Multiple Viewpoints Research Articles

Zero carbon, some nuisance: Exploring the viewpoints of heat pump owners and their neighbors in Groningen, Netherlands

ABSTRACT The domestic use of natural gas for heating is the prevalent option in the Netherlands. However, heat pumps will be mandatory in most Dutch households by 2026. Therefore, insights are needed in how citizens perceive this technology, by taking into account various societal, technological, economic, environmental, and political aspects. Our research offers a systematic investigation of the multiple viewpoints of heat pump users and their neighbors in Groningen, northern Netherlands. Using Q-methodology, we identified three distinct but interrelated and shared viewpoints: the realistic users, the hesitant neighbors and the enthusiastic advocates. All three shared viewpoints incorporate social influence and cognitive considerations, with the positive environmental impacts of heat pumps being highlighted in unison. Cognitive considerations relate mainly to technical and economic concerns. Social influence considerations often hint at the necessity of making prior agreements with the neighbors. We argue that the findings of this study can support policymakers toward the development of an integrated heat transition strategy.

A Passenger Detection and Action Recognition System for Public Transport Vehicles

In the past few years, advances of autonomous driving technologies have made significant progress. One successful deployment with great potential is for the self-driving public transportation. Consequently, the safety of passengers under unmanned operations is an important issue for the future development. In this paper, we propose a vision system for the detection, counting and action recognition of passengers on board a bus. To have a complete coverage of the passenger area, a pair of cameras mounted on the ceiling is adopted for image acquisition. A new cross-camera CNN structure is proposed for passenger pose recognition and action classification. To deal with the double counting problem caused by the overlap of multiple viewpoints, a region prediction algorithm is developed for matching validation. The experiments carried out on a minibus have demonstrated the feasibility of our proposed techniques.

Interdisciplinary STEM education foundational concepts: Implementation for knowledge creation

Interdisciplinary thinking is essential to understanding and solving real-life problems and requires multiple disciplinary viewpoints. Research into STEM education highlights that it promotes an interdisciplinary learning process integrating science, mathematics, engineering, and mathematics knowledge and skills. However, STEM definitions are varied, and implementation recommendations are scant, resulting in diversity in the development and implementation of the learning process. This study critically analyses the literature to determine the fundamental concepts of STEM education and STEM discipline integration. Our analysis discovers six key components of STEM education, encompassing the integration of discipline, utilization of multiple representations, engagement with realistic and relevant problems, application of the engineering design process, encouragement of active collaboration, and emphasis on student-centered learning approaches. Then, we transform these key components to a practical learning process. The STEM-DTaM (STEM with Design Thinking and Makerspace) learning model consists of seven steps. We then unfold how this proposed learning could facilitate interdisciplinary thinking construction.

Improve disaster literacy in nurses: a qualitative descriptive study.

The enhancement of nurses' disaster literacy is crucial for effective disaster emergency management, especially for clinical nurses with no prior experience in disaster rescue. This study aims to explore the perspectives of nurses who have been involved in disaster rescue operations on strategies to advance nurses' disaster literacy. A qualitative descriptive study, which follows unified qualitative study reporting guidelines. Thirty disaster rescue nurses were recruited to participate in this study, who came from 30 third-class hospitals in China. From October to December 2021, information was acquired by conducting semi-structured telephone interviews with participants. Qualitative content analysis was used to examine the data. Six categories and eleven subcategories were created. Strengthening disaster education and training was the main focus, and its subcategories included continuing education, nursing curriculum development, and disaster rescue experience guide. The second focus, which included scene simulation, actual combat/military simulation, and virtual reality simulation, was strengthening disaster simulation and drill. The third focus was on providing psychosocial support, which included improving self-psychological adjustment, developing resilience, and engaging in active coping. The fourth category was to strengthen nurse/hospital managers roles and leadership, which included enhancing professional identity and policies/leadership support. The fifth category was disaster preparedness, and the sixth was enhancing knowledge transformation. Government agencies or healthcare organizations should actively promote the formation of nurses' disaster literacy from multiple viewpoints. Disaster education and training, simulation exercises, and psychosocial support should be prioritized.

