Reviewerly/exHarmony huggingface dataset:
Impact of filtering on photonic time-delay reservoir computing

We analyze the modification of the computational properties of a time-delay photonic reservoir computer with a change in its feedback bandwidth. For a reservoir computing configuration based on a semiconductor laser subject to filtered optoelectronic feedback, we demonstrate that bandwidth selection can lead to a flat-topped eigenvalue spectrum for which a large number of system frequencies are weakly damped as a result of the attenuation of modulational instability by feedback filtering. This spectral configuration allows for the optimization of the reservoir in terms of its memory capacity, while its computational ability appears to be only weakly affected by the characteristics of the filter.
Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

Telemedicine support virtual consultations and evaluations in hand surgery for patients in remote areas during the COVID-19 era. However, traditional physical examination is challenging in telemedicine and it is inconvenient to manually measure the hand range of motion (ROM) from images or videos. Here, we propose an automatic method using the hand pose estimation technique, aiming to measure the hand ROM from smartphone images.Twenty-eight healthy volunteers participated in the study. An eight-hand gestures measurement protocol and the Google MediaPipe Hands were used to analyze images and calculate the ROM automatically. Manual goniometry was also performed according to the guideline of the American Medical Association. The correlation between the automatic and manual methods was analyzed by the intraclass correlation coefficient and Pearson correlation coefficient. The clinical acceptance was testified using Bland-Altman plots.A total of 32 parameters of each hand were measured by both methods, and 1792 measurement results were compared. The mean difference between automatic and manual methods is -2.21 ± 9.29° in the angle measurement and 0.48 ± 0.48 cm in the distance measurement. The intraclass correlation coefficient of 75% of parameters was higher than 0.75, the Pearson correlation coefficient of 84% of parameters was over 0.6, and 40.6% of parameters reached well-accepted clinical agreements.The proposed method provides a helpful protocol for automatic hand ROM measurement based on smartphone images and the MediaPipe Hands pose estimation technique. The automatic measurement is acceptable and comparable with existing methods, showing a possible application in the telemedicine examination of hand surgery.
A Probabilistic Contrastive Framework for Semi-Supervised Learning

Semi-supervised learning is a common way that investigates how to improve performance of a visual learning model, while data annotation is far from sufficient. Recent works in semi-supervised deep learning have successfully applied consistency regularization, which encourages a model to maintain consistent predictions for different perturbed versions of an image. However, most of such methods ignore the category correlation of image features, especially when exploiting strong augmentation methods for unlabeled images. To address this problem, we propose PConMatch, a model that leverages a probabilistic contrastive learning framework to separate the features of strongly-augmented versions from different classes. A semi-supervised probabilistic contrastive loss is designed, which takes both labeled and unlabeled samples into account and develops an auxiliary module to generate a probability score to measure the model prediction confidence for each sample. Specifically, PConMatch first generates a pair of weakly-augmented versions for each labeled sample, and produces a weakly-augmented version and a corresponding pair of strongly-augmented versions for each unlabeled sample. Second, a probability score module is proposed to assign pseudo-labeling confidence scores to strongly-augmented unlabeled images. Finally, the probability score of each sample is further passed to the contrastive loss, combining with consistency regularization to enable the model to learn better feature representations. Extensive experiments on four publicly available image classification benchmarks demonstrate that the proposed approach achieves state-of-the-art performance in image classification. Several rigorous ablation studies are conducted to validate the effectiveness of the method.
High-precision data acquisition for free-space continuous-variable quantum key distribution

Data acquisition in a continuous-variable quantum key distribution (CV-QKD) system is a necessary step to obtain secure secret keys. And the known data acquisition methods are commonly based on the assumption that the channel transmittance is constant. However, the channel transmittance in free-space CV-QKD fluctuates during the transmission of quantum signals, and the original methods are not applicable in this scenario. In this paper, we propose a data acquisition scheme based on the dual analog-to-digital converter (ADC). In this scheme, two ADCs with the same sampling frequency as the pulse repetition rate of the system and a dynamic delay module (DDM), which are used to construct a high-precision data acquisition system, eliminate the effect of transmittance fluctuation by a simple division operation of the data from the two ADCs. Simulation and proof-of-principle experimental results show that the scheme is effective for free-space channels and can achieve high-precision data acquisition under the condition of fluctuation of channel transmittance and very low signal-to-noise ratio (SNR). Furthermore, we introduce the direct application scenarios of the proposed scheme for free-space CV-QKD system and verify their feasibilities. This method is of great significance to promote the experimental realization and practical application of free-space CV-QKD.
Contactless Drink Intake Monitoring Using Depth Data

