nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of LCLC resonant converters for high-voltage high-frequency applications

This paper proposes a novel LCLC full-bridge resonant converter with ZCS and ZVS for high-voltage high-frequency applications. In this operating model, both of ZCS and ZVS can be achieved and the switching losses can be reduced to a minimum. The proposed converter adopts the parasitic parameters of the step-up voltage transformer and one other serial capacitor is needed to form the resonant tank. Compared with other resonant converters, where only ZCS or ZVS can be achieved, the proposed working model can increase conversion efficiency with soft-switching features. Finally, A 40 V input, 4800 V output and 300 W prototype with 500 kHz switching frequency was built to verify the benefits of the proposed converter. The achieved efficiency of the converter peaked at 96.7%.
Improved Style Transfer by Respecting Inter-layer Correlations

A popular series of style transfer methods apply a style to a content image by controlling mean and covariance of values in early layers of a feature stack. This is insufficient for transferring styles that have strong structure across spatial scales like, e.g., textures where dots lie on long curves. This paper demonstrates that controlling inter-layer correlations yields visible improvements in style transfer methods. We achieve this control by computing cross-layer, rather than within-layer, gram matrices. We find that (a) cross-layer gram matrices are sufficient to control within-layer statistics. Inter-layer correlations improves style transfer and texture synthesis. The paper shows numerous examples on ”hard” real style transfer problems (e.g. long scale and hierarchical patterns); (b) a fast approximate style transfer method can control cross-layer gram matrices; (c) we demonstrate that multiplicative, rather than additive style and content loss, results in very good style transfer. Multiplicative loss produces a visible emphasis on boundaries, and means that one hyper-parameter can be eliminated. 1 ar X iv :1 80 1. 01 93 3v 1 [ cs .C V ] 5 J an 2 01 8
Automatic fruit and vegetable classification from images

Vision and Pattern Recognition problems such as face recognition, fingerprinting identification, image c ategorization, and DNA sequencing often have an arbitrarily large num ber of classes and properties to consider. To deal with such c omplex problems using just one feature descriptor is a di fficult task and feature fusion may become mandatory. Although normal feature fusion is quite effective for some problems, it can yield unexpected classifica tion results when the di fferent features are not properly normalized and preprocessed. Besides it has the drawback of increasing the dimensionality which might require more tra ining data. To cope with these problems, this paper introduces a un ified approach that can combine many featuresandclassifiersthat requires less training and is more adequate to some problems than a naı̈ve method, where all features are simply concaten ated and fed independently to each classification algorithm. Beside s that, the presented technique is amenable to continuous le arning, both when refining a learned model and also when adding new classes to b discriminated. The introduced fusion approach is vali dated using a multi-class fruit-and-vegetable categorization t ask in a semi-controlled environment, such as a distributio n center or the supermarket cashier. The results show that the solution is a ble to reduce the classification error in up to 15 percentage p oints with respect to the baseline.
Standard Compliant Hazard and Threat Analysis for the Automotive Domain

The automotive industry has successfully collaborated to release the ISO 26262 standard for developing safe software for cars. The standard describes in detail how to conduct hazard analysis and risk assessments to determine the necessary safety measures for each feature. However, the standard does not concern threat analysis for malicious attackers or how to select appropriate security countermeasures. We propose to apply ISO 27001 for this purpose and show how it can be applied together with ISO 26262. We show how ISO 26262 documentation can be re-used and enhanced to satisfy the analysis and documentation demands of the ISO 27001 standard. We illustrate our approach based on an electronic steering column lock system.
Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment

Energy-aware design and evaluation of network protocols r equires knowledge of the energy consumption behavior of actu al wireless interfaces. But little practical information is available about the energy consumption behavior of well-known wireless network interfaces and device specifications do not provide information in a form that is helpful to protocol developers. This paper describes a series of exper iments which obtained detailed measurements of the energy consumption of a n IEEE 802.11 wireless network interface operating in an ad hoc networking environment. The data is presented as a collection of linear equations for calculating the energy consumed in sending, receiving and discarding broad cast and pointto-point data packets of various sizes. Some implications f or protocol design and evaluation in ad hoc networks are discussed. energy consumption, IEEE 802.11, ad hoc networks
Polysemy in Sentence Comprehension: Effects of Meaning Dominance.

