nils-herrmann/clean_scirepeval huggingface dataset:
Inter-AS traffic engineering with SDN

Egress selection in an Internet Service Provider (ISP) is the process of selecting an egress router to route interdomain traffic across the ISP such that a traffic engineering objective is achieved. In traditional ISP networks, traffic through the ISP is carried through the network and exits via an egress that is closest to the source in an attempt to minimize network resources for transit traffic. This exit strategy is known as Hot Potato Routing (HPR). The emerging field of Software-Defined Networking (SDN) has opened up many possibilities and promised to bring new flexibility to the rigid traditional paradigm of networking. In an ISP network, however, completely replacing legacy network devices with SDN nodes is neither simple nor straightforward. This has led to the idea of incremental and selective deployment of SDN nodes in an ISP network. Such a hybrid network gives us control over traffic flows that pass through the SDN nodes without requiring extensive changes to an existing ISP network. In this paper, we look at the problem of choosing an optimal set of egress routers to route inter-domain transit traffic in a hybrid SDN network such that the maximum link utilization of the egress links is minimized. We formulate the optimization problem, show that it is related to the makespan scheduling problem of unrelated parallel machines, and propose heuristics to solve it. We perform simulations to evaluate our heuristic on a real ISP topology and show that even with a small number of SDN nodes in the network, the maximum link utilization on the egress links can be lower than that of traditional HPR.
MNE software for processing MEG and EEG data

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time-frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research.
Learning in a mobile age: an investigation of student motivation

The purpose of this single-case study was to explore the lived experiences of a grade 6 teacher and students who used tablets as part of their classroom instruction. Malone and Lepper’s taxonomy of intrinsic motivations for learning is used as a framework for examining whether and how this particular theory of motivation applies equally well for mobile learning. This study reports on the grade 6 teacher’s and students’ perceptions regarding the motivational affordances of using these mobile devices for learning. The findings are consistent with those of Malone and Lepper that motivation can be enhanced through challenge, curiosity, control, recognition, competition and cooperation. This model is helpful in informing our understanding of the motivating features of using mobile devices for learning and how mobile technologies can be used to enhance learners’ motivation.
Extraction and classification texture of inflammatory cells and nuclei in normal pap smear images

The presence of inflammatory cells complicates the process of identifying the nuclei in the early detection of cervical cancer. Inflammatory cells need to be eliminated to assist pathologists in reading Pap smear slides. The texture of Grey-Level Run-Length Matrix (GLRLM) for inflammatory cells and nuclei types are investigated. The inflammatory cells and nuclei have different texture, and it can be used to differentiate them. To extract all of the features, firstly manual cropping of inflammatory cells and nuclei needs to be done. All of extracted features have been analyzed and selected by Decision Tree classifier (J48). Originally there have been eleven features in the direction of 135° which are extracted to classify cropping cells into inflammatory cells and nuclei. Then the eleven features are reduced into eight, namely low gray level run emphasis, gray level non uniformity, run length non-uniformity, long run low gray-level emphasis, short run high gray-level emphasis, short run low gray-level emphasis, long run high gray-level emphasis and run percentage based on the rule of classification. This experiment is applied into 957 cells which were from 50 images. The compositions of these cells were 122 cells of nuclei and 837 cells of inflammatory. The proposed algorithm applied to all of the cells and the result of classification by using these eight texture features obtains the sensitivity rates which show that there are still nuclei of cells that were considered as inflammatory cells. It was in accordance with the conditions of the difficulties faced by the pathologist while the specificity rate suggests that inflammatory cells detected properly and few inflammatory cells are considered as nucleus.
Albanian Part-of-Speech Tagging: Gold Standard and Evaluation

In this paper, we present a gold standard corpus for Albanian part-of-speech tagging and perform evaluation experiments with different statistical taggers. The corpus consists of more than 31,000 tokens and has been manually annotated with a medium-sized tagset that can adequately represent the syntagmatic aspects of the language. We provide mappings from the full tagset to both the original Google Universal Part-of-Speech Tags and the variant used in the Universal Dependencies project. We perform experiments with different taggers on the full tagset as well as on the coarser tagsets and achieve accuracies of up to 95.10%.
A Simplified Model for Normal Mode Helical Antennas

─ Normal mode helical antennas are widely used for RFID and mobile communications applications due to their relatively small size and omni-directional radiation pattern. However, their highly curved geometry can make the design and analysis of helical antennas that are part of larger complex structures quite difficult. A simplified model is proposed that replaces the curved helix with straight wires and lumped elements. The simplified model can be used to reduce the complexity of full-wave models that include a helical antenna. It also can be used to estimate the performance of a helical antenna without fullwave modeling of the helical structure. Index Terms─ Helical Antennas, RFID.
Antecedent- and response-focused emotion regulation: divergent consequences for experience, expression, and physiology.

