nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Voltage Glitch Attacks on Secure Devices

Secure devices are often subject to attacks and behavioural analysis in order to inject faults on them and/or extract otherwise secret information. Glitch attacks, sudden changes on the power supply rails, are a common technique used to inject faults on electronic devices. Detectors are designed to catch these attacks. As the detectors become more efficient, new glitches that are harder to detect arise. Common glitch detection approaches, such as directly monitoring the power rails, can potentially find it hard to detect fast glitches, as these become harder to differentiate from noise. This paper proposes a design which, instead of monitoring the power rails, monitors the effect of a glitch on a sensitive circuit, hence reducing the risk of detecting noise as glitches.
Personality and Persuasive Technology: An Exploratory Study on Health-Promoting Mobile Applications

Though a variety of persuasive health applications have been designed with a preventive standpoint toward diseases in mind, many have been designed largely for a general audience. Designers of these technologies may achieve more success if applications consider an individual’s personality type. Our goal for this research was to explore the relationship between personality and persuasive technologies in the context of health-promoting mobile applications. We conducted an online survey with 240 participants using storyboards depicting eight different persuasive strategies, the Big Five Inventory for personality domains, and questions on perceptions of the persuasive technologies. Our results and analysis revealed a number of significant relationships between personality and the persuasive technologies we evaluated. The findings from this study can guide the development of persuasive technologies that can cater to individual personalities to improve the likelihood of their success.
When 2.5D is not enough: Simultaneous reconstruction, segmentation and recognition on dense SLAM

While the main trend of 3D object recognition has been to infer object detection from single views of the scene - i.e., 2.5D data - this work explores the direction on performing object recognition on 3D data that is reconstructed from multiple viewpoints, under the conjecture that such data can improve the robustness of an object recognition system. To achieve this goal, we propose a framework whichreal-time segmentation is able (i) to carry out incremental real-time segmentation of a 3D scene while being reconstructed via Simultaneous Localization And Mapping (SLAM), and (ii) to simultaneously and incrementally carry out 3D object recognition and pose estimation on the reconstructed and segmented 3D representations. Experimental results demonstrate the advantages of our approach with respect to traditional single view-based object recognition and pose estimation approaches, as well as its usefulness in robotic perception and augmented reality applications.
Implementation Intentions and Effective Goal Pursuit

The theoretical distinction between goal intentions ("I intend to achieve -c") and implementation intentions ("I intend to perform goal-directed behavior y when I encounter situation z" ; P. M. Gollwitzer, 1993) is explored by assessing the completion rate of various goal projects. In correlational Study 1, difficult goal intentions were completed about 3 times more often when participants had furnished them with implementation intentions. In experimental Study 2, all participants were assigned the same difficult goal intention, and half were instructed to form implementation intentions. The beneficial effects of implementation intentions paralleled diose of Study 1. In experimental Study 3, implementation intentions were observed to facilitate the immediate initiation of goaldirected action when the intended opportunity was encountered. Implementation intentions are interpreted to be powerful self-regulatory tools for overcoming the typical obstacles associated with the initiation of goal-directed actions.
End-to-End Comparative Attention Networks for Person Re-Identification

Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem. One of the major challenges lies in the lack of spatial and temporal cues, which makes it difficult to deal with large variations of lighting conditions, viewing angles, body poses, and occlusions. Recently, several deep-learning-based person re-identification approaches have been proposed and achieved remarkable performance. However, most of those approaches extract discriminative features from the whole frame at one glimpse without differentiating various parts of the persons to identify. It is essentially important to examine multiple highly discriminative local regions of the person images in details through multiple glimpses for dealing with the large appearance variance. In this paper, we propose a new soft attention-based model, i.e., the end-to-end comparative attention network (CAN), specifically tailored for the task of person re-identification. The end-to-end CAN learns to selectively focus on parts of pairs of person images after taking a few glimpses of them and adaptively comparing their appearance. The CAN model is able to learn which parts of images are relevant for discerning persons and automatically integrates information from different parts to determine whether a pair of images belongs to the same person. In other words, our proposed CAN model simulates the human perception process to verify whether two images are from the same person. Extensive experiments on four benchmark person re-identification data sets, including CUHK01, CHUHK03, Market-1501, and VIPeR, clearly demonstrate that our proposed end-to-end CAN for person re-identification outperforms well established baselines significantly and offer the new state-of-the-art performance.
Evidence for an altered sex ratio in clinic-referred adolescents with gender dysphoria.

