nils-herrmann/clean_scirepeval huggingface dataset:
Named Entity Recognition through Classifier Combination

This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classifiers (robust linear classifier, maximum entropy, transformation-based learning, and hidden Markov model) are combined under different conditions. When no gazetteer or other additional training resources are used, the combined system attains a performance of 91.6F on the English development data; integrating name, location and person gazetteers, and named entity systems trained on additional, more general, data reduces the F-measure error by a factor of 15 to 21% on the English data.
Optional stopping: no problem for Bayesians.

Optional stopping refers to the practice of peeking at data and then, based on the results, deciding whether or not to continue an experiment. In the context of ordinary significance-testing analysis, optional stopping is discouraged, because it necessarily leads to increased type I error rates over nominal values. This article addresses whether optional stopping is problematic for Bayesian inference with Bayes factors. Statisticians who developed Bayesian methods thought not, but this wisdom has been challenged by recent simulation results of Yu, Sprenger, Thomas, and Dougherty (2013) and Sanborn and Hills (2013). In this article, I show through simulation that the interpretation of Bayesian quantities does not depend on the stopping rule. Researchers using Bayesian methods may employ optional stopping in their own research and may provide Bayesian analysis of secondary data regardless of the employed stopping rule. I emphasize here the proper interpretation of Bayesian quantities as measures of subjective belief on theoretical positions, the difference between frequentist and Bayesian interpretations, and the difficulty of using frequentist intuition to conceptualize the Bayesian approach.
Fabric Texture Analysis Using Computer Vision Techniques

This paper presents inexpensive computer vision techniques allowing to measure the texture characteristics of woven fabric, such as weave repeat and yarn counts, and the surface roughness. First, we discuss the automatic recognition of weave pattern and the accurate measurement of yarn counts by analyzing fabric sample images. We propose a surface roughness indicator FDFFT, which is the 3-D surface fractal dimension measurement calculated from the 2-D fast Fourier transform of high-resolution 3-D surface scan. The proposed weave pattern recognition method was validated by using computer-simulated woven samples and real woven fabric images. All weave patterns of the tested fabric samples were successfully recognized, and computed yarn counts were consistent with the manual counts. The rotation invariance and scale invariance of FDFFT were validated with fractal Brownian images. Moreover, to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from the 3-D fabric surface. According to the test results, we demonstrated that FDFFT is a fast and reliable parameter for fabric roughness measurement based on 3-D surface data.
Nanonewton force-controlled manipulation of biological cells using a monolithic MEMS microgripper with two-axis force feedback

As mechanical end-effectors, microgrippers enable the pick–transport–place of micrometer-sized objects, such as manipulation and positioning of biological cells in an aqueous environment. This paper reports on a monolithic MEMS-based microgripper with integrated force feedback along two axes and presents the first demonstration of forcecontrolled micro-grasping at the nanonewton force level. The system manipulates highly deformable biomaterials (porcine interstitial cells) in an aqueous environment using a microgripper that integrates a V-beam electrothermal microactuator and two capacitive force sensors, one for contact detection (force resolution: 38.5 nN) and the other for gripping force measurements (force resolution: 19.9 nN). The MEMS-based microgripper and the force control system experimentally demonstrate the capability of rapid contact detection and reliable force-controlled micrograsping to accommodate variations in size and mechanical properties of objects with a high reproducibility. (Some figures in this article are in colour only in the electronic version)
DeepMask: Masking DNN Models for robustness against adversarial samples

Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. To overcome this problem, we introduce a defensive mechanism called DeepMask. By identifying and removing unnecessary features in a DNN model, DeepMask limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs. Comparing with other defensive approaches, DeepMask is easy to implement and computationally efficient. Experimental results show that DeepMask can increase the performance of state-of-the-art DNN models against adversarial samples.
Text Categorization as a Graph Classification Problem

