Reviewerly/exHarmony huggingface dataset:
System parameter optimization with improved genetic algorithm for laser triangulation sensors

Laser triangulation ranging sensors, with their micron precision, simple principles and compact structures, are widely applied in the industry for instance for workpiece shape and defect measurements. However, the determination of system parameters for laser triangulation sensors currently depends on the experience and manual attempts of designers, which is not only time-consuming but also enables additional measurement errors. In this work, we present a genetic algorithm based method to accurately, fast, and adaptively determine the optimal system parameters for various measurement requirements. Based on the Scheimpflug principle, measurement range and resolution expressions are first derived for laser triangulation systems. The formulas of laser spot size in the x- and y-directions of image detectors are presented and the system geometric dimension is constrained when setting the measured surface on the calibration reference plane. Furthermore, a nonlinear programming genetic algorithm (NP-GA) is proposed to evaluate system parameters for certain measurement conditions, in which the local search capability is significantly improved with rapid convergence to the optimal solution. The algorithm allows determining the parameters by setting diverse measurement resolutions with a relative uncertainty below 3.7% in 3 s. ZEMAX simulations of the sensor systems are performed with non-sequential mode to validate NP-GA determined system parameters. Simulation and computed results show the relative differences of the parameters dependent RMS radius of the laser spot on the image detector are below 0.35% and 8.72% in the <i>x-</i> and <i>y</i>-directions, respectively.
Design and Fabrication of SNI Helmet on Free Fall Test Using the Ulrich Design Method

Abstract SNI helmets have the highest assessment criteria in the digestive process regarding design, production, and market-to-target buyers. The design of this helmet emphasizes functionality rather than aesthetics. Careful planning must be done because it is the primary goal of this research, the theoretical basis of the fourth and fifth editions of the Ulrich method of product design development with score screening and concept selection using the product design style to be able to find complex case problems with great detail and reduce large production costs because of solving cases by maximizing the results of planning and designing the third concept with a score of 3.4 and a rate of 13 is the best, and the concept was chosen because it excels in the four selected aspects. Hence, proceed to the fabrication process. In the following score assessment, it is hoped that the parameters will be much more detailed to maximize the function and get closer to the details of the assessment so that it can be valid and objective.
Navigating Awareness: Factors Influencing Students’ Perception of Maritime Environmental Issues in Islamic Education

Abstract This study employs a quantitative approach to explore the factors influencing students’ low awareness and attention to maritime environmental issues and recommends Islamic education management strategies to enhance maritime environmental awareness based on these factors. Given the backdrop of insufficient attention to maritime environmental issues within Islamic educational institutions, this study delves into various aspects related to the integration of maritime environmental concerns into the curriculum, the management of Islamic educational institutions, and the effectiveness of applied strategies. The sample for this study encompasses 80 students and 5 faculty members within the environment of one Islamic educational institution in Kabupaten Tasikmalaya, namely the University of Islam KH. Ruhiat. Data was collected through surveys and interviews. The research findings illuminate the primary factors influencing students’ limited awareness of and attention to maritime environmental issues. The distribution of student responses regarding these factors were as follows: (1) “Unfocused Studies that Neglect Environmental Aspects” (25%); (2) “Weak Discourse on Environmental Issues on Campus” (17.5%); and (3) “Non-Integrated Learning Curriculum” (15%). These findings underscore critical domains that educational institutions, particularly the University of Islam KH. Ruhiat, need to address in order to enhance students’ awareness of maritime environmental issues. Strategies encompass curriculum revisions that incorporate environmental themes, promoting academic discussions on environmental concerns, and creating platforms for community engagement that prioritize maritime environmental issues.
Event Detection with Word-Word Relation Classification

Abstract Event detection (ED) is a critical task in information extraction, aiming to identify triggers and types. Current research has increasingly focused on fine-grained types, where a single sentence may contain multiple trigger words and event types. Previous models only considered sentence-level features, neglecting word-word features and word positional information in the text. We propose a novel labeling scheme that treats event detection as a word-word relation recognition task. In this approach, we first identify relationships between word pairs and then utilize these relationships to perform trigger and event-type detection. By adopting this method, we can efficiently and concurrently identify triggers and types in a sentence. Leveraging word pair relationships effectively addresses scenarios in which multiple trigger words appear in a single sentence. The results demonstrate that our approach outperforms several baseline models.
Designing quantum blockchain system integrated with 6G network

