nils-herrmann/clean_scirepeval huggingface dataset:
Characterizing and modeling the distance of mobile calls: A metropolitan case study

During the past few years, with the democratization of open data, mobile network operators facilitated the sharing of mobile datasets. Such data are particularly interesting to better understand the way mobile users communicate, move and consummate services, but also to rethink the design or the evolution of current infrastructures with respects to new information and communication technologies and continuous increase of mobile usage. In this paper, we analyze a mobile dataset from a telecommunication provider in the city of Milan, Italy, and characterize mobile users' behavior. We show that, on the scale of the city, the number of short distance calls is massively higher than the number of long distance calls. Based on these observations, we propose a realistic yet simple model to describe the distance distribution of the calls in such a metropolitan environment. Finally, we focus on a specific area of the city, the soccer stadium area, and show that local events provoke regular deviation to the observed behaviors on the scale of the city. The macroscopic model we propose can be used to generate mobile datasets for any large city and thus support further simulations.
Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning

A recent imaging modality Diffusion Tensor Imaging completes information used from Structural MRI in studies of Alzheimer disease. A large number of recent studies has explored pathologic staging of Alzheimer disease using the Mean Diffusivity maps extracted from the Diffusion Tensor Imaging modality. The Deep Neural Networks are seducing tools for classification of subjects' imaging data in computer-aided diagnosis of Alzheimer's disease. The major problem here is the lack of a publicly available large amount of training data in both modalities. The lack number of training data yields over-fitting phenomena. We propose a method of a cross-modal transfer learning: from Structural MRI to Diffusion Tensor Imaging modality. Models pre-trained on a structural MRI dataset with domain-depended data augmentation are used as initialization of network parameters to train on Mean Diffusivity data. The method shows a reduction of the over-fitting phenomena, improves learning performance, and thus increases the accuracy of prediction. Classifiers are then fused by a majority vote resulting in augmented scores of classification between Normal Control, Alzheimer Patients and Mild Cognitive Impairment subjects on a subset of ADNI dataset.
DC–AC Cascaded H-Bridge Multilevel Boost Inverter With No Inductors for Electric/Hybrid Electric Vehicle Applications

This paper presents a cascaded H-bridge multilevel boost inverter for electric vehicle (EV) and hybrid EV (HEV) applications implemented without the use of inductors. Currently available power inverter systems for HEVs use a dc-dc boost converter to boost the battery voltage for a traditional three-phase inverter. The present HEV traction drive inverters have low power density, are expensive, and have low efficiency because they need a bulky inductor. A cascaded H-bridge multilevel boost inverter design for EV and HEV applications implemented without the use of inductors is proposed in this paper. Traditionally, each H-bridge needs a dc power supply. The proposed design uses a standard three-leg inverter (one leg for each phase) and an H-bridge in series with each inverter leg which uses a capacitor as the dc power source. A fundamental switching scheme is used to do modulation control and to produce a five-level phase voltage. Experiments show that the proposed dc-ac cascaded H-bridge multilevel boost inverter can output a boosted ac voltage without the use of inductors.
Sequential Deep Learning for Disaster-Related Video Classification

Videos serve to convey complex semantic information and ease the understanding of new knowledge. However, when mixed semantic meanings from different modalities (i.e., image, video, text) are involved, it is more difficult for a computer model to detect and classify the concepts (such as flood, storm, and animals). This paper presents a multimodal deep learning framework to improve video concept classification by leveraging recent advances in transfer learning and sequential deep learning models. Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) models are then used to obtain the sequential semantics for both audio and textual models. The proposed framework is applied to a disaster-related video dataset that includes not only disaster scenes, but also the activities that took place during the disaster event. The experimental results show the effectiveness of the proposed framework.
A First Step towards Eye State Prediction Using EEG

In this paper, we investigate how the eye state (open or closed) can be predicted by measuring brain waves with an EEG. To this end, we recorded a corpus containing the activation strength of the fourteen electrodes of a commercial EEG headset as well as the manually annotated eye state corresponding to the recorded data. We tested 42 different machine learning algorithms on their performance to predict the eye state after training with the corpus. The best-performing classifier, KStar, produced a classification error rate of only 2.7% which is a 94% relative reduction over the majority vote of 44.9% classification error.
Defending distributed systems against malicious intrusions and network anomalies

