Reviewerly/exHarmony huggingface dataset:
Smart healthcare in smart city using wireless body area network and 5G

Smart healthcare is a cloud-based continuous remote monitoring system that allows users to receive the alerts when abnormal body condition is detected. This monitoring is done using various sensors, viz. smart thermometer, sphygmomanometer, glucometer, electrocardiogram (ECG), and electroencephalogram (EEG) which are integrated into the body area network (BAN). After measuring the essential body parameters of the patient, it combines the data and communicates wirelessly to store in internet of things (IoT) cloud. The data can be accessed by the medical person and suggestions can be given in terms of medicine and precautions on a real-time basis. This method will be of greater priority due to the disruptions caused by the COVID-19 pandemic during home quarantine. The healthcare organizations assess patient’s risk profile and connect them with virtual care facilities. The smart healthcare providers stay safe with virtual triaging of patients with the Centers for Disease Control and Prevention (CDC). The vital parameters of the patient sensed by the sensors sent via mobile phone and follow-up are used for symptom tracking. All these things are feasible due to high-speed internet service by the 5G wireless network and indoor broadband solution by the femtocell, i.e., Home eNodeB (HeNB).
Highly accurate end-to-end image steganalysis based on auxiliary information and attention mechanism

The purpose of image steganalysis is to detect whether the secret information is hidden in the image. The current advanced adaptive steganography algorithm hides the secret information in the complex area of the image texture. However, it is difficult for the image steganalysis model to capture enough noise residual, which makes the detection ability of the model insufficient. To further improve the detection ability of the spatial image steganalysis model, a U-Net-based auxiliary information generation network is first constructed to increase the size of noise residual in the image so that the model can capture more favorable information. Besides, the spatial and channel attention mechanisms are fused to guide the model to pay more attention to the regions of the image that are globally favorable for steganalysis. To verify the effectiveness of the proposed model, experiments are conducted on the BOSSbase-v1.01 dataset through advanced spatial adaptive steganography algorithms S-UNIWARD and WOW. The experimental results show that the proposed model can improve the detection accuracy by up to 4.5% compared with the current optimal deep learning-based spatial image steganalysis models SR-Net and Siamese-Net.
Let Each Quantum Bit Choose Its Basis Gates

Near-term quantum computers are primarily limited by errors in quantum operations (or gates) between two quantum bits (or qubits). A physical machine typically provides a set of basis gates that include primitive 2-qubit (2Q) and 1-qubit (1Q) gates that can be implemented in a given technology. 2Q entangling gates, coupled with some 1Q gates, allow for universal quantum computation. In superconducting technologies, the current state of the art is to implement the same 2Q gate between every pair of qubits (typically an XX-or XY-type gate). This strict hardware uniformity requirement for 2Q gates in a large quantum computer has made scaling up a time and resource-intensive endeavor in the lab. We propose a radical idea – allow the 2Q basis gate(s) to differ between every pair of qubits, selecting the best entangling gates that can be calibrated between given pairs of qubits. This work aims to give quantum scientists the ability to run meaningful algorithms with qubit systems that are not perfectly uniform. Scientists will also be able to use a much broader variety of novel 2Q gates for quantum computing. We develop a theoretical framework for identifying good 2Q basis gates on “nonstandard” Cartan trajectories that deviate from “standard” trajectories like XX. We then introduce practical methods for calibration and compilation with nonstandard 2Q gates, and discuss possible ways to improve the compilation. To demonstrate our methods in a case study, we simulated both standard XY-type trajectories and faster, nonstandard trajectories using an entangling gate architecture with far-detuned transmon qubits. We identify efficient 2Q basis gates on these nonstandard trajectories and use them to compile a number of standard benchmark circuits such as QFT and QAOA. Our results demonstrate an 8x improvement over the baseline 2Q gates with respect to speed and coherence-limited gate fidelity.
DeepBurning-SEG: Generating DNN Accelerators of Segment-Grained Pipeline Architecture

