nils-herrmann/clean_scirepeval huggingface dataset:
HyPaFilter — A versatile hybrid FPGA packet filter

With network traffic rates continuously growing, security systems like firewalls are facing increasing challenges to process incoming packets at line speed without sacrificing protection. Accordingly, specialized hardware firewalls are increasingly used in high-speed environments. Hardware solutions, though, are inherently limited in terms of the complexity of the policies they can implement, often forcing users to choose between throughput and comprehensive analysis. On the contrary, complex rules typically constitute only a small fraction of the rule set. This motivates the combination of massively parallel, yet complexity-limited specialized circuitry with a slower, but semantically powerful software firewall. The key challenge in such a design arises from the dependencies between classification rules due to their relative priorities within the rule set: complex rules requiring software-based processing may be interleaved at arbitrary positions between those where hardware processing is feasible. We therefore discuss approaches for partitioning and transforming rule sets for hybrid packet processing, and propose HyPaFilter, a hybrid classification system based on tailored circuitry on an FPGA as an accelerator for a Linux netfilter firewall. Our evaluation demonstrates 30-fold performance gains in comparison to software-only processing.
Radio-Frequency Identification-Overview

Radio-frequency identification (RFID) is a wireless technology for automatic identification using electromagnetic fields in the radio frequency spectrum. In addition to the easy deployment and decreasing prices for tags, this technology has many advantages to bar codes and other common identification methods, such as no required line of sight and the ability to read several tags simultaneously. Therefore it enjoys large popularity among large businesses and continues to spread in the consumer market. Common applications include the fields of electronic article surveillance, access control, tracking, and identification of objects and animals. This paper introduces RFID technology, analyzes modern applications, and tries to point out strengths and weaknesses of RFID systems.
Cluster-based adaptive SVM: A latent subdomains discovery method for domain adaptation problems

Machine learning algorithms often suffer from good generalization in testing domains especially when the training (source) and test (target) domains do not have similar distributions. To address this problem, several domain adaptation techniques have been proposed to improve the performance of the learning algorithms when they face accuracy degradation caused by the domain shift problem. In this paper, we focus on the non-homogeneous distributed target domains and propose a new latent subdomain discovery model to divide the target domain into subdomains while adapting them. It is expected that applying adaptation on subdomains increase the rate of detection in comparing with the situation that the target domain is seen as one single domain. The proposed division method considers each subdomain as a cluster which has the definite ratio of positive to negative samples, linear discriminability and conditional distribution similarity to the source domain. This method divides the target domain into subdomains while adapting the trained target classifier for each subdomain using Adapt-SVM adaptation method. It also has a simple solution for selecting the appropriate number of subdomains. We call our proposed method Cluster-based Adaptive SVM or CA-SVM in short. We test CA-SVM on two different computer vision problems, pedestrian detection and image classification. The experimental results show the advantage in accuracy rate for our approach in comparison to several baselines.
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond

We propose ‘Hide-and-Seek’ a general purpose data augmentation technique, which is complementary to existing data augmentation techniques and is beneficial for various visual recognition tasks. The key idea is to hide patches in a training image randomly, in order to force the network to seek other relevant content when the most discriminative content is hidden. Our approach only needs to modify the input image and can work with any network to improve its performance. During testing, it does not need to hide any patches. The main advantage of Hide-and-Seek over existing data augmentation techniques is its ability to improve object localization accuracy in the weakly-supervised setting, and we therefore use this task to motivate the approach. However, Hide-and-Seek is not tied only to the image localization task, and can generalize to other forms of visual input like videos, as well as other recognition tasks like image classification, temporal action localization, semantic segmentation, emotion recognition, age/gender estimation, and person re-identification. We perform extensive experiments to showcase the advantage of Hide-and-Seek on these various visual recognition problems.
Differential private random forest

Organizations be it private or public often collect personal information about an individual who are their customers or clients. The personal information of an individual is private and sensitive which has to be secured from data mining algorithm which an adversary may apply to get access to the private information. In this paper we have consider the problem of securing these private and sensitive information when used in random forest classifier in the framework of differential privacy. We have incorporated the concept of differential privacy to the classical random forest algorithm. Experimental results shows that quality functions such as information gain, max operator and gini index gives almost equal accuracy regardless of their sensitivity towards the noise. Also the accuracy of the classical random forest and the differential private random forest is almost equal for different size of datasets. The proposed algorithm works for datasets with categorical as well as continuous attributes.
Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes.

Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies.
Ephemeral Content Popularity at the Edge and Implications for On-Demand Caching

The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and <italic>cache on <inline-formula><tex-math notation="LaTeX">$k$</tex-math> <alternatives><inline-graphic xlink:href="carlsson-ieq1-2614805.gif"/></alternatives></inline-formula></italic>th <italic>request</italic> for different <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="carlsson-ieq2-2614805.gif"/></alternatives></inline-formula>. The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect “oracle” policy, such improvements are unlikely to be achievable in practice.
A subject transfer framework for EEG classification

This paper proposes a subject transfer framework for EEG classification. It aims to improve the classification performance when the training set of the target subject (namely user) is small owing to the need to reduce the calibration session. Our framework pursues improvement not only at the feature extraction stage, but also at the classification stage. At the feature extraction stage, we first obtain a candidate filter set for each subject through a previously proposed feature extraction method. Then, we design different criterions to learn two sparse subsets of the candidate filter set, which are called the robust filter bank and adaptive filter bank, respectively. Given robust and adaptive filter banks, at the classification step, we learn classifiers corresponding to these filter banks and employ a two-level ensemble strategy to dynamically and locally combine their outcomes to reach a single decision output. The proposed framework, as validated by experimental results, can achieve positive knowledge transfer for improving the performance of EEG classification.
Brief mindfulness induction reduces inattentional blindness

Prior research has linked mindfulness to improvements in attention, and suggested that the effects of mindfulness are particularly pronounced when individuals are cognitively depleted or stressed. Yet, no studies have tested whether mindfulness improves declarative awareness of unexpected stimuli in goal-directed tasks. Participants (N=794) were either depleted (or not) and subsequently underwent a brief mindfulness induction (or not). They then completed an inattentional blindness task during which an unexpected distractor appeared on the computer monitor. This task was used to assess declarative conscious awareness of the unexpected distractor's presence and the extent to which its perceptual properties were encoded. Mindfulness increased awareness of the unexpected distractor (i.e., reduced rates of inattentional blindness). Contrary to predictions, no mindfulness×depletion interaction emerged. Depletion however, increased perceptual encoding of the distractor. These results suggest that mindfulness may foster awareness of unexpected stimuli (i.e., reduce inattentional blindness).
What's the role of perceived social support and coping styles in depression and anxiety?

Due to the excessive and pathologic effects of depression and anxiety, it is important to identify the role of protective factors, such as effective coping and social support. This study examined the associations between perceived social support and coping styles with depression and anxiety levels.



This cross sectional study was part of the Study on the Epidemiology of Psychological, Alimentary Health and Nutrition project. A total 4658 individuals aged ≥20 years was selected by cluster random sampling. Subjects completed questionnaires, which were used to describe perceived social support, coping styles, depression and anxiety. t-test, Chi-square test, pearson's correlation and Logistic regression analysis were used in data analyses.



The results of Logistic regression analysis showed after adjusting demographic characteristics for odd ratio of anxiety, active copings such as positive re-interpretation and growth with odds ratios; 95% confidence interval: 0.82 (0.76, 0.89), problem engagement (0.92 [0.87, 0.97]), acceptance (0.82 [0.74, 0.92]) and also among perceived social supports, family (0.77 [0.71, 0.84]) and others (0.84 [0.76, 0.91]) were protective. In addition to, for odd ratio of depression, active copings such as positive re-interpretation and growth (0.74 [0.69, 0.79]), problem engagement (0.89 [0.86, 0.93]), and support seeking (0.96 [0.93, 0.99]) and all of social support types (family [0.75 (0.70, 0.80)], friends [0.90 (0.85, 0.95)] and others [0.80 (0.75, 0.86)]) were protective. Avoidance was risk factor for both of anxiety (1.19 [1.12, 1.27]) and depression (1.22 [1.16, 1.29]).



