Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing and Evaluating a Mesh Simplification Algorithm for Virtual Reality

With the increasing accessibility of the mobile head-mounted displays (HMDs), mobile virtual reality (VR) systems are finding applications in various areas. However, mobile HMDs are highly constrained with limited graphics processing units (GPUs) and low processing power and onboard memory. Hence, VR developers must be cognizant of the number of polygons contained within their virtual environments to avoid rendering at low frame rates and inducing simulator sickness. The most robust and rapid approach to keeping the overall number of polygons low is to use mesh simplification algorithms to create low-poly versions of pre-existing, high-poly models. Unfortunately, most existing mesh simplification algorithms cannot adequately handle meshes with lots of boundaries or nonmanifold meshes, which are common attributes of many 3D models. In this article, we present QEM4VR, a high-fidelity mesh simplification algorithm specifically designed for VR. This algorithm addresses the deficiencies of prior quadric error metric (QEM) approaches by leveraging the insight that the most relevant boundary edges lie along curvatures while linear boundary edges can be collapsed. Additionally, our algorithm preserves key surface properties, such as normals, texture coordinates, colors, and materials, as it preprocesses 3D models and generates their low-poly approximations offline. We evaluated the effectiveness of our QEM4VR algorithm by comparing its simplified-mesh results to those of prior QEM variations in terms of geometric approximation error, texture error, progressive approximation errors, frame rate impact, and perceptual quality measures. We found that QEM4VR consistently yielded simplified meshes with less geometric approximation error and texture error than the prior QEM variations. It afforded better frame rates than QEM variations with boundary preservation constraints that create unnecessary lower bounds on overall polygon count reduction. Our evaluation revealed that QEM4VR did not fair well in terms of existing perceptual distance measurements, but human-based inspections demonstrate that these algorithmic measurements are not suitable substitutes for actual human perception. In turn, we present a user-based methodology for evaluating the perceptual qualities of mesh simplification algorithms.
MFPR

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users’ behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs), which are both useful for improving recommendations. However, as far as we are concerned, no existing works have integrated both explicit and multiple implicit feedback simultaneously. Therefore, we propose a unified and flexible model, named Multiple Feedback-based Personalized Ranking (MFPR), to make full use of multiple feedback, which uses a personalized ranking framework. To train model MFPR, we design an algorithm to generate ordered item pairs as labeled data, with consideration of both rating scores and multiple implicit feedback. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model. With the integration of multiple feedback, MFPR significantly improves recommendation performance.
Cold Tolerance of Photosynthetic Electron Transport System Is Enhanced in Wheat Plants Grown Under Elevated CO2

The effects of CO2 elevation on sensitivity of photosynthetic electron transport system of wheat in relation to low temperature stress are unclear. The performance of photosynthetic electron transport system and antioxidant system in chloroplasts was investigated in a temperature sensitive wheat cultivar Lianmai6 grown under the combination of low temperature (2 days at 2/−1°C in the day/night) and CO2 elevation (800 μmol l−1). It was found that CO2 elevation increased the efficiency of photosynthetic electron transport in wheat exposed to low temperature stress, which was related to the enhanced maximum quantum yield for electron transport beyond QA and the increased quantum yield for reduction of end electron acceptors at the PSI acceptor side in plants under elevated CO2. Also, under low temperature, the activities of ATPases, ascorbate peroxidase, and catalase in chloroplasts were enhanced in wheat under elevated CO2. It suggested that the cold tolerance of photosynthetic electron transport system is enhanced by CO2 elevation.
Exploiting Map Topology Knowledge for Context-Predictive Multi-Interface Car-to-Cloud Communication

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability.
Learning Acoustic Features from the Raw Waveform for Automatic Speech Recognition

