nils-herrmann/clean_scirepeval huggingface dataset:
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks

We introduce a new scene graph generation method called image-level attentional context modeling (ILAC). Our model includes an attentional graph network that effectively propagates contextual information across the graph using image-level features. Whereas previous works use an object-centric context, we build an imagelevel context agent to encode the scene properties. The proposed method comprises a single-stream network that iteratively refines the scene graph with a nested graph neural network. We demonstrate that our approach achieves competitive performance with the state-of-the-art for scene graph generation on the Visual Genome dataset, while requiring fewer parameters than other methods. We also show that ILAC can improve regular object detectors by incorporating relational image-level information.
A Survey on Antispoofing Schemes for Fingerprint Recognition Systems

Several issues related to the vulnerability of fingerprint recognition systems to attacks have been highlighted in the biometrics literature. One such vulnerability involves the use of artificial fingers, where materials such as Play-Doh, silicone, and gelatin are inscribed with fingerprint ridges. Researchers have demonstrated that some commercial fingerprint recognition systems can be deceived when these artificial fingers are placed on the sensor; that is, the system successfully processes the ensuing fingerprint images, thereby allowing an adversary to spoof the fingerprints of another individual. However, at the same time, several countermeasures that discriminate between live fingerprints and spoof artifacts have been proposed. While some of these antispoofing schemes are hardware based, several software-based approaches have been proposed as well. In this article, we review the literature and present the state of the art in fingerprint antispoofing.
Is smartphone addiction really an addiction?

In light of the rise in research on technological addictions and smartphone addiction in particular, the aim of this paper was to review the relevant literature on the topic of smartphone addiction and determine whether this disorder exists or if it does not adequately satisfy the criteria for addiction.  We reviewed quantitative and qualitative studies on smartphone addiction and analyzed their methods and conclusions to make a determination on the suitability of the diagnosis "addiction" to excessive and problematic smartphone use.  Although the majority of research in the field declares that smartphones are addictive or takes the existence of smartphone addiction as granted, we did not find sufficient support from the addiction perspective to confirm the existence of smartphone addiction at this time. The behaviors observed in the research could be better labeled as problematic or maladaptive smartphone use and their consequences do not meet the severity levels of those caused by addiction.  Addiction is a disorder with severe effects on physical and psychological health. A behavior may have a similar presentation as addiction in terms of excessive use, impulse control problems, and negative consequences, but that does not mean that it should be considered an addiction. We propose moving away from the addiction framework when studying technological behaviors and using other terms such as "problematic use" to describe them. We recommend that problematic technology use is to be studied in its sociocultural context with an increased focus on its compensatory functions, motivations, and gratifications.
Effects of a 6-month exercise program on patients with multiple sclerosis: a randomized study.

To improve walking and other aspects of physical function with a progressive 6-month exercise program in patients with multiple sclerosis (MS).



MS patients with mild to moderate disability (Expanded Disability Status Scale scores 1.0 to 5.5) were randomly assigned to an exercise or control group. The intervention consisted of strength and aerobic training initiated during 3-week inpatient rehabilitation and continued for 23 weeks at home. The groups were evaluated at baseline and at 6 months. The primary outcome was walking speed, measured by 7.62 m and 500 m walk tests. Secondary outcomes included lower extremity strength, upper extremity endurance and dexterity, peak oxygen uptake, and static balance. An intention-to-treat analysis was used.



Ninety-one (96%) of the 95 patients entering the study completed it. Change between groups was significant in the 7.62 m (p = 0.04) and 500 m walk tests (p = 0.01). In the 7.62 m walk test, 22% of the exercising patients showed clinically meaningful improvements. The exercise group also showed increased upper extremity endurance as compared to controls. No other noteworthy exercise-induced changes were observed. Exercise adherence varied considerably among the exercisers.



