nils-herrmann/clean_scirepeval huggingface dataset:
COVAREP — A collaborative voice analysis repository for speech technologies

Speech processing algorithms are often developed demonstrating improvements over the state-of-the-art, but sometimes at the cost of high complexity. This makes algorithm reimplementations based on literature difficult, and thus reliable comparisons between published results and current work are hard to achieve. This paper presents a new collaborative and freely available repository for speech processing algorithms called COVAREP, which aims at fast and easy access to new speech processing algorithms and thus facilitating research in the field. We envisage that COVAREP will allow more reproducible research by strengthening complex implementations through shared contributions and openly available code which can be discussed, commented on and corrected by the community. Presently COVAREP contains contributions from five distinct laboratories and we encourage contributions from across the speech processing research field. In this paper, we provide an overview of the current offerings of COVAREP and also include a demonstration of the algorithms through an emotion classification experiment.
Detection of New Vessels on the Optic Disc Using Retinal Photographs

Proliferative diabetic retinopathyis a rare condition likely to lead to severe visual impairment. It is characterized by the development of abnormal new retinal vessels. We describe a method for automatically detecting new vessels on the optic disc using retinal photography. Vessel-like candidate segments are first detected using a method based on watershed lines and ridge strength measurement. Fifteen feature parameters, associated with shape, position, orientation, brightness, contrast and line density are calculated for each candidate segment. Based on these features, each segment is categorized as normal or abnormal using a support vector machine (SVM) classifier. The system was trained and tested by cross-validation using 38 images with new vessels and 71 normal images from two diabetic retinal screening centers and one hospital eye clinic. The discrimination performance of the fifteen features was tested against a clinical reference standard. Fourteen features were found to be effective and used in the final test. The area under the receiver operator characteristic curve was 0.911 for detecting images with new vessels on the disc. This accuracy may be sufficient for it to play a useful clinical role in an automated retinopathy analysis system.
3D shape regression for real-time facial animation

We present a real-time performance-driven facial animation system based on 3D shape regression. In this system, the 3D positions of facial landmark points are inferred by a regressor from 2D video frames of an ordinary web camera. From these 3D points, the pose and expressions of the face are recovered by fitting a user-specific blendshape model to them. The main technical contribution of this work is the 3D regression algorithm that learns an accurate, user-specific face alignment model from an easily acquired set of training data, generated from images of the user performing a sequence of predefined facial poses and expressions. Experiments show that our system can accurately recover 3D face shapes even for fast motions, non-frontal faces, and exaggerated expressions. In addition, some capacity to handle partial occlusions and changing lighting conditions is demonstrated.
SENTIMENT ANALYSIS IN E-COMMERCE USING RECOMMENDATION SYSTEM

In the field of text mining the sentiment analysis is one of the current research topics. Sentiment analysis is the best solution for opinions and sentiment mining from natural language. Sentiment analysis gives significant information for decision making in various domains. The sentiments include ratings, reviews and emoticons. Several sentiment detection approaches are available which affect the quality of result. This paper finding the sentiments of people related to the services of E-shopping websites. The central goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best product. The proposed system use stochastic learning algorithm which analyze various feedbacks related to the services and then the sentiments are classified as negative, positive and neutral. The pre-processing of the data is greatly affecting the quality of detected sentiments so that the fake reviews in the website are analyzed. Finally the classification technique can be used to analysis the products which are posted in E-shopping website. The proposed system find out fake reviews made by posting fake comments about a product by identifying the MAC address along with review posting patterns. In order to find out the review is fake or genuine the proposed system find out the MAC address of the user if the system observes fake review send by the same MAC address many a times it will inform the admin to remove that review from the system. The proposed system helps the user to find out correct review of the product.
EventRiver: Visually Exploring Text Collections with Temporal References

Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.
Review of Charge Pump Topologies for Micro Energy Harvesting Systems

