Miracle-dz/UniFAR-FTUs huggingface dataset:
"This is why we play"

Professional sports constitute an important part of people's modern life. People spend substantial amounts of time and money supporting their favorite players and teams, and sometimes even riot after games. However, how team performance affects fan behavior remains understudied at a large scale. As almost every notable professional team has its own online fan community, these communities provide great opportunities for investigating this research question. In this work, we provide the first large-scale characterization of online fan communities of professional sports teams. Since user behavior in these online fan communities is inherently connected to game events and team performance, we construct a unique dataset that combines 1.5M posts and 43M comments in NBA-related communities on Reddit with statistics that document team performance in the NBA. We analyze the impact of team performance on fan behavior both at the game level and the season level. First, we study how team performance in a game relates to user activity during that game. We find that surprise plays an important role: the fans of the top teams are more active when their teams lose and so are the fans of the bottom teams in an unexpected win. Second, we study fan behavior over consecutive seasons and show that strong team performance is associated with fans of low loyalty, likely due to "bandwagon fans. " Fans of the bottom teams tend to discuss their team's future such as young talents in the roster, which may help them stay optimistic during adversity. Our results not only contribute to understanding the interplay between online sports communities and offline context but also provide significant insights into sports management.
Robust Dwt-Svd Based Blind Watermarking for Dibr 3D Video

Watermarking has been supplicate as a tool for the preservation of cognitive property rights of multimedia contents. Because of their digital nature, multimedia documents can be mimeographed, reformed, metamorphosed, and promulgated very effortlessly. In this ambience, it is indispensable to burgeon a system for copyright protection, preservation against mimeograph, and validation of contents. In this case, a watermark data is embedded into the actual multimedia content in such a way that it is long-lasting to the content itself. Later on, such watermark can be extracted to manifest ownership to pursue the distribution of the patent toil through the network. In this developed watermarking, predominantly concentrated on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Apart from this, it also prevents the copyright of virtual views which is generated by using a technique called Depth-Image-Based-Rendering. In this approach, secret data is inserted in the actual center vision using two mathematical transform, DWT and SVD. Then, multiple vision are automatically produced from the watermarked middle view and their corresponding profundity map at the content provider side. Finally watermark will be able to removed from all the three views in a sightless vogue without using the actual content of all the three views. In this proposed watermarking strategy is much better strong to image compression, noise addition and geometric distortion.
Understanding meta-population trends of the Australian fur seal, with insights for adaptive monitoring

Effective ecosystem-based management requires estimates of abundance and population trends of species of interest. Trend analyses are often limited due to sparse or short-term abundance estimates for populations that can be logistically difficult to monitor over time. Therefore it is critical to assess regularly the quality of the metrics in long-term monitoring programs. For a monitoring program to provide meaningful data and remain relevant, it needs to incorporate technological improvements and the changing requirements of stakeholders, while maintaining the integrity of the data. In this paper we critically examine the monitoring program for the Australian fur seal (AFS) Arctocephalus pusillus doriferus as an example of an ad-hoc monitoring program that was co-ordinated across multiple stakeholders as a range-wide census of live pups in the Austral summers of 2002, 2007 and 2013. This 5-yearly census, combined with historic counts at individual sites, successfully tracked increasing population trends as signs of population recovery up to 2007. The 2013 census identified the first reduction in AFS pup numbers (14,248 live pups, -4.2% change per annum since 2007), however we have limited information to understand this change. We analyse the trends at breeding colonies and perform a power analysis to critically examine the reliability of those trends. We then assess the gaps in the monitoring program and discuss how we may transition this surveillance style program to an adaptive monitoring program than can evolve over time and achieve its goals. The census results are used for ecosystem-based modelling for fisheries management and emergency response planning. The ultimate goal for this program is to obtain the data we need with minimal cost, effort and impact on the fur seals. In conclusion we identify the importance of power analyses for interpreting trends, the value of regularly assessing long-term monitoring programs and proper design so that adaptive monitoring principles can be applied.
Power and Conjugately Matched High Band UWB Power Amplifier

A common source RF amplifier can be designed to be either power or conjugately matched at the output but not both, since the required load impedances are distinct. In this paper, we have shown that using a drain-gate feedback network brings the power and conjugate match impedances closer together, up to a point where they coincide at a cost of slightly decreased efficiency. We have thoroughly analyzed the output power, return loss, and efficiency tradeoffs and developed a complete design methodology. As a design example, a broadband feedback class-A amplifier has been designed and fabricated in a 130-nm RFCMOS process. The amplifier achieves 7-dBm peak output power and maximum power efficiency of about 20% in the frequency range from 6 to 9 GHz. Input and output return losses are better than 8.5 and 9.5 dB, respectively.
Prediction of therapeutic response to pregabalin in subjects with neuropathic pain

