Reviewerly/exHarmony huggingface dataset:
A Secure and Decentralized Trust Management Scheme for Smart Health Systems

The Internet of Things (IoT) growth is extremely fast and it now has found its way to healthcare applications too. Many smart health gadgets and devices are helping practitioners in collecting medical information and monitoring patients. In this distributed system, information or service is sometimes shared and used by other devices. Considering the importance of health-related information and the decisions made based on it, there should be some sort of assurance on the security and quality of the services or information provided. Trust management is an efficient means of promoting application security and reliability in these cases. However, due to some limitations that are specific to IoT, traditional trust evaluation algorithms cannot be employed or do not yield satisfactory results. In this paper, evidence theory is exploited to design a decentralized service-oriented trust management model for healthcare IoT. A measure of evidence distance is used to reward well-behaving healthcare service/information providers as well as referrers and punish malicious entities. In this context-aware model, trust is estimated based on direct experiences and indirect feedbacks of recommenders. The process runs in two contexts; trust to healthcare service and trust to recommendation. When personal direct experience does not exist, trust to a source or service is estimated by applying the combinatorial laws of evidence theory and integrating indirect trust values. The proposed model is secure against bad-mouthing, good-mouthing, and on-off attacks due to its dynamic parameters and using the concept of evidence distance. Our results confirm the robustness and efficiency of this scheme.
Oriented Gaussian Function-Based Box Boundary-Aware Vectors for Oriented Ship Detection in Multiresolution SAR Imagery

As an important remote sensing means, synthetic aperture radar (SAR) has many superiorities to other sensors. How to effectively detect and locate ships in SAR images is also a popular field. In previous ship detection research, most algorithms focus on detecting the horizontal bounding box of ship targets, which ignore the rotation angle of each ships. Thus, too much background noise in the horizontal detection results makes them difficult to describe each ship accurately. Inspired by the powerful feature representation ability of convolutional neural networks (CNNs), a novel anchor-free and keypoint-based deep learning method is proposed for oriented ship detection in multiresolution SAR images. Our detector first extracts multilevel features from the input SAR image with a backbone network and feature pyramid network. Next, considering multiscale ships in multiresolution SAR images, we detect different sizes of ships on different levels of feature maps with identical head network structures. In each head network, the classification subnetwork determines each pixel in feature maps as the central pixel of this ship or not, and the regression subnetwork regresses the oriented bounding box for each ship. In the training process, the proposed oriented nonnormalized Gaussian function is used to describe the center point of ship targets, while the nonuniform weighting of the different level loss functions is used to suppress the imbalanced sample distribution. Experimental results on two authoritative SAR-oriented ship detection datasets and two Gaofen-3 images demonstrate the effectiveness and robustness of the proposed methods.
Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm

The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm.
Learning-Rate Annealing Methods for Deep Neural Networks

Deep neural networks (DNNs) have achieved great success in the last decades. DNN is optimized using the stochastic gradient descent (SGD) with learning rate annealing that overtakes the adaptive methods in many tasks. However, there is no common choice regarding the scheduled-annealing for SGD. This paper aims to present empirical analysis of learning rate annealing based on the experimental results using the major data-sets on the image classification that is one of the key applications of the DNNs. Our experiment involves recent deep neural network models in combination with a variety of learning rate annealing methods. We also propose an annealing combining the sigmoid function with warmup that is shown to overtake both the adaptive methods and the other existing schedules in accuracy in most cases with DNNs.
Embedded System for Eye Blink Detection Using Machine Learning Technique

Nowadays, eye tracking and blink detection are increasingly popular among researchers and have the potential to become a more important component of future perceptual user interfaces. The real-time eye-tracking system has been a fundamental and challenging problem for machine learning problems. The main purpose of this paper is to propose a new method to design an embedded eye blink detection system that can be used for various applications with the lowest cost. This study presents an efficient technique to determine the level of eyes that are closed and opened. We offered a real-time blink detection method by using machine learning and computer vision libraries. The proposed method consists of four phases: (1) taking frame by employing a raspberry pi camera that slotted to the raspberry pi 3 platform, (2) utilizing haar cascade algorithm to identify faces in the captured frames, (3) find facial landmarks by utilizing facial landmark detector algorithm, (4) detect the eyes' region and calculate the eye aspect ratio. The proposed method obtained a high accuracy to indicate eye closing or opening. In this study, an aspect ratio method was used to implement a robust and low-cost embedded eye blink detection system on the raspberry pi platform. This method is exact resourceful, fast, and easy to perform eye blink detection.
<i>pHeavy</i>: Predicting Heavy Flows in the Programmable Data Plane