Using a process model of emotion, a distinction between antecedent-focused and response-focused emotion regulation is proposed. To test this distinction, 120 participants were shown a disgusting film while their experiential, behavioral, and physiological responses were recorded. Participants were told to either (a) think about the film in such a way that they would feel nothing (reappraisal, a form of antecedent-focused emotion regulation), (b) behave in such a way that someone watching them would not know they were feeling anything (suppression, a form of response-focused emotion regulation), or (c) watch the film (a control condition). Compared with the control condition, both reappraisal and suppression were effective in reducing emotion-expressive behavior. However, reappraisal decreased disgust experience, whereas suppression increased sympathetic activation. These results suggest that these 2 emotion regulatory processes may have different adaptive consequences.
Reading the mind's eye: Decoding category information during mental imagery

Category information for visually presented objects can be read out from multi-voxel patterns of fMRI activity in ventral-temporal cortex. What is the nature and reliability of these patterns in the absence of any bottom-up visual input, for example, during visual imagery? Here, we first ask how well category information can be decoded for imagined objects and then compare the representations evoked during imagery and actual viewing. In an fMRI study, four object categories (food, tools, faces, buildings) were either visually presented to subjects, or imagined by them. Using pattern classification techniques, we could reliably decode category information (including for non-special categories, i.e., food and tools) from ventral-temporal cortex in both conditions, but only during actual viewing from retinotopic areas. Interestingly, in temporal cortex when the classifier was trained on the viewed condition and tested on the imagery condition, or vice versa, classification performance was comparable to within the imagery condition. The above results held even when we did not use information in the specialized category-selective areas. Thus, the patterns of representation during imagery and actual viewing are in fact surprisingly similar to each other. Consistent with this observation, the maps of "diagnostic voxels" (i.e., the classifier weights) for the perception and imagery classifiers were more similar in ventral-temporal cortex than in retinotopic cortex. These results suggest that in the absence of any bottom-up input, cortical back projections can selectively re-activate specific patterns of neural activity.
Can Instagram posts help characterize urban micro-events?

Social media content, from platforms such as Twitter and Foursquare, has enabled an exciting new field of “social sensing”, where participatory content generated by users has been used to identify unexpected emerging or trending events. In contrast to such text-based channels, we focus on image-sharing social applications (specifically Instagram), and investigate how such urban social sensing can leverage upon the additional multi-modal, multimedia content. Given the significantly higher fraction of geotagged content on Instagram, we aim to use such channels to go beyond identification of long-lived events (e.g., a marathon) to achieve finer-grained characterization of multiple micro-events (e.g., a person winning the marathon) that occur over the lifetime of the macro-event. Via empirical analysis from a corpus of Instagram data from 3 international marathons, we establish the need for novel data pre-processing as: (a) semantic annotation of image content indeed provides additional features distinct from text captions, and (b) an appreciable fraction of the posted images do not pertain to the event under consideration. We propose a framework, called EiM, that combines such preprocessing with clustering-based event detection. We show that our initial prototype of EiM shows promising results: it is able to identify many micro-events in the three marathons, with spatial and temporal resolution that is less than 1% and 10%, respectively, of the corresponding ranges for the macro-event.
Network densification: the dominant theme for wireless evolution into 5G

This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.
A Classification of Internet Retail Stores