Abstract Objective: To evaluate four models based on potential predictors for achieving a response to pregabalin treatment for neuropathic pain (NeP). Methods: In total, 46 pain studies were screened, of which 27 NeP studies met the criteria for inclusion in this analysis. Data were pooled from these 27 placebo-controlled randomized trials to assess if baseline characteristics (including mean pain and pain-related sleep interference [PRSI] scores), early clinical response during weeks 1–3 of treatment (change from baseline in pain and PRSI scores), and presence of treatment-emergent adverse events (AEs) were predictive of therapeutic response. Therapeutic response was defined as a ≥30% reduction from baseline in either pain and/or PRSI scores at week 5 with supplemental analyses to predict pain outcomes at weeks 8 and 12. Predictors of Patient Global Impression of Change (PGIC) were also evaluated. Four models were assessed: Random Forest, Logistic Regression, Naïve Bayes, and Partial Least Squares. Results: The number of pregabalin-treated subjects in the training/test datasets, respectively, were 2818/1407 (30% pain analysis), 2812/1405 (30% sleep analysis), and 2693/1345 (PGIC analysis). All four models demonstrated consistent results, and the most important predictors of treatment outcomes at week 5 and pain outcomes at weeks 8 and 12 were the reduction in pain score and sleep score in the first 1–3 weeks. The presence or absence of the most common AEs in the first 1–3 weeks was not correlated with any treatment outcome. Conclusions: Subjects with an early response to pregabalin are more likely to experience an end-of-treatment response.
Design of Miniaturized Vertical Split-Ring Resonator Based on Coupled Coplanar Waveguide

In this letter, a vertical split-ring resonator (VSRR) based on a coupled coplanar waveguide (CCPW) is proposed to reduce the longitudinal length of a VSRR. The resonant condition of the proposed resonator is obtained using the transmission line theory and the odd-mode analysis. The longitudinal length of the resonator is reduced using the CCPW. From full-wave simulations, the relationship between the loaded bandwidth and design parameters is investigated. The proposed design to miniaturize the resonator was fabricated and measured, and it is verified that the effective longitudinal length of the proposed VSRR based on CCPW is reduced by 50% compared with the previous VSRR.
Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning

Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multiple data resources to identify reliable protein communities that have biological significance and improve the performance of community detection methods for large-scale PPI networks. In this paper, we propose a Multi-source Learning based Protein Community Detection (MLPCD) algorithm by integrating Gene Expression Data (GED) and a parallel solution of MLPCD using cloud computing technology. GED under different conditions is integrated with the original PPI network to reconstruct a Weighted-PPI network. To flexibly identify protein communities of different scales, we define community modularity and functional cohesion measurements and detect protein communities from WPPI. In addition, we compare the detected communities with known protein complexes and evaluate the function enrichment of protein functional modules using Gene Ontology annotations. We implement a parallel version of MLPCD on the Apache Spark platform to enhance the performance of the algorithm. Extensive experimental results indicate the superiority and notable advantages of the MLPCD algorithm over the relevant algorithms in terms of accuracy and performance.
Causal Explanation Analysis on Social Media

Understanding causal explanations - reasons given for happenings in one’s life - has been found to be an important psychological factor linked to physical and mental health. Causal explanations are often studied through manual identification of phrases over limited samples of personal writing. Automatic identification of causal explanations in social media, while challenging in relying on contextual and sequential cues, offers a larger-scale alternative to expensive manual ratings and opens the door for new applications (e.g. studying prevailing beliefs about causes, such as climate change). Here, we explore automating causal explanation analysis, building on discourse parsing, and presenting two novel subtasks: causality detection (determining whether a causal explanation exists at all) and causal explanation identification (identifying the specific phrase that is the explanation). We achieve strong accuracies for both tasks but find different approaches best: an SVM for causality prediction (F1 = 0.791) and a hierarchy of Bidirectional LSTMs for causal explanation identification (F1 = 0.853). Finally, we explore applications of our complete pipeline (F1 = 0.868), showing demographic differences in mentions of causal explanation and that the association between a word and sentiment can change when it is used within a causal explanation.
Domain Adaptation for Gaussian Process Classification

