nils-herrmann/clean_scirepeval huggingface dataset:
Discovering Multidimensional Motifs in Physiological Signals for Personalized Healthcare

Personalized diagnosis and therapy requires monitoring patient activity using various body sensors. Sensor data generated during personalized exercises or tasks may be too specific or inadequate to be evaluated using supervised methods such as classification. We propose multidimensional motif (MDM) discovery as a means for patient activity monitoring, since such motifs can capture repeating patterns across multiple dimensions of the data, and can serve as conformance indicators. Previous studies pertaining to mining MDMs have proposed approaches that lack the capability of concurrently processing multiple dimensions, thus limiting their utility in online scenarios. In this paper, we propose an efficient real-time approach to MDM discovery in body sensor generated time series data for monitoring performance of patients during therapy. We present two alternative models for MDMs based on motif co-occurrences and temporal ordering among motifs across multiple dimensions, with detailed formulation of the concepts proposed. The proposed method uses an efficient hashing based record to enable speedy update and retrieval of motif sets, and identification of MDMs. Performance evaluation using synthetic and real body sensor data in unsupervised motif discovery tasks shows that the approach is effective for 1) concurrent processing of multidimensional time series information suitable for real-time applications, 2) finding unknown naturally occurring patterns with minimal delay, and 3) tracking similarities among repetitions, possibly during therapy sessions.
Feature extraction method for video based Human action recognitions: Extended Optical Flow algorithm

This paper focuses on the issue of improving the quality of low level 2D feature extraction for human action recognition. For instance, existing algorithms such as the Optical Flow algorithm detects noisy and irrelevant features because of its lack of ground truth data sets for complex scenes. For these features, it is difficult to extract data such as coordinate positions of the features, velocity and the direction of the moving objects, and the differential data information between different frames. Extracting such low level feature data is one of the major steps involved in video based Human action recognition. The paper proposes an extended Optical Flow algorithm focusing on human actions. This uses a Frame Jump technique along with thresholding of unwanted features to overcome the problems due to complex scenes. Frame Jump restricts to detecting only useful features by removing other features detected by the existing Optical Flow algorithm. In addition to the above, it also elucidates the integration of the proposed technique with other feature extraction algorithms.
Integration of Motion Capture and EMG data for Classifying the Human Motions

Three dimensional motion capture facility is a powerful tool for quantitative and qualitative assessment of multi-joint external movements. Electro-myograph (EMG) signals give the physiologic information of muscles while doing motions. In this paper, our objective is to integrate these two different bio-medical data together and to extract precise and accurate feature information for classifying the human motions. When both forms of data are integrated and analyzed together; the information achieved will be immensely useful to quantify the complex human motions for medical reasons or sport performances. These biological quantifications of biomechanical data, are useful for gait analysis and several orthopedic applications, such as joint mechanics, prosthetic designs, and sports medicines. Vie different dimensionality reduction approaches such Integral of Absolute value and Weighted Singular Value Decomposition are used to extract the preliminary features from EMG and motion capture data respectively. On combining these feature vectors, fuzzy clustering such as Fuzzy c-means (FCM) is performed on these vectors that are mapped as the points in multi-dimensional feature space. We get the degree of memberships with every cluster for each mapped point. This extracted information is used as the final feature vectors for classifying the human motions.
Describing a view alignment framework in 3D Tele-immersion systems

A collaborative 3D Tele-immersion (3DTI) system consists of multiple sensor devices sending streams of data to and from each other. We consider the data streams produced from Microsoft Kinect cameras here. A single camera produces large volumes of data every second. A large bandwidth and low network delay is required to support the streaming of large amounts of data in near real time in order to provide a good quality of experience (QoE), High network delay over the Internet causes a view disparity between the multiple sites and an inter-stream disparity between two streams from the same site. Both these issues hinder the QoE. We address these issues here and provide our solutions for it. We touch upon an interpolation based scheme which uses redundant data from the Kinect sensors to only send minimal amounts of data every second. We introduce a framework to put this interpolation scheme to effective use and to measure and minimize the view and inter-stream disparities with multiple sites using a time vector based solution.
Distributed Haptic Interactions with Physically Based 3D Deformable Models over Lossy Networks

