nils-herrmann/clean_scirepeval huggingface dataset:
Group Sparse Coding

Bag-of-words document representations are often used in te xt, image and video processing. While it is relatively easy to determine a suitab le word dictionary for text documents, there is no simple mapping from raw images or videos to dictionary terms. The classical approach builds a dictionary usin g vector quantization over a large set of useful visual descriptors extracted from a training set, and uses a nearest-neighbor algorithm to count the number of occurren ces of each dictionary word in documents to be encoded. More robust approaches have been proposed recently that represent each visual descriptor as a sparse w eighted combination of dictionary words. While favoring a sparse representation at the level of visual descriptors, those methods however do not ensure that images h av sparse representation. In this work, we use mixed-norm regularization to ac hieve sparsity at the image level as well as a small overall dictionary. This appro ach can also be used to encourage using the same dictionary words for all the images in a class, providing a discriminative signal in the construction of image repres entations. Experimental results on a benchmark image classification dataset show t at when compact image or dictionary representations are needed for computa tional efficiency, the proposed approach yields better mean average precision in c lassification.
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions

Education University of California, Berkeley (2008-2013) Ph.D. in Computer Science Thesis: Surface Web Semantics for Structured Natural Language Processing Advisor: Dan Klein. Committee members: Dan Klein, Marti Hearst, Line Mikkelsen, Nelson Morgan University of California, Berkeley (2012) Master of Science (M.S.) in Computer Science Thesis: An All-Fragments Grammar for Simple and Accurate Parsing Advisor: Dan Klein Indian Institute of Technology, Kanpur (2004-2008) Bachelor of Technology (B.Tech.) in Computer Science and Engineering GPA: 3.96/4.00 (Institute and Department Rank 2) Cornell University (Summer 2007) CS490 (Independent Research and Reading) GPA: 4.00/4.00 Advisors: Lillian Lee, Claire Cardie
A Flexible New Technique for Camera Calibration

We propose a flexible new technique to easily calibrate a camera. It is well suited for use without specialized knowledge of 3D geometry or computer vision. The technique only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique, and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one step from laboratory environments to real world use.
Dynamic Graph CNN for Learning on Point Clouds

Point clouds provide a flexible and scalable geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. Hence, the design of intelligent computational models that act directly on point clouds is critical, especially when efficiency considerations or noise preclude the possibility of expensive denoising and meshing procedures. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv is differentiable and can be plugged into existing architectures. Compared to existing modules operating largely in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked or recurrently applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. Beyond proposing this module, we provide extensive evaluation and analysis revealing that EdgeConv captures and exploits fine-grained geometric properties of point clouds. The proposed approach achieves state-of-the-art performance on standard benchmarks including ModelNet40 and S3DIS.
Security Analysis of Emerging Smart Home Applications

Recently, several competing smart home programming frameworks that support third party app development have emerged. These frameworks provide tangible benefits to users, but can also expose users to significant security risks. This paper presents the first in-depth empirical security analysis of one such emerging smart home programming platform. We analyzed Samsung-owned SmartThings, which has the largest number of apps among currently available smart home platforms, and supports a broad range of devices including motion sensors, fire alarms, and door locks. SmartThings hosts the application runtime on a proprietary, closed-source cloud backend, making scrutiny challenging. We overcame the challenge with a static source code analysis of 499 SmartThings apps (called SmartApps) and 132 device handlers, and carefully crafted test cases that revealed many undocumented features of the platform. Our key findings are twofold. First, although SmartThings implements a privilege separation model, we discovered two intrinsic design flaws that lead to significant overprivilege in SmartApps. Our analysis reveals that over 55% of SmartApps in the store are overprivileged due to the capabilities being too coarse-grained. Moreover, once installed, a SmartApp is granted full access to a device even if it specifies needing only limited access to the device. Second, the SmartThings event subsystem, which devices use to communicate asynchronously with SmartApps via events, does not sufficiently protect events that carry sensitive information such as lock codes. We exploited framework design flaws to construct four proof-of-concept attacks that: (1) secretly planted door lock codes, (2) stole existing door lock codes, (3) disabled vacation mode of the home, and (4) induced a fake fire alarm. We conclude the paper with security lessons for the design of emerging smart home programming frameworks.
Metric Learning for Novelty and Anomaly Detection

When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection —images of classes which are not in the training set but are related to those—, and anomaly detection —images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works.
Extraction, isolation and characterization of bioactive compounds from plants' extracts.