Traditional machining learning method aims at using the labeled data or unlabeled data to train a mathematic model then it can be used to predict the unlabeled data for Data mining problem, but it requires that the data which be trained should have same distribution with the predicting data. For the real world datasets, it is hard to get enough training datasets which has the same distribution. Thus, how to train a good mathematic model by using different distribution data is crucial problem, and the researchers using the probability view to solve transfer classification problem is relative less. In this paper, we propose a transfer classification algorithm based on the Gaussian Process model, which can be used to solve the homogeneous transfer classification problem. We use the probability theory to propose a novel classification transfer learning model based on the Gaussian Process (GP) model. We experiment on the synthetic and realworld datasets and compare to other method, the result has verified the effectiveness of our approach.
An Emperical Study: Relevance Feedback in Information Retrieval Systems

This paper examines the way of relevance feedback issue in persistent presentation space with regards to media information or data retrieval. Accentuation is put on investigating the unlikeness of issue and looking at the presumptions, executions, and benefits of different solutions in the literary works. This paper shows the study of relevance feedback techniques that has been utilized as a part of past research, suggests a use of different query remodeling mechanism that can be utilized in many retrieval frameworks or systems, and generates some rules for proficient outline of retrieval system’s element i.e. relevance feedback.
QoE-Aware Mobile VR HAS Cache Management With Coding Helper

Virtual reality (VR) video has attracted substantial research interests with the explosion of mobile VR devices in recent years. In this paper, we propose a novel cache management architecture with coding helper to meet the VR’s high bit rate requirement and the multi-rate feature in the HTTP adaptive streaming (HAS) transmission. The coding helper has not only storage capacity just as traditional caching helper, but also video coding function to encode the VR video into different rates. In this new architecture of VR HAS cache management, we consider the problem that how to distribute the bit rate of different VR videos in a certain cache size by taking into account the quality of experience (QoE) in different situations. In order to decrease the computing time of VR video service and guarantee a high level QoE as well, we propose a new maximum QoE increase (MQI) cache management method to obtain the solution of the optimal problem in multi-video and multi-cache (MVMC) situation. Furthermore, we propose a scheduling algorithm of coding helper to decrease the backhaul traffic and develop four different request scenarios in the experiments. Simulation results are finally provided to show that the coding helper scheme has about 16%–100% gain of usefulness than the traditional caching helper scheme. The proposed MQI cache allocation algorithm has more than 50% decreasing of computing time and almost has the same QoE compared with the exhaustive search. The framework can provide useful guidelines to distribute rate of VR video in cache over wireless networks and get better QoE.
Simulation centres in German hospitals and their organisational aspects: Expert survey on drivers and obstacles

Background: Simulation trainings offer the opportunity to replicate parts of or entire processes of medical care in a controlled environment. Some hospitals operate simulation centres dedicated to training their staff. Which organisational factors support or impede the implementation of such units in hospitals? Methods: In an exploratory survey experts and decision makers in German hospitals were interviewed. The answers were evaluated qualitatively in order to identify patterns in argumentation. Among the eleven participants were practitioners heading simulation centres (n=4), managers or executives in charge of finances (n=2), medical directors or head physicians with disciplinary responsibility for medical personnel (n=3), and researchers who deal with simulation trainings in medicine (n=2). The interview partners were guaranteed confidentiality in order to allow for them to speak freely about the obstacles and weaknesses in their respective organisations. Results: The interviewees showed a very heterogeneous picture of the simulation trainings in their respective hospitals in terms of content as well as target groups. Asked about purpose and benefit of simulation centres, subgroups of the interviewees gave various arguments. Financing is comprised of subsidies, fees from external participants, and of vocational training budgets. Several obstacles for the implementation were mentioned such as insufficient error culture, deficient interaction of quality and risk management, high equipment costs, or staffing levels that are too low to schedule entire teams for vocational trainings. Conclusions: Patterns in argumentation could be identified that support the implementation and operation of simulation centres in hospitals. Yet strikingly enough there were no coherent arguments i.e. there was no uniform reasoning of aim and purpose of simulation centres. Furthermore, the survey indicated the need to approach quality- and risk management more holistically wherefore simulation centres could offer laboratory environments. All in all, the survey indicates that the assessment of success and economic benefits of simulation centres haven’t yet been examined thoroughly.
A Hybrid Recommendation Algorithm Based on Heuristic Similarity and Trust Measure

In this paper, we propose a hybrid collaborative filtering recommendation algorithm based on heuristic similarity and trust measure, in order to alleviate the problem of data sparsity, cold start and trust measure. Firstly, a new similarity measure is implemented by weighted fusion of multiple similarity influence factors obtained from the rating matrix, so that the similarity measure becomes more accurate. Then, a user trust relationship computing model is implemented by constructing the user's trust network based on the trust propagation theory. On this basis, a SIMT collaborative filtering algorithm is designed which integrates trust and similarity instead of the similarity in traditional collaborative filtering algorithm. Further, an improved K nearest neighbor recommendation based on clustering algorithm is implemented for generation of a better recommendation list. Finally, a comparative experiment on FilmTrust dataset shows that the proposed algorithm has improved the quality and accuracy of recommendation, thus overcome the problem of data sparsity, cold start and trust measure to a certain extent.
Energy Efficiency Analysis of Elliptic Curve Based Cryptosystems