Network security breaches hinder the application of distributed computing systems manifested as the grids, clusters, intranets, extranets, or P2P systems. A new integrated approach is presented for building future, network-based intrusion detection systems (NIDS). We integrate the Snort (a NIDS) with a custom-designed anomaly detection system (ADS) to yield a powerful cyber defense system, called CAIDS. This system detects known attacks through signature matching and reveals network anomalies by Internet traffic data mining. The CAIDS design integrates two different detection engines for alert correlation between intrusions and anomalies. We aim to automate signature generation into Snort database. The system was tested over an Internet trace of 24 millions of packets containing 200 attacks. Our simulation experiments result in a 75% detection rate on all attacks with a low 5% false alarm rate. The system generates alerts on both intrusive attacks to distributed resources and anomalies detected in the Internet, intranet, and extranet connections.
Ways to React: Comparing Reactive Languages and Complex Event Processing

Reactive applications demand for detecting the changes that occur in a domain of interest and for timely reactions. Examples range from simple interactive applications to complex monitoring tasks involving distributed and heterogeneous systems. Over the last years, different programming paradigms and solutions have been proposed to support such applications. In this paper, we focus on two prominent approaches: event-based programming, specifically Complex Event Processing (CEP), and Reactive Languages (RLs). CEP systems enable the definition of high level situations of interest from low level primitive events detected in the external environment. On the other hand, RLs support time-changing values and their composition as dedicated language abstractions. These research fields have been investigated by different communities, belonging respectively to the database and the distributed systems areas and to the programming language area. It is our belief that a deeper understanding of these research fields, including their benefits and limitations, their similarities and differences, could drive further developments in supporting reactive applications. For this reason, we propose a first comparison of the two fields. Despite huge differences, we believe that such a comparison can trigger an interesting discussion across the communities, favor knowledge sharing, and let new ideas emerge.
Heart rate variability is associated with emotion recognition: direct evidence for a relationship between the autonomic nervous system and social cognition.

It is well established that heart rate variability (HRV) plays an important role in social communication. Polyvagal theory suggests that HRV may provide a sensitive marker of one's ability to respond and recognize social cues. The aim of the present study was to directly test this hypothesis. Resting-state HRV was collected and performance on the Reading the Mind in the Eyes Test was assessed in 65 volunteers. HRV was positively associated with performance on this emotion recognition task confirming our hypothesis and these findings were retained after controlling for a variety of confounding variables known to influence HRV - sex, BMI, smoking habits, physical activity levels, depression, anxiety, and stress. Our data suggests that increased HRV may provide a novel marker of one's ability to recognize emotions in humans. Implications for understanding the biological basis of emotion recognition, and social impairment in humans are discussed.
A taxonomy of temporal data visualization techniques

A wide range of visualization techniques have been designed to assist users to visually analyze and manipulate temporal data. All the proposed techniques have been designed independently. In this context it is therefore difficult to systematically explore the set of possibilities as well as to thoroughly envision visualization techniques of temporal data. Addressing this problem, we present a taxonomy of visualization techniques of temporal data. Based on a study of thirty-seven visual tools and visualization techniques of temporal data, our taxonomy structures the design space according to the Chi's visualization process steps.
An alternative text representation to TF-IDF and Bag-of-Words

