nils-herrmann/clean_scirepeval huggingface dataset:
View-Based Interpretation of Real-Time Optical Flow for Gesture Recognition

We have developed a real-time, view-based gesture recognition system. Optical flow is estimated and segmented into motion blobs. Gestures are recognized using a rule-based technique based on characteristics of the motion blobs such as relative motion and size. Parameters of the gesture (e.g., frequency) are then estimated using context specific techniques. The system has been applied to create an interactive environment for children.
FlowNet: Learning Optical Flow with Convolutional Networks

Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks CNNs succeeded at. In this paper we construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a large synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.
In vivo evaluation of bacterial cellulose/acrylic acid wound dressing hydrogel containing keratinocytes and fibroblasts for burn wounds

The healing of wounds, including those from burns, currently exerts a burden on healthcare systems worldwide. Hydrogels are widely used as wound dressings and in the field of tissue engineering. The popularity of bacterial cellulose-based hydrogels has increased owing to their biocompatibility. Previous study demonstrated that bacterial cellulose/acrylic acid (BC/AA) hydrogel increased the healing rate of burn wound. This in vivo study using athymic mice has extended the use of BC/AA hydrogel by the addition of human epidermal keratinocytes and human dermal fibroblasts. The results showed that hydrogel loaded with cells produces the greatest acceleration on burn wound healing, followed by treatment with hydrogel alone, compared with the untreated group. The percentage wound reduction on day 13 in the mice treated with hydrogel loaded with cells (77.34 ± 6.21%) was significantly higher than that in the control-treated mice (64.79 ± 6.84%). Histological analysis, the expression of collagen type I via immunohistochemistry, and transmission electron microscopy indicated a greater deposition of collagen in the mice treated with hydrogel loaded with cells than in the mice administered other treatments. Therefore, the BC/AA hydrogel has promising application as a wound dressing and a cell carrier.
MuDeL: a language and a system for describing and generating mutants

Testing is an approach for assessing the quality of a test case suite by analyzing its ability in distinguishing the product under test from a set of alternative products, the so-called mutants. The mutants are generated from the product under test by applying a set of mutant operators, which produce products with slight syntactical differences. The mutant operators are usually based on typical errors that occurs during the software development and can be related to a fault model. In this paper, we propose a language — named MuDeL — for describing mutant operators aiming not only at automating the mutant generation, but also at providing precision and formality to the operator descriptions. The language was designed using concepts that come from transformational and logical programming paradigms, as well as from context-free grammar theory. The language is illustrated with some simple examples. We also describe the mudelgen system, developed to support this language.
Characterizing Large-Scale Click Fraud in ZeroAccess

Click fraud is a scam that hits a criminal sweet spot by both tapping into the vast wealth of online advertising and exploiting that ecosystem's complex structure to obfuscate the flow of money to its perpetrators. In this work, we illuminate the intricate nature of this activity through the lens of ZeroAccess--one of the largest click fraud botnets in operation. Using a broad range of data sources, including peer-to-peer measurements, command-and-control telemetry, and contemporaneous click data from one of the top ad networks, we construct a view into the scale and complexity of modern click fraud operations. By leveraging the dynamics associated with Microsoft's attempted takedown of ZeroAccess in December 2013, we employ this coordinated view to identify "ad units" whose traffic (and hence revenue) primarily derived from ZeroAccess. While it proves highly challenging to extrapolate from our direct observations to a truly global view, by anchoring our analysis in the data for these ad units we estimate that the botnet's fraudulent activities plausibly induced advertising losses on the order of $100,000 per day.
A Fully Projective Formulation to Improve the Accuracy of Lowe's Pose-Estimation Algorithm

Both the original version of David Lowe's innuential and classic algorithm for tracking known objects and a reformulation of it implemented by Ishii et al. rely on (diierent) approximated imaging models. Removing their simplifying assumptions yields a fully pro-jective solution with signiicantly improved accuracy and convergence, and arguably better computation{ time properties.
Applying Business Intelligence Concepts to Medicaid Claim Fraud Detection

