Reviewerly/exHarmony huggingface dataset:
Distributed Particle Swarm Optimization for Multi-Robot System in Search and Rescue Operations

In this paper, we tackle the problem of finding victims in a search and rescue environment, taking into account that the terrain in a disaster is complex and dangerous for rescuers to traverse. Furthermore, time is crucial when saving lives from a disaster considering all of these challenges, a solution is proposed by using a cooperative robotics team, which speeds up the process of searching for survivors and avoids risking additional lives. This article focuses on the navigation of a swarm of robots that can avoid collisions with obstacles that can be either static or dynamic, and locate victims. The method we employed to navigate in the environment is based on a DPSO Distributed Particle Swarm Optimization where each particle swarm represents a single robot. We show the interaction between swarms, and we make use of artificial potential functions for collision avoidance and for attraction to victims. We perform several simulation experiments to test the navigation algorithm, avoiding obstacles, and finding victims. These experiments are carried out in different environments, varying the number of victims and also the size and number of obstacles. The results show how the algorithm allows the group to avoid obstacles and find possible victims, all experiments are implemented using a combination of Python with V-Rep.
The Immersion Principle in Multimedia Learning

The immersion principle in multimedia learning is that immersive virtual environments promote better learning when they incorporate multimedia design principles. In short, immersive media do not necessarily improve learning but effective instructional methods within immersive virtual environments do improve learning. The goal of effective instructional design in immersive virtual environments is to promote processes of selecting, organizing, and integrating information. Psychological presence – the subjective experience of “being there” – is an affective affordance of learning in immersive learning environments that can motivate learners to engage in deeper learning. The chapter also describes boundary conditions of immersive virtual environments, including the potential for instructional design to cause extraneous cognitive load and impose high metacognitive demands on learners. Multimedia learning principles developed based on research with less immersive media may generalize to learning in immersive environments.
The Critical Communication Challenges Between Geographically Distributed Agile Development Teams: Empirical Findings

<roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Background:</b></roman> Although a number of empirical studies have investigated communication challenges during recent years, we still need to discover the most critical challenges that face communication when agile development is geographically distributed. We also need to discover how successful geographically distributed agile development (GDAD) organizations deal with these challenges. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Literature review:</b></roman> Most previous studies reported that the critical challenges facing GDAD communication can be categorized into five themes: differences in cultures, different time zones, different spoken languages, different personal skills, and the efficiency and effectiveness of communication tools used. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Research questions:</b></roman> 1. What are the challenges of communication between GDAD teams? 2. How can the impact of GDAD communication challenges be mitigated? <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Methodology:</b></roman> Data were collected by interviewing 12 members of a three-team organization using distributed agile development. These teams are distributed over three countries; the main team located in Australia, the developers’ team located in China, and the testers’ team located in India. A thematic analysis technique was used to identify communication challenges and practices used to mitigate the effect of these challenges. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Results:</b></roman> Our findings reveal that the five challenges are still critical to GDAD. Moreover, we report a new critical challenge of communication in GDAD, the insufficient documentation provided by distributed teams and members. In addition, we recommend several practices to mitigate the impact of these challenges. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Conclusions:</b></roman> Communication among distributed agile development teams still faces several critical challenges, and the solutions to these challenges provided in recent years have not been sufficient. This fact prompts the need for more research on how the impact of these challenges can be lessened.
Experimenting Mobile and e-Health Services with Frail MCI Older People

The ageing population has become an increasing phenomenon world-wide, leading to a growing need for specialised help. Improving the quality of life of older people can lower the risk of depression and social isolation, but it requires a multi-dimensional approach through continuous monitoring and training of the main health domains (e.g., cognitive, motor, nutritional and behavioural). To this end, the use of mobile and e-health services tailored to the user’s needs can help stabilise their health conditions, in terms of physical, mental, and social capabilities. In this context, the INTESA project proposes a set of personalised monitoring and rehabilitation services for older people, based on mobile and wearable technologies ready to be used either at home or in residential long-term care facilities. We evaluated the proposed solution by deploying a suite of services in a nursing home and defining customised protocols to involve both guests (primary users) and nursing care personnel (secondary users). In this paper, we present the extended results obtained after the one-year period of experimentation in terms of technical reliability of the system, Quality of Experience, and user acceptance for both the user categories.
Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).
Pitfalls in using the geochronological information from the EarthChem Portal for Precambrian time-series analysis

