nils-herrmann/clean_scirepeval huggingface dataset:
RADAR INTERFEROMETRY AND ITS APPLICATION TO CHANGES IN THE EARTH ' S SURFACE

Geophysical applications of radar interferometry to measure changes in the Earth's surface have exploded in the early 1990s. This new geodetic technique calculates the interference pattern caused by the difference in phase between two images acquired by a spaceborne synthetic aperture radar at two distinct times. The resulting interferogram is a contour map of the change in distance between the ground and the radar instrument. These maps provide an unsurpassed spatial sampling density (---100 pixels km-2), a competitive precision (---1 cm), and a useful observation cadence (1 pass month-•). They record movements in the crust, perturbations in the atmosphere, dielectric modifications in the soil, and relief in the topography. They are also sensitive to technical effects, such as relative variations in the radar's trajectory or variations in its frequency standard. We describe how all these phenomena contribute to an interferogram. Then a practical summary explains the techniques for calculating and manipulating interferograms from various radar instruments, including the four satellites currently in orbit: ERS-1, ERS-2, JERS-1, and RADARSAT. The next chapter suggests some guidelines for interpreting an interferogram as a geophysical measurement: respecting the limits of the technique, assessing its uncertainty, recognizing artifacts, and discriminating different types of signal. We then review the geophysical applications published to date, most of which study deformation related to earthquakes, volcanoes, and glaciers using ERS-1 data. We also show examples of monitoring natural hazards and environmental alterations related to landslides, subsidence, and agriculture. In addition, we consider subtler geophysical signals such as postseismic relaxation, tidal loading of coastal areas, and interseismic strain accumulation. We conclude with our perspectives on the future of radar interferometry. The objective of the review is for the reader to develop the physical understanding necessary to calculate an interferogram and the geophysical intuition necessary to interpret it.
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model

Semantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence. However, datasets for semantic parsing contain many factoid questions that can be answered from a single web document. In this paper, we propose to evaluate semantic parsing-based question answering models by comparing them to a question answering baseline that queries the web and extracts the answer only from web snippets, without access to the target knowledge-base. We investigate this approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional language, and find that our model obtains reasonable performance (∼35 F1 compared to 41 F1 of state-of-the-art). We find in our analysis that our model performs well on complex questions involving conjunctions, but struggles on questions that involve relation composition and superlatives.
Revisiting Selectional Preferences for Coreference Resolution

Selectional preferences have long been claimed to be essential for coreference resolution. However, they are mainly modeled only implicitly by current coreference resolvers. We propose a dependencybased embedding model of selectional preferences which allows fine-grained compatibility judgments with high coverage. We show that the incorporation of our model improves coreference resolution performance on the CoNLL dataset, matching the state-of-the-art results of a more complex system. However, it comes with a cost that makes it debatable how worthwhile such improvements are.
Fusing laser and vision data with a genetic ICP algorithm

Knowledge about the environment is essential for humanoid and mobile robots to move and act safely. The most intuitive way to perceive information about the environment is through the vision system. However, the accuracy provided by stereo vision is insufficient for many tasks. A more accurate representation is created by a laser range-finder, which delivers no color information. This paper describes a novel approach to merge data obtained by stereo vision and laser range-finders by using a genetic ICP algorithm, which is able to register noisy point clouds with different resolutions and a small overlap. Furthermore, it describes an easy-to-use and robust method to calibrate the extrinsic parameters of two or more laser range-finders.
State of flat-panel display technology and future trends

The rivalry between the cathode-ray tube and flat-panel displays (FPDs) has intensified as performance of some FPDs now exceeds that of that entrenched leader in many cases. Besides the wellknown active-matrix-addressed liquid-crystal display, plasma, organic light-emitting diodes, and liquid-crystal-on-silicon displays are now finding new applications as the manufacturing, process engineering, materials, and cost structures become standardized and suitable for large markets.
Making Sense of Regulations with SBVR

The continuous increase in quantity and depth of regulation following the financial crisis has left the financial industry in dire need of making its compliance assessment activities more effective. The field of AI & Law provides models that, despite being fit for the representation of semantics of requirements, do not share the approach favoured by the industry which relies on business vocabularies such as SBVR. This paper presents Mercury, a solution for representing the requirements and vocabulary contained in a regulatory text (or business policy) in a SME-friendly way, for the purpose of determining compliance. Mercury includes a structured language based on SBVR, with a rulebook, containing the regulative and constitutive rules, and a vocabulary, containing the actions and factors that determine a rule’s applicability and its legal effect. Mercury includes an XML persistence model and is mapped to an OWL ontology called FIRO, enabling semantic applications.
Mobilyzer: An Open Platform for Controllable Mobile Network Measurements

