Reviewerly/exHarmony huggingface dataset:
A Comprehensive Survey on Robotics and Automation in Various Industries

Automation is the process of doing a work with motor or machines through a control architecture. Robotics is an advanced process of automation that makes the machine and motor to take decision with the virtual intelligence available in their control unit. Also the robotic equipment are incorporated with various sensors and monitoring units to observe its operation regularity and effectiveness. Nowadays the robotic systems are added with a neural network based algorithms for making the machine to analyze more complex problems same as like of human beings. The motive of this survey work is to analyze the recent attainments and effectives of the robotic based automation systems on various applications and industries. The discussion section included in the paper also projects the research gaps identified from the present robotic applications.
Ultra-Low-Power Reservoir Computing Based on Synthetic Antiferromagnetic Skyrmion Pairs

Reservoir computing (RC) is a promising method to realize the universal learning system. However, the complexity and power consumption limit its application. In this letter, we propose an ultra-low power RC device based on Synthetic antiferromagnetic skyrmion pairs (SAFSPs). By leveraging the Ruderman-Kittel-Kasuya- Yosida (RKKY) effect and spin-orbital torque, skyrmion pendulum motion can be realized. Furthermore, periodic oscillations can be achieved through magnetic anisotropy engineering. Based on this nonlinear motion of magnetic skyrmions, a RC system can be built. Our results show that the cognitive task of nonlinear time series can be carried out by a network of the proposed device and the power consumption can be reduced to 466nW.
A virtual reality classroom to teach and explore crystal solid state structures

Abstract We present an educational application of virtual reality that we created to help students gain an in-depth understanding of the internal structure of crystals and related key concepts. Teachers can use it to give lectures to small groups (10-15) of students in a shared virtual environment, both remotely (with teacher and students in different locations) and locally (while sharing the same physical space). Lectures can be recorded, stored in an online repository, and shared with students who can either review a recorded lecture in the same virtual environment or can use the application for self-studying by exploring a large collection of available crystal structures. We validated our application with human subjects receiving positive feedback.
Using Mechanical Metamaterials in Guitar Top Plates: A Numerical Study

It has recently been shown that the mechanical properties of thin, rectangular wooden plates can be tuned by carving them with specific patterns of perforations, effectively realising a 2D wooden mechanical metamaterial. Such a material is of great interest for the construction of musical instruments, as it could allow a new degree of creative control for makers. Furthermore, issues with the shrinking supplies of tone-woods could be alleviated as wood samples that don not meet the desired requirements could simply be altered, instead of being discarded. In this work, we study the effect of the use of these metamaterials in the soundboards of classical guitars. By way of simulations, we evaluate their impact on the modal behaviour and on the sound pressure level of the instrument, as well as on its ability to sustain the load exerted by the strings. Our results show that the metamaterials can tune the instrument’s response without compromising its structural integrity. We thus conclude that the use of wooden mechanical metamaterials in the soundboards of classical guitars is feasible and, in many ways, beneficial, not the least since it opens the door to using non-traditional woods with bespoke density and stiffness.
ExpFinder: A hybrid model for expert finding from text-based expertise data

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques such as vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder , a new hybrid model for expert finding, that integrates a novel N-gram vector space model, denoted as nVSM, and a graph-based model, denoted as μCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N-gram words, and ExpFinder incorporates nVSM into μCO-HITS to achieve expert finding. We comprehensively evaluate ExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder is an highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning

High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms solve these hybrid problems jointly to consider constraints between the discrete symbolic actions (i.e., the task plan) and their continuous geometric realization (i.e., motion plans). This joint approach solves more difficult problems than approaches that address the task and motion subproblems independently. TMP algorithms combine and extend results from both task and motion planning. TMP has mainly focused on computational performance and completeness and less on solution optimality. Optimal TMP is difficult because the independent optima of the subproblems may not be the optimal integrated solution, which can only be found by jointly optimizing both plans. This paper presents Task and Motion Informed Trees (TMIT*), an optimal TMP algorithm that combines results from makespan-optimal task planning and almost-surely asymptotically optimal motion planning. TMIT* interleaves asymmetric forward and reverse searches to delay computationally expensive operations until necessary and perform an efficient informed search directly in the problem's hybrid state space. This allows it to solve problems quickly and then converge towards the optimal solution with additional computational time, as demonstrated on the evaluated robotic-manipulation benchmark problems.
Frequency Adjustable Resonator as a Tunable Coupler for Xmon Qubits