Since heavy flows account for a significant fraction of network traffic, being able to predict heavy flows has benefited many network management applications for mitigating link congestion, scheduling of network capacity, exposing network attacks and so on. Existing machine learning based predictors are largely implemented on the control plane of Software Defined Networking (SDN) paradigm. As a result, frequent communication between the control and data planes can cause unnecessary overhead and additional delay in decision making. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> , a machine learning based scheme for predicting heavy flows directly on the programmable data plane, thus eliminating network overhead and latency to SDN controller. Considering the scarce memory and limited computation capability in the programmable data plane, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> includes a packet processing pipeline which deploys pre-trained decision tree models for in-network prediction. We have implemented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> in both bmv2 software switch and P4 hardware switch (i.e., Barefoot Tofino). Evaluation results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> has achieved 85% and 98% accuracy after receiving the first 5 and 20 packets of a flow respectively, while being able to reduce the size of decision tree by 5.4x on average. More importantly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pHeavy</i> can predict heavy flows at line rate on the P4 hardware switch.
The 2020 ESPnet Update: New Features, Broadened Applications, Performance Improvements, and Future Plans

This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to mainly deal with end-to-end speech recognition experiments based on sequence-to-sequence modeling. The project has grown rapidly and now covers a wide range of speech processing applications. Now ESPnet also includes text to speech (TTS), voice conversation (VC), speech translation (ST), and speech enhancement (SE) with support for beamforming, speech separation, denoising, and dereverberation. All applications are trained in an end-to-end manner, thanks to the generic sequence to sequence modeling properties, and they can be further integrated and jointly optimized. Also, ESPnet provides reproducible all-in-one recipes for these applications with state-of-the-art performance in various benchmarks by incorporating transformer, advanced data augmentation, and conformer. This project aims to provide up-to-date speech processing experience to the community so that researchers in academia and various industry scales can develop their technologies collaboratively.
Kalman filtering under unknown inputs and norm constraints

Due to its potential applications in robotics and navigation, recent years have witnessed some progress in Kalman filter (KF) with norm constraints on the state. A noticeable discovery of the existing literature is that the KF gain has an analytical expression, and the brute-force normalization (i.e., estimation without considering the norm constraints, followed by a normalization operation) is optimal in the mean-square sense. Although there are some extensions of the former works to situations with uncertainties, existing results are only limited to cases where models/bounds or statistical properties of the disturbances are known. The paper considers the design of KF for systems subject to norm constraints on the state and unknown inputs, whose models or statistical properties are not assumed to be available. Both cases with and without direct feedthrough will be discussed. For systems without direct feedthrough, we show that the KF gain can be derived explicitly and the brute-force normalization is optimal in the mean-square sense, thereby generalizing the above-mentioned works on norm-constrained KF. However, for the case with direct feedthrough, the KF gain does not seem to admit an analytical solution, as to be shown via a counterexample.
Combining outlier analysis algorithms to identify new physics at the LHC

A bstract The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a β -variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using super- symmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.
Multi-interest Diversification for End-to-end Sequential Recommendation

Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation . Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.
Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare

This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the understanding of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently.
A non-subsampled Shearlet transform based approach for heartbeat sound watermarking

To secure the heartbeat sound exchanged in telemedicine, we propose in this work a blind watermarking approach combining a Non-Subsampled Shearlet Transform and Singular Value Decomposition. This approach allows guaranteeing the patients' successful authentication by integrating a watermark containing the patient's specific information as well as his photography in the audio file. In this approach to decompose the signal into low and high frequencies, a Non-Subsampled Shearlet Transform is performed. Singular value decomposition is then applied to the obtained low frequency components and the watermark bits are integrated by modulating the singular values. The imperceptibility results have typically shown our approach can integrate a satisfactory amount of information while preserving the diagnostic content of the audio file. In addition, the robustness tests demonstrate the proposed approach can efficiently generate a watermark resisting to several attacks. The imperceptibility results have shown our approach can integrate a satisfactory amount of necessary information while preserving the diagnostic content of the audio file. Since the patient informations are embedded in the multimedia file, a watermark extraction will clearly authenticate the potential patient. Since the patient informations are embedded in the multimedia file, a watermark extraction will clearly authenticate the patient.
A Survey of Public-Key Encryption With Search Functionality for Cloud-Assisted IoT