The sixth-generation (6G) network utilizes state-of-the-art machine learning technology and obtains high attention, while the fifth-generation (5G) industry is still developing globally. Unfortunately, 6G encounters challenges to achieve performance superiority, such as scalability, massive connection, integrity, and trust. As a result, future network technologies are migrating away from centralized management entities and toward decentralized and distributed ledger technology, such as blockchain. However, the security of the blockchain is based on the computational complexity of solving specific mathematical problems that are impossible to solve on existing computers in real-time. On the other hand, quantum computers can effortlessly translate such problems with easy decryption. As a result, this study presents an architecture demonstrating the integration of quantum blockchain (QBC) with 6G networks. To show the quantum advantage, highly entangled/secured QBC of 5-, 6-, and 7-qubits are used to create the above system's quantum circuits. After circuit optimization, mitigation is executed with the efficiency analysis to show the advantage of the error mitigation approach in recreating the state of the QBC circuit and executing on quantum hardware. Furthermore, quantum algorithms of blockchain smart provenience contracts for the cloud-centric Internet of Things (IoT) are proposed, and corresponding quantum circuits are designed. The possible outcomes from these circuits based on the input transaction information are verified.
Multi-sensor based strategy learning with deep reinforcement learning for unmanned ground vehicle

As intelligent Unmanned Ground Vehicles (UGVs) find broader applications in areas such as transportation and logistics. The fusion of multiple sensors becomes crucial, since it not only amplifies UGV perception in dynamic scenarios but also underpins their autonomous decision-making capabilities. However, many existing methods only focus on single-sensor data, overlooking the multi-sensor data integration, thereby limiting UGV's scalability and adaptability. In this paper, we introduce the Multi-Sensor Collaborative Decision Network (MSCDN) for autonomous multi-sensor fusion policy learning designed specifically for UGVs. MSCDN is dedicated to integrate the data collected by multi-sensors in simulation environment and can be migrate to real environment. Firstly, a simulation environment mirroring real environment is created, using a framework that transfers UGV decision-making from simulated to real environment with deep reinforcement learning. Secondly, MSCDN uses a multi-sensor attention fusion network to adaptively integrate sensor data, refining UGV responses in dynamic settings. Thirdly, MSCDN's efficacy is tested on both simulated and real UGV lane-keeping tasks, showcasing its superior performance in comparative experiments. Compared to baseline methods, MSCDN reduces training steps and achieves a 35.71 % higher success rate and a 37.5 % quicker task completion time, underlining its proficient multi-sensor data fusion capability.
Applying blockchain technology for vaccination in the context of COVID-19 pandemic: a systematic review and meta-analysis

Blockchain, one of these new digital technologies, has special qualities like immutability, decentralization, and transparency that can be helpful in many different areas including managing electronic medical data and access rights, as well as mobile health. We reviewed all COVID-19-related and unrelated blockchain applications in the healthcare industry. MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar were searched for pertinent reports up to July 29, 2021. There were articles with both technical and clinical designs, with or without prototype development. A total of 85 375 articles were assessed, and 415 full-length reports—37 of which were connected to COVID-19 and 378 of which were unrelated—were ultimately incorporated into the study. The three primary COVID-19-related applications that were reported were contact tracing, monitoring of immunity or vaccination passports, and pandemic control and surveillance. Management of electronic medical records, internet of things (such as remote monitoring or mobile health), and supply chain monitoring were the top three non-COVID-19-related applications. The majority of publications (277 [667%] of 415] focused on the technical performance of blockchain prototype systems, whereas nine (2%) research indicated actual clinical use and uptake. Only technical studies (129 [311%] of 415) made up the remaining investigations. The most popular platforms were Hyperledger and Ethereum. Numerous COVID-19-related and unrelated health care applications of blockchain technology are possible. The necessity to adapt fundamental blockchain technology for use in healthcare settings is highlighted by the fact that the majority of current research is still in the technical stage and only a small number offers practical clinical applications.
‘We are not here to enforce; we are here for the people’ Factors influencing performance of contact tracing during the COVID-19 pandemic: A qualitative study

