nils-herrmann/clean_scirepeval huggingface dataset:
Frameworks for entity matching: A comparison

Entity matching is a crucial and difficult task for data integration. Entity matching frameworks provide several methods and their combination to effectively solve different match tasks. In this paper, we comparatively analyze 11 proposed frameworks for entity matching. Our study considers both frameworks which do or do not utilize training data to semiautomatically find an entity matching strategy to solve a given match task. Moreover, we consider support for blocking and the combination of different match algorithms. We further study how the different frameworks have been evaluated. The study aims at exploring the current state of the art in research prototypes of entity matching frameworks and their evaluations. The proposed criteria should be helpful to identify promising framework approaches and enable categorizing and comparatively assessing additional entity matching frameworks and their evaluations.
Lifetime Enhancement in Wireless Sensor Networks Using Fuzzy Approach and A-Star Algorithm

Wireless sensor networks (WSNs) are used in many applications to gather sensitive information which is then forwarded to an analysis center. Resource limitations have to be taken into account when designing a WSN infrastructure. Unbalanced energy consumption is an inherent problem in WSNs, characterized by multihop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. This paper proposes a new routing method for WSNs to extend network lifetime using a combination of a fuzzy approach and an A-star algorithm. The proposal is to determine an optimal routing path from the source to the destination by favoring the highest remaining battery power, minimum number of hops, and minimum traffic loads. To demonstrate the effectiveness of the proposed method in terms of balancing energy consumption and maximization of network lifetime, we compare our approach with the A-star search algorithm and fuzzy approach using the same routing criteria in two different topographical areas. Simulation results demonstrate that the network lifetime achieved by the proposed method could be increased by nearly 25% more than that obtained by the A-star algorithm and by nearly 20% more than that obtained by the fuzzy approach.
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity

To model semantic similarity for multilingual and cross-lingual sentence pairs, we first translate foreign languages into English, and then build an efficient monolingual English system with multiple NLP features. Our system is further supported by deep learning models and our best run achieves the mean Pearson correlation 73.16% in primary track.
Social robots as embedded reinforcers of social behavior in children with autism.

In this study we examined the social behaviors of 4- to 12-year-old children with autism spectrum disorders (ASD; N = 24) during three tradic interactions with an adult confederate and an interaction partner, where the interaction partner varied randomly among (1) another adult human, (2) a touchscreen computer game, and (3) a social dinosaur robot. Children spoke more in general, and directed more speech to the adult confederate, when the interaction partner was a robot, as compared to a human or computer game interaction partner. Children spoke as much to the robot as to the adult interaction partner. This study provides the largest demonstration of social human-robot interaction in children with autism to date. Our findings suggest that social robots may be developed into useful tools for social skills and communication therapies, specifically by embedding social interaction into intrinsic reinforcers and motivators.
Adtributor: Revenue Debugging in Advertising Systems

Advertising (ad) revenue plays a vital role in supporting free websites. When the revenue dips or increases sharply, ad system operators must find and fix the rootcause if actionable, for example, by optimizing infrastructure performance. Such revenue debugging is analogous to diagnosis and root-cause analysis in the systems literature but is more general. Failure of infrastructure elements is only one potential cause; a host of other dimensions (e.g., advertiser, device type) can be sources of potential causes. Further, the problem is complicated by derived measures such as costs-per-click that are also tracked along with revenue. Our paper takes the first systematic look at revenue debugging. Using the concepts of explanatory power, succinctness, and surprise, we propose a new multidimensional root-cause algorithm for fundamental and derived measures of ad systems to identify the dimension mostly likely to blame. Further, we implement the attribution algorithm and a visualization interface in a tool called the Adtributor to help troubleshooters quickly identify potential causes. Based on several case studies on a very large ad system and extensive evaluation, we show that the Adtributor has an accuracy of over 95% and helps cut down troubleshooting time by an order of magnitude.
Investigating web services on the world wide web

