nils-herrmann/clean_scirepeval huggingface dataset:
Demo paper: Demonstration of a 3D Tele-Immersion framework

Collaborative 3D Tele-immersive (3DTI) environments allow reconstruction of real world 3D scenes in the virtual world across multiple physical locations. This kind of reconstruction results in a lot of 3D data being transmitted over the internet in real time. The current systems allow for transmission at low frame rates due to the large volume of data and network bandwidth restrictions. The high delay over the internet also causes a view disparity between the two locations. We demonstrate our solutions to address these issues in our 3DTI framework. We use a baseball game as the environment.
Fault detection and isolation in motion monitoring system

Pervasive computing becomes very active research field these days. A watch that can trace human movement to record motion boundary as well as to study of finding social life pattern by one's localized visiting area. Pervasive computing also helps patient monitoring. A daily monitoring system helps longitudinal study of patient monitoring such as Alzheimer's and Parkinson's or obesity monitoring. Due to the nature of monitoring sensor (on-body wireless sensor), however, signal noise or faulty sensors errors can be present at any time. Many research works have addressed these problems any with a large amount of sensor deployment. In this paper, we present the faulty sensor detection and isolation using only two on-body sensors. We have been investigating three different types of sensor errors: the SHORT error, the CONSTANT error, and the NOISY SENSOR error (see more details on section V). Our experimental results show that the success rate of isolating faulty signals are an average of over 91.5% on fault type 1, over 92% on fault type 2, and over 99% on fault type 3 with the fault prior of 30% sensor errors.
Evaluating the Effect of Local Variations in Visually-Similar Motions on the Clustering of Body Sensor Features

Body Sensor Network-related applications such as assistive-living environment, orthopedic, physical medicines, and rehabilitation use wearable body sensors like motion trackers to track joint movements and electromyogram sensors to track muscular activities. These sensors provide information in the form of multidimensional time series data. Generally, for these applications, classification or similarity retrieval of human motions is performed by traditional clustering of dimensionally-reduced feature vectors based on joint movements and/or muscular activities. However, local variations in visually-similar sets of human motions cause them to group in different clusters resulting to a lower recall during retrieval. Hence, it is important to evaluate the effect of local variations on the given clustering of feature vectors. In this paper, we represent the local variations in the form of quantitative attributes that are measured from sensors' time series data. And further, we propose a multivariate analysis of variance technique for evaluating the effect of quantitative attributes on the clustering results that are based on different configurations of feature vectors representing joint movements and muscular activities.
MPEG Media Transport (MMT) for 3D Tele-Immersion Systems

3D Tele-Immersion (3DTI) environments are a new medium for highly interactive and immersive means of collaborations through a shared virtual 3D environment. They have many applications in the areas of education, entertainment, sports training, tele-medicine etc. The data in these systems are multi-modal, some high volume, some high frequency and all highly correlated. We identify three major challenges in a general 3DTI system, session management, synchronization and data format conversion. We discuss the shortcomings of some of the existing protocols/solutions to them. We describe some features relevant to 3DTI of the MPEG Media Transport (MMT) standard. In this paper we evaluate the use of MMT in a 3DTI application. We provide a feature comparison with the most popular protocols currently being used in such applications, RTP, RTSP, TCP and UDP etc. MPEG DASH was another protocol that was being considered, but that also fails to fully address some of the challenges that 3DTI applications face. Through this comparison study we advocate the use of MMT in 3DTI applications.
Real-Time, Curvature-Sensitive Surface Simplification Using Depth Images

With the rising popularity of handheld virtual reality (VR) devices and depth sensing RGB-D cameras, a variety of VR applications merging these two technologies has been suggested. However, immersive quality of experience in such VR applications is constrained mainly by the large data size and the hardware limitations to handle it. The depth data captured by RGB-D cameras provide a dense sampling of the surface, resulting in a high-poly mesh, which is difficult to be rendered on handheld VR devices due to their limited processing power. To improve the immersive VR experience, a sparse approximation of the depth data is needed. Traditional mesh and point cloud simplification methods are iterative and so are unsuitable for real-time applications. In this paper, we introduce a depth-image-based approach that is capable of generating a good quality sparse mesh for visualization in real time. We propose a curvature-sensitive surface simplification—$\text{CS}^3$  operator that assigns an importance measure to each point in the depth image, based on the local curvature. Further, it applies an importance-order-based restrictive sampling to generate a sparse representation that retains the overall shape as well as the finer features of the object. We also modify the 2-D sweep-line-based constrained Delaunay triangulation to generate 3-D meshes from the sparse point sampling obtained using $\text{CS}^3$. In addition, the proposed approach preserves key surface properties, such as texture coordinates and materials. We used three different datasets containing dense 3-D models with and without texture, which are scanned using various sensors to validate and compare the robustness, real-time performance, and accuracy of the proposed method over existing approaches. Based on the experimental results, we show that the proposed $\text{CS}^3$  operator and modified 2-D sweep-line-based triangulation generate sparse meshes from depth image in real time, performing significantly faster than current state-of-the-art methods while maintaining similar visual quality.
FaceFetch: A User Emotion Driven Multimedia Content Recommendation System Based on Facial Expression Recognition