We propose a scheme of tunable coupler based on a frequency adjustable resonator for scalable quantum integrated circuits. One side of the T-shape quarter-wave resonator is short to the ground through a DC SQUID, which is used to tune the frequency of the resonator coupler. In the other side with two open ends, either end capacitively couples to a different Xmon qubit, which determines the effective coupling between the two qubits. The fundamental mode of the tunable resonator dominates the interaction with the qubits and functions as a tunable coupler to switch off/on the qubit–qubit interaction. The required external magnetic flux for the tunable coupler's switching off frequency is affected by the asymmetry degree of the DC SQUID, which can help to choose a better noise environment (of the qubits) induced by the resonator coupler. The T-shape tunable resonator can help to reduce the direct interaction between two qubits, which should be an important way to suppress the ZZ crosstalk and realize high-fidelity quantum gates. The DC SQUID is several millimeters away from the Xmon qubits, which in principle creates less flux noises compared with the transmon-type coupler.
Learning Incrementally to Segment Multiple Organs in a CT Image

There exists a large number of datasets for organ segmentation, which are partially annotated, and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating the organs of interest. In other words, new datasets with annotations of new organ categories are built over time. To unleash the potential behind these partially labeled, sequentially-constructed datasets, we propose to learn a multi-organ segmentation model through incremental learning (IL). In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs. We give the first attempt to conjecture that the different distribution is the key reason for 'catastrophic forgetting' that commonly exists in IL methods, and verify that IL has the natural adaptability to medical image scenarios. Extensive experiments on five open-sourced datasets are conducted to prove the effectiveness of our method and the conjecture mentioned above.
Mild simulator sickness can alter heart rate variability, mental workload, and learning outcomes in a 360° virtual reality application for medical education: a post hoc analysis of a randomized controlled trial

Virtual reality (VR) applications could be beneficial for education, training, and treatment. However, VR may induce symptoms of simulator sickness (SS) such as difficulty focusing, difficulty concentrating, or dizziness that could impair autonomic nervous system function, affect mental workload, and worsen interventional outcomes. In the original randomized controlled trial, which explored the effectiveness of using a 360° VR video versus a two-dimensional VR video to learn history taking and physical examination skills, only the former group participants had SS. Therefore, 28 undergraduate medical students who participated in a 360° VR learning module were included in this post hoc study using a repeated measures design. Data of the Simulator Sickness Questionnaire (SSQ), heart rate variability (HRV) analysis, Task Load Index, and Mini-Clinical Evaluation Exercise were retrospectively reviewed and statistically analyzed. Ten (36%) participants had mild SS (total score > 0 and ≤ 20), and 18 (64%) had no SS symptom. Total SSQ score was positively related to the very low frequency (VLF) band power, physical demand subscale, and frustration subscale, and inversely related to physical examination score. Using multilevel modeling, the VLF power mediated the relationship between total SSQ score and physical examination score. Furthermore, frustration subscale moderated the mediating effects of the VLF power. Our results highlight the importance of documenting SS to evaluate a 360° VR training program. Furthermore, the combination of HRV analysis with mental workload measurement and outcome assessments provided the important clinical value in evaluating the effects of SS in VR applications in medical education.
Chromatic and Spatial Analysis of One-Pixel Attacks Against an Image Classifier

One-pixel attack is a curious way of deceiving neural network classifier by changing only one pixel in the input image. The full potential and boundaries of this attack method are not yet fully understood. In this research, the successful and unsuccessful attacks are studied in more detail to illustrate the working mechanisms of a one-pixel attack created using differential evolution. The data comes from our earlier studies where we applied the attack against medical imaging. We used a real breast cancer tissue dataset and a real classifier as the attack target. This research presents ways to analyze chromatic and spatial distributions of one-pixel attacks. In addition, we present one-pixel attack confidence maps to illustrate the behavior of the target classifier. We show that the more effective attacks change the color of the pixel more, and that the successful attacks are situated at the center of the images. This kind of analysis is not only useful for understanding the behavior of the attack but also the qualities of the classifying neural network.
Factors Influencing Analysis for Level of Engineering English Education Based on Artificial Intelligence Technology

