Reviewerly/exHarmony huggingface dataset:
Experiencing Mirror Therapy in a Virtual Reality Environment

The development of virtual reality technologies promises new opportunities for mirror therapy. It allows the addition of gamification elements and gives patients the opportunity to practice independently, in their own time and at their own location. Even though studies show positive difference between standard and virtual reality mirror therapy, underlying factors that could impact these differences are yet to be studied. In this study standard mirror therapy using a real mirror was compared to a virtual reality setup in terms of psychophysical effects, usability, and virtual presence of healthy participants. We found that although participants experienced psychophysical effects in both setups, this was significantly lower for the virtual reality setup. Additionally, participants found the system easy to use, but the virtual reality environment scored low in terms of experienced realism which could have attributed to the lower number of psychophysical effects in the virtual reality setup. These results provide additional grounds for further development and research into the application of virtual reality applications for mirror therapy.
Investigating and Comparing the Perceptions of Voice Interaction in Digital Games: Opportunities for Health and Wellness Applications

This study investigates and compares perceptions of voice interaction in digital games, focusing on three scenarios: game interacts with the player through voice, player interacts with another player through voice, and player interacts with the game through voice. We analyze the state-of-the-art, particularly in AAA games, and conduct a comparative study involving game expert participants from academia and industry, undergraduate students, and generative AIs. The study aims to provide insights to improve gameplay experiences, reduce disruptive behaviours, and identify the influence of different voice interaction modalities on player engagement, experience, motivations, and limitations concerning current technology. Based on this comparative analysis, we expect to identify opportunities for enhancing voice interaction in digital games, particularly in health and wellness applications, and anticipate future trends in this field.
Machine Learning Methods for Prediction of COVID-19 Patient Length of Stay: Using Texas PUDF Data

Accurately predicting the length of stay (LOS) has significant benefits for hospitals’ resource utilization. It’s especially important for COVID-19 patients since it is a highly contagious disease, and it places significant strain on healthcare systems worldwide. In this paper, we used the Texas Public Use Data File (PUDF) inpatient discharge record, which is a state-level dataset that contains data from over 600 hospitals, to make predictions regarding LOS of COVID-19 patients. Our analysis included 224,152 (n) records covering a time span of six quarters from Q4 2020 to Q1 2022. In addition, seven features are chosen to predict LOS - age group, sex code, race, first payment source, type of admission, illness severity, and risk mortality. Various machine learning techniques were employed, including Gradient Boosting (GB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Using regression models, we identified the best parameters for each model and predicted the numerical LOS for each quarter. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R2) were calculated to evaluate the performance of each prediction. The results revealed notable performance differences among models. SVM performs the least MAE for 6 quarters, and GB performs the highest R-squared for 6 quarters. It also showed varying performance across different quarters, possibly due to dissimilarities in the data distribution. Q4 2020 and Q2 2021 has a relatively better performance, and the data is relatively concentrated compared to others. Q4 2021 has the least performance with a highly dispersed data distribution.
Wearable Technology as Game Input for Active Exergames

Wearable technology and exergames are topics with a growing interest since the wide adoption of the Internet of Things and other technological advancements around machine to machine communication.Our paper extends on existing work in the area and proposes a development framework for exergames that use physiological signals through wearable technology as player input and combine with the Internet of Things to create a single exergame solution.We analyse the applicability of our proposed framework through an exergame that uses heart rate obtained from a wearable as the sole form of player input.We highlight the empirical nature of our work and present the future directions of our research along with recommendations for researchers that wish to introduce their own solutions in the area.
Retreating Nouns for Better Feature Extraction for Summarization: In Context with Narratives

Text summarization generates a precise summary of the original text while maintaining its meaning. We have collected information in the form of narratives for this project. In everyone's life, narratives are the most popular type of entertainment. It teaches us many things in our everyday lives. There are seven sorts of storylines, according to Christopher Booker: conquering the monster, rags to riches, the quest, voyage, return, comedy, tragedy, and rebirth. The goal of this research is to develop a system that aids in the extraction of important information or characteristics such as performer, position, association, and activities, as well as exhibiting the link between the retrieved data and creating a summary, with this process, we extract nouns and verbs; nouns are regarded actors, while verbs are considered spectators.
A Lower Complexity Deep Learning Method for Drones Detection

Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities can deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep-learning techniques to benchmark real data sets of flying drones. A Deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the SSD paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning techniques such as SVM is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were either RGB or IR data. Comparisons were made between all these types and conclusions are presented.
Visual Sentiment Analysis by Distillation of Instance-Based Sentiment Vectors

Sentiment Analysis is essential in the area of marketing or communication. In recent years, visual sentiment analysis that estimates how people feel when looking at an image has been widely studied along with the progress of machine learning. The main difficulty of sentiment analysis is to bridge the affective gap in translating from visual features to affective labels. SentiBank, a visual sentiment analysis framework, solved this problem and achieved high accuracy in emotion prediction by the text-based approach through the representation of adjective and noun pairs. However, there is a possibility that the texts need to reflect more sufficiently the information of an image, such as multiple objects or colors of the background. To reflect such information more directly, we propose training the visual sentiment analysis model by distilling the result of text classification that takes the pseudo text derived from patch images of object instances. The experiment shows that our method is lower than the original SentiBank in the Twitter dataset, with a more concrete image and highly accurate labels. However, our method achieved higher accuracy in the EmotionROI dataset, a more abstract dataset with comparably noisy labels than the Twitter dataset.
Autonomous Drone Landing on a Moving Naval Base using Vision-Based Robot Control

This paper proposes a novel technique of an Autonomous Drone landing on top of a moving Naval Vessel Base using Vision-Based Robot Control. To achieve an effective landing a camera is fixed under the drone which captures the images and detects the movement of the landing base. The landing base on top of the vessel is specially designed with a nested tag which is scanned by the fixed camera on the drone from a high altitude. This nested tag is a fiducial marker that is used by the imaging system of the drone as a point of reference in the Naval Vessel Base for precise landing. The controller is designed in the image coordinate frame captured by the imaging system on the drone with the help of the Jacobian Matrix. Whether the vessel remains stationary or moving, it always trembles, especially, when a vessel experiences bad weather at sea, it will be quite challenging for the drone to land on the trembling Naval Vessel Base. These unexpected movements cause the landing tag to disorient from the horizontal view of the captured frames, which makes it difficult to obtain the expected mid-point of the tag. To overcome this complication the translational and rotational motions of the drone are decoupled from the camera using the Virtual Camera Method where the mid-point of the tag is set as the feature point which helps to obtain the expected position during the horizontal plane disorientation of the tag image captured by the camera from the drone. Additionally, Kalman Filter is employed in the imaging system to reduce the observation noise so that precise estimation of the feature point can be achieved.
A Bit-Width Reducing Method for Ising Models Guaranteeing the Ground-State Output

Ising machines are being developed as an efficient computing alternative for solving combinatorial optimization problems. Ising machines solve a combinatorial optimization problem by transforming it into a data structure called an Ising model. However, Ising machines have hardware limitations and hence the input Ising model coefficients must be limited to a certain range. Existing coefficient bit-width reducing methods do not guarantee that the output solution is optimum without adding too many extra spins. In this paper, we propose a new bit-width reducing method for Ising models. In this method, we first partition an original Ising model into several Ising models, each of which has the same graph topology, and the coefficient bit-width is reduced to be dealt with by the target Ising machine. Next, we obtain a ground-state solution for every partitioned Ising model. At that time, we can theoretically prove that, if all the partitioned Ising models have a common ground-state solution, it also gives a ground-state solution for the original non-bit-width-reduced Ising model. Experimental results demonstrate that the theorem is empirically true.
A Comparative Study on Recent Trends to Secure Big Data

Big data has opened a wide window for business improvement in many ways, Nowadays, big data is most preferably used in corporate sectors, medium and large-scale industries, banks, healthcare etc. So Big data has enormous of applications in various fields. However, using such a powerful asset comes with big risks in security and privacy. This is a review paper, that addresses the recent challenges related to big data and their causes and solutions. We also investigate the recent available tools for managing big data security and integrity / quality and their limitation. In last we address the big data practices to prevent data theft and big data protection tools.
Inference of Infrastructure Network Flows via Physics-Inspired Implicit Neural Networks