The growing complexity and diversity of deep neural network (DNN) applications have inspired intensive research on specialized DNN accelerators and also the design automation frameworks. Previous specialized NN acceleratos roughly fall into two categories of implementation, either the no-pipelined architecture that relies on a generic processing unit (PU) to sequentially execute the DNN layers in a layer-wise way, or the fully-pipelined architecture that dedicates interconnected customized PUs to the corresponding DNN layers in the model. Thus, such designs often suffer from either the resource under-utilization issue faced by no-pipelined accelerators or the resource scalability problem brought by the over-deep pipeline designs. In this work, we propose a novel class of design solution for DNN acceleration, segment-grained pipeline architecture (SPA). In the SPA accelerator, the targeted workload of DNN models will be divided into many segments and each segment will be sequentially executed on the shared interconnected PUs in a pipeline manner, so that they will benefit from both the efficiency of pipelined execution and also the flexibility of sharing PUs across different model layers. Particularly, we found that the efficiency of the implemented SPA accelerator significantly depends on the segmentation strategies of the models and the hardware resources assignment policy for PUs. Therefore, we introduce an automated design framework, AutoSeg, that includes a parameterized SPA accelerator template and a co-design engine that will generate the efficient model segmentation solution and hardware pipeline design parameters for the acceleration workload. Experimental results show that the SPA solutions generated by the AutoSeg framework achieve $1.2\times to 6.3\times$ speedup when compared to ASIC-based general DNN processors, and the FPGA designs implemented by AutoSeg also achieve as high as $3.4\times$ DSP efficiency and $3.6\times$ throughput improvement.
Improved Single Shot Detector with Enhanced Hard Negative Mining Approach

Image classification and tiny-object detection are challenging tasks in computer vision domain. This is primarily due to their ability to tackle real-world problems, such as de-veloping self-driving cars, robot navigation, surveillance systems, and monitoring road safety. A hard negative mining approach is predominately utilized to train object detection networks, which use positive and negative samples to increase network gains, but the selection of negative samples is an expensive process as the network identifies many duplicates. Numerous research findings are being carried out to enhance hard negative mining. This research addresses the drawbacks of existing techniques in the hard negative mining approach and proposes utilizing medium priors to improve network performance. Medium priors can be defined as anchor boxes with 20 % to 50 % overlap with ground truth boxes. Since the tiny objects are much smaller than other objects in a frame, considering medium priors significantly enhances the detection probability. The proposed metric has been evaluated using Single Shot Multibox Detector (SSD) architec-ture. Experimental results on PASCAL VOC datasets indicate that the average precision of tiny objects such as potted plants and bottles increased by 4 % and 3.9 % with an overall increase in mAP of 0.9%.
Self-Recovery in Fragile Image Watermarking Using Integer Wavelet Transform

The growth of internet technology makes digital images can be easily distributed and modified to change image information. This research proposes fragile image watermarking based on LSB Adjustment on integer wavelet transform. This study focuses on self-recovery and authenticating the integrity of image contents. The proposed scheme embeds a watermark into the two-LSB by using LSB adjustment. The authentication bits are randomly located in the two-LSB with a secret key. The recovery data is generated based on IWT for the recovery process. The proposed watermarking method uses multi-layer authentication to achieve high precision and accuracy. This research also uses image inpainting techniques to improve the recovery under tamper attacks. The experimental results produce high imperceptibility with PSNR of 45.5 dB and SSIM of 0.997. The proposed scheme produces better self-recovered image quality than the existing scheme with PSNR of 37.98 dB and SSIM of 0.9928 against a tampering rate of 10%.
An offline English optical character recognition and NER using LSTM and adaptive neuro-fuzzy inference system

Everything becomes smart in the modern era, for everything we need a better plan or arrangements. In the olden days, essential information was noted as a document with the help of paper and pen or printed texts. But the intelligent world needs a paperless environment by converting handwritten or printed text documents into software copies. This can be achieved by the electronic data conversion concept called Optical Character Recognition (OCR). OCR of some documents is complex because of different writing styles and quality of scanned image issues, which can be solved by adopting a deep learning technique for better accuracy. We employed Long Short Term Memory (LSTM) for English Optical Character Recognition for paperless and effortless data storage and fast access in this work. Still, the records may contain the entities like names, contact details, drug details, diseases, educational qualifications, dates, etc. These entities cannot be separated by employing OCR alone; we need an entity recognition framework for deeper and faster data analysis. For efficient Named Entity Recognition, we utilize the Adaptive Fuzzy Inference System (ANFIS) powered by the algorithms CRF and BERT to automatically label each entity by training the vast amount of unlabeled text data. The ANFIS model is equipped with both linguistic and numerical knowledge. It is more accurate than the ANN when it comes to identifying patterns and classification data. Also, it is more transparent to the user. Our proposed framework aims to improve the performance of the character recognition system by using a feed-forward network. One of the main issues that have been identified in the development of this system is noise. Through this network, we can provide a single input and one output layer. The main components of the system are the training and recognition sections. These two sections are mainly focused on image acquisition and feature extraction. Besides these, they also include training and simulation of the classifier. The first step in the process of image recognition is to extract the features from the normalized image matrix. We then train the network using a proposed training algorithm. Experimentation on medical records attains a higher accuracy value of 0.9637, recall value of 0.9627, and f1 score of 0.9627, respectively.
Looking to the Sky for Better Tsunami Warnings

