nils-herrmann/clean_scirepeval huggingface dataset:
Where Mathematics Comes From : How the Embodied Mind Brings Mathematics into Being The only

The two authorsLakoff, a linguist and Nunez, a psychologistpurport to introduce a new field of study, i.e. "mathematical idea analysis", with this book. By "mathematical idea analysis", they mean to give a scientifically plausible account of mathematical concepts using the apparatus of cognitive science. This approach is meant to be a contribution to academics and possibly education as it helps to illuminate how we cognitise mathematical concepts, which are supposedly undecipherable and abstruse to laymen. The analysis of mathematical ideas, the authors claim, cannot be done within mathematics, for even metamathematicsrecursive theory, model theory, set theory, higherorder logic still requires mathematical idea analysis in itself! Formalism, by its very nature, voids symbols of their meanings and thus cognition is required to imbue meaning. Thus, there is a need for this new field, in which the authors, if successful, would become pioneers.
Enhanced three-factor security protocol for consumer USB mass storage devices

The Universal Serial Bus (USB) is an extremely popular interface standard for computer peripheral connections and is widely used in consumer Mass Storage Devices (MSDs). While current consumer USB MSDs provide relatively high transmission speed and are convenient to carry, the use of USB MSDs has been prohibited in many commercial and everyday environments primarily due to security concerns. Security protocols have been previously proposed and a recent approach for the USB MSDs is to utilize multi-factor authentication. This paper proposes significant enhancements to the three-factor control protocol that now makes it secure under many types of attacks including the password guessing attack, the denial-of-service attack, and the replay attack. The proposed solution is presented with a rigorous security analysis and practical computational cost analysis to demonstrate the usefulness of this new security protocol for consumer USB MSDs.
Continuity between waking activities and dream activities

Empirical studies largely support the continuity hypothesis of dreaming. Despite of previous research efforts, the exact formulation of the continuity hypothesis remains vague. The present paper focuses on two aspects: (1) the differential incorporation rate of different waking-life activities and (2) the magnitude of which interindividual differences in waking-life activities are reflected in corresponding differences in dream content. Using a correlational design, a positive, non-zero correlation coefficient will support the continuity hypothesis. Although many researchers stress the importance of emotional involvement on the incorporation rate of waking-life experiences into dreams, formulated the hypothesis that highly focused cognitive processes such as reading, writing, etc. are rarely found in dreams due to the cholinergic activation of the brain during dreaming. The present findings based on dream diaries and the exact measurement of waking activities replicated two recent questionnaire studies. These findings indicate that it will be necessary to specify the continuity hypothesis more fully and include factors (e.g., type of waking-life experience, emotional involvement) which modulate the incorporation rate of waking-life experiences into dreams. Whether the cholinergic state of the brain during REM sleep or other alterations of brain physiology (e.g., down-regulation of the dorsolateral prefrontal cortex) are the underlying factors of the rare occurrence of highly focused cognitive processes in dreaming remains an open question. Although continuity between waking life and dreaming has been demonstrated, i.e., interindividual differences in the amount of time spent with specific waking-life activities are reflected in dream content, methodological issues (averaging over a two-week period, small number of dreams) have limited the capacity for detecting substantial relationships in all areas. Nevertheless, it might be concluded that the continuity hypothesis in its present general form is not valid and should be elaborated and tested in a more specific way.
Wii all play: the console game as a computational meeting place

In this paper, we present results from a qualitative study of collocated group console gaming. We focus on motivations for, perceptions of, and practices surrounding the shared use of console games by a variety of established groups of gamers. These groups include both intragenerational groups of youth, adults, and elders as well as intergenerational families. Our analysis highlights the numerous ways that console games serve as a computational meeting place for a diverse population of gamers.
Passive active L- and S-band (PALS) microwave sensor for ocean salinity and soil moisture measurements

A passive/active L/S-band (PALS) microwave aircraft instrument to measure ocean salinity and soil moisture has been built and tested. Because the L-band brightness temperatures associated with salinity changes are expected to be small, it was necessary to build a very sensitive and stable system. This new instrument has dual-frequency, dual polarization radiometer and radar sensors. The antenna is a high beam efficiency conical horn. The PALS instrument was installed on the NCAR C-130 aircraft and soil moisture measurements were made in support of the Southern Great Plains 1999 experiment in Oklahoma from July 8–14, 1999. Data taken before and after a rainstorm showed significant changes in the brightness temperatures, polarization ratios and radar backscatter, as a function of soil moisture. Salinity measurement missions were flown on July 17–19, 1999, southeast of Norfolk, VA, over the Gulf Stream. The measurements indicated a clear and repeatable salinity signal during these three days, which was in good agreement with the Cape Hatteras ship salinity data. Data was also taken in the open ocean and a small decrease of 0.2 K was measured in the brightness temperature, which corresponded to the salinity increase of 0.4 psu measured by the M/V Oleander vessel.
Toward a standard benchmark for computer security research: the worldwide intelligence network environment (WINE)

Unlike benchmarks that focus on performance or reliability evaluations, a benchmark for computer security must necessarily include sensitive code and data. Because these artifacts could damage systems or reveal personally identifiable information about the users affected by cyber attacks, publicly disseminating such a benchmark raises several scientific, ethical and legal challenges. We propose the Worldwide Intelligence Network Environment (WINE), a security-benchmarking approach based on rigorous experimental methods. WINE includes representative field data, collected worldwide from 240,000 sensors, for new empirical studies, and it will enable the validation of research on all the phases in the lifecycle of security threats. We tackle the key challenges for security benchmarking by designing a platform for repeatable experimentation on the WINE data sets and by collecting the metadata required for understanding the results. In this paper, we review the unique characteristics of the WINE data, we discuss why rigorous benchmarking will provide fresh insights on the security arms race and we propose a research agenda for this area.
A Cost-Based Security Analysis of Symmetric and Asymmetric Key Lengths RSA Labs bulletin

Recently, there has been considerable debate concerning key sizes for publ i c key based cry p t o graphic methods. Included in the debate have been considerations about equivalent key sizes for diffe rent methods and considerations about the minimum re q u i red key size for diffe rent methods. In this paper we propose a method of a n a lyzing key sizes based upon the value of the data being protected and the cost of b reaking ke y s . I . I n t ro d u c t i o n A . W H Y I S K E Y S I Z E I M P O R T A N T ? In order to keep transactions based upon public key cryptography secure, one must ensure that the underlying keys are sufficiently large as to render the best possible attack infeasible. However, this really just begs the question as one is now left with the task of defining ‘infeasible’. Does this mean infeasible given access to (say) most of the Internet to do the computations? Does it mean infeasible to a large adversary with a large (but unspecified) budget to buy the hardware for an attack? Does it mean infeasible with what hardware might be obtained in practice by utilizing the Internet? Is it reasonable to assume that if utilizing the entire Internet in a key breaking effort makes a key vulnerable that such an attack might actually be conducted? If a public effort involving a substantial fraction of the Internet breaks a single key, does this mean that similar sized keys are unsafe? Does one need to be concerned about such public efforts or does one only need to be concerned about possible private, sur reptitious efforts? After all, if a public attack is known on a particular key, it is easy to change that key. We shall attempt to address these issues within this paper. number 13 Apr i l 2000 B u l l e t i n News and A dv i c e f rom RSA La bo rat o r i e s I . I n t ro d u c t i o n I I . M et ho ds o f At tac k I I I . H i s tor i ca l R es u l t s and t he R S A Ch a l le nge I V. Se cu r i t y E st i m ate s
Evolving Deep Convolutional Neural Networks for Image Classification

Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights. In this paper, we propose a new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems. In the proposed algorithm, an efficient variable-length gene encoding strategy is designed to represent the different building blocks and the unpredictable optimal depth in convolutional neural networks. In addition, a new representation scheme is developed for effectively initializing connection weights of deep convolutional neural networks, which is expected to avoid networks getting stuck into local minima which is typically a major issue in the backward gradient-based optimization. Furthermore, a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource. The proposed algorithm is examined and compared with 22 existing algorithms on nine widely used image classification tasks, including the stateof-the-art methods. The experimental results demonstrate the remarkable superiority of the proposed algorithm over the stateof-the-art algorithms in terms of classification error rate and the number of parameters (weights).
TRANSPORTATION NETWORK ANALYSIS BY USING REMOTE SENSING AND GIS A REVIEW

With the advent technology of Remote Sensing (RS) and Geographic Information Systems (GIS), a network transportation (Road) analysis within this environment has now become a common practice in many application areas. But a main problem in the network transportation analysis is the less quality and insufficient maintenance policies. This is because of the lack of funds for infrastructure. This demand for information requires new approaches in which data related to transportation network can be identified, collected, stored, retrieved, managed, analyzed, communicated and presented, for the decision support system of the organization. The adoption of newly emerging technologies such as Geographic Information System (GIS) can help to improve the decision making process in this area for better use of the available limited funds. The paper reviews the applications of GIS technology for transportation network analysis.
Lossless plenoptic image compression using adaptive block differential prediction

Plenoptic images are obtained from the projection of the light crossing a matrix of microlens arrays which replicates the scene from different direction into a camera device sensor. Plenoptic images have a different structure with respect to regular digital images, and novel algorithms for data compression are currently under research. This paper proposes an algorithm for the compression of plenoptic images. The micro images composing a plenoptic image are processed by an adaptive prediction tool, aiming at reducing data correlation before entropy coding takes place. The algorithm is compared with state-of-the-art image compression algorithms, namely, JPEG 2000 and JPEG XR. Obtained results demonstrate that the proposed algorithm improves the coding efficiency.
Community-based classification of noun phrases in twitter

Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the author's behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base.
Process implications of social networking-based requirements negotiation tools

Avoiding a major source of system and software project failures by finding more non-technical-user friendly methods of system definition and evolution has been a significant challenge. Five generations of the WinWin negotiation framework have improved such capabilities, but even the latest WikiWinWin toolset has encountered problems with non-technical stakeholder usage. With the advent of social networking and popularity of Facebook and Gmail, we have developed a radically different way for collaborative requirements management and negotiations. The new avatar of the WinWin framework called 'Winbook' is based on the social networking paradigm, similar to Facebook and content organization using color coded labels, similar to Gmail. Initial usage results on 14 small projects involving non-technical stakeholders have shown profound implications on the way requirements are negotiated and used through the system and software definition and development processes. Winbook has also been adopted as part of a project to bridge requirements and architecting for a major US government organization.
Flip-chip package with integrated antenna on a polyimide substrate for a 122-GHz bistatic radar IC

This paper presents the packaging technology and the integrated antenna design for a miniaturized 122-GHz radar sensor. The package layout and the assembly process are shortly explained. Measurements of the antenna including the flip chip interconnect are presented that have been achieved by replacing the IC with a dummy chip that only contains a through-line. Afterwards, radiation pattern measurements are shown that were recorded using the radar sensor as transmitter. Finally, details of the fully integrated radar sensor are given, together with results of the first Doppler measurements.
Maximizing DC-to-Load Efficiency for Inductive Power Transfer

Inductive power transfer (IPT) systems for transmitting tens to hundreds of watts have been reported for almost a decade. Most of the work has concentrated on the optimization of the link efficiency and has not taken into account the efficiency of the driver. Class-E amplifiers have been identified as ideal drivers for IPT applications, but their power handling capability at tens of megahertz has been a crucial limiting factor, since the load and inductor characteristics are set by the requirements of the resonant inductive system. The frequency limitation of the driver restricts the unloaded Q-factor of the coils and thus the link efficiency. With a suitable driver, copper coil unloaded Q factors of over 1000 can be achieved in the low megahertz region, enabling a cost-effective high Q coil assembly. The system presented in this paper alleviates the use of heavy and expensive field-shaping techniques by presenting an efficient IPT system capable of transmitting energy with a dc-to-load efficiency above 77% at 6 MHz across a distance of 30 cm. To the authors knowledge, this is the highest dc-to-load efficiency achieved for an IPT system without introducing restrictive coupling factor enhancement techniques.
Relevance Feedback Techniques in Interactive Content-Based Image Retrieval

Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research e orts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Speci cally, these e orts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which e ectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's relevance feedback. The experimental results show that the proposed approach greatly reduces the user's e ort of composing a query and captures the user's information need more precisely.
Video2vec: Learning semantic spatio-temporal embeddings for video representation

