nils-herrmann/clean_scirepeval huggingface dataset:
An Efficient Protocol for Yao?s Millionaires? Problem

The increase in volume and sensitivity of data communicated and processed over the Internet has been accompanied by a corresponding need for e-commerce techniques in which entities can participate in a secure and anonymous fashion. Even simple arithmetic operations over a set of integers partitioned over a network require sophisticated algorithms. As a part of our earlier work, we have developed a secure protocol for computing dot products of two vectors. In this paper,we present a secure protocol for Yao’s millionaires’ problem. In this problem, each of the two participating parties have a number and the objective is to determine whose number is larger without disclosing any information about the numbers. This problem has direct applications in on-line bidding and auctions. Furthermore, combined with a secure dot-product, a solution to this secure multiparty computation provides necessary building blocks for such basic operations as frequent item-set generation in association rule mining. Although an asymptotically optimal solution for the secure multiparty computation of the ‘less-or-equal’ predicate exists in literature, this protocol is not suited for practical applications. Here, we present a protocol which has a much simpler structure and is more efficient for numbers in ranges practically encountered in typical ecommerce applications. Furthermore, advances in cryptanalysis and the subsequent increase in key lengths for public-key cryptographic systems accentuate the advantage of the proposed protocol. We present experimental evidence demonstrating the efficiency of the proposed protocol both in terms of time and communication overhead.
Predicting Successful Memes using Network and Community Structure

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
Bilevel Feature Extraction-Based Text Mining for Fault Diagnosis of Railway Systems

A vast amount of text data is recorded in the forms of repair verbatim in railway maintenance sectors. Efficient text mining of such maintenance data plays an important role in detecting anomalies and improving fault diagnosis efficiency. However, unstructured verbatim, high-dimensional data, and imbalanced fault class distribution pose challenges for feature selections and fault diagnosis. We propose a bilevel feature extraction-based text mining that integrates features extracted at both syntax and semantic levels with the aim to improve the fault classification performance. We first perform an improved X2 statistics-based feature selection at the syntax level to overcome the learning difficulty caused by an imbalanced data set. Then, we perform a prior latent Dirichlet allocation-based feature selection at the semantic level to reduce the data set into a low-dimensional topic space. Finally, we fuse fault features derived from both syntax and semantic levels via serial fusion. The proposed method uses fault features at different levels and enhances the precision of fault diagnosis for all fault classes, particularly minority ones. Its performance has been validated by using a railway maintenance data set collected from 2008 to 2014 by a railway corporation. It outperforms traditional approaches.
Printed MIMO-Antenna System Using Neutralization-Line Technique for Wireless USB-Dongle Applications

A printed two-multiple-input multiple-output (MIMO)-antenna system incorporating a neutralization line for antenna port decoupling for wireless USB-dongle applications is proposed. The two monopoles are located on the two opposite corners of the system PCB and spaced apart by a small ground portion, which serves as a layout area for antenna feeding network and connectors for the use of standalone antennas as an optional scheme. It was found that by removing only 1.5 mm long inwards from the top edge in the small ground portion and connecting the two antennas therein with a thin printed line, the antenna port isolation can be effectively improved. The neutralization line in this study occupies very little board space, and the design requires no conventional modification to the ground plane for mitigating mutual coupling. The behavior of the neutralization line was rigorously analyzed, and the MIMO characteristics of the proposed antennas was also studied and tested in the reverberation chamber. Details of the constructed prototype are described and discussed in this paper.
Multicolor super-resolution imaging with photo-switchable fluorescent probes.

