nils-herrmann/clean_scirepeval huggingface dataset:
Selecting Receptive Fields in Deep Networks

Recent deep learning and unsupervised feature learning systems that learn from unlabeled data have achieved high performance in benchmarks by using extremely large architectures with many features (hidden units) at each layer. Unfortunately, for such large architectures the number of parameters can grow quadratically in the width of the network, thus necessitating hand-coded “local receptive fields” that limit the number of connections from lower level features to higher ones (e.g., based on spatial locality). In this paper we propose a fast method to choose these connections that may be incorporated into a wide variety of unsupervised training methods. Specifically, we choose local receptive fields that group together those low-level features that are most similar to each other according to a pairwise similarity metric. This approach allows us to harness the advantages of local receptive fields (such as improved scalability, and reduced data requirements) when we do not know how to specify such receptive fields by hand or where our unsupervised training algorithm has no obvious generalization to a topographic setting. We produce results showing how this method allows us to use even simple unsupervised training algorithms to train successful multi-layered networks that achieve state-of-the-art results on CIFAR and STL datasets: 82.0% and 60.1% accuracy, respectively.
A roof automobile module for LTE-MIMO antennas

A roof automobile antenna module for Long Term Evolution (LTE) application is proposed. The module consists of two LTE antennas for the multiple-input multiple-output (MIMO) method which requests low mutual coupling between the antennas for larger capacity. On the other hand, the installation location for a roof-top module is limited from safety or appearance viewpoint and this makes the multiple LTE antennas located there cannot be separated with enough space. In order to retain high isolation between the two antennas in such compact space, the two antennas are designed to have different shapes, different heights and different polarizations, and their ground planes are placed separately. In the proposed module, one antenna is a monopole type and has its element printed on a shark-fin-shaped substrate which is perpendicular to the car-roof. Another one is a planar inverted-F antenna (PIFA) and has its element on a lower plane parallel to the roof. In this manner, the two antennas cover the LTE-bands with omni-directional radiation in the horizontal directions and high radiation gain. The two antennas have reasonably good isolation between them even the module is compact with a dimension of 62×65×73 mm3.
Multicasting in delay tolerant networks: a social network perspective

Node mobility and end-to-end disconnections in Delay Tolerant Networks (DTNs) greatly impair the effectiveness of data dissemination. Although social-based approaches can be used to address the problem, most existing solutions only focus on forwarding data to a single destination. In this paper, we are the first to study multicast in DTNs from the social network perspective. We study multicast in DTNs with single and multiple data items, investigate the essential difference between multicast and unicast in DTNs, and formulate relay selections for multicast as a unified knapsack problem by exploiting node centrality and social community structures. Extensive trace-driven simulations show that our approach has similar delivery ratio and delay to the Epidemic routing, but can significantly reduce the data forwarding cost measured by the number of relays used.
Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas

In this paper, resource allocation for energy efficient communication in orthogonal frequency division multiple access (OFDMA) downlink networks with large numbers of base station (BS) antennas is studied. Assuming perfect channel state information at the transmitter (CSIT), the resource allocation algorithm design is modeled as a non-convex optimization problem for maximizing the energy efficiency of data transmission (bit/Joule delivered to the users), where the circuit power consumption and a minimum required data rate are taken into consideration. Subsequently, by exploiting the properties of fractional programming, an efficient iterative resource allocation algorithm is proposed to solve the problem. In particular, the power allocation, subcarrier allocation, and antenna allocation policies for each iteration are derived. Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations and unveil the trade-off between energy efficiency and the number of antennas.
Clustering for Simultaneous Extraction of Aspects and Features from Reviews

This paper presents a clustering approach that simultaneously identifies product features and groups them into aspect categories from online reviews. Unlike prior approaches that first extract features and then group them into categories, the proposed approach combines feature and aspect discovery instead of chaining them. In addition, prior work on feature extraction tends to require seed terms and focus on identifying explicit features, while the proposed approach extracts both explicit and implicit features, and does not require seed terms. We evaluate this approach on reviews from three domains. The results show that it outperforms several state-of-the-art methods on both tasks across all three domains.
Bayesian Model Averaging for Linear Regression Models

