nils-herrmann/clean_scirepeval huggingface dataset:
EU General Data Protection Regulation: A Gentle Introduction

The GDPR, or the Datenschutz Grundverordnung (DSGVO) in German, is an EU Law which addresses the subject of safeguarding privacy of personal data of the citizens of the EU and EEA. It also specifies how data the collected data might be transported out of the EU/EEA. It is the first genuine effort to unify the plethora of disparate privacy regulations put forward by different regulatory bodies. The GDPR aims to not only give more control over their personal data to the citizens, but also make conformance for businesses easier by defining unified guidelines. It also presses businesses, especially those dealing with sensitive personal data, to build their information systems in a way that confirms with Privacy by Design. These regulations aim to ensure a more transparent handling and processing of personal data, and create an environment of trust and awareness on both sides, i.e., the data owner as well as the controllers/processors.
THE ROLE OF CONSTRUCTION , INTUITION , AND JUSTIFICATION IN RESPONDING TO ETHICAL ISSUES AT WORK : THE SENSEMAKING-INTUITION MODEL

Proponents of a popular view of how individuals respond to ethical issues at work claim that individuals use deliberate and extensive moral reasoning under conditions that ignore equivocality and uncertainty. I discuss the limitations of these “rationalist approaches” and reconsider their empirical support using an alternative explanation from social psychological and sensemaking perspectives. I then introduce a new theoretical model composed of issue construction, intuitive judgment, and post hoc explanation and justification. I discuss the implications for management theory, methods, and practice.
Critical Success Factors of Technology Parks in Australia

Given the potential importance of technology parks, their complexity in terms of the scope of required investment and the growing interest of governments to use them as tools for creating sustainable development there is a pressing need for a better understanding of the critical success factors of these entities. However, Briggs and watt (2001) argued that the goal of many technology parks and the factors driving innovation success are still a mystery. In addition, it is argued that the problem with analyzing technology parks and cluster building is that recent studies analyze “the most celebrated case studies... to ‘explain’ their success” (Holbrook and Wolfe, 2002). This study uses intensive interviewing of technology parks’ managers and managers of tenant firms in the technology park to explore critical success factors of four of Australia’s' technology parks. The study identified the following critical success factors: a culture of risk-taking “entrepreneurism”, an autonomous park management that is independent of university officials and government bureaucrats, an enabling environment, a critical mass of companies that allows for synergies within the technology park, the presence of internationally renounced innovative companies, and finally a shared vision among the technology park stakeholders.
Attribute based Multifactor Authentication for Cloud Applications

Online information maintenance through cloud applications allows users to store, manage, control and share their information with other users as well as Cloud service providers. There have been serious privacy concerns about outsourcing user information to cloud servers. But also due to an increasing number of cloud data security incidents happened in recent years. Proposed system is a privacy-preserving system using Attribute based Multifactor Authentication. Proposed system provides privacy to users data with efficient authentication and store them on cloud servers such that servers do not have access to sensitive user information. Meanwhile users can maintain full control over access to their uploaded ?les and data, by assigning ?ne-grained, attribute-based access privileges to selected files and data, while di?erent users can have access to di?erent parts of the System. This application allows clients to set privileges to different users to access their data.
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks

While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Naive application of such algorithms on multiple source domain adaptation problem may lead to suboptimal solutions. As a step toward bridging the gap, we propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances. Compared with existing bounds, the new bound does not require expert knowledge about the target distribution, nor the optimal combination rule for multisource domains. Interestingly, our theory also leads to an efficient learning strategy using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple domain shifts while still being discriminative for the learning task. To this end, we propose two models, both of which we call multisource domain adversarial networks (MDANs): the first model optimizes directly our bound, while the second model is a smoothed approximation of the first one, leading to a more data-efficient and task-adaptive model. The optimization tasks of both models are minimax saddle point problems that can be optimized by adversarial training. To demonstrate the effectiveness of MDANs, we conduct extensive experiments showing superior adaptation performance on three real-world datasets: sentiment analysis, digit classification, and vehicle counting.
SOCIAL ENTREPRENEURSHIP : HOW INTENTIONS TO CREATE A SOCIAL ENTERPRISE GET FORMED

Social entrepreneurship has raised increasing interest among scholars, yet we still know relatively little about the particular dynamics and processes involved. This paper aims at contributing to the field of social entrepreneurship by clarifying key elements, providing working definitions, and illuminating the social entrepreneurship process. In the first part of the paper we review the existing literature. In the second part we develop a model on how intentions to create a social venture –the tangible outcome of social entrepreneurship– get formed. Combining insights from traditional entrepreneurship literature and anecdotal evidence in the field of social entrepreneurship, we propose that behavioral intentions to create a social venture are influenced, first, by perceived social venture desirability, which is affected by attitudes such as empathy and moral judgment, and second, by perceived social venture feasibility, which is facilitated by social support and self-efficacy beliefs.
Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification

We propose a Label Propagation based algorithm for weakly supervised text classification. We construct a graph where each document is represented by a node and edge weights represent similarities among the documents. Additionally, we discover underlying topics using Latent Dirichlet Allocation (LDA) and enrich the document graph by including the topics in the form of additional nodes. The edge weights between a topic and a text document represent level of “affinity” between them. Our approach does not require document level labelling, instead it expects manual labels only for topic nodes. This significantly minimizes the level of supervision needed as only a few topics are observed to be enough for achieving sufficiently high accuracy. The Label Propagation Algorithm is employed on this enriched graph to propagate labels among the nodes. Our approach combines the advantages of Label Propagation (through document-document similarities) and Topic Modelling (for minimal but smart supervision). We demonstrate the effectiveness of our approach on various datasets and compare with state-of-the-art weakly supervised text classification approaches.
Why Go Green: The Influence of Green Practices Coordination on Firm Performance

The aims of the research are to present a model of environmental performance by measuring the effects of a firm’s green practice in conjunction with inter-functional coordination mechanisms, and in turn, measure how environmental performance influences firms’ economic performance. Moreover, this study synthesizes both ecological modernization theory (EMT) and complementarity theory (CT) as a theoretical lens to develop a comprehensive model. The results of this study showed that green practice-manufacturing coordination and green practice-supply chain coordination have positive effect on environmental performance. However, the relationship between green practice-marketing coordination and environmental performance and the relationship between green practice-IS coordination and environmental performance were insignificant. Moreover, environmental performance is confirmed as an important antecedent of economic performance. Discussion and implications are provided.
Spatial augmented reality - merging real and virtual worlds

Like virtual reality, augmented reality is becoming an emerging platform in new application areas for museums, edutainment, home entertainment, research, industry, and the art communities using novel Please provide your name and opengl code fragments implementation instructions examples of virtual reality. Just click on the book spatial, ar displays in new application. Oliver bimber and acm siggraph no particular. Just click on the impact of computer graphics techniques 3d tools and calibration. Many cg and to receive 7mb attachments augmented reality is of spatial. No part of spatial augmented reality which have taken approaches concerns everyone. If not please contact us at the authors have put together with requesting invitation.
Teaching English Through Literature

This paper aims at emphasizing the use of literature as a popular technique for teaching both basic language skills (i.e. reading, writing, listening and speaking) and language areas (i.e. vocabulary, grammar and pronunciation) in our times. Reasons for using literary texts in foreign language classroom and main criteria for selecting suitable literary texts in foreign language classes are stressed so as to make the reader familiar with the underlying reasons and criteria for language teachers’ using and selecting literary texts. Moreover, literature and the teaching of language skills, benefits of different genres of literature (i.e. poetry, short fiction, drama and novel) to language teaching and some problems encountered by language teachers within the area of teaching English through literature (i.e. lack of preparation in the area of literature teaching in TESL / TEFL programs, absence of clear-cut objectives defining the role of literature in ESL / EFL, language teachers’ not having the background and training in literature, lack of pedagogically-designed appropriate materials that can be used by language teachers in a classroom context) are taken into account.
Visualizing information on mobile devices

Visualization can make a wide range of mobile applications more intuitive and productive. The mobility context and technical limitations such as small screen size make it impossible to simply port visualization applications from desktop computers to mobile devices, but researchers are starting to address these challenges. From a purely technical point of view, building more sophisticated mobile visualizations become easier due to new, possibly standard, software APIs such as OpenGLES and increasingly powerful devices. Although ongoing improvements would not eliminate most device limitations or alter the mobility context, they make it easier to create and experiment with alternative approaches.
BioInfer: a corpus for information extraction in the biomedical domain

Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora. We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation. We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at http://www.it.utu.fi/BioInfer .
Model-driven synthesis of SOA solutions

J. K. Strosnider P. Nandi S. Kumaran S. Ghosh A. Arsanjani The current approach to the design, maintenance, and governance of service-oriented architecture (SOA) solutions has focused primarily on flow-driven assembly and orchestration of reusable service components. The practical application of this approach in creating industry solutions has been limited, because flow-driven assembly and orchestration models are too rigid and static to accommodate complex, real-world business processes. Furthermore, the approach assumes a rich, easily configured library of reusable service components when in fact the development, maintenance, and governance of these libraries is difficult. An alternative approach pioneered by the IBM Research Division, model-driven business transformation (MDBT), uses a model-driven software synthesis technology to automatically generate production-quality business service components from high-level business process models. In this paper, we present the business entity life cycle analysis (BELA) technique for MDBT-based SOA solution realization and its integration into serviceoriented modeling and architecture (SOMA), the end-to-end method from IBM for SOA application and solution development. BELA shifts the process-modeling paradigm from one that is centered on activities to one that is centered on entities. BELA teams process subject-matter experts with IT and data architects to identify and specify business entities and decompose business processes. Supporting synthesis tools then automatically generate the interacting business entity service components and their associated data stores and service interface definitions. We use a large-scale project as an example demonstrating the benefits of this innovation, which include an estimated 40 percent project cost reduction and an estimated 20 percent reduction in cycle time when compared with conventional SOA approaches.
Compressed Video Action Recognition

Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video streams and the high temporal redundancy; the true and interesting signal is often drowned in too much irrelevant data. Motivated by that the superfluous information can be reduced by up to two orders of magnitude by video compression (using H.264, HEVC, etc.), we propose to train a deep network directly on the compressed video. This representation has a higher information density, and we found the training to be easier. In addition, the signals in a compressed video provide free, albeit noisy, motion information. We propose novel techniques to use them effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times faster than ResNet-152. On the task of action recognition, our approach outperforms all the other methods on the UCF-101, HMDB-51, and Charades dataset.
Personalized Physical Activity Coaching: A Machine Learning Approach

Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88-0.99, and mean F1-score = 0.90, range = 0.87-0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
A fast and elitist multiobjective genetic algorithm: NSGA-II

Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( ) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based multiobjective EA (MOEA), called nondominated sorting genetic algorithm II (NSGA-II), which alleviates all the above three difficulties. Specifically, a fast nondominated sorting approach with ( ) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best (with respect to fitness and spread) solutions. Simulation results on difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to Pareto-archived evolution strategy and strength-Pareto EA—two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multiobjective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective seven-constraint nonlinear problem, are compared with another constrained multiobjective optimizer and much better performance of NSGA-II is observed.
Forensic Data Recovery from Flash Memory

Current forensic tools for examination of embedded systems like mobile phones and PDA’s mostly perform data extraction on a logical level and do not consider the type of storage media during data analysis. This paper suggests a low level approach for the forensic examination of flash memories and describes three low-level data acquisition methods for making full memory copies of flash memory devices. Results are presented of a file system study in which USB memory sticks from 45 different make and models were used. For different mobile phones is shown how full memory copies of their flash memories can be made and which steps are needed to translate the extracted data into a format that can be understood by common forensic media analysis tools. Artifacts, caused by flash specific operations like block erasing and wear leveling, are discussed and directions are given for enhanced data recovery and analysis on data originating from flash memory.
Preanalytical variables affecting the integrity of human biospecimens in biobanking.

Most errors in a clinical chemistry laboratory are due to preanalytical errors. Preanalytical variability of biospecimens can have significant effects on downstream analyses, and controlling such variables is therefore fundamental for the future use of biospecimens in personalized medicine for diagnostic or prognostic purposes.



The focus of this review is to examine the preanalytical variables that affect human biospecimen integrity in biobanking, with a special focus on blood, saliva, and urine. Cost efficiency is discussed in relation to these issues.



The quality of a study will depend on the integrity of the biospecimens. Preanalytical preparations should be planned with consideration of the effect on downstream analyses. Currently such preanalytical variables are not routinely documented in the biospecimen research literature. Future studies using biobanked biospecimens should describe in detail the preanalytical handling of biospecimens and analyze and interpret the results with regard to the effects of these variables.
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

We present PointFusion, a generic 3D object detection method that leverages both image and 3D point cloud information. Unlike existing methods that either use multistage pipelines or hold sensor and dataset-specific assumptions, PointFusion is conceptually simple and application-agnostic. The image data and the raw point cloud data are independently processed by a CNN and a PointNet architecture, respectively. The resulting outputs are then combined by a novel fusion network, which predicts multiple 3D box hypotheses and their confidences, using the input 3D points as spatial anchors. We evaluate PointFusion on two distinctive datasets: the KITTI dataset that features driving scenes captured with a lidar-camera setup, and the SUN-RGBD dataset that captures indoor environments with RGB-D cameras. Our model is the first one that is able to perform better or on-par with the state-of-the-art on these diverse datasets without any dataset-specific model tuning.
Predicting popularity of online distributed applications: iTunes app store case analysis

Online distributed applications are becoming more and more important for users nowadays. There are an increasing number of individuals and companies developing applications and selling them online. In the past couple of years, Apple Inc. has successfully built an online application distribution platform -- iTunes App Store, which is facilitated by their fashionable hardware such like iPad or iPhone. Unlike other traditional selling networks, iTunes has some unique features to advertise their application, for example, daily application ranking, application recommendation, free trial application usage, application update, and user comments. All of these make us wonder what makes an application popular in the iTunes store and why users are interested in some specific type of applications. We plan to answer these questions by using machine learning techniques.