Reviewerly/exHarmony huggingface dataset:
Lightweight and Privacy-Preserving Remote User Authentication for Smart Homes

The rapid proliferation of embedded devices has led to the growth of the Internet of Things (IoT) with applications in numerous domains such as home automation, healthcare, education and agriculture.However, many of the connected devices particularly in smart homes are the target of attacks that try to exploit security vulnerabilities such as hard-coded passwords and insecure data transfer.Recent studies show that there is a considerable surge in the number of phishing attacks targeting smart homes during the COVID-19 pandemic.Moreover, many of the existing user authentication protocols in the literature incur additional computational overhead and need to be made more resilient to smart home targeted attacks.In this paper, we propose a novel lightweight and privacy-preserving remote user authentication protocol for securing smart home applications.Our approach is based on Photo Response Non-Uniformity (PRNU) to make our protocol resilient to smart home attacks such as smartphone capture attacks and phishing attacks.In addition, the lightweight nature of our solution is suitable for deployment on heterogeneous and resource constrained IoT devices.Besides, we leverage geometric secret sharing for establishing mutual authentication among the participating entities.We validate the security of the proposed protocol using the AVISPA formal verification tool and prototype it on a Raspberry Pi to analyze the power consumption.Finally, a comparison with existing schemes reveals that our scheme incurs a 20% reduction in communication overhead on smart devices.Furthermore, our proposed scheme is usable as it absolves users from memorizing passwords and carrying smart cards.
Quantum Computing in the Cloud: Analyzing job and machine characteristics

As the popularity of quantum computing continues to grow, quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis of resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing. This paper is a first-of-its-kind academic study, analyzing various trends in job execution and resources consumption / utilization on quantum cloud systems. We focus on IBM Quantum systems and analyze characteristics over a two year period, encompassing over 6000 jobs which contain over 600,000 quantum circuit executions and correspond to almost 10 billion “shots” or trials over 20+ quantum machines. Specifically, we analyze trends focused on, but not limited to, execution times on quantum machines, queuing/waiting times in the cloud, circuit compilation times, machine utilization, as well as the impact of job and machine characteristics on all of these trends. Our analysis identifies several similarities and differences with classical HPC cloud systems. Based on our insights, we make recommendations and contributions to improve the management of resources and jobs on future quantum cloud systems.
BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images

In clinical treatment, deep learning plays a pivotal role in medical image classification. Deep learning techniques provide opportunities for radiologists and orthopedic to ease out their lives with faster and more accurate results. The traditional deep learning approach nevertheless reached its performance ceiling. Therefore, in this paper, we investigate different enhancement techniques to boost the deep neural networks performance and provide a solution as BoostNet. The experiment is categorized into four different phases. We have selected ChampNet from benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet18, VGG19). This phase helps to obtain the best model. In the second phase, The ChampNet evaluates with different resolution datasets. This phase helps to finalize the dataset resolution to enhance the performance of ChampNet. In the third phase, Champ-Net merges with image enhancement techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE), High-frequency filtering (HEF), and Unsharp masking (UM). This phase helps to obtain Boost-Net with enriched performance. The last phase helps us to verify BoostNet results with Lightness Order Error. The presented research work fuses the image enhancement technique with ChampNet to generate BoostNet models. An assessment was performed on the Musculoskeletal Radiograph Bone Classification using classification schemes to demonstrate the proposed model's performance. The Classification accuracy of BoostNet was for the train a test dataset with and without enhancement techniques. The proposed model ChampNet + CLAHE, ChampNet + HEF, ChampNet + UM approach achieved 95.88%, 94.99%, and 94.18% accuracy, respectively. This experiment leads to a more accurate and efficient classification model. The main aim of this paper is to enhance techniques to boost the deep neural networks performance and provide a solution as BoostNet.
MF-BERT: Multimodal Fusion in Pre-Trained BERT for Sentiment Analysis

Multimodal sentiment analysis mainly concentrates on language, acoustic and visual information. Previous work based on BERT utilizes only text (language) representation to fine-tune BERT, while ignoring the importance of nonverbal information. Due to the fact that features extracted from a single modality may contain uncertainty, it is challenging for BERT to perform well in real-world applications. In this paper, we propose a multimodal fusion BERT that can explore the time-dependent interactions among different modalities. Additionally, prior BERT-based methods tend to train the models with only one optimizer to update the parameters. However, we argue that BERT has been pre-trained with a lot of corpora so it needs to be fine-tuned slightly. Therefore, an internal updating mechanism is introduced to avoid the overfitting of the model in the training process. We set two optimizers for multimodal fusion BERT and other components of the model with different learning rates, which enables the model to attain optimal parameters. The results of experiments on public datasets demonstrate that our model is superior to the baselines and achieves the state-of-the-art.
Born machine model based on matrix product state quantum circuit

