nils-herrmann/clean_scirepeval huggingface dataset:
Viewport Adaptation-Based Immersive Video Streaming: Perceptual Modeling and Applications

Immersive video offers the freedom to navigate inside virtualized environment. Instead of streaming the bulky immersive videos entirely, a viewport (also referred to as field of view, FoV) adaptive streaming is preferred. We often stream the high-quality content within current viewport, while reducing the quality of representation elsewhere to save the network bandwidth consumption. Consider that we could refine the quality when focusing on a new FoV, in this paper, we model the perceptual impact of the quality variations (through adapting the quantization stepsize and spatial resolution) with respect to the refinement duration, and yield a product of two closed-form exponential functions that well explain the joint quantization and resolution induced quality impact. Analytical model is crossvalidated using another set of data, where both Pearson and Spearman’s rank correlation coefficients are close to 0.98. Our work is devised to optimize the adaptive FoV streaming of the immersive video under limited network resource. Numerical results show that our proposed model significantly improves the quality of experience of users, with about 9.36% BD-Rate (Bjontegaard Delta Rate) improvement on average as compared to other representative methods, particularly under the limited bandwidth.
Geology of the Moscoviense Basin

TheMoscoviense Basin, on the northern portion of the lunar farside, displays topography with a partial peak ring, in addition to rings that are offset to the southeast. These rings do not follow the typical concentric ring spacing that is recognized with other basins, suggesting that they may have formed as a result of an oblique impact or perhaps multiple impacts. In addition to the unusual ring spacing present, the Moscoviense Basin contains diverse mare basalt units covering the basin floor and a few highland mafic exposures within its rings. New analysis of previously mapped mare units suggests that the oldest mare unit is the remnant of the impact melt sheet. The Moscoviense Basin provides a glimpse into the lunar highlands terrain and an opportunity to explore the geologic context of initial lunar crustal development and modification.
Improving literacy in rural India: cellphone games in an after-school program

Literacy is one of the great challenges in the developing world. But universal education is an unattainable dream for those children who lack access to quality educational resources such as well-prepared teachers and schools. Worse, many of them do not attend school regularly due to their need to work for the family in the agricultural fields or households. This work commitment puts formal education far out of their reach. On the other hand, educational games on cellphones hold the promise of making learning more accessible and enjoyable. In our projects 4th year, we reached a stage where we could implement a semester-long pilot on cellphone-based learning. The pilot study took the form of an after-school program in a village in India. This paper reports on this summative learning assessment. While we found learning benefits across the board, it seemed that more of the gains accrued to those children who were better equipped to take advantage of this opportunity. We conclude with future directions for designing educational games that target less well-prepared children in developing regions.
Data augmentation, feature combination, and multilingual neural networks to improve ASR and KWS performance for low-resource languages

This paper presents the progress of acoustic models for lowresourced languages (Assamese, Bengali, Haitian Creole, Lao, Zulu) developed within the second evaluation campaign of the IARPA Babel project. This year, the main focus of the project is put on training high-performing automatic speech recognition (ASR) and keyword search (KWS) systems from language resources limited to about 10 hours of transcribed speech data. Optimizing the structure of Multilayer Perceptron (MLP) based feature extraction and switching from the sigmoid activation function to rectified linear units results in about 5% relative improvement over baseline MLP features. Further improvements are obtained when the MLPs are trained on multiple feature streams and by exploiting label preserving data augmentation techniques like vocal tract length perturbation. Systematic application of these methods allows to improve the unilingual systems by 4-6% absolute in WER and 0.064-0.105 absolute in MTWV. Transfer and adaptation of multilingually trained MLPs lead to additional gains, clearly exceeding the project goal of 0.3 MTWV even when only the limited language pack of the target language is used.
A Case of Human Pulmonary Dirofilariasis in a 48-Year-Old Korean Man

Dirofilariasis is a rare disease in humans. We report here a case of a 48-year-old male who was diagnosed with pulmonary dirofilariasis in Korea. On chest radiographs, a coin lesion of 1 cm in diameter was shown. Although it looked like a benign inflammatory nodule, malignancy could not be excluded. So, the nodule was resected by video-assisted thoracic surgery. Pathologically, chronic granulomatous inflammation composed of coagulation necrosis with rim of fibrous tissues and granulations was seen. In the center of the necrotic nodules, a degenerating parasitic organism was found. The parasite had prominent internal cuticular ridges and thick cuticle, a well-developed muscle layer, an intestinal tube, and uterine tubules. The parasite was diagnosed as an immature female worm of Dirofilaria immitis. This is the second reported case of human pulmonary dirofilariasis in Korea.
Distributed computing with rules of thumb