We describe an empirical method for classifying Internet retail sites for electronic commerce. The technique used 35 observable Internet retail site attributes and features of on-line stores as the raw data to classify the on-line stores into meaningful groups. This paper presents a classification of on-line retail stores based upon an August 1996 convenience sample of 137 Internet retail stores. Descriptive statistics for 44 variables provide a snapshot of key attributes and features of on-line stores. Subsequent cluster and factor analysis identified five distinct Web catalog interface categories: super stores, promotional stores, plain sales stores, one page stores and product listings. On-line stores differ primarily on the three dimensions: size, service offerings, and interface quality. A preponderance of the stores in the study had limited product selection, few service features, and poor interfaces. This categorization provides a better understanding of the strategies pursued in Internet-based marketing and will be helpful for Internet retail store designers as well as for researchers to structure and target further analyses in this domain.
Collaborative Filtering Based Recommendation System : A survey

the most common technique used for recommendations is collaborative filtering. Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships from a group of user who share the same preferences and taste. In this paper we have explored various aspects of collaborative filtering recommendation system. We have categorized collaborative filtering recommendation system and shown how the similarity is computed. The desired criteria for selection of data set are also listed. The measures used for evaluating the performance of collaborative filtering recommendation system are discussed along with the challenges faced by the recommendation system. Types of rating that can be collected from the user to rate items are also discussed along with the uses of collaborative filtering recommendation system.
We don't need another hero.

Everybody loves the stories of heroes like Martin Luther King, Jr., Mother Teresa, and Gandhi. But the heroic model of moral leadership usually doesn't work in the corporate world. Modesty and restraint are largely responsible for the achievements of the most effective moral leaders in business. The author, a specialist in business ethics, says the quiet leaders he has studied follow four basic rules in meeting ethical challenges and making decisions. The rules constitute an important resource for executives who want to encourage the development of such leaders among their middle managers. The first rule is "Put things off till tomorrow." The passage of time allows turbulent waters to calm and lets leaders' moral instincts emerge. "Pick your battles" means that quiet leaders don't waste political capital on fights they can't win; they save it for occasions when they really want to fight. "Bend the rules, don't break them" sounds easier than it is--bending the rules in order to resolve a complicated situation requires imagination, discipline, restraint, flexibility, and entrepreneurship. The fourth rule, "Find a compromise," reflects the author's finding that quiet leaders try not to see situations as polarized tests of ethical principles. These individuals work hard to craft compromises that are "good enough"--responsible and workable enough--to satisfy themselves, their companies, and their customers. The vast majority of difficult problems are solved through the consistent striving of people working far from the limelight. Their quiet approach to leadership doesn't inspire, thrill, or provide story lines for uplifting TV shows. But the unglamorous efforts of quiet leaders make a tremendous difference every day in the corporate world.
A Practical Security Architecture for In-Vehicle CAN-FD

The controller area network with flexible data rate (CAN-FD) is attracting attention as the next generation of in-vehicle network technology. However, security issues have not been completely taken into account when designing CAN-FD, although every bit of information transmitted could be critical to driver safety. If we fail to solve the security vulnerabilities of CAN-FD, we cannot expect Vehicle-Information and Communications Technology (Vehicle-ICT) convergence to continue to develop. Fortunately, secure in-vehicle CAN-FD communication environments can be constructed using the larger data payload of CAN-FD. In this paper, we propose a security architecture for in-vehicle CAN-FD as a countermeasure (designed in accordance with CAN-FD specifications). We considered the characteristics of the International Organization for Standardization (ISO) 26262 Automotive Safety Integrity Level and the in-vehicle subnetwork to design a practical security architecture. We also evaluated the feasibility of the proposed security architecture using three kinds of microcontroller unit and the CANoe software. Our evaluation findings may be used as an indicator of the performance level of electronic control units for manufacturing next-generation vehicles.
Parsing C/C++ Code without Pre-processing

It is difficult to develop style-preserving source-to-source transformation engines for C and C++. The main reason is not the complexity of those languages, but the use of the C pre-processor (cpp), especially ifdefs and macros. This has for example hindered the development of refactoring tools for C and C++. In this paper we propose to combine multiple techniques and heuristics to parse C/C++ source files as-is, while still having only a few modifications to the original grammars of C and C++. We rely on the fact that in most C and C++ software, programmers follow a limited number of conventions on the use of cpp which makes it possible to disambiguate different situations by just looking at the context, names, or indentation of cpp constructs. We have implemented a parser, Yacfe, based on these techniques and evaluated it on 16 large open source projects. Yacfe can on average parse 96% of those projects correctly. As a side effect, we also found mistakes in code that was not compiled because it was protected by particular ifdefs, but that was still analyzed by Yacfe. Using Yacfe on new projects may require adapting some of our techniques. We found that as conventions and idioms are shared by many projects, the adaptation time is on average less than 2 hours for a new project.
TouchMover: actuated 3D touchscreen with haptic feedback

