nils-herrmann/clean_scirepeval huggingface dataset:
Power control in two-tier femtocell networks

In a two tier cellular network - comprised of a central macrocell underlaid with shorter range femtocell hotspots - cross-tier interference limits overall capacity with universal frequency reuse. To quantify near-far effects with universal frequency reuse, this paper derives a fundamental relation providing the largest feasible cellular Signal-to-Interference-Plus-Noise Ratio (SINR), given any set of feasible femtocell SINRs. We provide a link budget analysis which enables simple and accurate performance insights in a two-tier network. A distributed utility- based SINR adaptation at femtocells is proposed in order to alleviate cross-tier interference at the macrocell from cochannel femtocells. The Foschini-Miljanic (FM) algorithm is a special case of the adaptation. Each femtocell maximizes their individual utility consisting of a SINR based reward less an incurred cost (interference to the macrocell). Numerical results show greater than 30% improvement in mean femtocell SINRs relative to FM. In the event that cross-tier interference prevents a cellular user from obtaining its SINR target, an algorithm is proposed that reduces transmission powers of the strongest femtocell interferers. The algorithm ensures that a cellular user achieves its SINR target even with 100 femtocells/cell-site (with typical cellular parameters) and requires a worst case SINR reduction of only 16% at femtocells. These results motivate design of power control schemes requiring minimal network overhead in two-tier networks with shared spectrum.
Semantic File Systems

A semantic file system is an information storage system that provides flexible associative access to the system's contents by automatically extracting attributes from files with file type specific transducers. Associative access is provided by a conservative extension to existing tree-structured file system protocols, and by protocols that are designed specifically for content based access. Compatiblity with existing file system protocols is provided by introducing the concept of a virtual directory. Virtual directory names are interpreted as queries, and thus provide flexible associative access to files and directories in a manner compatible with existing software. Rapid attribute-based access to file system contents is implemented by automatic extraction and indexing of key properties of file system objects. The automatic indexing of files and directories is called "semantic" because user programmable transducers use information about the semantics of updated file system objects to extract the properties for indexing. Experimental results from a semantic file system implementation support the thesis that semantic file systems present a more effective storage abstraction than do traditional tree structured file systems for information sharing and command level programming.
A Random Sampling Scheme for Path Planning

Several randomized path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot’s configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilistically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the “goodness” of the robot’s free space, we show that the running time only grows as the absolute value of the logarithm of the probability of failure that we are willing to tolerate. We also show that it increases at a reasonable rate as the space goodness degrades. In the last section we suggest directions for future research.
Calibration of Ultrawide Fisheye Lens Cameras by Eigenvalue Minimization

We present a new technique for calibrating ultrawide fisheye lens cameras by imposing the constraint that collinear points be rectified to be collinear, parallel lines to be parallel, and orthogonal lines to be orthogonal. Exploiting the fact that line fitting reduces to an eigenvalue problem in 3D, we do a rigorous perturbation analysis to obtain a practical calibration procedure. Doing experiments, we point out that spurious solutions exist if collinearity and parallelism alone are imposed. Our technique has many desirable properties. For example, no metric information is required about the reference pattern or the camera position, and separate stripe patterns can be displayed on a video screen to generate a virtual grid, eliminating the grid point extraction processing.
The HMM-based speech synthesis system (HTS) version 2.0

A statistical parametric speech synthesis system based on hidden Markov models (HMMs) has grown in popularity over the last few years. This system simultaneouslymodels spectrum, excitation, and duration of speech using context-dependent HMMs and generatesspeechwaveforms from the HMMs themselves. Since December 2002, we have publicly released an open-source software toolkit named HMM-based speech synthesis system (HTS) to provide a research and development platform for the speech synthesis community. In December 2006, HTS version 2.0 was released. This version includes a number of new features which are useful for both speech synthesis researchers and developers. This paper describes HTS version 2.0 in detail, as well as future release plans.
A Kernel Method for the Two-Sample Problem

We propose a framework for analyzing and comparing distributions, allowing us to design statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS). We present two tests based on large deviation bounds for the test statistic, while a third is based on the asymptotic distribution of this statistic. The test statistic can be computed in quadratic time, although efficient linear time approximations are available. Several classical metrics on distributions are recovered when the function space used to compute the difference in expectations is allowed to be more general (eg. a Banach space). We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests.
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts

We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country i took action a toward country j at time t" - known as dyadic events - in order to form and test theories of international relations. We represent these event data as a tensor of counts and develop Bayesian Poisson tensor factorization to infer a low-dimensional, interpretable representation of their salient patterns. We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods. We also provide a comparison of our variational updates to their maximum likelihood counterparts. In doing so, we identify a better way to form point estimates of the latent factors than that typically used in Bayesian Poisson matrix factorization. Finally, we showcase our model as an exploratory analysis tool for political scientists. We show that the inferred latent factor matrices capture interpretable multilateral relations that both conform to and inform our knowledge of international a airs.
Refactoring with Unit Testing: A Match Made in Heaven?