Natural products from medicinal plants, either as pure compounds or as standardized extracts, provide unlimited opportunities for new drug leads because of the unmatched availability of chemical diversity. Due to an increasing demand for chemical diversity in screening programs, seeking therapeutic drugs from natural products, interest particularly in edible plants has grown throughout the world. Botanicals and herbal preparations for medicinal usage contain various types of bioactive compounds. The focus of this paper is on the analytical methodologies, which include the extraction, isolation and characterization of active ingredients in botanicals and herbal preparations. The common problems and key challenges in the extraction, isolation and characterization of active ingredients in botanicals and herbal preparations are discussed. As extraction is the most important step in the analysis of constituents present in botanicals and herbal preparations, the strengths and weaknesses of different extraction techniques are discussed. The analysis of bioactive compounds present in the plant extracts involving the applications of common phytochemical screening assays, chromatographic techniques such as HPLC and, TLC as well as non-chromatographic techniques such as immunoassay and Fourier Transform Infra Red (FTIR) are discussed.
Object Detection in Videos with Tubelet Proposal Networks

Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset. Different from object detection in static images, temporal information in videos is vital for object detection. To fully utilize temporal information, state-of-the-art methods [15, 14] are based on spatiotemporal tubelets, which are essentially sequences of associated bounding boxes across time. However, the existing methods have major limitations in generating tubelets in terms of quality and efficiency. Motion-based [14] methods are able to obtain dense tubelets efficiently, but the lengths are generally only several frames, which is not optimal for incorporating long-term temporal information. Appearance-based [15] methods, usually involving generic object tracking, could generate long tubelets, but are usually computationally expensive. In this work, we propose a framework for object detection in videos, which consists of a novel tubelet proposal network to efficiently generate spatiotemporal proposals, and a Long Short-term Memory (LSTM) network that incorporates temporal information from tubelet proposals for achieving high object detection accuracy in videos. Experiments on the large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed framework for object detection in videos.
Label-Noise Robust Generative Adversarial Networks

Generative adversarial networks (GANs) are a framework that learns a generative distribution through adversarial training. Recently, their class conditional extensions (e.g., conditional GAN (cGAN) and auxiliary classifier GAN (AC-GAN)) have attracted much attention owing to their ability to learn the disentangled representations and to improve the training stability. However, their training requires the availability of large-scale accurate class-labeled data, which are often laborious or impractical to collect in a real-world scenario. To remedy the drawback, we propose a novel family of GANs called label-noise robust GANs (rGANs), which, by incorporating a noise transition model, can learn a clean label conditional generative distribution even when training labels are noisy. In particular, we propose two variants: rAC-GAN, which is a bridging model between AC-GAN and the noise-robust classification model, and rcGAN, which is an extension of cGAN and is guaranteed to learn the clean label conditional distribution in an optimal condition. In addition to providing the theoretical background, we demonstrate the effectiveness of our models through extensive experiments using diverse GAN configurations, various noise settings, and multiple evaluation metrics (in which we tested 402 patterns in total).
Writing good software engineering research papers: minitutorial

Software engineering researchers solve problems of several different kinds. To do so, they produce several different kinds of results, and they should develop appropriate evidence to validate these results. They often report their research in conference papers. I analyzed the abstracts of research papers submitted to ICSE 2002 in order to identify the types of research reported in the submitted and accepted papers, and I observed the program committee discussions about which papers to accept. This report presents the research paradigms of the papers, common concerns of the program committee, and statistics on success rates. This information should help researchers design better research projects and write papers that present their results to best advantage.
Word-Entity Duet Representations for Document Ranking

