nils-herrmann/clean_scirepeval huggingface dataset:
Diagonal principal component analysis for face recognition

In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks the optimal projective vectors from diagonal face images without image-to-vector transformation. While in contrast to 2DPCA, DiaPCA reserves the correlations between variations of rows and those of columns of images. Experiments show that DiaPCA is much more accurate than both PCA and 2DPCA. Furthermore, it is shown that the accuracy can be further improved by combining DiaPCA with 2DPCA.
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

Identifying public misinformation is a complicated and challenging task. Stance detection, i.e. determining the relative perspective a news source takes towards a specific claim, is an important part of evaluating the veracity of the assertion. Automating the process of stance detection would arguably benefit human fact checkers. In this paper, we present our stance detection model which claimed third place in the first stage of the Fake News Challenge. Despite our straightforward approach, our model performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our model as the ‘simple but tough-to-beat baseline’ for the Fake News Challenge stance detection task.
The use of visual feedback, in particular mirror visual feedback, in restoring brain function.

This article reviews the potential use of visual feedback, focusing on mirror visual feedback, introduced over 15 years ago, for the treatment of many chronic neurological disorders that have long been regarded as intractable such as phantom pain, hemiparesis from stroke and complex regional pain syndrome. Apart from its clinical importance, mirror visual feedback paves the way for a paradigm shift in the way we approach neurological disorders. Instead of resulting entirely from irreversible damage to specialized brain modules, some of them may arise from short-term functional shifts that are potentially reversible. If so, relatively simple therapies can be devised--of which mirror visual feedback is an example--to restore function.
Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm

A robot manipulator sharing its workspace with humans should be able to quickly detect collisions and safely react for limiting injuries due to physical contacts. In the absence of external sensing, relative motions between robot and human are not predictable and unexpected collisions may occur at any location along the robot arm. Based on physical quantities such as total energy and generalized momentum of the robot manipulator, we present an efficient collision detection method that uses only proprioceptive robot sensors and provides also directional information for a safe robot reaction after collision. The approach is first developed for rigid robot arms and then extended to the case of robots with elastic joints, proposing different reaction strategies. Experimental results on collisions with the DLR-III lightweight manipulator are reported
A 200-mA Digital Low Drop-Out Regulator With Coarse-Fine Dual Loop in Mobile Application Processor

This paper proposes a coarse-fine dual-loop architecture for the digital low drop-out (LDO) regulators with fast transient response and more than 200-mA load capacity. In the proposed scheme, the output voltage is coregulated by two loops, namely, the coarse loop and the fine loop. The coarse loop adopts a fast current-mirror flash analog to digital converter and supplies high output current to enhance the transient performance, while the fine loop delivers low output current and helps reduce the voltage ripples and improve the regulation accuracies. Besides, a digital controller is implemented to prevent contentions between the two loops. Fabricated in a 28-nm Samsung CMOS process, the proposed digital LDO achieves maximum load up to 200 mA when the input and the output voltages are 1.1 and 0.9 V, respectively, with a chip area of 0.021 mm2. The measured output voltage drop of around 120 mV is observed for a load step of 180 mA.
Lessons learned in ITIL implementation failure

ITIL is the most widely used IT framework in majority of organizations in the world now. However, implementing such best practice experiences in an organization comes with some implementation challenges such as staff resistance, task conflicts and ambiguous orders. It means that implementing such framework is not easy and it can be caused of the organization destruction. This paper tries to describe overall view of ITIL framework and address major reasons on the failure of this framework’s implementation in the organizations
Transposition effects on bundle proximity losses in high-speed PM machines

Proximity effects caused by uneven distribution of current among the insulated wire strands of stator multi-strand windings can contribute significant bundle-level proximity losses in permanent magnet (PM) machines operating at high speeds. Three-dimensional finite element analysis is used to investigate the effects of transposition of the insulated strands in stator winding bundles on the copper losses in high-speed machines. The investigation confirms that the bundle proximity losses must be considered in the design of stator windings for high-speed machines, and the amplitude of these losses decreases monotonically as the level of transposition is increased from untransposed to fully-transposed (360°) wire bundles. Analytical models are introduced to estimate the currents in strands in a slot for a high-speed machine.
Predicting churn in mobile free-to-play games

In the mobile game industry, Free-to-Play games are dominantly released, and therefore player retention and purchases have become important issues. In this paper, we propose a game player model for predicting when players will leave a game. Firstly, we define player churn in the game and extract features that contain the properties of the player churn from the player logs. And then we tackle the problem of imbalanced datasets. Finally, we exploit classification algorithms from machine learning and evaluate the performance of the proposed prediction model using cross-validation. Experimental results show that the proposed model has high accuracy enough to predict churn for real-world application.
Media Effects: Theory and Research.