Researchers have faced great challenges when simulating complicated 3D volumetric deformable models in haptics-enabled collaborative/cooperative virtual environments (HCVEs) due to the expensive simulation cost, heavy communication load, and unstable network conditions. When general network services are applied to HCVEs, network problems such as packet loss, delay, and jitter can cause severe visual distortion, haptic instability, and system inconsistency. In this paper, we propose a novel approach to support haptic interactions with physically based 3D deformable models in a distributed virtual environment. Our objective is to achieve real-time sharing of deformable and force simulations over general networks. Combining linear modal analysis and corotational methods, we can effectively simulate physical behaviors of 3D objects, even for large rotational deformations. We analyze different factors that influence HCVEs' performance and focus on exploring solutions for streaming over lossy networks. In our system, 3D deformation can be described by a fairly small amount of data (several KB) using accelerations in the spectral domain, so that we can achieve low communication load and effective streaming. We develop a loss compensation and prediction algorithm to correct the errors/distortions caused by network problem, and a force prediction method to simulate force at users' side to ensure the haptic stability, and the visual and haptic consistency. Our system works well under both the client-server and the peer-to-peer distribution structures, and can be easily extended to other topologies. In addition to theoretical analysis, we have tested the proposed system and algorithms under various network conditions. The experimental results are remarkably good, confirming the effectiveness, robustness, and validity of our approach.
Resource negotiation for collaborative multimedia presentations

Distributed multimedia document systems and distributed video servers are examples of multimedia presentations involving collaboration among multiple information sources. In such applications, objects have to be retrieved from their sources and presented to users according to specified temporal relationships. Object retrieval in these collaborative applications is influenced by the presentation times, durations and network throughput available to their sources. Replication of objects amongst the set of collaborating systems gives a choice for object retrieval. We present a graph-search based algorithm for computing and negotiating throughput requirements of collaborating multimedia presentations with replicated objects. This algorithm optimizes the number of cached objects (that have already been played) for handling operations like reverse presentation.
Fault Detection Framework for Video Surveillance Systems

We consider cameras whose outputs do not reflect true scenes as faulty cameras. To build a fault detection video surveillance system without using additional hardware devices, we use the video outputs from cameras to do self-checking. We study two categories of faults: spatial faults and temporal faults, and reduce the two sub-problems into graph theoretical problems on two graphs (surveillance sharing graph (SSG) and surveillance partitioning graph (SPG)). We prove a theoretical upper bound for the spatial fault detection, and develop two algorithms for detecting the two types of faults respectively.Then, we integrate both in a framework, which is capable of the following: given the outputs from a video surveillance system, it isolates cameras which are faulty or suspected to be faulty. It gives warnings with types of faults, locations, detection confidence. Our experiments confirm the effectiveness of the framework's methodologies.
Motion Capture With Ellipsoidal Skeleton Using Multiple Depth Cameras

This paper introduces a novel motion capturing framework which works by minimizing the fitting error between an ellipsoid based skeleton and the input point cloud data captured by multiple depth cameras. The novelty of this method comes from that it uses the ellipsoids equipped with the spherical harmonics encoded displacement and normal functions to capture the geometry details of the tracked object. This method is also integrated with a mechanism to avoid collisions of bones during the motion capturing process. The method is implemented parallelly with CUDA on GPU and has a fast running speed without dedicated code optimization. The errors of the proposed method on the data from Berkeley Multimodal Human Action Database (MHAD) are within a reasonable range compared with the ground truth results. Our experiment shows that this method succeeds on many challenging motions which are failed to be reported by Microsoft Kinect SDK and not tested by existing works. In the comparison with the state-of-art marker-less depth camera based motion tracking work our method shows advantages in both robustness and input data modality.
Scheduling multimedia information delivery over unicast wireless channels

Wireless communication channels are generally characterized by the availability of low bandwidth. Hence, the exchange of multimedia information is normally done over a single or unicast channel. Multimedia information presentation can have certain constraints, such as precedence of one page of information with respect to another or the time of availability of information. Multimedia information delivery needs to be scheduled based on these constraints. The author models this scheduling problem using a linear programming approach. We suggest the use of approximation algorithms proposed by L.A. Hall et al. (1997) for this purpose. We also present the use of online approximation algorithms for scheduling multimedia information delivery when their availability varies with time. We also present our initial implementation experience.
Presentation Planning for Distributed VoD Systems

