nils-herrmann/clean_scirepeval huggingface dataset:
Meaningful maps with object-oriented semantic mapping

For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The majority of research to date has addressed these mapping challenges separately, focusing on either geometric or semantic mapping. In this paper we address the problem of building environmental maps that include both semantically meaningful, object-level entities and point- or mesh-based geometrical representations. We simultaneously build geometric point cloud models of previously unseen instances of known object classes and create a map that contains these object models as central entities. Our system leverages sparse, feature-based RGB-D SLAM, image-based deep-learning object detection and 3D unsupervised segmentation.
A Complete Traceability System for a Wine Supply Chain Using Radio-Frequency Identification and Wireless Sensor Networks [Wireless Corner]

Radio-frequency identification (RFID) technology provides an effective tool for managing traceability along food supply chains. This is because it allows automatic digital registration of data, and therefore reduces errors and enables the availability of information on demand. A complete traceability system can be developed in the wine production sector by joining this technology with the use of wireless sensor networks for monitoring at the vineyards. A proposal of such a merged solution for a winery in Spain has been designed, deployed in an actual environment, and evaluated. It was shown that the system could provide a competitive advantage to the company by improving visibility of the processes performed and the associated control over product quality. Much emphasis has been placed on minimizing the impact of the new system in the current activities.
Financial Fraud , Director Reputation , and Shareholder Wealth *

We investigate the reputational impact of financial fraud for outside directors based on a sample of firms facing shareholder class action lawsuits. Following a financial fraud lawsuit, outside directors do not face abnormal turnover on the board of the sued firm but experience a significant decline in other board seats held. The decline in other directorships is greater for more severe cases of fraud and when the outside director bears greater responsibility for monitoring fraud. Interlocked firms that share directors with the sued firm exhibit valuation declines at the lawsuit filing. When fraud-affiliated directors depart from boards of interlocked firms, these firms experience a significant increase in valuation.
Emotional and psychophysiological responses to tempo , mode , and percussiveness

People often listen to music to influence their emotional state. However, the specific musical characteristics which cause this process are not yet fully understood. We have investigated the influence of the musical characteristics of tempo, mode, and percussiveness on our emotions. In a quest towards ecologically valid results, 32 participants listened to 16 pop and 16 rock songs while conducting an office task. They rated experienced arousal, valence, and tension, while skin conductance and cardiovascular responses were recorded. An increase in tempo was found to lead to an increase in reported arousal and tension and a decrease in heart rate variability. More arousal was reported during minor than major mode songs. Level and frequency of skin conductance responses increased with an increase in percussiveness. Physiological responses revealed patterns that might not have been revealed by self-report. Interaction effects further suggest that musical characteristics interplay in modulating emotions. So, tempo, mode, and percussiveness indeed modulate our emotions and, consequently, can be used to direct emotions. Music presentation revealed subtly different results in a laboratory setting, where music was altered with breaks, from those in a more ecologically valid setting where continuous music was presented. All in all, this enhances our understanding of the influence of music on emotions and creates opportunities seamlessly to tap into listeners’ emotional state through their physiological responses.
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning

In this paper, we propose a new framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model [Mnih et al. (2016)]) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents’ information [Lample & Chaplot (2016)]. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35% higher score than the second place.
Study of Nida ’ s ( formal and dynamic equivalence ) and Newmark ’ s ( semantic and communicative translation ) translating theories on two short stories

M.A. in Translation Studies, Working as a Translator E-mail: shabnam_shakernia@yahoo.com This study aimed to investigate the use of Nida’s formal and dynamic equivalence and Newmark’s Semantic and communicative translation on two short stories. The present study aimed to investigate which of these approaches are the main focuses of the translators in the translations of the two short stories. In order to systematically conduct the study, two short stories with their corresponding Persian translations were analyzed. The findings obtained from the analysis show that the readability of the translation especially in short stories is more important than preserving the original wording. Moreover, the findings manifest that these translations are also tried to have naturalness.
A theory of reading: from eye fixations to comprehension.

This article presents a model of reading comprehension that accounts for the allocation of eye fixations of college students reading scientific passages. The model deals with processing at the level of words, clauses, and text units. Readers make longer pauses at points where processing loads are greater. Greater loads occur while readers are accessing infrequent words, integrating information from important clauses, and making inferences at the ends of sentences. The model accounts forthe gaze duration on each word of text as a function of the involvement of the various levels of processing. The model is embedded in a theoretical framework capable of accommodating the flexibility of reading.
Advantages and disadvantages of scenario approaches for strategic foresight