We present our recent work (ICS 2011) on dynamic environments in which computational nodes, or decision makers, follow simple and unsophisticated rules of behavior (e.g., repeatedly "best replying" to others' actions, and minimizing "regret") that have been extensively studied in game theory and economics. We aim to understand when convergence of the resulting dynamics to an equilibrium point is guaranteed if nodes' interaction is not synchronized (e.g., as in Internet protocols and large-scale markets). We take the first steps of this research agenda. We exhibit a general non-convergence result and consider its implications across a wide variety of interesting and timely applications: routing, congestion control, game theory, social networks and circuit design. We also consider the relationship between classical nontermination results in distributed computing theory and our result, explore the impact of scheduling on convergence, study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics.
2008-01-0201 Route Prediction from Trip Observations

This paper develops and tests algorithms for predicting the end-to-end route of a vehicle based on GPS observations of the vehicle’s past trips. We show that a large portion of a typical driver’s trips are repeated. Our algorithms exploit this fact for prediction by matching the first part of a driver’s current trip with one of the set of previously observed trips. Rather than predicting upcoming road segments, our focus is on making long term predictions of the route. We evaluate our algorithms using a large corpus of real world GPS driving data acquired from observing over 250 drivers for an average of 15.1 days per subject. Our results show how often and how accurately we can predict a driver’s route as a function of the distance already driven.
An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks

Distributed denial of service (DDoS) attacks are serious threats for availability of the internet services. These types of attacks command multiple agents to send a great number of packets to a victim and thus can easily exhaust the resources of the victim. In this paper we propose an anomaly-based DDoS detection method based on the various features of attack packets, obtained from study the incoming network traffic and using of Radial Basis Function (RBF) neural networks to analyze these features. We evaluate the proposed method using our simulated network and UCLA Dataset. The results show that the proposed system can make real-time detection accuracy better than 96% for DDoS attacks.
Structural effects of the antimicrobial peptide maculatin 1.1 on supported lipid bilayers

The interactions of the antimicrobial peptide maculatin 1.1 (GLFGVLAKVAAHVVPAIAEHF-NH2) with model phospholipid membranes were studied by use of dual polarisation interferometry and neutron reflectometry and dimyristoylphosphatidylcholine (DMPC) and mixed DMPC–dimyristoylphosphatidylglycerol (DMPG)-supported lipid bilayers chosen to mimic eukaryotic and prokaryotic membranes, respectively. In DMPC bilayers concentration-dependent binding and increasing perturbation of bilayer order by maculatin were observed. By contrast, in mixed DMPC–DMPG bilayers, maculatin interacted more strongly and in a concentration-dependent manner with retention of bilayer lipid order and structure, consistent with pore formation. These results emphasise the importance of membrane charge in mediating antimicrobial peptide activity and emphasise the importance of using complementary methods of analysis in probing the mode of action of antimicrobial peptides.
Duplicate Record Detection for Database Cleansing

Many organizations collect large amounts of data to support their business and decision making processes. The data collected from various sources may have data quality problems in it. These kinds of issues become prominent when various databases are integrated. The integrated databases inherit the data quality problems that were present in the source database. The data in the integrated systems need to be cleaned for proper decision making. Cleansing of data is one of the most crucial steps. In this research, focus is on one of the major issue of data cleansing i.e. “duplicate record detection” which arises when the data is collected from various sources. As a result of this research study, comparison among standard duplicate elimination algorithm (SDE), sorted neighborhood algorithm (SNA), duplicate elimination sorted neighborhood algorithm (DE-SNA), and adaptive duplicate detection algorithm (ADD) is provided. A prototype is also developed which shows that adaptive duplicate detection algorithm is the optimal solution for the problem of duplicate record detection. For approximate matching of data records, string matching algorithms (recursive algorithm with word base and recursive algorithm with character base) have been implemented and it is concluded that the results are much better with recursive algorithm with word base.
SUSIG: an on-line signature database, associated protocols and benchmark results