Automatic speech recognition (ASR) usually is performed by using hand crafted preprocessing, which extracts relevant information from the speech waveform, while reducing the redundancy of the resulting feature vectors. Prominent examples are the Mel frequency cepstral co-eﬃcients (MFCCs) or the Gammatone (GT) ﬁlter bank, originally designed for the use in Gaussian mixture hidden Markov models. However, the successful introduction of neural network (NN) acoustic models has raised the following question: can preprocessing become part of the acoustic modeling and training, taking unprocessed waveforms as direct input? Recent work shows that indeed a fully connected feed-forward NN, is able to learn the feature extraction as part of the acoustic model to a large extent. Introducing convolutional layers in the ﬁrst stages of the NN further closed the performance gap to hand crafted preprocessing. Improvements, even for multi-channel speech input, are reported on top of manually designed preprocessing, using large amounts of training data for a proprietary task. In this work, waveform based ASR modeling and training is investigated and analyzed for a publicly available medium sized data set, namely the CHiME-4 data set, which supplies real multichannel noisy data for training and evaluation.
A Translational Framework of Educational Neuroscience in Learning Disorders

Neuroimaging has undergone enormous progress during the last two and a half decades. The combination of neuroscientific methods and educational practice has become a focus of interdisciplinary research in order to answer more applied questions. In this realm, conditions that hamper learning success and have deleterious effects in the population – such as learning disorders (LD) – could especially profit from neuroimaging findings. At the moment, however, there is an ongoing debate about how far neuroscientific research can go to inform the practical work in educational settings. Here, we put forward a theoretical translational framework as a method of conducting neuroimaging and bridging it to education, with a main focus on dyscalculia and dyslexia. Our work seeks to represent a theoretical but mainly empirical guide on the benefits of neuroimaging, which can help people working with different aspects of LD, who need to act collaboratively to reach the full potential of neuroimaging. We provide possible ideas regarding how neuroimaging can inform LD at different levels within our multidirectional framework, i.e., mechanisms, diagnosis/prognosis, training/intervention, and community/education. In addition, we discuss methodological, conceptual, and structural limitations that need to be addressed by future research.
A Diagrammatic Study of Gauge and Gravity Amplitude Relations

We review modern methods of scattering amplitude computations, beginning with color-decomposition that decomposes the amplitude into a colordependent and a color-independent part of partial color-ordered amplitudes. We then show how a full amplitude can be described through the color-factor, propagator structure, and kinematic numerator of cubic color-ordered diagrams. The color-factors obey a Lie algebra and as such satisfy the Jacobi identity. We are able to impose the color-kinematics duality that states that the kinematic numerators also obey this identity. Because of this it is possible to write down sets of Jacobi equations for the numerator through their diagrammatic expression. These can be solved for a set of master numerators through which all other numerators can be expressed linearly. We find such solutions for the tree-level, one-loop, and two-loop diagrams for any number of particles. SAMMANFATTNING Vi ger en genomg̊ang av moderna metoder som används vid beräkningar av spridningsamplituder. Först beskriver vi färg-dekomposition (eng: colordecomposition) där amplituden delas upp i en färgberoende del och en färgoberoende del best̊aende av partiella färgdordnade amplituder. Därefter visar vi hur en fullständig amplitud kan beskrivas genom färgfaktorn, propageringsstrukturen, samt den kinematiska täljaren för kubiska färgordnade diagram. D̊a färgfaktorerna följer en liealgebra s̊a uppfyller de Jacobi identiteten. Vi kan införa den färg-kinematiska dualiteten som säger att även de kinematiska täljarna uppfyller denna identitet. Som en följd av detta är det möjligt att skriva ned ekvationssytem best̊aende av Jacobi ekvationer för täljarna genom deras diagrammatiska representation. Ekvationssystemen kan lösas för ett set av s̊a kallade master täljare genom vilka alla andra täljare kan beskrivas linjärt. Vi hittar s̊adana lösningar för diagrammen p̊a träd, en loop, samt tv̊a loop niv̊aerna som h̊aller oavsett antalet partiklar. CONTENTS
Semi-supervised Learning: Fusion of Self-supervised, Supervised Learning, and Multimodal Cues for Tactical Driver Behavior Detection

In this paper, we presented a preliminary study for tactical driver behavior detection from untrimmed naturalistic driving recordings. While supervised learning based detection is a common approach, it suffers when labeled data is scarce. Manual annotation is both time-consuming and expensive. To emphasize this problem, we experimented on a 104-hour real-world naturalistic driving dataset with a set of predefined driving behaviors annotated. There are three challenges in the dataset. First, predefined driving behaviors are sparse in a naturalistic driving setting. Second, the distribution of driving behaviors is long-tail. Third, a huge intra-class variation is observed. To address these issues, recent self-supervised and supervised learning and fusion of multimodal cues are leveraged into our architecture design. Preliminary experiments and discussions are reported.
Kitting in the Wild through Online Domain Adaptation

Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working conditions are needed. Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms. In this work we focus on robotic kitting in unconstrained scenarios. As a first contribution, we present a new visual dataset for the kitting task. Differently from standard object recognition datasets, we provide images of the same objects acquired under various conditions where camera, illumination and background are changed. This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified. Our second contribution is a novel online adaptation algorithm for deep models, based on batch-normalization layers, which allows to continuously adapt a model to the current working conditions. Differently from standard domain adaptation algorithms, it does not require any image from the target domain at training time. We benchmark the performance of the algorithm on the proposed dataset, showing its capability to fill the gap between the performances of a standard architecture and its counterpart adapted offline to the given target domain.
CoRec: a co-training approach for recommender systems

In Recommender Systems, a large amount of labeled data must be available beforehand to obtain good predictions. However, labeled data are often limited and expensive to obtain, since labeling typically requires human expertise, time, and labor. This paper proposes a framework, named CoRec, which is based on a co-training approach that drives two recommenders to agree with each other's predictions to generate their own. We used three publicly available datasets from movies, jokes and books domains, as well as two well-known recommender algorithms, to demonstrate the efficiency of the approach under different configurations. The experiments show that better accuracy can be obtained when recommender algorithms are simultaneously co-trained from multiple views to make predictions.
Preferred Interaction Styles for Human-Robot Collaboration Vary Over Tasks With Different Action Types

How do humans want to interact with collaborative robots? As robots become more common and useful not only in industry but also in the home, they will need to interact with humans to complete many varied tasks. Previous studies have demonstrated that autonomous robots are often more efficient and preferred over those that need to be commanded, or those that give instructions to humans. We believe that the types of actions that make up a task affect the preference of participants for different interaction styles. In this work, our goal is to explore tasks with different action types together with different interaction styles to find the specific situations in which different interaction styles are preferred. We have identified several classifications for table-top tasks and have developed a set of tasks that vary along two of these dimensions together with a set of different interaction styles that the robot can use to choose actions. We report on results from a series of human-robot interaction studies involving a PR2 completing table-top tasks with a human. The results suggest that people prefer robot-led interactions for tasks with a higher cognitive load and human-led interactions for joint actions.
Nonlocal feature transform with iterative refinement for video saliency detection

This paper proposes an effective approach to detect video saliency in unconstrained videos. Our approach explores mutual information among nonlocal frames, and feature differences between salient regions and backgrounds. First, motion saliency is computed by superpixel-level motion distinction from frame-level's. Second, we extract definite foreground and background samples. Each sample is described by high dimensional features. In human perception, salient regions have distinctive features from backgrounds such as color and texture. We attempt to learn optimal linear coefficients to separate the salient regions and backgrounds based on these samples. Finally, image filtering combined with generalized boundary is performed to refine the saliency map. Our approach yields high accurate saliency maps with well-defined boundaries. Quantitative and qualitative experiments are carried out on three benchmark video datasets, which show that our approach achieves state-of-the-art performance in video saliency detection.
Evaluation of Microhardness of Mineral Trioxide Aggregate After Immediate Placement of Different Coronal Restorations: An In Vitro Study

Objectives: The purpose of this research was to evaluate the effect of immediate placement of different restorative materials in comparison with a temporary restoration on the surface microhardness of mineral trioxide aggregate (MTA). Materials and Methods: Access cavities were prepared in 40 extracted human molars, and a 3-mm layer of MTA was placed in the pulp chamber. The samples were divided into eight groups (n=5). Ten minutes after the MTA placement, two groups were restored with Zonalin temporary restoration, while the other six groups were restored with glass-ionomer cement (GIC), resin-modified glass-ionomer (RMGI), or resin-based composite. In each group, the Vickers microhardness (VMH) of MTA was determined after 7 and 21 days. Data were entered into SPSS 17 software program and were analyzed by two-way analysis of variance (ANOVA). The significance level was set at 5%. Results: The type of restorative materials had a statistically significant effect on the microhardness of MTA (P=0.002). However, the microhardness of MTA was neither significantly influenced by the timing of final restoration (P=0.246) nor by the time-material interaction (P=0.116). Conclusions: Based on the results of the present study and by considering the limitations of laboratory studies, it is recommended to postpone the placement of final restorations until the underlying MTA is completely set. Otherwise, in the clinical conditions in which early covering of MTA is recommended, sufficient moist-curing and hydration should be guaranteed by selecting a restorative material with the lowest hydrophilic interaction energy.
To reveal or not to reveal: balancing user-centric social benefit and privacy in online social networks

