nils-herrmann/clean_scirepeval huggingface dataset:
Elysium: Automagically Healing Vulnerable Smart Contracts Using Context Aware Patching
Venue: ArXiv
Year: 2021

Smart contracts are programs that are deployed and executed on the blockchain. Typically, smart contracts govern assets; popular smart contracts can govern assets worth millions. Just like traditional programs, smart contracts are subject to programming mistakes. However, a major difference is that smart contracts cannot be patched once deployed. Several approaches have been studied to improve smart contract security, by detecting bugs prior to deployment, allowing contracts to be updated, or modifying clients to prevent attacking transactions. The most elegant solution would be to automatically eliminate bugs prior to deployment. Merely identifying bugs is not enough. This became evident when the Parity wallet was hacked a second time after being manually patched following a security audit. Automatic pre deployment patching offers a powerful promise to strengthen smart contract defenses. Current approaches are limited in the types of vulnerabilities that can be patched, in the flexibility of the patching process, and in scalability. In this paper we propose ELYSISUM, a scalable approach towards automatic smart contract repair, that combines template based patching with semantic patching by inferring context information from the bytecode. ELYSIUM can currently automatically patch 7 known vulnerabilities in smart contracts using external bugfinding tools, and it can easily be extended with new templates and new bug finding tools. We evaluate effectiveness and correctness of ELYSIUM using 3 different datasets by replaying more than 500K transactions against patched contracts. We find that ELYSIUM outperforms existing tools by patching at least 30% more contracts. Finally, we compare the overhead in terms of deployment and transaction cost increase. In comparison to other tools, ELYSIUM minimizes transaction cost (up to a factor of 1.9) for only a marginally higher deployment cost.
Performing Design Thinking Virtually A Socio Cognitive View on Virtual Design Thinking
Year: 2020

In today's world, corporate teamwork is increasingly characterized by co creation and dispersed stakeholders that need to collaborate for specific purposes, such as innovation development (Frow et al. 2015;  2013) The dispersed setting of team members motivates a virtual instead of an on site performance of teamwork to increase the possibility of uniting multiple and suitable stakeholders for specific tasks, while reducing the efforts to commonly be in the same place at the same time (Lipnack Stamps, 1999) To meet the needs of a contemporary, systematic procedure for Innovation Management in a globalized world, the collaborative and co creative innovation development approach Design Thinking (DT) needs to be facilitated toward a virtual performance (Furmanek Daurer, 2019; Grasler et al. 2017; Gumienny et al. 2011) In order to prepare the foundation for a Virtual Design Thinking (VDT) approach, an in depth examination of commonly analog performed DT was conducted, and its underlying principles identified. The examinations on DT enabled to further investigate how the innovation development approach can be applied virtually. This undertaking was accompanied by measurements that indicate the success of the VDT performance in adhering to a socio cognitive view on effectiveness (Gilson et al. 2015; Hjorland, 2002) With the aim to enable VDT, the research methodology Action Design Research was applied, which deploys practical, recurring, and meaningful interventions to gain insights regarding the collaboration of team members in a process of improving Information Systems in corporate problem contexts from a user centered perspective (Sein et al. 2011; Veling et al. 2016) The emerging ensemble artifact, the VDT approach, mirrors an improved setting of given, real life corporate problems (Purao et al. 2013) As a result, this dissertation presents an applicable VDT approach for companies that is perceived as satisfying and effective by the team members.
Packet Level and Physical Level Cross Technology Communications: A Brief Introduction
Year: 2020

In the Internet of things, cross technology communication (CTC) enables direct communication between heterogeneous devices without a gateway. CTC is receiving growing attention, because it can significantly reduce the hardware cost and the complexity of network deployment for communication between heterogeneous devices, as well as improve the network performance. At present, there are two types of CTC techniques, namely packet level CTC and physical level CTC. In this paper, we first provide an overview of the two types of techniques. Then, we detail the packet level CTC via two typical protocols: Freebee and ZigFi, and the physical level CTC via a typical protocol: WeBee. Finally, we compare the two CTC techniques in terms of hardware cost, communication direction, throughput, spectrum efficiency, and parallelism.
Influence of Surface Texture on Interface Bond Shear Strength of Asphalt Concrete
Year: 2020