Recognition of facial expressions of users allows researchers to build context-aware applications that adapt according to the users' emotional states. Facial expression recognition is an active area of research in the computer vision community. In this paper, we present Face Fetch, a novel context-based multimedia content recommendation system that understands a user's current emotional state (happiness, sadness, fear, disgust, surprise and anger) through facial expression recognition and recommends multimedia content to the user. Our system can understand a user's emotional state through a desktop as well as a mobile user interface and pull multimedia content such as music, movies and other videos of interest to the user from the cloud with near real time performance.
Spectral Watermarking for Parameterized Surfaces

This paper presents a blind spectral two-way watermarking framework for 3-D models with parametric information. We introduce a spectral geometric watermarking technique based on Dirichlet Manifold Harmonic Transform to alter the geometric shape, while the spectral basis functions are computed from the parametric mesh as the analysis domain. This new geometric method embeds watermarks into small surface patches without introducing discontinuity across the patch boundary, while at the same time is robust against various spatial attacks. By manipulating part of the geometric shape on the intermediate model instead of the original model, this method gains robustness against connectivity changing and cropping attacks. By combining the new geometric method with the existing texture method into the two-way watermarking framework, we can withstand various attacks applied to either geometric mesh or parametric information. Theoretical analysis and experiments show that this new geometric method is robust against the majority of attacks and the two-way watermarking framework helps achieve better robustness.
Analysis of human motions with arm constraint

This paper investigates a quantization and clustering issue on human motion performance constrained by disabilities. In a longitudinal study of medical therapy on motion disorder, stages of patient disability condition change over time. We investigate four different stages of one arm constrained walking motions by restricting 0%, 10%, 16% and 22% of arm swing angles. For analysis we use One-way ANOVA and K-mean clustering to indentify the most significant features and to partition four different motion constrained groups. Our experimental result shows that all four arm constraints during walking motion are clustered with an average accuracy of 91.7% on two different feature conditions: a mixture of singular value decomposition (SVD) and power spectral density (PSD); and SVD only on selected gait cycles. The proposed method can be integrated with a ubiquitous system (using wearable sensors) for a remote distance patient monitoring system analysis.
A forward error recovery technique for real-time MPEG-2 video transport and its performance over wireless IEEE 802.11 LAN

Real-time MPEG-2 video transport applications do not usually have the luxury of a reverse channel for recovering from any errors that might occur during communication. Degradation in quality of decoded video frames is immediately apparent in the presence of errors in headers. In this paper, we focus on protecting header information by replicating it in any free space that might be available in the defined MPEG-2 transport stream packets. We also present our implementation experience over wired ATM as well as wireless IEEE 802.11 LAN by incorporating this forward error recovery approach with a real-time MPEG-2 encoder. In our experiments, it is found that the free space available is generally more than adequate for replicating essential header information.
A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities

The evaluation of the postural control system (PCS) has applications in rehabilitation, sports medicine, gait analysis, fall detection, and diagnosis of many diseases associated with a reduction in balance ability. Standing involves significant muscle use to maintain balance, making standing balance a good indicator of the health of the PCS. Inertial sensor systems have been used to quantify standing balance by assessing displacement of the center of mass, resulting in several standardized measures. Electromyogram (EMG) sensors directly measure the muscle control signals. Despite strong evidence of the potential of muscle activity for balance evaluation, less study has been done on extracting unique features from EMG data that express balance abnormalities. In this paper, we present machine learning and statistical techniques to extract parameters from EMG sensors placed on the tibialis anterior and gastrocnemius muscles, which show a strong correlation to the standard parameters extracted from accelerometer data. This novel interpretation of the neuromuscular system provides a unique method of assessing human balance based on EMG signals. In order to verify the effectiveness of the introduced features in measuring postural sway, we conduct several classification tests that operate on the EMG features and predict significance of different balance measures.
Exploring Baseline Shift Prediction in Respiration Induced Tumor Motion

