nils-herrmann/clean_scirepeval huggingface dataset:
Wireless Sensor Networks: Technology, Protocols, and Applications

Infrastructure for Homeland Security Environments Wireless Sensor Networks helps readers discover the emerging field of low-cost standards-based sensors that promise a high order of spatial and temporal resolution and accuracy in an ever-increasing universe of applications. It shares the latest advances in science and engineering paving the way towards a large plethora of new applications in such areas as infrastructure protection and security, healthcare, energy, food safety, RFID, ZigBee, and processing. Unlike other books on wireless sensor networks that focus on limited topics in the field, this book is a broad introduction that covers all the major technology, standards, and application topics. It contains everything readers need to know to enter this burgeoning field, including current applications and promising research and development; communication and networking protocols; middleware architecture for wireless sensor networks; and security and management. The straightforward and engaging writing style of this book makes even complex concepts and processes easy to follow and understand. In addition, it offers several features that help readers grasp the material and then apply their knowledge in designing their own wireless sensor network systems: Examples illustrate how concepts are applied to the development and application of wireless sensor networks Detailed case studies set forth all the steps of design and implementation needed to solve real-world problems Chapter conclusions that serve as an excellent review by stressing the chapter's key concepts References in each chapter guide readers to in-depth discussions of individual topics This book is ideal for networking designers and engineers who want to fully exploit this new technology and for government employees who are concerned about homeland security. With its examples, it is appropriate for use as a coursebook for upper-level undergraduates and graduate students.
An Ontology of Meta-Level Categories

We focus in this paper on some meta-level ontological distinctions among unary predicates, like those between concepts and assertional properties. Three are the main contributions of this work, mostly based on a revisitation of philosophical (and linguistic) literature in the perspective of knowledge representation. The first is a formal notion of ontological commitment, based on a modal logic en dowed with mereological and topological primitives. The second is a formal account of Strawson's distinction between sortal and non-sortal predicates. Assertional properties like red belong to the latter category, while the former category is further refined by distinguishing substantial predicates (corresponding to types like person) from non-substantial predicates (corresponding to r les like student ). The third technical contribution is definition of countability which exploits the topological notion of connection to capture the intended semantics of unary predicates.
On the collective classification of email "speech acts"

We consider classification of email messages as to whether or not they contain certain "email acts", such as a request or a commitment. We show that exploiting the sequential correlation among email messages in the same thread can improve email-act classification. More specifically, we describe a new text-classification algorithm based on a dependency-network based collective classification method, in which the local classifiers are maximum entropy models based on words and certain relational features. We show that statistically significant improvements over a bag-of-words baseline classifier can be obtained for some, but not all, email-act classes. Performance improvements obtained by collective classification appears to be consistent across many email acts suggested by prior speech-act theory.
Exploiting efficient data mining techniques to enhance intrusion detection systems

Security is becoming a critical part of organizational information systems. Intrusion detection system (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. Data mining is being used to clean, classify, and examine large amount of network data to correlate common infringement for intrusion detection. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require processing. The amount of data accumulated each day by a network is huge. Several data mining techniques such as clustering, classification, and association rules are proving to be useful for gathering different knowledge for intrusion detection. This paper presents the idea of applying data mining techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems.
A Rule-Based Relation Extraction System using DBpedia and Syntactic Parsing

In this paper, we present a rule-based relation extraction approach which uses DBpedia and linguistic information provided by the syntactic parser Fips. Our goal is twofold: (i) the morpho-syntactic patterns are defined using the syntactic parser Fips to identify relations between named entities (ii) the RDF triples extracted from DBpedia are used to improve RE task by creating gazetteer relations. NEBHI, Kamel. A Rule-Based Relation Extraction System using DBpedia and Syntactic Parsing.
Inducing Document Plans for Concept-to-Text Generation

In a language generation system, a content planner selects which elements must be included in the output text and the ordering between them. Recent empirical approaches perform content selection without any ordering and have thus no means to ensure that the output is coherent. In this paper we focus on the problem of generating text from a database and present a trainable end-to-end generation system that includes both content selection and ordering. Content plans are represented intuitively by a set of grammar rules that operate on the document level and are acquired automatically from training data. We develop two approaches: the first one is inspired from Rhetorical Structure Theory and represents the document as a tree of discourse relations between database records; the second one requires little linguistic sophistication and uses tree structures to represent global patterns of database record sequences within a document. Experimental evaluation on two domains yields considerable improvements over the state of the art for both approaches.
Frequency estimation algorithm for an extended FMCW radar system with additional phase evaluation

Range detection applications based on radar can be separated into measurements of short distances with high accuracy or large distances with low accuracy. In this paper an approach is investigated to combine the advantages of both principles. Therefore an FMCW radar will be extended with an additional phase evaluation technique. In order to realize this combination an increased range resolution of the FMCW radar is required. This paper describes an frequency estimation algorithm to increase the frequency resolution and hence the range resolution of an FMCW radar at 24 GHz for a line based range detection system to evaluate the possibility of an extended FMCW radar using the phase information.
Joint Mention Extraction and Classification with Mention Hypergraphs

