nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Generation of Syntactically Well-formed and Semantically Appropriate Paraphrases

Paraphrases of an expression are alternative linguistic expressions conveying the same information as the original. Technology for handling paraphrases has been attracting increasing attention due to its potential in a wide range of natural language processing applications; e.g., machine translation, information retrieval, question answering, summarization, authoring and revision support, and reading assistance. In this thesis, we focus on lexical and structural paraphrases in Japanese, such as lexical and phrasal replacement, verb alternation, and topicalization, which can be generated relying on linguistic knowledge only. First, we address how to generate well-formed and appropriate paraphrases. One of the major problems is that it is practically impossible to take into account all sorts of semantic and discourse-related factors which affect the well-formedness and appropriateness of paraphrases. The knowledge, such as transformation rules, used for paraphrase generation tends to be underspecified, and thus would produce erroneous output. The revision process is introduced to detect and correct ill-formed and inappropriate candidates generated in the transfer stage. Within this framework, we first investigate what types of errors tend to occur in lexical and structural paraphrasing, and confirm the feasibility of our transfer-and-revision framework by revealing that most errors occur irrespective of classes of transformation rules. On the basis of another observation; that errors associated with case assignments form one of the major error types, we develop a model for detecting this type of error. The model utilizes a large collection of positive examples and a small collection of negative ones by combining supervised and unsupervised machine learning methods. Experimental results indicate that our model significantly outperforms conventional models. The second issue is to develop a mechanism that is capable of covering a wide variety of paraphrases. One way of gaining the coverage of paraphrase generation is to exploit the systemicity underlying several classes of paraphrases, such as verb alternation and compound noun decomposition. To capture the semantic properties required for generating these classes of paraphrases, we utilize the Lexical Conceptual Structure (LCS). The framework represents verbs as semantic structures with focus of statement and relationships between semantic arguments and syntactic cases. We implement a paraphrase generation model which consists of a case assignment rule and a handful of LCS transformation rules, with particular focus on ∗Doctoral Dissertation, Department of Information Processing, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-DD0261023, March 2005.
Stronger Security of Authenticated Key Exchange

Recent work by Krawczyk  and Menezes [16] has highlighted the importance of understanding well the guarantees and limitations of formal security models when using them to prove the security of protocols. In this paper we focus on security models for authenticated key exchange (AKE) protocols. We observe that there are several classes of attacks on AKE protocols that lie outside the scope of the Canetti-Krawczyk model. Some of these additional attacks have already been considered by Krawczyk . In an attempt to bring these attacks within the scope of the security model we extend the Canetti-Krawczyk model for AKE security by providing significantly greater powers to the adversary. Our contribution is a more compact, integrated, and comprehensive formulation of the security model. We then introduce a new AKE protocol called NAXOS and prove that it is secure against these stronger
QuASM: a system for question answering using semi-structured data

This paper describes a system for question answering using semi-structured metadata, QuASM (pronounced "chasm"). Question answering systems aim to improve search performance by providing users with specific answers, rather than having users scan retrieved documents for these answers. Our goal is to answer factual questions by exploiting the structure inherent in documents found on the World Wide Web (WWW). Based on this structure, documents are indexed into smaller units and associated with metadata. Transforming table cells into smaller units associated with metadata is an important part of this task. In addition, we report on work to improve question classification using language models. The domain used to develop this system is documents retrieved from a crawl of www.fedstats.gov.
Affective and Content Analysis of Online Depression Communities

