nils-herrmann/clean_scirepeval huggingface dataset:
Design Fiction : A Method Toolbox for Design Research in a Complex World

Current debates on design research, and its relation to other research fields and scientific disciplines, refer back to a fundamental distinction introduced by Herb Simon (Simon, 1996 (1981)): Design and design research do not primarily focus on explaining the world as it is; they share with engineering a fundamental interest in focusing on the world as it could be. In parallel, we observe a growing interest in the science studies to interpret scientific research as a constructive and creative practice (Knorr Cetina, 1999; 2002), organized as experimental systems (Rheinberger, 2001). Design fiction is a new approach, which integrates these two perspectives, in order to develop a method toolbox for design research for a complex world (Bleecker, 2009; Wiedmer & Caviezel, 2009; Grand 2010).
Text messaging as a tool for behavior change in disease prevention and management.

Mobile phone text messaging is a potentially powerful tool for behavior change because it is widely available, inexpensive, and instant. This systematic review provides an overview of behavior change interventions for disease management and prevention delivered through text messaging. Evidence on behavior change and clinical outcomes was compiled from randomized or quasi-experimental controlled trials of text message interventions published in peer-reviewed journals by June 2009. Only those interventions using text message as the primary mode of communication were included. Study quality was assessed by using a standardized measure. Seventeen articles representing 12 studies (5 disease prevention and 7 disease management) were included. Intervention length ranged from 3 months to 12 months, none had long-term follow-up, and message frequency varied. Of 9 sufficiently powered studies, 8 found evidence to support text messaging as a tool for behavior change. Effects exist across age, minority status, and nationality. Nine countries are represented in this review, but it is problematic that only one is a developing country, given potential benefits of such a widely accessible, relatively inexpensive tool for health behavior change. Methodological issues and gaps in the literature are highlighted, and recommendations for future studies are provided.
A vision for the future of genomics research

The completion of a high-quality, comprehensive sequence of the human genome, in this fiftieth anniversary year of the discovery of the double-helical structure of DNA, is a landmark event. The genomic era is now a reality. In contemplating a vision for the future of genomics research,it is appropriate to consider the remarkable path that has brought us here. The rollfold  shows a timeline of landmark accomplishments in genetics and genomics, beginning with Gregor Mendel’s discovery of the laws of heredity and their rediscovery in the early days of the twentieth century.Recognition of DNA as the hereditary material, determination of its structure, elucidation of the genetic code, development of recombinant DNA technologies, and establishment of increasingly automatable methods for DNA sequencing set the stage for the Human Genome Project (HGP) to begin in 1990 (see also www.nature.com/nature/DNA50). Thanks to the vision of the original planners, and the creativity and determination of a legion of talented scientists who decided to make this project their overarching focus, all of the initial objectives of the HGP have now been achieved at least two years ahead of expectation, and a revolution in biological research has begun. The project’s new research strategies and experimental technologies have generated a steady stream of ever-larger and more complex genomic data sets that have poured into public databases and have transformed the study of virtually all life processes. The genomic approach of technology development and large-scale generation of community resource data sets has introduced an important new dimension into biological and biomedical research. Interwoven advances in genetics, comparative genomics, highthroughput biochemistry and bioinformatics
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.

Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.
Software Engineering Challenges in Game Development

In Software Engineering (SE), video game development is unique yet similar to other software endeavors. It is unique in that it combines the work of teams covering multiple disciplines (art, music, acting, programming, etc.), and that engaging game play is sought after through the use of prototypes and iterations. With that, game development is faced with challenges that can be addressed using traditional SE practices. The industry needs to adopt sound SE practices for their distinct needs such as managing multimedia assets and finding the “fun” in game play. The industry must take on the challenges by evolving SE methods to meet their needs. This work investigates these challenges and highlights engineering practices to mitigate these challenges.
DEAP: a python framework for evolutionary algorithms

DEAP (Distributed Evolutionary Algorithms in Python) is a novel volutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition. Several examples illustrate the multiple properties of DEAP.
Improved routing strategies for Internet traffic delivery.

We analyze different strategies aimed at optimizing routing policies in the Internet. We first show that for a simple deterministic algorithm the local properties of the network deeply influence the time needed for packet delivery between two arbitrarily chosen nodes. We next rely on a real Internet map at the autonomous system level and introduce a score function that allows us to examine different routing protocols and their efficiency in traffic handling and packet delivery. Our results suggest that actual mechanisms are not the most efficient and that they can be integrated in a more general, though not too complex, scheme.
If you are not paying for it, you are the product: how much do advertisers pay to reach you?

