Reviewerly/exHarmony huggingface dataset:
Behavioral and Psychophysiological Measures of Engagement During Dynamic Di&amp;#64259;culty Adjustment in Immersive Virtual Reality

Dynamically Di&amp;#64259;culty Adjustment (DDA) has been widely used to preserve engagement in serious and entertaining games, reach better learning, and enhance user performance. A variety of studies suggests that in DDA, task performance (score) rises until hitting a plateau associated with the skill level. However, the sense of engagement is individual and context-dependent, and the e&amp;#64256;ect of DDA on other engagement indicators for immersive virtual environments is still unclear. This study measured objective indicators of engagement while study subjects played an immersive virtual game with DDA to &amp;#64257;nd evidence of dynamic response, similar to game performance. Participants were demanded to perform repetitive upper-limb motions while recording the following indicators: Response Latency as perceptive engagement (elapsed time after sensory stimulus), Exercise Intensity as motion engagement (hand velocity), and psychophysiological responses as emotional engagement (Heart Rate, Skin Conductance, and Respiratory Rate). In addition, 30 features were extracted from the signals to evaluate their variations between time windows. Results indicate that response latency, vertical hand velocity, and heart rate showed signi&amp;#64257;cant changes over time during DDA and grew until hitting a plateau, i.e., at the subject&amp;#39;s maximum performance. Moreover, some of the features extracted from the signals showed signi&amp;#64257;cant di&amp;#64256;erences between time windows, and having strong correlation with the mean of score: max Latency, min velocity on the Y-axis, and mean heart rate, which suggest a promising application for evaluating changes in engagement between di&amp;#64256;erent experimental conditions in VR.
A New Partitioned Spatial–Temporal Graph Attention Convolution Network for Human Motion Recognition

At present, human action recognition can be used in all walks of life, because the skeleton can transmit intuitive information without being affected by environmental factors. However, it only focuses on local information. In order to solve these problems, we introduce a neural network model for human body recognition in this paper. We propose a model named NEW-STGCN-CA. The model is based on a spatial–temporal graph convolution network (ST-GCN), which contains a new partition strategy and coordination attention (CA) mechanism. By integrating the CA attention mechanism model, we enable the network to focus on input-related information, ignore unnecessary information, and prevent information loss. Second, a new partitioning strategy is proposed for the sampled regions, which is used to enhance the connection between local information and global information. We proved that the Top-1 accuracy of the NEW-STGCN-CA model in the NTU-RGB+D 60 dataset reached 84.86%, which was 1.7% higher than the original model; the accuracy of Top-1 on the Kinetics-Skeleton dataset reached 32.40%, which was 3.17% higher than the original model. The experimental results show that NEW-STGCN-CA can effectively improve the algorithm’s accuracy while also having high robustness and performance.
A Blockchain-based Data Sharing Platform for Smart Cities

With the development of technology, big data applications have gradually penetrated the scope of smart cities.Therefore, building a new type of Smart City is essential to optimize the national governance system and its capacity.That requires the cooperation of all government sectors to form a positive incentive mechanism for information sharing.However, the massive amount of social operations data raises several issues, such as data islands, inconsistent data standards, data privacy, and security to smart city data governance.Hence, this paper proposes a data collaborative governance method based on blockchain and secure multi-party computing in a trusted execution environment, and incorporates it in designing a data-sharing platform for Smart Cities.The results of pressure tests showed that the functions of each module could operate normally, which were aligned with expectations.This design enables innovative urban governance and alleviates the security and privacy issues in traditional Smart Cities.Therefore, the proposed platform stands highly promising for optimizing data governance in Smart Cities.
Single-qubit quantum classifier based on gradient-free optimization algorithm

A single-qubit quantum classifier (SQC) based on a gradient-free optimization (GFO) algorithm, named the GFO-based SQC, is proposed to overcome the effects of barren plateaus caused by quantum devices. Here, a rotation gate R X ( ϕ ) is applied on the single-qubit binary quantum classifier, and the training data and parameters are loaded into ϕ in the form of vector multiplication. The cost function is decreased by finding the value of each parameter that yields the minimum expectation value of measuring the quantum circuit. The algorithm is performed iteratively for all parameters one by one until the cost function satisfies the stop condition. The proposed GFO-based SQC is demonstrated for classification tasks in Iris and MNIST datasets and compared with the Adam-based SQC and the quantum support vector machine (QSVM). Furthermore, the performance of the GFO-based SQC is discussed when the rotation gate in the quantum device is under different types of noise. The simulation results show that the GFO-based SQC can reach a high accuracy in reduced time. Additionally, the proposed GFO algorithm can quickly complete the training process of the SQC. Importantly, the GFO-based SQC has a good performance in noisy environments.
The Impact Technologies of Blockchain and IoT, on Food Security