Effective management of respiratory motion is essential for achieving the clinical goals of stereo tactic thoracic and abdominal radiotherapy, where highly potent radiation beams are precisely directed in order to ablate the tumor, while minimizing radiation damage to normal tissue and critical organs. Due to cycle-to-cycle variations in respiratory motion, it is important to be able to predict imminent anomalous or irregular tumor motion ahead of its occurrence. Such information can then be used to pause the radiation delivery, or to track the moving tumor. However, predicting tumor motion anomalies presents a challenge as the occurrence of these anomalies can vary from patient to patient and from day to day for the same patient. In this paper, we explore the use of observed data in predicting baseline trends, and baseline shifts, in particular. Using a tumor motion dataset obtained from 143 treatment fractions from 42 patients treated with Cyber knife Synchrony System, we execute multifaceted analyses, including offline and online scenarios. Given the variation in tumor motion patterns and the absence of standardized baselines and adequate personalized prior data, we compare performances of standard prediction algorithms with and without training on prior data. Our analyses yield promising results for baseline shift prediction, and real-time baseline trend estimation in general.
Evaluating the efficacy of RGB-D cameras for surveillance

RGB-D cameras have enabled real-time 3D video processing for numerous computer vision applications, especially for surveillance type applications. In this paper, we first present a real-time anti-forensic 3D object stream manipulation framework to capture and manipulate live RBG-D data streams to create realistic images/videos showing individuals performing activities they did not actually do. The framework uses computer vision and graphics methods to render photorealistic animations of live mesh models captured using the camera. Next, we conducted a visual inspection of the manipulated RGB-D streams (just like security personnel would do) by users who are computer vision and graphics scientists. The study shows that it was significantly difficult to distinguish between the real or reconstructed rendering of such 3D video sequences, thus clearly showing the potential security risk involved. Finally, we investigate the efficacy of forensic approaches for detecting such manipulations.
Video Human Motion Recognition Using Knowledge-Based Hybrid Method

Human motion recognition in video data has several interesting applications in fields such as gaming, senior/assisted living environments, and surveillance. In these scenarios, we might have to consider adding new motion classes (i.e. new types of human motions to be recognized) as well as new training data (say, for handling different type of subjects). Hence, both accuracy of classification and training time for the machine learning algorithms become important performance parameters in these cases. In this paper, we propose a Knowledge Based Hybrid (KBH) method that can compute the probabilities for Hidden Markov Models (HMMs) associated with different human motion classes. This computation is facilitated by appropriately mixing features from two different media types (3D motion capture and 2D video). We conducted a variety of experiments comparing the proposed KBH for HMMs and the traditional Baum-Welch algorithms. With the advantage of computing the HMMs parameters in a non-iterative manner, the KBH method outperforms the Baum-Welch algorithm both in terms of accuracy as well as reduced training time.
QoE Studies on Interactive 3D Tele-Immersion

Users' Quality of Experience (QoE) in Interactive 3D Tele-Immersion (i3DTI) systems is influenced by several factors such as the quality of the "live" 3D avatars of the users, network latency, rendering methodology (head mounted display or regular TV type of display), etc. Hence, it becomes important to answer the question: "Is Visual Quality (VQ) the only factor to be considered or do better immersion and faster interactions matter, in having good QoE?" To answer this question, in this paper, a highly optimized state-of-the-art i3DTI framework implementation is introduced along with a soccer-inspired penalty shootout game. This game allows users to experience various situations, view different angles, perceive delays, track virtual ball motion, and play naturally using their entire bodies. A head mounted display device - Oculus Rift allows the users to get completely immersed and perform better in the penalty shootout game compared to watching themselves play on a 3D TV. This scenario is obtained in a controlled lab setting with ultra-high-speed network that has ultra-low latency, high VQ, fast and realistic interactions. Such ideal conditions are not typically available in a wide area Internet. Hence, for faster interaction scenario, we used lesser RGB-D cameras for 3D reconstruction, thereby reducing the model quality significantly. The high responsiveness of the game masked the user's perception of quality, and resulted in them not noticing the lower VQ of the reconstructed 3D models. Based on the results from the user study that focused on immersion, interaction and VQ aspects; users felt the game to be visually appealing, intuitive, engaging, and highly entertaining in all of the scenarios.
Mining pattern sequences in respiratory tumor motion data

Management of respiration induced tumor motion during radiation therapy is crucial to effective treatment. Pattern sequences in the tumor motion signals can be valuable features in the analysis and prediction of irregular tumor motion. In this study, we put forward an approach towards mining pattern sequences in respiratory tumor motion data. We discuss the use of pattern sequence distributions as effective representations of motion characteristics, and find similarities between individual tumor motion instances. We also explore grouping of patients based on similarities in pattern sequence distributions exhibited by their respiratory motion traces.
Motion fault detection and isolation in Body Sensor Networks