This study shows active coping styles and perceived social supports particularly positive re-interpretation and family social support are protective factors for depression and anxiety.
A grasp planning for picking up an unknown object for a mobile manipulator

This paper describes a grasp planning for a mobile manipulator which works in real environment. Mobile robot studies up to now that manipulate an object in real world practically use ID tag on an object or an object model which is given to the robot in advance. The authors aim to develop a mobile manipulator that can acquire an object model through video images and can manipulate the object. In this approach, the robot can manipulate an unknown object autonomously. A grasp planning proposed in this paper can find a stable grasp pose from the automatically generated model which contains redundant data and the shape error of the object. Experiments show the effectiveness of the proposed method
Torque ripple and back EMF harmonic reduction of IPMSM with asymmetrical stator design

This paper suggests asymmetric stator teeth design to reduce torque ripple and back EMF Total Harmonic Distortion(THD) for Interior Permanent Magnet Synchronous Machine(IPMSM). IPMSM which has 8 poles, 12 slots is analyzed in this study. From changing design parameter in stator structure, 8 comparison models are analyzed. Analysis of proposed method is carried out using Finite Element Method(FEM). Suggested method has advantage to reduce torque ripple and back electromotive force(EMF) harmonics without average torque decrease. Comparison between reference model and comparison models applying proposed method proceeds to verify advantage of this method.
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction

Vector-space models, from word embeddings to neural network parsers, have many advantages for NLP. But how to generalise from fixed-length word vectors to a vector space for arbitrary linguistic structures is still unclear. In this paper we propose bag-of-vector embeddings of arbitrary linguistic graphs. A bag-of-vector space is the minimal nonparametric extension of a vector space, allowing the representation to grow with the size of the graph, but not tying the representation to any specific tree or graph structure. We propose efficient training and inference algorithms based on tensor factorisation for embedding arbitrary graphs in a bag-ofvector space. We demonstrate the usefulness of this representation by training bag-of-vector embeddings of dependency graphs and evaluating them on unsupervised semantic induction for the Semantic Textual Similarity and Natural Language Inference tasks.
The Clinical Spectrum of Leukocyte Adhesion Deficiency (LAD) III due to Defective CalDAG-GEF1

Leukocyte adhesion deficiency (LAD) type III is a rare syndrome characterized by severe recurrent infections, leukocytosis, and increased bleeding tendency. All integrins are normally expressed yet a defect in their activation leads to the observed clinical manifestations. Less than 20 patients have been reported world wide and the primary genetic defect was identified in some of them. Here we describe the clinical features of patients in whom a mutation in the calcium and diacylglycerol-regulated guanine nucleotide exchange factor 1 (CalDAG GEF1) was found and compare them to other cases of LAD III and to animal models harboring a mutation in the CalDAG GEF1 gene. The hallmarks of the syndrome are recurrent infections accompanied by severe bleeding episodes distinguished by osteopetrosis like bone abnormalities and neurodevelopmental defects.
Label-noise-tolerant classification for streaming data

Label noise-tolerant machine learning techniques address datasets which are affected by mislabelling of the instances. Since labelling quality is a severe issue in particular for large or streaming data sets, this setting becomes more and more relevant in the context of life-long learning, big data and crowd sourcing. In this contribution, we extend a powerful online learning method, soft robust learning vector quantisation, by a probabilistic model for noise tolerance, which is applicable for streaming data, including label-noise drift. The superiority of the technique is demonstrated in several benchmark problems.
Radical Analysis Network for Zero-Shot Learning in Printed Chinese Character Recognition

Chinese characters have a huge set of character categories, more than 20, 000 and the number is still increasing as more and more novel characters continue being created. However, the enormous characters can be decomposed into a compact set of about 500 fundamental and structural radicals. This paper introduces a novel radical analysis network (RAN) to recognize printed Chinese characters by identifying radicals and analyzing two-dimensional spatial structures among them. The proposed RAN first extracts visual features from input by employing convolutional neural networks as an encoder. Then a decoder based on recurrent neural networks is employed, aiming at generating captions of Chinese characters by detecting radicals and two-dimensional structures through a spatial attention mechanism. The manner of treating a Chinese character as a composition of radicals rather than a single character class largely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes, namely zero-shot learning.
Understanding the most satisfying and unsatisfying user experiences: Emotions, psychological needs, and context