We propose to learn semantic spatio-temporal embeddings for videos to support high-level video analysis. The first step of the proposed embedding employs a deep architecture consisting of two channels of convolutional neural networks (capturing appearance and local motion) followed by their corresponding Gated Recurrent Unit encoders for capturing longer-term temporal structure of the CNN features. The resultant spatio-temporal representation (a vector) is used to learn a mapping via a multilayer perceptron to the word2vec semantic embedding space, leading to a semantic interpretation of the video vector that supports high-level analysis. We demonstrate the usefulness and effectiveness of this new video representation by experiments on action recognition, zero-shot video classification, and “word-to-video” retrieval, using the UCF-101 dataset.
A Single-Phase Photovoltaic Inverter Topology With a Series-Connected Energy Buffer

Module integrated converters (MICs) have been under rapid development for single-phase grid-tied photovoltaic applications. The capacitive energy storage implementation for the double-line-frequency power variation represents a differentiating factor among existing designs. This paper introduces a new topology that places the energy storage block in a series-connected path with the line interface block. This design provides independent control over the capacitor voltage, soft-switching for all semiconductor devices, and the full four-quadrant operation with the grid. The proposed approach is analyzed and experimentally demonstrated.
Topic Segmentation and Labeling in Asynchronous Conversations

Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many Natural Language Processing (NLP) applications. We present two new corpora of email and blog conversations annotated with topics, and evaluate annotator reliability for the segmentation and labeling tasks in these asynchronous conversations. We propose a complete computational framework for topic segmentation and labeling in asynchronous conversations. Our approach extends state-of-the-art methods by considering a fine-grained structure of an asynchronous conversation, along with other conversational features by applying recent graph-based methods for NLP. For topic segmentation, we propose two novel unsupervised models that exploit the fine-grained conversational structure, and a novel graph-theoretic supervised model that combines lexical, conversational and topic features. For topic labeling, we propose two novel (unsupervised) random walk models that respectively capture conversation specific clues from two different sources: the leading sentences and the fine-grained conversational structure. Empirical evaluation shows that the segmentation and the labeling performed by our best models beat the state-of-the-art, and are highly correlated with human annotations.
Newsworthiness and Network Gatekeeping on Twitter: The Role of Social Deviance

Publishers of news information are keen to amplify the reach of their content by making it as re-sharable as possible on social media. In this work we study the relationship between the concept of social deviance and the re-sharing of news headlines by network gatekeepers on Twitter. Do network gatekeepers have the same predilection for selecting socially deviant news items as professionals? Through a study of 8,000 news items across 8 major news outlets in the U.S. we predominately find that network gatekeepers re-share news items more often when they reference socially deviant events. At the same time we find and discuss exceptions for two outlets, suggesting a more complex picture where newsworthiness for networked gatekeepers may be moderated by other effects such as topicality or varying motivations and relationships with their audience.
Pedestrian detection using HOG, LUV and optical flow as features with AdaBoost as classifier

Pedestrian detection has been used in applications such as car safety, video surveillance, and intelligent vehicles. In this paper, we present a pedestrian detection scheme using HOG, LUV and optical flow features with AdaBoost Decision Stump classifier. Our experiments on Caltech-USA pedestrian dataset show that the proposed scheme achieves promising results of about 16.7% log-average miss rate.
A Fast and Accurate Multilevel Inversion of the Radon Transform

A number of imaging technologies reconstruct an image function from its Radon projection using the convolution backprojection method. The convolution is an O(N2 logN) algorithm, where the image consists of N×N pixels, while the backprojection is an O(N3) algorithm, thus constituting the major computational burden of the convolution backprojection method. An O(N2 logN) multilevel backprojection method is presented here. When implemented with a Fourier-domain postprocessing technique, also presented here, the resulting image quality is similar or superior to the image quality of the classical backprojection technique.
Image Segmentation Using Local Variation

We present a new graph-theoretic approach to the problem of image segmentation. Our method uses local criteria and yet produces results that reflect global properties of the image. We develop a framework that provides specific definitions of what it means for an image to be underor over-segmented. We then present an efficient algorithm for computing a segmentation that is neither undernor over-segmented according to these definitions. Our segmentation criterion is based on intensity differences between neighboring pixels. An important characteristic of the approach is that it is able to preserve detail in low-variability regions while ignoring detail in high-variability regions, which we illustrate with several examples on both real and sythetic images.