A large number of people use online communities to discuss mental health issues, thus offering opportunities for new understanding of these communities. This paper aims to study the characteristics of online depression communities (CLINICAL) in comparison with those joining other online communities (CONTROL). We use machine learning and statistical methods to discriminate online messages between depression and control communities using mood, psycholinguistic processes and content topics extracted from the posts generated by members of these communities. All aspects including mood, the written content and writing style are found to be significantly different between two types of communities. Sentiment analysis shows the clinical group have lower valence than people in the control group. For language styles and topics, statistical tests reject the hypothesis of equality on psycholinguistic processes and topics between two groups. We show good predictive validity in depression classification using topics and psycholinguistic clues as features. Clear discrimination between writing styles and contents, with good predictive power is an important step in understanding social media and its use in mental health.
Unifying the Role of IT in Hyperturbulence and Competitive Advantage Via a Multilevel Perspective of IS Strategy

While information technology (IT) serves as a new source of sustainable competitive advantage for firms, it also induces hyperturbulent environments (or hyperturbulence) that erode that sustainable competitive advantage. In this paper, we posit that these contradictions might be due to cross-level nonlinear causality between firm-level IT-based strategic actions and collective-level IT-induced hyperturbulence. We develop a multilevel perspective of IS strategy for theorizing this causality, and unifying novel with established research. Complex adaptive systems theory is employed as the overarching framework for its strength in formalizing cross-level nonlinear causal paths. Using literature-based theorization and agent-based modeling, we establish two bottom-up nonlinear causal paths by which IT drives hyperturbulence: IT can act as an external force (i.e., component IT innovation) to locally instigate firm strategic actions that aggregate to temporary hyperturbulence or as an internal force (i.e., architectural IT innovation) to drive pervasive firm strategic interactions that aggregate to persistent hyperturbulence. Each causal path produces varied amounts of reducible and irreducible uncertainties and thereby renders a top-down nonlinear effect that reshapes the opportunity for IT to contribute to competitive advantage. This multilevel theorization paves the way for new, IS-specific theory regarding IT’s unique role in inducing nonlinear dynamics and in affording new business strategies in today’s competitive environments.
The comparison of soil sensors for integrated creation of IOT-based Wetting front detector (WFD) with an efficient irrigation system to support precision farming

This study investigates a prototyping of integrated system of Internet of Things based Wetting front detector (IOT-WFD) which focuses on how to enhance the IOT based Wetting front detector design for smart irrigation system. The empirical study was conducted with 2 sensors type to detect the wetting fronts which are the Frequency Domain Reflectrometry sensor (FDR) and Resistor-based sensor (RB) integrated and design with low-cost WFD. The results of this study point toward the IOT-WFD as an appropriated technology providing real time wetting front information in soil positively for application in terms of agricultural water management, with precision agriculture and efficient irrigation domain with a related decision knowledge that matches with the technology trend and smart farmer requirements. Evidence of positive results of this prototyping summary has been provided.
Simulation of CAN bus physical layer using SPICE

This paper deals with a computational simulation of high speed CAN bus physical layer using SPICE. At the beginning, the general electric parameters of the physical layer are described - these parameters are in compliance with the international standard ISO 11898-2. The next part of the paper describes a SPICE library, which contains all basic electronic parts of physical layer, including CAN transceivers, transmission lines, common mode chokes and ESD protections. The model of ESD generator is also included in the library. In the paper are shown the most important results from the illustrative simulation and the results are compared with the results of the experimental measurement.
MAGAN: Margin Adaptation for Generative Adversarial Networks

We propose a novel training procedure for Generative Adversarial Networks (GANs) to improve stability and performance by using an adaptive hinge loss objective function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive both a principled criterion for updating the margin and an approximate convergence measure. The resulting training procedure is simple yet robust on a diverse set of datasets. We evaluate the proposed training procedure on the task of unsupervised image generation, noting both qualitative and quantitative performance improvements.
From Entity Recognition to Entity Linking: A Survey of Advanced Entity Linking Techniques (人工知能学会全国大会(第26回)文化,科学技術と未来) -- (International Organized Session「Special Session on Web Intelligence & Data Mining」)