We present a new online signature database (SUSIG). The database consists of two parts that are collected using different pressure-sensitive tablets (one with and the other without an LCD display). A total of 100 people contributed to each part, resulting in a database of more than 3,000 genuine signatures and 2,000 skilled forgeries. The genuine signatures in the database are real signatures of the contributors. In collecting skilled forgeries, forgers were shown the signing process on the monitor and were given a chance to practice. Furthermore, for a subset of the forgeries (highly skilled forgeries), this animation was mapped onto the LCD screen of the tablet so that the forgers could trace over the mapped signature. Forgers in this group were also informed of how close they were to the reference signature, so that they could improve their forgery quality. We describe the signature acquisition process and several verification protocols for this database. We also report the performance of a state-of-the-art signature verification system using the associated protocols. The results show that the highly skilled forgery set is significantly more difficult compared to the skilled forgery set, providing researchers with challenging forgeries. The database is available through http://biometrics.sabanciuniv.edu .
Client Classification Policies for SLA Enforcement in Shared Cloud Datacenters

In Utility Computing business model, the owners of the computing resources negotiate with their potential clients to sell computing power. The terms of the Quality of Service (QoS) and the economic conditions are established in a Service-Level Agreement (SLA). There are many scenarios in which the agreed QoS cannot be provided because of errors in the service provisioning or failures in the system. Since providers have usually different types of clients, according to their relationship with the provider or by the fee that they pay, it is important to minimize the impact of the SLA violations in preferential clients. This paper proposes a set of policies to provide better QoS to preferential clients in such situations. The criterion to classify clients is established according to the relationship between client and provider (external user, internal or another privileged relationship) and the QoS that the client purchases (cheap contracts or extra QoS by paying an extra fee). Most of the policies use key features of virtualization: Selective Violation of the SLAs, Dynamic Scaling of the Allocated Resources, and Runtime Migration of Tasks. The validity of the policies is demonstrated through exhaustive experiments.
Deep learning with time-frequency representation for pulse estimation from facial videos

Accurate pulse estimation is of pivotal importance in acquiring the critical physical conditions of human subjects under test, and facial video based pulse estimation approaches recently gained attention owing to their simplicity. In this work, we have endeavored to develop a novel deep learning approach as the core part for pulse (heart rate) estimation by using a common RGB camera. Our approach consists of four steps. We first begin by detecting the face and its landmarks, and thereby locate the required facial ROI. In Step 2, we extract the sample mean sequences of the R, G, and B channels from the facial ROI, and explore three processing schemes for noise removal and signal enhancement. In Step 3, the Short-Time Fourier Transform (STFT) is employed to build the 2D Time-Frequency Representations (TFRs) of the sequences. The 2D TFR enables the formulation of the pulse estimation as an image-based classification problem, which can be solved in Step 4 by a deep Con-volutional Neural Network (CNN). Our approach is one of the pioneering works for attempting real-time pulse estimation using a deep learning framework. We have developed a pulse database, called the Pulse from Face (PFF), and used it to train the CNN. The PFF database will be made publicly available to advance related research. When compared to state-of-the-art pulse estimation approaches on the standard MAHNOB-HCI database, the proposed approach has exhibited superior performance.
Environmental and genetic control of insect abundance and herbivory along a forest elevational gradient

Environmental conditions and plant genotype may influence insect herbivory along elevational gradients. Plant damage would decrease with elevation as temperature declines to suboptimal levels for insects. However, host plants at higher elevations may exhibit traits that either reduce or enhance leaf quality to insects, with uncertain net effects on herbivory. We examined folivory, insect abundance and leaf traits along six replicated elevational ranges in Nothofagus pumilio forests of the northern Patagonian Andes, Argentina. We also conducted a reciprocal transplant experiment between low- and high-elevation sites to test the extent of environmental and plant genetic control on insect abundance and folivory. We found that insect abundance, leaf size and specific leaf area decreased, whereas foliar phosphorous content increased, from low-, through mid- to high-elevation sites. Path analysis indicated that changes in both insect abundance and leaf traits were important in reducing folivory with increasing elevation and decreasing mean temperature. At both planting sites, plants from a low-elevation origin experienced higher damage and supported greater insect loads than plants from a high-elevation origin. The differences in leaf damage between sites were twofold larger than those between plant origins, suggesting that local environment was more important than host genotype in explaining folivory patterns. Different folivore guilds exhibited qualitatively similar responses to elevation. Our results suggest an increase in insect folivory on high-elevation N. pumilio forests under future climate warming scenarios. However, in the short-term, folivory increases might be smaller than expected from insect abundance only because at high elevations herbivores would encounter more resistant tree genotypes.
Restoring highly corrupted images by impulse noise using radial basis functions interpolation

