Reviewerly/exHarmony huggingface dataset:
Automated Machine Learning for Healthcare and Clinical Notes Analysis

Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes.
BATCH: Machine Learning Inference Serving on Serverless Platforms with Adaptive Batching

Serverless computing is a new pay-per-use cloud service paradigm that automates resource scaling for stateless functions and can potentially facilitate bursty machine learning serving. Batching is critical for latency performance and cost-effectiveness of machine learning inference, but unfortunately it is not supported by existing serverless platforms due to their stateless design. Our experiments show that without batching, machine learning serving cannot reap the benefits of serverless computing. In this paper, we present BATCH, a framework for supporting efficient machine learning serving on serverless platforms. BATCH uses an optimizer to provide inference tail latency guarantees and cost optimization and to enable adaptive batching support. We prototype BATCH atop of AWS Lambda and popular machine learning inference systems. The evaluation verifies the accuracy of the analytic optimizer and demonstrates performance and cost advantages over the state-of-the-art method MArk and the state-of-the-practice tool SageMaker.
Machine learning based blind color image watermarking scheme for copyright protection

This work presents a blind and robust scheme using YCbCr color space, IWT (integer wavelet transform) and DCT (discrete cosine transform) for color image watermarking. During watermark insertion, Y channel is divided into blocks and Mersenne Twister random number generator is used to select the blocks for embedding. This randomized selection of blocks required a secret key, thus improving the security of the scheme. To reduce the computational complexity, the artificial neural network architecture is developed for watermark embedding. To check the robustness, several signal processing attacks such as JPEG compression, filtering attacks, noise attacks, cropping, resizing and other common attacks are applied on the watermarked images. The proposed work is tested on different images to verify the similarity in watermarking results. The scheme provides similar results (having little variation) for different test images. Experimental results demonstrate the superior performance in terms of imperceptibility and robustness. Further, the ANN framework provides faster embedding with approximately similar parametric results. The performance comparison with existing schemes demonstrates better performance for different attacks. The proposed work can be used in robust applications (i.e. copyright protection) for efficient results and less computational time.
Implementing graph-theoretic quantum algorithms on a silicon photonic quantum walk processor

Programmable silicon photonics realizes universal control over quantum walks.
Graph-Based Semi-Supervised Learning: A Comprehensive Review

Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the structure of the constructed graph. GSSL methods have demonstrated their advantages in various domains due to their uniqueness of structure, the universality of applications, and their scalability to large-scale data. Focusing on GSSL methods only, this work aims to provide both researchers and practitioners with a solid and systematic understanding of relevant advances as well as the underlying connections among them. The concentration on one class of SSL makes this article distinct from recent surveys that cover a more general and broader picture of SSL methods yet often neglect the fundamental understanding of GSSL methods. In particular, a significant contribution of this article lies in a newly generalized taxonomy for GSSL under the unified framework, with the most up-to-date references and valuable resources such as codes, datasets, and applications. Furthermore, we present several potential research directions as future work with our insights into this rapidly growing field.
TSCRNN: A novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT

In the Industrial Internet of Things (IIoT) in the 5G era, the growth of smart devices will generate a large amount of data traffic, bringing a huge challenge of network traffic classification, which is the prerequisite of IIoT traffic engineering, quality of service (QoS), cyberspace security, etc. It is difficult for current traffic classification methods to distinguish encrypted dataflow and design effective handcraft features. In this paper, a novel identification scheme of encrypted traffic, TSCRNN, is proposed to automatically extract features for efficient traffic classification, which is based on spatiotemporal features. TSCRNN includes the preprocessing phase and the classification phase. In the preprocessing phase, raw traffic data are processed with flow segmentation, sampling, and vectorization, etc. To solve the classification problem of long time flow, sampling strategies are used to collect samples from the middle of the long-lived flow. In the classification phase, TSCRNN extracts abstract spatial features by CNN and then introduces stack bidirectional LSTM to learn the temporal characteristics. The experiments were performed on the dataset ISCXTor2016. The experimental results show that TSCRNN outperforms other typical methods in all scenarios, which achieves the accuracy up to 99.4% and 95.0% respectively in Tor/nonTor binary classification tasks and sixteen classification tasks. Furthermore, TSCRNN is applied to other real network datasets obtained the satisfactory performance, which validates its feasibility and universality. It means that TSCRNN can effectively identify encrypted and anonymous traffic, provide a fine-grained traffic characterization mechanism, which will support the development of core technologies in the Industrial Internet of Things.
Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network

Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.
A Survey on Bias in Deep NLP

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as “pre-training”), versatile and performing models are released continuously for every new network design. These networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have been found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. In addition, available resources are identified and a strategy to deal with bias in deep NLP is proposed.
Real-Time Human Detection and Gesture Recognition for On-Board UAV Rescue