Michelle Lim Sern Mi Institute of Microengineering and Nanoelectronics (IMEN), UKM, 43600 Bangi, Selangor, Malaysia  This paper reviews CMOS based charge pump topologies used within autonomous embedded micro-systems. These charge pump structures have evolved from its simplistic diode-tied, single-branches with major threshold drops to exponential type, dual-branches with sophisticated gate and substrate control for lower voltage operation. Published charge pumps are grouped based on architecture, operation principles and pump optimization techniques with their pros and cons compared and results contrasted. The various charge pump topologies and schemes used are considered based on pumping efficiency, power efficiency, charge transferability, circuit complexity, pumping capacitors, form factor and minimum supply voltages with an optimum load. This article concludes with an overview of suitable techniques and recommendations that will aid a designer in selecting the most suitable charge pump topology especially for low ambient micro energy harvesting applications.
Deriving Concept-Based User Profiles from Search Engine Logs

User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e., positive preferences), but not the objects that users dislike (i.e., negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user's positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters.
Leading Edge Primer Ferroptosis : A Regulated Cell Death Nexus Linking Metabolism , Redox Biology , and Disease

Brent R. Stockwell,1,2,* José Pedro Friedmann Angeli,3,29 Hülya Bayir,4 Ashley I. Bush,5 Marcus Conrad,3 Scott J. Dixon,6 Simone Fulda,7 Sergio Gascón,8,28 Stavroula K. Hatzios,9,10 Valerian E. Kagan,11 Kay Noel,12 Xuejun Jiang,13 Andreas Linkermann,14 Maureen E. Murphy,15 Michael Overholtzer,13 Atsushi Oyagi,16 Gabriela C. Pagnussat,17 Jason Park,18 Qitao Ran,19 Craig S. Rosenfeld,12 Konstantin Salnikow,20 Daolin Tang,21,22 FrankM. Torti,23 Suzy V. Torti,24 Shinya Toyokuni,25 K.A. Woerpel,26 and Donna D. Zhang27
Static and dynamic 3D facial expression recognition: A comprehensive survey

a r t i c l e i n f o Keywords: Facial behaviour analysis Facial expression recognition 3D facial surface 3D facial surface sequences (4D faces) Automatic facial expression recognition constitutes an active research field due to the latest advances in computing technology that make the user's experience a clear priority. The majority of work conducted in this area involves 2D imagery, despite the problems this presents due to inherent pose and illumination variations. In order to deal with these problems, 3D and 4D (dynamic 3D) recordings are increasingly used in expression analysis research. In this paper we survey the recent advances in 3D and 4D facial expression recognition. We discuss developments in 3D facial data acquisition and tracking, and present currently available 3D/4D face databases suitable for 3D/4D facial expressions analysis as well as the existing facial expression recognition systems that exploit either 3D or 4D data in detail. Finally, challenges that have to be addressed if 3D facial expression recognition systems are to become a part of future applications are extensively discussed. Automatic human behaviour understanding has attracted a great deal of interest over the past two decades, mainly because of its many applications spanning various fields such as psychology, computer technology , medicine and security. It can be regarded as the essence of next-generation computing systems as it plays a crucial role in affective computing technologies (i.e. proactive and affective user interfaces), learner-adaptive tutoring systems, patient-profiled personal wellbeing technologies, etc. . Facial expression is the most cogent, naturally preeminent means for humans to communicate emotions, to clarify and give emphasis, to signal comprehension disagreement, to express intentions and, more generally, to regulate interactions with the environment and other people . These facts highlight the importance of automatic facial behaviour analysis, including facial expression of emotion and facial action unit (AU) recognition, and justify the interest this research area has attracted, in the past twenty years [3,4]. Until recently, most of the available data sets of expressive faces were of limited size containing only deliberately posed affective displays, mainly of the prototypical expressions of six basic emotions (i.e. anger, disgust, fear, happiness, sadness and surprise), recorded under highly controlled conditions. Recent efforts focus on the recognition of complex and spontaneous emotional phenomena (e.g. boredom or lack of attention, frustration , stress, etc.) rather than on the recognition of deliberately displayed prototypical expressions of emotions [5,4,6,7]. However, most …
Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more

