nils-herrmann/clean_scirepeval huggingface dataset:
One-to-many face recognition with bilinear CNNs

The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bilinear CNN (B-CNN), which has shown dramatic performance gains on certain fine-grained recognition problems [15]. We apply this new CNN to the challenging new face recognition benchmark, the IARPA Janus Benchmark A (IJB-A) . It features faces from a large number of identities in challenging real-world conditions. Because the face images were not identified automatically using a computerized face detection system, it does not have the bias inherent in such a database. We demonstrate the performance of the B-CNN model beginning from an AlexNet-style network pre-trained on ImageNet. We then show results for fine-tuning using a moderate-sized and public external database, FaceScrub . We also present results with additional fine-tuning on the limited training data provided by the protocol. In each case, the fine-tuned bilinear model shows substantial improvements over the standard CNN. Finally, we demonstrate how a standard CNN pre-trained on a large face database, the recently released VGG-Face model [20], can be converted into a B-CNN without any additional feature training. This B-CNN improves upon the CNN performance on the IJB-A benchmark, achieving 89.5% rank-1 recall.
IXPs: mapped?

Internet exchange points (IXPs) are an important ingredient of the Internet AS-level ecosystem - a logical fabric of the Internet made up of about 30,000 ASes and their mutual business relationships whose primary purpose is to control and manage the flow of traffic. Despite the IXPs' critical role in this fabric, little is known about them in terms of their peering matrices (i.e., who peers with whom at which IXP) and corresponding traffic matrices (i.e., how much traffic do the different ASes that peer at an IXP exchange with one another). In this paper, we report on an Internet-wide traceroute study that was specifically designed to shed light on the unknown IXP-specific peering matrices and involves targeted traceroutes from publicly available and geographically dispersed vantage points. Based on our method, we were able to discover and validate the existence of about 44K IXP-specific peering links - nearly 18K more links than were previously known. In the process, we also classified all known IXPs depending on the type of information required to detect them. Moreover, in view of the currently used inferred AS-level maps of the Internet that are known to miss a significant portion of the actual AS relationships of the peer-to-peer type, our study provides a new method for augmenting these maps with IXP-related peering links in a systematic and informed manner.
DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning

The high prevalence of spinal stenosis results in a large volume of MRI imaging, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this paper, we develop an efficient methodology to leverage the subject-matter-expertise stored in large-scale archival reporting and image data for a deep-learning approach to fully-automated lumbar spinal stenosis grading. Specifically, we introduce three major contributions: (1) a natural-language-processing scheme to extract level-by-level ground-truth labels from free-text radiology reports for the various types and grades of spinal stenosis (2) accurate vertebral segmentation and disc-level localization using a U-Net architecture combined with a spine-curve fitting method, and (3) a multiinput, multi-task, and multi-class convolutional neural network to perform central canal and foraminal stenosis grading on both axial and sagittal imaging series inputs with the extracted report-derived labels applied to corresponding imaging level segments. This study uses a large dataset of 22796 disc-levels extracted from 4075 patients. We achieve state-ofthe-art performance on lumbar spinal stenosis classification and expect the technique will increase both radiology workflow efficiency and the perceived value of radiology reports for referring clinicians and patients.
Structured Prediction Energy Networks

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using backpropagation to iteratively optimize the energy with respect to the labels. This deep architecture captures dependencies between labels that would lead to intractable graphical models, and performs structure learning by automatically learning discriminative features of the structured output. One natural application of our technique is multi-label classification, which traditionally has required strict prior assumptions about the interactions between labels to ensure tractable learning and prediction problems. We are able to apply SPENs to multi-label problems with substantially larger label sets than previous applications of structured prediction, while modeling high-order interactions using minimal structural assumptions. Overall, deep learning provides remarkable tools for learning features of the inputs to a prediction problem, and this work extends these techniques to learning features of structured outputs. Our experiments provide impressive performance on a variety of benchmark multi-label classification tasks, demonstrate that our technique can be used to provide interpretable structure learning, and illuminate fundamental trade-offs between feed-forward and iterative structured prediction techniques.
An overview of text-independent speaker recognition: From features to supervectors

This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.
From conditioning to category learning: an adaptive network model.