Several research results have shown that specifying the information about certain entities is the most common information demand of information retrieval users. The needs should be answered by returning specific entities, their properties or related concepts instead of just any type of documents. While some search engines are capable of recognizing specific types of entities, true entity-oriented search still has a long way to go because of the high ambiguity in names across documents. Entity linking (EL) goes beyond the entity recognition task by linking a textual named entity mention to a knowledge base entry. It is a difficult task involving several challenges. This paper gives a survey of the EL tasks in the general and the biomedical domain. In addition, results of our latest EL work are provided for reference, which uncover new EL challenges found in biomedical text mining, along with discussions regarding their possible solutions.
Max-pooling convolutional neural networks for vision-based hand gesture recognition

Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates noisy edges. Our big and deep MPCNN classifies 6 gesture classes with 96% accuracy, nearly three times better than the nearest competitor. Experiments with mobile robots using an ARM 11 533MHz processor achieve real-time gesture recognition performance.
Large gain linear series-fed microstrip antenna arrays at Ka and C bands

In this paper, the design of a series-fed microstrip antenna array is discussed and its design challenge of limited gain is explained. It discusses that the phase between the patches of the array plays a significant role in overcoming the challenge. A 23 element series fed linear array at Ka-band gives gain of 19dBi with beam in broadside direction and has SLL better than -15dB. A scaled down version at C-band has been designed and fabricated. A 7-element series fed antenna array yielded measured gain of 15.1 dBi at 5.79 GHz. This design can be extended for planar antenna arrays to obtain higher gain.
Help your mobile applications with fog computing

Cloud computing has paved a way for resource-constrained mobile devices to speed up their computing tasks and to expand their storage capacity. However, cloud computing is not necessary a panacea for all mobile applications. The high network latency to cloud data centers may not be ideal for delay-sensitive applications while storing everything on public clouds risks users' security and privacy. In this paper, we discuss two preliminary ideas, one for mobile application offloading and the other for mobile storage expansion, by leveraging the edge intelligence offered by fog computing to help mobile applications. Preliminary experiments conducted based on implemented prototypes show that fog computing can provide an effective and sometimes better alternative to help mobile applications.
SHEEP, GOATS, LAMBS and WOLVES: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation

Performance variabilit y in speech and speaker recognition systems can be attributed to many factors. One major factor, which is often acknowledged but seldom analyzed, is inherent differences in the recognizabilit y of different speakers. In speaker recognition systems such differences are characterized by the use of animal names for different types of speakers, including sheep, goats, lambs and wolves, depending on their behavior with respect to automatic recognition systems. In this paper we propose statistical tests for the existence of these animals and apply these tests to hunt for such animals using results from the 1998 NIST speaker recognition evaluation.
Lazy Lempel-Ziv Factorization Algorithms

For decades the Lempel-Ziv (LZ77) factorization has been a cornerstone of data compression and string processing algorithms, and uses for it are still being uncovered. For example, LZ77 is central to several recent text indexing data structures designed to search highly repetitive collections. However, in many applications computation of the factorization remains a bottleneck in practice. In this article, we describe a number of simple and fast LZ77 factorization algorithms, which consistently outperform all previous methods in practice, use less memory, and still offer strong worst-case performance guarantees. A common feature of the new algorithms is that they compute longest common prefix information in a lazy fashion, with the degree of laziness in preprocessing characterizing different algorithms.
Nonlinear total variation based noise removal algorithms

A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t → ∞ the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.
The effect of shame and shame memories on paranoid ideation and social anxiety.

Social wariness and anxiety can take different forms. Paranoid anxiety focuses on the malevolence of others, whereas social anxiety focuses on the inadequacies in the self in competing for social position and social acceptance. This study investigates whether shame and shame memories are differently associated with paranoid and social anxieties.



Shame, traumatic impact of shame memory, centrality of shame memory, paranoia and social anxiety were assessed using self-report questionnaires in 328 participants recruited from the general population.