Recent advances in far-field optical nanoscopy have enabled fluorescence imaging with a spatial resolution of 20 to 50 nanometers. Multicolor super-resolution imaging, however, remains a challenging task. Here, we introduce a family of photo-switchable fluorescent probes and demonstrate multicolor stochastic optical reconstruction microscopy (STORM). Each probe consists of a photo-switchable "reporter" fluorophore that can be cycled between fluorescent and dark states, and an "activator" that facilitates photo-activation of the reporter. Combinatorial pairing of reporters and activators allows the creation of probes with many distinct colors. Iterative, color-specific activation of sparse subsets of these probes allows their localization with nanometer accuracy, enabling the construction of a super-resolution STORM image. Using this approach, we demonstrate multicolor imaging of DNA model samples and mammalian cells with 20- to 30-nanometer resolution. This technique will facilitate direct visualization of molecular interactions at the nanometer scale.
Sample-efficient batch reinforcement learning for dialogue management optimization

Spoken Dialogue Systems (SDS) are systems which have the ability to interact with human beings using natural language as the medium of interaction. A dialogue policy plays a crucial role in determining the functioning of the dialogue management module. Handcrafting the dialogue policy is not always an option, considering the complexity of the dialogue task and the stochastic behavior of users. In recent years approaches based on Reinforcement Learning (RL) for policy optimization in dialogue management have been proved to be an efficient approach for dialogue policy optimization. Yet most of the conventional RL algorithms are data intensive and demand techniques such as user simulation. Doing so, additional modeling errors are likely to occur. This paper explores the possibility of using a set of approximate dynamic programming algorithms for policy optimization in SDS. Moreover, these algorithms are combined to a method for learning a sparse representation of the value function. Experimental results show that these algorithms when applied to dialogue management optimization are particularly sample efficient, since they learn from few hundreds of dialogue examples. These algorithms learn in an off-policy manner, meaning that they can learn optimal policies with dialogue examples generated with a quite simple strategy. Thus they can learn good dialogue policies directly from data, avoiding user modeling errors.
Layered Object Models for Image Segmentation

We formulate a layered model for object detection and image segmentation. We describe a generative probabilistic model that composites the output of a bank of object detectors in order to define shape masks and explain the appearance, depth ordering, and labels of all pixels in an image. Notably, our system estimates both class labels and object instance labels. Building on previous benchmark criteria for object detection and image segmentation, we define a novel score that evaluates both class and instance segmentation. We evaluate our system on the PASCAL 2009 and 2010 segmentation challenge data sets and show good test results with state-of-the-art performance in several categories, including segmenting humans.
A Theory of Focus Interpretation

According to the alternative semantics for focus, the semantic reflex of intonational focus is a second semantic value, which in the case of a sentence is a set of propositions. We examine a range of semantic and pragmatic applications of the theory, and extract a unitary principle specifying how the focus semantic value interacts with semantic and pragmatic processes. A strong version of the theory has the effect of making lexical or construction-specific stipulation of a focus-related effect in association with focus constructions impossible. Furthermore, while focus has a uniform import, the sources of meaning differences in association with focus are various. I thank Dorit Abusch, Manfred Bierwisch, Maria Bittner, Peter Blok, Gennaro Chierchia, Angelika Kratzer, Manfred Krifka, Irene Heim, Julia Hirschberg, Sjak de Mey, Manfred Pinkal, Arnim von Stechow, Ede Zimmermann, two reviewers, participants in ESCOL ’90 at Ohio State, the 1991 cognitive science workshop at the University of Texas, the greater New York computational linguistics seminar, the workshop on focus at the 1991 summer school at Saarbrücken, and colloquium audiences at UMass Amherst, Groeningen, Stuttgart and Saarbrücken for initial discussions of the ideas which were eventually realized in this paper, reactions to oral presentations of this and related work, or comments on the written version. This work was done at AT&T Bell Laboratories.
Integrating Object Affordances with Artificial Visual Attention

Affordances, as for example grasping possibilities, are known to play a role in the guidance of human attention but have not been considered in artificial attention systems so far. Extending our earlier work, we investigate the combination of affordance estimation and visual saliency in an artificial visual attention model. Different models based on saliency, affordance estimation, or their combination are suggested and evaluated via their predictions for a change detection task with human observers. Furthermore, we discuss the potential of Growing Neural Gases as a framework for consistently integrating bottom-up saliency, affordance-based and top-down attention mechanisms.
Japanese Text Normalization with Encoder-Decoder Model

Text normalization is the task of transforming lexical variants to their canonical forms. We model the problem of text normalization as a character-level sequence to sequence learning problem and present a neural encoder-decoder model for solving it. To train the encoder-decoder model, many sentences pairs are generally required. However, Japanese non-standard canonical pairs are scarce in the form of parallel corpora. To address this issue, we propose a method of data augmentation to increase data size by converting existing resources into synthesized non-standard forms using handcrafted rules. We conducted an experiment to demonstrate that the synthesized corpus contributes to stably train an encoder-decoder model and improve the performance of Japanese text normalization.
Papular epidermal nevus with "skyline" basal cell layer (PENS).