This paper presents the design and development of a novel visual+haptic device that co-locates 3D stereo visualization, direct touch and touch force sensing with a robotically actuated display. Our actuated immersive 3D display, called TouchMover, is capable of providing 1D movement (up to 36cm) and force feedback (up to 230N) in a single dimension, perpendicular to the screen plane. In addition to describing the details of our design, we showcase how TouchMover allows the user to: 1) interact with 3D objects by pushing them on the screen with realistic force feedback, 2) touch and feel the contour of a 3D object, 3) explore and annotate volumetric medical images (e.g., MRI brain scans) and 4) experience different activation forces and stiffness when interacting with common 2D on-screen elements (e.g., buttons). We also contribute the results of an experiment which demonstrates the effectiveness of the haptic output of our device. Our results show that people are capable of disambiguating between 10 different 3D shapes with the same 2D footprint by touching alone and without any visual feedback (85% recognition rate, 12 participants).
Interactions between location and task affect the spatial and directional firing of hippocampal neurons.

When rats forage for randomly dispersed food in a high walled cylinder the firing of their hippocampal "place" cells exhibits little dependence on the direction faced by the rat. On radial arm mazes and similar tasks, place cells are strongly directionally selective within their fields. These tasks differ in several respects, including the visual environment, configuration of the traversable space, motor behavior (e.g., linear and angular velocities), and behavioral context (e.g., presence of specific, consistent goal locations within the environment). The contributions of these factors to spatial and directional tuning of hippocampal neurons was systematically examined in rats performing several tasks in either an enriched or a sparse visual environment, and on different apparati. Place fields were more spatially and directionally selective on a radial maze than on an open, circular platform, regardless of the visual environment. On the platform, fields were more directional when the rat searched for food at fixed locations, in a stereotypic and directed manner, than when the food was scattered randomly. Thus, it seems that place fields are more directional when the animal is planning or following a route between points of special significance. This might be related to the spatial focus of the rat's attention (e.g., a particular reference point). Changing the behavioral task was also accompanied by a change in firing location in about one-third of the cells. Thus, hippocampal neuronal activity appears to encode a complex interaction between locations, their significance and the behaviors the rat is called upon to execute.
Using mixture design and neural networks to build stock selection decision support systems

There are three disadvantages of weighted scoring stock selection models. First, they cannot identify the relations between weights of stock-picking concepts and performances of portfolios. Second, they cannot systematically discover the optimal combination for weights of concepts to optimize the performances. Third, they are unable to meet various investors’ preferences. This study aimed to more efficiently construct weighted scoring stock selection models to overcome these disadvantages. Since the weights of stock-picking concepts in a weighted scoring stock selection model can be regarded as components in a mixture, we used the simplex-centroid mixture design to obtain the experimental sets of weights. These sets of weights are simulated with US stock market historical data to obtain their performances. Performance prediction models were built with the simulated performance data set and artificial neural networks. Furthermore, the optimization models to reflect investors’ preferences were built up, and the performance prediction models were employed as the kernel of the optimization models, so that the optimal solutions can now be solved with optimization techniques. The empirical values of the performances of the optimal weighting combinations generated by the optimization models showed that they can meet various investors’ preferences and outperform those of S&P’s 500 not only during the training period but also during the testing period.
Adiabatic charging of capacitors by Switched Capacitor Converters with multiple target voltages

A method for adiabatic charging of a capacitor by Switched Capacitor Converters (SCC) with multiple target voltages is presented, analyzed and verified experimentally. The SCC automatically changes the target voltages from low to high and back, thereby emulating voltage steps. This reduces the energy losses due to small voltage deviations between the SCC output and the charged capacitor and by enabling the stored energy to be recovered back into the power supply. In comparison with the previous method a considerable economy in hardware is achieved by replacing a set of voltage sources by a single SCC. It is shown that the voltage source and the switch resistance in the regular charging circuit can be emulated by the SCC equivalent circuit. Furthermore, such emulation provides the same step response if the SCC is operated close to the no-charge (NC) mode. This theoretical prediction was approved experimentally using the binary SCC.