U.S. governmental agencies are striving to do more with less. Controlling the costs of delivering healthcare services such as Medicaid is especially critical at a time of increasing program enrollment and decreasing state budgets. Fraud is estimated to steal up to ten percent of the taxpayer dollars used to fund governmentally supported healthcare, making it critical for government authorities to find cost effective methods to detect fraudulent transactions. This paper explores the use of a business intelligence system relying on statistical methods to detect fraud in one state’s existing Medicaid claim payment data. This study shows that Medicaid claim transactions that have been collected for payment purposes can be reformatted and analyzed to detect fraud and provide input for decision makers charged with making the best use of available funding. The results illustrate the efficacy of using unsupervised statistical methods to detect fraud in healthcare-related data.
Exploiting distributed version concurrency in a transactional memory cluster

We investigate a transactional memory runtime system providing scaling and strong consistency, i.e., 1-copy serializability on commodity clusters for both distributed scientific applications and database applications. We introduce a novel page-level distributed concurrency control algorithm, called Distributed Multiversioning (DMV). DMV automatically detects and resolves conflicts caused by data races for distributed transactions accessing shared in-memory data structures. DMV's key novelty is in exploiting the distributed data versions that naturally come about in a replicated cluster in order to avoid read-write conflicts, hence provide scaling. DMV runs conflicting read-only and update transactions in parallel on different replicas instead of using different physical data copies within a single node as in classic multiversioning. In its most general form, DMV can be used to implement a software transactional memory system on a cluster for scaling C++ applications. DMV supports highly multithreaded database applications as well by centralizing updates on a master replica and creating the required page versions for read-only transactions lazily, on a set of slave replicas. We also show that a version-aware scheduling technique can distribute the read-only transactions across the slaves in such a way to minimize version conflicts.In our evaluation, we use DMV as a lightweight approach to scaling a hash table microbenchmark workload and the industry-standard e-commerce workload of the TPC-W benchmark on a commodity cluster. Our measurements show scaling for both benchmarks. In particular, we show near-linear scaling up to 8 transactional nodes for the most common e-commerce workload, the TPC-W shopping mix. We further show that our scaling for the TPC-W e-commerce benchmark compares favorably with that of an existing coarse-grained asynchronous replication technique.
Actions and affordances in syntactic ambiguity resolution.

In 2 experiments, eye movements were monitored as participants followed instructions containing temporary syntactic ambiguities (e.g., "Pour the egg in the bowl over the flour"). The authors varied the affordances of task-relevant objects with respect to the action required by the instruction (e.g., whether 1 or both eggs in the visual workspace were in liquid form, allowing them to be poured). The number of candidate objects that could afford the action was found to determine whether listeners initially misinterpreted the ambiguous phrase ("in the bowl") as specifying a location. The findings indicate that syntactic decisions are guided by the listener's situation-specific evaluation of how to achieve the behavioral goal of an utterance.
SIFT-based local spectrogram image descriptor: a novel feature for robust music identification

Music identification via audio fingerprinting has been an active research field in recent years. In the real-world environment, music queries are often deformed by various interferences which typically include signal distortions and time-frequency misalignments caused by time stretching, pitch shifting, etc. Therefore, robustness plays a crucial role in music identification technique. In this paper, we propose to use scale invariant feature transform (SIFT) local descriptors computed from a spectrogram image as sub-fingerprints for music identification. Experiments show that these sub-fingerprints exhibit strong robustness against serious time stretching and pitch shifting simultaneously. In addition, a locality sensitive hashing (LSH)-based nearest sub-fingerprint retrieval method and a matching determination mechanism are applied for robust sub-fingerprint matching, which makes the identification efficient and precise. Finally, as an auxiliary function, we demonstrate that by comparing the time-frequency locations of corresponding SIFT keypoints, the factor of time stretching and pitch shifting that music queries might have experienced can be accurately estimated.
Efficient Planning in Non-Gaussian Belief Spaces and Its Application to Robot Grasping