This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations and entity-based representations. Ranking features are generated by the interactions between the two representations, incorporating information from the word space, the entity space, and the cross-space connections through the knowledge graph. To handle the uncertainties from the automatically constructed entity representations, an attention-based ranking model AttR-Duet is developed. With back-propagation from ranking labels, the model learns simultaneously how to demote noisy entities and how to rank documents with the word-entity duet. Evaluation results on TREC Web Track ad-hoc task demonstrate that all of the four-way interactions in the duet are useful, the attention mechanism successfully steers the model away from noisy entities, and together they significantly outperform both word-based and entity-based learning to rank systems.
Optimizing Semantic Coherence in Topic Models

Large organizations often face the critical challenge of sharing information and maintaining connections between disparate subunits. Tools for automated analysis of document collections, such as topic models, can provide an important means for communication. The value of topic modeling is in its ability to discover interpretable, coherent themes from unstructured document sets, yet it is not unusual to find semantic mismatches that substantially reduce user confidence. In this paper, we first present an expert-driven topic annotation study, undertaken in order to obtain an annotated set of baseline topics and their distinguishing characteristics. We then present a metric for detecting poor-quality topics that does not rely on human feedback or external reference corpora. Next we introduce a new topic model that incorporates salient properties of this metric. We show significant gains in topic quality on a substantial document collection from the National Institutes of Health, measured using both automated evaluation metrics and expert evaluations.
Issues about autonomous cars

The Autonomous car is a complex topic, different technical fields like: Automotive engineering, Control engineering, Informatics, Artificial Intelligence etc. are involved in solving the human driver replacement with an artificial (agent) driver. The problem is even more complicated because usually, nowadays, having and driving a car defines our lifestyle. This means that the mentioned (major) transformation is also a cultural issue. The paper will start with the mentioned cultural aspects related to a self-driving car and will continue with the big picture of the system.
Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach

In this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. We propose a novel probabilistic supervised joint aspect and sentiment model (SJASM) to deal with the problems in one go under a unified framework. SJASM represents each review document in the form of opinion pairs, and can simultaneously model aspect terms and corresponding opinion words of the review for hidden aspect and sentiment detection. It also leverages sentimental overall ratings, which often come with online reviews, as supervision data, and can infer the semantic aspects and aspect-level sentiments that are not only meaningful but also predictive of overall sentiments of reviews. Moreover, we also develop efficient inference method for parameter estimation of SJASM based on collapsed Gibbs sampling. We evaluate SJASM extensively on real-world review data, and experimental results demonstrate that the proposed model outperforms seven well-established baseline methods for sentiment analysis tasks.
Why distance matters: effects on cooperation, persuasion and deception

In this study, we examine how geographic distance affects collaboration using computer-mediated communication technology. We investigated experimentally the effects of cooperating partners being in the same or distant city on three behaviors: cooperation, persuasion, and deception using video conferencing and instant messaging (IM). Our results indicate that subjects are more likely to deceive, be less persuaded by, and initially cooperate less, with someone they believe is in a distant city, as opposed to in the same city as them. Although people initially cooperate less with someone they believe is far away, their willingness to cooperate increases quickly with interaction. Since the same media were used in both the far and near city conditions, these effects cannot be attributed to the media, but rather to social differences. This study confirms how CSCW needs to be concerned with developing technologies for bridging social distance, as well as geographic distance.
Placebos without Deception: A Randomized Controlled Trial in Irritable Bowel Syndrome

Placebo treatment can significantly influence subjective symptoms. However, it is widely believed that response to placebo requires concealment or deception. We tested whether open-label placebo (non-deceptive and non-concealed administration) is superior to a no-treatment control with matched patient-provider interactions in the treatment of irritable bowel syndrome (IBS).



Two-group, randomized, controlled three week trial (August 2009-April 2010) conducted at a single academic center, involving 80 primarily female (70%) patients, mean age 47 ± 18 with IBS diagnosed by Rome III criteria and with a score ≥ 150 on the IBS Symptom Severity Scale (IBS-SSS). Patients were randomized to either open-label placebo pills presented as "placebo pills made of an inert substance, like sugar pills, that have been shown in clinical studies to produce significant improvement in IBS symptoms through mind-body self-healing processes" or no-treatment controls with the same quality of interaction with providers. The primary outcome was IBS Global Improvement Scale (IBS-GIS). Secondary measures were IBS Symptom Severity Scale (IBS-SSS), IBS Adequate Relief (IBS-AR) and IBS Quality of Life (IBS-QoL).