Designations used by companies to distinguish their products are often claimed as trademarks or registered trademarks. In all instances in which Morgan Kaufmann Publishers is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopying, scanning, or otherwise—without prior written permission of the publisher. may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting " Support & Contact " then " Copyright and Permission " and then " Obtaining Permissions. "  " Make a new plan Stan. .. and get yourself free.  the database professional's guide to exploiting indexes, views, storage, and more / Sam Lightstone, Toby Teorey, and Tom Nadeau. p. cm.-(The Morgan Kaufmann series in database management systems) Includes bibliographical references and index.
The one-time pad revisited

The one-time pad, the mother of all encryption schemes, is well known to be information-theoretically secure, in contrast to most encryption schemes used in practice, which are at most computationally secure. In this paper, we focus on another, completely different aspect in which the one-time pad is superior to normal encryption, and which surfaces only when the receiver (not only the eavesdropper) is considered potentially dishonest, as can be the case in a larger protocol context in which encryption is used as a sub-protocol. For example, such a dishonest receiver (who is, say, coerced by the eavesdropper) can in normal encryption verifiably leak the message to the eavesdropper by revealing the secret key. While this leakage feature can provably not be avoided completely, it is more limited if the one-time pad is used. We use the constructive cryptography framework to make these statements precise.
Deep Transfer Learning for Person Re-Identification

Person re-identification (Re-ID) poses an inevitable challenge to deep learning: how to learn a robust deep model with millions of parameters on a small training set of few or no labels. In this paper, two deep transfer learning methods are proposed to address the training data sparsity problem, respectively from the supervised and unsupervised settings. First, a two-stepped fine-tuning strategy with proxy classifier learning is developed to transfer knowledge from auxiliary datasets. Second, given an unlabelled Re-Iddataset, an unsupervised deep transfer learning model is proposed based on a co-training strategy. Extensive experiments show that the proposed models achieve a good performance of deep Re-ID models.
Sources of Momentum Profits : Evidence on the Irrelevance of Characteristics ∗

Several recent studies document that sorting stocks first on certain stock-level characteristics and then on past returns results in elevated momentum profits. We show that such strategies enhance momentum profits simply by trading in stocks with more extreme past returns. Adjusted for this effect, elevated momentum profits resulting from characteristics (size, R2, turnover, age, analyst coverage, analyst forecast dispersion, market-to-book, price, illiquidity, credit rating) disappear almost entirely. Interaction patterns have been used to support behavioral and limits-to-arbitrage explanations of momentum; our findings imply that explanations of momentum should instead focus on the link between momentum profits and extreme past returns.
Interleukin-17 inhibitors. A new era in treatment of psoriasis and other skin diseases

Psoriasis is a chronic skin disease caused by the excessive secretion of inflammatory cytokines. Available therapeutic options include biologic drugs such as tumor necrosis factor alpha inhibitors and interleukin 12/23 (IL-12/23) inhibitors. The recent discovery of IL-17, which contributes to development of psoriasis, opened new possibilities for further treatment modalities. Currently, one anti-IL17 biological agent is approved for the treatment - a fully human monoclonal antibody that targets IL-17A (secukinumab). Further clinical trials, including a humanized IgG4 specific for IL-17 (ixekizumab) and a fully human antibody that targets the IL-17 receptor A (brodalumab).
An implementation of the FP-growth algorithm

The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. In this paper I describe a C implementation of this algorithm, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of the FP-growth algorithm). In addition, projected FP-trees are (optionally) pruned by removing items that have become infrequent due to the projection (an approach that has been called FP-Bonsai). I report experimental results comparing this implementation of the FP-growth algorithm with three other frequent item set mining algorithms I implemented (Apriori, Eclat, and Relim).
CMOS Low Noise Amplifier with Capacitive Feedback Matching

