Reviewerly/exHarmony huggingface dataset:
Exploring Efficient Microservice Level Parallelism

The microservice architecture has recently become a driving trend in the cloud by disaggregating a monolithic application into many scenario-oriented service blocks (microservices). The decomposition process results in a highly dynamic execution scenario, in which various chained microservices contend for computing resources in different ways. While parallelism has been exploited at both the instruction/thread level and the task/request level, very limited work has been done with the grain-size of a microservice. Current parallel processing solutions are sub-optimal as they neither capture the unique characteristics of microservices nor consider the uncertainty arises in the microservice environment. In this work we introduce microservice level parallelism (MLP), a technique that aims to precisely coalesce and align parallel microservice chains for better system performance and resource utilization. We identify major issues that prevent servers from effectively exploiting MLP and we define metrics that can guide MLP optimization. We propose v-MLP, a volatility-aware MLP that is able to adapt to a highly heterogeneous and dynamic microservice environment. We show that v-MLP can reduce tail latency by up to 50% and improve resource utilization by up to 15 % under various scenarios.
A Survey on Designing a Secure Smart Healthcare System with Blockchain

The Internet of Things (IoT) is a new development in information technology that aims to connect the physical and advanced virtual worlds. IoT depicts a vision of smart and connected environment by empowering connections between communication devices and people within a cyberspace. IoT can be widely integrated in all areas such as smart well-being, smart transportation, and smart cities, and so on. Within healthcare proposals, IoT facilitates correspondence between specialists and patients as the latter can be analyzed remotely in emergency situations over physique-based equipment and smart wearable devices. In any case, utilizing IoT in medical services frameworks can result in patient security breaches. As a result, security should be considered. Blockchain is one of the most active research areas these days, and it can be applied to the majority of IoT scenarios. There are only a few significant reasons for utilizing blockchain in medical care frameworks, and they are its recognizable components, particularly decentralization, data integrity, safety, confidentiality, and transparency. This paper provides a brief overview on blockchain-based security in healthcare IoT. The primary goal of this paper is to enhance the usefulness of medical services frameworks by utilizing emerging and innovative computing innovations such as IoT and blockchain. In this paper, a brief introduction to the fundamental concepts of IoT is followed by a discussion on blockchain. As a result, the application of IoT and blockchain in the clinical setting is being researched in three key areas: medication recognition, remote clinical observation, and clinical data board. Finally, the challenges of integrating IoT and blockchain in medical care frameworks are discussed.
FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks

Recent studies have demonstrated the potentials of federated learning (FL) in achieving cooperative and privacy-preserving data analytics. It would also be promising if FL can be employed in vehicular ad hoc networks (VANETs) for cooperative learning tasks, such as steering angle prediction, trajectory prediction, drivable road detection, etc., among integrated vehicles. However, since VANETs are characterized by ad hoc cooperating vehicles with non-independent and identically distributed (Non-IID) data, directly employing existing FL frameworks to VANETs may cause extensive communication overhead and compromised model performance. Further, most of the existing deep learning models incorporated in FL frameworks rely heavily on data with manual annotations, leading to a huge labor cost. To address these issues, in this paper we propose an efficient and effective Federated End-to-End Learning framework for cooperative learning tasks in VANETs, named FEEL. Specifically, we first formulate a distributed optimization problem for cooperative deep learning tasks with Non-IID data in multi-hop cluster VANETs. Second, two algorithms for inter-cluster learning and inner-cluster learning are respectively designed, to reduce the communication overhead and fit Non-IID data. Third, a Paillier-based communication protocol is crafted, allowing secure model parameter updates at the central server without knowing the real updates at each cooperating base station. Extensive experiments on two real-world datasets are conducted by considering various data distributions and VANET topologies, demonstrating the high efficiency and effectiveness of the proposed FEEL framework in both regression and classification tasks.
DeformableGAN: Generating Medical Images With Improved Integrity for Healthcare Cyber Physical Systems

