nils-herrmann/clean_scirepeval huggingface dataset:
Polymetric Views - A Lightweight Visual Approach to Reverse Engineering

Reverse engineering software systems has become a major concern in software industry because of their sheer size and complexity. This problem needs to be tackled, since the systems in question are of considerable worth to their owners and maintainers. In this article we present the concept of a polymetric view, a lightweight software visualization technique enriched with software metrics information. Polymetric views help to understand the structure and detect problems of a software system in the initial phases of a reverse engineering process. We discuss the benefits and limits of several predefined polymetric views we have implemented in our tool CodeCrawler. Moreover, based on clusters of different polymetric views we have developed a methodology which supports and guides a software engineer in the first phases of a reverse engineering of a large software system. We have refined this methodology by repeatedly applying it on industrial systems, and illustrate it by applying a selection of polymetric views to a case study.
Bypassing Passkey Authentication in Bluetooth Low Energy

Since the attack runs on the Security Manager layer , it can reuse a lot of the existing network stack that is already in place for this approach. This namely concerns every thing bellow HCI . Actually, the whole procedure starting with the authentication bypass and continuing to data in jection (which would be a regular communication any wa ) can be done using a general Bluetooth 4.0 Smart Ready USB dongle via HCI commands.
Autonomous Ground Vehicles—Concepts and a Path to the Future

Autonomous vehicles promise numerous improvements to vehicular traffic: an increase in both highway capacity and traffic flow because of faster response times, less fuel consumption and pollution thanks to more foresighted driving, and hopefully fewer accidents thanks to collision avoidance systems. In addition, drivers can save time for more useful activities. In order for these vehicles to safely operate in everyday traffic or in harsh off-road environments, a multitude of problems in perception, navigation, and control have to be solved. This paper gives an overview of the most current trends in autonomous vehicles, highlighting the concepts common to most successful systems as well as their differences. It concludes with an outlook into the promising future of autonomous vehicles.
Sketching Designs Using the Five Design-Sheet Methodology

Sketching designs has been shown to be a useful way of planning and considering alternative solutions. The use of lo-fidelity prototyping, especially paper-based sketching, can save time, money and converge to better solutions more quickly. However, this design process is often viewed to be too informal. Consequently users do not know how to manage their thoughts and ideas (to first think divergently, to then finally converge on a suitable solution). We present the Five Design Sheet (FdS) methodology. The methodology enables users to create information visualization interfaces through lo-fidelity methods. Users sketch and plan their ideas, helping them express different possibilities, think through these ideas to consider their potential effectiveness as solutions to the task (sheet 1); they create three principle designs (sheets 2,3 and 4); before converging on a final realization design that can then be implemented (sheet 5). In this article, we present (i) a review of the use of sketching as a planning method for visualization and the benefits of sketching, (ii) a detailed description of the Five Design Sheet (FdS) methodology, and (iii) an evaluation of the FdS using the System Usability Scale, along with a case-study of its use in industry and experience of its use in teaching.
Finding new terminology in very large corpora

Most technical and scientific terms are comprised of complex, multi-word noun phrases but certainly not all noun phrases are technical or scientific terms. The distinction of specific terminology from common non-specific noun phrases can be based on the observation that terms reveal a much lesser degree of distributional variation than non-specific noun phrases. We formalize the limited paradigmatic modifiability of terms and, subsequently, test the corresponding algorithm on bigram, trigram and quadgram noun phrases extracted from a 104-million-word biomedical text corpus. Using an already existing and community-wide curated biomedical terminology as an evaluation gold standard, we show that our algorithm significantly outperforms standard term identification measures and, therefore, qualifies as a high-performant building block for any terminology identification system. We also provide empirical evidence that the superiority of our approach, beyond a 10-million-word threshold, is essentially domain- and corpus-size-independent.
The five percent electrode system for high-resolution EEG and ERP measurements

A system for electrode placement is described. It is designed for studies on topography and source analysis of spontaneous and evoked EEG activity.



The proposed system is based on the extended International 10-20 system which contains 74 electrodes, and extends this system up to 345 electrode locations.



The positioning and nomenclature of the electrode system is described, and a subset of locations is proposed as especially useful for modern EEG/ERP systems, often having 128 channels available.