We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem involves averaging over all possible models (i.e., combinations of predictors) when making inferences about quantities of Adrian E. Raftery is Professor of Statistics and Sociology, David Madigan is Assistant Professor of Statistics, both at the Department of Statistics,University of Washington, Box 354322, Seattle, WA 98195-4322. Jennifer Hoeting is Assistant Professor of Statistics at the Department of Statistics, Colorado State University, Fort Collins, CO 80523. The research of Raftery and Hoeting was partially supported by ONR Contract N-00014-91-J-1074. Madigan's research was partially supported by NSF grant no. DMS 92111627. The authors are grateful to Danika Lew for research assistance and the Editor, the Associate Editor, two anonymous referees and David Draper for very helpful comments that greatly improved the article.
Subjective comparison and evaluation of speech enhancement algorithms

Making meaningful comparisons between the performance of the various speech enhancement algorithms proposed over the years, has been elusive due to lack of a common speech database, differences in the types of noise used and differences in the testing methodology. To facilitate such comparisons, we report on the development of a noisy speech corpus suitable for evaluation of speech enhancement algorithms. This corpus is subsequently used for the subjective evaluation of 13 speech enhancement methods encompassing four classes of algorithms: spectral subtractive, subspace, statistical-model based and Wiener-type algorithms. The subjective evaluation was performed by Dynastat, Inc. using the ITU-T P.835 methodology designed to evaluate the speech quality along three dimensions: signal distortion, noise distortion and overall quality. This paper reports the results of the subjective tests.
Lessons from a real world evaluation of anti-phishing training

Prior laboratory studies have shown that PhishGuru, an embedded training system, is an effective way to teach users to identify phishing scams. PhishGuru users are sent simulated phishing attacks and trained after they fall for the attacks. In this current study, we extend the PhishGuru methodology to train users about spear phishing and test it in a real world setting with employees of a Portuguese company. Our results demonstrate that the findings of PhishGuru laboratory studies do indeed hold up in a real world deployment. Specifically, the results from the field study showed that a large percentage of people who clicked on links in simulated emails proceeded to give some form of personal information to fake phishing websites, and that participants who received PhishGuru training were significantly less likely to fall for subsequent simulated phishing attacks one week later. This paper also presents some additional new findings. First, people trained with spear phishing training material did not make better decisions in identifying spear phishing emails compared to people trained with generic training material. Second, we observed that PhishGuru training could be effective in training other people in the organization who did not receive training messages directly from the system. Third, we also observed that employees in technical jobs were not different from employees with non-technical jobs in identifying phishing emails before and after the training. We conclude with some lessons that we learned in conducting the real world study.
An efficient omnidirectional vision system for soccer robots : From calibration to object detection

Robotic soccer is nowadays a popular research domain in the area of multi-robot systems. In the context of RoboCup, the Middle Size League is one of the most challenging. This paper presents an efficient omnidirectional vision system for real-time object detection, developed for the robotic soccer team of the University of Aveiro, CAMBADA. The vision system is used to find the ball and white lines, which are used for self-localization, as well as to find the presence of obstacles. Algorithms for detecting these objects and also for calibrating most of the parameters of the vision system are presented in this paper. We also propose an efficient approach for detecting arbitrary FIFA balls, which is an important topic of research in the Middle Size League. The experimental results that we present show the effectiveness of our algorithms, both in terms of accuracy and processing time, as well as the results that the team has been achieving: 1st place in  2008, 3rd place in 2009 and 1st place in the mandatory technical challenge in  2009, where the robots have to play with an arbitrary standard FIFA ball.
The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems c ©

The Levenberg-Marquardt method is a standard technique used to solve nonlinear least squares problems. Least squares problems arise when fitting a parameterized function to a set of measured data points by minimizing the sum of the squares of the errors between the data points and the function. Nonlinear least squares problems arise when the function is not linear in the parameters. Nonlinear least squares methods involve an iterative improvement to parameter values in order to reduce the sum of the squares of the errors between the function and the measured data points. The Levenberg-Marquardt curve-fitting method is actually a combination of two minimization methods: the gradient descent method and the Gauss-Newton method. In the gradient descent method, the sum of the squared errors is reduced by updating the parameters in the direction of the greatest reduction of the least squares objective. In the Gauss-Newton method, the sum of the squared errors is reduced by assuming the least squares function is locally quadratic, and finding the minimum of the quadratic. The Levenberg-Marquardt method acts more like a gradient-descent method when the parameters are far from their optimal value, and acts more like the Gauss-Newton method when the parameters are close to their optimal value. This document describes these methods and illustrates the use of software to solve nonlinear least squares curve-fitting problems.
Unsupervised Learning by Program Synthesis

