Reviewerly/exHarmony huggingface dataset:
A Machine Learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments

• A dataset is created using velocities of wheels and distance of obstacle from AMR. • The left and right turn of AMR is framed as a two class problem. • The parameters of the proposed algorithm are updated by SGD optimization technique. • The proposed algorithm provides better results with other existing algorithms. In this paper, a novel approach based on Machine Learning (ML) concept, i.e., the Adaptive Stochastic Gradient Descent Linear Regression (ASGDLR) algorithm, is developed to segregate an AMR's directional movement as right and left turn. Moreover, the developed algorithm is employed for path planning and navigational purposes. Here, real-time velocities of the right and left wheel and distance from obstacle data have been acquired by two Infrared (IR) sensors and one Ultrasonic (US) sensor positioned on the AMR. The weights of the proposed ASGDLR model are iteratively updated by applying Stochastic Gradient Descent (SGD) optimization technique by considering the difference between the actual velocity and model output velocity as an error signal. For the performance analysis of the proposed algorithm, three different performance indices, such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), have been evaluated. The efficacy of the proposed algorithm is compared with six different AI-based regression algorithms for validation purposes. Moreover, the performance of the proposed algorithm is investigated for both obstacle avoidance and path planning in dense and cluttered environments. The simulation findings indicate that our proposed algorithm can accomplish tasks more efficiently than others while eliminating their shortcomings.
Public service operational efficiency and blockchain – A case study of Companies House, UK

Despite the increasing interest and exploration of the use of blockchain technology in public service organisations (PSOs), academic understanding of its transformative impact on the operational excellence of PSOs remains limited. This study adopts an action design science research methodology to develop a proof of concept (POC) blockchain based application for Companies House, a government agency that is registering companies across UK. The application addresses the operational challenges of Companies House as well as issues citizens face when accessing its services. We draw from the public value framework proposed by Twizeyimana and Andersson (2019) and demonstrate the significance of the emerging blockchain technology in relation to their democratic practices based on six dimensions. We further discuss the related challenges and barriers for its implementation and evaluate the POC with the stakeholders of Companies House. We also present an illustrative case study, where we explored the appropriateness of the POC in relation to the draft legislation, “Registration of Overseas Entities and Beneficial Owners” (ROEBO) bill which proposes the introduction of a register of the beneficial owners of overseas legal entities that own real estate in the UK. Our research is one of the few studies that will provide in-depth empirical insights about the relationship between blockchain and operational excellence of PSOs.
Sustainable development of green reverse logistics based on blockchain

With the continuous convenience of life, logistics has become an indispensable existence in life. Logistics application, such as the southern banana transportation to the country, major cities can buy all the year round; Xinjiang ha honey melon, Ningxia white orchid melon, northeast rice, Tianjin small station rice are not seasonal supply market. However, with the increasing frequency of logistics activities, its damage to natural resources and ecological environment is becoming more and more serious. The waste produced in logistics is increasing day by day, which puts great pressure on the environment. Such as steel slag, industrial waste water, waste computers, waste batteries and other kinds of inorganic waste. Goods return is the most important activity in the green reverse logistics. Because reverse logistics is a kind of logistics activity including product return, logistics replacement, item reuse, waste disposal, reprocessing, repair and remanufacturing and other processes. Returns are the key part of it. In order to ensure the goods, return to the manufacturer and get good disposal, it is necessary to record the whole process information of the goods from the factory to the customer. Blockchain is a chain composed of one block after another. Each block holds certain information in each block, which are connected into chains in their respective chronological order. Based on this, this paper proposes a sustainable development strategy of green reverse logistics based on blockchain. In the strategy research, based on the characteristics of decentralization, non-tampering and high information security of blockchain technology(BT), this paper proposes a commodity traceability scheme based on blockchain. In this traceability scheme, this paper uses the structure of Merkel tree to design a license chain to store detailed commodity traceability information, and stores the Merkel tree root node of the license chain block in the public chain, forming a “double chain” structure of commodity traceability system. The simulation analysis shows that the traceability information stored in the license chain can use the high security of the public chain to ensure the security of information. Compared with the traditional blockchain, the double chain structure designed in the article, has a larger throughput, which can trace the source of the product to the manufacturer, ensure that the goods can be returned to the manufacturer for further processing, reduce the generation of waste in the logistics, and provide a guarantee for promoting the sustainable development of green reverse logistics.
Investigating intrinsic degradation factors by multi-branch aggregation for real-world underwater image enhancement