Searching for Web service access points is no longer attached to service registries as Web search engines have become a new major source for discovering Web services. In this work, we conduct a thorough analytical investigation on the plurality of Web service interfaces that exist on the Web today. Using our Web Service Crawler Engine (WSCE), we collect metadata service information on retrieved interfaces through accessible UBRs, service portals and search engines. We use this data to determine Web service statistics and distribution based on object sizes, types of technologies employed, and the number of functioning services. This statistical data can be used to help determine the current status of Web services. We determine an intriguing result that 63% of the available Web services on the Web are considered to be active. We further use our findings to provide insights on improving the service retrieval process.
Towards a Contextual Pragmatic Model to Detect Irony in Tweets

This paper proposes an approach to capture the pragmatic context needed to infer irony in tweets. We aim to test the validity of two main hypotheses: (1) the presence of negations, as an internal propriety of an utterance, can help to detect the disparity between the literal and the intended meaning of an utterance, (2) a tweet containing an asserted fact of the form Not(P1) is ironic if and only if one can assess the absurdity of P1. Our first results are encouraging and show that deriving a pragmatic contextual model is feasible.
Effects of Take-Over Requests and Cultural Background on Automation Trust in Highly Automated Driving

Appropriate automation trust is a prerequisite for safe, comfortable and efficient use of highly automated driving systems (HADS). Earlier research indicates that a drivers’ nationality and Take-Over Requests (TOR) due to imperfect system reliability might affect trust, but this has never been investigated in the context of highly automated driving. A driving simulator study (N = 80) showed that TORs only temporarily lowered trust in HADSs, and revealed similarities in trust formation between German and Chinese drivers. Trust was significantly higher after experiencing the system than before, both for German and Chinese participants. However, Chinese drivers reported significantly higher automation mistrust than German drivers. Self-report measures of automation trust were not connected to behavioral measures. The results support a distinction between automation trust and mistrust as separate constructs, shortand long-term effects of TORs on automation trust, and cultural differences in automation trust.
Teaching social influence : Demonstrations and exercises from the discipline of social psychology

Education is enhanced when students are able to be active, rather than passive, learners (McKeachie, 2002). Fortunately, social psychologists have a rich history of creating and publishing classroom demonstrations that allow for such active learning. Unfortunately, these demonstrations have been published in diverse journals, teaching manuals, and edited volumes that are not always readily available. The purpose of this article is to review demonstrations and exercises that have been developed for teaching students about social influence. Using an annotated bibliography format, we review more than five dozen techniques that assist instructors in demonstrating such social influence principles as cognitive dissonance, conformity, obedience, deindividuation, propaganda, framing, persuasion, advertising, social norms, and the selffulfilling prophecy.
Five-Factor Model personality profiles of drug users

Personality traits are considered risk factors for drug use, and, in turn, the psychoactive substances impact individuals' traits. Furthermore, there is increasing interest in developing treatment approaches that match an individual's personality profile. To advance our knowledge of the role of individual differences in drug use, the present study compares the personality profile of tobacco, marijuana, cocaine, and heroin users and non-users using the wide spectrum Five-Factor Model (FFM) of personality in a diverse community sample.



Participants (N = 1,102; mean age = 57) were part of the Epidemiologic Catchment Area (ECA) program in Baltimore, MD, USA. The sample was drawn from a community with a wide range of socio-economic conditions. Personality traits were assessed with the Revised NEO Personality Inventory (NEO-PI-R), and psychoactive substance use was assessed with systematic interview.



Compared to never smokers, current cigarette smokers score lower on Conscientiousness and higher on Neuroticism. Similar, but more extreme, is the profile of cocaine/heroin users, which score very high on Neuroticism, especially Vulnerability, and very low on Conscientiousness, particularly Competence, Achievement-Striving, and Deliberation. By contrast, marijuana users score high on Openness to Experience, average on Neuroticism, but low on Agreeableness and Conscientiousness.



In addition to confirming high levels of negative affect and impulsive traits, this study highlights the links between drug use and low Conscientiousness. These links provide insight into the etiology of drug use and have implications for public health interventions.
Improving Deep Neural Networks with Probabilistic Maxout Units

We present a probabilistic variant of the recently introduced maxout unit. The success of deep neural networks utilizing maxout can partly be attributed to favorable performance under dropout, when compared to rectified linear units. It however also depends on the fact that each maxout unit performs a pooling operation over a group of linear transformations and is thus partially invariant to changes in its input. Starting from this observation we ask the question: Can the desirable properties of maxout units be preserved while improving their invariance properties ? We argue that our probabilistic maxout (probout) units successfully achieve this balance. We quantitatively verify this claim and report classification performance matching or exceeding the current state of the art on three challenging image classification benchmarks (CIFAR-10, CIFAR-100 and SVHN).
Nonmonotonic relation of autogynephilia and heterosexual attraction.