Preserving details in restoring images highly corrupted by impulse noise remains a challenging problem. We proposed an algorithm based on radial basis functions (RBF) interpolation which estimates the intensities of corrupted pixels by their neighbors. In this algorithm, first intensity values of noisy pixels in the corrupted image are estimated using RBFs. Next, the image is smoothed. The proposed algorithm can effectively remove the highly dense impulse noise. Experimental results show the superiority of the proposed algorithm in comparison to the recent similar methods both in noise suppression and detail preservation. Extensive simulations show better results in measure of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), especially when the image is corrupted by very highly dense impulse noise.
Pulp revascularization of immature dog teeth with apical periodontitis.

This study examined the ability of a collagen solution to aid revascularization of necrotic-infected root canals in immature dog teeth. Sixty immature teeth from 6 dogs were infected, disinfected, and randomized into experimental groups: 1: no further treatment; 2: blood in canal; 3: collagen solution in canal, 4: collagen solution + blood, and 5: negative controls (left for natural development). Uncorrected chi-square analysis of radiographic results showed no statistical differences (p >or= 0.05) between experimental groups regarding healing of radiolucencies but a borderline statistical difference (p = 0.058) for group 1 versus group 4 for radicular thickening. Group 2 showed significantly more apical closure than group 1 (p = 0.03) and a borderline statistical difference (p = 0.051) for group 3 versus group 1. Uncorrected chi-square analysis revealed that there were no statistical differences between experimental groups for histological results. However, some roots in each of groups 1 to 4 (previously infected) showed positive histologic outcomes (thickened walls in 43.9%, apical closure in 54.9%, and new luminal tissue in 29.3%). Revascularization of disinfected immature dog root canal systems is possible.
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis

Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluations based on housing data in a major Canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 6.60%.
Macrolide, lincosamide, and streptogramin B resistance in lipophilic Corynebacteria inhabiting healthy human skin.

Corynebacteria exist as part of human skin microbiota. However, under some circumstances, they can cause opportunistic infections. The subject of the study was to examine the macrolide-lincosamide-streptogramin B (MLSB) antibiotic resistance in 99 lipophilic strains of Corynebacterium genus isolated from the skin of healthy men. Over 70% of the tested strains were resistant to erythromycin and clindamycin. All of which demonstrated a constitutive type of MLSB resistance mechanism. In all strains, there were being investigated the erm(A), erm(B), erm(C), erm(X), lin(A), msr(A), and mph(C) genes that could be responsible for the different types of resistance to marcolides, lincosamides, and streptogramin B. In all strains with the MLSB resistance phenotype, the erm(X) gene was detected. None of the other tested genes were discovered. Strains harboring the erm(X) gene were identified using a phenotypic method based on numerous biological and biochemical tests. Identification of the chosen strains was compared with the results of API Coryne, MALDI-TOF MS, and 16S rDNA sequencing methods. Only 7 out of the 23 investigated resistant strains provided successful results in all the used methods, showing that identification of this group of bacteria is still a great challenge. The MLSB resistance mechanism was common in most frequently isolated from healthy human skin Corynebacterium tuberculostearicum and Corynebacterium jeikeium strains. This represents a threat as these species are also commonly described as etiological factors of opportunistic infections.
Anonymous credentials on a standard java card

Secure identity tokens such as Electronic Identity (eID) cards are emerging everywhere. At the same time user-centric identity management gains acceptance. Anonymous credential schemes are the optimal realization of user-centricity. However, on inexpensive hardware platforms, typically used for eID cards, these schemes could not be made to meet the necessary requirements such as future-proof key lengths and transaction times on the order of 10 seconds. The reasons for this is the need for the hardware platform to be standardized and certified. Therefore an implementation is only possible as a Java Card applet. This results in severe restrictions: little memory (transient and persistent), an 8-bit CPU, and access to hardware acceleration for cryptographic operations only by defined interfaces such as RSA encryption operations.
 Still, we present the first practical implementation of an anonymous credential system on a Java Card 2.2.1. We achieve transaction times that are orders of magnitudes faster than those of any prior attempt, while raising the bar in terms of key length and trust model. Our system is the first one to act completely autonomously on card and to maintain its properties in the face of an untrusted terminal. In addition, we provide a formal system specification and share our solution strategies and experiences gained and with the Java Card.