Reviewerly/exHarmony huggingface dataset:
Typology development of earthquake displays in free-choice learning environments, to inform earthquake early warning education in the United States

Free-choice learning environments, such as museums, national parks, interpretive trails, and visitor centers, are trusted sources of information in their communities and support lifelong learning. Earthquake education in these spaces creates awareness of earthquake hazards and risk in areas where people live or visit and, in turn, may increase engagement in preparedness behavior. The ShakeAlert® Earthquake Early Warning System helps publics prepare by warning in advance of shaking from significant earthquakes along the West Coast of the United States. ShakeAlert can minimize earthquake damage by prompting automated actions (e.g., slowing trains, shutting off water valves) and prompting personal protective actions like “Drop, Cover, and Hold On” to significantly reduce damage, injury, and loss of life. Individuals and communities must have a basic understanding of earthquake hazards, as well as an awareness of ShakeAlert technology, to know how to respond if they feel shaking or receive an alert. Currently, there is a lack of contemporary scholarship on how free-choice learning environments approach earthquake education through exhibits and displays. We analyzed a sample of existing earthquake exhibits and their themes in the United States and explored how different display types are uniquely engaging. We found that most displays did not include information about how to prepare for an earthquake or associated protective actions. From this and the development of the typology, our research posits a foundational framework for how best to incorporate place-based learning on earthquakes and early warning into centers of free-choice learning which may apply to a range of other natural hazards and will enhance public awareness and safety.
The Ethics of Blockchain in Organizations

Blockchain is an open digital ledger technology that has the capability of significantly altering the way that people operations (i.e. human resource management) operate in organizations. This research takes a first step in proposing several ways in which the blockchain technology can be used to improve current organizational practices, while also considering the ethical implications. Specifically, the paper examines the role that blockchain technology plays in three primary areas of people operations: (1) entry to the organization (via recruitment and selection), (2) intraorganizational processes (including compensation via smart contracts, retention and motivation via shared leadership and conflict management via network-based dispute resolution, and performance management), and (3) exit (offboarding). In each section, the paper reviews the ethical implications from the lenses of virtue ethics, utilitarianism, deontology and contractarianism. The paper concludes that in whole the implementation of blockchain technology in people operations processes can create a more ethical work environment. However, careful implementation is necessary and requires extensive examination of ethical implications in advance.
A covariance-based Moth–flame optimization algorithm with Cauchy mutation for solving numerical optimization problems

Moth–Flame Optimization (MFO) algorithm, which is inspired by the navigation method of moths, is a nature-inspired optimization algorithm. The MFO is easy to implement and has been used to solve many real-world optimization problems. However, the MFO cannot balance exploration and exploitation well, and the information exchange between individuals is limited, especially in solving some complex numerical problems. To overcome these disadvantages of the MFO in solving the numerical optimization problems, a covariance-based Moth–Flame Optimization algorithm with Cauchy mutation (CCMFO) is proposed in this paper. In the CCMFO, the concept of covariance is used to transform the individuals of the moths and flames from the original space to the eigenspace and update the positions of moths, which can better improve the information exchange ability of the flames and moths in the eigenspace. In addition, Cauchy mutation is utilized to improve the exploration. And the CCMFO is compared with the other 22 algorithms on CEC 2020 test suite. The test results show that the CCMFO is better than other population-based optimization algorithms and MFO variants in search performance, while its performance is statistically similar to CEC competition algorithms. Furthermore, the CCMFO is compared with the other 12 algorithms on CEC 2020 real-world constrained optimization problems, and the results show that the CCMFO can effectively solve real-world constrained optimization problems. Finally, the CCMFO is used to optimize the tracking controller parameters of continuous casting mold vibration displacement. The experimental results based on the experimental platform show that the CCMFO can effectively reduce the difficulty of parameter selection and improve the tracking accuracy.
A Novel Fault Diagnosis Method Based on the KELM Optimized by Whale Optimization Algorithm

To solve the problem that fault features are difficult to extract and the time-frequency features cannot fully represent the state information, a novel method is proposed in this paper based on the whale optimization algorithm (WOA) and the kernel extreme learning machine (KELM). First, the vibration signals are processed by the ensemble empirical mode decomposition and sample entropy to obtain the feature vectors. Based on this, a KELM model for fault diagnosis is established. Then, the penalty factor and the kernel parameters in the KELM are optimized by WOA to improve the stability and classification accuracy. Taking faults of a ball-screw pair on a linear feed table as a case, the experimental results indicate that the proposed method can effectively extract the fault features of the ball-screw pair, and it can achieve higher classification accuracy, faster convergence speed, and greater convergence precision than the existing fault diagnosis methods.
An Echo State Network With Improved Topology for Time Series Prediction