Flat mirrors, virtual rear-view cameras, and camera-mirror calibration

Flat mirrors are useful to generate virtual cameras and capture a scene from multiple viewpoints. However, the resultant camera-mirror setup is cumbersome to calibrate because of the mirror image reversion. The typical calibration flaws are omitting the checkerboard symmetry and assuming that virtual cameras are front-view. This paper explains the rear-view abstraction and its usefulness in calibrating a camera-mirror setup to obtain the equivalent mirrorless configuration. The underlying theory is reviewed, including camera imaging, the calibration process, and the laws of reflection. The proposed approach is illustrated by calibrating a camera-mirror setup and then using the calibrated camera to generate mirror images synthetically. This work offers a practical insight into calibrating more sophisticated systems such as mirrored camera-projector profilometers.

A Survey On Image Registration Techniques Using FFT

Image Registration is the process of straightening two or more images of the same scene with testimonial to a particular image. The images are captured from various sensors at different times and at multiple viewpoints. Thus, to get a better picture of any change of a scene/object over a considerable period of time image registration is important. Image registration finds application in medical sciences, remote sensing and in computer vision. This paper presents a detailed review of several approaches which are classified accordingly along with their contributions and drawbacks. The main steps of an image registration procedure are also discussed. Different performance measures are presented that determine the registration quality and accuracy. The scope for the future research are presented as well. Applications based on Fast Fourier Transform (FFT) such as signal and image processing require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. At present, however, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realization of a High-Level framework for the implementation of 1-D and 2-D FFTs for real-time applications. Results show that the parallel implementation of 2-D FFT achieves virtually linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrizable environment based on the developed parallel 2-D FFT architecture is presented as a solution for frequency domain image filtering application

An efficient and precise multi-candidate viewpoint filtering algorithm for terrain viewshed selection

The goal of terrain viewshed point selection is to identify multiple viewpoints within a specific area that offer optimal visibility. However, as the resolution of terrain data becomes finer, the number of data points to be processed grows significantly, leading to a sharp rise in computational demands for viewshed point selection. This paper introduces the Efficient Precise Viewshed Point Selection (EPVPS) algorithm, which provides an important reference for multi-viewpoint planning. First, the Empty Circles based K-means (ECKM) algorithm is utilized to determine the initial centers for candidate viewpoint clustering. Second, a new viewpoint evaluation metric, Weighted Coverage Overlap Metric (WCOM), is proposed. This metric divides the viewshed of candidate points into coverage contribution points and overlap contribution points, computes WCOM based on this division, and stores the values in a min-heap. Finally, viewpoints are added and deleted from clusters by adjusting the sets of coverage contribution points and overlap contribution points. The EPVPS algorithm is compared with the Candidate Viewpoints Filtering (CVF) algorithm and the Simulated Annealing (SA) algorithm. Experimental results demonstrate that the EPVPS algorithm outperforms the others in computational efficiency, coverage rate, and overlap rate.

Stereo imaging inspired by bionic optics.

Stereo imaging has been a focal point in fields such as robotics and autonomous driving. This Letter discusses the imaging mechanisms of jumping spiders and human eyes from a biomimetic perspective and proposes a monocular stereo imaging solution with low computational cost and high stability. The stereo imaging mechanism of jumping spiders enables monocular imaging without relying on multiple viewpoints, thus avoiding complex large-scale feature point matching and significantly conserving computational resources. The foveal imaging mechanism of the human eye allows for complex imaging tasks to be completed only on the locally interested regions, resulting in more efficient execution of various visual tasks. By combining these two advantages, we have developed a more computationally efficient monocular stereo imaging method that can achieve stereo imaging on only the locally interested regions without sacrificing the performance of wide field-of-view (FOV) imaging. Finally, through experimental validation, we demonstrate that the method proposed in this Letter exhibits excellent stereo imaging performance.

