Multi Modality

The Image fusion is widely acknowledged as a useful tool for enhancing overall system performance in a variety of application areas such as battlefield surveillance, camouflaged ordnance detection, non-destructive testing defect detection, remote sensing, traffic control, machine learning and health care applications to name few, its own. There are, however, drawbacks to the information gathered from single-modality medical imaging. Medical diagnosis cannot be aided by extensive lesion information from single-modality imaging, which inevitably results in annoyance and poor clinical diagnosis performance. Medical image fusion is a method for resolving this issue; it does so by merging the benefits and supplementary data of several models of source images, eliminating redundant data, and providing a more thorough, accurate lesion description to support specialists in diagnosis and decision-making. Medical image fusion, which merges multi-modal images using image processing, may be useful here. A multiresolution image fusion method uses the Spatial Frequency DCTWT (SFDCT-DWT) technology. In the SF-DCT-DWT technique, the lowresolution MRI image is resampled to the high-resolution CT image, and fusion is performed by injecting the spectral and spatial information from CT and MRI images onto each other using their DCT-DWT coefficients and spatial frequency analysis. These images are from MR and CT imaging. According to experimental data, the recommended strategy surpasses other subjective and objective measures including Entropy (EN), Mutual Information (MI), and Structural Similarity Index Measure (SSIM).

Today in this digital world Image Fusion is the emerging field in the area of Image Processing. Its main goal is to merge two or more images in such a way as to retain the most desirable characteristics of each in resultant image. By observing medical fusion image, doctors could easily confirm the position of illness. In this field of medicine to evaluate or to examine the inner body parts different radiometric scanning techniques can be used. Some most commonly used scanning techniques include Computerized Tomography(CT scan), Magnetic Resonance Imaging scan(MRI) and Position Emission Tomography(PET)but the images of various body parts taken by using these scanning techniques are having their own merits and demerits. The idea of combining images from different modalities become very important and medical image fusion has emerged as a new promising research field. This paper presents two approaches to image fusion, namely Spatial Fusion and Transform Fusion . This paper describes Te...

Industrial measurement systems are frequently exposed to dynamic conditions such as thermal gradients, mechanical vibrations, and electromagnetic interference, which can cause sensor drift and degrade measurement accuracy. This paper presents the design and real-time implementation of an adaptive multi-modal sensor array architecture, integrating heterogeneous sensor types and an intelligent drift compensation algorithm. By combining data fusion techniques and adaptive filtering in real time, the system significantly reduces drift-induced errors and improves measurement precision. Benchmarked against conventional sensor setups, the proposed system achieves a 34% improvement in accuracy under dynamic industrial scenarios. The architecture is scalable and deployable in a range of high-precision industrial environments.

Collaborative problem solving (CPS) encourages children to communicate effectively and fosters creativity and critical thinking. New digital technologies can potentially provide opportunities for innovative ways to promote CPS at an early age. However, one key challenge of such technologies is to be engaging and effective while also pedagogically adapting to developmental needs of young learners. In this paper, we propose that a multimodal conversational AI system, Kid Space, providing interactive learning experiences could enrich CPS and promote positive educational outcomes. Kid Space combines multimodal classroom sensing with a projected 3D virtual environment and an animated conversational agent to engage with children via spoken dialogue and physical interactions. Within an exploratory case study, we formatively evaluated CPS behaviors of a set of children engaged with Kid Space to inform design modifications for the type of CPS learning analytics educators could benefit from and improved support of CPS behaviors in conversational AI-mediated learning environments. Our quantitative results indicate that there were significant correlations between CPS behaviors and (1) joint engagement of students working together and (2) pedagogical interventions provided by the instructional assistant facilitating the learning experience. The results imply that, with necessary design augmentations, Kid Space could potentially play a valuable role in supporting CPS processes of young learners.

The expanding online religious experience is increasingly challenging, if not supplanting, the traditional offline counterpart, necessitating a thorough examination of the mechanisms underlying this transformative influence. Furthermore, this cybermediated religious experience has a significant influence not just on individuals who engage in religious pursuits through online channels, but also on authority structures and identity dynamics. Particularly, an overarching inquiry arises: does this switch from offline to online domains delineate a hybrid portrayal of contemporary Western Muslim religious life? Therefore, a critical examination regarding the ensuing status of traditional religious authorities within the Western milieu subsequent to this transition towards the online sphere is necessary. By synthesizing insights from the existing literature on Digital Islam and a comprehensive analysis of contemporary circumstances, this study argues that the emergence of Islamic cyber experience in the western world which has forged a new dimension for Muslims. Notably, the Cyber Islamic Environment (CIE), which competes with the traditional physical religious sphere, has had a significant impact on several elements of Muslim religious life. Within this investigation, emphasis is placed on two key tenets-authority and identity-to elucidate the multifaceted impacts experienced by Muslim communities. In terms of authority, while the traditional Islamic authority remains stable, cohesive, and community-centric, the cyber authority is characterized by being individualistic, uncensored, and heightened stringency, albeit being more accessible. The same applies to identity. Unlike the relatively stable and authentic nature of spatial identity, characterized by its static and realistic features, cyber identity is characterized by volatility, lack of genuineness, and a propensity for constant change.