This review analyzes trends and commonalities among prominent theories of media effects. On the basis of exemplary meta-analyses of media effects and bibliometric studies of well-cited theories, we identify and discuss five features of media effects theories as well as their empirical support. Each of these features specifies the conditions under which media may produce effects on certain types of individuals. Our review ends with a discussion of media effects in newer media environments. This includes theories of computer-mediated communication, the development of which appears to share a similar pattern of reformulation from unidirectional, receiver-oriented views, to theories that recognize the transactional nature of communication. We conclude by outlining challenges and promising avenues for future research.
Internet of Things: Architectures, Protocols, and Applications

The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.
Practical Defenses for Evil Twin Attacks in 802.11

Open-access 802.11 wireless networks are commonly deployed in cafes, bookstores, and other public spaces to provide free Internet connectivity. These networks are convenient to deploy, requiring no out-of-band key exchange or prior trust relationships. However, such networks are vulnerable to a variety of threats including the evil twin attack where an adversary clones a client's previously-used access point for a variety of malicious purposes including malware injection or identity theft. We propose defenses that aim to maintain the simplicity, convenience, and usability of open-access networks while offering increased protection from evil twin attacks. First, we present an evil twin detection strategy called context-leashing that constrains access point trust by location. Second, we propose that wireless networks be identified by uncertified public keys and design an SSH-style authentication and session key establishment protocol that fits into the 802.1X standard. Lastly, to mitigate the pitfalls of SSH-style authentication, we present a crowd-sourcing-based reporting protocol that provides historical information for access point public keys while preserving the location privacy of users who contribute reports.
Curve Boxplot: Generalization of Boxplot for Ensembles of Curves

In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics.
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. Our method consists of both global and category specific adaptation techniques. Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. This initially adapted space then enables category specific adaptation through a generalization of constrained weak learning, with explicit transfer of the spatial layout from the source to the target domains. Our approach outperforms baselines across different settings on multiple large-scale datasets, including adapting across various real city environments, different synthetic sub-domains, from simulated to real environments, and on a novel large-scale dash-cam dataset.
Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing

This paper describes our work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing. The construction of each of these lexical resources has required many years of laborious human effort, and they all have their strengths and shortcomings. By linking them together, we build an improved resource in which (1) the coverage of FrameNet is extended, (2) the VerbNet lexicon is augmented with frame semantics, and (3) selectional restrictions are implemented using WordNet semantic classes. The synergistic exploitation of various lexical resources is crucial for many complex language processing applications, and we prove it once again effective in building a robust semantic parser.
2 Discourse Analysis as Ideology Analysis

This chapter focuses on the expression of ideologies in various structures of text and talk. It is situated within the broader framework of a research project on discourse and ideology which has been conducted at the University of Amsterdam since 1993. The theoretical premise of this study is that ideologies are typically, though not exclusively, expressed and reproduced in discourse and communication, including non-verbal semiotic messages, such as pictures, photographs and movies. Obviously, ideologies are also enacted in other forms of action and interaction, and their reproduction is often embedded in organizational and institutional contexts. Thus, racist ideologies may be expressed and reproduced in racist talk, comics or movies in the context of the mass media, but they may also be enacted in many forms of discrimination and institutionalized by racist parties within the context of the mass media or of Western parliamentary democracies. However, among the many forms of reproduction and interaction, discourse plays a prominent role as the preferential site for the explicit, verbal formulation and the persuasive communication of ideological propositions.
Estimating the Socio-Economic Impact of Product Reviews : Mining Text and Reviewer Characteristics

With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we re-examine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes like the extent of their perceived usefulness. Our approach explores multiple aspects of review text, such as lexical, grammatical, semantic, and stylistic levels to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences have a negative effect on product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are considered more informative (or helpful) by the users. By using Random Forest based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. Reviews for products that have received widely fluctuating reviews, also have reviews of widely fluctuating helpfulness. In particular, we find that highly detailed and readable reviews can have low helpfulness votes in cases when users tend to vote negatively not because they disapprove of the review quality but rather to convey their disapproval of the review polarity. We examine the relative importance of the three broad feature categories: ‘reviewer-related’ features, ‘review subjectivity’ features, and ‘review readability’ features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their socio-economic impact. Our results can have implications for judicious design of opinion forums.
Engineering Privacy