Unit testing is a basic principle of agile development. Its benefits include early defect detection, defect cause localization and removal of fear to apply changes to the code. As such, unit tests seem to be ideal companions during refactoring, as they provide a safety net which enables to quickly verify that behaviour is indeed preserved. In this study we investigate whether having unit tests available during refactoring actually leads to quicker refactorings and more high-quality code after refactoring. For this, we set up a two-group controlled experiment involving 42 participants. Results indicate that having unit tests available during refactoring does not lead to quicker refactoring or to higher-quality code after refactoring.
Classification of copy move forgery and normal images by ORB features and SVM classifier

Today, the characterization of the technological age is done by the digital images spread. They are the most common form of conveying information whether through internet, newspapers, magazines, or scientific journals. They are used as a strong proof of various crimes and as evidence used for various purposes. The modification, capturing or creating of the image has become easier and available with the emergence of means of image editing and processing tools. One of the most important and popular types of image forgery is a copy-move forgery in which an image part is copied and then pasted into the same image that has the intention of hiding something important or showing a false scene. Because the important properties of the copied parts come from the same image, such as brightness, noise, and texture which will be compatible with the entire image that makes more difficult for experts for the detection and distinguishing the alteration. Usually, the detecting copy move forgery conventional techniques suffer severely from the time-consuming problem. The evaluation of the improved method had been done using (150) images that were selected from two different datasets, “CoMoFoD” and “MICC-F2000”. Experimental results show that the improved method can accurately and quickly reveal the doubled regions of a tampered image. In addition, greatly reducing the processing time in comparison to the Khan algorithm, and the accuracy is kept at the same level. Owing to the availability and technological advancement of the image editing sophisticated tools, there is an increase in the loss of authentication in digital images. Thus, this led us to the proposal of different detection techniques that checks whether the digital images are forged or authentic. The specific type of forgery technique is copy move forgery in which widely used research topic is detection under digital image forensics. In this thesis, an enhancement of copy move image forgery classification is done by implementing hybrid features with classification algorithms like SIFT with SVM and EM algorithm and ORB with SVM and EM.The technique works by applying Firstly the DCT on an image and then on a resultant image, SIFT is obtained after applying DCT. A supervised learning method is proposed for classifying a copy-move image forgery of TIFF, JPEG, and BMP. The process starts with reducing the color of the photos. Achieve the accuracy more than 90%.
Random Forests--random Features

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost, but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. These ideas are al;so applicable to regression.
ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home

This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.
Medial temporal lobe damage causes deficits in episodic memory and episodic future thinking not attributable to deficits in narrative construction.

The medial temporal lobe (MTL) makes critical contributions to episodic memory, but its contributions to episodic future thinking remain a matter of debate. By one view, imagining future events relies on MTL mechanisms that also support memory for past events. Alternatively, it has recently been suggested that future thinking is independent of MTL-mediated processes and can be supported by regions outside the MTL. The current study investigated the nature and necessity of MTL involvement in imagining the future and tested the novel hypothesis that the MTL contributes to future thinking by supporting online binding processes related to narrative construction. Human amnesic patients with well characterized MTL damage and healthy controls constructed narratives about (1) future events, (2) past events, and (3) visually presented pictures. While all three tasks place similar demands on narrative construction, only the past and future conditions require memory/future thinking to mentally generate relevant narrative information. Patients produced impoverished descriptions of both past and future events but were unimpaired at producing detailed picture narratives. In addition, future-thinking performance positively correlated with episodic memory performance but did not correlate with picture narrative performance. Finally, future-thinking impairments were present when MTL lesions were restricted to the hippocampus and did not depend on the presence of neural damage outside the MTL. These results indicate that the ability to generate and maintain a detailed narrative is preserved in amnesia and suggest that a common MTL mechanism supports both episodic memory and episodic future thinking.
Graphical Models for Probabilistic and Causal Reasoning