In this paper, we consider the task of text categorization as a graph classification problem. By representing textual documents as graph-of-words instead of historical n-gram bag-of-words, we extract more discriminative features that correspond to long-distance n-grams through frequent subgraph mining. Moreover, by capitalizing on the concept of k-core, we reduce the graph representation to its densest part – its main core – speeding up the feature extraction step for little to no cost in prediction performances. Experiments on four standard text classification datasets show statistically significant higher accuracy and macro-averaged F1-score compared to baseline approaches.
A low-offset high-speed double-tail dual-rail dynamic latched comparator

This paper presents a new dynamic latched comparator which shows lower input-referred latch offset voltage and higher load drivability than the conventional dynamic latched comparators. With two additional inverters inserted between the input- and output-stage of the conventional double-tail dynamic comparator, the gain preceding the regenerative latch stage was improved and the complementary version of the output-latch stage, which has bigger output drive current capability at the same area, was implemented. As a result, the circuit shows up to 25% less input-referred latch offset voltage and 44% less sensitivity of the delay versus the input voltage difference (delay/log(ΔVin)), which is about 17.2ps/decade, than the conventional double-tail latched comparator at approximately the same area and power consumption.
Enhancement of latent fingerprints on banknotes using monogenic wavelets

Latent fingerprints on banknotes provide valuable information in forensic applications to identify suspects of robbery cases and counterfeiters. The image quality of latent fingerprints is poor due to the complex background of banknotes compared to live-scan images acquired in a controlled environment. An effective orientation field estimation algorithm is necessary for enhancing poor quality latents in the presence of background noise for reliable extraction of minutia features. There have been attempts in the literature to characterize a fingerprint using an AM-FM model. Motivated by the ability of the monogenic signal representation to demodulate such a signal to extract its features, this paper proposes a novel algorithm of latent fingerprint multiresolution orientation estimation with a modified Simoncelli isotropic wavelet design and by an appropriate use of the multiresolution orientation field. Experimental results on synthetic noisy images and real latents from our created latent fingerprint database on banknotes show that the enhanced image quality by using the proposed algorithm is better than the other existing methods for estimating the fingerprint orientation.
Repeatability and Reproducibility of Decisions by Latent Fingerprint Examiners

The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. We tested latent print examiners on the extent to which they reached consistent decisions. This study assessed intra-examiner repeatability by retesting 72 examiners on comparisons of latent and exemplar fingerprints, after an interval of approximately seven months; each examiner was reassigned 25 image pairs for comparison, out of total pool of 744 image pairs. We compare these repeatability results with reproducibility (inter-examiner) results derived from our previous study. Examiners repeated 89.1% of their individualization decisions, and 90.1% of their exclusion decisions; most of the changed decisions resulted in inconclusive decisions. Repeatability of comparison decisions (individualization, exclusion, inconclusive) was 90.0% for mated pairs, and 85.9% for nonmated pairs. Repeatability and reproducibility were notably lower for comparisons assessed by the examiners as "difficult" than for "easy" or "moderate" comparisons, indicating that examiners' assessments of difficulty may be useful for quality assurance. No false positive errors were repeated (n = 4); 30% of false negative errors were repeated. One percent of latent value decisions were completely reversed (no value even for exclusion vs. of value for individualization). Most of the inter- and intra-examiner variability concerned whether the examiners considered the information available to be sufficient to reach a conclusion; this variability was concentrated on specific image pairs such that repeatability and reproducibility were very high on some comparisons and very low on others. Much of the variability appears to be due to making categorical decisions in borderline cases.
If walls could talk: Patterns and anomalies in Facebook wallposts

How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friendship graph or are limited by the amount of users or the duration of the study. In this work, we model Facebook user behavior: we analyze the wall activities of users focusing on identifying common patterns and surprising phenomena. We conduct an extensive study of roughly 7K users over three years during four month intervals each year. We propose PowerWall, a lesser known heavy-tailed distribution to fit our data. Our key results can be summarized in the following points. First, we find that many wall activities, including number of posts, number of likes, number of posts of type photo, etc., can be described by the PowerWall distribution. What is more surprising is that most of these distributions have similar slope, with a value close to 1! Second, we show how our patterns and metrics can help us spot surprising behaviors and anomalies. For example, we find a user posting every two days, exactly the same count of posts; another user posting at midnight, with no other activity before or after. Our work provides a solid step towards a systematic and quantitative wall-centric profiling of Facebook user activity.
Exploring the Limits of Weakly Supervised Pretraining