Similar to the extension of the 10-20 system to the 10-10 system ("10% system"), proposed in 1985, the goal of this new extension to a 10-5 system is to further promote standardization in high-resolution EEG studies.
Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices

While indoor portable navigation using portable devices is becoming increasingly required, it faces many challenges in obtaining accurate positioning performance. One of the methods to improve navigation results is to recognize the mode of motion of the user carrying the portable device containing low-cost microelectromechanical systems (MEMS) motion sensors. Pattern recognition methodology has been employed to detect a group of motion modes that are common indoors, namely, walking, stationary, going up/down stairs, standing on an escalator, walking on an escalator, standing on a moving walkway, and walking on a moving walkway. The performance of the motion mode recognition module was examined on different types of mobile computing devices, including various brands of smartphones, tablets, smartwatches, and smartglasses, and the results obtained showed the capability of enhancing positioning performance. The module does not require satellite or wireless positioning signals, and depends only on MEMS sensors.
Planogram Compliance Checking Using Recurring Patterns

This paper proposes a novel automated planogram compliance checking method for retail chains without requiring product template images for modeling or training. Product layout information is extracted from one single input image by means of unsupervised recurring pattern detection and matched via graph matching with the expected product layout specified by a planogram. To improve the efficiency, a divide-conquer strategy is employed. Specifically, the input image is divided into several regions based on the planogram. Recurring patterns are detected in each region respectively and then merged together to estimate product layout information. Experimental results on real data from a supermarket chain have verified the effectiveness and efficiency of the proposed method.
The neural correlates of trustworthiness evaluations of faces and brands: Implications for behavioral and consumer neuroscience.

When we buy a product of a brand, we trust the brand to provide good quality and reliability. Therefore, trust plays a major role in consumer behavior. It is unclear, however, how trust in brands is processed in the brain and whether it is processed differently from interpersonal trust. In this study, we used fMRI to investigate the neural correlates of interpersonal and brand trust by comparing the brain activation patterns during explicit trustworthiness judgments of faces and brands. Our results showed that while there were several brain areas known to be linked to trustworthiness evaluations, such as the amygdalae, more active in trustworthiness judgments when compared to a control task (familiarity judgment) for faces, no such difference was found for brands. Complementary ROI analysis revealed that the activation of both amygdalae was strongest for faces in the trustworthiness judgments. The direct comparison of the brain activation patterns during the trustworthiness evaluations between faces and brands in this analysis showed that trustworthiness judgments of faces activated the orbitofrontal cortex, another region that was previously linked to interpersonal trust, more strongly than trustworthiness judgments of brands. Further, trustworthiness ratings of faces, but not brands, correlated with activation in the orbitofrontal cortex. Our results indicate that the amygdalae, as well as the orbitofrontal cortex, play a prominent role in interpersonal trust (faces), but not in trust for brands. It is possible that this difference is due to brands being processed as cultural objects rather than as having human-like personality characteristics.
Exploring the application of process mining to support self-regulated learning: An initial analysis with video lectures

Self-regulated learning involves students taking the responsibility of their own learning. Self-regulated learning students usually adopt a variety of learning strategies and behaviors, such as the performance of forethought-performance-reflection cycles or the regular and sequenced work over time, that eventually enable them to achieve a more significant and long-lasting learning. In this paper, we explore if these particular behaviors and strategies can be analyzed through the application of process mining techniques taking as data the events registered during the performance of learning activities. The discovery of the underlying processes followed by students can open new approaches to study the real self-regulated strategies used by students. The paper reviews the techniques and tools available to perform the process mining of events related to self-regulated learning and describes some initial works in this area. Furthermore, as an initial empirical study, we analyze the process followed by students regarding the visualization of videos provided in a first-year engineering subject. The obtained results are studied taking into account the grades obtained by the students. The results show that the students that obtained the best grades follow more varied routes than the students that obtained the worst grades. In addition, the best ones are more regular over time regarding weekly video visualization, mainly at the beginning of the term, while the worst ones visualize the videos mainly at the second part of the term.
Tracking Position and Orientation through Millimeter Wave Lens MIMO in 5G Systems

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location of the user terminals are largely undiscovered. In this paper, we propose a novel support detection-based channel training method for frequency selective millimeter-wave (mm-wave) multiple-input-multiple-output system with lens antenna arrays. We show that accurate position and orientation estimation and tracking is possible using signals from a single transmitter with lens antenna arrays. Particularly, the beamspace channel estimation is formulated as two sparse signal recovery problems in the downlink and uplink for the estimation of angle-of-arrival, angle-of-departure, and timeof-arrival. The proposed method offers a higher sparse detection probability compared to the compressed sensing based solutions. Finally, a joint heuristic beamformer design and user position and orientation tracking approach are proposed based on initial estimation of channel parameters obtained in the training phase.
Friends or Foes: Is Empathy Necessary for Moral Behavior?