The limited nature of robot sensors make many important robo tics problems partially observable. These problems may require the s yst m to perform complex information-gathering operations. One approach to so lving these problems is to create plans inbelief-space , the space of probability distributions over the underlying state of the system. The belief-space plan encodes a st rategy for performing a task while gaining information as necessary. Most approach es to belief-space planning rely upon representing belief state in a particular way (typically as a Gaussian). Unfortunately, this can lead to large errors between the assumed density representation of belief state and the true belief state. Th is paper proposes a new sample-based approach to belief-space planning that has fix ed computational complexity while allowing arbitrary implementations of Bayes filtering to be used to track belief state. The approach is illustrated in the conte xt of a simple example and compared to a prior approach. Then, we propose an applicatio n of the technique to an instance of the grasp synthesis problem where a robot mu st simultaneously localize and grasp an object given initially uncertain obje ct parameters by planning information-gathering behavior. Experimental results ar e presented that demonstrate the approach to be capable of actively localizing and graspi ng boxes that are presented to the robot in uncertain and hard-to-localize config urations.
Dissecting Video Server Selection Strategies in the YouTube CDN

In this paper, we conduct a detailed study of the YouTube CDN with a view to understanding the mechanisms and policies used to determine which data centers users download video from. Our analysis is conducted using week-long datasets simultaneously collected from the edge of five networks - two university campuses and three ISP networks - located in three different countries. We employ state-of-the-art delay-based geolocation techniques to find the geographical location of YouTube servers. A unique aspect of our work is that we perform our analysis on groups of related YouTube flows. This enables us to infer key aspects of the system design that would be difficult to glean by considering individual flows in isolation. Our results reveal that while the RTT between users and data centers plays a role in the video server selection process, a variety of other factors may influence this selection including load-balancing, diurnal effects, variations across DNS servers within a network, limited availability of rarely accessed video, and the need to alleviate hot-spots that may arise due to popular video content.
Bayesian Gaussian Process Classification with the EM-EP Algorithm

Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically related to the value of some latent function at that location. Starting from a Gaussian process prior over this latent function, data are used to infer both the posterior over the latent function and the values of hyperparameters to determine various aspects of the function. Recently, the expectation propagation (EP) approach has been proposed to infer the posterior over the latent function. Based on this work, we present an approximate EM algorithm, the EM-EP algorithm, to learn both the latent function and the hyperparameters. This algorithm is found to converge in practice and provides an efficient Bayesian framework for learning hyperparameters of the kernel. A multiclass extension of the EM-EP algorithm for GPCs is also derived. In the experimental results, the EM-EP algorithms are as good or better than other methods for GPCs or support vector machines (SVMs) with cross-validation
Prediction of air temperature using Multi-layer perceptrons with Levenberg-Marquardt training algorithm

related to the prediction of the air temperature in the region of Meknes in Morocco. Depending on weather parameters, such as atmospheric pressure (Pr), humidity (H), visibility (Vis), wind speed (V), dew point (Tr) and the precipitation (P). The database used contains a history of meteorological parameters covering 3288 days, from 2004 to 2012. This multilayer perceptron type of neuron (MLP) was used to approximate the relationship between these parameters with minimum square error. For a better air temperature prediction, we developed a stochastic neural model. The mean square error (MSE) and the correlation coefficient (R) were used to evaluate the performance of the developed models. The study of these statistical indicators demonstrated that the prediction of the air temperature was powerful with the Levenberg-Marquard algorithm, with architecture [610-1], the Tansig function in the hidden layer and the Purelin function in the output layer.
A Survey on Utilization of Data Mining Approaches for Dermatological (Skin) Diseases Prediction

— Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data.
Vertical Partitioning for Database Design: A Graphical Algorithm

Vertical partitioning is the process of subdividing the attributes of a relation or a record type, creating fragments. Previous approaches have used an iterative binary partitioning method which is based on clustering algorithms and mathematical cost functions. In this paper, however, we propose a new vertical partitioning algorithm using a graphical technique. This algorithm starts from the attribute affinity matrix by considering it as a complete graph. Then, forming a linearly connected spanning tree, it generates all meaningful fragments simultaneously by considering a cycle as a fragment. We show its computational superiority. It provides a cleaner alternative without arbitrary objective functions and provides an improvement over our previous work on vertical partitioning.
Self-Soldering Connectors for Modular Robots

