal segmentation due to their promising performance. However, the correlation between voxels in the target image is often ignored. In this study, an image segmentation method based on multi-atlas is proposed by combining the multi-task learning method with the semi-supervised label propagation algorithm. Both multi-task learning and semi-supervised label propagation can take advantage of the correlation between voxels in the target image. Specifically, instead of training an independent model for each voxel to be segmented in the target images, the multi-task learning method trains a joint classification model for a multi-task-voxel cluster in a target image patch, followed by majority voting to obtain a probabilistic segmentation for the central voxel. The probabilistic map is then used to guide a semi-supervised label propagation algorithm to get the final segmentation results. The proposed method was applied to hippocampus segmentation in MR images and compared with state-of-the-art multi-atlas segmentation methods. Experimental results demonstrated that our method had better segmentation performance for the hippocampal segmentation.
Secure Cloud Services Using Quantum-Blockchain Technology

Data privacy and data security are basic agenda prior to outsource data to cloud environment. It is observed that cloud technology is centralized so any attacker can easily tamper with data store in it. On the other hand, blockchain technology is decentralized. Cloud provides on demand computing, storage of data, etc. Blockchain technology provides a very strong security mechanism. In this manuscript, blockchain technology has been used to provide data security at cloud environments. Here, a novel framework of cloud computing has been proposed along with algorithms based on quantum mechanics. The quantum superimposition principle has been used to detect data tampering. Experimental results have been proposed to lay bare the effectiveness of proposed approach.
Real-Time Detection and Recognition of Cards in the Game of Set

This paper aims to present a procedure for real-time detection and recognition of the cards from the card game Set using computer vision techniques. Our approach to recognizing cards in images is done in three steps: segmenting cards from the image, extracting features from card images with horizontal and vertical lines, and card feature classification with support vector machine (SVM). Small number of features are extracted from relatively small subset of pixels to achieve real-time processing.
Fine and Coarse-Grained Feature Learning for Unsupervised Person Re-identification

Most supervised learning methods are currently used to solve the task of person re-identification (Re-ID) and have yielded good results. But these methods still have disadvantages such as the need for manual annotation of training data. Especially for large data sets, they need too high cost of manual annotation and the data are difficult to obtain for fully pairwise labeling. So unsupervised learning becomes a necessarily trend for person Re-ID, and we decide to solve the task via unsupervised learning method. Moreover, global features focus on spatial integrity of person features, and local ones help to highlight discriminative features of different patches. Therefore, we propose a fine and coarse-grained unsupervised (FCU) learning framework of global and local branches' feature learning to solve Re-ID task. Specifically, for local branch, extract patches from a feature map which learned on a PatchNet network of images, and learn their fine-grained features to close similar patches and push away dissimilar ones. For global branch, maximize the diversity between classes by repelled loss and similarity within classes by attracted loss, then similarity and diversity in the unlabeled datasets are used as information for unsupervised cluster merging and learning their coarse-grained features. The two branches are used to jointly achieve the effect of increasing inter-class differences and intra-class similarity. A large number of experiments verify the superiority of our method for unsupervised person re-identification.
AI-Based Modeling and Control of Robotic Systems: A Brief Tutorial

Accurate modeling and control of robotic systems are often critical to perform complex manipulation tasks. Artificial Intelligence (AI) based approaches have gained widespread popularity to deal with task complexity and uncertainty. This paper gives a detailed overview and tutorial of AI-based approaches for modeling and control of robotic systems. We provide an easy-to-understand explanation of the key AI-based techniques and demonstrate learning an inverse dynamics model and target reaching controller for a 2-link robotic manipulator using state-of-the-art AI-based methods. The experimental results present a thorough analysis of AI-based techniques and validate their advantages over conventional methods. The code used for the examples has also been made open-source for the research community on this link: https://www.github.com/ai-modeling-control. Thus, this paper aims to become a starting point for researchers interested in developing and implementing AI-based methods for robot modeling and control.
Unsupervised Person Re-Identification based on Feature Learning of Deep Convolutional Generative Adversarial Network

Most of the researchers are targeting the problem of person re-identification with supervised learning. Person re-identification problem requires a lot of pairwise manually annotated training data for each camera. This approach becomes impractical for large scale deployment because of the dependency on pairwise manually annotated data. In this paper, we propose an unsupervised approach for the person re-identification problem based on utilization of Generative Adversarial Network. We use Deep Convolutional Generative Adversarial Network for feature learning from image sequences of datasets as we prove that GANs learn the features effectively. In the feature learning process, we do not use any labels so this is a completely unsupervised process. Several similarity measures and a Cumulative Match Characteristic (CMC) Curve are used for the evaluation of our results. The experimental results show that our proposed approach gives comparable results to the existing state of the art approaches.