Improvements in analytical techniques over the past decades have led to an exponential growth of scientific data. The EarthChem Portal is the most comprehensive and popular open access online database in Earth sciences. This portal “operates a suite of data systems that assist geoscientists with accessing, sharing, and using geochemical, petrological, and geochronological data”. It gives users the capacity to search several dataset repositories (PetDB, SedDB, GEOROC, NavDat and DARWIN). In recent years, the EarthChem Portal has been used for numerous studies that applied time-series analysis to track Earth’s history and evolution over the past 4 billion years. A presumption of such studies is that the ages assigned to the samples are generally precise and accurate. Here, we investigate the age data reported in the EarthChem Portal. We show that only 17% of the samples recorded in the EarthChem Portal have a reported age with a relative age range of less than 5%, and only ∼3% of the samples can be used to investigate Precambrian global events. Our analyses show that careful filtering is mandatory before applying the data to time-series analysis to avoid reaching erroneous conclusions based on faulty results.
Developmental profiles of reading fluency and reading comprehension from grades 1 to 9 and their early identification.

This study examined developmental profiles of reading fluency and reading comprehension in Grades 1 to 9 (ages 7 to 15) in a large Finnish sample (N = 2,518). In addition, early predictors of the profiles were analyzed with respect to kindergarten cognitive skills (phonological awareness, letter knowledge, rapid automized naming [RAN], number counting, word reading, vocabulary, and listening comprehension), parental factors (level of education, reading difficulties), and gender. Four different profiles of reading fluency and reading comprehension development were identified using latent profile analysis. These comprised one profile with persistent reading difficulties across the grades, one with early poor reading skills but with a resolving tendency, one with average reading skills, and one with good readers who started with very high reading fluency but scored average over time. Of the kindergarten measures, parental reading difficulties, being male, low paternal level of education, slow RAN, difficulty in reading easy words, and low scores in phonological skills, letter knowledge, number counting, and vocabulary predicted reading difficulties. The children belonging to the profile with the resolving tendency showed an increased rate of family risk and multiple cognitive deficits but managed to resolve their reading difficulties. Being female, and good number counting and vocabulary skills predicted a tendency to resolve early reading difficulties. The results confirm the previous findings on the early predictors of reading difficulties and add to the literature by identifying skills that predict resolving patterns. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Mantaray modified multi-objective Harris hawk optimization algorithm expedites optimal load balancing in cloud computing

Task scheduling in the cloud is a difficult optimization challenge. The cloud system is assigned with a specific load based on the cloud architecture and user demands. However, both underloaded and overloaded situations result in a variety of system failures in terms of power consumption, machine failure, and so on. As a result, task-load balancing on virtual machines (VMs) is considered as an important part of cloud task scheduling. The present study proposes a dynamic load balancing algorithm based on the hybrid optimization algorithms named as Mantaray modified multi-objective Harris hawk optimization (MMHHO). The hybridization process updates the search space of Harris Hawk Optimization (HHO) by utilizing the Manta Ray Forging Optimization (MRFO) algorithm by considering the cost, response time, and resource utilization etc. The hybrid scheme, proposed in the present study, improves the system performance by enhancing the VMs throughput, balancing the load between the VMs, and sustaining the balance among priorities of tasks by adjusting the waiting time of the involved tasks. The proposed MMHHO based load balancing algorithm is implemented in CloudSim tool. The effectiveness of the suggested algorithm has been analyzed in terms of various parameters and compared with other existing algorithms. The simulation results show that the suggested MMHHO load balancing scheme outperforms other algorithms.
Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN

Cabin pose measurement is one of the key procedures in the assembly and docking process of large cabins, which provides important feedback information for the subsequent docking control system. As the basis of cabin pose measurement, the accuracy and robustness of cabin assembly image segmentation are particularly important. However, traditional image segmentation method based on RGB sensor is extremely susceptible to interference from the external environment, which greatly weakens the recognition effect. In this article, an image segmentation method of cabin assembly scene based on improved red-green-blue-depth (RGB-D) Mask R-CNN is proposed, and its network structure is designed to be able to specifically process four-channel images. The method can accurately extract the corresponding area of the cabin under complex and severe environmental disturbances, with high robustness and generalization capability. Meanwhile, the excellence of deep learning segmentation algorithms with depth channel information input is highlighted. In experiments, improved classic segmentation network U-Net, SegNet, pyramid scene parsing network (PSPNet), and Deeplab-v3 based on RGB-D were constructed as control, and these models were tested and evaluated on the enhanced test sets to verify their segmentation accuracy and robustness performance. Comparing experiments fully demonstrate the superiority of the segmentation network model of RGB-D four-channel input over RGB input. At the same time, vision system using the proposed Mask R-CNN algorithm based on RGB-D has the best cabin segmentation accuracy, robustness, and generalization capability, which has practical significance for industrial applications.
“Tricking the Brain” Using Immersive Virtual Reality: Modifying the Self-Perception Over Embodied Avatar Influences Motor Cortical Excitability and Action Initiation

To offer engaging neurorehabilitation training to neurologic patients, motor tasks are often visualized in virtual reality (VR). Recently introduced head-mounted displays (HMDs) allow to realistically mimic the body of the user from a first-person perspective (i.e., avatar) in a highly immersive VR environment. In this immersive environment, users may embody avatars with different body characteristics. Importantly, body characteristics impact how people perform actions. Therefore, alternating body perceptions using immersive VR may be a powerful tool to promote motor activity in neurologic patients. However, the ability of the brain to adapt motor commands based on a perceived modified reality has not yet been fully explored. To fill this gap, we “tricked the brain” using immersive VR and investigated if multisensory feedback modulating the physical properties of an embodied avatar influences motor brain networks and control. Ten healthy participants were immersed in a virtual environment using an HMD, where they saw an avatar from first-person perspective. We slowly transformed the surface of the avatar (i.e., the “skin material”) from human to stone. We enforced this visual change by repetitively touching the real arm of the participant and the arm of the avatar with a (virtual) hammer, while progressively replacing the sound of the hammer against skin with stone hitting sound via loudspeaker. We applied single-pulse transcranial magnetic simulation (TMS) to evaluate changes in motor cortical excitability associated with the illusion. Further, to investigate if the “stone illusion” affected motor control, participants performed a reaching task with the human and stone avatar. Questionnaires assessed the subjectively reported strength of embodiment and illusion. Our results show that participants experienced the “stone arm illusion.” Particularly, they rated their arm as heavier, colder, stiffer, and more insensitive when immersed with the stone than human avatar, without the illusion affecting their experienced feeling of body ownership. Further, the reported illusion strength was associated with enhanced motor cortical excitability and faster movement initiations, indicating that participants may have physically mirrored and compensated for the embodied body characteristics of the stone avatar. Together, immersive VR has the potential to influence motor brain networks by subtly modifying the perception of reality, opening new perspectives for the motor recovery of patients.
Implementation of the SMS Gateway in the Flood Early Warning Information System for Village Warning and Community Information

Flooding is a national disaster that often occurs in Indonesia. Flood disasters require long-term and short-term action. In the short-term system, the government currently emphasizes state and private institutions to jointly reduce flood victims by developing a flood disaster early warning system. Therefore, this study discusses the making of flood early warning information systems by utilizing GSM communication systems as a means of communication between clients and servers. The GSM communication service used is the SMS Gateway. The SMS gateway service is used for the first time sending data from a flood detection system to a flood information system. Second, disseminating flood information to the public. In this study, the flood warning system for flood early warning works with the integration of three modes.The three systems are flood detection systems, flood alarm systems, and flood early warning information systems. Flood detection systems are built using ultrasonic sensors and rain sensors as inputs, Arduino Uno as data processors and GSM SIM900 modules as outputs. The alarm system consists of GSM SIM900 module as Input, Arduino Uno as processor and electric alarm as output. The flood early warning information system was built using a Wavecom GSM modem, and data processing using PHP, MySQL DBMS, and Gammu. The communication system between each system uses SMS data. This method as a whole began in a flood detection system that sends flood and rain data to the flood early warning information system. And the flood warning system sends alarm activation data to the alarm system. Finally, the system distributes flood information to the public via SMS Gateway. This research is expected to help the community in anticipating more victims with flood information previously obtained.
The changing landscape of mobile learning pedagogy: A systematic literature review