Scenarios, as a prime technique of future studies, have long been used by government planners, corporate managers and military analysts as powerful tools to aid in decision making in the face of uncertainty. The idea behind them is to establish thinking about possible futures which can minimise surprises and broaden the span of managers’ thinking about different possibilities. Today the question of what scenarios are is unclear except with regard to one point they have become extremely popular. This paper attempts to shed light on differences in scenario approaches. It will describe the origin of scenarios and the development of different understandings and purposes for managers. Categories are developed to compare the different ways scenarios are performed. Finally, the advantages and disadvantages of scenario approaches are analysed.
Simple and Knowledge-intensive Generative Model for Named Entity Recognition

Almost all of the existing work on Named Entity Recognition (NER) consists of the following pipeline stages – part-of-speech tagging, segmentation, and named entity type classification. The requirement of hand-labeled training data on these stages makes it very expensive to extend to different domains and entity classes. Even with a large amount of hand-labeled data, existing techniques for NER on informal text, such as social media, perform poorly due to a lack of reliable capitalization, irregular sentence structure and a wide range of vocabulary. In this paper, we address the lack of hand-labeled training data by taking advantage of weak super vision signals. We present our approach in two parts. First, we propose a novel generative model that combines the ideas from Hidden Markov Model (HMM) and n-gram language models into what we call an N-gram Language Markov Model (NLMM). Second, we utilize large-scale weak supervision signals from sources such as Wikipedia titles and the corresponding click counts to estimate parameters in NLMM. Our model is simple and can be implemented without the use of Expectation Maximization or other expensive iterative training techniques. Even with this simple model, our approach to NER on informal text outperforms existing systems trained on formal English and matches state-of-the-art NER systems trained on hand-labeled Twitter messages. Because our model does not require hand-labeled data, we can adapt our system to other domains and named entity classes very easily. We demonstrate the flexibility of our approach by successfully applying it to the different domain of extracting food dishes from restaurant reviews with very little extra work.
Scalability Problems of Simple Genetic Algorithms

Scalable evolutionary computation has. become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithmnamely elitism, niching, and restricted mating are not significantly improving the scalability problems.
Crocodiles in the Regulatory Swamp: Navigating The Dangers of Outsourcing, SaaS and Shadow IT

Corporates are entering the brave new world of the internet and digitization without much regard for the fine print of a growing regulation regime. More traditional outsourcing arrangements are already falling foul of the regulators as rules and supervision intensifies. Furthermore, ‘shadow IT’ is proliferating as the attractions of SaaS, mobile, cloud services, social media, and endless new ‘apps’ drive usage outside corporate IT. Initial cost-benefit analyses of the Cloud make such arrangements look immediately attractive but losing control of architecture, security, applications and deployment can have far reaching and damaging regulatory consequences. From research in financial services, this paper details the increasing body of regulations, their inherent risks for businesses and how the dangers can be pre-empted and managed. We then delineate a model for managing these risks specifically focused on investigating, strategizing and governing outsourcing arrangements and related regulatory obligations.
Challenges of Microservices Architecture: A Survey on the State of the Practice

Microservices have been one of the fastest-rising trends in the development of enterprise applications and enterprise application landscapes. Even though various mapping studies investigated the open challenges around microservices from literature, it is difficult to have a clear view of existing challenges in designing, developing, and maintaining systems based on microservices architecture as it is perceived by practitioners. In this paper, we present the results of an empirical survey to assess the current state of practice and collect challenges in microservices architecture. Therefore, we synthesize the 25 collected results and produce a clear overview for answering our research questions. The result of our study can be a basis for planning future research and applications of microservices architecture.
Model-based transition optimization for a VTOL tailsitter

This paper addresses the problem of trajectory optimization for the transition of a Vertical Take-off and Landing (VTOL) tailsitter Unmanned Aerial Vehicle (UAV). The proposed strategy performs a model based optimization, where the model represents the closed-loop dynamics of the UAV with low-level control, ensuring attitude stabilization over the whole trajectory. We discuss the design of an optimization framework, vehicle modeling, and elaborate on the cost function construction. An additional feedback gain is implemented on the throttle channel with altitude discrepancies as its input to provide some level of robustness to wind disturbances. The overall approach is verified in simulation and experimental results with a focus on optimization of the back-transition (cruise-to-hover) of the Wingtra S100 VTOL tailsitter.
Taxonomy Construction Using Syntactic Contextual Evidence

Taxonomies are the backbone of many structured, semantic knowledge resources. Recent works for extracting taxonomic relations from text focused on collecting lexical-syntactic patterns to extract the taxonomic relations by matching the patterns to text. These approaches, however, often show low coverage due to the lack of contextual analysis across sentences. To address this issue, we propose a novel approach that collectively utilizes contextual information of terms in syntactic structures such that if the set of contexts of a term includes most of contexts of another term, a subsumption relation between the two terms is inferred. We apply this method to the task of taxonomy construction from scratch, where we introduce another novel graph-based algorithm for taxonomic structure induction. Our experiment results show that the proposed method is well complementary with previous methods of linguistic pattern matching and significantly improves recall and thus F-measure.
Defining and Designing Mixed Research Synthesis Studies.