Supply of food from the early stages of societies was one of the most important needs of humans. Sustainable supply of food is a demand in modern societies. The agri-food market grows as the population rises every year. The increase in the demand side of the market is more than the growth of the supply side. The rate of using technology in the supply side is increasing rapidly. By using technology in some parts, the efficiency of production improved and caused more production while resources are the same. Availability of resources in different areas causes different ways of production and nurturing innovative technologies to maintain food security. Water, soil, climate change, and growth of population are drivers of using technology in food security. To depict the role of different technologies in the food industry, the authors have reviewed the role of the most important technologies in this field. Knowing the trends of changes in the industry will help to focus on the most important questions and solutions. Having a share in the global food market requires the major use of technology in production processes. In this chapter, the authors will review the most important trends of technology absorption in the food industry.
Digital diagnostic cytopathology: Has the pandemic brought us closer?

The COVID-19 pandemic has acted as a powerful change driver in the field of pathology and has had relevant consequences on the practice of cytopathology, in terms of changes in workload, rates of malignancy, and the performance of cytology. At the same time, regulatory authorities have relaxed their requirements for the deployment of digital pathology for remote diagnostic reporting. However, most of these improvements have concerned digital histopathology. Data from a literature search show that experiences in digital cytopathology during the pandemic have concerned mainly educational and academic activities. From a broader point of view, when searching for all published literature on digital pathology, only a minority of papers deal with cytopathology, but a noticeable increase in publications has been seen in the last 10 years, with an upward trend toward a maximum of papers in 2021. Indeed, the pandemic has led to greater awareness of the possibility of digital for cytopathology as well.
Knowledge Infused Representations Through Combination of Expert Knowledge and Original Input

Abstract Sophisticated applications in natural language processing, such as conversational agents, often need to be able to generalize across a range of different tasks to generate natural-feeling language. In this paper, we introduce a model that aims to improve generalizability with regard to different tasks by combining the original input with the output of a task-specific expert. Through a combination mechanism, we create a new representation that has been enriched with the information given by the expert. These enriched representations then serve as input to a downstream model. We test three different combination mechanisms in two combination paradigms and evaluate the performance of the new enriched representation in a simple encoder-decoder model. We show that even very simple combination mechanisms are able to significantly improve performance of the downstream model. This means that the encoded expert information is transported through the new enriched input representation, leading to a beneficial impact on performance within the task domain. This opens the way for exciting future endeavors such as testing performance on different task domains and the combination of multiple experts.
Evaluation of an Immersive COVID-19 Data Visualization

COVID-19 restrictions have detrimental effects on the population, both socially and economically. However, these restrictions are necessary as they help reduce the spread of the virus. For the public to comply, easily comprehensible communication between decision makers and the public is thus crucial. To address this, we propose a novel 3-D visualization of COVID-19 data, which could increase the awareness of COVID-19 trends in the general population. We conducted a user study and compared a conventional 2-D visualization with the proposed method in an immersive environment. Results showed that the our 3-D visualization approach facilitated understanding of the complexity of COVID-19. A majority of participants preferred to see the COVID-19 data with the 3-D method. Moreover, individual results revealed that our method increases the engagement of users with the data. We hope that our method will help governments to improve their communication with the public in the future.
Hybrid recommender system based on matrix factorization

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Kaiser A. Reshak, Ban N. Dhannoon, Zainab N. Sultani; Hybrid recommender system based on matrix factorization. AIP Conference Proceedings 2 February 2023; 2457 (1): 040010. https://doi.org/10.1063/5.0118335 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
On Understanding Context Modelling for Adaptive Authentication Systems

In many situations, it is of interest for authentication systems to adapt to context (e.g., when the user’s behavior differs from the previous behavior). Hence, representing the context with appropriate and well-designed models is crucial. We provide a comprehensive overview and analysis of research work on C ontext M odelling f or A daptive A uthentication systems (CM4AA). To this end, we pursue three goals based on the Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) research methodologies. We first present a SMS to structure the research area of CM4AA ( goal 1 ). We complement the SMS with an SLR to gather and synthesise evidence about context information and its modelling for adaptive authentication systems ( goal 2 ). From the knowledge gained from goal 2, we determine the desired properties of the context information model and its use for adaptive authentication systems ( goal 3 ). Motivated to find out how to model context information for adaptive authentication, we provide a structured survey of the literature to date on CM4AA and a classification of existing proposals according to several analysis metrics. We demonstrate the ability of capturing a common set of contextual features that are relevant for adaptive authentication systems independent from the application domain. We emphasise that despite the possibility of a unified framework, no standard for CM4AA exists.
Improving Masked Face Recognition Using Dense Residual Unit Aided with Quadruplet Loss

