nils-herrmann/clean_scirepeval huggingface dataset:
Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks

Deep convolutional neural networks have achieved great success in computer vision and many other areas. They automatically extract translational-invariant spatial features and integrate with neural network-based classifier. This letter investigates the suitability and potential of deep convolutional neural network in supervised classification of polarimetric synthetic aperture radar (POLSAR) images. The multilooked POLSAR data in the format of coherency or covariance matrix is first converted into a normalized 6-D real feature vector. The six-channel real image is then fed into a four-layer convolutional neural network tailored for POLSAR classification. With two cascaded convolutional layers, the designed deep neural network can automatically learn hierarchical polarimetric spatial features from the data. Two experiments are presented using the AIRSAR data of San Francisco, CA, and Flevoland, The Netherlands. Classification result of the San Francisco case shows that slant built-up areas, which are conventionally mixed with vegetated area in polarimetric feature space, can now be successfully distinguished after taking into account spatial features. Quantitative analysis with respect to ground truth information available for the Flevoland test site shows that the proposed method achieves an accuracy of 92.46% in classifying the considered 15 classes. Such results are comparable with the state of the art.
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks. These models appear promising for applications such as language modeling and machine translation. However, they scale poorly in both space and time as the amount of memory grows — limiting their applicability to real-world domains. Here, we present an end-to-end differentiable memory access scheme, which we call Sparse Access Memory (SAM), that retains the representational power of the original approaches whilst training efficiently with very large memories. We show that SAM achieves asymptotic lower bounds in space and time complexity, and find that an implementation runs 1,000⇥ faster and with 3,000⇥ less physical memory than non-sparse models. SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring 100,000s of time steps and memories. As well, we show how our approach can be adapted for models that maintain temporal associations between memories, as with the recently introduced Differentiable Neural Computer.
COMPARISON AND EVALUATION OF FEATURE POINT DETECTORS

Low-level feature extraction is the first step in any image analysis procedure and is essential for the performance of stereo vision and object recognition systems. Research concerning the detection of corners, blobs and circular or point like features is particularly rich and many procedures have been proposed in the literature. In this paper, several frequently used methods and some novel ideas are tested and compared. We measure the performance of the detectors under the criteria of their detection and repeatability rate as well as the localization accuracy. We present a short review of the major interest point detectors, propose some improvements and describe the experimental setup used for our comparison. Finally, we determine which detector leads to the best results and show that it satisfies the criteria specified above.
Language modeling by variable length sequences: theoretical formulation and evaluation of multigrams

The multigram model assumes that language can be described as the output of a memoryless source that emits variable-length sequences of words. The estimation of the model parameters can be formulated as a Maximum Likelihood estimation problem from incomplete data. We show that estimates of the model parameters can be computed through an iterative Expectation-Maximization algorithm and we describe a forward-backward procedure for its implementation. We report the results of a systematical evaluation of multi-grams for language modeling on the ATIS database. The objective performance measure is the test set perplexity. Our results show that multigrams outperform conventional n-grams for this task.
Spectral Networks and Deep Locally Connected Networks on Graphs

Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for lowdimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures.
Unsupervised Nearest Neighbors Clustering With Application to Hyperspectral Images

We address the problem of unsupervised clustering of multidimensional data when the number of clusters is not known a priori. The proposed iterative approach is a stochastic extension of the kNN density-based clustering (KNNCLUST) method which randomly assigns objects to clusters by sampling a posterior class label distribution. In our approach, contextual class-conditional distributions are estimated based on a k nearest neighbors graph, and are iteratively modified to account for current cluster labeling. Posterior probabilities are also slightly reinforced to accelerate convergence to a stationary labeling. A stopping criterion based on the measure of clustering entropy is defined thanks to the Kozachenko-Leonenko differential entropy estimator, computed from current class-conditional entropies. One major advantage of our approach relies in its ability to provide an estimate of the number of clusters present in the data set. The application of our approach to the clustering of real hyperspectral image data is considered. Our algorithm is compared with other unsupervised clustering approaches, namely affinity propagation (AP), KNNCLUST and Non Parametric Stochastic Expectation Maximization (NPSEM), and is shown to improve the correct classification rate in most experiments.
Dcell: a scalable and fault-tolerant network structure for data centers