State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Yet, ImageNet is now nearly ten years old and is by modern standards “small”. Even so, relatively little is known about the behavior of pretraining with datasets that are multiple orders of magnitude larger. The reasons are obvious: such datasets are difficult to collect and annotate. In this paper, we present a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images. Our experiments demonstrate that training for large-scale hashtag prediction leads to excellent results. We show improvements on several image classification and object detection tasks, and report the highest ImageNet-1k single-crop, top-1 accuracy to date: 85.4% (97.6% top-5). We also perform extensive experiments that provide novel empirical data on the relationship between large-scale pretraining and transfer learning performance.
Static Detection of Second-Order Vulnerabilities in Web Applications

Web applications evolved in the last decades from simple scripts to multi-functional applications. Such complex web applications are prone to different types of security vulnerabilities that lead to data leakage or a compromise of the underlying web server. So called secondorder vulnerabilities occur when an attack payload is first stored by the application on the web server and then later on used in a security-critical operation. In this paper, we introduce the first automated static code analysis approach to detect second-order vulnerabilities and related multi-step exploits in web applications. By analyzing reads and writes to memory locations of the web server, we are able to identify unsanitized data flows by connecting input and output points of data in persistent data stores such as databases or session data. As a result, we identified 159 second-order vulnerabilities in six popular web applications such as the conference management systems HotCRP and OpenConf. Moreover, the analysis of web applications evaluated in related work revealed that we are able to detect several critical vulnerabilities previously missed.
The true self online: Personality correlates of preference for self-expression online, and observer ratings of personality online and offline

Theorists have suggested some people find it easier to express their ‘‘true selves’’ online than in person. Among 523 participants in an online study, Shyness was positively associated with online ‘Real Me’ self location, while Conscientiousness was negatively associated with an online self. Extraversion was indirectly negatively associated with an online self, mediated by Shyness. Neuroticism was positively associated with an online self, partly mediated by Shyness. 107 online and offline friends of participants provided ratings of them. Overall, both primary participants and their observers indicated that offline relationships were closer. However, participants who located their Real Me online reported feeling closer to their online friends than did those locating their real selves offline. To test whether personality is better expressed in online or offline interactions, observers’ ratings of participants’ personalities were compared. Both online and offline observers’ ratings of Extraversion, Agreeableness and Conscientiousness correlated with participants’ self-reports. However, only offline observers’ ratings of Neuroticism correlated with participants’ own. Except for Neuroticism, the similarity of online and offline observers’ personality ratings to participants’ self-reports did not differ significantly. The study provides no evidence that online self-presentations are more authentic; indeed Neuroticism may be more visibly
Anomaly Detection Using an Ensemble of Feature Models

We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of ``normal'' training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show selects against noisy and irrelevant features. Our results on 47 data sets show that for most data sets, this approach significantly improves performance over current state-of-the-art feature space distance and density-based approaches.
Watermarking-based copyright protection of sequential functions

Watermarking is one of several techniques available today to deter copyright infringement in electronic systems. The technique consists of implanting indelible stamps in the circuit's inner structure, while not disrupting its functionality nor degrading its performance significantly. In this paper, a novel method is proposed for the creation of watermarks in regular sequential functions. This is an important class of functions, as it is the basis of most digital controllers. Algorithms are proposed for implanting robust watermarks to minimize the overhead and, ultimately, to reduce the impact on performance. Detection methods have been discussed in the presence of infringement attacks. The resilience of the method in several tampering regimes has been estimated. Examples illustrate the suitability of the approach.
Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning

Hepatocellular carcinoma (HCC) is one of the most common and aggressive human malignancies. Its high mortality rate is mainly a result of intra-hepatic metastases. We analyzed the expression profiles of HCC samples without or with intra-hepatic metastases. Using a supervised machine-learning algorithm, we generated for the first time a molecular signature that can classify metastatic HCC patients and identified genes that were relevant to metastasis and patient survival. We found that the gene expression signature of primary HCCs with accompanying metastasis was very similar to that of their corresponding metastases, implying that genes favoring metastasis progression were initiated in the primary tumors. Osteopontin, which was identified as a lead gene in the signature, was over-expressed in metastatic HCC; an osteopontin-specific antibody effectively blocked HCC cell invasion in vitro and inhibited pulmonary metastasis of HCC cells in nude mice. Thus, osteopontin acts as both a diagnostic marker and a potential therapeutic target for metastatic HCC.
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering

Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users’ preferences toward them. In domains like clothing recommendation, explaining users’ preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity and subtlety of people’s ‘visual preferences,’ as well as the scale and dimensionality of the data and features involved. Ultimately, a successful model should be capable of capturing considerable variance across different categories and styles, while still modeling the commonalities explained by ‘global’ structures in order to combat the sparsity (e.g. cold-start), variability, and scale of real-world datasets. Here, we address these challenges by building such structures to model the visual dimensions across different product categories. With a novel hierarchical embedding architecture, our method accounts for both high-level (colorfulness, darkness, etc.) and subtle (e.g. casualness) visual characteristics simultaneously.
Automatically classifying database workloads

The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS, and then use the model to identify any change in the workload type. We construct a workload classifier from the Browsing and Ordering profiles of the TPC-W benchmark. Experiments with an industry-supplied workload show that our classifier accurately identifies the mix of OLTP and DSS work within an application workload.
Drivers of consumer-brand identification

a r t i c l e i n f o Keywords: Consumer–brand identification Consumer self-identity Brand relationships Product category involvement The concept of consumer–brand identification (CBI) is central to our understanding of how, when, and why brands help consumers articulate their identities. This paper proposes and tests an integrative theoretical framework of the antecedents of CBI. Six drivers of CBI, a moderator, and two consequences are posited and tested with survey data from a large sample of German household consumers. The results confirm the influence of five of the six drivers, namely, brand–self similarity, brand distinctiveness, brand social benefits, brand warmth, and memorable brand experiences. Further, we find that all five of these antecedents have stronger causal relationships with CBI when consumers have higher involvement with the brand's product category. Finally, CBI is tied to two important pro-company consequences, brand loyalty and brand advocacy. Theoretical and managerial significance of the findings are discussed. " Choices are made more easily—either more routinely or more impulsively , seemingly—because one object is symbolically more harmonious with our goals, feelings, and self-definitions than another. " Sidney J. Levy (1959, p. 120) " Why has the Toyota Prius enjoyed such success … when most other hybrid models struggle to find buyers? One answer may be that buyers of the Prius want everyone to know they are driving a hybrid …. In fact, more than half the Prius buyers surveyed this spring … said the main reason they purchased their car was that 'it makes a statement about me.' " Micheline Maynard (2007)
Semantic Role Labeling via FrameNet, VerbNet and PropBank

This article describes a robust semantic parser that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. The  corpus contains the examples annotated with semantic roles whereas the VerbNet lexicon provides the knowledge about the syntactic behavior of the verbs. We connect VerbNet and FrameNet by mapping the FrameNet frames to the VerbNet Intersective Levin classes. The PropBank corpus, which is tightly connected to the VerbNet lexicon, is used to increase the verb coverage and also to test the effectiveness of our approach. The results indicate that our model is an interesting step towards the design of more robust semantic parsers.
An end-to-end system for designing mechanical structures for print-and-fold robots

This work presents a script-based development environment aimed at allowing users to easily design and create mechanical bodies for folded plastic robots. The origami-inspired fabrication process is inexpensive and widely accessible, and the tools developed in this work allow for open source design sharing and modular reuse. Designs are generated by recursively combining mechanical components - from primitive building blocks, through mechanisms and assemblies, to full robots - in a flexible yet well-defined manner. This process was used to design robotic elements of increasing complexity up to a multi-degree-of-freedom compliant manipulator arm, demonstrating the power of this system. The developed system is extensible, opening avenues for further research ultimately leading to the development of a complete robot compiler.