Recently, improving the visual quality of underwater images has received extensive attentions in both computer vision and ocean engineering fields. However, existing works mostly focus on directly learning clear images from degraded observations but without careful investigations on the intrinsic degradation factors, thus require mass training data and lack generalization ability. In this work, we propose a new method, named Multi-Branch Aggregation Network (termed as MBANet) to partially address the above issue. Specifically, by analyzing underwater degradation factors from the perspective of both color distortions and veil effects, MBANet first constructs a multi-branch multi-variable architecture to obtain one intermediate coarse result and two degraded factors. We then establish a physical model inspired process to fully utilize our estimated degraded factors and thus obtain the desired clear output images. A series of evaluations on multiple datasets show the superiority of our method against existing state-of-the-art approaches, both in execution speed and accuracy. Furthermore, we demonstrate that our MBANet can significantly improve the performance of salience object detection in the underwater environment.
Automatic captioning for medical imaging (MIC): a rapid review of literature

Automatically understanding the content of medical images and delivering accurate descriptions is an emerging field of artificial intelligence that combines skills in both computer vision and natural language processing fields. Medical image captioning is involved in various applications related to diagnosis, treatment, report generation and computer-aided diagnosis to facilitate the decision making and clinical workflows. Unlike generic image captioning, medical image captioning highlights the relationships between image objects and clinical findings, which makes it a very challenging task. Although few review papers have already been published in this field, their coverage is still quite limited and only particular problems are addressed. This motivates the current paper where a rapid review protocol was adopted to review the latest achievements in automatic medical image captioning from the medical domain perspective. We aim through this review to provide the reader with an up-to-date literature in this field by summarizing the key findings and approaches in this field, including the related datasets, applications and limitations as well as highlighting the main competitions, challenges and future directions.
A transdisciplinary review and framework of consumer interactions with embodied social robots: Design, delegate, and deploy

Abstract Social robots are gradually entering the organizational frontline, and research is beginning to unveil the implications for consumer–firm interactions. While empirical studies on human–robot service interaction (HRSI) are scarce in business literature, other scientific fields have generated an abundance of empirical findings that can inform consumer research on successfully integrating embodied social robots in consumer‐facing services. In this light, a systematic literature review was conducted across scientific fields, screening over 13,500 research articles. Through a thorough review process, 199 service‐relevant empirical research articles were identified. Emanating from these data, an organizing meta‐framework is advanced (D 3 : design, delegate, and deploy). Leveraging this D 3 framework, a comprehensive overview of several dimensions of the literature is provided, and key insights for each framework dimension are presented. Based on this overview, implications for whether, how, and when to integrate social robots in practice and a comprehensive future research agenda are developed.
A Systematic evaluation of blockchain-enabled contract administration in construction projects

Inefficient and ineffective Construction Contract Administration (CCA) is at the root of many of the sector challenges. Emerging digital technologies such as blockchain are revealing some capabilities with the potential for improving CCA. This paper addresses the research question of whether and how can blockchain contribute to improving CCA and what are the specific challenges to its adoption in CCA. The study adopts a systematic review of studies in this domain and their subsequent analysis against a CCA framework. Most applications were found to be centered on two CCA functions; the financial management, and the document and record management. CCA functions that embed complex contractual logics such as claims and disputes resolution management have received scant attention. To advance blockchain application in CCA, a structured roadmap of its key challenges relating to technology, process, policy and society is proposed for future research. The outcome of this paper provides scholars, practitioners and policymakers, interested in construction contracts, with the evidence about the current level of blockchain contribution to CCA and a structured set of recommendations for future research.
Crowdsourcing platform for QoE evaluation for cloud multimedia services