All over the world, people are wearing face masks and practising social distancing to protect themselves against the Coronavirus disease (COVID-19). The need for contactless biometric systems has increased to avoid the common point of contact. Among contactless biometric systems, facial recognition systems are the most economical and effective ones. Conventional face recognition systems rely heavily upon the facial features of the eyes, nose, and mouth. But due to people wearing face masks, the important facial features of the nose and mouth get hidden under the mask, resulting in degraded performance by the facial recognition systems on masked faces. In this paper, we propose a Dense Residual Unit (DRU) aided with Quadruplet loss on top of existing facial recognition systems. This solution tries to unveil the masked faces by producing embeddings for masked faces, which are similar to embeddings of unmasked faces of the same identity but different from embeddings of different identities. We have evaluated our method using two pre-trained facial recognition models’ backbones, i.e. Resnet-101 and MobileFaceNet, and upon two datasets, among them, one is a real-world dataset, i.e. MFR2, and one is a simulated masked face dataset i.e. masked version of LFW. We have achieved improvement in the performance of masked face recognition in terms of False Match Rate, False Non-Match Rate, Fisher Discriminant ratio, and Equal Error Rate.
Immersive Learning and Participatory Engagement

Guided by constructivist theory, this study examines health professions student learning and engagement in the virtual classroom (VC) setting. Students (N=52) participated in a one-week VC prior to and during the COVID-19 pandemic. After participation, students were surveyed to discern their impressions of the experience, including the administration of a presence questionnaire, a key indicator of virtual environment (VE) efficacy. High student presence scores were significantly correlated with the perception that the VC facilitated learning (r = .573, p = .001). Conversely, students who perceived the course content as challenging were less likely to recommend the VC as a viable alternative learning platform. Furthermore, in terms of presence, undergraduate and graduate students were not significantly different. In summary, health professions students view immersive technologies favorably and may benefit from using such platforms as alternative or supplemental learning tools regardless of their academic level.
A design of laser triangulation system with combined diffuse and specular reflection modes for dull and shiny surface measurements

Abstract Conventional laser triangulations are designed to measure the depth or height on a dull or rough surface. This can be done by projecting a light perpendicularly to the surface and capturing the spot of the diffuse reflection which is scattered from the surface. However, for a shiny or smooth surface, the specular reflection dominates, and it is difficult to detect diffuse reflection. We may modify the system setting to capture the specular reflection, with the cost of losing the diffuse reflection information. In this article, we proposed a design of dual diffuse and specular reflection mode of laser triangulation. The advantage is that there is no need to repeat alignment and calibration processes when we change the measuring mode, so we can easily select the measuring mode that is suitable for each surface. We have described the system setting and the calibration results where we can achieve the resolution of micrometre for both measuring modes.
Towards precision medicine based on a continuous deep learning optimization and ensemble approach

We developed a continuous learning system (CLS) based on deep learning and optimization and ensemble approach, and conducted a retrospective data simulated prospective study using ultrasound images of breast masses for precise diagnoses. We extracted 629 breast masses and 2235 images from 561 cases in the institution to train the model in six stages to diagnose benign and malignant tumors, pathological types, and diseases. We randomly selected 180 out of 3098 cases from two external institutions. The CLS was tested with seven independent datasets and compared with 21 physicians, and the system's diagnostic ability exceeded 20 physicians by training stage six. The optimal integrated method we developed is expected accurately diagnose breast masses. This method can also be extended to the intelligent diagnosis of masses in other organs. Overall, our findings have potential value in further promoting the application of AI diagnosis in precision medicine.
How to Register a Live onto a Liver ? Partial Matching in the Space of Varifolds