We introduce an unsupervised learning algorithm that combines probabilistic modeling with solver-based techniques for program synthesis. We apply our techniques to both a visual learning domain and a language learning problem, showing that our algorithm can learn many visual concepts from only a few examples and that it can recover some English inflectional morphology. Taken together, these results give both a new approach to unsupervised learning of symbolic compositional structures, and a technique for applying program synthesis tools to noisy data.
Agent-based software engineering

The technology of intelligent agents and multi-agent systems seems set to radically alter the way in which complex, distributed, open systems are conceptualized and implemented. The purpose of this paper is to consider the problem of building a multi-agent system as a software engineering enterprise. The article focuses on three issues: (i) how agents might be specified; (ii) how these specifications might be refined or otherwise transformed into efficient implementations; and (iii) how implemented agents and multi-agent systems might subsequently be verified, in order to show that they are correct with respect to their specifications. These issues are discussed with reference to a number of casestudies. The article concludes by setting out some issues and open problems for future
Privacy Preservation by Disassociation

In this work, we focus on the preservation of user privacy in the publication of sparse multidimensional data. Existing works protect the users’ sensitive information by generalizing or suppressing quasi identifiers in the original data. In many real world cases, neither generalization nor the distinction between sensitive and non-sensitive items is appropriate. For example, web search query logs contain millions of terms that are very hard to categorize as sensitive or non sensitive. At the same time, a generalization-based anonymization would remove the most valuable information in the dataset; the original terms. Motivated by this problem, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. Up to now, such techniques were used to sever the link between quasiidentifiers and sensitive values in settings with a clear distinction between these types of values. Our proposal generalizes these techniques for sparse multidimensional data, where no such distinction holds. We protect the users’ privacy by disassociating combinations of terms that can act as quasi-identifiers from the rest of the record or by disassociating the constituent terms, so that the identifying combination cannot be accurately recognized. To this end, we present an algorithm that anonymizes the data by first clustering them and then locally disassociating identifying combinations of terms. We analyze the attack model and extend the km-anonymity guaranty to the aforementioned setting. We empirically evaluate our method on real and synthetic datasets.
mm Wave Initial Cell Search Analysis under UE Rotational Motion

Millimeter wave (mmW) communication has emerged as a promising component of the access link for 5G cellular systems. In order to overcome the higher free-space path loss at these frequencies, high gain, and therefore highly directional, antennas are being proposed at both ends of the link. Furthermore, in order to maintain a mobile connection with an acceptable quality of service (QOS), these highly directional antennas also need to be electronically steerable. This required dual-end steerability adds significant system complexity in terms of both initial access and connected mode procedures. Therefore, these various procedures which were originally designed for the current sub 6 GHz systems need to be re-investigated in light of this added complexity. Traditionally, sub 6 GHz mobility studies mainly focused on translational motion; however, with the dual-end highly directional links being proposed for mmW communications, rotational motion also becomes a concern. In this paper, we study the effects these steerable links have on initial network access procedures, especially in the presence of user equipment (UE) rotational motion.
Towards Automatically-Tuned Neural Networks

Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. However, current AutoML tools do not yet support modern neural networks effectively. In this work, we present a first version of Auto-Net, which provides automatically-tuned feed-forward neural networks without any human intervention. We report results on datasets from the recent AutoML challenge showing that ensembling Auto-Net with Auto-sklearn often performs better than either alone and report the first results on winning competition datasets against human experts with automatically-tuned neural networks.
Online Identification of PMSM Parameters: Parameter Identifiability and Estimator Comparative Study

In this paper, a model-reference-based online identification method is proposed to estimate permanent-magnet synchronous machine (PMSM) parameters during transients and in steady state. It is shown that all parameters are not identifiable in steady state and a selection has to be made according to the user's objectives. Then, large signal convergence of the estimated parameters is analyzed using the second method of Lyapunov and the singular perturbations theory. It is illustrated that this method may be applied with a decoupling control technique that improves convergence dynamics and overall system stability. This method is compared with an extended Kalman filter (EKF)-based online identification approach, and it is shown that, in spite of its implementation complexity with respect to the proposed method, EKF does not give better results than the proposed method. It is also shown that the use of a simple PMSM model makes estimated parameters sensitive to those supposed to be known whatever the estimator is (both the proposed method and EKF). The simulation results as well as the experimental ones, implemented on a nonsalient pole PMSM, illustrate the validity of the analytic approach and confirm the same conclusions.
Complex problem solving: a case for complex cognition?