The aim of this research was to study the structure of the most satisfying and unsatisfying user experiences in terms of experienced emotions, psychological needs, and contextual factors. 45 university students wrote descriptions of their most satisfying and unsatisfying recent user experiences and analyzed those experiences using the Positive and Negative Affect Schedule (PANAS) method for experienced emotions, a questionnaire probing the salience of 10 psychological needs, and a self-made set of rating scales for analyzing context. The results suggested that it was possible to capture variations in user experiences in terms of experienced emotions, fulfillment of psychological needs, and context effectively by using psychometric rating scales. The results for emotional experiences showed significant differences in 16 out of 20 PANAS emotions between the most satisfying and unsatisfying experiences. The results for psychological needs indicated that feelings of autonomy and competence emerged as highly salient in the most satisfying experiences and missing in the unsatisfying experiences. High self-esteem was also notably salient in the most satisfying experiences. The qualitative results indicated that most of the participants’ free-form qualitative descriptions, especially for the most unsatisfying user experiences, gave important information about the pragmatic aspects of the interaction, but often omitted information about hedonic and social aspects of user experience. 2011 British Informatics Society Limited.
Fundamental movement skills and physical fitness as predictors of physical activity: A 6-year follow-up study.

The purpose of this study was to examine the extent to which fundamental movement skills and physical fitness scores assessed in early adolescence predict self-reported physical activity assessed 6 years later. The sample comprised 333 (200 girls, 133 boys; M age = 12.41) students. The effects of previous physical activity, sex, and body mass index (BMI) were controlled in the main analyses. Adolescents' fundamental movement skills, physical fitness, self-report physical activity, and BMI were collected at baseline, and their self-report energy expenditure (metabolic equivalents: METs) and intensity of physical activity were collected using the International Physical Activity Questionnaire 6 years later. Results showed that fundamental movement skills predicted METs, light, moderate, and vigorous intensity physical activity levels, whereas fitness predicted METs, moderate, and vigorous physical activity levels. Hierarchical regression analyses also showed that after controlling for previous levels of physical activity, sex, and BMI, the size of the effect of fundamental movement skills and physical fitness on energy expenditure and physical activity intensity was moderate (R(2) change between 0.06 and 0.15), with the effect being stronger for high intensity physical activity.
ActiveMath: A Generic and Adaptive Web-Based Learning Environment

ActiveMath is a generic web-based learning system that dynamically generates interactive (mathematical) courses adapted to the student’s goals, preferences, capabilities, and knowledge. The content is represented in an semantic xml-based format. For each user, the appropriate content is retrieved from a knowledge base and the course is generated individually according to pedagogical rules. Then the course is presented to the user via a standard webbrowser. One of the exceptional features of ActiveMath is its integration of stand-alone mathematical service systems. This offers the means for exploratory learning, realistically complex exercises as well as for learning proof methods. The article provides a comprehensive account of the current version of ActiveMath.
Fourth International Consultation on Incontinence Recommendations of the International Scientific Committee: Evaluation and treatment of urinary incontinence, pelvic organ prolapse, and fecal incontinence.

P. Abrams , K.E. Andersson, L. Birder, L. Brubaker, L. Cardozo, C. Chapple, A. Cottenden, W. Davila, D. de Ridder, R. Dmochowski, M. Drake, C. DuBeau, C. Fry, P. Hanno, J. Hay Smith, S. Herschorn, G. Hosker, C. Kelleher, H. Koelbl, S. Khoury,* R. Madoff, I. Milsom, K. Moore, D. Newman, V. Nitti, C. Norton, I. Nygaard, C. Payne, A. Smith, D. Staskin, S. Tekgul, J. Thuroff, A. Tubaro, D. Vodusek, A. Wein, and J.J. Wyndaele and the Members of the Committees