With the rapid evolution of mobile technology, the adoption of mobile learning in education has also increased. Several studies have conducted various systematic literature reviews to discuss mobile learning from different perspectives, such as its advantages and limitations. However, limited attention has been paid to pedagogy in mobile learning. To address this gap, this study conducts a systematic review of 165 empirical studies on mobile learning to investigate the changing landscape of mobile learning pedagogy. The findings reveal that, despite the ubiquity and flexibility of mobile technologies, most mobile learning studies were conducted in formal settings. The findings also reveal that game-based learning and collaborative learning were the most frequently used pedagogical approaches. Furthermore, redefinition is the most frequently used pedagogical functionality of technology in mobile learning, based on the Substitution Augmentation Modification Redefinition (SAMR) model. The findings of this systematic literature review could be beneficial to both researchers and practitioners in highlighting how mobile learning is designed and implemented, and what gaps should be focused on to enhance pedagogical experiences on mobile devices and thus achieve better mobile learning outcomes.
A Complex Matrix Private Key to Enhance the Security Level of Image Cryptography

Standard methods used in the encryption and decryption process are implemented to protect confidential data. These methods require many arithmetic and logical operations that negatively affect the performance of the encryption process. In addition, they use private keys of a specific length, in addition to the fixed length of the data block used in encryption, which may provide the possibility of penetration of these methods, thus decreasing the level of security. In this research paper, a new method of digital image cryptography is introduced. This method is based on using a color image as an image_key to generate a sophisticated matrix private key (MPK) that cannot be hacked. The proposed method uses an initial state to set the required parameters, with secret information needed to generate the private key. The data-block size is variable, and the complicity of the MPK depends on the number of selected rounds and the data-block size. The proposed method is appropriate for publication in Symmetry because it employs a symmetrical complex matrix key to encrypt and decrypt digital images. The proposed method is simple yet very efficient in terms of throughput and scalability. The experiments show that the proposed method meets the quality requirements and can speed up the encryption–decryption process compared with standard methods, including DES, 3DES, AES, and Blowfish.
Quantum-inspired evolutionary algorithm applied to neural architecture search

The success of machine learning models over the last few years is mostly related to the significant progress of deep neural networks. These powerful and flexible models can even surpass human-level performance in tasks such as image recognition and strategy games. However, experts need to spend considerable time and resources to design the network structure. The demand for new architectures drives interest in automating this design process. Researchers have proposed new algorithms to address the neural architecture search (NAS) problem, including efforts to reduce the high computational cost of such methods. A common approach to improve efficiency is to reduce the search space with the help of expert knowledge, searching for cells rather than entire networks. Motivated by the faster convergence promoted by quantum-inspired evolutionary methods, the Q-NAS algorithm was proposed to address the NAS problem without relying on cell search. In this work, we consolidate Q-NAS, adding a new penalization feature, enhancing its retraining scheme, and also investigating more challenging search spaces than before. In CIFAR-10, we reached 93.85% of test accuracy in 67 GPU days, considering the addition of an early-stopping mechanism. We also applied Q-NAS to CIFAR-100, without modifying the parameters, and our best accuracy was 74.23%, which is comparable to ResNet164. The enhancements and results presented in this work show that Q-NAS can automatically generate network architectures that outperform hand-designed models for CIFAR-10 and CIFAR-100. Also, compared to other NAS methods, Q-NAS results are promising regarding the balance between performance, runtime efficiency, and automation. We believe that our results enrich the discussion on this balance, considering alternatives to the cell search approach.