In text mining, information retrieval, and machine learning, text documents are commonly represented through variants of sparse Bag of Words (sBoW) vectors (e.g. TF-IDF ). Although simple and intuitive, sBoW style representations suffer from their inherent over-sparsity and fail to capture word-level synonymy and polysemy. Especially when labeled data is limited (e.g. in document classification), or the text documents are short (e.g. emails or abstracts), many features are rarely observed within the training corpus. This leads to overfitting and reduced generalization accuracy. In this paper we propose Dense Cohort of Terms (dCoT), an unsupervised algorithm to learn improved sBoW document features. dCoT explicitly models absent words by removing and reconstructing random sub-sets of words in the unlabeled corpus. With this approach, dCoT learns to reconstruct frequent words from co-occurring infrequent words and maps the high dimensional sparse sBoW vectors into a low-dimensional dense representation. We show that the feature removal can be marginalized out and that the reconstruction can be solved for in closed-form. We demonstrate empirically, on several benchmark datasets, that dCoT features significantly improve the classification accuracy across several document classification tasks.  From sBoW to dCoT, Marginalized Encoders for Text Representation Permission to make digital or hard copies of all or part of thi s work for personal or classroom use is granted without fee provided th at copies are not made or distributed for profit or commercial advantage an d that copies bear this notice and the full citation on the first page. To cop y therwise, to republish, to post on servers or to redistribute to lists, re quires prior specific permission and/or a fee. CIKM’12, October 29–November 2, 2012, Maui, HI, USA.
Modeling behavioral RESTful web service interfaces in UML

A web service interface contains information about the names of the operations that can be invoked on the service and the input and output parameters of these operations. The Web Application Description Language (WADL) is a language to describe the interface of a web service that follows the Representation State Transfer (REST) architectural style. Currently, WADL descriptions do not describe the behavioral semantics of the operations neither ensure that the published interfaces follow the REST style, that is they are RESTful. In this paper, we present an approach to model the structural and behavioral interface of a RESTful web service using UML class and UML protocol diagrams. These models lead to RESTful interfaces that describe the behavior of operations in terms of preconditions and postconditions. The contracts can then be published in an extended version of the WADL language and used for documentation, stub generation, testing and monitoring purposes.
Focal loss dense detector for vehicle surveillance

Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for vehicle detection. State-of-the-art performance result has been showed on the DETRAC vehicle dataset.
Characteristics and variability of structural networks derived from diffusion tensor imaging

Structural brain networks were constructed based on diffusion tensor imaging (DTI) data of 59 young healthy male adults. The networks had 68 nodes, derived from FreeSurfer parcellation of the cortical surface. By means of streamline tractography, the edge weight was defined as the number of streamlines between two nodes normalized by their mean volume. Specifically, two weighting schemes were adopted by considering various biases from fiber tracking. The weighting schemes were tested for possible bias toward the physical size of the nodes. A novel thresholding method was proposed using the variance of number of streamlines in fiber tracking. The backbone networks were extracted and various network analyses were applied to investigate the features of the binary and weighted backbone networks. For weighted networks, a high correlation was observed between nodal strength and betweenness centrality. Despite similar small-worldness features, binary networks and weighted networks are distinctive in many aspects, such as modularity and nodal betweenness centrality. Inter-subject variability was examined for the weighted networks, along with the test-retest reliability from two repeated scans on 44 of the 59 subjects. The inter-/intra-subject variability of weighted networks was discussed in three levels - edge weights, local metrics, and global metrics. The variance of edge weights can be very large. Although local metrics show less variability than the edge weights, they still have considerable amounts of variability. Weighting scheme one, which scales the number of streamlines by their lengths, demonstrates stable intra-class correlation coefficients against thresholding for global efficiency, clustering coefficient and diversity. The intra-class correlation analysis suggests the current approach of constructing weighted network has a reasonably high reproducibility for most global metrics.
Secure kNN computation on encrypted databases

Service providers like Google and Amazon are moving into the SaaS (Software as a Service) business. They turn their huge infrastructure into a cloud-computing environment and aggressively recruit businesses to run applications on their platforms. To enforce security and privacy on such a service model, we need to protect the data running on the platform. Unfortunately, traditional encryption methods that aim at providing "unbreakable" protection are often not adequate because they do not support the execution of applications such as database queries on the encrypted data. In this paper we discuss the general problem of secure computation on an encrypted database and propose a SCONEDB Secure Computation ON an Encrypted DataBase) model, which captures the execution and security requirements. As a case study, we focus on the problem of k-nearest neighbor (kNN) computation on an encrypted database. We develop a new asymmetric scalar-product-preserving encryption (ASPE) that preserves a special type of scalar product. We use APSE to construct two secure schemes that support kNN computation on encrypted data; each of these schemes is shown to resist practical attacks of a different background knowledge level, at a different overhead cost. Extensive performance studies are carried out to evaluate the overhead and the efficiency of the schemes.
Eddy Current Losses in Transformer Windings and Circuit Wiring