Partial shapes correspondences is a problem that often occurs in computer vision (occlusion, evolution in time...). In medical imaging, data may come from different modalities and be acquired under different conditions which leads to variations in shapes and topologies. In this paper we use an asymmetric data dissimilarity term applicable to various geometric shapes like sets of curves or surfaces, assessing the embedding of a shape into another one without relying on correspondences. It is designed as a data attachment for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, allowing to compute a meaningful deformation of one shape onto a subset of the other. We refine it in order to control the resulting non-rigid deformations and provide consistent deformations of the shapes along with their ambient space. We show that partial matching can be used for robust multi-modal liver registration between a Computed Tomography (CT) volume and a Cone Beam Computed Tomography (CBCT) volume. The 3D imaging of the patient CBCT at point of care that we call __live__ is truncated while the CT pre-intervention provides a full visualization of the __liver__. The proposed method allows the truncated surfaces from CBCT to be aligned non-rigidly, yet realistically, with surfaces from CT with an average distance of $2.6mm(+/- 2.2)$. The generated deformations extend consistently to the liver volume, and are evaluated on points of interest for the physicians, with an average distance of $5.8mm (+/- 2.7)$ for vessels bifurcations and $5.13mm (+/- 2.5)$ for tumors landmarks. Such multi-modality volumes registrations would help the physicians in the perspective of navigating their tools in the patient's anatomy to locate structures that are hardly visible in the CBCT used during their procedures.&lt;br&gt;Our code is available at &lt;a href='https://github.com/plantonsanti/PartialMatchingVarifolds'&gt;https://github.com/plantonsanti/PartialMatchingVarifolds&lt;/a&gt;.
Analisis User Interface (UI) Dan User Experience (UX) Sudut Elevasi Pemukul Gamelan Metaverse Virtual Reality Menggunakan User Centered Design (UCD)

User Interface (UI) and User Experience (UX) are important things in an application that is being developed, how UI and UX take part in an interaction with the application being created, cannot be separated from a Virtual Reality (VR), also requires a UI/UX.In VR we can see UI that have been made in such a way as to resemble the appearance of an application that is close to the real world or looks realistic.UI in VR can also be used in this case to facilitate interaction between objects in the application and the user.The discussion in this article will discuss Virtual Reality Gamelan, where users will interact with gamelan beaters so that they can play gamelan like real gamelan.Therefore, the User Centred Design (UCD) method will pay attention to the user, in this case it will be the centre of the design, in order to get the results from the elevation angle of the gamelan beater to approach the elevation angle of the gamelan beater when playing the real gamelan.The hope is to develop an application that is user-friendly and when playing gamelan it will feel like playing gamelan in the real world.
Multidimensional Author Profiling for Social Business Intelligence

Abstract This paper presents a novel author profiling method specially aimed at classifying social network users into the multidimensional perspectives for social business intelligence (SBI) applications. In this scenario, being the user profiles defined on demand for each particular SBI application, we cannot assume the existence of labelled datasets for training purposes. Thus, we propose an unsupervised method to obtain the required labelled datasets for training the profile classifiers. Contrary to other author profiling approaches in the literature, we only make use of the users’ descriptions, which are usually part of the metadata posts. We exhaustively evaluated the proposed method under four different tasks for multidimensional author profiling along with state-of-the-art text classifiers. We achieved performances around 88% and 98% of F1 score for a gold standard and a silver standard datasets respectively. Additionally, we compare our results to other supervised approaches previously proposed for two of our tasks, getting very close performances despite using an unsupervised method. To the best of our knowledge, this is the first method designed to label user profiles in an unsupervised way for training profile classifiers with a similar performance to fully supervised ones.
Process anti-pattern detection – a case study

Anti-patterns are harmful phenomena repeatedly occurring, e.g., in software development projects. Though widely recognized and well-known, their descriptions are traditionally not fit for automated detection. The detection is usually performed by manual audits, or on business process models. Both options are time-, effort- and expertise-heavy, prone to biases, and/or omissions. Meanwhile, collaborative software projects produce much data as a natural side product, capturing their status and day-to-day history. Long-term, our research aims at deriving models for the automated detection of process and project management anti-patterns, applicable to project data. Here, we present a general approach for studies investigating occurrences of these types of anti-patterns in projects and discuss the entire process of such studies in detail, starting from the anti-pattern descriptions in literature. We demonstrate and verify our approach with the Fire Drill anti-pattern detection as a case study, applying it to data from 15 student projects. The results of our study suggest that reliable detection of at least some process and project management anti-patterns in project data is possible, with 13 projects assessed accurately for Fire Drill presence by our automated detection when compared to the ground truth gathered from independent data. The overall approach can be similarly applied to detecting patterns and other phenomena with manifestations in Application Lifecycle Management data.