The current study tries to investigate changes in a content of English textbook to the 9 th standard that is authorized under the Sindh Textbook Board Jamshoro, Sindh, Pakistan. The textbook of English is four decades old. However, few changes have been observed in its content with respect to the socio-cultural and political ideologies. The changes are more visible in the presentation of texts in the given units and attractive with the help of colorful images. Therefore, the aim of this study is utilization of the multi-modal analysis to analyze the relation of colorful images with the text in the units. The study tends to utilize specific tools to analyze textbooks that create impact on ideological mindset of its learners at their secondary level of education in English. The analysis has shown a fact that the textbook of English is deeply influenced by the particular cultural agendas of Pakistan and specifically to the Sindh Province. The alteration of English textbook is not only limited to its layout and the design, however there has been few changes in its ideological perspectives. The study provided a fact in the end that the recent edition of the English textbook to the 9 th standard is more attractive and interactive compared to its past editions in terms of its learning that creates motivation in learners.

In this work, we propose a novel and custom-designed framework for few-shot anomaly detection and localization in medical imaging, inspired by the foundational principles of Contrastive Language-Image Pretraining (CLIP) but distinctly engineered to address the unique challenges of clinical data. Our architecture fuses visual and textual modalities through a parameter-efficient prompt tuning strategy, which optimizes a set of learnable textual tokens to robustly characterize the distribution of healthy images and facilitate the identification of deviations indicative of anomalies without requiring explicit anomaly training data. By leveraging only healthy training samples, the model learns a comprehensive representation space that captures normal anatomical and physiological variability. Anomalies are detected as out-of-distribution patterns through patch-level embedding comparisons, enabling precise spatial attention maps and bounding box predictions for interpretable anomaly localization critical for clinical decision support. We validate our approach on three heterogeneous medical imaging datasets-chest X-ray (CheXpert), brain MRI (BrainMRI), and breast ultrasound (BUSI)-under multiple few-shot prompt tuning configurations with (K = {4, 8, 16, 32}) samples of healthy images. Our method consistently achieves state-of-the-art anomaly detection and localization performance, demonstrating superior generalization and robustness across diverse clinical modalities and limited supervision regimes. This establishes our framework as an effective and annotation-efficient solution for advancing medical anomaly analysis through multi-modal representation learning.

Vehicles on highway on-ramps are one of the leading contributors to congestion. In this paper, we propose a prediction framework that predicts the longitudinal trajectories and lane changes (LCs) of vehicles on highway on-ramps and tapers. Specifically, our framework adopts a combination of prediction models that inputs a 4 seconds duration of a trajectory to output a forecast of the longitudinal trajectories and LCs up to 15 seconds ahead. Training and Validation based on next generation simulation (NGSIM) data show that the prediction power of the developed model and its accuracy outperforms a traditional long-short term memory (LSTM) model. Ultimately, the work presented here can alleviate the congestion experienced on on-ramps, improve safety, and guide effective traffic control strategies.

Context: Several studies have investigated the integration of Intelligent Tutoring Systems (ITS) in education. However, there is still a gap in understanding approaches that promote student engagement in online learning. Problem: The literature lacks specific analyses that clarify the role of ITS in supporting student engagement in online learning environments, particularly before the advent of Large Language Models (LLM). Solution: This study analyzes the (inter)national literature to identify indicators of ITS contributions to student engagement, focusing on solutions implemented prior to the adoption of LLM. ITS Theory: The research is grounded in ITS theory by addressing ITS as mediating tools in online learning interactions, emphasizing their potential for enhancement through novel technological approaches. Method: Relevant articles from the literature were selected and reviewed to map themes addressed by ITS, identify the main types of solutions, and evaluate their implications for future ITS designs. Summary of Results: The results highlight the themes explored by ITS, the primary solutions developed, and their implications. Among the 15 studies analyzed in the Brazilian context, they also emphasize the potential of combining earlier and current solutions while maintaining the crucial role of human tutors in the teaching-learning process. Contributions and Impact on ITS: This study advances the ITS field by offering theoretical and practical insights for designing ITS that integrate traditional and modern approaches. By focusing on the relationship between ITS and student engagement, the research contributes to the development of tools that enhance online learning effectiveness and foster better interactions between students, systems, and human tutors.

Traffic flow forecasting is a key problem of intelligent transport systems and represents a challenging task due to the spatial-temporal correlation features and long temporal interdependence of the considered data. Conventional methods deal with this either by spatial forecasting given observed counts at previous times or by temporal forecasting given observed traffic counts in neighbouring locations. In order to fully exploit the spatio-temporal properties observed in the data, a hybrid multimodal deep learning method for short-term traffic flow forecasting called HaMDeepT is proposed. Specifically, the HaMDeepT method can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data through an attention-based auxiliary multimodal deep learning architecture. The base module of this method consists of a 1D CNN and GRU with the attention mechanism. The forecasted spatio-temporal traffic demand (counts of traffic passing through different locations at regular time intervals) is dependent on far more critical spatial factors than other point sensors such as weather stations. This HaMDeepT method, in terms of 3D CNN-GRU, which uses a stack of 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Units (GRUs) combined with the Correlation and Relative Operation layers to model both the spatial context features and the temporal dependencies of traffic count data at all locations, has a better performance compared to other network architectures. It overcomes the drawback of a fixed and handcrafted graph Laplacian matrix representation of the spatial relationships of the locations used by the ST-Graph. It uses the Correlation layer to estimate the spatial correlation features for each traffic count data point with others, focusing on the stations with major impacts on the target location, and the Relative Operation layer to model the relative distances thereafter. Using these novel methods, the traffic flow forecasting results for the miniNYC dataset are more accurate and more intuitive visualisation of the spatial structure that affects the performance of the predictions.