Annealing Temporal–Spatial Contrastive Learning for multi-view Online Action Detection

In open-world scenarios, the analysis of action events from multiple viewpoints is crucial for achieving a holistic understanding, a concept that resonates with human perception. However, achieving a synthesis of information from multiple viewpoints presents a challenge, as the data can induce inter-view regularization, complicating the learning process. This paper is the first to delve into Online Action Detection (OAD) through a multi-view lens, underscoring the value of cross-observation in enriching view-level information. By harnessing the spatiotemporal dynamics inherent in multi-view video sequences, an Annealing Temporal–Spatial Contrastive Learning (ATSCL) consisting of Annealing Temporal Contrastive Learning (ATCL) and Spatial Contrastive Learning (SCL) is proposed, optimized for compatibility with RNN-based models. ATCL employs an annealing temporal loss to uncover the intrinsic video structures via a temporal annealing sampling mechanism. Concurrently, SCL utilizes a spatial loss to draw representations from various viewpoints closer together, mitigating the regularization effects. The ATSCL liberates training multi-view OAD from the stringent requirements of synchronized training videos, enabling the execution of OAD tasks asynchronously. Experiments demonstrate that the RNN-based models realize an average improvement of 5.92% on the DAHLIA dataset, 3.36% on the IKEA ASM dataset, 2.92% on the BREAKFAST dataset and 0.6% (mAP) on the THUMOS’14 dataset following the integration of the ATSCL framework, underscoring the ATSCL’s efficacy across different RNN structures.

Blinded by the Data: When Analytics Overshadows Judgment

This article discusses the potential risks that can emerge when organizations overly on data-driven decision making by focusing excessively on quantitative analysis and metrics to the exclusion of important qualitative human factors. The article explores common pitfalls such as leaders losing sight of broader strategic perspectives and higher-level business drivers when immersed in granular data points, organizational chaos and paralysis resulting from constant A/B testing without clear objectives, over-quantification discounting important but difficult to measure "soft" elements like culture, talent and leadership that profoundly shape performance, and overconfidence in the perceived objectivity of models overlooks inherent biases. The article advocates balancing quantitative and qualitative lenses to gain a more textured understanding of complex dynamics by supplementing metrics with perspectives from ethnography and stakeholder input to provide needed context as numbers alone do not replace prudent application of human judgment and experience, and for data-driven initiatives to achieve tangible impacts, analytics must inform innovation and change management rather than replace continuous improvement processes, such that with moderation and integration of multiple viewpoints, data can empower rather than control strategic decision making.

How residents and volcanoes co-produce risk knowledge: Ways of knowing and affective attunement to the rhythms of Lonquimay volcano, Chile

In this paper, we present our understanding of the importance of affects in people's sense-making of volcanic risk in everyday life. In doing so, we explore how local knowledge on volcanism is produced and circulated through communities' ongoing affective encounters with volcanoes. Through ethnographic fieldwork and semi-structured interviews, we draw on the heterogeneous experiences and narratives of Malalcahuello residents living next to the Lonquimay volcanic complex in the Southern Andes of Chile. Its last eruption in 1988–1990 formed a new cone on the NE flank, called Navidad (Christmas), which has allowed residents to experience active volcanism in a twofold sense: being affected by its impacts during the eruption, and responding affectively to the volcano in everyday life. The results pave the way for a typology of affect-based ways of knowing volcanism. These are constituted by multiple people's viewpoints: 1) knowing the ground, 2) knowing the territory, 3) knowing the risk, and 4) knowing the behaviour. These ways of knowing vary according to, and are in part determined by, the different rhythms of the volcano itself. Therefore, active volcanism becomes a more-than-human agent of knowledge through its rhythmic presence in people's everyday lives. Over time, the local population has become affectively attuned to both ‘hazardous situations’ related to volcanic eruption and ‘risk and safe situations’ during volcanic quiescence. These attributes of human-volcano encounters turn hazardous spaces into affect-laden spaces at different times, raising the need to rethink spatio-temporal dimensions in knowledge dialogue and disaster risk reduction. Overall, the paper underlines the importance of affect-oriented risk research in Chile and worldwide to account for the pre-existent viewpoints from which a volcano is at the heart of people's concerns.

Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.