Walking speed improved in this randomized study. The results confirm that exercise is safe for multiple sclerosis patients and should be recommended for those with mild to moderate disability.
How does social software change knowledge management? Toward a strategic research agenda

0963-8687/$ see front matter 2012 Elsevier B.V http://dx.doi.org/10.1016/j.jsis.2012.04.003 q I am very grateful to the reviewer team at JSIS, Bo Sebastian Spaeth, and Stefan Haefliger, for excellent Science Foundation (Grant 100014-125513). ⇑ Tel.: +41 44 632 88 50; fax: +41 44 632 10 45. E-mail address: gvkrogh@ethz.ch Knowledge management is commonly understood as IS implementations that enable processes of knowledge creation, sharing, and capture. Knowledge management at the firm level is changing rapidly. Previous approaches included centrally managed, proprietary knowledge repositories, often involving structured and controlled search and access. Today the trend is toward knowledge management by social software, which provides open and inexpensive alternatives to traditional implementations. While social software carries great promise for knowledge management, this also raises fundamental questions about the very essence and value of firm knowledge, the possibility for knowledge protection, firm boundaries, and the sources of competitive advantage. I draft a strategic research agenda consisting of five fundamental issues that should reinvigorate research in knowledge management.
Machine learning methods in chemoinformatics

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.
Effects of increasing salinity on freshwater ecosystems in Australia.

Salt is a natural component of the Australian landscape to which a number of biota inhabiting rivers and wetlands are adapted. Under natural flow conditions periods of low flow have resulted in the concentration of salts in wetlands and riverine pools. The organisms of these systems survive these salinities by tolerance or avoidance. Freshwater ecosystems in Australia are now becoming increasingly threatened by salinity because of rising saline groundwater and modification of the water regime reducing the frequency of high-flow (flushing) events, resulting in an accumulation of salt. Available data suggest that aquatic biota will be adversely affected as salinity exceeds 1000 mg L (1500 EC) but there is limited information on how increasing salinity will affect the various life stages of the biota. Salinisation can lead to changes in the physical environment that will affect ecosystem processes. However, we know little about how salinity interacts with the way nutrients and carbon are processed within an ecosystem. This paper updates the knowledge base on how salinity affects the physical and biotic components of aquatic ecosystems and explores the needs for information on how structure and function of aquatic ecosystems change with increasing salinity. BT0215 Ef ect of s al ini ty on f r eshwat er ecosys t em s in A us t rali a D. L. Niel e etal
On Median Graphs: Properties, Algorithms, and Applications

ÐIn object prototype learning and similar tasks, median computation is an important technique for capturing the essential information of a given set of patterns. In this paper, we extend the median concept to the domain of graphs. In terms of graph distance, we introduce the novel concepts of set median and generalized median of a set of graphs. We study properties of both types of median graphs. For the more complex task of computing generalized median graphs, a genetic search algorithm is developed. Experiments conducted on randomly generated graphs demonstrate the advantage of generalized median graphs compared to set median graphs and the ability of our genetic algorithm to find approximate generalized median graphs in reasonable time. Application examples with both synthetic and nonsynthetic data are shown to illustrate the practical usefulness of the concept of median graphs. Index TermsÐMedian graph, graph distance, graph matching, genetic algorithm,
How Digital Nudges Influence Consumers - Experimental Investigation in the Context of Retargeting

Retargeting is an innovative online marketing technique in the modern age. Although this advertising form offers great opportunities of bringing back customers who have left an online store without a complete purchase, retargeting is risky because the necessary data collection leads to strong privacy concerns which in turn, trigger consumer reactance and decreasing trust. Digital nudges – small design modifications in digital choice environments which guide peoples’ behaviour – present a promising concept to bypass these negative consequences of retargeting. In order to prove the positive effects of digital nudges, we aim to conduct an online experiment with a subsequent survey by testing the impacts of social nudges and information nudges in retargeting banners. Our expected contribution to theory includes an extension of existing research of nudging in context of retargeting by investigating the effects of different nudges in retargeting banners on consumers’ behaviour. In addition, we aim to provide practical contributions by the provision of design guidelines for practitioners to build more trustworthy IT artefacts and enhance retargeting strategy of marketing practitioners.
Full on-chip CMOS low dropout voltage regulator using MOS capacitor compensation

In This paper, we presents a full on-chip and area efficient low-dropout voltage regulator (LDO) which, exploiting the technique nested miller compensation with active capacitor (NMCAC) to eliminate the external capacitor without compromising the stability of the system in the full output current range. The external capacitor is removed allowing for greater power system integration for system on-chip applications. The proposed LDO is that provides a fast transient response, a low quiescent current 51 μA with a dropout voltage of 192 mV and output variation 69 mV when a full load step 0-50 mA is applied. It designed in 0.18 μm CMOS process.
Software-Defined Network Function Virtualization: A Survey

Diverse proprietary network appliances increase both the capital and operational expense of service providers, meanwhile causing problems of network ossification. Network function virtualization (NFV) is proposed to address these issues by implementing network functions as pure software on commodity and general hardware. NFV allows flexible provisioning, deployment, and centralized management of virtual network functions. Integrated with SDN, the software-defined NFV architecture further offers agile traffic steering and joint optimization of network functions and resources. This architecture benefits a wide range of applications (e.g., service chaining) and is becoming the dominant form of NFV. In this survey, we present a thorough investigation of the development of NFV under the software-defined NFV architecture, with an emphasis on service chaining as its application. We first introduce the software-defined NFV architecture as the state of the art of NFV and present relationships between NFV and SDN. Then, we provide a historic view of the involvement from middlebox to NFV. Finally, we introduce significant challenges and relevant solutions of NFV, and discuss its future research directions by different application domains.
Clone Detection Using Abstract Syntax Suffix Trees

Reusing software through copying and pasting is a continuous plague in software development despite the fact that it creates serious maintenance problems. Various techniques have been proposed to find duplicated redundant code (also known as software clones). A recent study has compared these techniques and shown that token-based clone detection based on suffix trees is extremely fast but yields clone candidates that are often no syntactic units. Current techniques based on abstract syntax trees-on the other hand-find syntactic clones but are considerably less efficient. This paper describes how we can make use of suffix trees to find clones in abstract syntax trees. This new approach is able to find syntactic clones in linear time and space. The paper reports the results of several large case studies in which we empirically compare the new technique to other techniques using the Bellon benchmark for clone detectors
Seasonality of the common cutworm Spodoptera litura in a soybean ecosystem

Seasonal occurrence of the common cutworm Spodoptera litura (Fab.) (Lepidoptera: Noctuidae) moths captured in synthetic sex pheromone traps and associated field population of eggs and larvae in soybean were examined in India from 2009 to 2011. Male moths of S. litura first appeared in late July or early August and continued through October. Peak male trap catches occurred during the second fortnight of September, which was within soybean reproductive stages. Similarly, the first appearance of S. litura egg masses and larval populations were observed after the first appearance of male moths in early to mid-August, and were present in the growing season up to late September to mid-October. The peak appearance of egg masses and larval populations always corresponded with the peak activity of male moths recorded during mid-September in all years. Correlation studies showed that weekly mean trap catches were linearly and positively correlated with egg masses and larval populations during the entire growing season of soybean. Seasonal means of male moth catches in pheromone traps during the 2010 and 2011 seasons were significantly lower than the catches during the 2009 season. However, seasonal means of the egg masses and larval populations were not significantly different between years. Pheromone traps may be useful indicators of the onset of numbers of S. litura eggs and larvae in soybean fields.
Factors affecting running economy in trained distance runners.

Running economy (RE) is typically defined as the energy demand for a given velocity of submaximal running, and is determined by measuring the steady-state consumption of oxygen (VO2) and the respiratory exchange ratio. Taking body mass (BM) into consideration, runners with good RE use less energy and therefore less oxygen than runners with poor RE at the same velocity. There is a strong association between RE and distance running performance, with RE being a better predictor of performance than maximal oxygen uptake (VO2max) in elite runners who have a similar VO2max). RE is traditionally measured by running on a treadmill in standard laboratory conditions, and, although this is not the same as overground running, it gives a good indication of how economical a runner is and how RE changes over time. In order to determine whether changes in RE are real or not, careful standardisation of footwear, time of test and nutritional status are required to limit typical error of measurement. Under controlled conditions, RE is a stable test capable of detecting relatively small changes elicited by training or other interventions. When tracking RE between or within groups it is important to account for BM. As VO2 during submaximal exercise does not, in general, increase linearly with BM, reporting RE with respect to the 0.75 power of BM has been recommended. A number of physiological and biomechanical factors appear to influence RE in highly trained or elite runners. These include metabolic adaptations within the muscle such as increased mitochondria and oxidative enzymes, the ability of the muscles to store and release elastic energy by increasing the stiffness of the muscles, and more efficient mechanics leading to less energy wasted on braking forces and excessive vertical oscillation. Interventions to improve RE are constantly sought after by athletes, coaches and sport scientists. Two interventions that have received recent widespread attention are strength training and altitude training. Strength training allows the muscles to utilise more elastic energy and reduce the amount of energy wasted in braking forces. Altitude exposure enhances discrete metabolic aspects of skeletal muscle, which facilitate more efficient use of oxygen. The importance of RE to successful distance running is well established, and future research should focus on identifying methods to improve RE. Interventions that are easily incorporated into an athlete's training are desirable.
INTRODUCTION À LA THÉORIE DE GALOIS ET LA GÉOMÉTRIE ALGÉBRIQUE