We study the problem of inferring edge flows and nodal injections in infrastructure networks. Leveraging the Thomson’s Principle from the electric circuits literature, we setup a framework to jointly learn network parameters and missing states. Despite being application agnostic, the proposed approach captures the fundamental physics of the infrastructure, and is able to handle partial observation, node and edge features as well as operational constraints. The physics inspired learning framework leads to a bilevel optimization problem, which is NP hard in general. By exploiting convexity properties, we reformulate the problem as a single level optimization, composed of a graph neural network and an additional implicit layer. The resulting architecture can be trained using standard gradient-based methods. We assess the validity of the proposed approach on two different infrastructure networks (power and traffic), and show it outperforms the current state of the art.
Collision-free Path Planning and Control of Coordinated UAVs with Tethered Payloads

Unmanned aerial vehicles (UAVs) have been found in various fields, in particular, with emphasis on the load transport function. As maintaining the load integrity is crucial, these applications benefit from controllers enabling stable and safe flights. State-of-the-art methods for path planning and control of slung-load UAVs are often concerned with swing-minimizing trajectories or control of either the load or the vehicle to enable agile maneuvers. Optimization methods have found more success in uniting the planning and control for slung-load UAVs as a whole. In this paper, we employ a nonlinear model predictive control (NMPC) framework to navigate a pair of slung-load UAVs around an obstacle. In the preliminary simulations conducted, NMPC is first used to reach a desired goal state considering the two independent system dynamics concurrently. This approach yields an unsuccessful mission under certain parameter settings. An alternative method separates the planning and control to use NMPC as a tracking controller of a pre-generated trajectory. The advantages of the latter approach are demonstrated.
A Systematic Review on Software Design Patterns in Today's Perspective

Larger Object Oriented(OO) software systems can benefit greatly from the use of software design patterns. Because design pattern offers a well-structured and proven solution to recurrent issues that developers deal with. Previous studies explored the effects of design patterns and created several models for mining design patterns. Some of them were developed using machine learning (ML) methods. This paper concentrated on giving a brief summary of the difficulties with design patterns from the developer's point of view, as well as their impact on some quality aspects. Along with that, we discussed the shortcomings of contemporary classic pattern mining techniques. We compared both conventional mining tools and ML-based mining tools. Findings show that both the random forest model and PatternScout, which is based on SPARQL queries, can effectively discover all patterns with higher precision and recall values.
Advancing Human Fall Detection with TsetFall: A Comprehensive, Fine-Grained, and Publicly Available Dataset

Human fall detection research remains an under-developed area, particularly with respect to utilizing dynamic images for analysis. Existing fall detection datasets often suffer from size, diversity, and representativeness limitations, as they generally comprise a small number of videos featuring a limited range of actions, camera perspectives, and lighting conditions. Moreover, these datasets typically lack the complexity of real-world fall scenarios, as they often exclude distracting objects and offer only one or two camera angles. To address these challenges, this work introduces TsetFall, a comprehensive, fine-grained, and publicly available dataset generated for fall detection research. Employing a novel AI technique devised by the present authors, the dataset annotation process was notably expedited. By bridging the current gaps in available resources, the TsetFall dataset serves as a valuable benchmark for advancing the field and tackling the multifaceted nature of human fall detection.
Redefining Redirected Movement for Wheelchair Based Interaction for Virtual Reality

Among all possible interaction experiences in VR, real locomotion is the most immersive and natural form. However, due to space constraints, this technique is limited in real applications and may require redirect movement models for making it possible. In this paper we propose enhancing real wheelchair locomotion as an interaction scheme in VR, by including and developing redirect movement techniques within this context. To evaluate and determine the effectiveness of redirect walking in this scenario, we have created an accessibility-based application. Our experimental results indicate that users in wheelchairs are unable to accurately notice redirection gains from 0.45 up to 1.7. In other words, they cannot distinguish between a physical distance of 1.7m and 0.45m while moving 1m in the virtual world. These findings suggest that redirect walking can be a useful tool for enhancing real wheelchair locomotion in VR, without compromising the immersive and natural experience of locomotion.
Dynamic Obstacle Avoidance for Nonholonomic Vehicles using Collision Cones: Theory and Experiments <sup>*</sup>