Results from path analyses show that external shame is specifically associated with paranoid anxiety. In contrast, internal shame is specifically associated with social anxiety. In addition, shame memories, which function like traumatic memories, or that are a central reference point to the individual's self-identity and life story, are significantly associated with paranoid anxiety, even when current external and internal shame are considered at the same time. Thus, traumatic impact of shame memory and centrality of shame memory predict paranoia (but not social anxiety) even when considering for current feelings of shame.



Our study supports the evolutionary model suggesting there are two different types of 'conspecific' anxiety, with different evolutionary histories, functions and psychological processes. Paranoia, but less so social anxiety, is associated with traumatic impact and the centrality of shame memories. Researchers and clinicians should distinguish between types of shame memory, particularly those where the self might have felt vulnerable and subordinate and perceived others as threatening and hostile, holding malevolent intentions towards the self.
A fast iterative nearest point algorithm for support vector machine classifier design

In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Friess is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive.
PAST-TENSE GENERATION FROM FORM VERSUS MEANING: BEHAVIOURAL DATA AND SIMULATION EVIDENCE.

The standard task used to study inflectional processing of verbs involves presentation of the stem form from which the participant is asked to generate the past tense. This task reveals a processing disadvantage for irregular relative to regular English verbs, more pronounced for lower-frequency items. Dual- and single-mechanism theories of inflectional morphology are both able to account for this pattern; but the models diverge in their predictions concerning the magnitude of the regularity effect expected when the task involves past-tense generation from meaning. In this study, we asked normal speakers to generate the past tense from either form (verb stem) or meaning (action picture). The robust regularity effect observed in the standard form condition was no longer reliable when participants were required to generate the past tense from meaning. This outcome would appear problematic for dual-mechanism theories to the extent that they assume the process of inflection requires stem retrieval. By contrast, it supports single-mechanism models that consider stem retrieval to be task-dependent. We present a single-mechanism model of verb inflection incorporating distributed phonological and semantic representations that reproduces this task-dependent pattern.
The Interrelationships Among Attachment Style , Personality Traits , Interpersonal Competency , and Facebook Use

Young adults are increasingly integrating social media into their daily lives and social behavior, and empirical work on the personality traits and interpersonal competency associated with use and the potential consequences of use on social behavior is still emerging. The present study sought to investigate the interrelationships among constructs relevant to the developmental tasks associated with emerging adulthood and Facebook use, including adult attachment style, Five-Factor Model personality traits, and interpersonal competency. Using data collected from 617 emerging adults, we used structural equation modeling to test a theoretical model explaining interrelationships among the constructs under study. Results from our data yielded a well-fitting model indicating that insecure attachment had direct and positive effects on neuroticism, direct and negative effects on extraversion, direct and negative effects on interpersonal competency, and negative indirect effects on Facebook use. In addition, only extraversion was related to interpersonal competency and Facebook use when first accounting for attachment style. These results highlight the role of attachment style, and its importance in both developing personality traits and interpersonal skills, and online social behavior, which aligns well with an attachment theory framework. Lastly, we discussed future directions for research, as well as theoretical and practice implications for psychologists.
Concept and Implementation of a PLL-Controlled Interlaced Chirp Sequence Radar for Optimized Range–Doppler Measurements

In this paper, we introduce a novel concept for frequency-modulated continuous-wave radar. The approach is based on an interlaced chirp sequence waveform, which is controlled by phase-locked loops (PLLs). The use of PLLs improves frequency chirp linearity and reduces phase noise compared with open-loop voltage-controlled oscillators commonly used to generate the fast chirps. In contrast to basic range-Doppler processing, it enables higher target velocities to be detected unambiguously at the same resolution, which makes it suitable for automotive applications. Separate frequency synthesizers generate the interlaced ramps in the system. An RF switch combines these two signals to suppress transients caused by oscillator overshoot and to avoid incoherencies due to programming times of the PLL ICs. We built a prototype radar system in K-band. The test results are promising and bode well for other applications.