Significant amount of research and development is being directed on monitoring activities of daily living of senior citizens who live alone as well as those affected with certain disorders such as Alzheimer's and Parkinson's. A combination of sophisticated inertial sensing, wireless communication and signal processing technologies have made such a pervasive and remote monitoring possible. Due to the nature of the sensing and communication mechanisms, these monitoring sensors are susceptible to errors and failures. In this paper, we address the issue of identifying and isolating faulty sensors in a Body Sensor Network that is used for remote monitoring of daily living activities. We identify three different types of fault isolation strategies and propose both history-based and non-history based approaches.
Flexible disk scheduling for multimedia presentation servers

Multimedia presentations (e.g., lectures, digital libraries) normally include discrete media objects, such as text and images, along with continuous media objects, such as video and audio. Delivery of the objects composing a multimedia presentation is based on temporal relationships specified by the author(s). Hence, even discrete media objects (that do not normally have any real-time characteristics) have temporal constraints on their presentation. Composition of multimedia presentations may be light (without any accompanying video or large multimedia data) or heavy (accompanied by video for the entire presentation duration). This varying nature of the composition of multimedia presentations provides some flexibility for scheduling their retrieval. Depending on the composition of multimedia presentations, disk access for retrieval may not be needed in every round. For instance, a medium multimedia presentation may need few slots once in, say, every five/six rounds, since the client will take time to consume (view) the presentation. Hence, a presentation can skip a certain number of disk scheduling rounds before retrieving the next required chunk of data. We present a min-max skip round disk scheduling strategy that can admit multimedia presentations in a flexible manner depending on their composition.
Adaptive Frame Concatenation Mechanisms for QoS in Multi-Rate Wireless Ad Hoc Networks

Providing quality of service (QoS) to users in a wireless ad-hoc network is a key concern for service providers. With the availability of multiple rates in IEEE 802.11a/b/g wireless LANs, it is desirable to improve the network capacity and temporal fairness by sending multiple consecutive frames (also referred as frame concatenation mechanism) over high rate links, as proposed in opportunistic auto rate (OAR). However, OAR does not consider the effect of frame sizes and may yield unsatisfactory performance for high priority multimedia flows transmitting over low rate links. Therefore, a more appropriate frame concatenation strategy and a corresponding service differentiation scheme should be devised to provide better performance for high priority voice/video flows than low priority data flows, under various channel rate scenarios. In this paper, we first analyze the effect of frame size on the performance of OAR. Then, we propose a general concatenation mechanism (GCM), a more accurate frame concatenation mechanism for multi-rate MAC with better fairness. Finally, we propose two mechanisms: adaptive weighted fair frame concatenation mechanism (AWFCM) and adaptive QoS aware frame concatenation mechanism (AQCM), for supporting service differentiation and QoS in multi-rate wireless ad hoc networks. The primary idea is to adjust the number of concatenated frames based on flow weights/priorities, frame sizes, link rates, and network traffic. Simulation results show that the proposed mechanisms achieve desirable performance on supporting multimedia applications in multi-rate wireless ad-hoc networks.
2012 IEEE international symposium on a world of wireless, mobile, and multimedia networks WoWMoM

It is our great pleasure to welcome you to the Thirteenth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2012. Over the years, WoWMoM has emerged to be a flagship forum that brings together researchers from academia, industry, and government laboratories who are involved in various aspects of mobile multimedia networking technologies, ranging from communications platforms to services and applications.
Mobile tracking and resource reservation scheme for cellular networks

Providing and maintaining quality of service (QoS) in cellular networks has been investigated by many researchers. The main aim of all the approaches is to maximize the utilization of available resources like bandwidth. Location technology has helped widely in the conservation of resources. One of the algorithms that make use of the position of the mobile to reserve resources is the shadow clustering scheme. By tracking the mobile more accurately we can help any reservation protocol to reserve resources using its reservation algorithm. In this paper we propose a method to increase the utilization of resources by making more accurate predictions of the location of the mobile. This involves making use of a new mobile tracking scheme which takes tracking the mobile to the intra cellular level as against the exiting tracking at cellular level. The information obtained form this mobile positioning scheme is then passed on to a reservation algorithm. Simulation of the proposed scheme upon two mobility models has been conducted and compared against reservation schemes that uses inter cellular tracking. The mean that the error in determining the position of the mobile is greatly reduced and also the accuracy with which we are able to predict the motion of the user is also increased.