Mobile Internet availability, performance and reliability have remained stubbornly opaque since the rise of cellular data access. Conducting network measurements can give us insight into user-perceived network conditions, but doing so requires careful consideration of device state and efficient use of scarce resources. Existing approaches address these concerns in ad-hoc ways.
 In this work we propose Mobilyzer, a platform for conducting mobile network measurement experiments in a principled manner. Our system is designed around three key principles: network measurements from mobile devices require tightly controlled access to the network interface to provide isolation; these measurements can be performed efficiently using a global view of available device resources and experiments; and distributing the platform as a library to existing apps provides the incentives and low barrier to adoption necessary for large-scale deployments. We describe our current design and implementation, and illustrate how it provides measurement isolation for applications, efficiently manages measurement experiments and enables a new class of experiments for the mobile environment.
PCA vs. ICA: A Comparison on the FERET Data Set

Over the last ten years, face recognition has become a specialized applications area within the field of computer vision. Sophisticated commercial systems have been developed that achieve high recognition rates. Although elaborate, many of these systems include a subspace projection step and a nearest neighbor classifier. The goal of this paper is to rigorously compare two subspace projection techniques within the context of a baseline system on the face recognition task. The first technique is principal component analysis (PCA), a well-known “baseline” for projection techniques. The second technique is independent component analysis (ICA), a newer method that produces spatially localized and statistically independent basis vectors. Testing on the FERET data set (and using standard partitions), we find that, when a proper distance metric is used, PCA significantly outperforms ICA on a human face recognition task. This is contrary to previously
Neural Associative Memory for Dual-Sequence Modeling

Many important NLP problems can be posed as dual-sequence or sequence-tosequence modeling tasks. Recent advances in building end-to-end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis demonstrates that long range dependencies between source and target-sequence can be bridged effectively using Dual AM-RNNs. However, an initial experiment on autoencoding reveals that these benefits are not exploited by the system when learning to solve sequence-to-sequence tasks which indicates that additional supervision or regularization is needed.
Wireless Energy Transfer Using Magnetic Resonance

In 1899, Nikola Tesla, who had devised a type of resonant transformer called the Tesla coil, achieved a major breakthrough in his work by transmitting 100 million volts of electric power wirelessly over a distance of 26 miles to light up a bank of 200 light bulbs and run one electric motor. Tesla claimed to have achieved 95% efficiency, but the technology had to be shelved because the effects of transmitting such high voltages in electric arcs would have been disastrous to humans and electrical equipment in the vicinity. This technology has been languishing in obscurity for a number of years, but the advent of portable devices such as mobiles, laptops, smartphones, MP3 players, etc warrants another look at the technology. We propose the use of a new technology, based on strongly coupled magnetic resonance. It consists of a transmitter, a current carrying copper coil, which acts as an electromagnetic resonator and a receiver, another copper coil of similar dimensions to which the device to be powered is attached. The transmitter emits a non-radiative magnetic field resonating at MHz frequencies, and the receiving unit resonates in that field. The resonant nature of the process ensures a strong interaction between the sending and receiving unit, while interaction with rest of the environment is weak.
F-CNN: An FPGA-based framework for training Convolutional Neural Networks

This paper presents a novel reconfigurable framework for training Convolutional Neural Networks (CNNs). The proposed framework is based on reconfiguring a streaming datapath at runtime to cover the training cycle for the various layers in a CNN. The streaming datapath can support various parameterized modules which can be customized to produce implementations with different trade-offs in performance and resource usage. The modules follow the same input and output data layout, simplifying configuration scheduling. For different layers, instances of the modules contain different computation kernels in parallel, which can be customized with different layer configurations and data precision. The associated models on performance, resource and bandwidth can be used in deriving parameters for the datapath to guide the analysis of design trade-offs to meet application requirements or platform constraints. They enable estimation of the implementation specifications given different layer configurations, to maximize performance under the constraints on bandwidth and hardware resources. Experimental results indicate that the proposed module design targeting Maxeler technology can achieve a performance of 62.06 GFLOPS for 32-bit floating-point arithmetic, outperforming existing accelerators. Further evaluation based on training LeNet-5 shows that the proposed framework achieves about 4 times faster than CPU implementation of Caffe and about 7.5 times more energy efficient than the GPU implementation of Caffe.
Practical, appropriate, empirically-validated guidelines for designing educational games