This paper presents a novel web-based crowdsourcing platform for the assessment of the subjective and objective quality of experience (QoE) of the video service in the cloud-server environment. The user has the option to enter subjective QoE data for video service by filling out a web questionnaire. The objective QoE data of the cloud-server, network condition, and the user device is automatically captured by the crowdsourcing platform. Our proposed system collects both objective and subjective QoE simultaneously in real-time. The paper presents the key technologies used in the development of the platform and describes the functional requirements and design ideas of the system in detail. The system collects real-time comprehensive data to enhance the quality of the user experience to provide a valuable reference. The system is tested in a real-time environment and the test results are given in terms of the system performance. The crowdsourcing platform has new features of real-time network monitoring, the client device, and cloud monitoring, which currently has not been provided by existing web platforms and crowdsourcing frameworks. The results show that 1MB buffer is filled 100% very soon after starting watching videos from the crowdsourcing platform.
Improved YOLOv5 with Transformer for Large Scene Military Vehicle Detection on SAR Image

With the development of SAR technology, large scene object detection on SAR images has attracted more and more attention. Exiting large scene object detection is mainly based on the CNN network, which limits the obtaining of global context information. On the other hand, due to the high acquisition cost of SAR images, there are no existing public datasets in military vehicle detection. To solve these problems, we adopt the Transformer module to construct the neck block based on YOLOv5. This design can gain global context information, and also has better performance for small objects detection. Furthermore, to achieve the detection of large-scale military ground vehicles, we construct a dataset based on the MSTAR dataset, named LSGVOD. Extensive experiments have been conducted on LSGVOD, and experimental results show that the proposed method greatly improves detection accuracy. Compared to other methods, it achieves the best accuracy with 93.3% mAP.
A Coupled Spintronics Neuromorphic Approach for High‐Performance Reservoir Computing

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information‐processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information‐processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high‐dimensional dynamics of coupled spin‐torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information‐processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information‐processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle‐based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses.
Multiple Object Tracking in Robotic Applications: Trends and Challenges

The recent advancement in autonomous robotics is directed toward designing a reliable system that can detect and track multiple objects in the surrounding environment for navigation and guidance purposes. This paper aims to survey the recent development in this area and present the latest trends that tackle the challenges of multiple object tracking, such as heavy occlusion, dynamic background, and illumination changes. Our research includes Multiple Object Tracking (MOT) methods incorporating the multiple inputs that can be perceived from sensors such as cameras and Light Detection and Ranging (LIDAR). In addition, a summary of the tracking techniques, such as data association and occlusion handling, is detailed to define the general framework that the literature employs. We also provide an overview of the metrics and the most common benchmark datasets, including Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), MOTChallenges, and University at Albany DEtection and TRACking (UA-DETRAC), that are used to train and evaluate the performance of MOT. At the end of this paper, we discuss the results gathered from the articles that introduced the methods. Based on our analysis, deep learning has introduced significant value to the MOT techniques in recent research, resulting in high accuracy while maintaining real-time processing.
Convolutional Neural Network-Based Human Activity Recognition for Edge Fitness and Context-Aware Health Monitoring Devices

The vital signs can vary significantly depending on the daily physical activities, which may not be due to defects of the organs. Under remote human health-monitoring applications, for reliable disease diagnosis, recorded biomedical signals such as an electrocardiogram (ECG) and a photoplethysmogram (PPG) must be indexed with physical activity information, as it is unknown to the physicians and also to the computer-aided diagnostic system. Since deep-learning (DL) networks were explored for various vital signs extraction and disease classification, we present an effective convolutional neural network (CNN)-based human activity recognition (HAR) method by exploring suitable hyperparameters. CNN-based methods are evaluated using the acceleration signals taken from the standard HAR benchmark database, University of California, Irvine (UCI) with accuracy (ACC), model size (in kB), and processing time (PT), and also implemented on the Raspberry Pi 4 (R-Pi-4) to study real-time feasibility. Evaluation results showed that higher HAR accuracy can be achieved with the activation function of the exponential linear unit (ELU) for the 2-layer CNN with a segment size of 2.5 s (ACC of 89.05% and PT of 0.142 ms), the 4-layer CNN with a segment size of 1 s (ACC of 91.66% and PT of 0.541 ms), and the 6-layer CNN with a segment size of 2 s (ACC of 91.18% and PT of 1.672 ms). Results demonstrated that the selection of an optimal number of layers and hyperparameters plays a major role in achieving higher accuracy with lower computational time on both personal computer central processing unit (PC-CPU) and R-Pi computing platforms, which were not addressed in past studies.
Group-preserving label-specific feature selection for multi-label learning