It is important for humans to remain hydrated, particularly for older adults who are at a greater risk of dehydration and may forget to drink. Monitoring liquid intake and getting reminders to drink throughout the day is a useful solution to increase hydration levels. The objective of this paper is to automatically detect drink events from multiple containers in a simulated home environment using a vision-based approach. The proposed work compares the use of depth and RGB (red, green, blue) cameras for this task. In this paper, we compared 2D and 3D Convolutional Neural Networks (CNN) using RGB and depth cameras. We collected data from nine participants performing drinking, eating and other Activities of Daily Living (ADL) in a simulated home environment. We found that for the 3D models, the RGB and depth camera inputs provided very similar F1-scores for both 10-Fold (94.3% vs 93.9%, respectively) and Leave-One-Subject-Out (LOSO) cross validation (84.2% vs 86.2%, respectively). This is a promising result as depth cameras also mitigate the challenges to privacy of RGB-based models. The 3D CNN models outperformed the 2D models, thereby creating a more robust system. Depth cameras are a useful alternative to RGB cameras with equal performance in identifying drinking events.
Improving transaction safety via anti-fraud protection based on blockchain

Financial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed to alleviate the issue. To solve the problem, we propose a gradient-boosting decision tree based anti-fraud protection with blockchain Technology, referred to as GBDT-APBT, which treats anti-fraud transaction model as the accumulation of the classfiers' weakness and builds up a classifiers' to judge whether the transaction is fraudulent. Each user's private data is trained offline at the local blockchain node, then the trained model is directly uploaded to the cloud, and the final consensus model is obtained by voting. Due to incorporating blockchain technology, GBDT-APBT demonstrates decentralisation, openness, autonomy, anonymity, and immutability, showing its ability to satisfying the demand for an effective and beneficial anti-counterfeiting system, with high performance and effectiveness in detecting fraud information. Experiments show that compared with other methods, GBDT-APBT offers a promising approach to the security of credit card transactions with reference to the detection accuracy.
Intuitionistic Fuzzy Deep Neural Network

The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological way the whole process of IFDNN development, starting with the simplest form—an intuitionistic fuzzy neural network (IFNN) with one layer with single-input neuron, passing through IFNN with one layer with one multi-input neuron, further subsequent complication—an IFNN with one layer with many multi-input neurons, and finally—the true IFDNN with many layers with many multi-input neurons. The formulas for strongly optimistic, optimistic, average, pessimistic and strongly pessimistic formulas for NN parameters estimation, represented in the form of intuitionistic fuzzy pairs, are given here for the first time for each one of the presented IFNNs. To demonstrate its workability, an example of an IFDNN application to biomedical data is here presented.
Enhanced Cloud Storage Encryption Standard for Security in Distributed Environments

With the growing number of cloud users, shared data auditing is becoming increasingly important. However, these schemes have issues with the certificate management. Although there is a certificate-shared auditing scheme, it is ineffective in dealing with dynamic data and protecting data privacy. The verifier cannot access the data content to ensure data integrity due to security concerns. This paper proposes a novel technique to ensure the integrity and improve the access control. A novel enhanced storage retrieval mechanism is used to improve the performance of the cloud’s storage and retrieval mechanisms to achieve this. The technique is evaluated in concern of the upload, download, encryption, and decryption time. As the file size grows, so does the time it takes to upload it. Similarly, the time taken to encrypt files of various formats and sizes evidenced that it depends on the file size and format. Thus, the encryption time increases as the file sizes increases, demonstrating the performance of the proposed system.
Tilt Correction of Panoramic Images for a Holistic Visual Homing Method with Planar-Motion Assumption

Holistic local visual homing based on warping of panoramic images relies on some simplifying assumptions about the images and the environment to make the problem more tractable. One of these assumptions is that images are captured on flat ground without tilt. While this might be true in some environments, it poses a problem for a wider real-world application of warping. An extension of the warping framework is proposed where tilt-corrected images are used as inputs. The method combines the tilt correction of panoramic images with a systematic search through hypothetical tilt parameters, using an image distance measure produced by warping as the optimization criterion. This method not only improves the homing performance of warping on tilted images, but also allows for a good estimation of the tilt without requiring additional sensors or external image alignment. Experiments on two newly collected tilted panoramic image databases confirm the improved homing performance and the viability of the proposed tilt-estimation scheme. Approximations of the tilt-correction image transformations and multiple direct search strategies for the tilt estimation are evaluated with respect to their runtime vs. estimation quality trade-offs to find a variant of the proposed methods which best fulfills the requirements of practical applications.
DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection

Recent advances in convolutional neural network (CNN)-based object detection have a trade-off between accuracy and computational cost in various industrial tasks and essential consideration. However, the fully convolutional one-stage detector (FCOS) demonstrates low accuracy compared with its computational costs owing to the loss of low-level information. Therefore, we propose a module called a dual-path lightweight module (DLM) that efficiently utilizes low-level information. In addition, we propose a DLMFCOS based on DLM to achieve an optimal trade-off between computational cost and detection accuracy. Our network minimizes feature loss by extracting spatial and channel information in parallel and implementing a bottom-up feature pyramid network that improves low-level information detection. Additionally, the structure of the detection head is improved to minimize the computational cost. The proposed method was trained and evaluated by fine-tuning parameters through experiments and using public datasets PASCAL VOC 07 and MS COCO 2017 datasets. The average precision (AP) metric is used for our quantitative evaluation matrix for detection performance, and our model achieves an average 1.5% accuracy improvement at about 33.85% lower computational cost on each dataset than the conventional method. Finally, the efficiency of the proposed method is verified by comparing the proposed method with the conventional method through an ablation study.
Psychology and AI at a Crossroads: How Might Complex Systems Explain Themselves?

Abstract A challenge in building useful artificial intelligence (AI) systems is that people need to understand how they work in order to achieve appropriate trust and reliance. This has become a topic of considerable interest, manifested as a surge of research on Explainable AI (XAI). Much of the research assumes a model in which the AI automatically generates an explanation and presents it to the user, whose understanding of the explanation leads to better performance. Psychological research on explanatory reasoning shows that this is a limited model. The design of XAI systems must be fully informed by a model of cognition and a model of pedagogy, based on empirical evidence of what happens when people try to explain complex systems to other people and what happens as people try to reason out how a complex system works. In this article we discuss how and why C. S. Peirce's notion of abduction is a best model for XAI. Peirce's notion of abduction as an exploratory activity can be regarded as supported by virtue of its concordance with models of expert reasoning that have been developed by modern applied cognitive psychologists.
MedSkim: Denoised Health Risk Prediction via Skimming Medical Claims Data

Health risk prediction is a challenge task that aims to predict whether patients would suffer from a certain disease/condition in the near future based on their historical EHR data. Although existing approaches can achieve better performance, none of them can deal with the noise existing in the EHR data explicitly. In this paper, we hypothesize that automatically removing noise from EHR data should help the models further improve the performance. Correspondingly, we propose a novel model named MedSkim, which is able to automatically rule out irrelevant visits and codes by effectively skimming through the EHR data. In particular, the proposed model has a code selection module that can directly make a skipping decision to each individual diagnosis codes and then remove the target-irrelevant ones. A backward probing RNN (BPRNN) is designed to reversely process the EHR data and provide a coarse grained representation learning for visits. Besides, a forward skipping RNN (FSRNN) is proposed to read the EHR in a preceding way and dynamically select important visits and codes based on the results of previous two modules. Finally, the risk prediction module uses the output hidden states from FSRNN for generating the final representation to make predictions. Additionally, we also design an extra regularization term based on the skip rate of the model and combine it with standard cross entropy loss to train the model in an end-to-end setting. Experimental results show that MedSkim achieves the best performance on three real-world datasets compared with the state-of-the-art baselines in terms of PR-AUC, F1 and Cohen’s Kappa. Moreover, the ablation study and case study confirm that the proposed MedSkim is reasonable and effective for removing noise from EHR data <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The source code of the proposed MedSkim is available at https://github.com/SH-Src/MedSkim
Beware the Sirens: Prototyping an Emergency Vehicle Detection System for Smart Cars

Vehicle drivers should be able to react coherently in anomalous circumstances, such as the quick arrival of an emergency vehicle with sirens wailing. This situation requires all regular vehicles to give way or slow down, depending on the road and traffic conditions. In this paper, we address an automatic system that assists the driver in reacting to the arrival of an emergency vehicle by employing audio and video algorithms based on Deep Learning. More specifically, by leveraging sound recognition algorithms, the vehicle is able to detect the arrival of the emergency vehicle by its siren sound. In such an event, by making use of computer vision algorithms, the vehicle intelligence can monitor the driver's gaze and awareness towards the emergency vehicle and assess his/her awareness. The paper describes the process of integrating these technologies into a commercial car, the creation of new datasets and the challenges encountered.
Efficient Navigation for Constrained Shortest Path with Adaptive Expansion Control

