nils-herrmann/clean_scirepeval huggingface dataset:
A New Proposed Dynamic Quantum with Re-Adjusted Round Robin Scheduling Algorithm and Its Performance Analysis

Scheduling is the central concept used frequently in Operating System. It helps in choosing the processes for execution.  Robin (RR) is one of the most widely used CPU scheduling algorithm. But, its performance degrades with respect to context switching, which is an overhead and it occurs during each scheduling. Overall performance of the system depends on choice of an optimal time quantum, so that context switching can be reduced. In this paper, we have proposed a new variant of RR scheduling algorithm, known as Dynamic Quantum with Re-adjusted Round Robin (DQRRR) algorithm. We have experimentally shown that performance of DQRRR is better than RR by reducing number of context switching, average waiting time and average turnaround time.
Child pornography in peer-to-peer networks.

The presence of child pornography in peer-to-peer networks is not disputed, but there has been little effort done to quantify and analyze the distribution and nature of that content to-date. By performing an analysis of queries and query hits on the largest peer-to-peer network, we are able to both quantify and describe the nature of querying by child pornographers as well as the content they are sharing.



Child pornography related content was identified and analyzed in 235,513 user queries and 194,444 query hits.



The research confirmed a large amount of peer-to-peer traffic is dedicated to child pornography, but supply and demand must be separated for a better understanding. The most prevalent query and the top two most prevalent filenames returned as query hits were child pornography related. However, it would be inaccurate to state child pornography dominates peer-to-peer as 1% of all queries were related to child pornography and 1.45% of all query hits (unique filenames) were related to child pornography, consistent with a smaller study (Hughes et al., 2008). In addition to the above, research indicates that the median age searched for was 13 years old, and the majority of queries were gender-neutral, but of those with gender-related terms, 79% were female-oriented. Distribution-wise, the vast majority of content-specific searches are for movies at 99%, though images are still the most prevalent in availability.



There is no shortage of child pornography supply and demand on peer-to-peer networks and by analyzing how consumers seek and distributors advertise content we can better understand their motivations.



Understanding the behavior of child pornographers and how they search for content when contrasted with those sharing content provides a basis for finding and combating that behavior. For law enforcement, knowing the specific terms used allows more timely and accurate forensics and better identification of those seeking and distributing child pornography. For Internet researchers, better filtering and monitoring is possible. For mental health professionals, understanding the preferences and behaviors of those searching supports more effective treatment.
Training Spiking Deep Networks for Neuromorphic Hardware

We describe a method to train spiking deep networks that can be run using leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012 benchmark. Our method for transforming deep artificial neural networks into spiking networks is scalable and works with a wide range of neural nonlinearities. We achieve these results by softening the neural response function, such that its derivative remains bounded, and by training the network with noise to provide robustness against the variability introduced by spikes. Our analysis shows that implementations of these networks on neuromorphic hardware will be many times more power-efficient than the equivalent non-spiking networks on traditional hardware.
Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution

While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach.
Exploiting Language Models to Classify Events from Twitter

Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events.
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos

In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation. However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space. In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment. The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps. To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively. Experiments on 300W, 300VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-ofthe-art models.  Face alignment aims to estimate a set of facial landmarks given a face image or video sequence. It is a classic computer vision problem that has attributed to many advanced machine learning algorithms Fan et al. (2018); Bulat and Tzimiropoulos (2017); Trigeorgis et al. (2016); Peng et al. (2015, 2016); Kowalski, Naruniec, and Trzcinski (2017); Chen et al. (2017); Liu et al. (2017); Hu et al. (2018). Nowadays, with the rapid development of consumer hardwares (e.g., mobile phones, digital cameras), High-Resolution (HR) video sequences can be easily collected. Estimating facial landmarks on such highresolution facial data has tremendous applications, e.g., face makeup Chen, Shen, and Jia (2017), editing with special effects Korshunova et al. (2017) in live broadcast videos. However, most existing face alinement methods work on faces with medium image resolutions Chen et al. (2017); Bulat and Tzimiropoulos (2017); Peng et al. (2016); Liu et al. (2017). Therefore, developing face alignment algorithms for high-resolution videos is at the core of this paper. To this end, we propose an accurate and stable algorithm for high-resolution video-based face alignment, named Fractional Heatmap Regression (FHR). It is well known that ∗ indicates equal contributions. Conventional Heatmap Regression (CHR) Loss Fractional Heatmap Regression (FHR) Loss 930 744 411
Beyond Fitts' Law: Models for Trajectory-Based HCI Tasks