The development of deep learning enables the production of new images via generative adversarial networks (GANs). The GANs have now been widely applied in the industry as well as academic research that brought tremendous progress to our community. Many researchers in medical imaging also introduced this novel technology for medical image reconstruction, segmentation, synthesis, etc. On the other hand, the GAN-generated images may also suffer from exhibiting unique textures, namely the checkerboard artifacts. For medical diagnosis and healthcare, such artifacts could bring negative impacts as they may distort information collected in medical images. Improper treatment and rehabilitation plans based on the disinformation of checkerboard artifacts could be harmful for patients and healthcare cyber physical systems. Thus, we investigate the checkerboard artifact synthesized during adversarial training in this paper. Based on the theoretical analysis, we propose a method for GANs to generate images without producing checkerboard artifacts. It could protect medical images preserving high integrity for healthcare cyber physical systems. Our experiments justify the efficiency of proposed method when associating with a variety of GANs for image synthesis. Also, we prove that it is feasible to detect GAN-generated images by tracing the checkerboard artifacts.
A Novel Strategy for Solar Irradiance Forecasting Using Deep Learning Techniques and Validation for a Himalayan Location in India as a Case Study

Reliable estimation of solar irradiance data is required for a number of solar energy applications. The Deep Learning (DL) techniques have achieved a high prediction accuracy due to their ability to analyze complex datasets. In this study Multi-Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) techniques are used for daily forecasting of global solar irradiance with improved accuracy. A novel strategy is followed to ensure unbiased comparison of the models following the grid search technique to optimize the network architecture. As a case study, these models are used to predict solar irradiance for a location in a hilly region of India for validation, using 9 years data for training. The model is validated by comparing the forecasts of the succeeding year with the real values. The comparative analysis shows that use of grid search technique, results in almost same accuracy with mean square error close to 0.017 for the three models. However, the speed of training and the complexity of the models differ for each model. The MLP is found to be the most efficient model due to low number of parameters. The study is of importance for reliable solar irradiance forecasting for any location worldwide.
GMM discriminant analysis with noisy label for each class

Real-world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance. In this paper, we propose a Gaussian Mixture Discriminant Analysis (GMDA) with noisy label for each class. We introduce flipping probability and class probability and use EM algorithms to solve the discriminant problem with label noise. We also provide the detail proofs of convergence. Experimental results on synthetic and real-world datasets show that the proposed approach notably outperforms other four state-of-the-art methods.
Modeling a session of subject-system interaction in a wireless communication infrastructure with a mixed resource

The article examines the subject-system interaction session, where the system is understood as a base station, and the subject is understood as a mobile communication device. The peculiarity of the study is taking into account the phenomenon relevant to modern communication infrastructures, which is that the base station supports the division of information traffic into a subspace of guaranteed personalized traffic and a subspace of general-purpose traffic. The study considers a highly critical empirical emergency when the general-purpose traffic subspace may cease to be available at any time. The presented mathematical apparatus describes the impact of such an emergency on the active communication sessions supported by the system in receiving new incoming requests of increasing intensity. To characterize this emergency situation, expressions adapted for practical application are presented to calculate such qualitative parameters as the probability of stability, the probability of failure, and unavailability.
Driving Behavior Recognition Algorithm Combining Attention Mechanism and Lightweight Network

In actual driving scenes, recognizing and preventing drivers' non-standard driving behavior is helpful in reducing traffic accidents. To resolve the problems of various driving behaviors, a large range of action, and the low recognition accuracy of traditional detection methods, in this paper, a driving behavior recognition algorithm was proposed that combines an attention mechanism and lightweight network. The attention module was integrated into the YOLOV4 model after improving the feature extraction network, and the structure of the attention module was also improved. According to the 20,000 images of the Kaggle dataset, 10 typical driving behaviors were analyzed, processed, and recognized. The comparison and ablation experimental results showed that the fusion of an improved attention mechanism and lightweight network model had good performance in accuracy, model size, and FLOPs.
Recursive Least Squares for Near-Lossless Hyperspectral Data Compression

The hyperspectral image compression scheme is a trade-off between the limited hardware resources of the on-board platform and the ever-growing resolution of the optical instruments. Predictive coding attracts researchers due to its low computational complexity and moderate memory requirements. We propose a near-lossless prediction-based compression scheme that removes spatial and spectral redundant information, thereby significantly reducing the size of hyperspectral images. This scheme predicts the target pixel’s value via a linear combination of previous pixels. The weight matrix of the predictor is iteratively updated using a recursive least squares filter with a loop quantizer. The optimal number of bands for prediction was analyzed experimentally. The results indicate that the proposed scheme outperforms state-of-the-art compression methods in terms of the compression ratio and quality retrieval.
A robust context attention network for human hand detection