Born machine model based on the probability interpretation of the wave function combining quantum information theory with machine learning method provides a new tool to study the generative models. The Born machine model with a general parameterized quantum circuit generally requires the same number of qubits as the sample feature size of the dataset to be processed, while each sample usually contains thousands of features in actual dataset. A novel Born machine model with a matrix product state quantum circuit is proposed, which requires less qubits than that with a general parameterized quantum circuit, so it can make better use of scarce qubit resources in near-term quantum devices. And the presented Born machine model is trained with the maximal mean discrepancy loss function. The learning process of the proposed Born machine model is numerically simulated on the Bars-and-Stripes dataset. The simulation results verify the feasibility of the Born machine model with the matrix product state quantum circuit.
Multiple birth support vector machine based on dynamic quantum particle swarm optimization algorithm

At present, the parameters of the multiple birth support vector machine (MBSVM) are mainly determined by experience or artificially specified by the grid method. Both of these methods rely too much on the experience value, which is easy to cause the selection of the parameter value to be insufficient. The selection of parameters directly affects the performance of MBSVM, so how to find a set of optimal parameters plays a vital role in improving the performance of MBSVM. To solve this problem, a dynamic quantum particle swarm optimization algorithm (DQPSO) was proposed. Focusing on the contraction – expansion (CE) coefficient control mode, authors propose the concept of the ability factor of particle search and realize the dynamic regulation of CE coefficient by taking it as feedback. Then DQPSO algorithm is used to optimize the parameters of MBSVM. The experimental results show that the proposed DQPSO algorithm has better optimization performance and faster convergence speed than that of the classical QPSO. Meanwhile, experiments on the UCI datasets show that the proposed DQPSO-MBSVM algorithm is effective to improve the classification performance of MBSVM.
Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks

High-resolution digital elevation models (DEMs) play a critical role in geospatial databases, which can be applied to many terrain-related studies such as facility siting, hydrological analysis, and urban design. However, due to the limitation of precision of equipment, there are big gaps to collect high-resolution DEM data. A practical idea is to recover high-resolution DEMs from easily obtained low-resolution DEMs, and this process is termed DEM super-resolution (SR). However, traditional DEM SR methods (e.g., bicubic interpolation) tend to over-smooth high-frequency regions on account of the operation of averaging local variations. With the recent development of machine learning, image SR methods have made great progress. Nevertheless, due to the complexity of terrain characters (e.g., peak and valley) and the huge difference between elevation field and image RGB (Red, Green, and Blue) value field, there are few works that apply image SR methods to the task of DEM SR. Therefore, this paper investigates the question of whether the state-of-the-art image SR methods are appropriate for DEM SR. More specifically, the traditional interpolation method and three excellent SR methods based on neural networks are chosen for comparison. Experimental results suggest that SRGAN (Super-Resolution with Generative Adversarial Network) presents the best performance on accuracy evaluation over a series of DEM SR experiments.
A Novel Hybrid Hunger Games Search Algorithm With Differential Evolution for Improving the Behaviors of Non-Cooperative Animals

Inspired by the behaviors of animals in the state of starvation, hunger games search algorithm (HGS) is proposed. HGS has shown competitive performance among other meta-heuristic (MH) algorithms. However, HGS tends to stagnate in local optimal for some complex optimization problems and remains premature convergence. Therefore, to solve these problems and enhance the diversity of the population, a modified HGS based on the operators of the differential evolution algorithm (DE), chaotic local search (CLS) strategies, and evolutionary population dynamics technique (EPD) is proposed (named DECEHGS). The proposed DECEHGS algorithm consists of two stages: in the first stage, based on the animals’ behaviors, we use different evolutionary methods to update animals’ positions; in the second stage, the CLS strategy and EPD technique are combined to prevent premature convergence and stagnation in a local optimum. The proposed algorithm was evaluated using IEEE CEC2014 and IEEE CEC2017 mathematical functions and four engineering problems. The experimental results demonstrate that DECEHGS has competitive performance in global optimization tasks and engineering problems compared with state-of-the-art algorithms.
3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D