Traffic flow forecasting is a key problem of intelligent transport systems and represents a challenging task due to the spatial-temporal correlation features and long temporal interdependence of the considered data. Conventional methods deal with this either by spatial forecasting given observed counts at previous times or by temporal forecasting given observed traffic counts in neighbouring locations. In order to fully exploit the spatio-temporal properties observed in the data, a hybrid multimodal deep learning method for short-term traffic flow forecasting called HaMDeepT is proposed. Specifically, the HaMDeepT method can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data through an attention-based auxiliary multimodal deep learning architecture. The base module of this method consists of a 1D CNN and GRU with the attention mechanism. The forecasted spatio-temporal traffic demand (counts of traffic passing through different locations at regular time intervals) is dependent on far more critical spatial factors than other point sensors such as weather stations. This HaMDeepT method, in terms of 3D CNN-GRU, which uses a stack of 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Units (GRUs) combined with the Correlation and Relative Operation layers to model both the spatial context features and the temporal dependencies of traffic count data at all locations, has a better performance compared to other network architectures. It overcomes the drawback of a fixed and handcrafted graph Laplacian matrix representation of the spatial relationships of the locations used by the ST-Graph. It uses the Correlation layer to estimate the spatial correlation features for each traffic count data point with others, focusing on the stations with major impacts on the target location, and the Relative Operation layer to model the relative distances thereafter. Using these novel methods, the traffic flow forecasting results for the miniNYC dataset are more accurate and more intuitive visualisation of the spatial structure that affects the performance of the predictions.

In contrast to these new-generation techniques, the evolution of data generation also introduced new issues in terms of data quality preservation, mainly on cloud-based Extract, Transform, and Load pipelines. Real-time stream anomaly detection has been pivotal to correcting the decision-making process, as inconsistent data could provide false positives of decision-making. This paper deals with the various machine learning-based techniques applied in anomaly detection concerning real-time stream data. The paper investigates supervised, unsupervised, and hybrid models for their accuracy, scalability, and computational efficiency. The paper's findings are benchmarked against synthetic and real-world datasets, providing actionable insights for practitioners and researchers. Anomaly detection mechanisms are integrated within cloud-native architectures, and some of the challenges include data latency, system scalability, and model interpretability. With vast experimentation and analysis, this research establishes the best practices to achieve data quality and integrity in various real-time environments, which further opened paths for improvements in automated ETL processes. It further explores the implications of anomaly detection on high-speed data streams. In doing so, it develops the trade-offs between model complexity and detection latency. This paper describes strategies to develop scalable, fault-tolerant cloud infrastructure supporting these methods, utilizing microservices and containerization technologies. The comparative framework not only evaluates classic techniques but also explores emerging techniques, which include graph-based and ensemble models, to overcome the shortcomings of existing approaches. The extensive information discussed is intended to assist practitioners in choosing the most suitable tools and configurations for anomaly detection, ultimately enhancing the robustness and reliability of cloud-based ETL pipelines.

Metode til styrkelse af den praenatale tilknytning mellem mor og ufødt barn (med elementer af "BONDING ANALYSIS" som udviklet af Hidas og Raffai) GRIN Verlag GRIN-Your knowledge has value Since its foundation in 1998, GRIN has specialized in publishing academic texts by students, college teachers and other academics as e-book and printed book. The website www.grin.com is an ideal platform for presenting term papers, final papers, scientific essays, dissertations and specialist books.

The swift expansion of varied data sources demands novel methods for data analytics. Structured and unstructured
data are combined in unified multi-modal data analytics to yield more thorough insights. This study examines current
approaches, investigates problems and solutions related to data integration, and talks about useful applications in a
variety of fields. The goal of the study is to close the gap between various data modalities and to indicate future trends
and potential industry consequences.

Machine Learning (ML) has emerged as a powerful tool for assessing vulnerability in water disaster relief operations, offering unprecedented predictive insights that enhance decision-making processes. This paper examines the importance of vulnerability assessments in disaster relief efforts and explores various ML techniques used for this purpose. A comprehensive analysis of case studies demonstrates the practical applications of ML in identifying vulnerable populations and improving disaster response effectiveness. Key challenges, including data quality and model interpretability, are discussed. The paper concludes by highlighting the potential of ML to transform disaster relief operations and offers recommendations for future research in improving ML techniques for vulnerability assessments in water-related disasters.