There has recently been a great deal of interest in the potential of computer games to function as innovative educational tools. However, there is very little evidence of games fulfilling that potential. Indeed, the process of merging the disparate goals of education and games design appears problematic, and there are currently no practical guidelines for how to do so in a coherent manner. In this paper, we describe the successful, empirically validated teaching methods developed by behavioural psychologists and point out how they are uniquely suited to take advantage of the benefits that games offer to education. We conclude by proposing some practical steps for designing educational games, based on the techniques of Applied Behaviour Analysis. It is intended that this paper can both focus educational games designers on the features of games that are genuinely useful for education, and also introduce a successful form of teaching that this audience may not yet be familiar with.
Publish/Subscribe in a mobile enviroment

A publish/subscribe system dynamically routes and delivers events from sources to interested users, and is an extremely useful communication service when it is not clear in advance who needs what information. In this paper we discuss how a publish/subscribe system can be extended to operate in a mobile environment, where events can be generated by moving sensors or users, and subscribers can request delivery at handheld and/or mobile devices. We describe how the publish/subscribe system itself can be distributed across multiple (possibly mobile) computers to distribute load, and how the system can be replicated to cope with failures, message loss, and disconnections.
Spoken Digits Recognition using Weighted MFCC and Improved Features for Dynamic Time Warping

In this paper, we propose novel techniques for feature parameter extraction based on MFCC and feature recognition using dynamic time warping algorithm for application in speaker-independent isolated digits recognition. Using the proposed Weighted MFCC (WMFCC), we achieve low computational overhead for the feature recognition stage since we use only 13 weighted MFCC coefficients instead of the conventional 39 MFCC coefficients including the delta and double delta features. In order to capture the trends or patterns that a feature sequence presents during the alignment process, we compute the local and global features using Improved Features for DTW algorithm (IFDTW), rather than using the pure feature values or their estimated derivatives. The experiments based on TI-Digits corpus demonstrate the effectiveness of proposed techniques leading to higher recognition accuracy of 98.13%.
Discriminative Cellets Discovery for Fine-Grained Image Categories Retrieval

Fine-grained image categories recognition is a challenging task aiming at distinguishing objects belonging to the same basic-level category, such as leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and etc. Most of the existing methods have difficulties to automatically detect discriminative object components. In this paper, we propose a new fine-grained image categorization model that can be deemed as an improved version spatial pyramid matching (SPM). Instead of the conventional SPM that enumeratively conducts cell-to-cell matching between images, the proposed model combines multiple cells into cellets that are highly responsive to object fine-grained categories. In particular, we describe object components by cellets that connect spatially adjacent cells from the same pyramid level. Straightforwardly, image categorization can be casted as the matching between cellets extracted from pairwise images. Toward an effective matching process, a hierarchical sparse coding algorithm is derived that represents each cellet by a linear combination of the basis cellets. Further, a linear discriminant analysis (LDA)-like scheme is employed to select the cellets with high discrimination. On the basis of the feature vector built from the selected cellets, fine-grained image categorization is conducted by training a linear SVM. Experimental results on the Caltech-UCSD birds, the Leeds butterflies, and the COSMIC insects data sets demonstrate our model outperforms the state-of-the-art. Besides, the visualized cellets show discriminative object parts are localized accurately.
New model of semantic similarity measuring in wordnet

This paper presents a new model of measuring semantic similarity in the taxonomy of WordNet. The model takes the path length between two concepts and IC value of each concept as its metric, furthermore, the weight of two metrics can be adapted artificially. In order to evaluate our model, traditional and widely used datasets are used. Firstly, coefficients of correlation between human ratings of similarity and six computational models are calculated, the result shows our new model outperforms their homologues. Then, the distribution graphs of similarity value of 65 word pairs are discussed our model having no faulted zone more centralized than other five methods. So our model can make up the insufficient of other methods which only using one metric(path length or IC value) in their model.
Information Bottleneck for Gaussian Variables