A distributed video-on-demand (VoD) system is one where a collection of video data is located at dispersed sites across a computer network. In a single site environment, a local video server retrieves video data from its local storage device. However, in distributed VoD systems, when a customer requests a movie from the local server, the server may need to interact with other servers located across the network. In this paper, we present different types of presentation plans that a local server can construct in order to satisfy a customer request. Informally speaking, a presentation plan is a temporally synchronized sequence of steps that the local server must perform in order to present the requested movie to the customer. This involves obtaining commitments from other video servers, obtaining commitments from the network service provider, as well as making commitments of local resources, while keeping within the limitations of available bandwidth, available buffer, and customer data consumption rates. Furthermore, in order to evaluate the quality of a presentation plan, we introduce two measures of optimality for presentation plans: minimizing wait time for a customer and minimizing access bandwidth which, informally speaking, specifies how much network/disk bandwidth is used. We develop algorithms to compute three different optimal presentation plans that work at a block level, or at a segment level, or with a hybrid mix of the two, and compare their performance through simulation experiments. We have also mathematically proven effects of increased buffer or bandwidth and data replications for presentation plans which had previously been verified experimentally in the literature.
A novel method for post-surgery face recognition using sum of facial parts recognition

Plastic surgery is becoming more and more commonplace today due to its increasing acceptance in society and its cost-affordability. This in turn has led to the need for developing highly accurate post-surgery face recognition techniques, a problem space which differs significantly from traditional face recognition. In this paper we first conduct a statistical study to show that facial plastic surgery operations correlate with a desire to conform to a golden ratio with respect to the human face. We then apply this knowledge, with the notion of considering a face in terms of the sum of its parts, to propose a novel face recognition technique. The proposed technique is then evaluated against well known datasets, and as per our experiments achieves a recognition rate of 85.35%, which significantly outperforms other state of the art techniques.
An on-line repository for embedded software

The use of off-the-shelf components (COTS) can significantly reduce the time and cost of developing large-scale software systems. However, there are some difficult problems with the component-based approach. First, the developers have to be able to effectively retrieve components. This requires the developers to have an extensive knowledge of available components and how to retrieve them. After identifying the components, the developers also face a steep learning curve to master the use of these components. We are developing an On-line Repository for Embedded Software (ORES) to facilitate component management and retrieval. In this paper, we address the issues of designing software repository systems to assist users in obtaining appropriate components and learning to understand and use the components efficiently. We use an ontology to construct an abstract view of the organization of the components in ORES. The ontology structure facilitates repository browsing and effective search. We also develop a set of tools to assist with component comprehension, including a tutorial manager and a component explorer.
Similarity measure for multi-attribute data [haptic data recognition]

Efficient recognition of haptic data such as 3D motion capture data and sign language sensory data can have wide applications in the interactive computer animation and sign language automatic translation areas. For this purpose, we propose a similarity measure for multi-attribute haptic data, a new form of multimedia signal. The proposed similarity measure, based on singular value decomposition, captures the most important features of the signal data, allows for different signal generating rates and reasonable variations in similar signals. Experiments with real life and synthetic data demonstrate that the proposed similarity measure can capture the similarities of motions with different speeds and different lengths and can have up to 100% recognition rates.
Quantifying the Makeup Effect in Female Faces and Its Applications for Age Estimation

In this paper, a comprehensive statistical study of makeup effect on facial parts (skin, eyes, and lip) is conducted first. According to the statistical study, a method to detect whether makeup is applied or not based on input facial image is proposed, then the makeup effect is further quantified as Young Index (YI) for female age estimation. An age estimator with makeup effect considered is presented in this paper. Results from the experiments find that with the makeup effect considered, the method proposed in this paper can improve accuracy by 0.9-6.7% in CS (Cumulative Score) and 0.26-9.76 in MAE (Mean of Absolute Errors between the estimated age and the ground truth age labeled or acquired from the data) comparing with other age estimation methods.
A study on lidar data forensics