In many real-world application domains, e.g., text categorization and image annotation, objects naturally belong to more than one class label, giving rise to the multi-label learning problem. The performance of multi-label learning greatly relies on the quality of available features, whereas the data generally involve a lot of irrelevant, redundant, even noisy features. This fact has led to that a surge of research on feature selection methods that select significant features for multi-label learning. Nevertheless, most of the previous approaches suffer from the deficiency that label-specific features are not taken into account, and they are also inefficient in exploiting labeling information such as local label correlations. Moreover, these methods lack interpretability, which can only find a feature subset for all labels, however, cannot show how features are related to different labels. Based on this, we present a new group-preserving label-specific feature selection (GLFS) framework for multi-label learning, which simultaneously considers the features special to the labels in the same group and specific features owned by each label to execute feature selection. In addition, we further consider to learn label-group and instance-group correlations for the exploitation of labeling information, and make a collaborative use of them to improve the model generalization. Extensive experiments validate the advantages of the proposed GLFS method.
Mobile learning-technology barriers in school education: teachers’ views

Despite the significant growth and capabilities of mobile technologies, there is limited empirical evidence on teachers' perceived mobile technology-learning barriers. This study investigated Greek teachers' perceptions of barriers to using mobile technology in school classrooms. A 28-item questionnaire was administered to 557 teachers and six barrier-factors were extracted: 'lack of resources', 'lack of support', 'lack of time', 'lack of teacher confidence', 'class conditions' and 'concerns about students'. The questionnaire was a valid and reliable tool for the investigation of various dimensions of teachers' perceived barriers. Gender, school level, age and ICT training had significant but small effects on specific barrier-factors. The order of importance of barrier-factors was similar for users and non-users ('lack of resources', 'support' and 'class conditions') independently of the type of mobile device used in class. 'Concerns about students' was identified as a major barrier only by non-users. Implications for educational policy and professional training are discussed.
Risk Stratification Index 3.0, a Broad Set of Models for Predicting Adverse Events during and after Hospital Admission

Risk stratification helps guide appropriate clinical care. Our goal was to develop and validate a broad suite of predictive tools based on International Classification of Diseases, Tenth Revision, diagnostic and procedural codes for predicting adverse events and care utilization outcomes for hospitalized patients.Endpoints included unplanned hospital admissions, discharge status, excess length of stay, in-hospital and 90-day mortality, acute kidney injury, sepsis, pneumonia, respiratory failure, and a composite of major cardiac complications. Patient demographic and coding history in the year before admission provided features used to predict utilization and adverse events through 90 days after admission. Models were trained and refined on 2017 to 2018 Medicare admissions data using an 80 to 20 learn to test split sample. Models were then prospectively tested on 2019 out-of-sample Medicare admissions. Predictions based on logistic regression were compared with those from five commonly used machine learning methods using a limited dataset.The 2017 to 2018 development set included 9,085,968 patients who had 18,899,224 inpatient admissions, and there were 5,336,265 patients who had 9,205,835 inpatient admissions in the 2019 validation dataset. Model performance on the validation set had an average area under the curve of 0.76 (range, 0.70 to 0.82). Model calibration was strong with an average R 2 for the 99% of patients at lowest risk of 1.00. Excess length of stay had a root-mean-square error of 0.19 and R 2 of 0.99. The mean sensitivity for the highest 5% risk population was 19.2% (range, 11.6 to 30.1); for positive predictive value, it was 37.2% (14.6 to 87.7); and for lift (enrichment ratio), it was 3.8 (2.3 to 6.1). Predictive accuracies from regression and machine learning techniques were generally similar.Predictive analytical modeling based on administrative claims history can provide individualized risk profiles at hospital admission that may help guide patient management. Similar results from six different modeling approaches suggest that we have identified both the value and ceiling for predictive information derived from medical claims history.
Efficient quantum state tomography with convolutional neural networks

Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.