We present a novel model for the task of joint mention extraction and classification. Unlike existing approaches, our model is able to effectively capture overlapping mentions with unbounded lengths. The model is highly scalable, with a time complexity that is linear in the number of words in the input sentence and linear in the number of possible mention classes. Our model can be extended to additionally capture mention heads explicitly in a joint manner under the same time complexity. We demonstrate the effectiveness of our model through extensive experiments on standard datasets.
Vision-based SLAM using the Rao-Blackwellised Particle Filter

We consider the problem of Simultaneous Localization and Mapping (SLAM) from a Bayesian point of view using the Rao-Blackwellised Particle Filter (RBPF). We focus on the class of indoor mobile robots equipped with only a stereo vision sensor. Our goal is to construct dense metric maps of natural 3D point landmarks for large cyclic environments in the absence of accurate landmark position measurements and reliable motion estimates. Landmark estimates are derived from stereo vision and motion estimates are based on visual odometry. We distinguish between landmarks using the Scale Invariant Feature Transform (SIFT). Our work defers from current popular approaches that rely on reliable motion models derived from odometric hardware and accurate landmark measurements obtained with laser sensors. We present results that show that our model is a successful approach for vision-based SLAM, even in large environments. We validate our approach experimentally, producing the largest and most accurate vision-based map to date, while we identify the areas where future research should focus in order to further increase its accuracy and scalability to significantly larger
Design and Building of a Cheap Smart Meter

This paper shows the design of a cheap smart meter which is designed not only to measure the customer’s power consumption and generation but also to enable and support the new operation and control functions in the distribution networks. It is based on open source hardware (Arduino and Arduino Ethernet Board) and offers a plurality of communication possibilities, like USB, Ethernet, ZigBee or Bluetooth. The smart meter uses an ADE7753 as converter and saves only the most important data (voltage, current, frequency, activeand reactive energy) on a SD-card so that all further calculations can be performed by an external central system. The smart meter can be used as energy meter (few minutes average) or as power quality meter (10 seconds average).
Auditory Beat Stimulation and its Effects on Cognition and Mood States

Auditory beat stimulation may be a promising new tool for the manipulation of cognitive processes and the modulation of mood states. Here, we aim to review the literature examining the most current applications of auditory beat stimulation and its targets. We give a brief overview of research on auditory steady-state responses and its relationship to auditory beat stimulation (ABS). We have summarized relevant studies investigating the neurophysiological changes related to ABS and how they impact upon the design of appropriate stimulation protocols. Focusing on binaural-beat stimulation, we then discuss the role of monaural- and binaural-beat frequencies in cognition and mood states, in addition to their efficacy in targeting disease symptoms. We aim to highlight important points concerning stimulation parameters and try to address why there are often contradictory findings with regard to the outcomes of ABS.
Big Data Analytics with Datalog Queries on Spark

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, ). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation

In this work we present a novel method for the challenging problem of depth image up sampling. Modern depth cameras such as Kinect or Time-of-Flight cameras deliver dense, high quality depth measurements but are limited in their lateral resolution. To overcome this limitation we formulate a convex optimization problem using higher order regularization for depth image up sampling. In this optimization an an isotropic diffusion tensor, calculated from a high resolution intensity image, is used to guide the up sampling. We derive a numerical algorithm based on a primal-dual formulation that is efficiently parallelized and runs at multiple frames per second. We show that this novel up sampling clearly outperforms state of the art approaches in terms of speed and accuracy on the widely used Middlebury 2007 datasets. Furthermore, we introduce novel datasets with highly accurate ground truth, which, for the first time, enable to benchmark depth up sampling methods using real sensor data.
Topical web crawlers: Evaluating adaptive algorithms

Topical crawlers are increasingly seen as a way to address the scalability limitations of universal search engines, by distributing the crawling process across users, queries, or even client computers. The context available to such crawlers can guide the navigation of links with the goal of efficiently locating highly relevant target pages. We developed a framework to fairly evaluate topical crawling algorithms under a number of performance metrics. Such a framework is employed here to evaluate different algorithms that have proven highly competitive among those proposed in the literature and in our own previous research. In particular we focus on the tradeoff between exploration and exploitation of the cues available to a crawler, and on adaptive crawlers that use machine learning techniques to guide their search. We find that the best performance is achieved by a novel combination of explorative and exploitative bias, and introduce an evolutionary crawler that surpasses the performance of the best nonadaptive crawler after sufficiently long crawls. We also analyze the computational complexity of the various crawlers and discuss how performance and complexity scale with available resources. Evolutionary crawlers achieve high efficiency and scalability by distributing the work across concurrent agents, resulting in the best performance/cost ratio.
A robotically-augmented walker for older adults