Documentary cinema is a film genre that aims to reproduce authentic reality and provide awareness to the audience by addressing real events, people or subjects with a claim of objectivity. Such films generally use a narrative language that is not based on fiction and reflect reality as it is. There are several standpoints a documentary film-maker may follow and adopt. These standpoints usually determine the formal characteristics of the film. A documentary may be ethnographic, journalistic, poetic, etc. Likewise, a documentary film-maker may seek to approach reality without any self-involvement and he/she may choose to observe rather than participate in the content or vice versa. Utilizing a number of methods, documentary cinema has flourished in both content and form in recent years. New ways of making documentaries are flourishing and minor realities as well as major ones are brought to the audience. One approach is the ethnographic documentary, where the filmmaker adopts a participant-observer role, immersing themselves in the socio-cultural phenomenon to authentically convey a particular truth. Taking ethnography as a starting point, a film-maker makes numerous stylistic choices and enrich his/her narrative following different patterns. This study aims to interrogate the prospects of documentary cinema in recent years and define the relationship between the film-maker's standpoint and the stylistic choices he/she makes, using the film Your Turn (Capai, 2019) as a case study. The sample film was chosen via purposive sampling method and a textual analysis was conducted. The results of the analysis shows that Your Turn (Capai, 2019) utilizes ethnography and turn the participant-observation method into a collective practice which contributes to the overall aesthetics of the film as well as its narrative foundation.

Late fusion schemes with deep learning classification patterns set up with multi-modality images have an essential role in pedestrian protection systems since they have achieved prominent results in the pedestrian recognition task. In this paper, the late fusion scheme merged with Convolutional Neural Networks (CNN) is investigated for pedestrian recognition based on the Daimler stereo vision data sets. An independent CNN-based classifier for each imaging modality (Intensity, Depth, and Optical Flow) is handled before the fusion of its probabilistic output scores with a Multi-Layer Perceptron which provides the recognition decision. In this paper, we set out to prove that the incremental cross-modality deep learning approach enhances pedestrian recognition performances. It also outperforms state-of-the-art pedestrian classifiers on the Daimler stereovision data sets.

The aim of image fusion is to combine appropriate information from two or more source images into one single image such that the single image is more informative than source images. Digital negative which is one of the spatial domain image enhancement techniques,enhances white or gray detail on dark regions, especially, when black areas are dominant in size.. Considering this, a method of image fusion is proposed, specifically for multi-focus images.Each of these images are fused with digital negative of each other for getting augmentation in white or gray detail on dark regions.Act of this method is evaluated with the help of fusion appraisal parameters like RMSE, PSNR, Mean Intensity value Mutual Information etc.

Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train s...

Om problemer i kvindebevcegelsen og i jeministisk teori omkring jorskels-og lighedsbegreberne. i AfDrude Dahlerup Diskussionen om begrebsparret srerart-lighed eller forskelle-ligheder synes aldrig at ophore. I det mindste ikke i Sverige. Som underviser i Sverige modtager jeg til stadighed studenteropgaver, som skriver, at der er to hovedlini er inden for feminismen: lighedsfeminismen kontra forskelsfeminismen. Ogsa i Danmark horer man dette. Som analytiske begreber er disse dikotomier imidlertid tvivlsomme.

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we propose a vehicular sensor-rich platform and learning algorithms for maneuver anticipation. For this purpose we equip a car with cameras, Global Positioning System (GPS), and a computing device to capture the driving context from both inside and outside of the car. In order to anticipate maneuvers, we propose a sensory-fusion deep learning architecture which jointly learns to anticipate and fuse multiple sensory streams. Our architecture consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to capture long temporal dependencies. We propose a novel training procedure which allows the network to predict the future given only a partial temporal context. We introduce a diverse data set with 1180 miles of natural freeway and city driving, and show that we can anticipate maneuvers 3.5 seconds before they occur in realtime with a precision and recall of 90.5% and 87.4% respectively.

We present a new AI task-Embodied Question Answering (EmbodiedQA)-where an agent is spawned at a random location in a 3D environment and asked a question ('What color is the car?'). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ('orange'). This challenging task requires a range of AI skills-active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.

Problem-based learning (PBL) designs are addressing the demands and potentials of an information-saturated era where accessing inquiry resources and new information is reconfiguring tutor-facilitated dialogues. Unclear is how incorporation of CSCL tools and the rich digital multimodal resources they collaboratively access and generate are reshaping the traditional problem-based cycle of inquiry and intersubjective sense-making. This study in higher education adopts Interactional Ethnography (IE) as a logic-of-inquiry to examine how a group of medical undergraduate students and their facilitator (n = 12) collaborated to access, review, appropriate and devise multimodal digital and visual texts within and across one problem cycle (three face-to-face tutorials and self-directed learning). Drawing on concepts of 'multimodality' and 'intervisuality' from literacy theory, we extend theoretical understandings of how multimodal texts become actors within a developing PBL event, not just objects of study or cultural tools. Through this multifocal approach, we make visible how what occurs at one point in time with these texts in the developing dialogic space is consequential for what students can and do undertake in subsequent engagements with such texts in and across one bounded cycle of learning activities. Arising from this analysis, we propose the concept of dialogic intervisualizing to characterize the dynamic interplay between and among information problem-solving processes, textual negotiations and purposeful, facilitated dialogue for deep knowledge co-construction within and across collaborative, computer supported learning activity in an inquiry cycle.