As a developing discipline, research results in the field of human computer interaction (HCI) tends to be "soft". Many workers in the field have argued that the advancement of HCI lies in "hardening" the field with quantitative and robust models. In reality, few theoretical, quantitative tools are available in user interface research and development. A rare exception to this is Fitts' law. Extending information theory to human perceptual-motor system, Paul Fitts (1954) found a logarithmic relationship that models speed accuracy tradeoffs in aimed movements. A great number of studies have verified and / or applied Fitts' law to HCI problems, such as pointing performance on a screen, making Fitts' law one of the most intensively studied topic in the HCI literature.
The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

The INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified; in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem; in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart; and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions, and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the ‘usual’ ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series.
The forgettable-watcher model for video question answering

A number of visual question answering approaches have been proposed recently, aiming at understanding the visual scenes by answering the natural language questions. While the image question answering has drawn significant attention, video question answering is largely unexplored. Video-QA is different from Image-QA since the information and the events are scattered among multiple frames. In order to better utilize the temporal structure of the videos and the phrasal structures of the answers, we propose two mechanisms: the re-watching and the re-reading mechanisms and combine them into the forgettable-watcher model. Then we propose a TGIF-QA dataset for video question answering with the help of automatic question generation. Finally, we evaluate the models on our dataset. The experimental results show the effectiveness of our proposed models.
Large-scale adaptive semi-supervised learning via unified inductive and transductive model

Most semi-supervised learning models propagate the labels over the Laplacian graph, where the graph should be built beforehand. However, the computational cost of constructing the Laplacian graph matrix is very high. On the other hand, when we do classification, data points lying around the decision boundary (boundary points) are noisy for learning the correct classifier and deteriorate the classification performance. To address these two challenges, in this paper, we propose an adaptive semi-supervised learning model. Different from previous semi-supervised learning approaches, our new model needn't construct the graph Laplacian matrix. Thus, our method avoids the huge computational cost required by previous methods, and achieves a computational complexity linear to the number of data points. Therefore, our method is scalable to large-scale data. Moreover, the proposed model adaptively suppresses the weights of boundary points, such that our new model is robust to the boundary points. An efficient algorithm is derived to alternatively optimize the model parameter and class probability distribution of the unlabeled data, such that the induction of classifier and the transduction of labels are adaptively unified into one framework. Extensive experimental results on six real-world data sets show that the proposed semi-supervised learning model outperforms other related methods in most cases.
Toxicity in goats caused by oleander (Nerium oleander).

Cases of poisoning by oleander (Nerium oleander) were observed in several species, except in goats. This study aimed to evaluate the pathological effects of oleander in goats. The experimental design used three goats per group: the control group, which did not receive oleander and the experimental group, which received leaves of oleander (50 mg/kg/day) for six consecutive days. On the seventh day, goats received 110 mg/kg of oleander leaves four times at one-hourly interval. A last dose of 330 mg/kg of oleander leaves was given subsequently. After the last dose was administered, clinical signs such as apathy, colic, vocalizations, hyperpnea, polyuria, and moderate rumen distention were observed. Electrocardiogram revealed second-degree atrioventricular block. Death occurred on an average at 92 min after the last dosing. Microscopic evaluation revealed renal necrosis at convoluted and collector tubules and slight myocardial degeneration was observed by unequal staining of cardiomyocytes. Data suggest that goats appear to respond to oleander poisoning in a manner similar to other species.
Fixation distance estimation using vergence eye movement for automatic focusing glasses

In this study, we propose a method for estimating fixation distance on the basis of measurements of vergence eye movements. The aim of this approach is to control the lens focus of automatic focusing glasses. To reduce user effort at the time of calibration, the calibration was performed at infinite distance gazing, and the parameters were determined from the premeasured pupillary distance at infinity and iris diameters. To clarify the effectiveness of the proposed method, we conducted evaluation experiments using prototype glasses. The results showed that even participants requiring myopic correction could perform accurate motion vergence movements. Fixation distance estimation showed that, with the eye calibrated at infinite distance gazing, shorter distances could be estimated with an average accuracy exceeding 90%.
Generative Attribute Controller with Conditional Filtered Generative Adversarial Networks

We present a generative attribute controller (GAC), a novel functionality for generating or editing an image while intuitively controlling large variations of an attribute. This controller is based on a novel generative model called the conditional filtered generative adversarial network (CFGAN), which is an extension of the conventional conditional GAN (CGAN) that incorporates a filtering architecture into the generator input. Unlike the conventional CGAN, which represents an attribute directly using an observable variable (e.g., the binary indicator of attribute presence) so its controllability is restricted to attribute labeling (e.g., restricted to an ON or OFF control), the CFGAN has a filtering architecture that associates an attribute with a multi-dimensional latent variable, enabling latent variations of the attribute to be represented. We also define the filtering architecture and training scheme considering controllability, enabling the variations of the attribute to be intuitively controlled using typical controllers (radio buttons and slide bars). We evaluated our CFGAN on MNIST, CUB, and CelebA datasets and show that it enables large variations of an attribute to be not only represented but also intuitively controlled while retaining identity. We also show that the learned latent space has enough expressive power to conduct attribute transfer and attribute-based image retrieval.
Finger Vein Recognition Using Minutiae Extraction and Curve Analysis