This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network.  1 illustrates a simple yet typical Bayesian network. It describes the causal relationships among the season of the year (X1), whether rain falls (X2) during the season, whether the sprinkler is on (X3) during that season, whether the pavement would get wet (X4), and whether the pavement would be slippery (X5). All variables in this figure are binary, taking a value of either true or false, except the root variable X1 which can take one of four values: Spring, Summer, Fall, or Winter. Here, the absence of a direct link between X1 and X5, for example, captures our understanding that the influence of seasonal variations on the slipperiness of the pavement is mediated by other conditions (e.g., the wetness of the pavement). As this example illustrates, a Bayesian network constitutes a model of the environment rather than, as in many other knowledge representation schemes (e.g., logic, rule-based systems and neural networks), a model of the reasoning process. It simulates, in fact, the causal mechanisms that operate in the environment, and thus allows the investigator to answer a variety of queries, including: associational queries, such as “Having observed A, what can we expect of B?”; abductive queries, such as “What is the most plausible explanation for a given set of observations?”; and control queries; such as “What will happen if we intervene and act on the environment?” Answers to the first type of query depend only on probabilistic
From Event Detection to Storytelling on Microblogs Janani Kalyanam

The problem of detecting events from content published on microblogs has garnered much interest in recent times. In this paper, we address the questions of what happens after the outbreak of an event in terms of how the event gradually progresses and attains each of its milestones, and how it eventually dissipates. We propose a model based approach to capture the gradual unfolding of an event over time. This enables the model to automatically produce entire timeline trajectories of events from the time of their outbreak to their disappearance. We apply our model on the Twitter messages collected about Ebola during the 2014 outbreak and obtain the progression timelines of several events that occurred during the outbreak. We also compare our model to several existing topic modeling and event detection baselines in literature to demonstrate its efficiency.
Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.
A 3.7-kW axial-gap switched-reluctance motor robustly designed by using a mathematical model

An axial-gap switched-reluctance motor (SRM) was designed with high power density by using a mathematical model. To shorten the analysis time by 3D finite element analysis (3D-FEA) to calculate the torque characteristics of the axial-gap SRM, an analytical method was proposed and used for designing the SRM. The key feature of the method is expressing the mutual flux with a mathematical model by employing transient analysis using pre-processed inductance tables. A 3.7-kW prototype axial-gap SRM designed was designed by this method and tested. According to the test results, output density of the prototype SRM is 16% higher than that of conventional induction motors.
Analogy just looks like high level perception: why a domain-general approach to analogical mapping is right

Hofstadter and his colleagues have criticized current accounts of analogy, claiming that such accounts do not accurately capture interactions between processes of representation construction and processes of mapping. They suggest instead that analogy should be viewed as a form of high level perception that encompasses both representation building and mapping as indivisible operations within a single model. They argue speci® cally against SM E, our model of analogical matching, on the grounds that it is modular, and oŒer instead programs such as Mitchell and Hofstadter’ s Copycat as examples of the high level perception approach. In this paper we argue against this position on two grounds. First, we demonstrate that most of their speci® c arguments involving SM E and Copycat are incorrect. Second, we argue that the claim that analogy is high-level perception, while in some ways an attractive metaphor, is too vague to be useful as a technical proposal. We focus on ® ve issues : (1) how perception relates to analogy, (2) how  ̄ exibility arises in analogical processing, (3) whether analogy is a domain-general process, (4) how micro-worlds should be used in the study of analogy, and (5) how best to assess the psychological plausibility of a model of analogy. W e illustrate our discussion with examples taken from computer models embodying both views.
Efficient High-Quality Volume Rendering of SPH Data

High quality volume rendering of SPH data requires a complex order-dependent resampling of particle quantities along the view rays. In this paper we present an efficient approach to perform this task using a novel view-space discretization of the simulation domain. Our method draws upon recent work on GPU-based particle voxelization for the efficient resampling of particles into uniform grids. We propose a new technique that leverages a perspective grid to adaptively discretize the view-volume, giving rise to a continuous level-of-detail sampling structure and reducing memory requirements compared to a uniform grid. In combination with a level-of-detail representation of the particle set, the perspective grid allows effectively reducing the amount of primitives to be processed at run-time. We demonstrate the quality and performance of our method for the rendering of fluid and gas dynamics SPH simulations consisting of many millions of particles.