Nowadays, Internet of Things (IoT) is an attractive system to provide broad connectivity of a wide range of applications, and clouds are natural promoters. Cloud-assisted IoT combines the advantages of cloud computing and IoT, which is able to collect data from the real world and maximizes the value of the collected data by the means of data sharing and data analysis. Meanwhile, secure and convenient data retrieval in cloud servers becomes an important requirement for both enterprises and individual users. Public-key encryption with search functionality (shorten as PKE-SF) is a widely used cryptographic technique that allows users to retrieve encrypted data without decryption. PKE-SF mainly contains the primitives of public-key encryption with keyword search (PKE-KS), public-key encryption with equality test (PKE-ET), and plaintext-checkable encryption (PCE). In light of the overwhelming variety and multitude of PKE-SF schemes, this survey presents these schemes from different perspectives to provide better comprehension for beginners and advanced researchers. More concretely, this survey concentrates on the state of the art of PKE-SF by analyzing the design rationale, examining the framework and security model, and assessing the existing schemes in accordance with theoretic efficiency, security properties, and experimental performance. Furthermore, we discuss the extensions of traditional PKE-SF schemes which feature with the access control delegation, conjunctive keyword search, certificate-free, and offline keyword guessing attack resilience. Finally, we point out some promising directions for readers.
Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors

Artificial Intelligence (AI) for accessibility is a rapidly growing area, requiring datasets that are inclusive of the disabled users that assistive technology aims to serve. We offer insights from a multi-disciplinary project that constructed a dataset for teachable object recognition with people who are blind or low vision. Teachable object recognition enables users to teach a model objects that are of interest to them, e.g., their white cane or own sunglasses, by providing example images or videos of objects. In this paper, we make the following contributions: 1) a disability-first procedure to support blind and low vision data collectors to produce good quality data, using video rather than images; 2) a validation and evolution of this procedure through a series of data collection phases and 3) a set of questions to orient researchers involved in creating datasets toward reflecting on the needs of their participant community.
A Cloud-Assisted Reliable Trust Computing Scheme for Data Collection in Internet of Things

Large number of Internet of Things (IoT) applications with intelligent sensing devices (ISDs) are combined with cloud computing to collect and process data more efficiently. However, ISDs are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud, causing damage to IoT applications. Therefore, it is a critical issue to design an effective mechanism to ensure the security of data collection in cloud computing. In this article, a cloud-assisted reliable trust computing (CRTC) scheme is proposed to identify the trust of ISDs at low cost, providing high-quality data for IoT applications. The CRTC scheme mainly includes the following parts: First, a reliable approach of obtaining the real data of ISDs at a low cost is proposed to identify the trust of ISDs. In the proposed method, ISDs fits the forwarding packets information to form inspection information (II) with a small amount of data and then sends II to inspection nodes, effectively reducing the cost of routing II. Second, an effective method of trust computing is given to evaluate the trustworthiness of ISDs based on the data and II collected by unmanned aerial vehicles (UAV). Then, the aggregators are selected from high-trust ISDs to ensure secure data collection. Third, to obtain more reliable trust at lower cost, a trajectory optimization algorithm for UAV is proposed to collect as much II as possible and reduce the moving distance. Theoretical analysis and experimental results show that the proposed CRTC scheme is superior to previous strategies in terms of the success rate of data collection, the speed of identifying the trust of ISDs, and the UAV's trajectory distance.
Lightweight cryptography system for IoT devices using DNA

Many lightweight encryption algorithms have been developed for tackling the limitations of resources on the Internet of Things (IoT) devices. Such devices have limited processing capabilities in terms of speed, storage, and memory. In this paper, we designed and implemented a flexible lightweight encryption system with strong and simple substitution, and transposition operations to encrypt and decrypt data that meets limited processing capabilities within IoT devices. We used a variable block size to make the proposed system more flexible to be implemented on various IoT devices that have different memory sizes. Besides, the DeoxyriboNucleic Acid (DNA) sequence is utilized to generate random encryption keys that make it hard to break by the criminals. The experimental results of the proposed lightweight encryption system show promising results to be used for any IoT device with respect to memory size and encryption time compared to well-known cryptographic systems. This is achieved by using variable block size to minimize the size of memory use and minimizing the encryption time by using logical and rotate operations. For fulfilling the security goals, the developed lightweight encryption system has shown a better avalanche effect value which is over 50% compared to other existing encryption systems. The avalanche effect proves that such system is capable to secure IoT data against real-time attacks.