Pairing navigation satellites and CubeSats could provide earlier, more accurate warnings of approaching tsunamis and other impacts of extreme events.
A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams

Nowadays the amount of networks of devices and sensors, such as smart homes or smart cities, is rapidly increasing. Each of these devices generates massive amounts of data on a continuous basis where an interpretable description of its state is interesting for the experts. This knowledge can be extracted by means of emerging pattern mining techniques. In fact, it can be extracted locally on each device and joined together afterwards in order to obtain a global vision of the system without transferring any data. However, the traditional massive data processing frameworks are focused on the extraction of this global model, which produces huge amounts of data transfers throughout the network. This paper proposes a distributed method based on evolutionary fuzzy systems for both the extraction and subsequent fusion of descriptive emerging patterns in data streams from different sources of the same kind. First, an evolutionary algorithm following an informed approach for efficient data processing is presented for the extraction of emerging patterns on the data stream generated by each device, in order to obtain a local model for each stream. Then, several fusion methods are proposed for the aggregation of these patterns in order to extract the global model. A wide experimental study has been carried out to analyse the suitability of the evolutionary algorithm for the extraction of high-quality emerging patterns and its capacity to deal with concept drift. Finally, the quality of the proposed fusion methods is also analysed.
Vision Transformers for Road Accident Detection from Dashboard Cameras

Road accidents are increasing at a worrying rate and have raised one of the major concerns in traffic road monitoring. Their detection is becoming a very important aspect for intelligent traffic management systems. Unlike most of the existing anomaly detection systems that mainly monitor traffic status from static cameras, we focus in this paper on more challenging scenario using dashboard cameras. To handle this problem, we propose to adopt vision transformers with positional embeddings and based on multi-head attention mechanism for traffic monitoring following the increasing development of such models in natural language processing and computer vision communities. Precisely, to accomplish accident identification while exploiting the spatio-temporal aspect of videos, we employ a mix architecture. This architecture has the advantage of incorporating convolutional layers to capture local correlations of different patterns within the same image and vision transformer to learn the sequential correlations between the extracted features. Extensive experiments on two popular datasets DAD and CCD have been conducted to demonstrate the effectiveness of the proposed approach in terms of detection accuracy. The obtained results are compared to some recurrent neural networks commonly used to process sequential input data such as CNN-RNN, Conv-LSTM, and LCRN.
Evidential classification for defending against adversarial attacks on network traffic

Research interest in demonstrating vulnerability of Machine Learning (ML) algorithms against sophisticated Adversarial Machine Learning (AML) perturbation attacks has become more prominent in recent years. Adversarial attacks perturb dataset instances by finding the nearest decision boundary and moving the instance values towards the boundary. Thus, a popular challenge in this field is combating such adversarial attacks by increasing model accuracy. Making a model more robust often requires the ML engineer to have preemptive knowledge not only that an adversarial attack will occur, but also which attack will occur. This work is the first to reinforce a Neural Network (NN) model in a network security environment against AML attacks by leveraging an evidential classification approach. Evidential approaches allow for measuring an extra degree of insight of uncertainty between features to enable classification of ambiguous instances as uncertain. Crucially, the proposed approach does not require any training of perturbed datasets or any knowledge that an adversarial attack may take place. Recent advances in making ML models more robust against single-step adversarial attacks have been greatly successful, but researchers have found greater issue in making their models more robust against complex, iterative attacks. The proposed approach is evaluated using a modern network security dataset, and compared against a conventional Bayesian NN. Rather than training a model to increase Accuracy, the proposed approach aims to reduce the misclassification rate of perturbed data. By allowing instances in a dataset to be classified as uncertain, comparing against a conventional NN, the proposed approach produces results that decrease the misclassification rates on the two perturbed malicious classes from 70.53% to 13.09%, and from 99.67% to 1.33%, respectively.
Using learning analytics to investigate learning processes and behavioural patterns in an augmented reality language learning environment

Abstract Background Augmented reality has been widely applied in various fields, and its benefits in language learning have been increasingly recognized. However, the investigation of effective learning behaviours and processes in augmented reality learning environments, taking into account temporality and analysis of differences in learning behaviours between learning achievements from the perspective of learning strategies, is still a topic that requires further research. Objectives The goal of this research is to explore effective learning processes in AR language learning environments and learning behavioural patterns of learners with different performance levels via LA taking into account temporality. Methods We used the learning application designed and developed in previous studies to understand the learning process and behavioural patterns of acquiring Japanese compound verbs with augmented reality. Lag sequential analyses were conducted to analyse the learning behavioural transitions using learning data. Additionally, frequent sequences were analysed using frequent sequence mining to compare the learning behavioural patterns of learners with different learning performances. Results and Conclusions We investigated the learning process and behavioural patterns and discussed the language learning strategies employed in the augmented reality learning environment. The results indicated that learners primarily performed strategies of performed expressions, recombining, and structured reviewing in AR compound verb learning activities. Furthermore, the results revealed the different types of strategies applied by different learning performance learners, whereby the low group learners used the least variety of strategies, and the middle and high group learners employed repeating and deductive reasoning strategies that were not followed by the low group.
Quantum Algorithm for Numerical Energy Gradient Calculations at the Full Configuration Interaction Level of Theory