Online Social Network (OSN) profiles help users to create first impressions on other users and therefore lead to various social benefits. However, users can become the victims of privacy harms such as identity theft, stalking or discrimination due to the personal data revealed in these profiles. So they have to carefully select the privacy settings for their profile attributes, keeping in mind this trade-off between privacy and social benefit. Since a profile consists of several attributes and users usually do not fully understand how the revelation of different attribute combinations can lead to privacy harms, this task is not easy. Without any support, privacy concerned users may take decisions that lead to sub-optimal social benefits or expose them to privacy risks or both. Therefore, in this paper, we develop a user-friendly model, based on Integer Programming (IP), to aid in this decision process. More precisely, our model provides an OSN user with easy-to-implement suggestions about the privacy settings of his profile attributes such that he can achieve the maximum social benefit while protecting himself from all or at least some major privacy risks. We propose methods to evaluate the privacy risks based on harm trees and the social benefits based on existing studies on the benefits of data sharing in OSNs. They form the founding pillars of our model.
Measuring structural similarity between RDF graphs

In the latest years, there has been a huge effort to deploy large amounts of data, making it available in the form of RDF data thanks, among others, to the Linked Data initiative. In this context, using shared ontologies has been crucial to gain interoperability, and to be able to integrate and exploit third party datasets. However, using the same ontology does not suffice to successfully query or integrate external data within your own dataset: the actual usage of the vocabulary (e.g., which concepts have instances, which properties are actually populated and how, etc.) is crucial for these tasks. Beingable to compare different RDF graphs at the actual usage level would indeed help in such situations. Unfortunately, the complexity of graph comparison is an obstacle to the scalability of many approaches. In this article, we present our structural similarity measure, designed to compare structural similarity of low-level data between two different RDF graphs according to the patterns they share. To obtain such patterns, we leverage a data mining method (KRIMP) which allows to extract the most descriptive patterns appearing in a transactional database. We adapt this method to the particularities of RDF data, proposing two different conversions for an RDF graph. Once we have the descriptive patterns, we evaluate how much two graphs can compress each other to give a numerical measure depending on the common data structures they share. We have carried out several experiments to show its ability to capture the structural differences of actual vocabulary usage.
Remove RATs from your code: automated optimization of resource inefficient database writes for mobile applications

Developers strive to build feature-filled apps that are responsive and consume as few resources as possible. Most of these apps make use of local databases to store and access data locally. Prior work has found that local database services have become one of the major drivers of a mobile device's resource consumption. In this paper we propose an approach to reduce the energy consumption and improve runtime performance of database operations in Android apps by optimizing inefficient database writes. Our approach automatically detects database writes that happen within loops and that will trigger inefficient autocommit behaviors. Our approach then uses additional analyses to identify those that are optimizable and rewrites the code so that it is more efficient. We evaluated our approach on a set of marketplace Android apps and found it could reduce the energy and runtime of events containing the inefficient database writes by 25% to 90% and needed, on average, thirty-six seconds to analyze and transform each app.
Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot

We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise.
Research on Hotspots Finding in Indoor Space Based on Regression Analysis

Finding hotspots in indoor space plays an important role in getting popular locations and building efficient path planning. Previous hotspots studies in indoor space have been usually built upon single-feature (e.g., density or residence time) which cannot cover the hotspots, inevitably causing inaccuracy. For example, a location with high density may be an entrance and the location is not the hotspots we want to get. To address this issue, in this paper we propose an approach to find hotspots with multi-feature in indoor trajectory data by extracting four hotspots features including visit times, residence time, density and location weight. Then, we use the theory of machine learning to analyze. we compare three regression models to obtain a better model and adjust parameters to get accurate results. Then we compared our method with three basic methods, the experimental results show that our method can get higher accuracy in hotspots finding which also provide a new method for prediction of hotspots.