Many older adults use walkers to improve their stability and safety while walking. We have developed a robotically augmented walker to reduce fall risk and confusion, and to increase walker convenience and enjoyment. Using a modified version of the CARMEN navigation software suite [11], the walker is capable of parking itself and returning to the user when signaled by remote control. The system also supports navigation in large indoor environments by providing simple directions to target locations such as a cafeteria. The walker received positive reviews during informal testing with residents of a Pennsylvania residence facility for older adults. This research is sponsored by the National Science Foundation under the ITR Program, which is gratefully acknowledged. The views and concussions contained in this document are those of the authors and should not be interpreted as necessarily representing official policies of endorsements, either expressed or implied, of the United States Government of the National Science Foundation.
Failure-atomic msync(): a simple and efficient mechanism for preserving the integrity of durable data

Preserving the integrity of application data across updates is difficult if power outages and system crashes may occur during updates. Existing approaches such as relational databases and transactional key-value stores restrict programming flexibility by mandating narrow data access interfaces. We have designed, implemented, and evaluated an approach that strengthens the semantics of a standard operating system primitive while maintaining conceptual simplicity and supporting highly flexible programming: Failureatomic msync() commits changes to a memory-mapped file atomically, even in the presence of failures. Our Linux implementation of failure-atomic msync() has preserved application data integrity across hundreds of whole-machine power interruptions and exhibits good microbenchmark performance on both spinning disks and solid-state storage. Failure-atomic msync() supports higher layers of fully general programming abstraction, e.g., a persistent heap that easily slips beneath the C++ Standard Template Library. An STL <map> built atop failure-atomic msync() outperforms several local key-value stores that support transactional updates. We integrated failure-atomic msync() into the Kyoto Tycoon key-value server by modifying exactly one line of code; our modified server reduces response times by 26--43% compared to Tycoon's existing transaction support while providing the same data integrity guarantees. Compared to a Tycoon server setup that makes almost no I/O (and therefore provides no support for data durability and integrity over failures), failure-atomic msync() incurs a three-fold response time increase on a fast Flash-based SSD---an acceptable cost of data reliability for many.
Discriminative Phrase Embedding for Paraphrase Identification

This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN to learn the discriminative weights of words and phrases specific to paraphrase task, so that a weighted sum of embeddings can represent sentences more effectively. Based on these two innovations we get competitive state-of-the-art performance on paraphrase identification.
Restoration of images corrupted by mixed Gaussian-impulse noise via l1-l0 minimization

In this paper, we study the restoration of images corrupted by Gaussian plus salt-and-pepper noise, and propose a l1-l0 minimization approach where the l1 term is from impulse noise and the l0 term is from the assumption that the clean image patches admit a sparse representation over certain unknown dictionary. The main algorithm contains three phases. First, we identify the outlier candidates which are likely to be corrupted by salt-and-pepper impulse noise. Second, we recover the image via dictionary learning on the free-outlier pixels. Finally, an alternating minimization algorithm is employed to solve the proposed minimization energy function, leading to an further restoration based on the recovered image in the second step. Comparative experiments are carried out to show the leading performance of the proposed method.
Reconfigurable RFICs in Si-based technologies for a compact intelligent RF front-end

This paper presents reconfigurable RF integrated circuits (ICs) for a compact implementation of an intelligent RF front-end for multiband and multistandard applications. Reconfigurability has been addressed at each level starting from the basic elements to the RF blocks and the overall front-end architecture. An active resistor tunable from 400 to 1600 /spl Omega/ up to 10 GHz has been designed and an equivalent model has been extracted. A fully tunable active inductor using a tunable feedback resistor has been proposed that provides inductances between 0.1-15 nH with Q>50 in the C-band. To demonstrate reconfigurability at the block level, voltage-controlled oscillators with very wide tuning ranges have been implemented in the C-band using the proposed active inductor, as well as using a switched-spiral resonator with capacitive tuning. The ICs have been implemented using 0.18-/spl mu/m Si-CMOS and 0.18-/spl mu/m SiGe-BiCMOS technologies.
Social Structure of Facebook Networks

We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes—gender, class year, major, high school, and residence—at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on the user characteristics. We thereby compare the relative importances of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.  to Social Networks February 11, 2011
Cultural influences on memory.

Research reveals dramatic differences in the ways that people from different cultures perceive the world around them. Individuals from Western cultures tend to focus on that which is object-based, categorically related, or self-relevant whereas people from Eastern cultures tend to focus more on contextual details, similarities, and group-relevant information. These different ways of perceiving the world suggest that culture operates as a lens that directs attention and filters the processing of the environment into memory. The present review describes the behavioral and neural studies exploring the contribution of culture to long-term memory and related processes. By reviewing the extant data on the role of various neural regions in memory and considering unifying frameworks such as a memory specificity approach, we identify some promising directions for future research.
Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach

A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases.