nils-herrmann/clean_scirepeval huggingface dataset:
No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting and most approaches currently use a pipeline of processing steps connecting a sequence of components. In this article, we analyse and micro evaluate the behaviour of 29 available QA components for the DBpedia knowledge graph that were released by the research community since 2010. As a result, we provide a perspective on collective failure cases, suggest characteristics of QA components that prevent them from performing better and provide future challenges and research directions in the field.
Enterprise and process architecture patterns

For more than 30 years, many authors have attempted to synthesize the knowledge about how an enterprise should structure its business processes, the people that execute them, the Information Systems that support both of these and the IT layer on which such systems operate, in such a way that they will be aligned with the business strategy. This is the challenge of Enterprise Architecture design, which is the theme of this paper. We will provide a brief review of the literature on this subject, with an emphasis on more recent proposals and methods that have been applied in practice. We also select approaches that propose some sort of framework that provides a general Enterprise Architecture in a given domain that can be reused as a basis for specific designs in such a domain. Then we present our proposal for Enterprise Architecture design, which is based on general domain models that we call Enterprise Architecture Patterns.
Forensic hash for multimedia information

Digital multimedia such as images and videos are prevalent on today’s internet and cause significant social impact, which can be evidenced by the proliferation of social networking sites with user generated contents. Due to the ease of generating and modifying images and videos, it is critical to establish trustworthiness for online multimedia information. In this paper, we propose a new framework to perform multimedia forensics by using compact side information to reconstruct the processing history of a multimedia document. We refer to this framework as FASHION, standing for Forensic hASH for informatION assurance. As a first step in the modular design for FASHION, we propose new algorithms based on Radon transform and scale space theory to effectively estimate the parameters of geometric transforms and detect local tampering that an image may have undergone. The FASHION framework is designed to answer a much broader range of questions regarding the processing history of multimedia data than simple binary decision from robust image hashing, and also offers more efficient and accurate forensic analysis than multimedia forensic techniques that do not use any side information.
So far away from one's partner, yet so close to romantic alternatives: avoidant attachment, interest in alternatives, and infidelity.

Temptation pervades modern social life, including the temptation to engage in infidelity. The present investigation examines one factor that may put individuals at a greater risk of being unfaithful to their partner: dispositional avoidant attachment style. The authors hypothesize that avoidantly attached people may be less resistant to temptations for infidelity due to lower levels of commitment in romantic relationships. This hypothesis was confirmed in 8 studies. People with high, vs. low, levels of dispositional avoidant attachment had more permissive attitudes toward infidelity (Study 1), showed attentional bias toward attractive alternative partners (Study 2), expressed greater daily interest in meeting alternatives to their current relationship partner (Study 5), perceived alternatives to their current relationship partner more positively (Study 6), and engaged in more infidelity over time (Studies 3, 4, 7, and 8). This effect was mediated by lower levels of commitment (Studies 5-8). Thus, avoidant attachment predicted a broad spectrum of responses indicative of interest in alternatives and propensity to engage in infidelity, which were mediated by low levels of commitment.
TOWARD A PSYCHOLOGY OF FRAMING EFFECTS

Framing is the process by which a communication source constructs and defines a social or political issue for its audience. While many observers of political communication and the mass media have discussed framing, few have explicitly described how framing affects public opinion. In this paper we offer a theory of framing effects, with a specific focus on the psychological mechanisms by which framing influences political attitudes. We discuss important conceptual differences between framing and traditional theories of persuasion that focus on belief change. We outline a set of hypotheses about the interaction between framing and audience sophistication, and test these in an experiment. The results support our argument that framing is not merely persuasion, as it is traditionally conceived. We close by reflecting on the various routes by which political communications can influence attitudes.
Interactive Deep Colorization With Simultaneous Global and Local Inputs

Colorization methods using deep neural networks have become a recent trend. However, most of them do not allow user inputs, or only allow limited user inputs (only global inputs or only local inputs), to control the output colorful images. The possible reason is that it’s difficult to differentiate the influence of different kind of user inputs in network training. To solve this problem, we present a novel deep colorization method, which allows simultaneous global and local inputs to better control the output colorized images. The key step is to design an appropriate loss function that can differentiate the influence of input data, global inputs and local inputs. With this design, our method accepts no inputs, or global inputs, or local inputs, or both global and local inputs, which is not supported in previous deep colorization methods. In addition, we propose a global color theme recommendation system to help users determine global inputs. Experimental results shows that our methods can better control the colorized images and generate state-of-art results.
Capacitor Bank Designing for Power Factor Improvement

Various inductive loads used in all industries deals with the problem of power factor improvement. Capacitor bank connected in shunt helps in maintaining the power factor closer to unity. They improve the electrical supply quality and increase the efficiency of the system. Also the line losses are also reduced. Shunt capacitor banks are less costly and can be installed anywhere. This paper deals with shunt capacitor bank designing for power factor improvement considering overvoltages for substation installation.  Capacitor Bank, Overvoltage Consideration, Power Factor, Reactive Power
Link prediction for partially observed networks

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many existing supervised learning approaches. We develop a new method which treats the observed network as a sample of the true network with different sampling rates for positive and negative examples. We obtain a relative ranking of potential links by their probabilities, utilizing information on node covariates as well as on network topology. Empirically, the method performs well under many settings, including when the observed network is sparse. We apply the method to a protein-protein interaction network and a school friendship network.
Implementation of Lean Manufacturing through Supplier Kaizen Framework-A Case Study

Lean manufacturing is a production philosophy derived from the Toyota Production System (TPS). It has the ultimate aim of reducing cost by thoroughly eliminating wastes. Lean manufacturing aims to improve productivity and creating continuous flow in the production and strive to create value in the every aspect of the production. This paper studies on the implementation a Lean Manufacturing project done at an automotive component company. The Lean project uses the Supplier Kaizen frameworksteps and methodology to implement the lean manufacturing at a Malaysian components manufacturer through kaikaku and kaizen approach. This resulted in substantial improvements in quality, timely delivery and cost saving which in total improves the bottom line.
Detection of microaneurysms using multi-scale correlation coefficients

This paper presents a new approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR)—a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since microaneurysms are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on multi-scale correlation filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, microaneurysm candidate detection (coarse level) and true microaneurysm classification (fine level). The approach was evaluated based on two public datasets—ROC (retinopathy on-line challenge, http://roc.healthcare.uiowa.edu) and DIARETDB1 (standard diabetic retinopathy database, http://www.it.lut.fi/project/imageret/diaretdb1). We conclude our method to be effective and efficient.
A Decision Fusion Model for 3D Detection of Autonomous Driving

This paper proposes a multimodal fusion model for 3D car detection inputting both point clouds and RGB images and generates the corresponding 3D bounding boxes. Our model is composed of two subnetworks: one is point-based method and another is multi-view based method, which is then combined by a decision fusion model. This decision model can absorb the advantages of these two sub-networks and restrict their shortcomings effectively. Experiments on the KITTI 3D car detection benchmark show that our work can achieve state of the art performance.
A Papier-Mache Approach to Learning 3D Surface Generation

We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) autoencoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potential for other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.
Attendance management system using hybrid face recognition techniques

Attendance recording of a student in an academic organization plays a vital role in judging students performance. As manual labor involved in this process is time consuming, an automated Attendance Management System (AMS) based on face detection and face recognition techniques is proposed in this paper. The system employs modified Viola-Jones algorithm for face detection, and alignment- free partial face recognition algorithm for face recognition. After successful recognition of a student, the system automatically updates the attendance in the excel sheet. The proposed system improves the performance of existing attendance management systems by eliminating manual calling, marking and entry of attendance in institutional websites.
A Method for Computing Optimal SetPoint Schedule for HVAC Systems

We propose a method for model-based control of building air conditioning systems that minimizes energy costs while maintaining occupant comfort. The method uses a building thermal model in the form of a thermal circuit identified from collected sensor data, and reduces the building thermal dynamics to a Markov decision process (MDP) whose decision variables are the sequence of temperature set-points over a suitable horizon, for example one day. The main advantage of the resulting MDP model is that it is completely discrete, which allows for a very fast computation of the optimal sequence of temperature set-points. Experiments on thermal models demonstrate savings that can exceed 50% with respect to usual control strategies in buildings such as night setup. 2013 REHVA World Congress (CLIMA) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.  We propose a method for model-based control of building air conditioning systems that minimizes energy costs while maintaining occupant comfort. The method uses a building thermal model in the form of a thermal circuit identified from collected sensor data, and reduces the building thermal dynamics to a Markov decision process (MDP) whose decision variables are the sequence of temperature set-points over a suitable horizon, for example one day. The main advantage of the resulting MDP model is that it is completely discrete, which allows for a very fast computation of the optimal sequence of temperature set-points. Experiments on thermal models demonstrate savings that can exceed 50% with respect to usual control strategies in buildings such as night setup.
Application of Data Mining for Enterprise Digital Marketing Strategy Making

With the popularity of broadband Internet and the booming development of network technology in recent years, the traditional marketing is also gradually moving to digital marketing. The dissemination of information through the internet is different from traditional marketing. In today's competitive market, there are various products made by enterprises, how to stand out in a highly competitive environment, and how to go with marketing tactics and product through the product advertising to cause high degree of customer purchase intentions and interests effectively, is one of the important keys. Furthermore, due to the international business environment and intense competition, it is necessary to response and satisfies market and customer demand immediately, and use information technology to assist enterprises in doing business marketing. Therefore, in order to grasp market characteristics and consumer preferences to select the appropriate products marketing strategies. In this study, the structure of digital marketing is proposed aims to provide the enterprise relevant marketing information, to identify appropriate marketing practices of market segment, and thus to achieve the majority of the responses or purchase intention in marketing effectiveness.
Defending Against Advanced Persistent Threats Using Game-Theory

Advanced persistent threats (APT) combine a variety of different attack forms ranging from social engineering to technical exploits. The diversity and usual stealthiness of APT turns them into a central problem of contemporary practical system security, since information on attacks, the current system status or the attacker's incentives is often vague, uncertain and in many cases even unavailable. Game theory is a natural approach to model the conflict between the attacker and the defender, and this work investigates a generalized class of matrix games as a risk mitigation tool for an advanced persistent threat (APT) defense. Unlike standard game and decision theory, our model is tailored to capture and handle the full uncertainty that is immanent to APTs, such as disagreement among qualitative expert risk assessments, unknown adversarial incentives and uncertainty about the current system state (in terms of how deeply the attacker may have penetrated into the system's protective shells already). Practically, game-theoretic APT models can be derived straightforwardly from topological vulnerability analysis, together with risk assessments as they are done in common risk management standards like the ISO 31000 family. Theoretically, these models come with different properties than classical game theoretic models, whose technical solution presented in this work may be of independent interest.
Dual-polarized aperture-coupled wideband microstrip patch antenna with high isolation for C-Band

An aperture-coupled dual-polarized wideband microstrip antenna with high port-to-port isolation in C-Band is presented in this paper. The proposed antenna contains reflector, substrate, patch and director. By means of four shorting pins the bandwidth increases noticeably. It has 50% bandwidth of -10dB return loss at the 5.25GHz center frequency, and minimum 31.5dB isolation across the band. The reflector helps to improve Front to Back (F/B) ratio of better than 20dB. Moreover, it has a peak gain of 9.2dBi. The structure of the antenna is compact and easy to fabricate. Therefore, it's a suitable candidate for arrays, and could be used in base stations for Wi-Fi applications.
Single Password Authentication

Users frequently reuse their passwords when authenticating to various online services. Combined with the use of weak passwords or honeypot/phishing attacks, this brings high risks to the security of the user’s account information. In this paper, we propose several protocols that can allow a user to use a single password to authenticate to multiple services securely. All our constructions provably protect the user from dictionary attacks on the password, and cross-site impersonation or honeypot attacks by the online service providers. Our solutions assume the user has access to either an untrusted online cloud storage service (as per Boyen ), or a mobile storage device that is trusted until stolen. In the cloud storage scenario, we consider schemes that optimize for either storage server or online service performance, as well as anonymity and unlinkability of the user’s actions. In the mobile storage scenario, we minimize the assumptions we make about the capabilities of the mobile device: we do not assume synchronization, tamper resistance, special or expensive hardware, or extensive cryptographic capabilities. Most importantly, the user’s password remains secure even after the mobile device is stolen. Our protocols provide another layer of security against malware and phishing. To the best of our knowledge, we are the first to propose such various and provably secure password-based authentication schemes. Lastly, we argue that our constructions are relatively easy to deploy, especially if a few single sign-on services (e.g., Microsoft, Google, Facebook) adopt our proposal.
Robosemantics: How Stanley the Volkswagen Represents the World

One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that the car constitutes a refutation of semantic arguments against the possibility of strong artificial intelligence.
Leadership and the fate of organizations.

This article concerns the real-world importance of leadership for the success or failure of organizations and social institutions. The authors propose conceptualizing leadership and evaluating leaders in terms of the performance of the team or organization for which they are responsible. The authors next offer a taxonomy of the dependent variables used as criteria in leadership studies. A review of research using this taxonomy suggests that the vast empirical literature on leadership may tell us more about the success of individual managerial careers than the success of these people in leading groups, teams, and organizations. The authors then summarize the evidence showing that leaders do indeed affect the performance of organizations--for better or for worse--and conclude by describing the mechanisms through which they do so.
Sleep deprivation and drowsiness of medical residents and medical students.

to evaluate sleep quality and daytime sleepiness of residents and medical students.



we applied a socio-demographic questionnaire, the Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) to a population of residents and medical students.



hundred five residents and 101 undergraduate medical students participated. Residents presented higher mean PSQI (6.76±2.81) with poorer sleep quality when compared with undergraduates (5.90±2.39); Both had similar measures of sleepiness by ESS (p=0.280), but residents showed lower duration and lower subjective sleep quality.



medical students and residents presented sleep deprivation, indicating the need for preventive actions in the medical area.
Defining the Business Model in the New World of Digital Business

Recent rapid advances in ICTs, specifically in Internet and mobile technologies, have highlighted the rising importance of the Business Model (BM) in Information Systems (IS). Despite agreement on its importance to an organization’s success, the concept is still fuzzy and vague, and there is no consensus regarding its definition. Furthermore, understanding the BM domain by identifying its meaning, fundamental pillars, and its relevance to other business concepts is by no means complete. In this paper we aim to provide further clarification by first presenting a classification of definitions found in the IS literature; second, proposing guidelines on which to develop a more comprehensive definition in order to reach consensus; and third, identifying the four main business model concepts and values and their interaction, and thus place the business model within the world of digital business. Based on this discussion, we propose a new definition for the business model that we argue is more appropriate to this new world.