Several types of epidermal keratinocytic nevus are recognized.



We sought to describe a previously unreported keratinocytic nevus with distinctive clinical and histopathologic features in 5 patients.



We performed a clinical and photographic review, and obtained skin biopsy samples for histopathologic examination from each patient. Genetic analysis to screen for fibroblast growth factor receptor 3 and phosphatidylinositol 3-kinase, catalytic, alpha hotspot mutations was performed on lesional skin from two patients.



Five infants (2 male, 3 female) had from 1 to 11 lesions present since birth. These consisted of 1- to 7-mm hyperkeratotic papules with a rough, flat surface and a round, commalike, rectangular, or polygonal shape. Histopathologic examination showed acanthosis with broad and rectangular rete ridges, and strikingly arranged basal cells with palisaded nuclei. Genetic testing on paraffin-embedded specimens from two patients ruled out hotspot mutations in the fibroblast growth factor receptor 3 and phosphatidylinositol 3-kinase, catalytic, alpha genes.



A small number of patients are presented.



We propose the name "papular epidermal nevus with 'skyline' basal cell layer" (PENS) for this newly recognized condition.
Accurate Floating-Point Summation Part II: Sign, K-Fold Faithful and Rounding to Nearest

In this Part II of this paper we first refine the analysis of error-free vector transformations presented in Part I. Based on that we present an algorithm for calculating the rounded-to-nearest result of s := ∑ pi for a given vector of floatingpoint numbers pi, as well as algorithms for directed rounding. A special algorithm for computing the sign of s is given, also working for huge dimensions. Assume a floating-point working precision with relative rounding error unit eps. We define and investigate a K-fold faithful rounding of a real number r. Basically the result is stored in a vector Resν of K non-overlapping floating-point numbers such that ∑ Resν approximates r with relative accuracy epsK , and replacing ResK by its floating-point neighbors in ∑ Resν forms a lower and upper bound for r. For a given vector of floating-point numbers with exact sum s, we present an algorithm for calculating a K-fold faithful rounding of s using solely the working precision. Furthermore, an algorithm for calculating a faithfully rounded result of the sum of a vector of huge dimension is presented. Our algorithms are fast in terms of measured computing time because they allow good instruction-level parallelism, they neither require special operations such as access to mantissa or exponent, they contain no branch in the inner loop, nor do they require some extra precision: The only operations used are standard floating-point addition, subtraction and multiplication in one working precision, for example double precision. Certain constants used in the algorithms are proved to be optimal.
Protection of Location Privacy using Dummies for Location-based Services

Recently, highly accurate positioning devices enable us to provide various types of location-based services. On the other hand, because position data obtained by such devices include deeply personal information, protection of location privacy is one of the most significant issues of location-based services. Therefore, we propose a technique to anonymize position data. In our proposed technique, the psrsonal user of a location-based service generates several false position data (dummies) sent to the service provider with the true position data of the user. Because the service provider cannot distinguish the true position data, the user’s location privacy is protected. We conducted performance study experiments on our proposed technique using practical trajectory data. As a result of the experiments, we observed that our proposed technique protects the location privacy of users.
Scalable MCMC for Mixed Membership Stochastic Blockmodels

We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on the stochastic gradient Riemannian Langevin sampler and achieves both faster speed and higher accuracy at every iteration than the current state-of-the-art algorithm based on stochastic variational inference. In addition we develop an approximation that can handle models that entertain a very large number of communities. The experimental results show that SG-MCMC strictly dominates competing algorithms in all cases.
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method