The bonding strength provided by the tack coat between asphalt concrete layers at the interface is considered as an essential issue in the performance of the flexible pavement throughout its service life. However, the surface texture of the pavement surface exhibits another essential issue in the bond strength. In this investigation, three layers of asphalt concrete (base, binder and wearing) courses with (25, 19, and 12.5) mm of nominal maximum size of aggregates have been prepared using roller compactor. Rapid curing cutback RC 70 and cationic medium setting emulsion CMS have been implemented as tack coats with three application rates. The slab specimens were subjected to mean texture depth determination using sand patch method. Core specimens of 102 mm diameter were obtained from the prepared slab samples and subjected to interface bond strength test with the aid of a special manufactured mold. Test results were analyzed and compared. It was concluded that when RC70 tack coat was implemented, asphalt concrete binder course laid on base course exhibited the highest shear strength of 1600 kPa at an application rate of 0.33 liter/m2 when compared to other application rates. Asphalt concrete wearing course laid on binder course exhibited the highest shear strength of 1515 kPa at an application rate of 0.15 liter/m2 when compared to other application rates. When CMS tack coat was implemented, asphalt concrete binder course laid on base course exhibited the highest shear strength of 1620 kPa at an application rate of 0.23 liter/m2 when compared to other application rates. Finally, the asphalt concrete wearing course laid on binder course exhibited the highest shear strength of 2272 kPa at an application rate of 0.23 liter/m2 when compared to other application rates.
Clyde: A Deep Reinforcement Learning DOOM Playing Agent
Venue: AAAI Workshops
Year: 2017

In this paper we present the use of deep reinforcement learning techniques in the context of playing partially observable multi agent 3D games. These techniques have traditionally been applied to fully observable 2D environments, or navigation tasks in 3D environments. We show the performance of Clyde in comparison to other competitors within the context of the ViZDOOM competition that saw 9 bots compete against each other in DOOM death matches. Clyde managed to achieve 3rd place in the ViZDOOM competition held at the IEEE Conference on Computational Intelligence and Games 2016. Clyde performed very well considering its relative simplicity and the fact that we deliberately avoided a high level of customisation to keep the algorithm generic.
Identity, morals, and taboos: beliefs as assets.
Venue: The quarterly journal of economics
Year: 2011

We develop a theory of moral behavior, individual and collective, based on a general model of identity in which people care about "who they are" and infer their own values from past choices. The model sheds light on many empirical puzzles inconsistent with earlier approaches. Identity investments respond nonmonotonically to acts or threats, and taboos on mere thoughts arise to protect beliefs about the "priceless" value of certain social assets. High endowments trigger escalating commitment and a treadmill effect, while competing identities can cause dysfunctional capital destruction. Social interactions induce both social and antisocial norms of contribution, sustained by respectively shunning free riders or do gooders.
Maximum likelihood approach to speech enhancement for noisy reverberant signals
Venue: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2008

This paper proposes a speech enhancement method for signals contaminated by room reverberation and additive background noise. The following conditions are assumed: (1) The spectral components of speech and noise are statistically independent Gaussian random variables. (2) The convolutive distortion channel is modeled as an auto regressive system in each frequency bin. (3) The power spectral density of speech is modeled as an all pole spectrum, while that of noise is assumed to be stationary and given in advance. Under these conditions, the proposed method estimates the parameters of the channel and those of the all pole speech model based on the maximum likelihood estimation method. Experimental results showed that the proposed method successfully suppressed the reverberation and additive noise from three second noisy reverberant signals when the reverberation time was 0.5 seconds and the reverberant signal to noise ratio was 10 dB.
Moral Distress During COVID 19: Residents in Training Are at High Risk
Venue: AEM education and training
Year: 2020

The ethics and moral dilemmas of rationing care are particularly acute to all health care providers as we tackle the COVID19 crisis. In normal times, first responders and clinicians face moral angst in situations when they are not able to follow their inner values, however studies show this angst is highly intensified in a disaster setting.1 3Why? Public health emergencies may require clinicians to change their practice, such as the need to prioritize the community above the individual in fairly allocating scarce resources.
Eliminating overfitting of probabilistic topic models on short and noisy text: The role of dropout
Venue: Int. J. Approx. Reason.
Year: 2019

Probabilistic topic models are powerful tools for discovering hidden structures/semantics in discrete data, e.g. texts, images, links. However, on short and noisy texts, directly applying topic models may not work well or face severe overfitting. In this article, we investigate the benefits of dropout for preventing topic models from overfitting. We integrate dropout into several stochastic methods for learning latent Dirichlet allocation (LDA) From extensive experiments on four large scale datasets, our findings are: (1) dropout helps to prevent overfitting and significantly enhance predictiveness and generalization of LDA on short texts; (2) for long documents, dropout may provide little benefit; (3) dropout can be easily integrated into any learning methods to avoid overfitting for short and noisy text. Furthermore, dropout can be straightforwardly employed in a wide range of topic models. In evidence, we apply dropout to BTM (Biterm topic model) one of the state of the art models for short texts. Our experiments illustrate that BTM with dropout not only remains its good results in term of predictiveness, but also improves the learning time significantly.
Corporate image or social engagement: Twitter discourse on corporate social responsibility (CSR) in public relations strategies in the energy sector
Year: 2020