Online advertising is progressively moving towards a programmatic model in which ads are matched to actual interests of individuals collected as they browse the web. Letting the huge debate around privacy aside, a very important question in this area, for which little is known, is: How much do advertisers pay to reach an individual?
 In this study, we develop a first of its kind methodology for computing exactly that - the price paid for a web user by the ad ecosystem - and we do that in real time. Our approach is based on tapping on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted prices for winning bids paid by advertisers in order to place targeted ads. Our main technical contribution is a method for tallying winning bids even when they are encrypted. We achieve this by training a model using as ground truth prices obtained by running our own "probe" ad-campaigns. We design our methodology through a browser extension and a back-end server that provides it with fresh models for encrypted bids. We validate our methodology using a one year long trace of 1600 mobile users and demonstrate that it can estimate a user's advertising worth with more than 82% accuracy.
Lightfield Recovery from Its Focal Stack

The Focal Stack Transform integrates a 4D lightfield over a set of appropriately chosen 2D planes. The result of such integration is an image focused on a determined depth in 3D space. The set of such images is the Focal Stack of the lightfield. This paper studies the existence of an inverse for this transform. Such inverse could be used to obtain a 4D lightfield from a set of images focused on several depths of the scene. In this paper, we show that this inversion cannot be obtained for a general lightfield and introduce a subset of lightfields where this inversion can be computed exactly. We examine the numerical properties of such inversion process for general lightfields and examine several regularization approaches to stabilize the transform. Experimental results are provided for focal stacks obtained from several plenoptic cameras. From a practical point of view, results show how this inversion procedure can be used to recover, compress, and denoise the original 4D lightfield.
IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type

Previous empirical studies examining the relationship between IT capability and accountingbased measures of firm performance report mixed results. We argue that extant research (1) has relied on aggregate overall measures of the firm’s IT capability, ignoring the specific type and nature of IT capability; and (2) has not fully considered important contextual (environmental) conditions that influence the IT capability-firm performance relationship. Drawing on the resource-based view (RBV), we advance a contingency perspective and propose that IT capabilities’ impact on firm resources is contingent on the “fit” between the type of IT capability/resource a firm possesses and the demands of the environment (industry) in which it competes. Specifically, using publicly available rankings as proxies for two types of IT capabilities (internally-focused and externally-focused capabilities), we empirically examines the degree to which three industry characteristics (dynamism, munificence, and complexity) influence the impact of each type of IT capability on measures of financial performance. After controlling for prior performance, the findings provide general support for the posited contingency model of IT impact. The implications of these findings on practice and research are discussed.
Adaptive Virtual Machine Management in the Cloud: A Performance-Counter-Driven Approach

The success of cloud computing technologies heavily depends on both the underlying hardware and system software support for virtualization. In this study, we propose to elevate the capability of the hypervisor to monitor and manage co-running virtual machines (VMs) by capturing their dynamic behavior at runtime and adaptively schedule and migrate VMs across cores to minimize contention on system resources hence maximize the system throughput. Implemented at the hypervisor level, our proposed scheme does not require any changes or adjustments to the VMs themselves or the applications running inside them, and minimal changes to the host OS. It also does not require any changes to existing hardware structures. These facts reduce the complexity of our approach and improve portability at the same time. The main intuition behind our approach is that because the host OS schedules entire virtual machines, it loses sight of the processes and threads that are running within the VMs; it only sees the averaged resource demands from the past time slice. In our design, we sought to recreate some of this low level information by using performance counters and simple virtual machine introspection techniques. We implemented an initial prototype on the Kernel Virtual Machine (KVM) and our experimental results show the presented approach is of great potential to improve the overall system throughput in the Cloud environment. Adaptive Virtual Machine Management in the Cloud: A Performance-Counter-Driven Approach
Interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal-journal citations

Using “Analyze Results” at the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Science Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling’s “quadratic entropy.” Since this indicator of interdisciplinarity is normalized between zero and one, the interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel et al.’s (2008) community-finding algorithms operating on the relations journals included in JCRs. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started and/or animated (e.g., using PowerPoint). The “citing” dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal literatures are further discussed: local and global maps are based on different assumptions and can be expected to serve different purposes for the explanation.
Forgery Detection Based on Intrinsic Document Contents

Nowadays, Document forgery detection is becoming increasingly important as forgery techniques are becoming available even to untrained users. Hence, documents that do not contain any extrinsic security features (e.g. invoices) have become easier to forge. We previously presented a method to detect manipulated documents based on distortions introduced during the forgery creation process. In this paper, several approaches are explored to improve accuracy and time taken to detect forgeries based on document distortions. The main idea behind the presented approaches is to automatically identify which parts of a document belong to the template (and hence would remain static across different documents originating from the same source) and then detect distortions in those parts only. An improvement up to 29% in accuracy of forgery detection is observed compared to our previous work. Furthermore, we also present an approximation of the original method that results in a reduction in run time of the method by several orders of magnitude, while having only a marginal reduction in its accuracy.
Comparison among dimensionality reduction techniques based on Random Projection for cancer classification

Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. However, the performance of RP is usually lower. We attempt to improve classification accuracy of RP through combining other reduction dimension methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Feature Selection (FS). We compared classification accuracy and running time of different combination methods on three microarray datasets and a simulation dataset. Experimental results show a remarkable improvement of 14.77% in classification accuracy of FS followed by RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield a more discriminative subspace with an increase of 13.65% on classification accuracy on the same dataset. FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.
Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.
Deliberate practice and the modifiability of body and mind : toward a science of the structure and acquisition of expert and elite performance

Some researchers in sports attribute elite performance to genetic talent. However, they do not offer complete genetic accounts that specify the causal processes involved in the activation and expression of the dormant genes in DNA during practice in the athletes' development that lead to the emergence of the distinctive physiological and anatomical attributes (innate talent). This article argues that it is possible to account for the development of elite performance among healthy children without recourse to unique talent (genetic endowment) — excepting the innate determinants of body size. This account based on the expert-performance approach shows that the distinctive characteristics of elite performers are adaptations to extended and intense practice activities that selectively activate dormant genes that all healthy children's DNA contain. The expert-performance approach has provided accounts for elite performance in several domains of expertise, such as music, ballet, chess, and medicine. This article shows how the superior performance of athletes can be captured and reproduced under laboratory conditions to discover the mechanisms mediating superior performance. The discovered mechanisms have, so far, been shown to reflect predominantly complex skills and physiological adaptations acquired over years and decades as a result of high daily levels of activities, which were specially designed to improve performance (deliberate practice). The second part of this article describes the development of expert performance in sports as an extended series of stable states of adaptation with associated physiological mechanisms that mediate performance. One section describes how frequent intense engagement in certain types of practice activities is shown to induce physiological strain which cause biochemical changes that stimulate growth and transformation of cells, which in turn leads to associated improved adaptations of physiological systems and the brain. A careful review of the published evidence on the heritability of acquisition of elite sports achievement failed to reveal reproducible evidence for any genetic constraints for attaining elite levels by healthy individuals (excluding, of course, the evidence on body size). The theoretical framework of expert performance explains individual differences in attained performance by the factors that influence the engagement in sustained extended deliberate practice, such as motivation, parental support, and access to the best training environments and teachers. Consequently, the development of expert performance will be primarily constrained, by individuals engagement in deliberate practice and the quality of the available training resources.
A secure location verification method for ADS-B

At the heart of the NextGen Air Traffic Control System, there is the ADS-B (Automatic Dependent Surveillance - Broadcast) that provides aircraft location data. Despite its importance, ADS-B has been developed without consideration of security risks and is subject to various types of attacks. Although a number of techniques have been proposed to protect ADS-B, there are many challenges remaining for applying them to real world. We have developed a practical method that can reject virtually all spoofed ADS-B messages by monitoring the radio propagation time between senders and receivers. To measure the actual propagation time, it uses a small timestamp value; hence we call it ADS-B with Timestamp (ADS-BT). ADS-BT monitors the discrepancy between the time of flight based on the timestamp values and the time of flight based on the location data. In spoofed ADS-B messages, the discrepancy between them diverges over time, which allows us to identify spoofed ADS-B messages accurately. Our study shows a 99% detection rate within a few seconds and 100% rate within 25 seconds, with 0% false positive ratio. ADS-BT does not require any special hardware or 3rd party stations to collaborate. It does not require synchronized clocks or cryptographic processing. In this paper, we describe how the time discrepancies are measured, how the timestamps are implemented and used, and how the spoofed message frames are rejected passively and actively. We then demonstrate its performance through simulation study under various attack scenarios. If ADS-BT is adopted in the NextGen ATC, the security risk for the ADS-B can be substantially reduced.
Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex

Object and face representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from a functional neuroimaging experiment. A pattern-based classification algorithm learned to categorize individual brain maps according to the object category being viewed by the subject. An identical algorithm learned to classify an image-based, view-dependent representation of the stimuli. High correlations were found between the confusability of object categories and the confusability of brain activity maps. This occurred even with the inclusion of multiple views of objects, and when the object classification model was tested with high spatial frequency line drawings of the stimuli. Consistent with a distributed representation of objects in VT cortex, the data indicate that object categories with shared image-based attributes have shared neural structure.
Reciprocal Velocity Obstacles for real-time multi-agent navigation

In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept , which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.