The number of adolescents referred to specialized gender identity clinics for gender dysphoria appears to be increasing and there also appears to be a corresponding shift in the sex ratio, from one favoring natal males to one favoring natal females.



We conducted two quantitative studies to ascertain whether there has been a recent inversion of the sex ratio of adolescents referred for gender dysphoria.



The sex ratio of adolescents from two specialized gender identity clinics was examined as a function of two cohort periods (2006-2013 vs. prior years). Study 1 was conducted on patients from a clinic in Toronto, and Study 2 was conducted on patients from a clinic in Amsterdam.



Across both clinics, the total sample size was 748. In both clinics, there was a significant change in the sex ratio of referred adolescents between the two cohort periods: between 2006 and 2013, the sex ratio favored natal females, but in the prior years, the sex ratio favored natal males. In Study 1 from Toronto, there was no corresponding change in the sex ratio of 6,592 adolescents referred for other clinical problems.



Sociological and sociocultural explanations are offered to account for this recent inversion in the sex ratio of adolescents with gender dysphoria.
Survey and comparative analysis of entropy and relative entropy thresholding techniques

Entropy-based image thresholding has received considerable interest in recent years. Two types of entropy are generally used as thresholding criteria: Shannon’s entropy and relative entropy, also known as Kullback–Leibler information distance, where the former measures uncertainty in an information source with an optimal threshold obtained by maximising Shannon’s entropy, whereas the latter measures the information discrepancy between two different sources with an optimal threshold obtained by minimising relative entropy. Many thresholding methods have been developed for both criteria and reported in the literature. These two entropybased thresholding criteria have been investigated and the relationship among entropy and relative entropy thresholding methods has been explored. In particular, a survey and comparative analysis is conducted among several widely used methods that include Pun and Kapur’s maximum entropy, Kittler and Illingworth’s minimum error thresholding, Pal and Pal’s entropy thresholding and Chang et al.’s relative entropy thresholding methods. In order to objectively assess these methods, two measures, uniformity and shape, are used for performance evaluation.
Automatic Generation of Review Matrices as Multi-document Summarization of Scientific Papers

A synthesis matrix is a table that summarizes various aspects of multiple documents. In our work, we specifically examine a problem of automatically generating a synthesis matrix for scientific literature review. As described in this paper, we first formulate the task as multidocument summarization and question-answering tasks given a set of aspects of the review based on an investigation of system summary tables of NLP tasks. Next, we present a method to address the former type of task. Our system consists of two steps: sentence ranking and sentence selection. In the sentence ranking step, the system ranks sentences in the input papers by regarding aspects as queries. We use LexRank and also incorporate query expansion and word embedding to compensate for tersely expressed queries. In the sentence selection step, the system selects sentences that remain in the final output. Specifically emphasizing the summarization type aspects, we regard this step as an integer linear programming problem with a special type of constraint imposed to make summaries comparable. We evaluated our system using a dataset we created from the ACL Anthology. The results of manual evaluation demonstrated that our selection method using comparability improved
Vaginal orgasm is associated with less use of immature psychological defense mechanisms.

Freud implied a link between inability to have a vaginal orgasm and psychosexual immaturity. Since Kinsey, many sexologists have asserted that no such link exists. However, empirical testing of the issue has been lacking.



The objective was to determine the relationship between different sexual behavior triggers of female orgasm and use of immature psychological defense mechanisms.



Women reported their past month frequency of different sexual behaviors and corresponding orgasm rates and completed the Defense Style Questionnaire (DSQ-40).



