Reviewerly/exHarmony huggingface dataset:
Why are many businesses instilling a DevOps culture into their organization?

DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effective. Although this movement is relatively recent, there exist an intensive research around DevOps. However, the real reasons why companies move to DevOps and the results they expect to obtain have been paid little attention in real contexts. This paper aims to help practitioners and researchers to better understand the context and the problems that many companies face day to day in their organizations when they try to accelerate software delivery and the main drivers that move these companies to adopting DevOps. We conducted an exploratory study by leveraging in depth, semi-structured interviews to relevant stakeholders of 30 multinational software-intensive companies, together with industrial workshops and observations at organizations’ facilities that supported triangulation. Additionally, we conducted an inter-coder agreement analysis, which is not usually addressed in qualitative studies in software engineering, to increase reliability and reduce authors bias of the drawn findings. The research explores the problems and expected outcomes that moved companies to adopt DevOps and reveals a set of patterns and anti-patterns about the reasons why companies are instilling a DevOps culture. This study aims to strengthen evidence and support practitioners in making better informed about which problems trigger a DevOps transition and most common expected results.
Breast Cancer Prediction Analysis using Machine Learning Algorithms

Most common diseases and the leading cause of death to most women across the globe is Breast Cancer (BC). Although many individuals who suffer breast cancer have no family history but women who have blood relatives suffering from the same disease are at higher risk. Besides, a high risk of developing breast cancer includes aging, genes, thick breast tissues, obesity, and radiation exposure. Malignant and benign are two different types of tumors and to distinguish between these two, physicians need a reliable diagnostic procedure. The mammography method is used to detect breast cancer but radiologists exhibit significant variation in interpretation. Fine Needle Aspiration Cytology (FNAC) is commonly adopted in the diagnosis of breast cancer. Moreover, early diagnosis is vital to treatment with a better chance of success. Classification and data mining attributes are an efficient and effective way of categorizing results. Using machine learning models that will play a vital role in early prediction. In this paper, we present a prediction of breast cancer with different machine learning algorithms compare their prediction accuracy, area under the receiver operating characteristic curve (AUC) and performance parameters. For Simulation purposes, we are using the Wisconsin Dataset of Breast Cancer (WDBC). After analysis, the Support Vector Machine (SVM) model has achieved 96.25% accuracy with AUC of 99.4. Further, these algorithms can be modified with their mathematical models to increase the prediction of breast cancer.
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things

Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clients or end devices. However, their comparative training performance under real-world resource-restricted Internet of Things (IoT) device settings remains barely studied. This work provides empirical comparisons of FL and SL in real-world IoT settings regarding (i) learning performance with heterogeneous data distributions and (ii) on-device execution overhead. Our analyses in this work demonstrate that the learning performance of SL is better than FL under an imbalanced data distribution but worse than FL under an extreme non-IID data distribution. Recently, FL and SL are combined to form splitfed learning (SFL) to leverage each of their benefits (e.g., parallel training of FL and lightweight on-device computation requirement of SL). Our work considers FL, SL, and SFL, and mounts them on Raspberry Pi devices to evaluate their performance, including training time, communication overhead, power consumption, and memory usage with resource-restricted IoT devices. Besides evaluations, we apply two optimizations. First, we generalize SFL by carefully examining the possibility of a hybrid type of model training at the server-side. The generalized SFL merges sequential (dependent) and parallel (independent) processes of model training and thus is beneficial to a system with a large scale of IoT devices, specifically at the server-side operations. Second, we propose pragmatic techniques to substantially reduce the communication overhead by up to four times for the SL and (generalized) SFL.
Endogenous Trusted DRL-Based Service Function Chain Orchestration for IoT

With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement.
Review on Strain Localization Phenomena Studied by High‐Resolution Digital Image Correlation