An echo state network with improved topology (IESN) is proposed for accurate and efficient time series prediction. In this approach, a tighter bound of the echo state property related to the Lipshitz constant of reservoir activation function and the maximum structured singular value of reservoir is firstly researched to run the model at the edge of chaos. A smooth composite reservoir activation function is then designed to enhance the ESN. The exact echo state property bound is solved by computing the Lipshitz constant of the composite function. Finally, a decoupling matrix with eigenvalues distributing uniformly in the complex plane is built as the reservoir for abundant dynamic characteristics. Six classical benchmarks are employed to test the IESN. Besides, combined with amplitude-frequency separation based on the Hilbert transform, the IESN predicts a set of engine vibration signals in knock. Compared with several popular models, the proposed IESN shows the best performance.
A Distributed Authentication Scheme Based on Smart Contract for Roaming Service in Mobile Vehicular Networks

Secure and real-time communication is an essential condition in mobile vehicular networks, and this requires secure authentication and seamless access enabled by roaming services. As a security inspector, roaming authentication ensures that legitimate users can access the network securely. However, today’s roaming authentication protocols authenticate users with the help of centralized authentication servers, leading to the risk of the single point of failure and roaming fraud. The massive device access in 5G networks further exacerbates the losses when problems occur. In light of it, we propose a decentralized fraud-proof roaming authentication framework based on blockchain. We leverage smart contracts to implement a roaming authentication protocol, including user/AP registration, authentication, and revocation. For higher efficiency, we utilize the Bloom filter for the revocation process. In addition, we design an unforgeable and undeniable billing scheme based on hash chain technology. Security and performance analysis show that the proposed roaming authentication scheme can provide the required security features while incurring an acceptable authentication delay.
QuantumLeap: Hybrid quantum neural network for financial predictions

This paper introduces a new hybrid deep quantum neural network for financial predictions, the QuantumLeap system. This system consists of an encoder that transforms a partitioned financial time series into a sequence of density matrices; a deep quantum network that predicts the density matrix at a later time; and a classical network that measures, from the output density matrix, the maximum price reached by a security at a later time. The deep quantum network is isomorphic to a deep classical network and is computationally tractable. A hybrid deep network is associated with each time stride, allowing for parallelisation of the learning process. The classical network is a learnable measurement apparatus which infers, from the output density matrix, the maximum price reached by a security for a given time. Experimental results associated with 24 securities clearly demonstrate the accuracy and efficiency of the system in both the regression and extrapolation regimes.
A Systematic Review of the Adoption of Blockchain for Supply Chain Processes

This paper systematically reviews the literature on the adoption of Blockchain technology in Supply Chain Management (SCM) processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology, 53 peer-reviewed research publications from five different databases (IEEE Xplore, Science Direct, Scopus, Google Scholar, and EBSCOhost) were selected and analyzed using a classification coding framework. The findings reveal that Agri-food traceability, Blockchain security, smart contracts, and the Internet of Things (IoT) were the significant identified current trends in the use of Blockchain in SCM processes. The key identified challenges include high costs of transactions and a lack of trust between stakeholders. Identified solutions were Blockchain traceability systems and the use of smart contracts and IoTs. In addition, this paper identified gaps in the literature that need to be addressed in future studies.
A Holistic Approach to Reactive Mobile Manipulation

We present the design and implementation of a taskable reactive mobile manipulation system. In contrary to related work, we treat the arm and base degrees of freedom as a holistic structure which greatly improves the speed and fluidity of the resulting motion. At the core of this approach is a robust and reactive motion controller which can achieve a desired end-effector pose, while avoiding joint position and velocity limits, and ensuring the mobile manipulator is manoeuvrable throughout the trajectory. This can support sensor-based behaviours such as closed-loop visual grasping. As no planning is involved in our approach, the robot is never stationary thinking about what to do next. We show the versatility of our holistic motion controller by implementing a pick and place system using behaviour trees and demonstrate this task on a 9-degree-of-freedom mobile manipulator. Additionally, we provide an open-source implementation of our motion controller for both non-holonomic and omnidirectional mobile manipulators available at jhavl.github.io/holistic.
A Smart Surveillance System for Pedestrian Tracking and Counting using Template Matching