Unmanned aerial vehicles (UAVs) play an important role in numerous technical and scientific fields, especially in wilderness rescue. This paper carries out work on real-time UAV human detection and recognition of body and hand rescue gestures. We use body-featuring solutions to establish biometric communications, like yolo3-tiny for human detection. When the presence of a person is detected, the system will enter the gesture recognition phase, where the user and the drone can communicate briefly and effectively, avoiding the drawbacks of speech communication. A data-set of ten body rescue gestures (i.e., Kick, Punch, Squat, Stand, Attention, Cancel, Walk, Sit, Direction, and PhoneCall) has been created by a UAV on-board camera. The two most important gestures are the novel dynamic Attention and Cancel which represent the set and reset functions respectively. When the rescue gesture of the human body is recognized as Attention, the drone will gradually approach the user with a larger resolution for hand gesture recognition. The system achieves 99.80% accuracy on testing data in body gesture data-set and 94.71% accuracy on testing data in hand gesture data-set by using the deep learning method. Experiments conducted on real-time UAV cameras confirm our solution can achieve our expected UAV rescue purpose.
The use of online coding platforms as additional distance tools in programming education

Abstract This study analyzes various publications of scientists on the training of future IT specialists and the features of training programming using online simulators. The authors of the article made a comparative description of different online platforms for teaching programming according to certain criteria, selected interesting tasks from the online platform hackerrank.com, which have already been used to teach students. Online programming simulators have significant potential in organizing an effective distance learning system in Ukrainian universities. It is important to use online simulators in the learning process as an additional tool for the formation of professional competencies, which provides more intensive involvement of students in the process of writing code and practical (situational) application of existing knowledge. Gamification of the process of training future IT specialists helps to increase cognitive activity, and hence – the quality of the educational process and distance learning in particular. The authors recommend the use of online programming simulators as an additional tool for teaching computer science disciplines, taking into account their functionality, as well as the level of preparation of students and the expected learning outcomes.
Quantum secret sharing using discretely modulated coherent states

Point-to-point quantum privacy communication over a standard telecommunication fiber link can be implemented by continuous-variable quantum key distribution (CV QKD). However, as communication networks develop, the two-party CV QKD system may hardly meet the requirements of secret key sharing of multiple users (at least three users). In this paper, we consider a protocol called quantum secret sharing (QSS) which allows a legitimate user, a so-called dealer, to share a secret key with multiple remote users through an insecure quantum channel. These users can correctly recover the dealer's secret key only when they work cooperatively. We carry out QSS with discretely modulated coherent states (DMCSs) because they are easy to prepare and resilient to losses. An asymptotic security proof for the proposed DMCS-based QSS protocol against both eavesdroppers and dishonest users is presented. Numerical simulation based on a linear bosonic channel shows that the maximal transmission distance of the DMCS-based QSS protocol reaches more than 100 km, and it can be further lengthened by exploiting a higher-dimensional discrete modulation strategy. Moreover, the composable security of the DMCS-based QSS protocol is also presented.
A Blockchain-Based Artificial Intelligence-Empowered Contagious Pandemic Situation Supervision Scheme Using Internet of Drone Things

Contagious disease pandemics present a significant threat worldwide in terms of both human health and economic damage. New diseases emerge annually and place enormous burdens on many countries. Additionally, using humans to handle pandemic situations increases the chances of disease spreading. Therefore, various technologies that do not directly involve humans should be employed to handle pandemic situations. The Internet of Drones (IoDT), artificial intelligence (AI), and blockchain are emerging technologies that have revolutionized the modern world. This article presents a blockchain-based AI-empowered pandemic situation supervision scheme in which a swarm of drones embedded with AI is engaged to autonomously monitor pandemic outbreaks, thereby keeping human involvement as low as possible. A use case based on a recent pandemic (i.e., COVID-19) is discussed. Two types of drone swarms are used to handle multiple tasks (e.g., checking face masks and imposing lockdowns). A lightweight blockchain is considered to handle situations in remote areas with poor network connectivity. Additionally, a two-phase lightweight security mechanism is adopted to validate the entities in the proposed scheme. A proof of concept is established using an experimental environment setup and dataset training. An analysis of the experimental results demonstrates the feasibility of the proposed scheme.
TrSeg: Transformer for semantic segmentation

Recent efforts in semantic segmentation using deep learning frameworks have made notable advances. However, capturing the existence of objects in an image at multiple scales still remains a challenge. In this paper, we address the semantic segmentation task based on transformer architecture. Unlike existing methods that capture multi-scale contextual information through infusing every single-scale piece of information from parallel paths, we propose a novel semantic segmentation network incorporating a transformer (TrSeg) to adaptively capture multi-scale information with the dependencies on original contextual information. Given the original contextual information as keys and values, the multi-scale contextual information from the multi-scale pooling module as queries is transformed by the transformer decoder. The experimental results show that TrSeg outperforms the other methods of capturing multi-scale information by large margins.
Simple mitigation of global depolarizing errors in quantum simulations

