Reviewerly/exHarmony huggingface dataset:
Çok Sınıflı ve Dengesiz Eğitimsel Veri Kümesiyle Yükseköğretim Planlama ve Karar Destek Sistemi: Teknoloji Fakültesi Örneği

Studies on academic performance prediction, a sub-branch of Educational Data Mining, have increased in recent years.Educational datasets in real environments often have class imbalanced and multi-class target variables.However, studies with these datasets are very few.In this context, in this study, with the ethical no decision of 23.05.2022-286783, using the data set of Marmara University (MU) Faculty of Technology (TF) students, a student graduation status estimation was made with the multiclass imbalanced educational dataset to identify the students at risk.1394 samples and 11 features were obtained through data preprocessing and feature selection (FS) stages.153 students belonging to 2016 were used for robustness control.3 different datasets containing 11, 7 and 5 features obtained with 7 different FS were created.Using 9 different sampling methods and 16 different machine learning algorithms, 750 different models were created.Models were checked for robustness.F1 Score and Repeated Stratified 5*5 fold-CV were used as success criteria.Hyperparameter settings were made with GridSearchCV.As a result, although ROS+RF was the most successful algorithm with an F1 Score of 0.9935, the most successful and most consistent models were the 7-featured None+ET, None+MLP, None+Bagging_DT and None+RF models.With these models, the decision support system web application was developed and presented to MU TF faculty members. Çok Sınıflı ve Dengesiz Eğitimsel
DC: Research on Augmented Reality

Augmented reality technology can integrate virtual information (objects, pictures, videos, sounds, etc.) into the real environment, enrich the real world, and build a more comprehensive and better world. Outdoor illumination is essential for AR applications. We will discuss our proposed two methods which predict outdoor illumination from a single outdoor image. With predicted illumination, the virtual object can be inserted into the real environment, with consistent shadow information and occlusion relationship.
Volumetric Avatar Reconstruction with Spatio-Temporally Offset RGBD Cameras

RGBD cameras can capture users and their actions in the real world for reconstruction of photo-realistic volumetric avatars that allow rich interaction between spatially distributed telepresence parties in virtual environments. In this paper, we present and evaluate a system design that enables volumetric avatar reconstruction at increased frame rates. We demonstrate that we can overcome the limited capturing frame rate of commodity RGBD cameras such as the Azure Kinect by dividing a set of cameras into two spatio-temporally offset reconstruction groups and implementing a real-time reconstruction pipeline to fuse the temporally offset RGBD image streams. Comparisons of our proposed system against capture configurations possible with the same number of RGBD cameras indicate that it is beneficial to use a combination of spatially and temporally offset RGBD cameras, allowing increased reconstruction frame rates and scene coverage while producing temporally consistent volumetric avatars.
Interactive Panoramic Ray Tracing for Mixed 360° RGBD Videos

This paper introduces an interactive panoramic ray tracing method for rendering real-time photo-realistic illumination and shadow effects when inserting virtual objects into 360° RGBD videos. First, we approximate the geometry of a real scene with a panoramic depth buffer and a screen space depth buffer. Then, a sparse sampling ray generation method is proposed to accelerate the tracing process by reducing the number of rays that need to be emitted in ray tracing. After this, an irradiance estimation pass is introduced to generate a noisy Monte-Carlo image. At last, the final result is smoothed by interpolation, spatial-temporal filtering, and differential rendering. We tested our method in some natural and synthetic scenes and compared the results of our approach with the ground truth and that of the Image-Based Lighting method. The results show that our method can generate visually photo-realistic frames for virtual objects in 360° RGBD videos in real-time, making the rendering results more natural and credible.
Does Cognitive Workload Impact the Effect on Pain Distraction in Virtual Reality? A Study Design

Pain is an important issue for post-surgery patients. Virtual reality treatment (VRT) has been used to reduce pain perception, while there are few studies have investigated the effect of cognitive workload (CWL). The present study design hypothesizes that higher CWL will lead to stronger distraction in a virtual environment. To this end, a soothing psychological relief environment with a cognitive task, in which participants' CWL can be controlled, is constructed. In the future, this study will be applied to patients with pain to explore whether CWL is one of the key factors in reducing pain in VRT.
Projector-Camera Calibration on Dynamic, Deformable Surfaces

Dynamic projection mapping (DPM) enables viewers to visualize information on dynamic, deformable objects. Such systems often comprise of a RGB-D camera and projector pair that must be calibrated apriori. Most calibration techniques require specific static calibration objects of known geometry. In this paper, we propose the first projector-camera calibration technique for DPM that uses the dynamic, deformable surface itself to calibrate the devices, without needing to bring in a static, rigid calibration object. Our method is hardware agnostic, fast, and accurate and allows quick recalibration.
A Simple Approach to Animating Virtual Characters by Facial Expressions Reenactment