Human Detection and tracking have become a focal area of research as it plays a major role in computer vision applications. Methods and equipment for the detection and tracking of humans are constantly being changed as advanced methodologies are being discovered. Even though this is a huge topic in the computer vision field, pedestrian detection in infrared images is relatively new. In this Research paper, we designed a new methodology for pedestrian detection, tracking, and counting in infrared images. The main objective of our research is to give machines a technique for the detection of pedestrians in real-time. Our proposed method of human detection detects and tracks pedestrians effectively and efficiently using the OCTVBS benchmark Pedestrian Infrared/visible Stereo Video Dataset. First, we used Template matching for Pedestrian detection. After detections tracking is done using Kalman filter. Lastly, headcount is acquired from head detections gained from applying Haar-like features. We evaluated our method on the OCTVBS dataset and gained detection accuracy of 91.04%, tracking accuracy of 92.47%, and count accuracy of 82.11%. The presented method should be useful for the monitoring of pedestrians in IR video surveillance.
Detecting privacy requirements from User Stories with NLP transfer learning models

To provide privacy-aware software systems, it is crucial to consider privacy from the very beginning of the development. However, developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems. We present an approach to decrease privacy risks during agile software development by automatically detecting privacy-related information in the context of user story requirements, a prominent notation in agile Requirement Engineering (RE). The proposed approach combines Natural Language Processing (NLP) and linguistic resources with deep learning algorithms to identify privacy aspects into User Stories. NLP technologies are used to extract information regarding the semantic and syntactic structure of the text. This information is then processed by a pre-trained convolutional neural network, which paved the way for the implementation of a Transfer Learning technique. We evaluate the proposed approach by performing an empirical study with a dataset of 1680 user stories. The experimental results show that deep learning algorithms allow to obtain better predictions than those achieved with conventional (shallow) machine learning methods. Moreover, the application of Transfer Learning allows to considerably improve the accuracy of the predictions, ca. 10%. Our study contributes to encourage software engineering researchers in considering the opportunities to automate privacy detection in the early phase of design, by also exploiting transfer learning models.
Non-dominated Sorting Genetic Algorithm II and Particle Swarm Optimization for design optimization of Shell and Tube Heat Exchanger

Optimization methods are applied in Shell and Tube Heat Exchanger (STHE) design to reduce the cost of the device. Various existing optimization techniques such as Particle Swarm Optimization (PSO), Adaptive Range Genetic Algorithm (ARGA) are applied in the design of STHE. Existing optimization methods used in STHE design, have the limitation of lower convergence and easily trap into local optima. In this research, the hybrid method of Non-dominated Sorting Genetic Algorithm II (NSGA II) and PSO method is proposed to reduce the cost in STHE design. The NSGA II method is applied to improve the exploration and PSO method is applied to improve exploitation of search process. The hybrid method has objective function of total cost and overall heat transfer of the model to improve the performance. The NSGA II has strong exploration in the search due to the non-dominated search process and also provides good convergence. The PSO method is applied in the best solution of NSGA II and the PSO method has the advantage of strong exploitation that escapes from the local optima. The hybrid NSGA II-PSO method is tested on three test cases and is compared with existing optimization methods to analyze its performance. The result shows that the hybrid NSGA II-PSO method has a 4.85% lesser total cost in case 1 and 1.51% lesser total cost in case 2, when compared to the ARGA method.
PhotoHelper: Portrait Photographing Guidance Via Deep Feature Retrieval and Fusion

We introduce a new photographing guidance (PhotoHelper) for amateur photographers to enhance their portrait photo quality using deep feature retrieval and fusion. In our model, we comprehensively integrate empirical aesthetic rules, traditional machine learning algorithms and deep neural networks to extract different kinds of features in both color and space aspects. With these features, we build a modified random forest with a structured photograph collection to identify types of photos. We also define the composition matching score to measure the similarity between the given photo and the reference photo. By combining all of the above processes, a one-stop deep portrait photographing guidance is constructed to provide users with professional reference photographs that are similar to the current scene and automatically generate spatial composition guidance according to the user-selected reference photo. Experiments and evaluations show that the aesthetic quality of portrait photos can be significantly improved via the composition guidance of our photographing guidance approach.
An intelligent UAV path planning optimization method for monitoring the risk of unattended offshore oil platforms