The essence of English for engineering is English for professional purposes (ESP). The assessment of the level of education in engineering English classrooms is one of the key issues currently being discussed in all schools. While the traditional English classroom teaching model has been criticized due to many problems, the changes in the new English curriculum and the changes in the assessment methods have also created new demands on the English teaching methods. The artificial intelligence technology brings a new direction for the optimization of English classroom, and it also provides new support for the realization of intelligent and collaborative English classroom teaching. In view of the current situation that the college English teaching evaluation mode is still dominated by summative evaluation, this paper summarizes the current problems of poor teaching effect, unbalanced ability cultivation, and mismatch between evaluation and teaching in engineering English. On this basis, it adopts the BPNN network combined with the interactive mechanism of English teaching to establish a multi-dimensional interactive English learning framework of teacher, student, corpus, and AI resource base. And the BPNN algorithm process was optimized using the gray wolf algorithm to improve the engineering English teaching model. Finally, an experiment on teaching engineering English was conducted within a university, and the experimental results showed that teaching objectives, teaching contents, teaching methods, and teaching effects had important effects on teaching effectiveness. In addition, the teaching framework constructed using the improved BPNN algorithm was better in terms of the learning effect at the same time, especially in writing, during the teaching process. Finally, the experimental results show that BPNN optimized by gray wolf algorithm can achieve better teaching effect than BPNN.
A path planning approach for mobile robots using short and safe Q-learning

Path planning is a major challenging problem for mobile robots, as the robot is required to reach the target position from the starting position while simultaneously avoiding conflicts with obstacles. This paper refers to a novel method as short and safe Q-learning to alleviate the short and safe path planning task of mobile robots. To solve the slow convergence of Q-learning, the artificial potential field is utilized to avoid random exploration and provides a priori knowledge of the environment for mobile robots. Furthermore, to speed up the convergence of the Q-learning and reduce the computing time, a dynamic reward is proposed to facilitate the mobile robot towards the target point. The experiments are divided into two parts: short and safe path planning. The mobile robot can reach the target with the optimal path length in short path planning, and away from obstacles in safe path planning. Experiments compared with the state-of-the-art algorithm demonstrate the effectiveness and practicality of the proposed approach. Concluded, the path length, computing time and turning angle of SSQL is increased by 2.83%, 23.98% and 7.98% in short path planning, 3.64%, 23.42% and 12.61% in safe path planning compared with classical Q-learning. Furthermore, the SSQL outperforms other optimization algorithms with shorter path length and smaller turning angles.
The Effects of Sensory Cues on Immersive Experiences for Fostering Technology-Assisted Sustainable Behavior: A Systematic Review

Games are consistently acknowledged as a powerful approach that can significantly impact people’s behavior towards living in a sustainable way. Sensory cues are regarded as influential factors in facilitating immersive experiences in gamified applications to foster sustainable behavior. As our perception of an environment is influenced not only by what we can see but also by additional sensory input such as sound and touch, additional sensory information can be part of the participant’s experience. This study systematically scrutinized game-based applications containing sensory cues to interpret current technology-assisted sustainable behavior development. This study provides a review of the impact of the sensory signals offered by video games, virtual reality, and augmented reality on pro-environmental behavioral intention. This research found that human senses can change the perception of immersion in multiple ways: visual (dimensions, angles, color), auditory (music, dialogue), and haptic, and these can affect sustainable behavior. Thus, we argue that multiple sensory modalities provide more opportunities to influence users to act sustainably. Based on the results, the theoretical contribution of this paper emphasizes the level of immersion, which is closely related to various sensory perceptions, and explains the correlation between them. In terms of industrial applications, it provides game designers, developers of VR and AR applications, and planners of sustainable education guidelines for the adoption of immersive scenarios.
Workloads and Emotional Factors Derived from the Transition towards Online and/or Hybrid Teaching among Postgraduate Professors: Review of the Lessons Learned