In this paper, a new type of 3D bin packing problem (BPP) is proposed, in which a number of cuboidshaped items must be put into a bin one by one orthogonally. The objective is to find a way to place these items that can minimize the surface area of the bin. This problem is based on the fact that there is no fixed-sized bin in many real business scenarios and the cost of a bin is proportional to its surface area. Our research shows that this problem is NP-hard. Based on previous research on 3D BPP, the surface area is determined by the sequence, spatial locations and orientations of items. Among these factors, the sequence of items plays a key role in minimizing the surface area. Inspired by recent achievements of deep reinforcement learning (DRL) techniques, especially Pointer Network, on combinatorial optimization problems such as TSP, a DRL-based method is applied to optimize the sequence of items to be packed into the bin. Numerical results show that the method proposed in this paper achieve about 5% improvement than heuristic method.
De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.
A general-purpose graph dynamical system modeling framework

We describe InterSim, a general purpose flexible framework for simulating graph dynamical systems (GDS) and their generalizations. GDS provide a powerful formalism to model and analyze agent-based systems (ABS) because there is a direct mapping between nodes and edges (which denote interactions) in a GDS and agents and interactions in an ABS, thereby providing InterSim with great expressive power. We describe the design, implementation, capabilities, and features of InterSim; e.g., it enables users to quickly produce simulations of ABS in many application domains. We present illustrative case studies that focus on the simulation of social phenomena. InterSim has been used to simulate networks with 4 million agents and to execute large parametric simulation studies.
A SiC-based power converter module for medium-voltage fast charger for plug-in electric vehicles

This paper presents an isolated power converter module for medium-voltage (2.4 kV), high-power-quality (PF ≥ 0.98, current THD ≤ 2%), 50 kW fast charger for plug-in electric vehicles. The proposed high-efficiency (above 96%), and reduced-footprint converter module utilizes off-the-shelf Silicon Carbide (SiC) devices to step down the rectified single-phase medium-voltage input. The developed module can also serve as a building block for other medium voltage rectifier applications, including power supplies for data centers and other dc power distribution systems. Based on the system requirements, the appropriate unidirectional converter topology was selected, its operation was simulated and validated by experiments on the developed converter prototype.
Peformance analysis of data encryption algorithms

The principal goal guiding the design of any encryption algorithm must be security against unauthorized attacks. However, for all practical applications, performance and speed are also important concerns. These are the two main characteristics that differentiate one encryption algorithm from another. This paper provides the performance comparison between four of the most commonly used encryption algorithms: DES(Data Encryption Standard), 3DES(Triple DES), BLOWFISH and AES (Rijndael). The comparison has been conducted by running several setting to process different sizes of data blocks to evaluate the algorithms encryption and decryption speed. Based on the performance analysis of these algorithms under different hardware and software platform, it has been concluded that the Blowfish is the best performing algorithm among the algorithms under the security against unauthorized attack and the speed is taken into consideration.
Induction of Word and Phrase Alignments for Automatic Document Summarization

Current research in automatic single-document summarization is dominated by two effective, yet nave approaches: summarization by sentence extraction and headline generation via bagof-words models. While successful in some tasks, neither of these models is able to adequately capture the large set of linguistic devices utilized by humans when they produce summaries. One possible explanation for the widespread use of these models is that good techniques have been developed to extract appropriate training data for them from existing document/abstract and document/ headline corpora. We believe that future progress in automatic summarization will be driven both by the development of more sophisticated, linguistically informed models, as well as a more effective leveraging of document/abstract corpora. In order to open the doors to simultaneously achieving both of these goals, we have developed techniques for automatically producing word-to-word and phrase-to-phrase alignments between documents and their human-written abstracts. These alignments make explicit the correspondences that exist in such document/abstract pairs and create a potentially rich data source from which complex summarization algorithms may learn. This paper describes experiments we have carried out to analyze the ability of humans to perform such alignments, and based on these analyses, we describe experiments for creating them automatically. Our model for the alignment task is based on an extension of the standard hidden Markov model and learns to create alignments in a completely unsupervised fashion. We describe our model in detail and present experimental results that show that our model is able to learn to reliably identify word- and phrase-level alignments in a corpus of (document, abstract) pairs.