Skin Effect Figure 1 shows the magnetic field (flux lines) in and around a conductor carrying dc or low frequency current I. The field is radially symmetrical, as shown, only if the return current with its associated field is at a great distance. At low frequency, the energy in the magnetic field is trivial compared to the energy loss in the resistance of the wire. Hence the current Introduction As switching power supply operating frequencies increase, eddy current losses and parasitic inductances can greatly impair circuit performance. These high frequency effects are caused by the magnetic field resulting from current flow in transformer windings and circuit wiring. This paper is intended to provide insight into these phenomena so that improved high frequency. performance can be achieved. Among other things, it explains (I) why eddy current losses increase so dramatically with more winding layers, (2) why parallelling thin strips doesn't work, (3) how passive conductors (Faraday shields and C. T .windings) have high losses, and (4) why increasing conductor surface area will actually worsen losses and parasitic inductance if the configuration is not correct.
Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors.

Statistical power analysis provides the conventional approach to assess error rates when designing a research study. However, power analysis is flawed in that a narrow emphasis on statistical significance is placed as the primary focus of study design. In noisy, small-sample settings, statistically significant results can often be misleading. To help researchers address this problem in the context of their own studies, we recommend design calculations in which (a) the probability of an estimate being in the wrong direction (Type S [sign] error) and (b) the factor by which the magnitude of an effect might be overestimated (Type M [magnitude] error or exaggeration ratio) are estimated. We illustrate with examples from recent published research and discuss the largest challenge in a design calculation: coming up with reasonable estimates of plausible effect sizes based on external information.
A Variational Observation Model of 3 D Object for Probabilistic Semantic SLAM

We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping. However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered. Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned. Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging. In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution. We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view. In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.
SCRUM: An extension pattern language for hyper productive software development

The patterns of the SCRUM development method are presented as an extension pattern language to the existing organizational pattern languages. In the last few years, the SCRUM development method has rapidly gained recognition as an effective tool to hyper-productive software development. However, when SCRUM patterns are combined with other existing organizational patterns, they lead to highly adaptive, yet well-structured software development organizations. Also, decomposing SCRUM into patterns can guide adoption of only those parts of SCRUM that are applicable to a specific situation.
Evolved GANs for generating pareto set approximations

In machine learning, generative models are used to create data samples that mimic the characteristics of the training data. Generative adversarial networks (GANs) are neural-network based generator models that have shown their capacity to produce realistic samples in different domains. In this paper we propose a neuro-evolutionary approach for evolving deep GAN architectures together with the loss function and generator-discriminator synchronization parameters. We also propose the problem of Pareto set (PS) approximation as a suitable benchmark to evaluate the quality of neural-network based generators in terms of the accuracy of the solutions they generate. The covering of the Pareto front (PF) by the generated solutions is used as an indicator of the mode-collapsing behavior of GANs. We show that it is possible to evolve GANs that generate good PS approximations. Our method scales to up to 784 variables and that it is capable to create architecture transferable across dimensions and functions.
Bootstrapping coreference resolution using word associations

In this paper, we present an unsupervised framework that bootstraps a complete coreference resolution (CoRe) system from word associations mined from a large unlabeled corpus. We show that word associations are useful for CoRe – e.g., the strong association between Obama and President is an indicator of likely coreference. Association information has so far not been used in CoRe because it is sparse and difficult to learn from small labeled corpora. Since unlabeled text is readily available, our unsupervised approach addresses the sparseness problem. In a self-training framework, we train a decision tree on a corpus that is automatically labeled using word associations. We show that this unsupervised system has better CoRe performance than other learning approaches that do not use manually labeled data.