Words like church are polysemous, having two related senses (a building and an organization). Three experiments investigated how polysemous senses are represented and processed during sentence comprehension. On one view, readers retrieve an underspecified, core meaning, which is later specified more fully with contextual information. On another view, readers retrieve one or more specific senses. In a reading task, context that was neutral or biased towards a particular sense preceded a polysemous word. Disambiguating material consistent with only one sense followed, in a second sentence (Experiment 1) or the same sentence (Experiments 2 & 3). Reading the disambiguating material was faster when it was consistent with that context, and dominant senses were committed to more strongly than subordinate senses. Critically, following neutral context, the continuation was read more quickly when it selected the dominant sense, and the degree of sense dominance partially explained the reading time advantage. Similarity of the senses also affected reading times. Across experiments, we found that sense selection may not be completed immediately following a polysemous word but is completed at a sentence boundary. Overall, the results suggest that readers select an individual sense when reading a polysemous word, rather than a core meaning.
Inverse Kinematic Infrared Optical Finger Tracking

Human hand and finger tracking is an important input modality for both virtual and augmented reality. We present a novel device that overcomes the disadvantages of current glove-based or visionbased solutions by using inverse-kinematic models of the human hand. The fingertips are tracked by an optical infrared tracking system and the pose of the phalanxes is calculated from the known anatomy of the hand. The new device is lightweight and accurate and allows almost arbitrary hand movement. Examinations of the flexibility of the hand have shown that this new approach is practical because ambiguities in the relationship between finger tip positions and joint positions, which are theoretically possible, occur only scarcely in practical use.
Crop disease classification using texture analysis

With the Agriculture Sector being the backbone of not only a large number of industries but also society as a whole, there is a rising need to grow good quality crops which in turn will give a high yield. For this to happen, it is crucial to monitor the crops throughout their growth period. In this paper, Image processing is used to detect and classify sunflower crop diseases based on the image of their leaf. The images are taken through a high resolution digital camera and after preprocessing, are subjected to k-means clustering to get the diseased part of the leaf. These are then run through the various machine learning algorithms and classified based on their color and texture features. A comparison based on accuracy between various machine learning algorithms is done namely K-Nearest Neighbors, Multi-Class Support Vector Machine, Naive Bayes and Multinomial Logistic Regression to achieve maximum accuracy. The implementation has been done using MATLAB.
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu .
Descriptive versus interpretive phenomenology: their contributions to nursing knowledge.

A number of articles in the nursing literature discuss the differences between descriptive and interpretive approaches to doing phenomenology. A review of studies demonstrates, however, that many researchers do not articulate which approach guides the study, nor do they identify the philosophical assumptions on which the study is based. Such lack of clarity makes it difficult for the reader to obtain a sense of how the knowledge produced by the study is to be evaluated and used. In this article, the authors compare the philosophical components of descriptive and interpretive approaches to doing phenomenology and illustrate types of knowledge produced by each through reviewing specific studies. They focus on the various uses of phenomenology in generating useful knowledge for health care practice.
Decomposing Adult Age Differences in Working Memory

Two studies, involving a total of 460 adults between 18 and 87 years of age, were conducted to determine which of several hypothesized processing components was most responsible for age-related declines in working memory functioning. Significant negative correlations between age and measures of working memory (i.e., from -.39 to -.52) were found in both studies, and these relations were substantially attenuated by partialing measures hypothesized to reflect storage capacity, processing efficiency, coordination effectiveness, and simple comparison speed. Because the greatest attenuation of the age relations occurred with measures of simple processing speed, it was suggested that many of the age differences in working memory may be mediated by age-related reductions in the speed of executing elementary operations.
Predicting Matchups and Preferences in Context