EXPLORATORY STUDY ON THE USE OF STORY METHODS IN INCREASING STUDENT INVOLVEMENT IN HISTORY LEARNING AT SMAN 1 MEDAN

This research aims to investigate the use of the story method in increasing student involvement in history learning at SMA Negeri 1 Medan. The story method is an approach that utilizes narrative to bring historical content to students in an interesting and engaging way. This research uses a qualitative approach with an exploratory study design. Data was collected through classroom observations, interviews with history teachers, and questionnaires given to students. Data analysis was carried out using content analysis techniques. The research results show that using the story method can increase student involvement in history learning. Students responded positively to the use of narrative in understanding historical content and felt more involved and enthusiastic in the learning process. The implications of this research provide encouragement for history teachers to consider using the story method as an effective learning strategy for increasing student engagement in history lessons. The story method also encourages students to think critically and reflectively. They are invited to see multiple points of view and understand the complexity of historical events, which helps them develop analytical and critical skills. The use of stories creates a more inclusive and enjoyable classroom atmosphere. Students feel more involved and valued, which in turn increases their motivation to study history. However, this research also identified several challenges in implementing the story method, such as the need for additional time to design interesting and relevant story material as well as differences in student learning preferences. However, these challenges can be overcome through collaboration between teachers and support from the school. Overall, this research concludes that the story method is an effective and innovative learning strategy for increasing student involvement in history learning. The recommendation put forward is to continue developing and perfecting this method, as well as providing training and workshops for teachers so that they are more skilled in implementing the story method. In this way, it is hoped that history learning in high schools can become more interesting, interactive and meaningful for students and help them develop a deeper understanding and appreciation of history.

In Some Ways: Syādvāda as the Synthesis of Anekāntavāda and Nayavāda in Akalaṅka’s Philosophical Treatises

AbstractThis paper investigates the relationship between the theory of many-sidedness (anekāntavāda), the theory of viewpoints (nayavāda), and the theory of sevenfold conditional predication (syādvāda) in the philosophical monographs of the Jaina philosopher Akalaṅka (c. 720-780 CE). Despite being celebrated as novel Jaina contributions to Indian philosophy, the mutual relationship between these three theories is poorly understood. Do these three theories represent three distinct frameworks with distinct purposes? Or do these three theories ultimately synthesise to form a single unified framework? Through a careful study of the Laghīyastraya and the Nyāyaviniścaya, this paper shows how Akalaṅka understands syādvāda as the central organising framework for integrating the theory of many-sidedness and the theory of viewpoints. On the one hand, syādvāda is analysed as the synthesis of multiple viewpoints because each of the seven conditional predications, when taken individually, represents a genuine viewpoint. On the other hand, the seven conditional predications of syādvāda, when taken collectively and iterated with respect to different characteristics, represent the linguistic formalisation of many-sided (anekānta) Jaina ontology. This paper shows how syādvāda as the grand synthesis of the theory of many-sidedness and the theory of viewpoints creates new possibilities as well as new problems within Akalaṅka’s philosophical system.

Query-Based Multiview Detection for Multiple Visual Sensor Networks.

In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use multiple camera perspectives to address occlusion challenges with multiview aggregation being a crucial component. In these applications, data from various interconnected cameras are combined to create a detailed ground plane feature. This feature is formed by projecting convolutional feature maps from multiple viewpoints and fusing them using uniform weighting. However, simply aggregating data from all cameras is not ideal due to different levels of occlusion depending on object positions and camera angles. To overcome this, we introduce QMVDet, a new query-based learning multiview detector, which incorporates an innovative camera-aware attention mechanism for aggregating multiview information. This mechanism selects the most reliable information from various camera views, thus minimizing the confusion caused by occlusions. Our method simultaneously utilizes both 2D and 3D data while maintaining 2D-3D multiview consistency to guide the multiview detection network's training. The proposed approach achieves state-of-the-art accuracy on two leading multiview detection benchmarks, highlighting its effectiveness for IoT-based multiview detection scenarios.

Multi-Dimensional Analysis of Offshore Wind Industry: The Case of French Wind Farms

The development of the offshore wind industry faces a number of organizational and economic hurdles, often strong societal resistance and the instability of public policies, thus drawing interest from academics who have analyzed this process from multiple viewpoints. This body of knowledge has served as the basis for a study aiming at characterizing France’s first three offshore wind farm projects, which finally reached the energy production stage after over ten years of development. Results show that although this development has been the subject of atypically strong social opposition, the projects’ features are quite close to those highlighted by the literature.

Seeing Double: Can We Reduce The Number Of Cameras In Tomo-PTV?

In this work, we explore the possibility of reducing the number of cameras for tomographic particle tracking velocimetry (PTV) by using beamsplitters to project multiple viewpoints on a single camera sensor. This optical train allows a trade-off between particle seeding density and number of cameras, which enables the reduction of camera cost at the highest end of high-speed tomographic PTV applications where camera cost is considerable. Results are presented with synthetically-generated images for unmodified versions of state-of-the-art PTV algorithms (OpenLPT and DaVis 10.1), and a modification is implemented to the OpenLPT algorithm to include the knowledge that the views are multiplied, enabling the removal of more particles at every iteration of the iterative particle reconstruction (IPR) step. We observe that incorporating the knowledge of the image-multiplied projection brings the performance of the OpenLPT PTV algorithm very close to the baseline performance for equivalent particle seeding density values. However, when using real doubled experimental images we note a major challenge when performing volume self-calibration. The volume self-calibration process is significantly affected by the image doubling, and the knowledge of image doubling has to also be incorporated into the volume self-calibration process, which is left as future work.

A dialogic approach to transform teaching, learning & assessment with generative AI in secondary education: a proof of concept

ABSTRACT This paper explores the pedagogical potential of Generative Artificial Intelligence (GenAI) in secondary education through a dialogic approach to teaching, learning and assessment. It presents an ongoing action research project in collaboration with a high school in Western Australia, involving four teachers to integrate GenAI in their classrooms. The study aims to develop and evaluate innovative pedagogies for leveraging GenAI to enhance educational practices and student learning outcomes across three action research teams focusing on critical questioning, assessment and differentiation. Drawing on Bakhtin’s concept of heteroglossia, the study conceptualizes GenAI not as a definitive knowledge provider but as a dialogic agent that facilitates collaborative dialogue and co-construction of knowledge among students. This perspective aims to encourage students to critically engage with AI-generated content and integrate multiple viewpoints into their learning, thus fostering key epistemic skills. Initial findings demonstrate active student engagement in dialogues with GenAI, highlighting the use of follow-up questions that indicate critical thinking and creativity. These findings underscore the significance of integrating multiple perspectives and fostering epistemic skills among students, promoting a comprehensive and ethical approach to AI use in education. The research calls for further exploration of GenAI’s pedagogic potential and its broader implications for educational practices, suggesting a promising avenue for pedagogical innovation and the development of critical thinking skills in the digital age.

A snapshot of parallelism in distributed deep learning training

The accelerated development of applications related to artificial intelligence has generated the creation of increasingly complex neural network models with enormous amounts of parameters, currently reaching up to trillions of parameters. Therefore, it makes your training almost impossible without the parallelization of training. Parallelism applied with different approaches is the mechanism that has been used to solve the problem of training on a large scale. This paper presents a glimpse of the state of the art related to parallelism in deep learning training from multiple points of view. The topics of pipeline parallelism, hybrid parallelism, mixture-of-experts and auto-parallelism are addressed in this study, which currently play a leading role in scientific research related to this area. Finally, we develop a series of experiments with data parallelism and model parallelism. The objective is that the reader can observe the performance of two types of parallelism and understand more clearly the approach of each one.

A Teaching Practicum Model for Constructing Cogenerative Dialogue Amongst Preservice Teachers to Improve Science Teaching

The specific focus of this study is how a team of four preservice teachers experienced a collaborative practicum model to support the development of cogenerative dialogue and foster professional growth. Data sources included individual video club annotations and the associated group discussions facilitated by comparison of groups members selected annotations. The analysis found that participation in peer collaboration provided multiple viewpoints of shared teaching experiences that enabled preservice teachers' different ways to notice student thinking. Providing a structured framework for reflection, namely the individual video club annotations, served as the genesis for cogenerative dialogues centered on instructional change for the preservice teachers. This work's implications showcase the importance of allowing for the iterative enactment and reflection on pedagogical choices by preservice teachers early in their professional development.