To ensure the safe operation of static and dynamic devices in a limited platform space and prevent security threats caused by accidents and external intruders, the unmanned aerial vehicle (UAV) is becoming an important tool to monitor and reduce the operational risk of unattended offshore oil platforms. However, UAVs may encounter location errors, adversely affecting patrol efficiency and accuracy. Therefore, the optimal UAV path planning is vital to ensure complete monitoring routes and reduce the risk presented by the marine environment, devices, and employees on unattended offshore platforms. This paper established a multi-objective mathematical model with the shortest flight path and minimum correction times for the intelligent UAV patrol of unattended offshore platforms. An intelligent algorithm with large-scale constraints for UAV path planning was proposed based on non-dominated sorting genetic algorithms. A flight path length of 8076.11 m and 25 correction times presented the optimal solution. The results indicated that the proposed algorithm could be used to plan an effective and accurate three-dimensional (3D) UAV flight path according to the size of the offshore oil platform. This is highly significant for the intelligent risk monitoring of unattended offshore platforms in practical engineering in the future.
Deep learning for microscopic examination of protozoan parasites

The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.
A Vision-based System for Recognition of Words used in Indian Sign Language Using MediaPipe

Indian Sign Language (ISL) is a form of communication used in India by the speech and hearing impaired community. It conveys linguistic information through gestures of the hands, arms, face, and head. However, the gestures used may not always be directly related to the referent term, resulting in a significant communication gap. Hence there is a need for a translator that can translate ISL into text or speech. The proposed system aims to recognize signs of ISL and translate them into texts that can be easily read. The ISL recognition system is based on Google&#x2019;s MediaPipe as a feature extractor and Random Forest Classifier is used for classification. An accuracy of 97.4% is achieved. The results show that the integration of MediaPipe with ML algorithms may be effectively employed to correctly recognise signs of ISL.
Image Encryption Through Lucas Sequence, S-Box and Chaos Theory

The need for image encryption schemes that are of low computation complexity is ever increasing. Meanwhile, the use of chaotic functions and mathematical sequences to administer security has been shown to provide very good results in recent literature. This paper proposes a lightweight image encryption scheme that is based on 3 stages. The first stage incorporates the use of the Lucas sequence, the second stage incorporates the use of an S-box, while the third stage makes use of the Sin Logistic map. Performance evaluation of the proposed encryption scheme indicates great resistivity to various forms of attacks, attained at a low computation cost. This renders the proposed image encryption ideal for real-time applications. Furthermore, a comparison of the proposed scheme with the state-of-the-art indicates its superiority.
Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises

Health professionals often prescribe patients to perform specific exercises for rehabilitation of several diseases (e.g., stroke, Parkinson, backpain). When patients perform those exercises in the absence of an expert (e.g., physicians/therapists), they cannot assess the correctness of the performance. Automatic assessment of physical rehabilitation exercises aims to assign a quality score given an RGBD video of the body movement as input. Recent deep learning approaches address this problem by extracting CNN features from co-ordinate grids of skeleton data (body-joints) obtained from videos. However, they could not extract rich spatio-temporal features from variable-length inputs. To address this issue, we investigate Graph Convolutional Networks (GCNs) for this task. We adapt spatio-temporal GCN to predict continuous scores(assessment) instead of discrete class labels. Our model can process variable-length inputs so that users can perform any number of repetitions of the prescribed exercise. Moreover, our novel design also provides self-attention of body-joints, indicating their role in predicting assessment scores. It guides the user to achieve a better score in future trials by matching the same attention weights of expert users. Our model successfully outperforms existing exercise assessment methods on KIMORE and UI-PRMD datasets.
Breathing Scarf: Using a First-Person Research Method to Design a Wearable for Emotional Regulation

The cognitive complexities of emotions and individualized coping strategies make it a difficult space for design. Collecting first-person data can provide nuanced understanding of the lived experience of emotional life, to better inform the design of wearable technologies for emotional self-regulation. We present a preliminary study of our first-person phenomenological approach to autobiographical design. The methodology is unique for the intertwining of emotional activities and mindfulness exercises, as a strategy for controlling emotional repercussions. Self-observation and documentation included journaling and sketching using the Inside-Out Probe workbook, followed by material prototyping and testing in-the-wild. The Breathing Scarf prototype embodies the design considerations. In designing for one to support personalized self-regulation strategies, key considerations include designing for personal comfort, ownership, and individual-over-social meaning-making. Of equal importance in the design research process are the well-being of the designer/researcher, the ability to self-regulate emotions, and the ethics of care and emotion work.