We present a general probabilistic framework for predicting the outcome of pairwise matchups (e.g. two-player sport matches) and pairwise preferences (e.g. product preferences), both of which have widespread applications ranging from matchmaking in computer games to recommendation in e-commerce. Unlike existing models for these tasks, our model not only learns representations of the items in a more expressive latent vector space, but also models how context modifies matchup and preference outcomes. For example, the context "weather" may alter the winning probability in a tennis match, or the fact that the user is on a mobile device may alter his preferences among restaurants. More generally, the model is capable of handling any symmetric game/comparison problem that can be described by vectorized player/item and game/context features. We provide a comprehensive evaluation of its predictive performance with real datasets from both domains to show its ability to predict preference and game outcomes more accurately than existing models. Furthermore, we demonstrate on synthetic datasets the expressiveness of the model when compared against theoretical limits.
A holistic approach to service survivability

We present SABER (Survivability Architecture: Block, Evade, React), a proposed survivability architecture that blocks, evades and reacts to a variety of attacks by using several security and survivability mechanisms in an automated and coordinated fashion. Contrary to the ad hoc manner in which contemporary survivable systems are built-using isolated, independent security mechanisms such as firewalls, intrusion detection systems and software sandboxes-SABER integrates several different technologies in an attempt to provide a unified framework for responding to the wide range of attacks malicious insiders and outsiders can launch.
 This coordinated multi-layer approach will be capable of defending against attacks targeted at various levels of the network stack, such as congestion-based DoS attacks, software-based DoS or code-injection attacks, and others. Our fundamental insight is that while multiple lines of defense are useful, most conventional, uncoordinated approaches fail to exploit the full range of available responses to incidents. By coordinating the response, the ability to survive successful security breaches increases substantially.
 We discuss the key components of SABER, how they will be integrated together, and how we can leverage on the promising results of the individual components to improve survivability in a variety of coordinated attack scenarios. SABER is currently in the prototyping stages, with several interesting open research topics.
User Association for Load Balancing in Heterogeneous Cellular Networks

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.
Attribute-Based Signatures

We introduce Attribute-Based Signatures (ABS), a versatile primitive that allows a party to sign a message with fine-grained control over identifying information. In ABS, a signer, who possesses a set of attributes from the authority, can sign a message with a predicate that is satisfied by his attributes. The signature reveals no more than the fact that a single user with some set of attributes satisfying the predicate has attested to the message. In particular, the signature hides the attributes used to satisfy the predicate and any identifying information about the signer (that could link multiple signatures as being from the same signer). Furthermore, users cannot collude to pool their attributes together. We give a general framework for constructing ABS schemes, and then show several practical instantiations based on groups with bilinear pairing operations, under standard assumptions. Further, we give a construction which is secure even against a malicious attribute authority, but the security for this scheme is proven in the generic group model. We describe several practical problems that motivated this work, and how ABS can be used to solve them. Also, we show how our techniques allow us to extend Groth-Sahai NIZK proofs to be simulation-extractable and identity-based with low overhead.
Security in IPv 6-enabled Wireless Sensor Networks : An Implementation of TLS / DTLS for the Contiki Operating System

During the last several years the advancements in technology made it possible for small sensor nodes to communicate wirelessly with the rest of the Internet. With this achievement the question of securing such IP-enabled Wireless Sensor Networks (IP-WSNs) emerged and has been an important research topic since. In this thesis we discuss our implementation of TLS and DTLS protocols using a pre-shared key cipher suite (TLS PSK WITH AES 128 CCM 8) for the Contiki operating system. Apart from simply adding a new protocol to the set of protocols supported by the Contiki OS, this project allows us to evaluate how suitable the transport-layer security and pre-shared key management schemes are for IP-WSNs.
A hybrid dragonfly algorithm with extreme learning machine for prediction