A fundamental challenge in data center networking is how to efficiently interconnect an exponentially increasing number of servers. This paper presents DCell, a novel network structure that has many desirable features for data center networking. DCell is a recursively defined structure, in which a high-level DCell is constructed from many low-level DCells and DCells at the same level are fully connected with one another. DCell scales doubly exponentially as the node degree increases. DCell is fault tolerant since it does not have single point of failure and its distributed fault-tolerant routing protocol performs near shortest-path routing even in the presence of severe link or node failures. DCell also provides higher network capacity than the traditional tree-based structure for various types of services. Furthermore, DCell can be incrementally expanded and a partial DCell provides the same appealing features. Results from theoretical analysis, simulations, and experiments show that DCell is a viable interconnection structure for data centers.
Learning with Opponent-Learning Awareness

Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multiagent reinforcement learning, but also can be extended to hierarchical reinforcement learning, generative adversarial networks and decentralised optimization. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results. We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes an additional term that accounts for the impact of one agent’s policy on the anticipated parameter update of the other agents. Preliminary results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners’ dilemma (IPD), while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods. Applied to infinitely repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents can successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the likelihood ratio policy gradient estimator, making the method suitable for model-free reinforcement learning. This method thus scales to large parameter and input spaces and nonlinear function approximators. We also apply LOLA to a grid world task with an embedded social dilemma using deep recurrent policies and opponent modelling. Again, by explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest. Our code is available at github.com/alshedivat/lola.
The Dos and Don'ts of Client Authentication on the Web

Client authenticationhasbeena continuoussourceof problemson theWeb. Althoughmany well-studiedtechniquesexist for authentication, Websitescontinueto use extremely weak authenticationschemes,especiallyin non-enterprise nvironmentssuchasstorefronts. These weaknesses oftenresultfrom carelessuseof authenticators within Web cookies. Of the twenty-seven siteswe investigated,we weakenedthe client authenticationon two systems,gainedunauthorizedaccesson eight, and extractedthesecretkey usedto mint authenticatorsfrom one. We provide a descriptionof the limitations, requirements,andsecuritymodelsspecificto Webclientauthentication. This includesthe introductionof the interrogative adversary, a surprisinglypowerful adversarythat canadapti vely querya Website. We proposea setof hintsfor designinga secureclient authenticationscheme.Usingthesehints,wepresenthe designand analysisof a simple authenticationscheme secureagainstforgeriesby the interrogati ve adversary. In conjunctionwith SSL, our schemeis secureagainst forgeriesby theactiveadversary.
Human selection of mnemonic phrase-based passwords

Textual passwords are often the only mechanism used to authenticate users of a networked system. Unfortunately, many passwords are easily guessed or cracked. In an attempt to strengthen passwords, some systems instruct users to create mnemonic phrase-based passwords. A mnemonic password is one where a user chooses a memorable phrase and uses a character (often the first letter) to represent each word in the phrase.In this paper, we hypothesize that users will select mnemonic phrases that are commonly available on the Internet, and that it is possible to build a dictionary to crack mnemonic phrase-based passwords. We conduct a survey to gather user-generated passwords. We show the majority of survey respondents based their mnemonic passwords on phrases that can be found on the Internet, and we generate a mnemonic password dictionary as a proof of concept. Our 400,000-entry dictionary cracked 4% of mnemonic passwords; in comparison, a standard dictionary with 1.2 million entries cracked 11% of control passwords. The user-generated mnemonic passwords were also slightly more resistant to brute force attacks than control passwords. These results suggest that mnemonic passwords may be appropriate for some uses today. However, mnemonic passwords could become more vulnerable in the future and should not be treated as a panacea.
Energy dissipation in general purpose microprocessors

In this paper we investigate possible ways to improve the energy efficiency of a general purpose microprocessor. We show that the energy of a processor depends on its performance, so we chose the energy-delay product to compare different processors. To improve the energy-delay product we explore methods of reducing energy consumption that do not lead to performance loss (i.e., wasted energy), and explore methods to reduce delay by exploiting instruction level parallelism. We found that careful design reduced the energy dissipation by almost 25%. Pipelining can give approximately a 2x improvement in energydelay product. Superscalar issue, however, does not improve the energy-delay product any further since the overhead required offsets the gains in performance. Further improvements will be hard to come by since a large fraction of the energy (5040%) is dissipated in the clock network and the on-chip memories. Thus, the efficiency of processors will depend more on the technology being used and the algorithm chosen by the programmer than the micro-architecture.
A Deep Hybrid Model for Weather Forecasting