Complex problem solving (CPS) emerged in the last 30 years in Europe as a new part of the psychology of thinking and problem solving. This paper introduces into the field and provides a personal view. Also, related concepts like macrocognition or operative intelligence will be explained in this context. Two examples for the assessment of CPS, Tailorshop and MicroDYN, are presented to illustrate the concept by means of their measurement devices. Also, the relation of complex cognition and emotion in the CPS context is discussed. The question if CPS requires complex cognition is answered with a tentative “yes.”
Detecting SQL injection attacks using SNORT IDS

SQL injection attack poses a serious security threats among the Internet community nowadays and it's continue to increase exploiting flaws found in the Web applications. In SQL injection attack, the attackers can take advantage of poorly coded web application software to introduce malicious code into the system and/or could retrieve important information. Web applications are under siege from cyber criminals seeking to steal confidential information and disable or damage the services offered by these application. Therefore, additional steps must be taken to ensure data security and integrity of the applications. In this paper we propose an innovative solution to filter the SQL injection attack using SNORT IDS. The proposed detection technique uses SNORT tool by augmenting a number of additional SNORT rules. We evaluate the proposed solution by comparing our method with several existing techniques. Experimental results demonstrate that the proposed method outperforms other similar techniques using the same data set.
Design of a flexible tactile sensor for classification of rigid and deformable objects

For both humans and robots, tactile sensing is important for interaction with the environment: it is the core sensing used for exploration and manipulation of objects. In this paper, we present a novel tactile-array sensor based on flexible piezoresistive rubber. We describe the design of the sensor and data acquisition system.We evaluate the sensitivity and robustness of the sensor, and show that it is consistent over time with little relaxation. Furthermore, the sensor has the benefit of being flexible, having a high resolution, it is easy to mount, and simple to manufacture. We demonstrate the use of the sensor in an active object-classification system. A robotic gripper with two sensors mounted on its fingers performs a palpation procedure on a set of objects. By squeezing an object, the robot actively explores the material properties, and the system acquires tactile information corresponding to the resulting pressure. Based on a k nearest neighbor classifier and using dynamic time warping to calculate the distance between different time series, the system is able to successfully classify objects. Our sensor demonstrates similar classification performance to the Weiss Robotics tactile sensor, while having additional benefits.
PipeGen: Data Pipe Generator for Hybrid Analytics

As the number of big data management systems continues to grow, users increasingly seek to leverage multiple systems in the context of a single data analysis task. To efficiently support such hybrid analytics, we develop a tool called PipeGen for efficient data transfer between database management systems (DBMSs). PipeGen automatically generates data pipes between DBMSs by leveraging their functionality to transfer data via disk files using common data formats such as CSV. PipeGen creates data pipes by extending such functionality with efficient binary data transfer capabilities that avoid file system materialization, include multiple important format optimizations, and transfer data in parallel when possible. We evaluate our PipeGen prototype by generating 20 data pipes automatically between five different DBMSs. The results show that PipeGen speeds up data transfer by up to 3.8× as compared to transferring using disk files.
By their apps you shall understand them: mining large-scale patterns of mobile phone usage

Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usage patterns exhibit not only what people are consistently interested in but also the way in which they use their phones, and can help improving phone design and personalized services. This work aims at mining automatically usage patterns from apps data recorded continuously with smartphones. A new probabilistic framework for mining usage patterns is proposed. Our methodology involves the design of a bag-of-apps model that robustly represents level of phone usage over specific times of the day, and the use of a probabilistic topic model that jointly discovers patterns of usage over multiple applications and describes users as mixtures of such patterns. Our framework is evaluated using 230 000+ hours of real-life app phone log data, demonstrates that relevant patterns of usage can be extracted, and is objectively validated on a user retrieval task with competitive performance.