Mixed research synthesis is the latest addition to the repertoires of mixed methods research and systematic review. Mixed research synthesis requires that the problems generated by the methodological diversity within and between qualitative and quantitative studies be resolved. Three basic research designs accommodate this diversity, including the segregated, integrated, and contingent designs. Much work remains to be done before mixed research synthesis can secure its place in the repertoires of mixed methods research and systematic review, but the effort is well worth it as it has the potential to enhance both the significance and utility for practice of the many qualitative and quantitative studies constituting shared domains of research.
Examining the Impact of Contextual Ambiguity on Search Advertising Keyword Performance : A Topic Model

A Topic Model Approach (with Abhishek, Vibhanshu and Beibei Li), Job Market Paper, invited for resubmission to Marketing Science. Substitution or Promotion? The Impact of Price Discounts on Cross-Channel Sales of Digital Movies (with Michael D. Smith, and Rahul Telang), conditionally accepted at the Journal of Retailing.
Attention U-Net: Learning Where to Look for the Pancreas

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The source code for the proposed architecture is publicly available.
Firefly Algorithm, Lévy Flights and Global Optimization

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
Single case studies vs . multiple case studies : A comparative study

There are several different definitions and kinds of case studies. Because of different reasons the case studies can be either single or multiple. This study attempts to answer when to write a single case study and when to write a multiple case study. It will further answer the benefits and disadvantages with the different types. The literature review, which is based on secondary sources, is about case studies. Then the literature review is discussed and analysed to reach a conclusion. The conclusion is that there are several different opinions if a single case study or a multiple case study is the best choice. Different causes to consider in the choice to make a single case study or a multiple case study are presented. Some causes are that the amount depends on the context, upon how much is known and how much new information the cases bring. Another conclusion from the case studies I looked among is that it is generally more number of pages in the multiple case studies than in the single case studies.
Detection of Violations in Credit Cards of Banks and Financial Institutions based on Artificial Neural Network and Metaheuristic Optimization Algorithm

Due to popularity of the World Wide Web and ecommerce, electronic communications between people and different organizations through virtual world of the Internet have provided a good basis for commercial and economic relations. These developments, although occurring for less than a century, electronic communications have always been subject to interference, cheating, fraud, and other acts of sabotage. Along with this increase in trading volume, there is a huge increase in the number of online fraud which results in billions of dollars of losses annually worldwide; this has a direct effect on customer service of banking systems, particularly electronic banking systems, and survival as a reliable financial service provider. Therefore, attention to fraud detection techniques is essential to prevent fraudulent acts and is the motive for many scientific researches. For this reason, business intelligence is used to identify financial violations in various economic, banking and other fields. Here, the focus is on algorithms and methods presented in data mining to deal with fraud by using neural networks. The main objective is to improve these methods or present new algorithms by studying the behavioral patterns of customers and the combined use of genetic algorithm to improve the performance of neural network and find the appropriate models for better decision making by implementing and testing the performance of the suggested algorithms. The results show that more strength was given to neural network by using genetic algorithm. In fact, genetic algorithm can raise our ability to control the training process. Moreover, it was concluded that criteria such as age, gender, marital status were not effective on detection; in fact, the most important effective criteria are information related to transaction. Financial fraud detection; neural networks; data mining; genetic algorithm
Direct evidence of quantum transport in photosynthetic light-harvesting complexes.

The photosynthetic light-harvesting apparatus moves energy from absorbed photons to the reaction center with remarkable quantum efficiency. Recently, long-lived quantum coherence has been proposed to influence efficiency and robustness of photosynthetic energy transfer in light-harvesting antennae. The quantum aspect of these dynamics has generated great interest both because of the possibility for efficient long-range energy transfer and because biology is typically considered to operate entirely in the classical regime. Yet, experiments to date show only that coherence persists long enough that it can influence dynamics, but they have not directly shown that coherence does influence energy transfer. Here, we provide experimental evidence that interaction between the bacteriochlorophyll chromophores and the protein environment surrounding them not only prolongs quantum coherence, but also spawns reversible, oscillatory energy transfer among excited states. Using two-dimensional electronic spectroscopy, we observe oscillatory excited-state populations demonstrating that quantum transport of energy occurs in biological systems. The observed population oscillation suggests that these light-harvesting antennae trade energy reversibly between the protein and the chromophores. Resolving design principles evident in this biological antenna could provide inspiration for new solar energy applications.
Gamification by Design - Implementing Game Mechanics in Web and Mobile Apps

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