Herein, a comprehensive review on the application of digital image correlation (DIC) for investigations of strain localization phenomena in the field of materials science and engineering (MSE) is provided. The Review is divided into three parts. In Part 1, the method of DIC is reviewed in terms of basic principles, correlation algorithms, and sources of errors. Part 2 is focused on the application of DIC in the field of MSE. This part is subdivided into the application of DIC in 1) combination with optical microscopy and 2) in combination with scanning electron microscopy (SEM). In Part 3, results of high‐resolution DIC obtained in combination with SEM are presented for high‐strength austenitic stainless steels—transformation‐induced plasticity steels and twinning‐induced plasticity steels—exhibiting microscopic strain localization phenomena. These investigations are supplemented by electron backscattered diffraction for interpretation of strain fields. Although DIC allows for both 2D and 3D calculations of displacement and strain fields, herein, 2D digital correlation is referred to only, as in particular with SEM only in‐plane displacements are calculated so far.
Visual Navigation With Multiple Goals Based on Deep Reinforcement Learning

Learning to adapt to a series of different goals in visual navigation is challenging. In this work, we present a model-embedded actor-critic architecture for the multigoal visual navigation task. To enhance the task cooperation in multigoal learning, we introduce two new designs to the reinforcement learning scheme: inverse dynamics model (InvDM) and multigoal colearning (MgCl). Specifically, InvDM is proposed to capture the navigation-relevant association between state and goal and provide additional training signals to relieve the sparse reward issue. MgCl aims at improving the sample efficiency and supports the agent to learn from unintentional positive experiences. Besides, to further improve the scene generalization capability of the agent, we present an enhanced navigation model that consists of two self-supervised auxiliary task modules. The first module, which is named path closed-loop detection, helps to understand whether the state has been experienced. The second one, namely the state-target matching module, tries to figure out the difference between state and goal. Extensive results on the interactive platform AI2-THOR demonstrate that the agent trained with the proposed method converges faster than state-of-the-art methods while owning good generalization capability. The video demonstration is available at https://vsislab.github.io/mgvn.
Dyadic relational graph convolutional networks for skeleton-based human interaction recognition

Skeleton-based human interaction recognition is a challenging task requiring all abilities to recognize spatial, temporal, and interactive features. These abilities rarely co-exist in existing methods. Graph convolutional network (GCN) based methods fail to extract interactive features. Traditional interaction recognition methods cannot effectively capture spatial features from skeletons. Toward this end, we propose a novel Dyadic Relational Graph Convolutional Network (DR-GCN) for interaction recognition. Specifically, we make four contributions: (i) we design a Relational Adjacency Matrix (RAM) that represents dynamic relational graphs. These graphs are constructed combining both geometric features and relative attention from the two skeleton sequences; (ii) we propose a Dyadic Relational Graph Convolution Block (DR-GCB) that extracts spatial-temporal interactive features; (iii) we stack the proposed DR-GCBs to build DR-GCN and integrate our methods with an advanced model. (iv) Our models achieve state-of-the-art results on SBU and significant improvements on the mutual action sub-datasets of NTU-RGB+D and NTU-RGB+D 120.
9.8 A 25mm<sup>2</sup> SoC for IoT Devices with 18ms Noise-Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET

Automatic speech recognition (ASR) using deep learning is essential for user interfaces on IoT devices. However, previously published ASR chips [4-7] do not consider realistic operating conditions, which are typically noisy and may include more than one speaker. Furthermore, several of these works have implemented only small-vocabulary tasks, such as keyword-spotting (KWS), where context-blind deep neural network (DNN) algorithms are adequate. However, for large-vocabulary tasks (e.g., >100k words), the more complex bidirectional RNNs with an attention mechanism [1] provide context learning in long sequences, which improve ASR accuracy by up to 62% on the 200kwords LibriSpeech dataset, compared to a simpler unidirectional RNN (Fig. 9.8.1). Attention-based networks emphasize the most relevant parts of the source sequence during each decoding time step. In doing so, the encoder sequence is treated as a soft-addressable memory whose positions are weighted based on the state of the decoder RNN. Bidirectional RNNs learn past and future temporal information by concatenating forward and backward time steps.
Connecting Human-Robot Interaction and Data Visualization

Human-robot interaction (HRI) research frequently explores how to design interfaces that enable humans to effectively teleoperate and supervise robots. One of the principle goals of such systems is to support data collection, analysis, and human decision making, which requires representing robot data in ways that support fast and accurate analyses by humans. However, the interfaces for these systems do not always use best-practice principles for effectively visualizing data. We present a new framework to scaffold reasoning about robot interface design that emphasizes the need to consider data visualization for supporting analysis and decision making processes, detail several data visualization best practices relevant to HRI, identify a set of core data tasks that commonly occur in HRI, and highlight several promising opportunities for further synergistic activities at the intersection of these two research areas.
Incremental concept cognitive learning based on three-way partial order structure