A capacitive shunt feedback LNA input matching network is demonstrated which offers matching, good noise performance, low component count and low area consumption. A prototype 9 GHz LNA vehicle amplifier is designed and fabricated in a 130 nm RF-CMOS process. The measured amplifier has 20.5 dB of gain at 8.8 GHz with input and output match of -15 dB and -8 dB respectively. It has 1.4 GHz of 3 dB-bandwidth around 9 GHz with the noise figure of 1.7 dB at the center frequency and below 2 dB across the band. Large signal measurements reveal that the amplifier can deliver -2 dBm of power to the 50Omega output load at its 1 dB compression point. It draws 23 mA of current from a 1.2 V supply. The chip occupies an area of 0.64 mm2.
EXPOSURE: Finding Malicious Domains Using Passive DNS Analysis

The domain name service (DNS) plays an important role in the operation of the Internet, providing a two-way mapping between domain names and their numerical identifiers. Given its fundamental role, it is not surprising that a wide variety of malicious activities involve the domain name service in one way or another. For example, bots resolve DNS names to locate their command and control servers, and spam mails contain URLs that link to domains that resolve to scam servers. Thus, it seems beneficial to monitor the use of the DNS system for signs that indicate that a certain name is used as part of a malicious operation. In this paper, we introduce EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in malicious activity. We use 15 features that we extract from the DNS traffic that allow us to characterize different properties of DNS names and the ways that they are queried. Our experiments with a large, real-world data set consisting of 100 billion DNS requests, and a real-life deployment for two weeks in an ISP show that our approach is scalable and that we are able to automatically identify unknown malicious domains that are misused in a variety of malicious activity (such as for botnet command and control, spamming, and phishing).
Is a picture really worth a thousand words? Exploring the feasibility of graphical authentication systems

The weakness of knowledge-based authentication systems, such as passwords and Personal Identification Numbers (PINs), is well known, and reflects an uneasy compromise between security and human memory constraints. Research has been undertaken for some years now into the feasibility of graphical authentication mechanisms in the hope that these will provide a more secure and memorable alternative. The graphical approach substitutes the exact recall of alphanumeric codes with the recognition of previously learnt pictures, a skill at which humans are remarkably proficient. So far, little attention has been devoted to usability and initial research has failed to conclusively establish significant memory improvement. This paper reports two user studies comparing several implementations of the graphical approach with PINs. Results demonstrate that pictures can be a solution to some problems relating to traditional knowledge-based authentication but that they are not a simple panacea, since a poor design can eliminate the picture superiority effect in memory. The paper concludes by discussing the potential of the graphical approach and providing guidelines for developers contemplating using these mechanisms.
Feature Learning with Raw-Waveform CLDNNs for Voice Activity Detection

Voice Activity Detection (VAD) is an important preprocessing step in any state-of-the-art speech recognition system. Choosing the right set of features and model architecture can be challenging and is an active area of research. In this paper we propose a novel approach to VAD to tackle both feature and model selection jointly. The proposed method is based on a CLDNN (Convolutional, Long Short-Term Memory, Deep Neural Networks) architecture fed directly with the raw waveform. We show that using the raw waveform allows the neural network to learn features directly for the task at hand, which is more powerful than using log-mel features, specially for noisy environments. In addition, using a CLDNN, which takes advantage of both frequency modeling with the CNN and temporal modeling with LSTM, is a much better model for VAD compared to the DNN. The proposed system achieves over 78% relative improvement in False Alarms (FA) at the operating point of 2% False Rejects (FR) on both clean and noisy conditions compared to a DNN of comparable size trained with log-mel features. In addition, we study the impact of the model size and the learned features to provide a better understanding of the proposed architecture.
Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms

We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular, we analyze particle swarm optimization (PSO), differential evolution (DE), and covariance matrix adaptation-evolution strategy (CMA-ES). Each evolutionary algorithm is contrasted with the others and with a robust nonstochastic gradient follower (i.e., a hill climber) based on Newton-Raphson. The evolved benchmark problems yield insights into the operation of PSOs, illustrate benefits and drawbacks of different population sizes, velocity limits, and constriction (friction) coefficients. The fitness landscapes made by genetic programming reveal new swarm phenomena, such as deception, thereby explaining how they work and allowing us to devise better extended particle swarm systems. The method could be applied to any type of optimizer.