Weather forecasting is a canonical predictive challenge that has depended primarily on model-based methods. We explore new directions with forecasting weather as a data-intensive challenge that involves inferences across space and time. We study specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables. We show how the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. We also derive an efficient learning and inference procedure that allows for large scale optimization of the model parameters. We evaluate the methods with experiments on real-world meteorological data that highlight the promise of the approach.
RoleMiner: mining roles using subset enumeration

Role engineering, the task of defining roles and associating permissions to them, is essential to realize the full benefits of the role-based access control paradigm. Essentially, there are two basic approaches to accomplish this: the top-down and the bottom-up. The top-down approach relies on a careful analysis of the business processes to define job functions and then specify appropriate roles from them. While this approach can aid in defining roles more accurately, it is tedious and time consuming since it requires that the semantics of the business processes be well understood. Moreover, it ignores existing permissions within an organization and does not utilize them. On the other hand, the bottom-up approach starts with existing permissions and attempts to derive roles from them, thus helping to automate role definition. In this paper, we present an unsupervised approach called RoleMiner that mines roles from existing user-permission assignments. Since a role is nothing but a set of permissions, when no semantics are available, the task of role mining is essentially that of clustering users that have same (or similar) permissions. However, unlike the traditional applications of data mining that ideally require identification of non-overlapping clusters, roles will have overlapping permission needs and thus permission sets that define roles should be allowed to overlap. It is this distinction from traditional clustering that makes the problem of role mining non-trivial. Our experiments with real and simulated data sets indicate that our role mining process is quite accurate and efficient.
Improving source code search with natural language phrasal representations of method signatures

As software continues to grow, locating code for maintenance tasks becomes increasingly difficult. Software search tools help developers find source code relevant to their maintenance tasks. One major challenge to successful search tools is locating relevant code when the user's query contains words with multiple meanings or words that occur frequently throughout the program. Traditional search techniques, which treat each word individually, are unable to distinguish relevant and irrelevant methods under these conditions. In this paper, we present a novel search technique that uses information such as the position of the query word and its semantic role to calculate relevance. Our evaluation shows that this approach is more consistently effective than three other state of the art search techniques.
Adaptive 360-Degree Video Streaming using Scalable Video Coding

Virtual reality and 360-degree video streaming are growing rapidly, yet, streaming high-quality 360-degree video is still challenging due to high bandwidth requirements. Existing solutions reduce bandwidth consumption by streaming high-quality video only for the user's viewport. However, adding the spatial domain (viewport) to the video adaptation space prevents the existing solutions from buffering future video chunks for a duration longer than the interval that user's viewport is predictable. This makes playback more prone to video freezes due to rebuffering, which severely degrades the user's Quality of Experience especially under challenging network conditions. We propose a new method that alleviates the restrictions on buffer duration by utilizing scalable video coding. Our method significantly reduces the occurrence of rebuffering on links with varying bandwidth without compromising playback quality or bandwidth efficiency compared to the existing solutions. We demonstrate the efficiency of our proposed method using experimental results with real world cellular network bandwidth traces.
Large-scale synthesis of copper nanoparticles by chemically controlled reduction for applications of inkjet-printed electronics.