Current state-of-the-art approaches for human hand detection have achieved great success by making good use of multiscale and contextual information but still remain unsatisfactory for hand detection, especially in complex scenarios. The main reason is that there are some parts similar to human hands, such as wrists, faces and feet. Simply using contextual information makes it difficult to address these problems. In this paper, we propose a Context Attention Feature Pyramid Network (CA-FPN) for human hand detection. In this method, a novel Context Attention Module (CAM) is inserted into the feature pyramid networks. The CAM is designed to capture relative contextual information for hands and build long-range dependencies around hands. Our CA-FPN can achieve state-of-the-art results on two public hand detection datasets: the Oxford and Vision for Intelligent and Applications (VIVA) datasets. Furthermore, the inference time of our CA-FPN is approximately 8.5 FPS on one TITAN X GPU, indicating that it can be used in real-time applications. Besides, the CAM helps improve head detection on the HollywoodHeads dataset, demonstrating its robustness in other detection tasks. The code has been made available at https://github.com/IC-LAB/CA-FPN . • A context attention module to utilize dilated and typical convolutions. • The proposed module with FPN achieves top performance on challenging hand detection. • The proposed module can be applied to other vision tasks, such as head detection.
Progressive multi-branch embedding fusion network for underwater image enhancement

The underwater image enhancement techniques are essential for ocean research and engineering applications. In this paper, we propose a progressive multi-branch embedding fusion network (PMEFN) to improve image quality. Specifically, a multi-branch embedding fusion module (MEFM) is designed. The distorted images and its sharpened versions are used as the input, which are fused to learn the contextualized features based on a two-branch hybrid encoder–decoder module (HEDM2) combined with the triple attention module to focus on the noise region. Afterwards, we use the multi-stage refining framework to decompose the image enhancement or marine snow removal tasks into multiple stages and progressively learn the nonlinear functions from the distorted inputs. Additionally, the outputs generated at each stage are further refined and enhanced based on a three-branch hybrid encoder–decoder module (HEDM3). We perform experiments using real underwater datasets, including EUVP, UFO-120, UIEB, and synthetic dataset MSRB. The experimental results show that the proposed method has a superior performance as compared to other methods in terms of quantitative performance and visual quality. In addition, the effectiveness of each component is further validated by performing ablation experiments.
Examining the Prediction of COVID-19 Contact-Tracing App Adoption Using an Integrated Model and Hybrid Approach Analysis

COVID-19 contact-tracing applications (CTAs) offer enormous potential to mitigate the surge of positive coronavirus cases, thus helping stakeholders to monitor high-risk areas. The Kingdom of Saudi Arabia (KSA) is among the countries that have developed a CTA known as the Tawakkalna application, to manage the spread of COVID-19. Thus, this study aimed to examine and predict the factors affecting the adoption of Tawakkalna CTA. An integrated model which comprises the technology acceptance model (TAM), privacy calculus theory (PCT), and task-technology fit (TTF) model was hypothesized. The model is used to understand better behavioral intention toward using the Tawakkalna mobile CTA. This study performed structural equation modeling (SEM) analysis as well as artificial neural network (ANN) analysis to validate the model, using survey data from 309 users of CTAs in the Kingdom of Saudi Arabia. The findings revealed that perceived ease of use and usefulness has positively and significantly impacted the behavioral intention of Tawakkalna mobile CTA. Similarly, task features and mobility positively and significantly influence task-technology fit, and significantly affect the behavioral intention of the CTA. However, the privacy risk, social concerns, and perceived benefits of social interaction are not significant factors. The findings provide adequate knowledge of the relative impact of key predictors of the behavioral intention of the Tawakkalna contact-tracing app.
From Low to High Order Motion Planners: Safe Robot Navigation Using Motion Prediction and Reference Governor