While photovoltaic (PV) systems are being installed at an unprecedented rate, it is challenging to keep track of them due to their decentralized character and large number. In this paper, we present the 3D-PV-Locator for large-scale detection of roof-mounted PV systems in three dimensions (3D). The 3D-PV-Locator combines information extracted from aerial images and 3D building data by means of deep neural networks for image classification and segmentation, as well as 3D spatial data processing techniques. It thereby extends existing approaches for the automated detection of PV systems from aerial images by also providing their azimuth and tilt angles. We evaluate the 3D-PV-Locator using a large dataset gathered from the official German PV registry in a real-world study with more than one million buildings. In terms of azimuth and tilt angles, our evaluation shows that the 3D-PV-Locator and the official registry coincide for about two thirds of the observations and are within neighboring classes for 84 and 99 percent of the observations, respectively. In terms of detected PV system capacity, we show that the 3D-PV-Locator clearly outperforms existing approaches. It performs particularly well for the groups of small and medium-sized PV systems (3.6–33.1 percent error reduction) and PV systems tilted beyond 40° (25.6–38.1 percent error reduction). The 3D PV system data generated by the 3D-PV-Locator can inform several practical applications, such as improved forecasting of solar generation, the optimized planning and operation of distribution networks, improved integration of electric vehicles, and others. All datasets and pre-trained models associated with this paper are available online.
Reflectance spectroscopy for assessing heavy metal pollution indices in mangrove sediments using XGBoost method and physicochemical properties

Single and composite pollution indices (SPI and CPI) of heavy metals are calculated with background heavy metal concentrations. They are efficient evaluation indicators of sediment pollution levels and could be directly compared among different regions and habitat types, however, they are rarely investigated in mangrove sediment with visible and near-infrared spectroscopy (VNIRS). With mangrove sediment samples collected from five regions and four habitat types, this study aimed to develop estimation models of heavy metal (Cr, Ni, Cu, Zn and Pb) pollution indices using XGBoost (extreme gradient boosting) method, and further to explore the effect of physicochemical properties (Fe, organic carbon (OC), clay and salinity) on the estimation accuracy. Kruskal-Walls test showed that regions and habitat types were significant factors affecting the six pollution indices. Compared with first derivative, second derivative and continuum removal reflectance, standard normal variate (SNV) reflectance was more accurate for estimating SPIs and CPI using XGBoost model. The XGBoost models with sensitive wavelengths of SNV reflectance outperformed that with whole spectrum (410 bands) in estimating the six pollution indices, and the ranking of estimation accuracy was CPI > SPICu > SPIZn > SPIPb > SPINi > SPICr. Compared with the XGBoost model with sensitive wavelengths alone, the model with additional physicochemical properties could increase R2 and RPD (the ratio of standard deviation to RMSE of validation in the test subset) values by 0.795–7.282% and 1.199–8.780%, and decrease RMSE values by 4.101–26.866% in estimating SPICr, SPINi and SPICu. Moreover, Fe and OC had greater effect on the estimation accuracy than clay and salinity. We conclude the integration of XGBoost and physicochemical properties holds potentials for accurately assessing heavy metal pollution indices in mangrove sediment with VNIRS, which can provide methodological basis for evaluating environment quality and ecological risk in mangrove ecosystem at the plot or landscape scale.
Artificial intelligence for dermatopathology: Current trends and the road ahead

Artificial intelligence (AI), including deep learning methods that leverage neural network-based algorithms, hold significant promise for dermatopathology and other areas of diagnostic pathology in research and clinical practice. There has been significant progress over past several years in applying AI to analyzing digital histopathology images for diagnosis. While much work in AI analysis of histopathology data remains investigational, recent regulatory agency approval in Europe and United States of AI-assisted tools for clinical use in histopathologic diagnosis of prostate and breast cancer herald broader movement of AI into the clinical diagnostic realm of anatomic pathology, including dermatopathology. However, significant challenges remain in translating AI from research into clinical practice, including algorithmic real-world performance, robustness to variation in data sets and practice settings, effective integration into clinical workflows, and cost effectiveness. This review introduces core concepts and terminology in AI, and assesses current progress and challenges in applying AI to dermatopathology.
Evolutionary Competitive Multitasking Optimization

This article introduces a special multitasking optimization problem (MTOP) called the competitive MTOP (CMTOP). Its distinctive characteristics are that all tasks&#x2019; objectives are comparable, and its optimal solution is the best one among the optimal solutions of all the individual problems. This article proposes an evolutionary algorithm with an online resource allocation strategy and an adaptive information transfer mechanism to solve the CMTOP. The experimental results on benchmark and real-world problems show that our proposed algorithm is effective and efficient.
Differentiable neural architecture learning for efficient neural networks