With the vigorous development of the information technology industry, the information data available to mankind has shown an explosive growth trend. Dynamic concept learning is an approach that can effectively process the acquired massive data and extract valuable information from them. Concept cognitive learning (CCL) is a very active research direction in the field of dynamic concept learning, while partial order formal structure analysis (POFSA) is a concrete and practical model of CCL. However, the existing CCL algorithms in POFSA face some challenges when processing constantly changing data. Therefore, this paper is devoted to explore an incremental CCL algorithm based on three-way object partial order structure diagram (OPOSD) in POFSA with the incorporation of the thoughts of incremental learning. The features of five object categories are considered, and their incremental influences on three-way OPOSD are analyzed and their incremental CCL algorithms in three-way OPOSD are established. Based on some real famous formal contexts, this paper conducts numerical experiments, and the results show that the incremental CCL algorithm based on three-way OPOSD is consistent with human cognitive principles, and can improve the CCL performance of POFSA as well.
Information representation of blockchain technology: Risk evaluation of investment by personalized quantifier with cubic spline interpolation

With the applications of blockchain technology in various fields, the research on blockchain has attracted much attention. Different from the researches focusing on specific applications of blockchain technology in a certain field, this study devotes to capturing the attitudes of investors regarding different risk criteria in blockchain technology investment decision making. We use personalized quantifiers to extract investors’ preferences on each risk evaluation criterion. At present, the personalized quantifier that can reflect individual attitudes and behavior intentions have been fitted by various functions, but there are still limitations. In this regard, this paper introduces a cubic spline interpolation function to fit the personalized quantifier, and addresses the consistency of the personalized quantifier in the ordered weighted averaging aggregation. Moreover, we employ a qualitative information representation model called probabilistic linguistic term sets to express decision-makers' evaluations on each criterion. We give a case study to illustrate the usability of the proposed personalized quantifier in blockchain risk evaluation. The comparative analysis with other four types of personalized quantifiers shows that our proposed personalized quantifier with cubic spline interpolation has ideal geometric characteristics in terms of smooth curve and high fitting accuracy, thus having strong applicability. Further, we show that this method is relatively easy to operate.
Workflow scheduling using particle swarm optimization and gray wolf optimization algorithm in cloud computing

Abstract Cloud computing is one of the emerging technologies in computer science in which services are provided through the internet on‐demand. Workflow scheduling is considered to be an NP‐hard problem and has a significant issue in the cloud environment. Finding the polynomial‐time solutions for workflow scheduling problem is difficult with most of the existing algorithms designed for traditional computing platforms. Some existing meta‐heuristics algorithms proposed for workflow scheduling problem are stuck in the local optimal solution and fails to give the global optimal solution. In this article, a hybrid of particle swarm optimization and gray wolf optimization, named the PSO‐GWO algorithm, is proposed for workflow scheduling. The proposed algorithm was tested to reduce the total executing cost (TEC) and total execution time (TET) of the dependent tasks in the cloud computing environment. The proposed algorithm takes advantage of both the standard PSO and GWO algorithms and does not stick in the local optimal solution. The experiment results show that the PSO‐GWO outperformed compared with the standard PSO and GWO algorithm in TEC and TET.
Comparisons of performance between quantum-enhanced and classical machine learning algorithms on the IBM Quantum Experience

Abstract Machine learning is now widely used almost everywhere, primarily for forecasting. In the broadest sense, the machine learning objective may be summarized as an approximation problem, and the issues solved by various training methods can be reduced to finding the optimal value of an unknown function or restoring a function. At the moment, we have only experimental samples of quantum computers based on classical-quantum logic, when quantum gates are used instead of ordinary logic gates, and probabilistic quantum bits are used instead of deterministic bits. Namely, the probabilistic nature problems that provide for the determination of a certain optimal state from a large set of possible ones on which quantum computers can achieve “quantum supremacy” – an extraordinary (by many orders of magnitude) reduction in the time required to solve the task. The main idea of the work is to identify the possibility of achieving, if not quantum supremacy, then at least a quantum advantage when solving machine learning problems on a quantum computer.
Quantum Electromagnetic Transients Program