x + a1x + · · ·+ an = 0. On s’interesse surtout à la résolution “par radicaux”, c’est-à-dire à la résolution qui n’utilise que des racines m √ a. Il est bien connu depuis le 16 siècle que l’on peut résoudre par radicaux des équations de degré n ≤ 4. Par contre, selon un résultat célèbre d’Abel, l’équation générale de degré n ≥ 5 n’est pas résoluble par radicaux. L’idée principale de la théorie de Galois est d’associer à chaque équation son groupe de symétrie. Cette construction permet de traduire des propriétés de l’équation (telles que la résolubilité par radicaux) aux propriétés du groupe associé. Le cours ne suivra pas le chemin historique. L’ouvrage [Ti 1, 2] est une référence agréable pour l’histoire du sujet.
A Methodology For Minimizing The Oscillations In Supply Chains Using System Dynamics And Genetic Algorithms

Supply Chain Management (SCM) is a critically significant strategy that enterprises depend on to meet challenges that they face because of highly competitive and dynamic business environments of today. Supply chain management involves the entire network of processes from procurement of raw materials/services/technologies to manufacturing or servicing intermediate products/services to converting them into final products or services and then distributing and retailing them till they reach final customers. A supply chain network by nature is a large and complex, engineering and management system. Oscillations occurring in a supply chain because of internal and/or external influences and measures to be taken to mitigate/minimize those oscillations are a core concern in managing the supply chain and driving an organization towards a competitive advantage. The objective of this thesis is to develop a methodology to minimize the oscillations occurring in a supply chain by making use of the techniques of System Dynamics (SD) and Genetic Algorithms (GAs). System dynamics is a very efficient tool to model large and complex systems in order to understand their complex, non-linear dynamic behavior. GAs are stochastic search algorithms, based on the mechanics of natural selection and natural genetics, used to search complex and non-linear search spaces where traditional techniques may be unsuitable.
How Retailers at different Stages of E-Commerce Maturity Evaluate Their Entry to E-Commerce Activities?

This paper investigates how retailers at different stages of e-commerce maturity evaluate their entry to e-commerce activities. The study was conducted using qualitative approach interviewing 16 retailers in Saudi Arabia. It comes up with 22 factors that are believed the most influencing factors for retailers in Saudi Arabia. Interestingly, there seem to be differences between retailers in companies at different maturity stages in terms of having different attitudes regarding the issues of using e-commerce. The businesses that have reached a high stage of e-commerce maturity provide practical evidence of positive and optimistic attitudes and practices regarding use of e-commerce, whereas the businesses that have not reached higher levels of maturity provide practical evidence of more negative and pessimistic attitudes and practices. The study, therefore, should contribute to efforts leading to greater e-commerce development in Saudi Arabia and other countries with similar context.
Managing Byzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario

While swarm robotics systems are often claimed to be highly faulttolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robotsÐi.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.
WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility

WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs an object-oriented representation of the model. The software offers a user-interface, based on dialogue boxes and menu commands, through which the model may then be analysed using Markov chain Monte Carlo techniques. In this paper we discuss how and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software’s design. We also discuss how the framework may be extended. It is possible to write specific applications that form an apparently seamless interface with WinBUGS for users with specialized requirements. It is also possible to interface with WinBUGS at a lower level by incorporating new object types that may be used by WinBUGS without knowledge of the modules in which they are implemented. Neither of these types of extension require access to, or even recompilation of, the WinBUGS source-code.