A Bayesian phase difference estimation (BPDE) algorithm allows us to compute the energy gap of two electronic states of a given Hamiltonian directly by utilizing the quantum superposition of their wave functions. Here we report an extension of the BPDE algorithm to the direct calculation of the energy difference of two molecular geometries. We apply the BPDE algorithm for the calculation of numerical energy gradients based on the two-point finite-difference method, enabling us to execute geometry optimization of one-dimensional molecules at the full-CI level on a quantum computer. Results of numerical quantum circuit simulations of the geometry optimization of the H2 molecule with the STO-3G and 6-31G basis sets, the LiH and BeH2 molecules at the full-CI/STO-3G level, and the N2 molecule at the CASCI(6e,6o)/6-311G* level are given.
Multi-granularity Pseudo-label Collaboration for unsupervised person re-identification

Unsupervised person re-identification (ReID) aims at training a model through using fully unlabeled training images. Most successful approaches combine clustering-based pseudo-label prediction with ReID model learning and perform the two steps in an alternating fashion. However, there are some person images, which have common appearance but different identities or have same identity but large intra-variations, and they are prone to be assigned wrong pseudo-labels. Existing methods typically infer person image pseudo-labels in a single pseudo-label space, which is insufficient due to losing the accurate labels of these hard images. This limits model’s power to cope with varying changes. To address this problem, we propose a Multi-granularity Pseudo-label Collaboration (MPC) method for unsupervised person ReID. Firstly, a multi-granularity pseudo-label prediction (MPP) method is proposed to predict the pseudo-labels of person images. MPP generates multiple pseudo-label spaces with each one inferring the person image pseudo-labels under a different granularity at cluster size. Secondly, the multiple pseudo-label spaces are applied to learn a mixture of experts (MoE) model. Each expert model is supervised by the pseudo-labels in a specific pseudo-label space, enabling MoE model to handle different variations. The output of these expert models are fused critically by a bilateral attention aggregation (BAA) to generate aggregation image features. Meanwhile, a label mix-up method is designed for refining pseudo-labels. It fuses labels across different pseudo-label spaces based on label co-occurrence to promote aggregation feature learning. We extensively evaluate the proposed MPC method under both image-based and video-based unsupervised person ReID settings. MPC significantly improves the current baseline method by a margin of 5.4% and 3.7% in mAP, and 3.4% and 3.9% in Rank1 accuracy on the image-based datasets Market-1501 and DukeMTMC-ReID, respectively. In particular, MPC method achieves state-of-the-art performance on video-based datasets with mAP of 71.4% and 87.3% on MARS and DukeMTMC-VideoReID, respectively.
Quantum algorithm for learning secret strings and its experimental demonstration

In this paper, we consider the secret-string-learning problem in the teacher–student setting: the teacher has a secret string s∈{0,1}n, and the student wants to learn the secret s by question–answer interactions with the teacher, where at each time, the student can ask the teacher with a pair (x,q)∈{0,1}n×{0,1,…,n−1} and the teacher returns a bit given by the oracle fs(x,q) that indicates whether the length of the longest common prefix of s and x is greater than q or not. Our contributions are as follows. (i) We prove that any classical deterministic algorithm needs at least n queries to the oracle fs to learn the n-bit secret string s in both the worst case and the average case, and also present an optimal classical deterministic algorithm learning any s using n queries. (ii) We propose a quantum algorithm learning the n-bit secret string s with certainty using n/2 queries to the oracle fs, thus proving a double speedup over classical counterparts. (iii) Experimental demonstrations of our quantum algorithm on the IBM cloud quantum computer are presented, with the average success probabilities of 85.3% and 82.5% for all cases with n=2 and n=3, respectively.
Controlled quantum teleportation in the presence of an adversary

We present a device independent analysis of controlled quantum teleportation where the receiver is not trusted. We show that the notion of genuine tripartite nonlocality allows us to certify control power in such a scenario. By considering a specific adversarial attack strategy on a device characterized by depolarizing noise, we find that control power is a monotonically increasing function of genuine tripartite nonlocality. These results are relevant for building practical quantum communication networks and also shed light on the role of nonlocality in multipartite quantum information processing.