The association between ability to have vaginal intercourse orgasm (versus clitoral orgasm) and the use of DSQ-40 immature psychological defense mechanisms (associated with various psychopathologies) was examined.



In a sample of 94 healthy Portuguese women, vaginal orgasm (triggered solely by penile-vaginal intercourse) was associated with less use of DSQ-40 immature defenses. Vaginal orgasm was associated with less somatization, dissociation, displacement, autistic fantasy, devaluation, and isolation of affect. Orgasm from clitoral stimulation or combined clitoral-intercourse stimulation was not associated with less use of immature defenses, and was associated with more use of some immature defenses. In one regression analysis, more masturbation and less vaginal orgasm consistency made independent contributions to the statistical prediction of immature defenses. In another regression analysis, any use of extrinsic clitoral stimulation for intercourse orgasm, and lack of any vaginal orgasm, made independent contributions to the statistical prediction of immature defenses. Vaginally anorgasmic women had immature defenses scores comparable to those of established (depression, social anxiety disorder, panic disorder, and obsessive-compulsive disorder) outpatient psychiatric groups. Results were not confounded by social desirability responding or relationship quality.



The results linking penile-vaginal orgasm with less use of immature psychological defense mechanisms are consistent with both early psychoanalytic personality theory and recent advances in sexual physiology. Implications for diagnosis and sex therapy are noted.
Visual social attention in autism spectrum disorder: Insights from eye tracking studies

We review different aspects of visual social attention in autism spectrum disorders (ASD) from infancy to adulthood in light of the eye-tracking literature. We first assess the assumption that individuals with ASD demonstrate a deficit in social orienting together with decreased attention to socially relevant stimuli such as faces compared to TD individuals. Results show that social orienting is actually not qualitatively impaired and that decreased attention to faces does not generalized across contexts. We also assess the assumption that individuals with ASD demonstrate excess mouth and diminished eye gaze compared to TD individuals. We find that this assumption receives little support across ages and discuss some factors that might have initially lead to this conjecture. We report that the assessment of the ability to follow the direction of another person's gaze needs to be further examined and that eye-tracking studies add to the evidence that individuals with ASD demonstrate difficulties in interpreting gaze cues. Finally, we highlight innovative data acquisition and analyses that are increasingly shedding light on the more subtle nature of the profound social difficulties experienced by individuals with ASD.
A MultiTask Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks

Monitoring the future health status of patients from the historical Electronic Health Record (EHR) is a core research topic in predictive healthcare. The most important challenges are to model the temporality of sequential EHR data and to interpret the prediction results. In order to reduce the future risk of diseases, we propose a multi-task framework that can monitor the multiple status of diagnoses. Patients’ historical records are directly fed into a Recurrent Neural Network (RNN) which memorizes all the past visit information, and then a task-specific layer is trained to predict multiple diagnoses. Moreover, three attention mechanisms for RNNs are introduced to measure the relationships between past visits and current status. Experimental results show that the proposed attention-based RNNs can significantly improve the prediction accuracy compared to widely used approaches. With the attention mechanisms, the proposed framework is able to identify the visit information which is important to the final prediction.
Variation Autoencoder Based Network Representation Learning for Classification

Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study introduces a deep network representation model that seamlessly integrates the text information and structure of a network. The model captures highly non-linear relationships between nodes and complex features of a network by exploiting the variational autoencoder (VAE), which is a deep unsupervised generation algorithm. The representation learned with a paragraph vector model is merged with that learned with the VAE to obtain the network representation, which preserves both structure and text information. Comprehensive experiments is conducted on benchmark datasets and find that the introduced model performs better than state-of-the-art techniques.
Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations

Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data.