Vein pattern is the network of blood vessels beneath person’s skin. Vein patterns are sufficiently different across individuals, and they are stable unaffected by ageing and no significant changed in adults by observing. It is believed that the patterns of blood vein are unique to every individual, even among twins. Finger vein authentication technology has several important features that set it apart from other forms of biometrics as a highly secure and convenient means of personal authentication. This paper presents a finger-vein image matching method based on minutiae extraction and curve analysis. This proposed system is implemented in MATLAB. Experimental results show that the proposed method performs well in improving finger-vein matching accuracy.
The enrichment of BPMN business process model with SBVR business vocabulary and rules

Aspects of business process models' integration with structured business vocabularies & rules are analyzed in this paper. Despite the fact that business process (BP) modeling has its long-lasting traditions in various areas of application, this discipline remains in the constant process of improvement and issue-solving. The paper deals with one of such issues, namely, the existing gap between BP modeling and specification of business vocabularies & rules. In not dealt appropriately, this may lead to some issues while developing, reading and interpreting business models themselves, also to miscommunication issues within and among the organizations dependent on such models, and so on. The gap could be diminished by the means of integration of BP models with business vocabularies & rules; the paper presents some argumentation to back such statements. Later, basic principles of the approach for BPMN BP model integration with SBVR business vocabulary & rules are presented and briefly described in this paper.
High bandwidth 0.35μm CMOS transimpedance amplifier

A transimpedance amplifier (TIA) has been designed in a 0.35 μm digital CMOS technology for Gigabit Ethernet. It is based on the structure proposed by Mengxiong Li . This paper presents an amplifier which exploits the regulated cascode (RGC) configuration as the input stage with an integrated optical receiver which consists of an integrated photodetector, thus achieving as large effective input transconductance as that of Si Bipolar or GaAs MESFET. The RGC input configuration isolates the input parasitic capacitance including photodiode capacitance from the bandwidth determination better than common-gate TIA. A series inductive peaking is used for enhancing the bandwidth. The proposed TIA has transimpedance gain of 51.56 dBΩ, and 3-dB bandwidth of 6.57 GHz with two inductor between the RGC and source follower for 0.1 pF photodiode capacitance. The proposed TIA has an input courant noise level of about 21.57 pA/Hz0.5 and it consumes DC power of 16 mW from 3.3 V supply voltage.
Environment and Policy Factors Shaping Global E-Commerce Diffusion: A Cross-Country Comparison

This article examines the key global, environmental and policy factors that act as determinants of e-commerce diffusion. It is based on systematic comparison of case studies from 10 countries— Brazil, China, Denmark, France, Germany, Mexico, Japan, Singapore, Taiwan, and the United States. It finds that B2B e-commerce seems to be driven by global forces, whereas B2C seems to be more of a local phenomenon. A preliminary explanation for this difference is that B2B is driven by global competition and MNCs that “push” e-commerce to their global suppliers, customers, and subsidiaries. This in turn creates pressures on local companies to adopt e-commerce to stay competitive. In contrast, B2C is “pulled” by consumer markets, which are mainly local and therefore divergent. While all consumers desire convenience and low prices, consumer preferences and values, national culture, and distribution systems differ markedly across countries and define differences in local consumer markets. These findings support the transformation perspective about globalization and its impacts. In terms of policy, the case studies suggest that enabling policies such as trade and telecommunications liberalization are likely to have the biggest impact on e-commerce, by making ICT and
Tropical rat mites (Ornithonyssus bacoti) - serious ectoparasites.

In Germany there is limited information available about the distribution of the tropical rat mite (Ornithonyssus bacoti) in rodents. A few case reports show that this hematophagous mite species may also cause dermatitis in man. Having close body contact to small rodents is an important question for patients with pruritic dermatoses. The definitive diagnosis of this ectoparasitosis requires the detection of the parasite, which is more likely to be found in the environment of its host (in the cages, in the litter or in corners or cracks of the living area) than on the hosts' skin itself. A case of infestation with tropical rat mites in a family is reported here. Three mice that had been removed from the home two months before were the reservoir. The mites were detected in a room where the cage with the mice had been placed months ago. Treatment requires the eradication of the parasites on its hosts (by a veterinarian) and in the environment (by an exterminator) with adequate acaricides such as permethrin.