In the past decade, a flurry of empirical and theoretical research on morality and empathy has taken place, and interest and usage in the media and the public arena have increased. At times, in both popular culture and academia, morality and empathy are used interchangeably, and quite often the latter is considered to play a foundational role for the former. In this article, we argue that although there is a relationship between morality and empathy, it is not as straightforward as apparent at first glance. Moreover, it is critical to distinguish among the different facets of empathy (emotional sharing, empathic concern, and perspective taking), as each uniquely influences moral cognition and predicts differential outcomes in moral behavior. Empirical evidence and theories from evolutionary biology as well as developmental, behavioral, and affective and social neuroscience are comprehensively integrated in support of this argument. The wealth of findings illustrates a complex and equivocal relationship between morality and empathy. The key to understanding such relations is to be more precise on the concepts being used and, perhaps, abandoning the muddy concept of empathy.
Space and space-time modeling using process convolutions

A continuous spatial model can be constructed by convolving a very simple, perhaps independent, process with a kernel or point spread function. This approach for constructing a spatial process offers a number of advantages over specification through a spatial covariogram. In particular, this process convolution specification leads to compuational simplifications and easily extends beyond simple stationary models. This paper uses process convolution models to build space and space-time models that are flexible and able to accomodate large amounts of data. Data from environmental monitoring is considered.
Decision trees and forests: a probabilistic perspective

Decision trees and ensembles of decision trees are very popular in machine learning and often achieve state-of-the-art performance on black-box prediction tasks. However, popular variants such as C4.5, CART, boosted trees and random forests lack a probabilistic interpretation since they usually just specify an algorithm for training a model. We take a probabilistic approach where we cast the decision tree structures and the parameters associated with the nodes of a decision tree as a probabilistic model; given labeled examples, we can train the probabilistic model using a variety of approaches (Bayesian learning, maximum likelihood, etc). The probabilistic approach allows us to encode prior assumptions about tree structures and share statistical strength between node parameters; furthermore, it offers a principled mechanism to obtain probabilistic predictions which is crucial for applications where uncertainty quantification is important. Existing work on Bayesian decision trees relies on Markov chain Monte Carlo which can be computationally slow and suffer from poor mixing. We propose a novel sequential Monte Carlo algorithm that computes a particle approximation to the posterior over trees in a top-down fashion. We also propose a novel sampler for Bayesian additive regression trees by combining the above top-down particle filtering algorithm with the Particle Gibbs (Andrieu et al., 2010) framework. Finally, we propose Mondrian forests (MFs), a computationally efficient hybrid solution that is competitive with non-probabilistic counterparts in terms of speed and accuracy, but additionally produces well-calibrated uncertainty estimates. MFs use the Mondrian process (Roy and Teh, 2009) as the randomization mechanism and hierarchically smooth the node parameters within each tree (using a hierarchical probabilistic model and approximate Bayesian updates), but combine the trees in a non-Bayesian fashion. MFs can be grown in an incremental/online fashion and remarkably, the distribution of online MFs is the same as that of batch MFs.
Wind Speed and Rotor Position Sensorless Control for Direct-Drive PMG Wind Turbines

This paper proposes a wind speed and rotor position sensorless control for wind turbines directly driving permanent magnetic generators (PMGs). A sliding-mode observer is designed to estimate the rotor position of the PMG by using the measured stator currents and the commanded stator voltages obtained from the control scheme of the machine-side converter of the PMG wind turbine. The rotor speed of the PMG (i.e., the turbine shaft speed) is estimated from its back electromotive force using a model adaptive reference system observer. Based on the measured output electrical power and estimated rotor speed of the PMG, the mechanical power of the turbine is estimated by taking into account the power losses of the wind turbine generator system. A back-propagation artificial neural network is then designed to estimate the wind speed in real time by using the estimated turbine shaft speed and mechanical power. The estimated wind speed is used to determine the optimal shaft speed reference for the PMG control system. Finally, a sensorless control is developed for the PMG wind turbines to continuously generate the maximum electrical power without using any wind speed or rotor position sensors. The validity of the proposed estimation and control algorithms are shown by simulation studies on a 3-kW PMG wind turbine and are further demonstrated by experimental results on a 300-W practical PMG wind turbine.
Esthetic anterior composite resin restorations using a single shade: Step-by-step technique.