In many route planning applications, finding constrained shortest paths (CSP) is an important and fundamental problem. CSP aims to find the shortest path between two nodes on a graph while satisfying a path constraint. Solving CSPs requires a large search space and is prohibitively slow on large graphs, even with the state-of-the-art parallel solution on GPUs. The reason lies in the lack of effective navigational information and pruning strategies in the search procedure. In this paper, we propose SPEC, a Shortest Path Enhanced approach for solving the exact CSP problem. Our design rationales of SPEC rely on the observation that the shortest path (SP) provides valuable information in the search procedure of CSP. Hence, we propose a label priority that distinguishes promising candidate paths based on SP. We further devise efficient pruning and teleporting strategies utilizing SP lengths and costs, which eliminates unfeasible paths at an early stage. Furthermore, we observe that the expansion number at each search iteration affects the overall performance significantly. Thus, we devise an adaptive controller based on reinforcement learning. We also show that SPEC works seamlessly with the parallel implementation. Extensive experimental results on 8 read-world graphs reveal that single thread SPEC achieves an order of magnitude speedup over the state-of-the-art GPU-based method. The parallel implementation boosts SPEC 3 to 5 times further.
Automated Pre-Play Analysis of American Football Formations Using Deep Learning

Annotation and analysis of sports videos is a time-consuming task that, once automated, will provide benefits to coaches, players, and spectators. American football, as the most watched sport in the United States, could especially benefit from this automation. Manual annotation and analysis of recorded videos of American football games is an inefficient and tedious process. Currently, most college football programs focus on annotating offensive formations to help them develop game plans for their upcoming games. As a first step to further research for this unique application, we use computer vision and deep learning to analyze an overhead image of a football play immediately before the play begins. This analysis consists of locating individual football players and labeling their position or roles, as well as identifying the formation of the offensive team. We obtain greater than 90% accuracy on both player detection and labeling, and 84.8% accuracy on formation identification. These results prove the feasibility of building a complete American football strategy analysis system using artificial intelligence. Collecting a larger dataset in real-world situations will enable further improvements. This would likewise enable American football teams to analyze game footage quickly.
Classification of Breast Cancer Ultrasound Images with Deep Learning-Based Models

Breast cancer is the type of cancer that affects women the most frequently in the world. Additionally, it is the highest cause of death for women. For the detection and treatment of breast cancer, there are numerous imaging techniques. For medical image analysts, making a diagnosis is arduous, routine, time-consuming and tedious. Additionally, the growing volume of ultrasounds to interpret has overloaded practitioners and analysts. In the past, investigations have been performed using mammogram images. This research aims to take a different approach. The hypothesis is that by using artificial intelligence (AI) for ultrasound analysis, the process of computer-aided diagnosis (CAD) can be made more effective, interesting and free from subjectivity. The research&rsquo;s purpose is to classify benign (non-cancerous), malignant (cancerous) and normal samples. The dataset contains 780 images in total. Data were split into 70% for training and 30% for validation. In this dataset, data augmentation and data preprocessing are also applied. Three models are used to classify samples. While ResNet50 scores 85.4% accuracy, ResNeXt50 scores 85.83%, and VGG16 scores 81.11%. Making the diagnosis by artificial intelligence will provide relief in the field of medicine. Computer vision models may be used in medicine. Therefore, providing more data and testing data more broadly will help improve the model.
Healthcare 4P: Systematic Review of Applications of Decentralized Trust Using Blockchain Technology

Sharing of Global Healthcare information using secure digital channels will be a key accelerator in developing a knowledge society with improved health care. Sharing of information brings both risks and benefits to Healthcare institutes. Blockchain technology provides an immutable and decentralized based platform. However, as highlighted by the scalability Trilemma there is a balance to be achieved between scalability, security, and decentralization. There are different Blockchain industry standard models, each of which provides a varying degree of reliability, investment cost, and security. This chapter focusses on information sharing challenges in IoT-enabled health care and analysing the effectiveness of Blockchain frameworks to resolve the same. It goes further to create a repeatable framework of trust requirements of the use cases in health care for verifying the Blockchain capability matrix.