Safe navigation around obstacles is a fundamental challenge for highly dynamic robots.The state-of-the-art approach for adapting simple reference path planners to complex robot dynamics using trajectory optimization and tracking control is brittle and requires significant replanning cycles.In this letter, we introduce a novel feedback motion planning framework that extends the applicability of low-order (e.g.position-/velocity-controlled) reference motion planners to high-order (e.g., acceleration-/jerkcontrolled) robot models using motion prediction and reference governors.We use predicted robot motion range for safety assessment and establish a continuous bidirectional interface between high-level planning and low-level control via a reference governor.We describe the generic fundamental building blocks of our feedback motion planning framework and give specific example designs for motion control, prediction, and reference planning.We prove the correctness of our planning framework and demonstrate its performance in numerical simulations.We conclude that accurate motion prediction is crucial for closing the gap between high-level planning and low-level control.
An Approach to Science and Risk-Aware Planetary Rover Exploration

This work grapples with the challenge of directing autonomous decision making by planetary rovers conducting science investigations. Most of the related work addresses obstacle avoidance and traversabilty, while less work seeks to directly improve science yield. This research develops a comprehensive approach for planetary rovers that accounts for both science investigation and mobility risk. We present a probabilistic framework that quantifies these two attributes of rover exploration and generates paths that constrain risk while increasing science return. Specifically, science productivity is measured and improved using formal principles from information theory and statistical learning for decision making. Risk is estimated using a probabilistic model that predicts rover wheel slippage based on geometric and semantic information. Our method is evaluated in a simulation study using real Mars surface data that is relevant for both science and terrain investigations. Experimental analysis verifies the effectiveness of our approach.
Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Recognizing irregular text in natural images is a challenging task in computer vision.The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes.In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture.The proposed framework is divided into four main modules.Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image.Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain enriched feature maps.Thirdly, a deep bi-directional long short-term memory (Bi-LSTM) network is used to make a contextual feature map out of a visual feature map generated from a Convolutional Neural Network (CNN).Finally, we propose a Dual Step Attention Mechanism (DSAM) integrated with the Connectionist Temporal Classification (CTC) -Attention decoder to re-weights visual features and focus on the intra-sequence relationships to generate a more accurate character sequence.The effectiveness of our proposed framework is verified through extensive experiments on various benchmarks datasets, such as SVT, ICDAR, CUTE80, and IIIT5k.The performance of the proposed text recognition framework is analyzed with the accuracy metric.Demonstrate that our proposed method outperforms the existing approaches on both regular and irregular text.Additionally, the robustness of our approach is evaluated using the grocery datasets, such as GroZi-120, Web-Market, SKU-110K, and Freiburg Groceries datasets that contain complex text images.Still, our framework produces superior performance on grocery datasets.
Benchmarking Built-In Tracking Systems for Indoor AR Applications on Popular Mobile Devices

As one of the most promising technologies for next-generation mobile platforms, Augmented Reality (AR) has the potential to radically change the way users interact with real environments enriched with various digital information. To achieve this potential, it is of fundamental importance to track and maintain accurate registration between real and computer-generated objects. Thus, it is crucially important to assess tracking capabilities. In this paper, we present a benchmark evaluation of the tracking performances of some of the most popular AR handheld devices, which can be regarded as a representative set of devices for sale in the global market. In particular, eight different next-gen devices including smartphones and tablets were considered. Experiments were conducted in a laboratory by adopting an external tracking system. The experimental methodology consisted of three main stages: calibration, data acquisition, and data evaluation. The results of the experimentation showed that the selected devices, in combination with the AR SDKs, have different tracking performances depending on the covered trajectory.
Toward a design theory for virtual companionship

KEYWORDS: Virtual companionconversational agentvirtual assistantdesign sciencedesign theorydesign principles
Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers.
Evolving explainable rule sets

Most AI systems work like black boxes tasked with generating reasonable outputs for given inputs. Many domains, however, have explainablity and trustworthiness requirements not fulfilled by these approaches. Various methods exist to analyze or interpret black-box models post training. When it comes down to sensitive domains in which there is a mandate for white-box models, a better choice would be to use transparent models. In this work, we present a method which evolves explainable rule-sets using inherently transparent ordinary logic to make models. We showcase some sample domains we tackled and discuss their major desirable properties like bias detection, knowledge discovery, and modifiablity, to name a few.