Open-label placebo produced significantly higher mean (±SD) global improvement scores (IBS-GIS) at both 11-day midpoint (5.2 ± 1.0 vs. 4.0 ± 1.1, p<.001) and at 21-day endpoint (5.0 ± 1.5 vs. 3.9 ± 1.3, p = .002). Significant results were also observed at both time points for reduced symptom severity (IBS-SSS, p = .008 and p = .03) and adequate relief (IBS-AR, p = .02 and p = .03); and a trend favoring open-label placebo was observed for quality of life (IBS-QoL) at the 21-day endpoint (p = .08).



Placebos administered without deception may be an effective treatment for IBS. Further research is warranted in IBS, and perhaps other conditions, to elucidate whether physicians can benefit patients using placebos consistent with informed consent.
The Boolean Constraint Solver of SWI-Prolog (System Description)

We present a new constraint solver over Boolean variables, available as library(clpb) in SWI-Prolog. Our solver distinguishes itself from other available CLP(B) solvers by several unique features: First, it is written entirely in Prolog and is hence portable to different Prolog implementations. Second, it is the first freely available BDDbased CLP(B) solver. Third, we show that new interface predicates allow us to solve new types of problems with CLP(B) constraints. We also use our implementation experience to contrast features and state necessary requirements of attributed variable interfaces to optimally support CLP(B) constraints in different Prolog systems. Finally, we also present some performance results and comparisons with SICStus Prolog.
BIOMECHANICS OF HUMAN MOVEMENT

The biomechanics of human motion is a fascinating field. Who among us has never marveled at the graceful motions of a dancer or the rapid finger movements of a musician? From the time of Aristotle onward there have been countless books written on the topic of movement in animals and humans. Despite the great minds that have considered the topic, it is just recently that much advancement has been made experimentally. Historically, the study of human movement has been costly and very time consuming. This is because, in order to study them, movements are almost always discretized and then analyzed step by step, with the size of the steps determined by the speed of the movement (and the questions being asked). Whether it be frames of film from a movie camera or digitized samples from an electrogoniometer, most movements are recorded as series of static images that are then reassembled to provide kinematic and kinetic information. There has been a tremendous growth in the study of human movement in the past 2 decades because of the low cost of digital data acquisition systems that make possible the storage and analysis of massive amounts of data that are required to accurately characterize complex motion. This growing interest in the study of human movement is coming from five predominate groups:
A review of iris anti-spoofing

To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured physical synthetic sample, is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. The whole biometric community, including researchers, developers, standardizing bodies and vendors, has thrown itself into the very challenging task of proposing and developing efficient protection methods against this threat, known as spoofing. The goal of this paper is to provide a comprehensive and structured overview on the work that has been carried out over the last decade in the field of iris anti-spoofing. In brief, the paper has been thought as a tool to provide biometric researchers an overall picture of the current panorama in the mentioned area following a systematic approach.
Wrangler: interactive visual specification of data transformation scripts

Though data analysis tools continue to improve, analysts still expend an inordinate amount of time and effort manipulating data and assessing data quality issues. Such "data wrangling" regularly involves reformatting data values or layout, correcting erroneous or missing values, and integrating multiple data sources. These transforms are often difficult to specify and difficult to reuse across analysis tasks, teams, and tools. In response, we introduce Wrangler, an interactive system for creating data transformations. Wrangler combines direct manipulation of visualized data with automatic inference of relevant transforms, enabling analysts to iteratively explore the space of applicable operations and preview their effects. Wrangler leverages semantic data types (e.g., geographic locations, dates, classification codes) to aid validation and type conversion. Interactive histories support review, refinement, and annotation of transformation scripts. User study results show that Wrangler significantly reduces specification time and promotes the use of robust, auditable transforms instead of manual editing.