The objective of the present study was to discover which tasks resulted in a higher workload, leading to a higher anxiety of postgraduate professors at different Ibero-American universities, when dealing with the transition from face-to-face teaching to a virtual one. A questionnaire was constructed that was completed by 125 professors once validated. The results showed that with respect to tasks that required a higher workload, the professors had to re-think methodological strategies and activities, correct the student’s work, provide tutoring, and search for materials. As for the degree of anxiety, it was higher, given the increased number of hours in front of the computer, the lack of immediate feedback from the students, the feeling of not reaching all the students, having to look for activities to invigorate the online classes, and the need to prepare more materials and the assessment tasks. The results were the same irrespective of the university, thereby enabling us to conclude that this phenomenon was globalized, and the workload and anxiety were replicated in different contexts. Therefore, it is necessary to implement formative proposals that help manage online or hybrid teaching, beyond the mere transfer of the face-to-face teaching models to virtual ones.
A social robot-based platform for health behavior change toward prevention of childhood obesity

Abstract Childhood obesity is a major public health challenge which is linked with the occurrence of diseases such as diabetes and cancer. The COVID-19 pandemic has forced changes to the lifestyle behaviors of children, thereby making the risk of developing obesity even greater. Novel preventive tools and approaches are required to fight childhood obesity. We present a social robot-based platform which utilizes an interactive motivational strategy in communication with children, collects self-reports through the touch of tangible objects, and processes behavioral data, aiming to: (a) screen and assess the behaviors of children in the dimensions of physical activity, diet, and education, and (b) recommend individualized goals for health behavior change. The platform was integrated through a microservice architecture within a multi-component system targeting childhood obesity prevention. The platform was evaluated in an experimental study with 30 children aged 9–12 years in a real-life school setting, showing children’s acceptance to use it, and an 80% success rate in achieving weekly personal health goals recommended by the social robot-based platform. The results provide preliminary evidence on the implementation feasibility and potential of the social robot-based platform toward the betterment of children’s health behaviors in the context of childhood obesity prevention. Further rigorous longer-term studies are required.
Software security during modern code review: the developer’s perspective

To avoid software vulnerabilities, organizations are shifting security to earlier stages of the software development, such as at code review time. In this paper, we aim to understand the developers’ perspective on assessing software security during code review, the challenges they encounter, and the support that companies and projects provide. To this end, we conduct a two-step investigation: we interview 10 professional developers and survey 182 practitioners about software security assessment during code review. The outcome is an overview of how developers perceive software security during code review and a set of identified challenges. Our study revealed that most developers do not immediately report to focus on security issues during code review. Only after being asked about software security, developers state to always consider it during review and acknowledge its importance. Most companies do not provide security training, yet expect developers to still ensure security during reviews. Accordingly, developers report the lack of training and security knowledge as the main challenges they face when checking for security issues. In addition, they have challenges with third-party libraries and to identify interactions between parts of code that could have security implications. Moreover, security may be disregarded during reviews due to developers’ assumptions about the security dynamic of the application they develop. Preprint: https://arxiv.org/abs/2208.04261 Data and materials: https://doi.org/10.5281/zenodo.6969369
Model of Flood Attenuation by Combining Dry Dam and Retention Pond Distribution

This research aims to find the most optimum condition for the position and capacity of the retention pond for decreasing the design flood and increasing the time concentration. This research was conducted in the Ciawi and Sukamadu dams-Indonesia. Based on the design, the Ciawi and Sukamadu dry dams only can reduce the flood peak to amount of 13%, so it is still needed the supporting infrastructure for increasing the efficiency in reducing flood. The combination of high- and low-intensity rainfall-runoff management methods can support the performance of dry dams in making an effort to reduce the design flood. The methodology consists of collecting the primary and secondary data, conducting the analysis based on the concepts of flood control and low impact development, then analyzing by using the HEC-HMS software. The results show that by adding the Ciawi and Sukamahi dams that have a storage capacity of 15 million m3, there is success to decrease the flood peak to 13% and the additional time concentration (Tc) is during 20 min. The additional retention pond will be able to reduce the number of flood peaks. The results show that the additional retention pond that is combined with the Ciawi dan Sukamahi dams can increase the performance of the Ciawi and Sukamahi dams with the percentage flood reduction is 19% and the additional time concentration is 50 min. The formulation that illustrates the flood reduction in the downstream of the Ciliwung watershed was proposed.