We extended BANNER, a biomedical NER system, with the proposed method. This yields an integrated system that can be applied to chemical and drug NER or biomedical NER. We call our branch of the BANNER system BANNER-CHEMDNER, which is scalable over millions of documents, processing about 530 documents per minute, is configurable via XML, and can be plugged into other systems by using the BANNER Unstructured Information Management Architecture (UIMA) interface. BANNER-CHEMDNER achieved an 85.68% and an 86.47% F-measure on the testing sets of CHEMDNER Chemical Entity Mention (CEM) and Chemical Document Indexing (CDI) subtasks, respectively, and achieved an 87.04% F-measure on the official testing set of the BioCreative II gene mention task, showing remarkable performance in both chemical and biomedical NER. BANNER-CHEMDNER system is available at: https://bitbucket.org/tsendeemts/banner-chemdner.
The Design and Implementation of INGRES

The currently operational (March 1976) version of the INGRES database management system is described. This multiuser system gives a relational view of data, supports two high level nonprocedural data sublanguages, and runs as a collection of user processes on top of the UNIX operating system for Digital Equipment Corporation PDP 11/40, 11/45, and 11/70 computers. Emphasis is on the design decisions and tradeoffs related to (1) structuring the system into processes, (2) embedding one command language in a general purpose programming language, (3) the algorithms implemented to process interactions, (4) the access methods implemented, (5) the concurrency and recovery control currently provided, and (6) the data structures used for system catalogs and the role of the database administrator.
Also discussed are (1) support for integrity constraints (which is only partly operational), (2) the not yet supported features concerning views and protection, and (3) future plans concerning the system.
OC-192 transmitter in standard 0.18 /spl mu/m CMOS

A fully integrated SONET OC-192 transmitter IC using a standard CMOS process consists of an input data register, FIFO, CMU, and 16:1 multiplexer to give a 10Gb/s serial output. A higher FEC rate, 10.7Gb/s, is supported. This chip, using a 0.18/spl mu/m process, exceeds SONET requirements, dissipating 450mW.
Exploring the Value of Online Reviews to Organizations: Implications for Revenue Forecasting and Planning

¶ Despite the widespread popularity of online opinion forums among consumers, the business value that such systems bring to organizations has, so far, remained an unanswered question. This paper addresses this question by studying the value of online movie ratings in forecasting motion picture revenues. First, we conduct a survey where a nationally representative sample of subjects who do not rate movies online is asked to rate a number of recent movies. Their ratings exhibit high correlation with online ratings for the same movies. We thus provide evidence for the claim that online ratings can be considered as a useful proxy for word-of-mouth about movies. Inspired by the Bass model of product diffusion, we then develop a motion picture revenue-forecasting model that incorporates the impact of both publicity and word of mouth on a movie's revenue trajectory. Using our model, we derive notably accurate predictions of a movie's total revenues from statistics of user reviews posted on Yahoo! Movies during the first week of a new movie's release. The results of our work provide encouraging evidence for the value of publicly available online forum information to firms for real-time forecasting and competitive analysis. ¶ This is a preliminary draft of a work in progress. It is being distributed to seminar participants for comments and discussion.
Significance and Challenges of Big Data Research

In recent years, the rapid development of Internet, Internet of Things, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from academia, industry, and governments around the world. In this position paper, we first briefly introduce the concept of big data, including its definition, features, and value. We then identify from different perspectives the significance and opportunities that big data brings to us. Next, we present representative big data initiatives all over the world. We describe the grand challenges (namely, data complexity, computational complexity, and system complexity), as well as possible solutions to address these challenges. Finally, we conclude the paper by presenting several suggestions on carrying out big data projects.
Aesthetic-based Clothing Recommendation

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers’ decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the aesthetic features, is seldom considered. It plays a vital role in clothing recommendation since a users’ decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.
Tennis Winner Prediction based on Time-Series History with Neural Modeling

Tennis is one of the most popular sports in the world. Many researchers have studied in tennis model to find out whose player will be the winner of the match by using the statistical data. This paper proposes a powerful technique to predict the winner of the tennis match. The proposed method provides more accurate prediction results by using the statistical data and environmental data based on Multi-Layer Perceptron (MLP) with back-propagation learning algorithm.