We used adaptive network theory to extend the Rescorla-Wagner (1972) least mean squares (LMS) model of associative learning to phenomena of human learning and judgment. In three experiments subjects learned to categorize hypothetical patients with particular symptom patterns as having certain diseases. When one disease is far more likely than another, the model predicts that subjects will substantially overestimate the diagnosticity of the more valid symptom for the rare disease. The results of Experiments 1 and 2 provide clear support for this prediction in contradistinction to predictions from probability matching, exemplar retrieval, or simple prototype learning models. Experiment 3 contrasted the adaptive network model with one predicting pattern-probability matching when patients always had four symptoms (chosen from four opponent pairs) rather than the presence or absence of each of four symptoms, as in Experiment 1. The results again support the Rescorla-Wagner LMS learning rule as embedded within an adaptive network model.
A Literature Review on Opinion Mining and Sentiment Analysis

Opinion mining also called sentiment analysis, involves edifice a scheme to collect and classify opinions about a product. It is a process for tracking the humor of the people about a certain product. Due to cheap availability of internet, people are more dependent on Internet. People purchase product on internet and gives their opinion about the product. There are lots research has been done in the area of opinion mining or sentiment analysis. In this paper we presented the overview on Opinion Mining or Sentiment analysis. We also given the methods, tools and dataset used by Researchers with
Defending Connected Vehicles Against Malware: Challenges and a Solution Framework

Vehicles face growing security threats as they become increasingly connected with the external world. Hackers, researchers, and car hobbyists have compromised security keys used by the electronic control units (ECUs) on vehicles, modified ECU software, and hacked wireless transmissions from vehicle key fobs and tire monitoring sensors, using low-cost commercially available tools. However, the most damaging security threats to vehicles are only emerging. One such threat is malware, which can infect vehicles in a variety of ways and cause severe consequences. Defending vehicles against malware attacks must address many unique challenges that have not been well addressed in other types of networks. This paper identifies those vehicle-specific challenges, discusses existing solutions and their limitations, and presents a cloud-assisted vehicle malware defense framework that can address these challenges.
Attending to Attention: Detecting and Combating Mind Wandering during Computerized Reading

Mind wandering (MW) is a ubiquitous phenomenon that has a negative influence on performance and productivity in many contexts. We propose that intelligent interfaces should have some mechanism to detect and respond to MW in real-time. Towards this end, we developed an interface that automatically detects MW from eye-gaze during computerized reading. When MW is detected, the interface intervenes by asking just-in-time questions and encouraging re-reading as needed. After multiple rounds of iterative refinement, we summatively compared the interface to a yoked control condition in a randomized control trial with 104 participants. Preliminary results suggest that the system was successful in correcting comprehension deficits attributed to MW, thereby highlighting the potential for intelligent interfaces that improve performance by "attending to attention."
Global grasp planning using triangular meshes

In this paper we present an algorithm to determine the location of contact points to obtain force closure grasps on tree dimensional objects. The shape of the object is assumed to be given by a triangle mesh - a format widely used in CAD software. Our algorithm can handle an arbitrary number of contact points and does nor require any prior information about their initial locations. Through an iterative process, contact point locations are updated aiming at improving a commonly used grasp quality metric. The process is global in the sense that during the process the whole surface of the object can be explored, and contact point locations can cross sharp edges that usually represent a problem for optimization algorithms relying on smooth surface representations. Extensive simulation results illustrate the performance of the proposed method, outlining strengths and directions for further research.
Analysis on Rectifier Diode Stress Issue of the DCM Active Clamped Dual-Switch Forward Converter and its Solutions