To get the best possible results from current quantum devices error mitigation is essential. In this work we present a simple but effective error mitigation technique based on the assumption that noise in a deep quantum circuit is well described by global depolarizing error channels. By measuring the errors directly on the device, we use an error model ansatz to infer error-free results from noisy data. We highlight the effectiveness of our mitigation via two examples of recent interest in quantum many-body physics: entanglement measurements and real-time dynamics of confinement in quantum spin chains. Our technique enables us to get quantitative results from the IBM quantum computers showing signatures of confinement, i.e., we are able to extract the meson masses of the confined excitations which were previously out of reach. Additionally, we show the applicability of this mitigation protocol in a wider setting with numerical simulations of more general tasks using a realistic error model. Our protocol is device-independent, simply implementable, and leads to large improvements in results if the global errors are well described by depolarization.
Machine Learning and feature engineering-based study into sarcasm and irony classification with application to cyberbullying detection

Irony and sarcasm detection is considered a complex task in Natural Language Processing. This paper set out to explore the sarcasm and irony on Twitter, using Machine Learning and Feature Engineering techniques. First we review and clarify the definition of irony and sarcasm by discussing various studies focusing on the terms. Next the first experiment is conducted comparing between various types of classification methods including some popular classifiers for text classification task. For the second experiment, different types of data preprocessing methods were compared and analyzed. Finally, the relationship between irony, sarcasm, and cyberbullying are discussed. The results are interesting as we observed high similarity between them.
Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

Abstract Prediction of breast tumour malignancy using ultrasound imaging, is an important step for early detection of breast cancer. An efficient prediction system can be a great help to improve the survival chances of the involved patients. In this work, a machine learning (ML)—radiomics based classification pipeline is proposed, to perform this predictive modelling task, in a much more efficient manner. Multiple different types of image features of the region of interests are considered in this work, followed by a recursive feature elimination based feature selection step. Furthermore, a synthetic minority oversampling technique based step is also included in the pipeline, to deal with the class imbalance problem, that is often present in medical imaging datasets. Various ML models are considered in the subsequent model training phase, on a publicly available breast ultrasound image dataset (BUSI). From experimental analysis it has been observed that, shape, texture and histogram oriented gradients related features are the most informative, with respect to the predictive modelling task. Furthermore, it was observed that ensemble learners such as random forest, gradient boosting and AdaBoost classifiers are able to achieve significant results with respect to multiple evaluation metrics. The proposed approach achieved the state‐of‐the‐art accuracy, area under the curve, F 1‐score and Mathews correlation coefficient values of 0.974, 0.97, 0.94 and 0.959, respectively, on the BUSI dataset. Such kind of impressive results suggest that the proposed approach can have a very high practical utility, in real medical diagnostic settings.
Improved Residual Networks for Image and Video Recognition

Residual networks (ResNets) represent a powerful type of convolutional neural network (CNN) architecture, widely adopted and used in various tasks. In this work we propose an improved version of ResNets. Our proposed improvements address all three main components of a ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut. We are able to show consistent improvements in accuracy and learning convergence over the baseline. For instance, on ImageNet dataset, using the ResNet with 50 layers, for top-1 accuracy we can report a 1.19% improvement over the baseline in one setting and around 2% boost in another. Importantly, these improvements are obtained without increasing the model complexity. Our proposed approach allows us to train extremely deep networks, while the baseline shows severe optimization issues. We report results on three tasks over six datasets: image classification (ImageNet, CIFAR-10 and CIFAR-100), object detection (COCO) and video action recognition (Kinetics-400 and Something-Something-v2). In the deep learning era, we establish a new milestone for the depth of a CNN. We successfully train a 404-layer deep CNN on the ImageNet dataset and a 3002-layer network on CIFAR-10 and CIFAR-100, while the baseline is not able to converge at such extreme depths. Code and models are publicly available at: https://github.com/iduta/iresnet.
MobileNetV2 Model for Image Classification

Machine learning has been increasingly prevailing all over the world, especially in the computer vision field. This paper mainly focused on the performance of MobileNetV2 model for image classification. To verify the advanced performance of MobileNetV2 model better, this paper adopted MobileNetVl model as the control group and introduced an experiment of identifying images in a variety of datasets extracted from TensorFlow. With the T-SNE visualization tool, the conclusion can be generated by comparing the accuracy and effectiveness of these two models. The experimental results demonstrated that the proficiency of MobileNetV2 model achieved higher accuracy rates compared to MobileNetVl model. In order to enhance the performance of MobileNetV2, extensive experiments are performed.