In this study I investigated the relation between normal heterosexual attraction and autogynephilia (a man's propensity to be sexually aroused by the thought or image of himself as a woman). The subjects were 427 adult male outpatients who reported histories of dressing in women's garments, of feeling like women, or both. The data were questionnaire measures of autogynephilia, heterosexual interest, and other psychosexual variables. As predicted, the highest levels of autogynephilia were observed at intermediate rather than high levels of heterosexual interest; that is, the function relating these variables took the form of an inverted U. This finding supports the hypothesis that autogynephilia is a misdirected type of heterosexual impulse, which arises in association with normal heterosexuality but also competes with it.
Why are average faces attractive? The effect of view and averageness on the attractiveness of female faces.

Images of faces manipulated to make their shapes closer to the average are perceived as more attractive. The influences of symmetry and averageness are often confounded in studies based on full-face views of faces. Two experiments are reported that compared the effect of manipulating the averageness of female faces in profile and full-face views. Use of a profile view allows a face to be "morphed" toward an average shape without creating an image that becomes more symmetrical. Faces morphed toward the average were perceived as more attractive in both views, but the effect was significantly stronger for full-face views. Both full-face and profile views morphed away from the average shape were perceived as less attractive. It is concluded that the effect of averageness is independent of any effect of symmetry on the perceived attractiveness of female faces.
SEMAFOR 1.0: A Probabilistic Frame-Semantic Parser

An elaboration on (Das et al., 2010), this report formalizes frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. SEMAFOR 1.0 finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two feature-based, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results and is released for public use.
In vivo bio-integration of three hyaluronic acid fillers in human skin: a histological study.

Hyaluronic acid (HA) formulations are used for aesthetic applications. Different cross-linking technologies result in HA dermal fillers with specific characteristic visco-elastic properties.



Bio-integration of three CE-marked HA dermal fillers, a cohesive (monophasic) polydensified, a cohesive (monophasic) monodensified and a non-cohesive (biphasic) filler, was analysed with a follow-up of 114 days after injection. Our aim was to study the tolerability and inflammatory response of these fillers, their patterns of distribution in the dermis, and influence on tissue integrity.



Three HA formulations were injected intradermally into the iliac crest region in 15 subjects. Tissue samples were analysed after 8 and 114 days by histology and immunohistochemistry, and visualized using optical and transmission electron microscopy.



Histological results demonstrated that the tested HA fillers showed specific characteristic bio-integration patterns in the reticular dermis. Observations under the optical and electron microscopes revealed morphological conservation of cutaneous structures. Immunohistochemical results confirmed absence of inflammation, immune response and granuloma.



The three tested dermal fillers show an excellent tolerability and preservation of the dermal cells and matrix components. Their tissue integration was dependent on their visco-elastic properties. The cohesive polydensified filler showed the most homogeneous integration with an optimal spreading within the reticular dermis, which is achieved by filling even the smallest spaces between collagen bundles and elastin fibrils, while preserving the structural integrity of the latter. Absence of adverse reactions confirms safety of the tested HA dermal fillers.
Moving toward more perfect unions: daily and long-term consequences of approach and avoidance goals in romantic relationships.

In 2 daily experience studies and a laboratory study, the authors test predictions from approach-avoidance motivational theory to understand how dating couples can maintain feelings of relationship satisfaction in their daily lives and over the course of time. Approach goals were associated with increased relationship satisfaction on a daily basis and over time, particularly when both partners were high in approach goals. Avoidance goals were associated with decreases in relationship satisfaction over time, and people were particularly dissatisfied when they were involved with a partner with high avoidance goals. People high in approach goals and their partners were rated as relatively more satisfied and responsive to a partner's needs by outside observers in the lab, whereas people with high avoidance goals and their partners were rated as less satisfied and responsive. Positive emotions mediated the link between approach goals and daily satisfaction in both studies, and responsiveness to the partner's needs was an additional behavioral mechanism in Study 2. Implications of these findings for approach-avoidance motivational theory and for the maintenance of satisfying relationships over time are discussed.
Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks

In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these images. Then, generic image features from a local image corresponding to each region proposal are extracted by a combination model of 2-D reduction convolutional neural networks (CNNs). Finally, to improve the location accuracy, we propose an unsupervised score-based bounding box regression (USB-BBR) algorithm, combined with a nonmaximum suppression algorithm to optimize the bounding boxes of regions that detected as objects. Experiments show that the dimension-reduction model performs better than the retrained and fine-tuned models and the detection precision of the combined CNN model is much higher than that of any single model. Also our proposed USB-BBR algorithm can more accurately locate objects within an image. Compared with traditional features extraction methods, such as elliptic Fourier transform-based histogram of oriented gradients and local binary pattern histogram Fourier, our proposed localization framework shows robustness when dealing with different complex backgrounds.
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges

Fog computing is an emerging paradigm that extends computation, communication, and storage facilities toward the edge of a network. Compared to traditional cloud computing, fog computing can support delay-sensitive service requests from end-users (EUs) with reduced energy consumption and low traffic congestion. Basically, fog networks are viewed as offloading to core computation and storage. Fog nodes in fog computing decide to either process the services using its available resource or send to the cloud server. Thus, fog computing helps to achieve efficient resource utilization and higher performance regarding the delay, bandwidth, and energy consumption. This survey starts by providing an overview and fundamental of fog computing architecture. Furthermore, service and resource allocation approaches are summarized to address several critical issues such as latency, and bandwidth, and energy consumption in fog computing. Afterward, compared to other surveys, this paper provides an extensive overview of state-of-the-art network applications and major research aspects to design these networks. In addition, this paper highlights ongoing research effort, open challenges, and research trends in fog computing.
An Adaptive Pre-Distortion Technique to Mitigate the DTC Nonlinearity in Digital PLLs

Digital fractional-N phase-locked loops (PLLs) are an attractive alternative to analog PLLs in the design of frequency synthesizers for wireless applications. However, the main obstacle to their full acceptance in the wireless-systems arena is their higher content of output spurious tones, whose level is ultimately set by the nonlinearity of the time-to-digital converter (TDC). The known methods to improve the linearity of the TDC either increase its dissipation and phase noise or require slow foreground calibrations. By contrast, the class of digital PLLs based on a one-bit TDC driven by a multibit digital-to-time converter (DTC) substantially reduces power dissipation and eliminates the TDC nonlinearity issues. Although its spur performance depends on DTC linearity, the modified architecture enables the application of a background adaptive pre-distortion which does not compromise the PLL phase-noise level and power consumption and is much faster than other calibration techniques. This paper presents a 3.6-GHz digital PLL in 65-nm CMOS, with in-band fractional spurs dropping from -39 to -52 dBc when the pre-distortion is enabled, in-band phase noise of -103 dBc/Hz and power consumption of 4.2 mW.
Superionic glass-ceramic electrolytes for room-temperature rechargeable sodium batteries.

Innovative rechargeable batteries that can effectively store renewable energy, such                 as solar and wind power, urgently need to be developed to reduce greenhouse gas                 emissions. All-solid-state batteries with inorganic solid electrolytes and                 electrodes are promising power sources for a wide range of applications because of                 their safety, long-cycle lives and versatile geometries. Rechargeable sodium                 batteries are more suitable than lithium-ion batteries, because they use abundant                 and ubiquitous sodium sources. Solid electrolytes are critical for realizing                 all-solid-state sodium batteries. Here we show that stabilization of a                 high-temperature phase by crystallization from the glassy state dramatically                 enhances the Na(+) ion conductivity. An ambient temperature                 conductivity of over 10(-4) S cm(-1)                 was obtained in a glass-ceramic electrolyte, in which a cubic                     Na(3)PS(4) crystal with superionic conductivity was first                 realized. All-solid-state sodium batteries, with a powder-compressed                     Na(3)PS(4) electrolyte, functioned as a rechargeable battery                 at room temperature.