• We build a new standalone control module based on the scaled sigmoid function to enrich the neural network module family to enable the neural architecture optimization. • Our DNAL method produces no candidate neural architectures but one, thus drastically improving the learning efficiency, i.e., costing 20 epochs for CIFAR-10 and 10 epochs for ImageNet. • It is applicable to conventional CNNs, lightweight CNNs, and stochastic supernets. • Extensive experiments confirm that our DNAL method achieves excellent performance on various CNN architectures, including VGG16, ResNet50, MobileNetV2, and ProxylessNAS, over the task of CIFAR-10 and ImageNet-1K classification. Efficient neural networks has received ever-increasing attention with the evolution of convolutional neural networks (CNNs), especially involving their deployment on embedded and mobile platforms. One of the biggest problems to obtaining such efficient neural networks is efficiency, even recent differentiable neural architecture search (DNAS) requires to sample a small number of candidate neural architectures for the selection of the optimal neural architecture. To address this computational efficiency issue, we introduce a novel architecture parameterization based on scaled sigmoid function , and propose a general Differentiable Neural Architecture Learning (DNAL) method to obtain efficient neural networks without the need to evaluate candidate neural networks. Specifically, for stochastic supernets as well as conventional CNNs, we build a new channel-wise module layer with the architecture components controlled by a scaled sigmoid function. We train these neural network models from scratch. The network optimization is decoupled into the weight optimization and the architecture optimization, which avoids the interaction between the two types of parameters and alleviates the vanishing gradient problem. We address the non-convex optimization problem of efficient neural networks by the continuous scaled sigmoid method instead of the common softmax method. Extensive experiments demonstrate our DNAL method delivers superior performance in terms of efficiency, and adapts to conventional CNNs (e.g., VGG16 and ResNet50), lightweight CNNs (e.g., MobileNetV2) and stochastic supernets (e.g., ProxylessNAS). The optimal neural networks learned by DNAL surpass those produced by the state-of-the-art methods on the benchmark CIFAR-10 and ImageNet-1K dataset in accuracy, model size and computational complexity. Our source code is available at https://github.com/QingbeiGuo/DNAL.git .
Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks

In this paper, we propose a novel deep learning method for joint classification and segmentation of breast masses based on radio-frequency (RF) ultrasound (US) data. In comparison to commonly used classification and segmentation techniques, utilizing B-mode US images, we train the network with RF data (data before envelope detection and dynamic compression), which are considered to include more information on tissue's physical properties than standard B-mode US images. Our multi-task network, based on the Y-Net architecture, can effectively process large matrices of RF data by mixing 1D and 2D convolutional filters. We use data collected from 273 breast masses to compare the performance of networks trained with RF data and US images. The multi-task model developed based on the RF data achieved good classification performance, with area under the receiver operating characteristic curve (AUC) of 0.90. The network based on the US images achieved AUC of 0.87. In the case of the segmentation, we obtained mean Dice scores of 0.64 and 0.60 for the approaches utilizing US images and RF data, respectively. Moreover, the interpretability of the networks was studied using class activation mapping technique and by filter weights visualizations.
A deep learning model for breast ductal carcinoma in situ classification in whole slide images

The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pathologists using microscopes is limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately diagnose large numbers of histopathological specimens. Computational pathology tools which can assist pathologists in detecting and classifying DCIS and IDC from whole slide images (WSIs) would be of great benefit for routine pathological diagnosis. In this paper, we trained deep learning models capable of classifying biopsy and surgical histopathological WSIs into DCIS, IDC, and benign. We evaluated the models on two independent test sets (n= 1382, n= 548), achieving ROC areas under the curves (AUCs) up to 0.960 and 0.977 for DCIS and IDC, respectively.
Human-Centric Image Captioning

In this paper, we propose a new topic, Human-Centric Captioning, to mainly describe the human behavior in an image. Human activities and relationships are the primary objectives of visual understanding in daily applications. However, existing image captioning systems cannot differently treat humans and other objects, which limits the ability to understand and describe diverse human activities. As the first explorer of this new task, we build a novel Human-Centric COCO dataset concentrating on humans. Accordingly, we propose a novel Human-Centric Captioning Model (HCCM) that focuses on human-centric feature hierarchization and sentence generation. Specifically, our model first utilizes human body part-level knowledge to hierarchize the image features and then applies a novel three-branch captioning model to process these hierarchical features independently to calibrate the descriptions of human actions. Comprehensive experiments demonstrate that our HCCM achieves the state-of-the-art performance with BLEU-4, CIDEr and SPICE scores of 41.5, 127.3, 23.5 respectively. Dataset and code are publicly available at https://github.com/JohnDreamer/HCCM/.