3D LiDAR (Light Imaging Detection and Ranging) data has recently been used in a wide range of applications such as vehicle automation and crime scene reconstruction. Decision making in such applications is highly dependent on LiDAR data. Thus, it becomes crucial to authenticate the data before using it. Though authentication of 2D digital images and video has been widely studied, the area of 3D data forensic is relatively unexplored. In this paper, we investigate and identify three possible attacks on the LiDAR data. We also propose two novel forensic approaches as a countermeasure for such attacks and study their effectiveness. The first forensic approach utilises the density consistency check while the second method leverages the occlusion effect for revealing the forgery. Experimental results demonstrate the effectiveness of the proposed forgery attacks and raise the awareness against unauthenticated use of LiDAR data. The performance analyses of the proposed forensic approaches indicate that the proposed methods are very efficient and provide the detection accuracy of more than 95% for certain kinds of forgery attacks. While the forensic approach is unable to handle all forgery attacks, the study motivates to explore more sophisticated forensic methods for LiDAR data.
3D content fingerprinting

Fingerprint is a set of features that uniquely characterizes a video. The aim of content fingerprinting is to determine the duplicate videos over the Internet. In this paper, a method for content fingerprinting of Depth-Image-Based-Rendering (DIBR) 3D videos is proposed. The proposed method is two pronged approach: (i) histogram based global fingerprint and (ii) keypoint based local fingerprint. Though global fingerprint is fast and robust towards DIBR 3D pre-processing, it is not robust against severe distortions such as change in brightness. To make the proposed method robust against such severe distortions, we have complemented the global fingerprint with widely used keypoint based local fingerprint. Experimental results show that the proposed two pass method as robust as keypoint based local fingerprinting method. Additionally, the proposed method improves the video matching time of keypoint based local fingerprint method by 60% to 100%.
Haptic-enabled interactive rendering of deformable objects based on shape matching

In this paper, we propose a novel framework to simulate 3D deformation of flexible objects in real time with haptic interaction as well as force rendering. The deformation is computed using the shape matching method, which is a meshless geometry-based approach and well known for the simplicity and robustness. We introduce new mechanism named Constraint Particle Coupling for the real time deformable interaction with haptic devices. When haptic tool collides with the soft object during the simulation, the nearest position on the object's surface to the haptic tool is identified and a new mass particle is generated so that, the user always `hits' a particle. Another particle is further added to preserve the position of the local mass centroid. The corresponding feedback force can be naturally calculated as the derivative of the local deformation energy with respect to the displacement of the collision particle. The material stiffness can also be adjusted and edited interactively by modifying the local particle mass density making the framework more flexible.
Quantifying and Improving User Quality of Experience in Immersive Tele-Rehabilitation

3D Tele-Immersion (3DTI) environments are emerging as a new medium for human interactions and collaborations in the areas of education, sports training, physical medicine and rehabilitation. By adding a tactile element to a visually centered 3DTI environment, such applications can be made even more engaging. But it also opens up a few challenges in terms of fusing the visual and tactile data streams in a synchronous way. In this paper we describe a 3DTI Tele-Rehabilitation system with Microsoft Kinect cameras and hap tic devices. We describe some of the challenges we face in providing as well as quantifying a good quality of experience (QoE) in this system. We propose a set of solutions that: (i) improve the user's QoE (by using multi-modal prediction for handling latencies, better synchronization that accounts for the global state of the system, etc.), (ii) quantify the QoE (by designing a controlled virtual environment and by defining appropriate user QoE metrics for immersive tele-rehabilitation). The experimental results show a marked improvement in the performance of the system, consequently improving the user-experience. This is also verified by the results of the user performance study.
A Comprehensive Approach for Streaming 3D Progressive Meshes

Fast and efficient streaming of detailed 3D model over lossy network has long been a challenge, al-though progressive compression techniques were pro-posed long time ago. One reason is that packet loss occurring in unreliable networks is highly unpredictable, and leads to connectivity inconsistency and distortions. In this paper, we address this problem by proposing a receiver-based loss tolerance scheme based on a prediction technique. Our method works without introducing protection bits and retransmission. We stream mesh refinement data on reliable and unreliable networks separately so as to reduce the transmission delay as well as to obtain a satisfactory decompression result. The tests indicate that the decompression is completed quickly, suggesting that it is a practical solution. Moreover, the proposed prediction technique achieves a good approximation of the original mesh with low distortion.