Social media have opened up new opportunities for the creation of innovative public relations strategies focused on establishing and cultivating relationships with stakeholders on the basis of meaningful dialogue. Consideration of the interrelation between corporate social responsibility (CSR) and public relations highlights new areas for exploration and engagement. Both the dialogical and semantic perspectives reveal the performative and conversational aspects of social media. In general, both the linguistic panorama of CSR and digital media as part of a PR strategy open new possibilities for a dialogical, interactive, meaningful relationship strategy for corporate image management. Based on the linguistic approach to CSR and the Communication Management Approach, this paper explores the linguistic use of Twitter as a primary dialogical strategy to effectively enhance interactive dialogue based relationships with the stakeholders of the top 50 companies in the energy sector based on tweet data from 2016. Semantic analysis was conducted by advanced text mining and clustering techniques on 3042 tweets monitored in 2017 that contained the leading CSR related hashtags and keywords. The results demonstrated that the top energy companies apply a defensive and symbolic perspective, mainly for branding purposes. The corporate discourse dominates over a meaningful conversational strategy to foster interaction with stakeholders around sustainability issues on Twitter. The study reveals a homogenized interrelation between CSR, social media, and public relations. The results reveal a tendency for isomorphy in the communication models applied by the companies in the energy sector. Furthermore, similarities in semantics and thus strong tendencies to mutually mimic dialogical strategies are also observed. The semantic narrative built around the brand indicates a limited orientation towards CSR and sustainability. As such, it does not contribute to the creation of a dialogical interaction and meaningful relationships with multiple stakeholders on Twitter, in the high risk sector represented by the energy industry.
Climate change, ambient ozone, and health in 50 US cities
Year: 2007

We investigated how climate change could affect ambient ozone concentrations and the subsequent human health impacts. Hourly concentrations were estimated for 50 eastern US cities for five representative summers each in the 1990s and 2050s, reflecting current and projected future climates, respectively. Estimates of future concentrations were based on the IPCC A2 scenario using global climate, regional climate, and regional air quality models. This work does not explore the effects of future changes in anthropogenic emissions, but isolates the impact of altered climate on ozone and health. The cities' ozone levels are estimated to increase under predicted future climatic conditions, with the largest increases in cities with present day high pollution. On average across the 50 cities, the summertime daily 1 h maximum increased 4.8 ppb, with the largest increase at 9.6 ppb. The average number of days/summer exceeding the 8 h regulatory standard increased 68% Elevated ozone levels correspond to approximately a 0.11% to 0.27% increase in daily total mortality. While actual future ozone concentrations depend on climate and other influences such as changes in emissions of anthropogenic precursors, the results presented here indicate that with other factors constant, climate change could detrimentally affect air quality and thereby harm human health.
Nonlinear Correlation of Stock and Commodity Indices in Emerging and Developed Market
Year: 2013

The interrelationship between stock and commodity markets has been an issue of interest for both the academia and practitioners in the field of investment and wealth management. Traditionally, commodity has been a popular avenue for diversification in a mixed portfolio. However, this works well as long as there is little or no correlation between the two markets. This chapter presents an empirical investigation of the daily movement of stock and commodity index of two different countries to throw some light on the interrelationship between stock and commodity market. The uniqueness of this study lies in the choice of markets as also the methodology. The authors have chosen a developed market, viz. the US market, and an emerging market, viz. the Indian market. This study uses the major stock and commodity indices respectively for both countries for a period of three years. For analysis the authors have used the tools from nonlinear dynamics like recurrence analysis, power spectrum analysis, and delay based crosscorrelation function. The investigation revealed that the dynamics of the time path of daily movement of Indian stock and commodity exchanges are much similar in nature while those of the US market are quite different. This chapter also models the respective time series using Geometric Brownian Motion and finds that the Indian data set performed much better than the US ones. This has a strong impact on strategy for designing mixed portfolios in Indian market.
The traffic signal control problem for intersections: a review
Year: 2020