This letter devises a quantum electromagnetic transients program (QEMTP) which is the first attempt to tackle the computational challenges in solving EMTP through quantum computing. The main contributions lie in: (1) A quantum-enabled EMTP formulation with Dommel&#x0027;s model encoded in quantum states; 2) An Harrow-Hassidim-Lloyd (HHL)-based QEMTP to solve the discrete-time nodal equations; 3) An iteration-based HHL revision for mitigating temporal errors to achieve high accuracy of QEMTP with limited quantum resources. Case studies verify the correctness and efficacy of QEMTP in simulating both fast and slow dynamics.
An empirical framework for domain generalization in clinical settings

Clinical machine learning models experience significantly degraded performance in datasets not seen during training, e.g., new hospitals or populations. Recent developments in domain generalization offer a promising solution to this problem by creating models that learn invariances across environments. In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data. We introduce a framework to induce synthetic but realistic domain shifts and sampling bias to stress-test these methods over existing non-healthcare benchmarks. We find that current domain generalization methods do not achieve significant gains in out-of-distribution performance over empirical risk minimization on real-world medical imaging data, in line with prior work on general imaging datasets. However, a subset of realistic induced-shift scenarios in clinical time series data exhibit limited performance gains. We characterize these scenarios in detail, and recommend best practices for domain generalization in the clinical setting.
FSoD-Net: Full-Scale Object Detection From Optical Remote Sensing Imagery

Object detection is an essential task in computer vision. Recently, several convolution neural network (CNN)-based detectors have achieved a great success in natural scenes. However, for optical remote sensing images with a large scale of view, lower proportion of foreground target pixels and drastic differences in object scale present considerable challenges. To address these problems, we propose a novel one-stage detector called the full-scale object detection network (FSoD-Net) which consists of proposed multiscale enhancement network (MSE-Net) backbone cascaded with scale-invariant regression layers (SIRLs). First, MSE-Net provides the multiscale description enhancement by integrated the Laplace kernel with fewer parallel multiscale convolution layers. Second, SIRLs contain three different isolated regression branch layers (i.e., corresponding to small, medium, and large scales), which make default discrete scale bounding boxes (bboxes) cover full-scale object information in regression procedure. A novel specific scale joint loss is also designed that uses the softmax function combined with a strong <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>-norm constraint in each regression branch layer. It can further speed up the convergence and improve the classification scores of predicted bboxes. Finally, extensive experiments are carried on challenge data sets of large-scale dataset for object detection in aerial images (DOTA) and object detection in optical remote sensing images (DIOR) which contain multiple instances from different imaging platforms, and these results demonstrate that FSoD-Net can achieve better performance than other state-of-the-art one-stage detectors, and it can reach a mean average precision (mAP) of 75.33&#x0025; on DOTA and 71.80&#x0025; mAP on DIOR, respectively. Especially, the average precision (AP) of tiny object detection can improve 10&#x0025;&#x2013;20&#x0025; approximately.
A Vehicle Detection Technique Using Binary Images for Heterogeneous and Lane-Less Traffic

Nowadays, providing a low-cost traffic management system in developing countries or in heterogeneous and lane-less traffic conditions is highly essential. It can help to manage traffic congestion, save fuel, save travel time, and enhance user safety. By keeping these as an objective, this article presents a vehicle detection method in heterogeneous and lane-less traffic by extracting a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binary image</i> from a discrete sensor array. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binary image</i> is formed with a logic 1 or 0, which are recorded based on the occupancy status of the vehicles in an observed zone. The proposed method is demonstrated with virtual loops in video and with an array of micro-LiDARs. The width and length of the vehicle are obtained from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binary image</i> , which is extracted from virtual loops in a video recording and classified the vehicles. Similarly, the width and height information is obtained using an array of micro-LiDARs and classified the vehicles. The proposed method can easily be implemented with minimal storage, minimum cost, less bandwidth, and less computation complexity than the conventional methods, such as image processing or video processing-based vehicle classification. The proposed classification methods are mathematically derived, implemented, and measured performance over real traffic scenarios. It can be adopted automatically to high or light traffic scenarios by adjusting the distance between observation zones. The detection accuracy of 98% is observed while extracting data from video and 91.3% while using micro-LiDARs. The proposed works are compared with existing techniques.