Obstacle avoidance is a critical part in the control of autonomous vehicles. This paper proposes an obstacle avoidance algorithm based on the computationally advantageous collision cone concept, for unicycles which may be subject to strict input constraints and dynamic, non-compliant obstacles which may even actively pursue a collision. The paper presents both a theoretical analysis together with experiments demonstrating the beneficial features of the algorithm. We analytically derive explicit, and practically intuitive, conditions on the control parameters, under which safety is guaranteed. Experiments illustrate the safe, intuitive, and minimally invasive behaviors generated by the proposed algorithm.
Validating Structural Cognitive Training Using Immersive Virtual Reality

As people age, they may experience difficulties in performing everyday activities, which can have a negative impact on their quality of life and independence. This paper proposes a novel methodology, called the SCT, which combines virtual reality games with cognitive exercises to improve the performance of Instrumental Activities of Daily Living (IADLs) among older adults. The SCT aims to overcome the challenges associated with far transfer, where skills learned in one context may not be effectively applied in another context. To demonstrate the effectiveness of the SCT, the paper presents a virtual reality game that combines IADLs with cognitive exercises. The game includes four core mechanics: medication management, grocery shopping, breakfast preparation, and city exploration. Each mechanic targets specific IADLs and cognitive functions, such as planning, decision-making, and problem-solving. Future work will involve testing the game with a group of older adults and implementing an adaptive difficulty system based on individual performance. The authors also plan to add additional game mechanics, such as a pill organization task, to further improve IADL performance among older adults. Overall, the SCT represents a promising approach for enhancing the quality of life and independence of older adults.
Simplified Searching for Similarities in Documents

This paper analyses the current state of the plagiarism problem. The result of this work is the implementation of designed tool and a verification of this solution’s accuracy. The analyses and design deal with pre-processing techniques, file conversions to different formats, algorithms aiming at comparing document similarities and the graphical techniques representing these similarities. The paper is mainly focused on frequency methods and the graphical representation named tag Cloud.
A Writer-Dependent Approach for Offline Signature Verification Using Feature Learning and One-Class Support Vector Machine

In many administrative and financial entities, signatures are frequently employed to confirm a person’s identification as well as the authenticity of various documents. Being a biometric characteristic, a signature is a fairly secure form of identification. Hence, efforts are being made to continually raise the bar for the security signatures, particularly by creating effective procedures for verifying signatures. Both traditional and automatic techniques have been used to verify signatures, whiles the former has recorded moderately accurate results, the latter in recent studies have demonstrated great success in separating authentic signatures from forgeries. The purpose of this work is to explore the implementation and deployment of offline writer-dependent signature verification systems, by proposing a model that can greatly minimize computation while simultaneously increasing accuracy, to achieve this, we propose combining the VGG-16 architecture as a feature extractor with a One-Class Support Vector Machine (OC-SVM) as a writer-dependent classifier.
Tactical Autonomous Maneuver Testbed for Multi-Agent Air-Ground Teams

We present a testbed for multi-agent teams through the integration of small unmanned aerial systems with small autonomous ground vehicles. Using the Quanser Autonomous Research Studio, Quanser's QDrone, and QCar, we set up an environment where researchers can run behavior models to observe the interaction between aerial and ground systems. The testbed strives to be agnostic of the programming language and lower-level autonomy so that the higher-level interactions can be studied more effectively. For the purposes of this indoor testbed, we found that both internal positioning systems and external reference systems (motion capture cameras) are required for maintaining accurate pose odometry and collecting ground-truth data. Based on the observed and collected data on the QDrone and QCar, this study aims to develop the capabilities of ground/aerial autonomous system integration, while creating a basic testbed that can serve additional research on these topics.
Fourier-Legendre series neural networks for classification

This paper presents a newly proposed artificial neural network constructed from double Fourier-Legendre series expansions, named FLSNN, for classification analyses in artificial intelligence. Numerical examples include 2-category problems (make classification and make moons) and 3-category problems (PL-PW and SL-SW of IRIS problem), respectively. The computed FLSNN results, including accuracy score, loss of RSME, decision boundary and topology, are checked with results analyzed from backward propagation neural networks (BPANN) and radial basis function neural networks (RBFNN). Results show that FLSNNs performs well as expected.