Provider-initiated contact tracing (CT) is an important measure to slow down the spread of infectious diseases such as COVID-19. However, carrying out effective CT depends on the collaboration between the patient and the contact tracer. To improve CT, it is important to understand which factors influence contact tracers in being able to carry out CT during large pandemics.We performed individual semi-structured interviews with nine contact tracers working for the COVID-19 unit of the Public Health Service (PHS) Rotterdam-Rijnmond, the Netherlands, to explore their experiences with carrying out CT. Data were collected between July 2020 and December 2020. The interview protocol was structured based on the CT tasks and guided by the literature and the framework explaining adherence to clinical practice guidelines.In general, CT seemed to be carried out satisfactorily. Individual factors (interviewing techniques and skills, attitude towards the patient and attitude towards CT), factors related to the patient (cooperativeness and engagement, emotions, language and culture and (mis)information), guideline-related factors (characteristics) and factors related to the organisation (interactions with colleagues, support from management, workload and training) were found to influence the carrying out of CT.To be well prepared for future pandemics, it is important to explore strategies that can be effective to support the contact tracer in performing CT, support patients in feeling comfortable to be engaged and ways to reach more consistency in policies and protocols.
Immersive virtual reality as a social research environment. The impact of the presence of an embodied agent on the Stroop task performance based on own research

This empirical study examined the effects of the presence of others (physical observers and embodied agents) on the performance of the Stroop task within an immersive virtual reality (VR) environment. Drawing from the research conducted by Pascal Huguet and his colleagues, who explored the social facilitation and inhibition effects in traditional lab conditions using the Stroop test, the current study sought to test these effects in VR conditions. Participants were divided into four groups, each experiencing different conditions – performing the task alone, under the awareness of being observed, or in the presence of embodied agents (formally dressed as Dr. Piotr or casually as Piotrek). A one-way analysis of variance (ANOVA) for independent samples was conducted to evaluate the results. Findings showed a significant reduction in Stroop interference when participants were aware of being observed by the experimenter. However, the presence of embodied agents in the VR environment, regardless of their regardless of their dress or manner of presentation, did not significantly influence the results. These results not only confirmed the occurrence of the Stroop interference effect in VR conditions, comparable to traditional lab conditions, but they also suggested that the social presence of the experimenter could enhance task performance in the VR environment. There was no significant influence observed from the presence of embodied agents, though. These observations can greatly contribute to the development of VR. However, further research is required to expand these findings, considering factors such as sample size and realism of embodied agents.
Roadmap for Implementation Optical Extreme Learning Machines

The principles of machine learning are discussed in terms of non-linear mappings and signal mixing. We considered the characteristics of the capabilities of algorithmic reservoir computers based on the software implementation of artificial neurons with random weights of input signals, and physical reservoir computers using various random and non-linear phenomena. The main elements of the concept of extremal learning machines are outlined, their features and the algorithm for learning a linear output with comb regularization by the method of pseudo-inverse Moore-Penrose matrices are described. The study found the subdivision of optical reservoir computers by types and spatial scales of the physical processes used. Optical Extreme Learning Machines (OELM) are identified as a promising area of implementation. The structure of the diffractive OELM, its principle of operation based on the scattering by random elements, and its inherent limitations are described. The use of plasmonic metal nanostructures to reduce the size of such processors is proposed. To implement the quantum version of the OELM signal with frequency modulation, a new approach to processors operating on systems of artificial atoms with random interactions has been formulated. The study proposed to incite and read signals using the methods of three-pulse femtosecond pump-probe spectroscopy. The need for a low level of relaxation during the processor cycle is noted for stable operation of the circuit. The study discussed an individual atom generating high harmonics in a strong laser field as the fundamental quantum limit of the processor, and described the physical mechanism providing this effect. For each of the three OELM options, critically important tasks are formulated, the consistent solution of which will significantly bring the creation of technologically significant OELM closer.
Enhancing Creative Performance

The COVID-19 pandemic has disrupted firms' prior working modes. As more employees are required to work from home, firms need to successfully implement remote working and distance learning conditions. In this context, knowledge management systems (KMSs) are helpful in sharing knowledge and enhancing employee creativity, regardless of their place of work. This study elucidates the antecedents that moderate the impact of KMS usage on employees' creative performance. It explores how absorptive capacity (AC), as well as extrinsic and intrinsic motivation, directly influences employees' creative performance and their moderating effects. Results from an online survey of 415 employees indicate that KMS usage increases employees' creative performance. Moreover, AC as well as extrinsic and intrinsic motivation have a positive direct effect on employees' creative performance. However, the authors find no positive moderating effect of AC or extrinsic and intrinsic motivation on the relationship between KMS usage and employees' creative performance.
Integrated Immunization Information System in Indonesia: Prototype Design Using Quantitative and Qualitative Data