Copper nanoparticles are being given considerable attention as of late due to their interesting properties and potential applications in many areas of industry. One such exploitable use is as the major constituent of conductive inks and pastes used for printing various electronic components. In this study, copper nanoparticles were synthesized through a relatively large-scale (5 l), high-throughput (0.2 M) process. This facile method occurs through the chemical reduction of copper sulfate with sodium hypophosphite in ethylene glycol within the presence of a polymer surfactant (PVP), which was included to prevent aggregation and give dispersion stability to the resulting colloidal nanoparticles. Reaction yields were determined to be quantitative while particle dispersion yields were between 68 and 73%. The size of the copper nanoparticles could be controlled between 30 and 65 nm by varying the reaction time, reaction temperature, and relative ratio of copper sulfate to the surfactant. Field emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) images of the particles revealed a spherical shape within the reported size regime, and x-ray analysis confirmed the formation of face-centered cubic (FCC) metallic copper. Furthermore, inkjet printing nanocopper inks prepared from the polymer-stabilized copper nanoparticles onto polyimide substrates resulted in metallic copper traces with low electrical resistivities (≥3.6 µΩ cm, or ≥2.2 times the resistivity of bulk copper) after a relatively low-temperature sintering process (200 °C for up to 60 min).
One Day Ahead Prediction of Wind Speed and Direction

This paper presents a new technique for predicting wind speed and direction. This technique is based on using a linear time-series-based model relating the predicted interval to its corresponding one- and two-year old data. The accuracy of the model for predicting wind speeds and directions up to 24 h ahead have been investigated using two sets of data recorded during winter and summer season at Madison weather station. Generated results are compared with their corresponding values when using the persistent model. The presented results validate the effectiveness and accuracy of the proposed prediction model for wind speed and direction.
EVENODD: An Efficient Scheme for Tolerating Double Disk Failures in RAID Architectures

AbstructWe present a novel method, that we call EVENODD, for tolerating up to two disk failures in RAID architectures. EVENODD employs the addition of only two redundant disks and consists of simple exclusive-OR computations. This redundant storage is optimal, in the sense that two failed disks cannot be retrieved with less than two redundant disks. A major advantage of EVENODD is that it only requires parity hardware, which is typically present in standard RAID-5 controllers. Hence, EVENODD can be implemented on standard RAID-5 controllers without any hardware changes. The most commonly used scheme that employes optimal redundant storage (Le., two extra disks) is based on ReedSolomon (RS) error-correcting codes. This scheme requires computation over finite fields and results in a more complex implementation. For example, we show that the complexity of implementing EVENODD in a disk array with 15 disks is about 50% of the one required when using the RS scheme. The new scheme is not limited to RAID architectures: it can be used in any system requiring large symbols and relatively short codes, for instance, in multitrack magnetic recording. To this end, we also present a decoding algorithm for one column (track) in error.
Differences in hymenal morphology between adolescent girls with and without a history of consensual sexual intercourse.

To compare the morphology of the hymen in adolescent girls who have and have not had sexual intercourse involving penile-vaginal penetration.



Female patients aged 13 to 19 years, recruited from an urban adolescent medicine practice.



Subjects were interviewed in private after completing detailed questionnaires and then underwent a physical examination. External genital inspections were performed using a colposcope with an attached 35-mm camera to document the appearance of the hymen. The presence of notches or clefts was recorded during the examination, and photographs taken at x10 magnification were used to take measurements of the width of the posterior hymenal rim.



Posterior hymenal notches and clefts were more common among girls admitting past intercourse (13/27 [48%]) than in girls who denied intercourse (2/58 [3%]; P =.001), but the mean width of the posterior hymenal rim was not significantly different between the 2 groups (2.5 mm vs 3.0 mm; P =.11). Two subjects who denied intercourse but had posterior hymenal clefts described a painful first experience with tampon insertion.



Deep notches or complete clefts in the posterior rim of the hymen were rare in girls who denied intercourse. Subjects who admitted past intercourse still had nondisrupted, intact hymens in 52% of cases.
The relational self: an interpersonal social-cognitive theory.

The authors propose an interpersonal social-cognitive theory of the self and personality, the relational self, in which knowledge about the self is linked with knowledge about significant others, and each linkage embodies a self-other relationship. Mental representations of significant others are activated and used in interpersonal encounters in the social-cognitive phenomenon of transference (S. M. Andersen & N. S. Glassman, 1996), and this evokes the relational self. Variability in relational selves depends on interpersonal contextual cues, whereas stability derives from the chronic accessibility of significant-other representations. Relational selves function in if-then terms (W. Mischel & Y. Shoda, 1995), in which ifs are situations triggering transference, and thens are relational selves. An individual's repertoire of relational selves is a source of interpersonal patterns involving affect, motivation, self-evaluation, and self-regulation.