The problem of extracting the relevant aspects of data was pr eviously addressed through the information bottleneck(IB) method, through (soft) clustering one variable while p r serving information about another relevancevariable. The current work extends these ideas to obtain co ntinuous representations that preserve relevant information, rather t han discrete clusters, for the special case of multivariate Gaussian variables. While the general continu ous IB problem is difficult to solve, we provide an analytic solution for the optimal representatio n and tradeoff between compression and relevance for the this important case. The obtained optimal representation is a noisy linear projection to eigenvectors of the normalized regression matrix Σx|yΣ x , which is also the basis obtained in canonical correlation analysis. However, in Gaussian IB , the compression tradeoff parameter uniquely determines the dimension, as well as the scale of ea ch igenvector, through a cascade of structural phase transitions. This introduces a novel in t rpretation where solutions of different ranks lie on a continuum parametrized by the compression lev el. Our analysis also provides a complete analytic expression of the preserved information as a function of the compression (the “information-curve”), in terms of the eigenvalue spectrum of the data. As in the discrete case, the information curve is concave and smooth, though it is made of different analytic segments for each optimal dimension. Finally, we show how the algorithmic the ory developed in the IB framework provides an iterative algorithm for obtaining the optimal G ussian projections.
Automated Detection and Analysis for Android Ransomware

Along with the rapid growth of new science and technology, the functions of smartphones become more and more powerful. Nevertheless, everything has two aspects. Smartphones bring so much convenience for people and also bring the security risks at the same time. Malicious application has become a big threat to the mobile security. Thus, an efficiency security analysis and detection method is important and necessary. Due to attacking of malicious application, user could not use smartphone normally and personal information could be stolen. What is worse, attacking proliferation will impact the healthy growth of the mobile Internet industry. To limit the growing speed of malicious application, the first thing we need to know what malicious application is and how to deal with. Detecting and analyzing their behaviors helps us deeply understand the attacking principle such that we can take effective countermeasures against malicious application. This article describes the basic Android component and manifest, the reason that Android is prevalent and why attacking came in. This paper analyzed and penetrated malicious ransom ware which threats mobile security now with our developed automated analysis approach for such mobile malware detection.
Don't be SCAREd: use SCalable Automatic REpairing with maximal likelihood and bounded changes

Various computational procedures or constraint-based methods for data repairing have been proposed over the last decades to identify errors and, when possible, correct them. However, these approaches have several limitations including the scalability and quality of the values to be used in replacement of the errors. In this paper, we propose a new data repairing approach that is based on maximizing the likelihood of replacement data given the data distribution, which can be modeled using statistical machine learning techniques. This is a novel approach combining machine learning and likelihood methods for cleaning dirty databases by value modification. We develop a quality measure of the repairing updates based on the likelihood benefit and the amount of changes applied to the database. We propose SCARE (SCalable Automatic REpairing), a systematic scalable framework that follows our approach. SCARE relies on a robust mechanism for horizontal data partitioning and a combination of machine learning techniques to predict the set of possible updates. Due to data partitioning, several updates can be predicted for a single record based on local views on each data partition. Therefore, we propose a mechanism to combine the local predictions and obtain accurate final predictions. Finally, we experimentally demonstrate the effectiveness, efficiency, and scalability of our approach on real-world datasets in comparison to recent data cleaning approaches.
On Combining Classifiers

We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions—the sum rule—outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.
A Computational Model of Narrative Generation for Surprise Arousal

This paper describes our effort for a planning-based computational model of narrative generation that is designed to elicit surprise in the reader's mind, making use of two temporal narrative devices: flashback and foreshadowing. In our computational model, flashback provides a backstory to explain what causes a surprising outcome, while foreshadowing gives hints about the surprise before it occurs. Here, we present Prevoyant, a planning-based computational model of surprise arousal in narrative generation, and analyze the effectiveness of Prevoyant. The work here also presents a methodology to evaluate surprise in narrative generation using a planning-based approach based on the cognitive model of surprise causes. The results of the experiments that we conducted show strong support that Prevoyant effectively generates a discourse structure for surprise arousal in narrative.
The Native POSIX Thread Library for Linux

In diesem Bericht wird die neue Implementation von Threads unter Linux behandelt. Die bis jetzt noch eingesetzte Implementation ist veraltet und basiert auf nicht mehr aktuellen Voraussetzungen. Es ist wichtig zuerst die fundamentalen Kenntnisse über ThreadImplementationen zu erhalten und die Probleme der aktuellen Implementation zu erkennen, um die nötigen Änderungen zu sehen. Florian Dürrbaum 14.12.2003 2 FH Aargau Enterprise Computing