In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of ELM. Also, there is no guarantee of optimality of weights and biases settings on the hidden layer. DA is a recently promising optimization algorithm that mimics the moving behavior of moths. DA is exploited here to select less number of nodes in the hidden layer to speed up the performance of the ELM. It also is used to choose the optimal hidden layer weights and biases. A set of assessment indicators is used to evaluate the proposed and compared methods over ten regression data sets from the UCI repository. Results prove the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy. The proposed model was compared against the set of commonly used optimizers and regression systems. These optimizers are namely, particle swarm optimization (PSO) and genetic algorithm (GA). The proposed DA-ELM model proved an advance overall compared methods in both accuracy and generalization ability.
Retrieving documents with mathematical content

Many documents with mathematical content are published on the Web, but conventional search engines that rely on keyword search only cannot fully exploit their mathematical information. In particular, keyword search is insufficient when expressions in a document are not annotated with natural keywords or the user cannot describe her query with keywords. Retrieving documents by querying their mathematical content directly is very appealing in various domains such as education, digital libraries, engineering, patent documents, medical sciences, etc. Capturing the relevance of mathematical expressions also greatly enhances document classification in such domains.
 Unlike text retrieval, where keywords carry enough semantics to distinguish text documents and rank them, math symbols do not contain much semantic information on their own. In fact, mathematical expressions typically consist of few alphabetical symbols organized in rather complex structures. Hence, the structure of an expression, which describes the way such symbols are combined, should also be considered. Unfortunately, there is no standard testbed with which to evaluate the effectiveness of a mathematics retrieval algorithm.
 In this paper we study the fundamental and challenging problems in mathematics retrieval, that is how to capture the relevance of mathematical expressions, how to query them, and how to evaluate the results. We describe various search paradigms and propose retrieval systems accordingly. We discuss the benefits and drawbacks of each approach, and further compare them through an extensive empirical study.
A novel evolutionary data mining algorithm with applications to churn prediction

Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification models and they have been shown to be very effective. However, when it comes to determining the likelihood of each classification made, many of them are not designed with such purpose in mind. For this, they are not readily applicable to such problem as churn prediction. For such an application, the goal is not only to predict whether or not a subscriber would switch from one carrier to another, it is also important that the likelihood of the subscriber’s doing so be predicted. The reason for this is that a carrier can then choose to provide special personalized offer and services to those subscribers who are predicted with higher likelihood to churn. Given its importance, we propose a new data mining algorithm, called data mining by evolutionary learning (DMEL), to handle classification problems of which the accuracy of each predictions made has to be estimated. In performing its tasks, DMEL searches through the possible rule space using an evolutionary approach that has the following characteristics: 1) the evolutionary process begins with the generation of an initial set of first-order rules (i.e., rules with one conjunct/condition) using a probabilistic induction technique and based on these rules, rules of higher order (two or more conjuncts) are obtained iteratively; 2) when identifying interesting rules, an objective interestingness measure is used; 3) the fitness of a chromosome is defined in terms of the probability that the attribute values of a record can be correctly determined using the rules it encodes; and 4) the likelihood of predictions (or classifications) made are estimated so that subscribers can be ranked according to their likelihood to churn. Experiments with different data sets showed that DMEL is able to effectively discover interesting classification rules. In particular, it is able to predict churn accurately under different churn rates when applied to real telecom subscriber data.
TR 0710 A Fast Algorithm for Image Deblurring with Total Variation Regularization

We propose and test a simple algorithmic framework for recovering images from blurry and noisy observations based on total variation (TV) regularization when a blurring point-spread function is given. Using a splitting technique, we construct an iterative procedure of alternately solving a pair of easy subproblems associated with an increasing sequence of penalty parameter values. The main computation at each iteration is three Fast Fourier Transforms (FFTs). We present numerical results showing that a rudimentary implementation of our algorithm already performs favorably in comparison with two of the existing start-of-the-art algorithms. In particular, it runs orders of magnitude faster than a number of existing algorithms for solving TVL2-based de-convolution problems to good accuracies.