The key to effective collaboration is awareness; traditional groupware systems have relied on visual cues to achieve it. Audio has been used to complement and overcome the limitations of graphical displays to maintain awareness. However, the emergence of alternative user interface strategies, such as haptic feedback, presents new opportunities to promote awareness. Yet, designing effective haptic awareness mechanisms is challenging due to the lack of documented experience. Therefore, we suggest using flexible prototyping and evaluation tools to facilitate an exploratory design process. We also propose that the design of awareness features should consider modalities to be used and the relationships between them from the outset. To support this idea, we introduce a modeling language and supporting tools to express haptic awareness features. We show how the language can be used in the context of model-driven development of groupware.

Along with climate change, more frequent extreme events, such as flooding and tropical cyclones, threaten the livelihoods and wellbeing of poor and vulnerable populations. One of the most immediate needs of people affected by a disaster is finding shelter. While the proliferation of data on disasters is already helping to save lives, identifying damages in buildings, assessing shelter needs, and finding appropriate places to establish emergency shelters or settlements require a wide range of data to be combined rapidly. To address this gap and make a headway in comprehensive assessments, this paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. This workflow is built around open and online data to ensure scalability and broad accessibility. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used. This technique allows the collected multimodal data to share a common semantic space that facilitates the prediction of individual variables. Each fused numerical vector was fed into an unsupervised clustering algorithm called Self-Organizing-Maps (SOM). The trained SOM serves as a predictor for future cases, allowing predicting consequences such as total deaths, total people affected, and total damage, and provides specific recommendations for assessments in the shelter and housing sector. To achieve such prediction, a satellite image from before the disaster and the geographic and demographic conditions are shown to the trained model, which achieved a prediction accuracy of 62%.

Computer modeling of protein-ligand interactions is one of the most important phases in a drug design process. Part of the process involves the optimization of highly multi-modal objective (scoring) functions. This research presents the Minimum Population Search heuristic as an alternative for solving these global unconstrained optimization problems. To determine the effectiveness of Minimum Population Search, a comparison with seven state-of-the-art search heuristics is performed. Being specifically designed for the optimization of large scale multi-modal problems, Minimum Population Search achieves excellent results on all of the tested complexes, especially when the amount of available function evaluations is strongly reduced. A first step is also made toward the design of hybrid algorithms based on the exploratory power of Minimum Population Search. Computational results show that hybridization leads to a further improvement in performance.

Educational technology research has found that parents of young children widely share concerns about extended screen time, lack of physical activity, and lack of social interaction. Kid Space was developed to address these concerns by enabling multi-modal and immersive collaborative play-based learning. Kid Space utilizes multiple sensing technologies with an immersive physical space through a human-scale wall projection and incorporates a conversational AI agent to interact with children, understand individual progress, and personalize learning experiences in a blended physical and digital environment. To evaluate Kid Space in the wild, we conducted a multi-method user study involving a quasi-experimental design and exploratory case study with 14 students and three educators in an elementary school. Mixed methods for data collection and analysis were used to understand the students' and educators' perceptions of Kid Space and its impact on the students' educational outcomes (learning engagement, experience, and performance). The findings showed (1) positive perceptions toward Kid Space, (2) high levels of engagement-with decreased screen time (41% of the time), increased physical activity (99.3% of the time), and increased social interactions with conversational AI agent and the other collaborating student (52% of the time), and (3) significant learning gains after experiencing Kid Space (24% gain, paired ttest: p < 0.01). These positive results are accompanied by critical user insights for improving future iterations of Kid Space to validate long-term educational outcomes.

The key to effective collaboration is awareness; traditional groupware systems have relied on visual cues to achieve it. Audio has been used to complement and overcome the limitations of graphical displays to maintain awareness. However, the emergence of alternative user interface strategies, such as haptic feedback, presents new opportunities to promote awareness. Yet, designing effective haptic awareness mechanisms is challenging due to the lack of documented experience. Therefore, we suggest using flexible prototyping and evaluation tools to facilitate an exploratory design process. We also propose that the design of awareness features should consider modalities to be used and the relationships between them from the outset. To support this idea, we introduce a modeling language and supporting tools to express haptic awareness features. We show how the language can be used in the context of model-driven development of groupware.

Logistics plays a pivotal role in a country’s economic development. Recent policy initiatives in India, such as PM Gati Shakti National Master Plan (NMP), the National Logistics Policy, State Logistics Policies, and Bharatmala Pariyojana, signify the country's proactive measures to enhance its logistics and transportation infrastructure. There has been growing emphasis on development of Multi-Modal Logistics Parks (MMLPs) in India as these are expected to play a pivotal role in facilitating intermodal transfer and providing value-added logistics services for both domestic and EXIM cargo in the country Keeping in view their growing importance in the country’s policy landscape the planning of MMLPs assume critical significance. This paper critically analyses the activity mix/land use patterns of eleven selected existing and proposed MMLPs in India and evaluates their land use disposition with the national level policy guidelines. Overall, the paper offers valuable insights for policymakers, researchers, and stakeholders in planning and development of multi modal logistics facilities like multi -modal logistics parks

The primary aim of this essay is to clarify the visual aspects of reading order in the early Viking Age Scandinavian runic material. The investigation in part identi fies the visual principles of reading which occur in the inscriptions and in part uses these principles to support new reading orders in three inscrip tions: Bo Boije4 Skee, U ANF1937;163 Björkö and DR NOR1988;5 Malt. The read ing order which is examined is the one offered by the text itself, for which reason the intention of the carver and the behaviour of the actual reader is ignored. The theoretical framework of the study is based on social semiotics and a multimodal approach to text, i.e. meaning is expressed not only by lan guage but also via tools such as image and layout. The analysis model of Kress and van Leeuwen (2006) of the grammar of visual design is specifically em ployed as the point of departure. The result establishes a number of visual semiotic resources. These resources express concepts such as salience, creation of associations or demarcation, which can be connected in diverse ways to func tions of reading order such as start of reading or the reading pathway. Some resources seem to express none of these three concepts and can therefore be assumed to be directly linked to reading order via convention. Bo Boije4, U ANF1937;163 and DR NOR1988;5 offer several visual possibilities of reading order which are considered in turn. New reading orders have been proposed for all of these cases.

Robot manipulation tasks by natural language instructions need common understanding of the target object between human and the robot. However, the instructions often have an interpretation ambiguity, because the instruction lacks important information, or does not express the target object correctly to complete the task. To solve this ambiguity problem, we hypothesize that "naming" the target objects in advance will reduce the ambiguity of natural language instructions. We propose a robot system and method that incorporates naming with appearance of the objects in advance, so that in the later manipulation task, instruction can be performed with its unique name to disambiguate the objects easily. To demonstrate the effectiveness of our approach, we build a system that can memorize the target objects, and show that naming the objects facilitates detection of the target objects and improves the success rate of manipulation instructions. With this method, the success rate of object manipulation task increases by 31% in ambiguous instructions.

Black and William introduced the term 'inside the black box' to research in educational assessment in the late 1990s. This metaphor can be applied to current research in problem-based learning (PBL). This chapter addresses the need to look inside the 'black box' of PBL by exploring two under-researched aspects-independent study and online learning. Using the Interactional Ethnographic (IE) approach to collect and analyse data in context and over time (across contexts), we systematically examined how students learn between tutorials to explore how online learning supports independent study in a PBL curriculum. Despite PBL's 40-year history as an instructional method in undergraduate education, surprisingly few studies have examined and documented the in situ enactment of student learning in PBL contexts from an interactional perspective. While there is a growing body of student evaluation and outcomes data to support the efficacy of PBL programmes, research to date has relied mainly upon student and staff questionnaires and interviews. In dentistry, for example, the majority of studies have focused on problem design, course evaluation, and student achievement or performance. From a methodological perspective, the reliance on self-report data such as student course evaluation questionnaires across clinical education and staff surveys has come under some criticism. Concerns have been expressed regarding the status quo of this research agenda with a recent call to 'look inside PBL programmes', due to a perceived lack of studies into 'the way students experience and understand' PBL courses (Prosser, 2004, p. 204). Of critical importance is the need to contribute further interactional data and analysis on PBL-inaction to support theory building. This is particularly the case given that the central, constructivist tenet of PBL is its 'process' approach

Generative models have recently shown the ability to realistically generate data and model the distribution accurately. However, joint modeling of an image with the attribute that it is labeled with requires learning a cross modal correspondence between image and attribute data. Though the information present in a set of images and its attributes possesses completely different statistical properties altogether, there exists an inherent correspondence that is challenging to capture. Various models have aimed at capturing this correspondence either through joint modeling of a variational autoencoder or through separate encoder networks that are then concatenated. We present an alternative by proposing a bridged variational autoencoder that allows for learning cross-modal correspondence by incorporating cross-modal hallucination losses in the latent space. In comparison to the existing methods, we have found that by using a bridge connection in latent space we not only obtain better generation results, but also obtain highly parameterefficient model which provide 40% reduction in training parameters for bimodal dataset and nearly 70% reduction for trimodal dataset. We validate the proposed method through comparison with state of the art methods and benchmarking on standard datasets.

Technological advances enable the capture and management of complex data sets that need to be correctly understood. Visualisation techniques can help in complex data analysis and exploration, but sometimes the visual channel is not enough, or it is not always available. Some authors propose using the haptic channel to reinforce or substitute the visual sense, but the limited human haptic short-term memory still poses a challenge. We present the haptic tuning fork, a reference signal displayed before the haptic information for increasing the discriminability of haptic icons. With this reference, the user does not depend only on short-term memory. We have decided to evaluate the usefulness of the haptic tuning fork in impedance kinesthetic devices as these are the most common. Furthermore, since the renderable signal ranges are device-dependent, we introduce a methodology to select a discriminable set of signals called the haptic scale. Both the haptic tuning fork and the haptic scale...

An approach to providing tangible feedback to users of a mobile device in both highly visual touchscreen-based and eyes-free interaction scenarios and the transition between the two is presented. A rotational dynamical systems metaphor for the provision of feedback is proposed, which provides users with physically based feedback via the audio, tactile and visual senses. By using a consistent metaphor in this way it is possible to support the seamless movement between highly visual touch-based interaction and eyes-free gestural interaction.

Medical image fusion is a technique where multiple imaging modalities are merged together to obtain a single image that contains maximum information. Spatial domain approach such as IHS-PCA fusion provides best visual quality but required large storage space and also lags directional information. We propose a unique technique to improve the fusion of MRI with PET/SPECT images. To achieve this, we integrate Dual Tree Complex Wavelet Transform with PCA based histogram matching and IHS space.IHS transform is applied to convert RGB channels of multispectral image PET/SPECT to intensity, hue and saturation components. Pathological information in the images can be highlighted in multi-scale and multi-direction by using DTCWT. PCA with weighted average fusion rule is used for extracting tumor features by using principal components. The qualitative and quantitative analysis of the proposed method is compared with existing techniques and proved that our algorithm provides high structural information content, high mutual information with high spatial and spectral resolution thereby enhancing the tumor region in the merged image.

Robot manipulation tasks by natural language instructions need common understanding of the target object between human and the robot. However, the instructions often have an interpretation ambiguity, because the instruction lacks important information, or does not express the target object correctly to complete the task. To solve this ambiguity problem, we hypothesize that "naming" the target objects in advance will reduce the ambiguity of natural language instructions. We propose a robot system and method that incorporates naming with appearance of the objects in advance, so that in the later manipulation task, instruction can be performed with its unique name to disambiguate the objects easily. To demonstrate the effectiveness of our approach, we build a system that can memorize the target objects, and show that naming the objects facilitates detection of the target objects and improves the success rate of manipulation instructions. With this method, the success rate of object manipulation task increases by 31% in ambiguous instructions.

Since the 1980s, the learning sciences have been an emerging field of research that focuses on learning of individuals and groups in authentic social, material, and digital contexts from a multilevel perspective. The learning sciences have substantially advanced theoretical conceptions of learning and its support as well as methodological approaches, for example by introducing designbased research. The article provides an overview of the history, important theories and concepts

Powerful correspondence for the visually impaired can be upgraded by haptic interfaces with the world around individuals living with visual deficiency. This writing audit will endeavor to respond to extreme inquiries concerning where haptic innovation is going throughout the following decade predicted by an overview of bleeding edge haptic innovation blended in with a discourse of the upsides and downsides of deliberately chose high effect research articles at last, hypothesizing the eventual fate of haptics through designing patterns and development of arrangement changes.

BVI (blind and visually impaired) persons do not have the same degree of access to concepts and procedures in learning algebraic equations as sighted persons. As tactile resources are uncommon, most rely on auditory-based methods that place heavy stress on working memory. We propose a novel approach that leverages haptic technology to communicate mathematical symbols via vibrotactile patterns; this design addresses open challenges found in existing resources for BVI people and explores the potential utility of this medium for all users. We conducted three separate studies to evaluate the feasibility of our gloves that included BVI participants. The outcomes of our studies yielded insightful design discussions, including a blind participant who found the device immediately intuitive and exciting in potential. CCS CONCEPTS • Human-centered computing → Interface design prototyping; Accessibility technologies; Haptic devices.

The article describes the training of the first Danish bed bug detection  dog, which took place with a grant from the Danish Research and Innovation Agency. The bed bug detection dog was trained in collaboration between the company Danminar and Aarhus University with the author as dog trainer.

Logistics plays a pivotal role in a country’s economic development. Recent policy initiatives in India, such as PM Gati Shakti National Master Plan (NMP), the National Logistics Policy, State Logistics Policies, and Bharatmala Pariyojana, signify the country's proactive measures to enhance its logistics and transportation infrastructure. There has been growing emphasis on development of Multi-Modal Logistics Parks (MMLPs) in India as these are expected to play a pivotal role in facilitating intermodal transfer and providing value-added logistics services for both domestic and EXIM cargo in the country Keeping in view their growing importance in the country’s policy landscape the planning of MMLPs assume critical significance. This paper critically analyses the activity mix/land use patterns of eleven selected existing and proposed MMLPs in India and evaluates their land use disposition with the national level policy guidelines. Overall, the paper offers valuable insights for policymakers, researchers, and stakeholders in planning and development of multi modal logistics facilities like multi -modal logistics parks

Without the help of several persons I could not have achieved the thesis which now lays in front of you. I write these Acknowledgements in the hope that I would not forget anybody who helped me get to the point where I am able to defend this work. First and foremost I would like to thank my advisor, Brian Katz. For too many aspects I can mention here, but among others his patience, his critical eye on my work, his co-authoring of articles etc. Too many deeds which words can describe here. The AA group at LIMSI I via this way thank for their warm welcome in France. The team member I am most grateful and indebted to is David Poirier-Quinot. Already early on in my thesis I noticed his willingness to help others. His aid can be found throughout the thesis but is most noticeable in the multi-modal auralizations. Even after he left LIMSI his help was ongoing. Second, I would like to thank Areti Andreopolou for her overall help, teaching me how to design listening tests, and assistance during my many MatLab questions. Best of luck to you in your new job in Greece. I admire Laurent Simon for his love of the field of acoustics and thank him for his help with my many MatLab questions. I wish him all the best for his new job in Switzerland. Christophe d'Alessandro, the on-and-off head of the AA-group, I thank him for his interesting discussions about in particular French history. I wish him all the best for his new job at LAM. Albert Rilliard has helped me with denoising of anechoic files for which I am thankful but foremost for his fun discussions during lunch. I wish him all the best. I thank Marc Evrard for his friendship during this thesis, I hope you are having a fun time in Japan. During my first months I was in an office with Olivier Perrottin and Lionel Fuegere. I would like to thank them for showing me the French ways from French taxes to aid in language skills. I wish Olivier all the best for his stay in Scotland and Lionel for his new job in England. I am thankful to Peter Stitt for his help with the tracking system and ambisonic renderings. I wish him all the best for his job search in France. Samuel Delalez, the last of the Mohicans of the AA group, I am grateful for his aid with Max/MSP and PRAAT. Best of luck during the last months of your thesis. Finally, I am very happy that the presented frameworks are going to be improved and further studies are going to be carried out based on my work. This will be done by David Thery, I wish him all luck and promise I am also available for all questions. Another great help during my PhD were my interns: Daniel Furlan, Julie Meyer, and Hugo Demontis. I would like to thank Daniel for his assistance during the measurements in the Amphithéâtre, Théâtre de l'Athénée, and Saint-Germain-des-Prés, Julie for her work on the Notre-Dame model and measurement, and Hugo for his contributions to the dynamic voice auralizations. I would like to thank the personnel of the Théâtre de l'Athénée, Notre-Dame cathedral, and Saint-Germain-des-Prés church for their assistance and patience during the measurements. Additional thanks to THALIM and LAM for their help in hosting the listening test and to all participants of the listening test for their time. Special thanks to Bengt-Inge Dalenbäck (CATT-Acoustic) for the numerous and ii lengthy informative discussions, critical ear during the preparation of auralizations, and comments in preparing manuscripts for several journal articles. Finally, I would like to thank my wife, Claudia Postma-Bawelski, for everything but foremost for her patience. It was not easy living apart for more than three years. Now, I am looking forward living together with her in Germany.

This paper seeks to (re)think what constitutes records within an ethnographic archive by making visible the challenges that our external IE team faced as we entered the records archived by the internal IE team that we were invited to support in the documentation and analysis of a developing Instructional Development project. Although both teams shared a common conceptual logic-of-inquiry – Interactional Ethnography – It became evident that there were limits to certainty (Green & Baker 2007) that led us to (re)examine why some particular records were archived and what they were records of.

Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices. Two tasks -- text-based visual question answering and text-based image captioning, with a text extension from existing vision-language applications, are catching on rapidly. To address these problems, many sophisticated multi-modality encoding frameworks (such as heterogeneous graph structure) are being used. In this paper, we argue that a simple attention mechanism can do the same or even better job without any bells and whistles. Under this mechanism, we simply split OCR token features into separate visual- and linguistic-attention branches, and send them to a popular Transformer decoder to generate answers or captions. Surprisingly, we find this simple baseline model is rather strong -- it consistently outperforms state-of-the-art (SOTA) models on two popular benchmarks, TextVQA and all three tasks of S...

Opinnäyte tutkii vuodepotilaan tarpeisiin suunnattua digitaalisen apuvälineen palvelukonseptia. Se perustuu oletukseen audiovisuaalisen materiaalin suotuisista vaikutuksista ihmisen hyvinvointiin. Laadullinen tutkimus on konseptin ja prototyypin suunnitteluprosessi ja se toimii laajemman MAI Garden1 -tutkimushankkeen esitutkimuksena. Hankkeessa kehittyvä helppokäyttöinen ja interaktiivinen elämysteknologia (jolla tarkoitan käyttöliittymän, kosketus/piirtonäytön ja kädessä pideltävän pehmo-ohjaimen muodostamaa palvelukokonaisuutta) pyrkii täydentämään – ei korvaamaan – hoitolaitosten niukkoja inhimillisiä resursseja uuden teknologian avulla. Taiteen ja kulttuurin elävöittävää vaikutusta hyödyntävä hanke pyrkii kohottamaan elämänlaatua laitoshoidossa, joten sen yhteiskunnallinen merkitys voi olla huomattava. Opinnäytteen lähestymistapa on käytännöllinen, laadullinen ja empiirinen, ja se antaa puheenvuoron vanhenevalle ja sairaalle tekniikan käyttäjälle. Tavoitteena on ymmärtää tällais...

Any piece of information is meaningful only when it is able to convey the content about it. The clarity and quality of information is important. Image Fusion is a technique to improve the quality and quantity of information from a set of images. By the process of image fusion the more information from each of the given images is combining together to generate a resultant image whose quality is maximum to any of the input images. We proposed a Hybrid DWT-DCT method to fuse multi focus images. In this technique we convert the image data from spatial domain to transform domain.. Then decompose the transform data into four parts that is LL, LH, HL, HH part. This decomposition Process again applies in LL part at two levels. After this three level decomposition we combine the input image data by applying average method using DCT and get fused data. This data are in transform domain again convert to spatial domain by applying IDCT and IDWT method and get final fused image with better visual Quality. After getting the result of fused image we compare the quality measure parameters of different technique like PCA, DCT, average pixel, maximum pixel, minimum pixel, HDWT method to hybrid DWT-DCT method. And conclude that the PSNR value and Entropy of fused image have better result as compare to other techniques. Due to this the fused image has better visual Quality as well as more informative data would contain in fused image.