Animating virtual characters is one of the core problems in virtual reality. Facial animations are able to intuitively express emotion and attitudes of virtual characters. However, creating facial animations is a non-trivial task. It depends on either expensive motion capture devices or human designers' time and effort to tune the animation parameters. In this work, we propose a learning-based approach to animate virtual characters by facial expression reenactment from abundant image data. This approach is simple yet effective, and is generalizable to various 3D characters. Preliminary evaluation results demonstrate its effectiveness and its potential to accelerate the development of VR applications.
A Divide-and-conquer Solution to 3D Human Motion Estimation from Raw MoCap Data

Marker-based optical motion capture (MoCap) aims to estimate 3D human motions from a sequence of input raw markers. In this paper, we propose a divide-and-conquer strategy based MoCap solving network that accurately retrieves 3D human skeleton motions from raw marker sequences in real-time. Our core idea is to decompose the task of direct estimation of global human motion from all markers into first solving sub-motions of local parts and then aggregating sub-motions into a global one to achieve accurate motion estimation.
Scene Transformer: Automatic Transformation from Real Scene to Virtual Scene

Given a real scene and a virtual scene, the indoor scene transformation problem is defined as transforming the layout of the input virtual scene. The transformed layout preserves as much as possible the relationship between the furniture in the input virtual scene, and the input real scene provides the user with as much passive haptic as possible when exploring the virtual scene. We propose a real-scene-constrained deep scene transformer to solve this problem. First, we introduce the deep scene matching network to predict the matching relationship between real furniture and virtual furniture. Then we introduce a layout refinement algorithm based on the refinement parameter network to arrange the matched virtual furniture into the new virtual scene. At last, we introduce a deep scene generating network to arrange the unmatched virtual furniture into the new virtual scene.
Prototyping Large Scale Projection Based Experiences in VR

The aim of this research was to develop a technique to simulate large scale, location-based installations in VR. The project is a col-laboration with Aardman Animations, a world-renowned animation studio developing a projection-based installation. We developed a room-scale VR experience to simulate the installation in order to visualise the projection distortion under different conditions. A study was then conducted using our simulation to investigate the effect of distortion of the projections on the experience of immersion and how three factors effected the distortion.
Comparing Context-sharing Interfaces in XR Remote Collaboration

eXtended Reality (XR) remote collaboration refers to working to- gether on virtual data or real objects through various types of reality. To achieve a higher immersive collaboration experience, it is critical to converge heterogeneous realities in one shared workspace, where environmental changes are continuously updated. In this study, We extend our previous work, the Webized XR system, in terms of sharing the local environmental context. This system can provide three types of context-sharing interface (2D video, 360 video, and 3D reconstruction), delivering spatial information about the workspace. We also performed a pilot user study to measure the quality of this system.
Multimodal Apology: Using WebXR to Repair Trust with Virtual Companion

Research on trust repair in human-agent cooperation is limited, but it can mitigate trust violations and prevent distrust. We conducted a study with 45 participants who interacted with a companion agent through mixed-reality interfaces while playing a language-learning quiz game. Our results showed that displaying the agent's intentions during mistake explanations helped repair trust. We also found that this had positive effects on affect, user experience, and cooperation willingness. Our findings suggest that including agent intentions in mistake explanations is an effective way to repair trust in human-agent interactions.
Tracing a Linear Subspace: Application to Linearly-Homomorphic Group Signatures

When multiple users have power or rights, there is always the risk of corruption or abuse. Whereas there is no solution to avoid those malicious behaviors, from the users themselves or from external adversaries, one can strongly deter them with tracing capabilities that will later help to revoke the rights or negatively impact the reputation. On the other hand, privacy is an important issue in many applications, which seems in contradiction with traceability. In this paper, we first extend usual tracing techniques based on codes so that not just one contributor can be traced but the full collusion. In a second step, we embed suitable codes into a set $$\mathcal V$$ of vectors in such a way that, given a vector $$\textbf{U}\in \textsf{span}(\mathcal V)$$ , the underlying code can be used to efficiently find a minimal subset $$\mathcal X \subseteq \mathcal V$$ such that $$\textbf{U}\in \textsf{span}(\mathcal X)$$ . To meet privacy requirements, we then make the vectors of $$\textsf{span}(\mathcal V)$$ anonymous while keeping the efficient tracing mechanism. As an interesting application, we formally define the notion of linearly-homomorphic group signatures and propose a construction from our codes: multiple signatures can be combined to sign any linear subspace in an anonymous way, but a tracing authority is able to trace back all the contributors involved in the signatures of that subspace.
WikiCoder: Learning to Write Knowledge-Powered Code