The connection mechanism between neighboring modules is the most critical subsystem of each module in a modular robot. Here, we describe a strong, lightweight, and solid-state connection method based on heating a low melting point alloy to form reversible soldered connections. No external manipulation is required for forming or breaking connections between adjacent connectors, making this method suitable for reconfigurable systems such as self-reconfiguring modular robots. Energy is only consumed when switching connectivity, and the ability to transfer power and signal through the connector is inherent to the method. Soldering connectors have no moving parts, are orders of magnitude lighter than other connectors, and are readily mass manufacturable. The mechanical strength of the connector is measured as 173 N, which is enough to support many robot modules, and hundreds of connection cycles are performed before failure.
Optimizing the CVaR via Sampling

Conditional Value at Risk (CVaR) is a prominent risk measure that is being used extensively in various domains. We develop a new formula for the gradient of the CVaR in the form of a conditional expectation. Based on this formula, we propose a novel sampling-based estimator for the gradient of the CVaR, in the spirit of the likelihood-ratio method. We analyze the bias of the estimator, and prove the convergence of a corresponding stochastic gradient descent algorithm to a local CVaR optimum. Our method allows to consider CVaR optimization in new domains. As an example, we consider a reinforcement learning application, and learn a risksensitive controller for the game of Tetris.
Detection and localization of brain strokes in realistic 3-D human head phantom

Stroke is the second leading cause of death worldwide. The treatment depends on the location and extent of stroke and it should be started within 4.5 hours of the attack. Thus rapid detection is a must. Existing imaging systems do not provide a solution to monitor the patient in real time and not portable, thus requires patient's movement. Addressing these limitations, a portable diagnostic system is proposed in this paper employing monostatic radar approach using only one antenna covering 73% fractional bandwidth centered at 1.85 GHz. A realistic three-dimensional (3-D) head phantom is generated to simulate different stroke conditions. The phantom anatomically holds the structure of a real human head with frequency dispersive electrical properties of respective biological tissues of head that are rigorously fitted with fourth-order Debye model for the simulation environment. A confocal imaging technique is used to map the abnormalities inside the head. The obtained images indicate the potential of the presented technique to detect and locate the position of both hemorrhagic and ischemic strokes.
AUTOMATIC RECONSTRUCTION OF SINGLE TREES FROM TERRESTRIAL LASER SCANNER DATA

The investigation of single trees in a forest is of ecological and economical interest. One aim is to capture the geometric aspects of a tree: the length and diameter of the trunk and individual branches, the change of the radius along the branch and similar measures. These measures can be determined automatically from terrestrial laser scanner data. The conditions for scanning in the forest, but also the irregular structure and surface of trees aggravate the reconstruction process. The branches of the trees are locally modelled by circular cylinders. With the radius, the axis direction and the axis position the main parameters of interest are captured. We describe a set of algorithms for automatically tting and tracking cylinders along branches and reconstructing the entire tree. Especially for coniferous trees the computation of an outer hull, giving the extent in different directions and at different heights is an alternative, as the dense foliage coverage renders a distinction between branches and needles impossible. Examples for the different reconstructions of trees are presented.
A Framework for Structural Risk Minimisation

The paper introduces a framework for studying structural risk minimisation. The model views structural risk minimisation in a PAC context. It then considers the more general case when the hierarchy of classes is chosen in response to the data. This theoretically explains the impressive performance of the maximal margin hyperplane algorithm of Vapnik. It may also provide a general technique for exploiting serendipitous simplicity in observed data to obtain better prediction accuracy from small training sets.
What Is Coefficient Alpha ? An Examination of Theory and Applications

Psychological research involving scale construction has been hindered considerably by a widespread lack of understanding of coefficient alpha and reliability theory in general. A discussion of the assumptions and meaning of coefficient alpha is presented. This discussion is followed by a demonstration of the effects of test length and dimensionality on alpha by calculating the statistic for hypothetical tests with varying numbers of items, numbers of orthogonal dimensions, and average item intercorrelations. Recommendations for the proper use of coefficient alpha are offered.