This paper illustrates the rectifier stress issue of the active clamped dual switch forward converters operating on discontinuous current mode (DCM), and analyzes the additional reverse voltage on the rectifier diode of active clamped dual switch forward converter at DCM operation, which does not appear in continuous current mode (CCM). The additional reverse voltage stress, plus its spikes, definitely causes many difficulties in designing high performance power supplies. In order to suppress this voltage spike to an acceptable level and improve the working conditions for the rectifier diode, this paper carefully explains and presents the working principles of active clamped dual switch forward converter in DCM operation, and theoretically analyzes the causes of the additional reverse voltage and its spikes. For conquering these difficulties, this paper also innovate active clamped snubber (ACS) cell to solve this issue. Furthermore, experiments on a 270W active clamped dual switch forward converter prototype were designed to validate the innovation. Finally, based on the similarities of the rectifier network in forward-topology based converters, this paper also extents the utility of this idea into even wider dc-dc converters.
Informative art: using amplified artworks as information displays

Informative art is computer augmented, or amplified, works of art that not only are aesthetical objects but also information displays, in as much as they dynamically reflect information about their environment. Informative art can be seen as a kind of slow technology, i.e. a technology that promotes moments of concentration and reflection. Our aim is to present the design space of informative art. We do so by discussing its properties and possibilities in relation to work on information visualisation, novel information display strategies, as well as art. A number of examples based on different kinds of mapping relations between information and the properties of the composition of an artwork are described.
Learning to Learn from Noisy Web Videos

Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training videos is feasible for some action classes but doesnt scale to the full long-tailed distribution of actions. A promising way to address this is to leverage noisy data from web queries to learn new actions, using semi-supervised or webly-supervised approaches. However, these methods typically do not learn domain-specific knowledge, or rely on iterative hand-tuned data labeling policies. In this work, we instead propose a reinforcement learning-based formulation for selecting the right examples for training a classifier from noisy web search results. Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts. Experiments on the challenging Sports-1M action recognition benchmark as well as on additional fine-grained and newly emerging action classes demonstrate that our method is able to learn good labeling policies for noisy data and use this to learn accurate visual concept classifiers.
On the Importance of Text Analysis for Stock Price Prediction

We investigate the importance of text analysis for stock price prediction. In particular, we introduce a system that forecasts companies’ stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents. Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features. This impact is most important in the short term (i.e., the next day after the financial event) but persists for up to five days.
Visual Analysis of Movement Behavior Using Web Data for Context Enrichment

With increasing use of GPS devices more and more location-based information is accessible. Thus not only more movements of people are tracked but also POI (point of interest) information becomes available in increasing geo-spatial density. To enable analysis of movement behavior, we present an approach to enrich trajectory data with semantic POI information and show how additional insights can be gained. Using a density-based clustering technique we extract 1.215 frequent destinations of ~150.000 user movements from a large e-mobility database. We query available context information from Foursquare, a popular location-based social network, to enrich the destinations with semantic background. As GPS measurements can be noisy, often more then one possible destination is found and movement patterns vary over time. Therefore we present highly interactive visualizations that enable an analyst to cope with the inherent geospatial and behavioral uncertainties.
The Portuguese version of the body image scale (BIS) - psychometric properties in a sample of breast cancer patients.

The aim of this study was to analyse the psychometric properties of the Portuguese version of the body image scale (BIS; Hopwood, P., Fletcher, I., Lee, A., Al Ghazal, S., 2001. A body image scale for use with cancer patients. . This is a brief and psychometric robust measure of body image for use with cancer patients, independently of age, cancer type, treatment or stage of the disease and it was developed in collaboration with the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Study Group.



The sample is comprised of 173 Portuguese postoperative breast cancer patients that completed a battery of measures that included the BIS and other scales of body image and quality of life, in order to explore its construct validity.



The Portuguese version of BIS confirmed the original unidimensional structure and demonstrated adequate internal consistency, both in the global sample (alpha=.93) as in surgical subgroups (mastectomy=.92 and breast-conserving surgery=.93). Evidence for the construct validity was provided through moderate to largely sized correlations between the BIS and other related measures. In further support of its discriminant validity, significant differences in BIS scores were found between women who underwent mastectomy and those who underwent breast-conserving surgery, with the former presenting higher scores. Age and time since diagnosis were not associated with BIS scores.