The intersection traffic signal control problem (ITSCP) has become even more important as traffic congestion has been more intractable. The ITSCP seeks an efficient schedule for traffic signal settings at intersections with the goal of maximizing traffic flow while considering various factors such as real time strategies, signal timing constraints, rapid developments in traffic systems, and practical implementation. Since the factors constituting the ITSCP exhibit stochastically complicated interactions, it is essential to identify these factors to propose solution methods that can address this complexity and still be practically implemented.  The objective of this review is to provide a survey of problems, methods, and practices in the evaluation of the ITSCP. In this paper, a unified terminology for the ITSCP and a citation network of the current body of relevant research are accordingly presented, and various assumptions, constraints, and solution approaches are summarized. A review across the entire body of knowledge throughout the history of the ITSCP is therefore provided. This review also highlights open issues and challenges that remain to be addressed by future research.
The mental turmoil of hospital nurses in the COVID 19 pandemic.
Venue: Psychological trauma theory, research, practice and policy
Year: 2020

At this time, nurses within hospitals are working hard, but they potentially will have long term mental health effects as a result of the 2019 novel coronavirus (COVID 19) pandemic. Both short term interventions, such as daily huddles and debriefings, and long term interventions, including follow ups on the mental health of nurses, need to be implemented to prevent mental disorders among nurses during and after the pandemic.
Training needs of businesses in the retail industry: A business case in the United Arab Emirates
Venue: Journal of Research in Emerging Markets
Year: 2019

People in the organization are said to be an important asset. They need to be equipped with the needed skills to contribute to become abreast with the latest trends and demands in the industry. This research focused on an inquiry about how to make the retail industry employees skillful and relevant in the industry. The quantitative results have indicated the significance of training employees in terms of how well they apply the necessary skills at their workplaces. People who have been trained on various skills use the required skills to complete their jobs, which translates effectively to positive productivity, quality output, and satisfactory customer services. Apart from providing services to clients, employees are the cornerstone of productivity and implement the policies of the organization based on its mission, vision and core values. The results have also indicated that the majority of the employees lack the skills needed in executing daily activities. This study is significant in that it has proved beyond doubt that training needs analysis is the first step towards understanding the skill gap in an organization. That way, the company can create the needed measures and strategies for training to equip its employees with the necessary skills for increased production, quality, and improved customer services. This could result in a seamless operation that would significantly cut the overall cost of operations for the firm.
Automatic Understanding of the Visual World
Venue: SIGIR
Year: 2019

One of the central problems of artificial intelligence is machine perception, i.e. the ability to understand the visual world based on input from sensors such as cameras. In this talk, I will present recent progress of my team in this direction. I will start with presenting results on how to generate additional training data using weak annotations, motion information and synthetic data. Next, I will discuss our results for action recognition in videos, where human tubelets have shown to be successful. Our tubelet approach moves away from state of the art frame based approaches and improves classification and localization by relying on joint information from several frames. We show how to extend this type of method to weakly supervised learning of actions, which allows us to scale to large amounts of data with sparse manual annotation. Finally, I will present recent work on grasping with a robot arm based on learning long horizon manipulations with a hierarchy of RL and imitation based skills.
Expected number of real zeroes of random Taylor series
Venue: Communications in Contemporary Mathematics
Year: 2019

Let  see text] be i.i.d. random variables with zero mean and unit variance. Consider a random Taylor series of the form  see text] where  see text] is a real sequence such that  see text] is regularly varying with index  see text] where  We prove that  see text] where  see text] denotes the number of real zeroes of  see text] in the interval
"The Great Turkes Defiance" Revisited
Year: 2019

Four decades after his monograph on the apocryphal correspondence of the Ottoman sultan was published, the author reviews the previous study of the subject, the origins of his book, its skeptical reception then, and the current acceptance of its main argument that most of the Russian versions of that correspondence are translations from Western European pamphlets and newspapers. Recent scholarship has located additional proof, and the current article presents further information which should help identify the sources for some of the Russian texts.
Climate change effects and adaptation strategies in the wine sector: a quantitative literature review
Year: 2016

This paper presents a quantitative literature review focusing on how scientific research analysed climate change impact on wine chain as well as potential adaptation strategies. The work is based on content analysis and text mining and takes into account researches from 1990 to 2015. A particular emphasis was given to the evaluation of suggested or implemented adaptation strategies at both global and national levels. Data were analysed using cluster analysis, multidimensional scaling and specificity evaluation. Results show that the study of climate change impacts on the wine sector is a recently emerging research topic. Adaptation strategies have not yet been explored thoroughly in the literature, and in depth uncertainty quantification is also needed. Finally, additional research gaps and potential future issues are suggested.