Energy consumption is an important factor for any cryptoscheme, implemented on devices having limited energy resource. In this paper, we analyze the energy consumption during the Diffie-Hellman key exchange implemented on both supersingular and ordinary elliptic curves. The former protocol is Supersingular Isogeny based Diffie-Hellman (SIDH) and the latter is Elliptic Curve based Diffie-Hellman (ECDH) respectively. Implementations are executed on 64 bit Intel Skylake processor. The energy consumption is analysed for the classical bit security levels of 128 and 192. In this paper, a detailed comparison of power and energy consumption by elliptic curve point addition and doubling operations is presented for affine and standard projective coordinates. Projective coordinates based elliptic curve operations are found to be around 50 to 60 times more energy efficient. We then analyze SIDH and ECDH, implemented using projective coordinates. Our results show that, SIDH consumes around 37 to 47 times more energy in comparison to ECDH for the above mentioned bit security levels.
Authentication of IoT Device and IoT Server Using Secure Vaults

Internet of Things is a topic of much interest and, in last few years, security of the IoT systems is a field of tremendous research activities. Mutual authentication between IoT devices and IoT servers is an important part of secure IoT systems. Single password-based authentication mechanisms, which are widely used, are vulnerable to side-channel and dictionary attacks. In this paper, we present a multi-key (or multi-password) based mutual authentication mechanism. In our approach, the shared secret between the IoT server and the IoT device is called secure vault, which is a collection of equal sized keys. Initial contents of the secure vault are shared between the server and the IoT device and contents of the secure vault change after every successful communication session. We have implemented this mechanism on an Arduino device to prove our algorithm is feasible on IoT devices with memory and computational power constraints.
A Cartesian B-Spline Vehicle Model for Extended Object Tracking

In this paper a novel representation of the contour of an spatially extended object is proposed, which is tailored to vehicles with an unknown size and orientation that are tracked based on measurements from an automotive Light Detection and Ranging (LIDAR). We deploy quadratic uniform periodic B-Splines to directly represent a star-convex shape approximation of the object in Cartesian space. In contrast to previous approaches that work in polar space, we introduce a new walk parameter to model the contour function of an object such that the shapes parameters are well defined and lie within the same space as the measurements. A major advantage of the approach is that a scaling of the length and the width can be performed independently by scaling the basis points of the Splines.
VegaStar: An Illegal Domain Detection System on Large-Scale Video Traffic

Today online video is growing and becoming increasingly popular on the Web. It is no secret that illegal content is now a one-click-away from everyone, including children and minors. Intelligent video analysis methods can help to automatically detect and isolate questionable content in media. Unfortunately, these methods are hugely costly, and affecting public privacy. In this paper, we present an illegal domain detection system on large-scale video traffic, VegaStar. Using metadata of over 5 million URLs of video, VegaStar: (i) provides lexical and behavior characteristics of video domain names, (ii) proposes a model to detect illegal video domains constructed by twelve feature sets, (iii) detects website domains hosting illegal video content even before the videos are being downloaded, and (iv) understand different CDNs and cloud providers that host content for a particular resource. We conduct extensive experimental analysis and the result shows that the proposed model can classify domains with accuracy approximately 90% by cross validation experiments on Random Forest. We argue that VegaStar represents an important development in the field of video traffic identification, and it can be significantly improve the efficiency of former methods.
Chiral Material With Tunable Double Plates

A double slab with chiral structures based on three-segment cranks is analyzed for linearly polarized and normal incident waves. Both slabs are equal and are constituted by a two-dimensional lattice of four same-handedness metallic cranks. The slabs are parallel, with the unit cells perfectly aligned, and each layer is built on a standard FR4 board. Using a free-wave experimental setup and numerical simulations, the transmission and reflection coefficients were determined under normal incidence of linearly polarized waves. The retrieved values for the rotation angle, chirality parameter, refractive index, and ellipticity show a clear chiral behavior for a spacing lower than the slab width. It has also found that as the distance between the two layers increases, the resonant frequency also increases, and this dependence is close to exponential. Based on these observations, a system composed of two chiral slabs can be used as a tunable rotator or a polarization filter.