The Portuguese BIS proved to be a reliable and valid measure of body image concerns in a sample of breast cancer patients, allowing a brief and comprehensive assessment, both on clinical and research settings.
Comparative analysis of high- and low-fidelity prototypes for more valid usability evaluations of mobile devices

Validation of low-fidelity prototyping test results is difficult because we cannot claim whether the results are the effect of the prototype itself or the essence of the design concept we try to evaluate. However, it will cost too much if we implement a fully functional prototype for more valid evaluation. In this research, we provide a qualitative and reflective analysis of usability evaluations of a text messaging functionality of a mobile phone by comparing three types of prototyping techniques---paper-based and computer-based and fully functional prototype. This analysis led us to realize how significantly the unique characteristics of each different prototype affect the usability evaluation in different ways. We identify what characteristics of each prototype causes the differences in finding usability problems, and then suggest key considerations for designing more valid low-fidelity prototypes based on this analysis.
Diatom Autofocusing in Brightfield Microscopy: a Comparative Study

We present a number of autofocusing methods in lighting microscopy for its use in diatom identi cation. Among these, the Tenengrad method has been considered one of the best. The basic requirements for a practical autofocusing system are speed, sharpness and robustness to noise. Recently other focus measures based on a modi ed Laplacian method are said to perform better than Tenengrad. We investigate two sound methods based on a modi ed Tenengrad and a modied Laplacian. Measurements show that they provide a reliable and suitable focus measure that outperform similar methods. We investigate the window size analysis dependency and perform an univariate analysis on the focus measures. The focusing techniques are implemented in an automatic slide scanning system for diatom detection and identi cation for its use in the ADIAC project.
Challenges of component-based development

It is generally understood that building software systems with components has many advantages but the difficulties of this approach should not be ignored. System evolution, maintenance, migration and compatibilities are some of the challenges met with when developing a component-based software system. Since most systems evolve over time, components must be maintained or replaced. The evolution of requirements affects not only specific system functions and particular components but also component-based architecture on all levels. Increased complexity is a consequence of different components and systems having different life cycles. In component-based systems it is easier to replace part of system with a commercial component. This process is however not straightforward and different factors such as requirements management, marketing issues, etc., must be taken into consideration. In this paper we discuss the issues and challenges encountered when developing and using an evolving component-based software system. An industrial control system has been used as a case study.
Use of mass media campaigns to change health behaviour

Mass media campaigns are widely used to expose high proportions of large populations to messages through routine uses of existing media, such as television, radio, and newspapers. Exposure to such messages is, therefore, generally passive. Such campaigns are frequently competing with factors, such as pervasive product marketing, powerful social norms, and behaviours driven by addiction or habit. In this Review we discuss the outcomes of mass media campaigns in the context of various health-risk behaviours (eg, use of tobacco, alcohol, and other drugs, heart disease risk factors, sex-related behaviours, road safety, cancer screening and prevention, child survival, and organ or blood donation). We conclude that mass media campaigns can produce positive changes or prevent negative changes in health-related behaviours across large populations. We assess what contributes to these outcomes, such as concurrent availability of required services and products, availability of community-based programmes, and policies that support behaviour change. Finally, we propose areas for improvement, such as investment in longer better-funded campaigns to achieve adequate population exposure to media messages.
From Word Embeddings to Item Recommendation

Social network platforms can archive data produced by their users. Then, the archived data is used to provide better services to the users. One of the services that these platforms provide is the recommendation service. Recommendation systems can predict the future preferences of users using various different techniques. One of the most popular technique for recommendation is matrix-factorization, which uses lowrank approximation of input data. Similarly, word embedding methods from natural language processing literature learn lowdimensional vector space representation of input elements. Noticing the similarities among word embedding and matrix factorization techniques and based on the previous works that apply techniques from text processing to recommendation, Word2Vec’s skip-gram technique is employed to make recommendations. The aim of this work is to make recommendation on next check-in venues. Unlike previous works that use Word2Vec for recommendation, in this work non-textual features are used. For the experiments, a Foursquare check-in dataset is used. The results show that use of vector space representations of items modeled by skip-gram technique is promising for making recommendations. Recommendation systems, Location based social networks, Word embedding, Word2Vec, Skip-gram technique