We tackle the problem of automatic generation of computer programs from a few pairs of input-output examples. The starting point of this work is the observation that in many applications a solution program must use external knowledge not present in the examples: we call such programs knowledge-powered since they can refer to information collected from a knowledge graph such as Wikipedia. This paper makes a first step towards knowledge-powered program synthesis. We present WikiCoder, a system building upon state of the art machine-learned program synthesizers and integrating knowledge graphs. We evaluate it to show its wide applicability over different domains and discuss its limitations. WikiCoder solves tasks that no program synthesizers were able to solve before thanks to the use of knowledge graphs, while integrating with recent developments in the field to operate at scale.
THE THREATENING OF SKOUW LANGUAGE IN JAYAPURA

The Threatening of Skouw Language in Jayapura. This local research aims mainly to faind out (1) The What factors that threatening the Skouw Language in Jayapura ?This local research takes places in Jayapura particularly in Skow area. The method used descriptive qualitative research the data are in the questionnaire form this study uses the descriptive method and supported by quantitative data. This study used two instruments, namely: observation and questionnaire, as a data collection tool. From the research, a community of Skouw residing in the Skouw Mabo village, Skouw Yambe village and Skouw Sae village, which use Skouw language only the elderly speakers or parents, while the young generation, use the Indonesian language. In general, seen from the results of research, will be extinct in a relatively short period of time when there was no awareness of the native speakers to teach the young generation and local governments, especially in Jayapura city government issued a policy for the preservation of Skouw language, which was one of Papua rich culture.
Influence of Simulated Aerodynamic Forces on Weight Perception and Realism in Virtual Reality

In this research, the impact of aerodynamics on the perception of weight in virtual environments was studied. A real-time aerodynamic simulation was created for objects with different shapes and weights, and an experiment was conducted to determine the correlation between subjects' weight perception and the presence of simulated aerodynamic forces on virtual objects. The results showed that the weight of light objects was more accurately conveyed and the perceived realism was improved when simulated aerodynamic forces were included.
VGTC Virtual Reality Technical Achievement Award

Steinicke studied mathematics with a minor in computer science at the University of Münster, from which he also received his Ph.D. in 2006 and Venia Legendi in 2010, both in Computer Science.Dr. Steinicke's research is driven by understanding and exploiting human perceptual, cognitive and motor abilities and limitations to reform how people interact with and experience computer-mediated realities, including virtual, augmented, and mixed realities.He is perhaps best known for his fundamental and transformative contributions to the topic of redirected walking, where in 2009 he used psychophysical methods to determine points of subjective equality between physical actions in the real world and corresponding visual feedback in VR, and detection thresholds for the implementation of a range of imperceptible modifications to the mapping between one's physical actions and the result of those actions in VR.These results, and the approach used to obtain them, have since been widely applied in a diversity of similar contexts, and have been instrumental to advancing VR locomotion research.Also, Dr. Steinicke's rigorous scientific approach to integrating behavioral measures into VR research has provided valuable inspiration to others in his research community.Dr. Steinicke has additionally made significant contributions to convergent research in multi-touch interaction in semi-immersive 3D environments.His work in perceptually inspired spatial user interfaces addresses the significant research challenges associated with using 2D touch interfaces to support the direct manipulation of 3D objects.He has been instrumental in building up the research
IEEE VR 2023 Steering Committee Message

The IEEE VR Steering Committee congratulates and offers gratitude to the enormous efforts of the IEEE VR Conference Organizing Committees — with special recognition for the multiple years of hard work of the General Chairs, Xubo Yang, Kun Zhou, Tobias Langlotz, and Stephan Lukosch. To realize the conference requires a sizeable, motivated team of volunteers to work with the IEEE conference management team. Maintaining the conference's status as the premier international virtual reality conference is a testament to the commitment of all the organizers and participants that make up the IEEE Virtual Reality community.
Development of a Clinical Decision Support System to Predict Unplanned Cancer Readmissions.

Unplanned 30-day cancer readmissions are an important outcome of cancer hospitalization and can significantly raise mortality rates and costs for both the patient and the hospital. This paper aimed to develop a predictive model using machine learning and electronic health records to predict unplanned 30-day cancer readmissions and further develop it as a clinical decision support system. The three-stage study design followed the 2022 AMIA Artificial Intelligence Evaluation Showcase. In the first stage, the technical performance of the model was determined (81% of AUROC) and contributing factors were identified. In the second stage, the technical feasibility and workflow considerations of using such a predictive model were explored through semi-structured interviews. In the third stage, a decision tree analysis and a cost estimation showed that the model can reduce unplanned readmissions significantly if timely action is taken and that preventing a single readmission may significantly reduce costs.
Tensors