In this paper we integrate insights from diverse islands of research on electronic privacy to offer a holistic view of privacy engineering and a systematic structure for the discipline's topics. First we discuss privacy requirements grounded in both historic and contemporary perspectives on privacy. We use a three-layer model of user privacy concerns to relate them to system operations (data transfer, storage and processing) and examine their effects on user behavior. In the second part of the paper we develop guidelines for building privacy-friendly systems. We distinguish two approaches: "privacy-by-policy" and "privacy-by-architecture." The privacy-by-policy approach focuses on the implementation of the notice and choice principles of fair information practices (FIPs), while the privacy-by-architecture approach minimizes the collection of identifiable personal data and emphasizes anonymization and client-side data storage and processing. We discuss both approaches with a view to their technical overlaps and boundaries as well as to economic feasibility. The paper aims to introduce engineers and computer scientists to the privacy research domain and provide concrete guidance on how to design privacy-friendly systems.
Energy Sharing Control Scheme for State-of-Charge Balancing of Distributed Battery Energy Storage System

This paper presents an energy sharing state-of-charge (SOC) balancing control scheme based on a distributed battery energy storage system architecture where the cell balancing system and the dc bus voltage regulation system are combined into a single system. The battery cells are decoupled from one another by connecting each cell with a small lower power dc-dc power converter. The small power converters are utilized to achieve both SOC balancing between the battery cells and dc bus voltage regulation at the same time. The battery cells' SOC imbalance issue is addressed from the root by using the energy sharing concept to automatically adjust the discharge/charge rate of each cell while maintaining a regulated dc bus voltage. Consequently, there is no need to transfer the excess energy between the cells for SOC balancing. The theoretical basis and experimental prototype results are provided to illustrate and validate the proposed energy sharing controller.
Design Optimization of Direct-Coupled Ironless Axial Flux Permanent Magnet Synchronous Wind Generator With Low Cost and High Annual Energy Yield

In this paper, an improved design of an ironless axial flux permanent magnet synchronous generator (AFPMSG) is presented for direct-coupled wind turbine application considering wind speed characteristics. The partial swarm optimization method is used to perform a multi-objective design optimization of the ironless AFPMSG in order to decrease the active material cost and increase the annual energy yield of the generator over the entire range of operating wind speed. General practical and mechanical limitations in the design of the generator are considered as optimization constraints. For accurate analytical design of the generator, distribution of the flux in all parts of the machine is obtained through a modified magnetic equivalent circuit model of AFPMSG. In this model, the magnetic saturation of the rotor back iron cores is considered using a nonlinear iterative algorithm. Various combinations of pole and coil numbers are studied in the design of a 30 kW AFPMSG via the optimization procedure. Finally, 3-D finite-element model of the generator was prepared to confirm the validity of the proposed design procedure and the generator performance for various wind speeds.
A Dynamic-Model-Based Wheel Slip Detector for Mobile Robots on Outdoor Terrain

This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, inertial measurement unit, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.
TinySLAM improvements for indoor navigation

TinySLAM  is one of the most simple SLAM methods but the original implementation  is based on the specific robot model and provided as the ad-hoc application. Its key feature is simplicity of implementation and configuration at cost of accuracy (as our tests shown). Some changes were made in the original algorithm in order to minimize an error of estimated trajectories. The introduced model of cell leads to an error decrease on almost all tested indoor sequences. The proposed dynamic probability estimator improves usage of coarse-grained maps when memory efficiency is more desirable than accuracy. Obtained quantitative measurements justify the changes made in tinySLAM in case the method is used in a relatively small environment.
Generating Sentences by Editing Prototypes

We propose a new generative language model for sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional language models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies.
Using sensitivity analysis and visualization techniques to open black box data mining models

0020-0255/$ see front matter 2012 Elsevier Inc http://dx.doi.org/10.1016/j.ins.2012.10.039 ⇑ Corresponding author. E-mail addresses: pcortez@dsi.uminho.pt (P. Cor In this paper, we propose a new visualization approach based on a Sensitivity Analysis (SA) to extract human understandable knowledge from supervised learning black box data mining models, such as Neural Networks (NNs), Support Vector Machines (SVMs) and ensembles, including Random Forests (RFs). Five SA methods (three of which are purely new) and four measures of input importance (one novel) are presented. Also, the SA approach is adapted to handle discrete variables and to aggregate multiple sensitivity responses. Moreover, several visualizations for the SA results are introduced, such as input pair importance color matrix and variable effect characteristic surface. A wide range of experiments was performed in order to test the SA methods and measures by fitting four well-known models (NN, SVM, RF and decision trees) to synthetic datasets (five regression and five classification tasks). In addition, the visualization capabilities of the SA are demonstrated using four real-world datasets (e.g., bank direct marketing and white wine quality).