Children and young adults often present to a dental practice after accidents that affect their anterior teeth. In many situations, only 1 tooth is affected by the trauma, and choosing the right treatment option can be complex. Esthetic restoration of the maxillary anterior dentition can be accomplished by using direct or indirect techniques. Direct resin composite restoration can be characterized as a life-like definitive restoration and is a predictable, conservative, and reliable chairside procedure. This article describes a straightforward protocol used to restore a patient's appearance with a single-shade composite resin after a Class IV fracture received during a sporting event.
Cognitive training changes hippocampal function in mild cognitive impairment: a pilot study.

A randomized pilot experiment examined the neural substrates of response to cognitive training in participants with mild cognitive impairment (MCI). Participants performed exercises previously demonstrated to improve verbal memory and an active control group performed other computer activities. An auditory-verbal fMRI task was conducted before and after the two-month training program. Verbal memory scores improved significantly and left hippocampal activation increased significantly in the experimental group (gains in 5 of 6 participants) relative to the control group (reductions in all 6 participants). Results suggest that the hippocampus in MCI may retain sufficient neuroplasticity to benefit from cognitive training.
A new sub-threshold 7T SRAM cell design with capability of bit-interleaving in 90 nm CMOS

In this paper we present a new Static Random Access Memory (SRAM) that has 7 transistors in each cell. This idea allows for bit-interleaving that makes the SRAM more reliable against the soft errors. One of the challenges of a conventional 6 transistor (6T) SRAM cell in sub-threshold region is sizing of its access transistors. Here by separating access transistors of reading and writing, we mitigate this challenge. By using a minimum-size access transistor for reading, probability of unsuccessful read reduces. To have a more successful write operation, we make one of the inverters of the cell that is fighting with the write access transistor, weaker during write operation by floating its supply voltage and ground rails. After write operation, this inverter returns back to normal mode. Simulation results in 90 nm CMOS technology show that our design satisfies 4.5-sigma criterion for reading and writing at supply voltage of 300 mV. Compared to conventional 6T SRAM cell, our design improves read-time and write-time significantly. For example at supply voltage of 500 mV, these improvements are 137 and 83 percents, respectively. Comparing power and energy consumption for single write operation of the proposed 7T SRAM cell at supply voltage of 300mV with conventional 6T SRAM cell at 800mV (i.e. minimum achievable voltage for this SRAM cell) shows improvements of 133.4X and 266.78X, respectively.
Scholarly communication trends in the digital age: Informal scholarly publishing and dissemination, a grounded theory approach

The purpose of this paper is to investigate scholars’ attitudes toward informal publishing and dissemination to provide a view of the challenges and advantages of using such channels. Although considerable research has been carried out in relation to peer-reviewed scholarly publishing, relatively few studies have investigated the adoption of informal scholarly communication platforms in the scholarly publishing process.  The paper deployed a grounded theory approach using semi-structured interviews as a qualitative research tool. A theoretical sample of 40 researchers in 4 universities were interviewed to gather data regarding informal publishing, platforms, factors that affect the researchers’ decision and the use of informal channels in dissemination.  Results of the interviews suggest that there is an increasing trend among researchers toward informal publishing and dissemination throughout the scholarly communication cycle. The paper shows that there are three types of scholars who are involved in the scholarly communication process: conventional, modern and liberal scholars. Each of these scholars carries different beliefs regarding the scholarly communication process.  This paper was conducted on a relatively small sample of academic researchers, and therefore, the results cannot be easily generalized into a wider community of scholars.  The paper provides insight into informal scholarly publishing practices using a grounded theory approach. This approach helped to capture the changes in both scholarly publishing practices and the adoption of informal techniques among the scholarly community.
A data lifeCycle model for smart cities

Smart Cities are the most challenging and promising technological solutions for absorbing the increasing pressure of population growth, while simultaneously enforcing a sustainable economic progress as well as a higher quality of life. Several technologies are involved in a potential Smart City deployment, although data are the fuel to achieve the demanded and mandatory smartness. Data can be obtained from multiple sources, in large quantities, and with a variety of formats, therefore, an appropriate management is critical for their effective usage. Data life cycle models constitute an effective trend towards developing an integral and efficient data management framework, from data creation to data consumption and removal. In this paper we present the Smart City Comprehensive Data LifeCycle (SCC-DLC) model, a data management architecture generated from a comprehensive scenario agnostic model, tailored for the particular scenario of Smart Cities. We define the management of each data life phase, and describe its implementation on a Smart City with Fog-to-Cloud (F2C) resources management, an architecture that combines the advantages of both cloud and fog strategies.