As the volume of immunization records increases, problems with fragmented records arise, especially since the majority of records in developing countries, including Indonesia, remain paper based. Implementing an immunization information system (IIS) offers a solution to this problem.In this study, we designed an integrated IIS prototype in Indonesia using the design science research (DSR) methodology.The stages of the DSR methodology followed in this study included identifying problems and motivating and defining objectives for a solution, design and development, demonstration, evaluation, communication, and drawing conclusions and suggestions. Specifically, this study began with problem formulation and a literature review. We then applied quantitative (questionnaire with 305 members of the public) and qualitative (interviews with 15 health workers including nurses, midwives, and doctors) data collection approaches.The resulting high-fidelity prototype follows the 8 golden rules. There are 2 IIS designs, one for the public as immunization recipients and another for health workers. The functionalities include immunization history, schedule, recommendations, verification, certificates, reminders and recalls, coverage, monitoring, news, and reports of adverse events. Evaluation of the prototype was carried out through interviews and a questionnaire designed according to the System Usability Scale (SUS) and Post-Study System Usability Questionnaire (PSSUQ). The SUS value was 72.5 or "Good (Acceptable)," while the system usefulness, information quality, interface quality, and overall value on the PSSUQ were 2.65, 2.94, 2.48, and 2.71, respectively, which indicate it has an effective design.This provides a guide for health facilities, health regulators, and health application developers on how to implement an integrated IIS in Indonesia.
An attention mechanism and multi-feature fusion network for medical image segmentation

Recently, deep learning has been applied to medical image segmentation. However, existing methods based on deep learning still suffer from several disadvantages, such as blurred edge segmentation of image lesion regions and weak context information extraction. To tackle these problems, this paper proposes an attention mechanism and multi-feature fusion network with the encoder-decoder structure for medical image segmentation. In the proposed network, the convolutional group encoder module and the self-attention module are applied to divide images. The convolutional group encoder uses multiple convolution and dilated convolution to enhance the multi-scale information capturing capability of the model. The extracted image features will be useful for precise segmentation. Moreover, the self-attention module is introduced into the network for mining and complementing the edge details of segmented images. In the proposed model, convolutional group encoders and self-attention are applied repeatedly to capture changes in contextual relationships and continuously refine boundary information. Several experiments have been conducted on the BUSI and ISIC datasets to verify the effectiveness of the proposed method. Compared with other methods, the proposed method can achieve better segmentation results.
Evaluating the Worker Technology Acceptance of a Mixed Reality Technical Documentation

Mixed Reality (MR) technology has gained popularity in the manufacturing industry over the last few decades. In the maintenance field, it helps workers in document analysis and decision-making during operations. While numerous studies have demonstrated the empirical benefits of MR, few have examined its applicability in a real industrial context. Among them, many focus on user performance. The novelty of our work is to evaluate mainly the acceptance of MR technology by experienced technicians using smart glasses for maintenance tasks in a real industrial context. We conducted a user study involving 7 workers who were asked to follow the instructions provided by an MR Technical Documentation (MRTD) developed considering authoring guidelines already validated in the literature but only in a laboratory context. The industrial operators performed both navigation tasks within the menu of the MRTD and a disassembly task, following the instructions displayed on the smart glass. The evaluation in the real working scenario showed that MRTD is highly appreciated by workers even if they have no previous experience with smart glasses. These results allow us to confirm the goodness of the design guidelines followed for the authoring of the MRTD.
Ubiquity of VR: Towards Investigating Ways of Interrupting VR Users to Obtain Their Attention in Public Spaces

The portability of mobile virtual reality (VR) head-mounted displays (HMDs) allows users to immerse themselves in a virtual environment wherever they want at any physical place. While mobile VR has the potential to become ubiquitous as smartphones, it is still rarely used. Social acceptability is an important factor determining the usage of mobile technology. Previous work suggests that VR HMDs are considered not socially acceptable during social interaction. However, it is still unknown why the acceptance of mobile VR is reduced when being surrounded by other people. Therefore, we conducted two studies to investigate if and how interruptions by bystanders while wearing a mobile VR HMD contribute to reduced social acceptability. In the first study, we performed an observation in natural environments to find out how people interrupt VR users. In the second study, we used the same set of scenarios in an online survey to gather the opinions of a larger user group. Our results reveal still high skepticism towards VR use in public spaces, which can be mitigated partly by VR experience.