General relativity is a subject that most undergraduates in physics are particularly curious about, but it has a reputation for being very difficult. This book provides as gentle an introduction to general relativity as possible, leading you through the necessary mathematics in order to arrive at important results. Of course, you cannot avoid the mathematics of general relativity altogether, but, using this book, you can gain an appreciation of tensors and differential geometry at a pace you can keep up with. Early chapters build up to a complete derivation of Einstein's Equations, while the final chapters cover the key applications on black holes, cosmology and gravitational waves. It is designed as a coursebook with just enough material to cover in a one-semester undergraduate class, but it is also accessible to any numerate readers who wish to appreciate the power and beauty of Einstein's creation for themselves.
A multi-camera person tracking and re-identification system based on edge computing

Multi-object tracking (MOT) is to track multiple objects simultaneously in a video. The main application scenarios of MOT are security monitoring and automatic driving, etc. In these scenarios, we often need to track and identify many targets at the same time. In this work, we have presented a multi-camera person tracking and re-identification system based on edge computing which comprises of communication solution, a FairMOT based tracking system and a MoT based re-identification system. We apply our system in real scene online, the results show that our system is efficient for multi-camera person tracking and re-identification tasks and can run with frame rate at real-time.
Speaker diarization based on multi-timescale feature fusion

Deep and convolutional neural networks have performed well in capturing speaker characteristics, while the ECAPA-TDNN model has demonstrated outstanding performance in both the fields of speaker validation and speaker diarization. Within this essay, during the speech segmentation stage, we uniformly redivide the speech on multiple time scales based on the oracle voice activity detection. Meanwhile, we fine-tune the ECAPA-TDNN architecture by adding a RepVGG module to extract more abundant features, then aggregate all of the outputs. Finally, we use DOVER-Lap to integrate the results obtained after the clustering of multiple schemes in a way to obtain the final temporal labeling. The best results achieves 1.91% of the diarization error rate on the classical AMI conference corpus.
Seen and unseen Chinese intent recognition using automatic probability thresholds

Intent recognition is the first part which needs to be accurately recognized in the conversation between ai and the user. Due to the diversity of user intent, it is difficult to manually define all categories of intent in advance during the training phase. The inability to distinguish undefined intent categories can cause the ai to keep replying to the user with seriously wrong answers, which can greatly reduce the user's sense of experience. Therefore, it is very necessary to realize the recognition of seen intention categories and the distinction of unseen intention categories. In this paper, a model with Automatic probability threshold which based on the bert model is used to ensure accurate recognition of seen intentions while distinguishing unseen intentions. Due to the inadequacy of training samples, the automatic probability threshold model in the paper uses text vectors from two bert models containing different dropout parameters as the training set, which can improve accuracy of the model.
Eye Pupil Controlled Wheelchair

Elderly and disabled people find it very difficult to move freely to their desired areas at any opportune time in today's fastpaced environment.Thankfully, some people are physically fit and have good eyesight, which helps to assure their survival.This paper attempts to investigate the use of a design approach for a system that aids the disabled who are unable of operating a wheelchair in the conventional manner.This system uses information collected from the sensors and offers a system that will help replace traditional method.An intelligent vision system that analyses head movements, pupil position, the patient's angle of view, and other factors can be used to determine the necessary movements of the chair.In order to detect the pupil's position and control the wheelchair to travel in the desired direction, the suggested technology uses image processing algorithms.Project is built with the use of ultrasonic sensor to avoid the obstacles in the way.
A data poisoning attack method for recommendation system based on dynamic adaptation of data and model

Data poisoning attack has been one of the most prominent threats for data-driven machine learning model. Specifically, in the field of recommendation system, an attacker could manipulate recommendation results by injecting some crafted fake data into recommendation model. In this paper, a data poisoning attack method based on dynamic adaptation of data and model is proposed for the recommendation system, referred to as dynamic attack, which solves the problem that the fake data fails to keep aggressive due to difference between models. Experimental results on the two real datasets, MovieLens-100K and MovieLens-1M, show that dynamic attack outperforms existing heuristic-based attacks and the average attack success rate is increased by more than 10 times.
Establish automatic operation and maintenance system of power monitoring system data center based on cloud platform

With the gradual maturity of cloud computing technology, the power data center begins to migrate to the cloud platform, so there are many new operation and maintenance requirements, such as scalability, security, configurability, etc. In order to meet these requirements, this paper proposes an automatic operation and maintenance system of power data center based on cloud platform. This system uses cloud computing platform and virtualization technology to model the hardware resources, data resources, configuration resources, security resources, etc. in the power data center, digitize the resources, and realize the automatic management of resources through the system's data collection, analysis, and transmission, so as to achieve the goal of automatic and efficient operation and maintenance, It can better meet the new demand in the operation and maintenance of power data center.