nils-herrmann/clean_scirepeval huggingface dataset:
Policy Uncertainty and Corporate Investment
Year: 2015

Using a news based index of policy uncertainty, we document a strong negative relationship between firm level capital investment and the aggregate level of uncertainty associated with future policy and regulatory outcomes. More importantly, we find evidence that the relation between policy uncertainty and capital investment is not uniform in the cross section, being significantly stronger for firms with a higher degree of investment irreversibility and for firms that are more dependent on government spending. Our results lend empirical support to the notion that policy uncertainty can depress corporate investment by inducing precautionary delays due to investment irreversibility. Received January 2, 2014; accepted July 27, 2015 by Editor David Denis.
Sign changes of a product of Dirichlet character and Fourier coefficients of half integral weight modular forms
Year: 2017

Let $f\in S_{k+1/2}(N,\chi) be a Hecke eigenform of half integral weight $k+1/2\(k\geq 2) and the real nebentypus \chi=\pm 1$ where the Fourier coefficients $a(n) are reals. We prove that the sequence \chi(p^\nu})a(tp^{2\nu}}_{\nu\in\N} has infinitely many sign changes for almost all primes $p$ where $t$ is a squarefree integer such that $a(t)\neq 0$ The same result holds for the sequences of Fourier coefficients {a(tp^{2(2\nu+1)}_{\nu\in\N} and {a(tp^{4\nu}}_{\nu\in\N}
Improved PSO based K Means Clustering Applied to Fault Signals Diagnosis
Venue: 2018 International Conference on Control, Automation and Diagnosis (ICCAD)
Year: 2018

In this paper, we attempt to detect and diagnose anomalous from fault signals in industrial processes using K means clustering technique with Particle Swarm Optimization (PSO)algorithm. In addition to healthy (normal)engines, we consider three types of engine faults: joints problem, faulty bearings and mechanical loosening. Data are characterized by six discriminant variables and the aim is to classify them into four classes. To carry out this analysis, three algorithms are used. First, conventional K means is applied to the considered data. In a second approach, K means is combined with the PSO technique. The improvement proposed in the third algorithm concerns the use of PSO based K means with Global K means initialization. The performances of our approach are improved and the results compare favorably with the use of the traditional K means algorithm.
Political Risk Factors on Perfomance of Public Private Partnership Renewable Energy Projects: The Case of Geothermal Renewable Enrgy Projects in Kenya
Year: 2020

To solve the problem of limited fiscal funds and demand for energy, PPP has been touted as an effective approach. However political risks have been blamed for failure to attract private investments in equally measure as their developed partners. This article looked at political risks influencing the performance of PPP renewable energy projects in Kenya. The study adopted a pragmatic paradigm and employed a mixed methods approach, correlational and descriptive survey design. Quantitative data was collected by use of a self administered questionnaire and an interview guide was used to collect qualitative data. A sample size of 263 respondents was drawn from a target population of 769 using the Yamane formula. For descriptive statistics the study used the mean and standard deviation. For inferential statistics the study used Pearson's Product Moment Correlation (r) and Multiple Regression while the F tests were used in hypothesis testing. The study established a significant influence of political risks r 0.572, F (1,205) 99.771, R2 0.327 at p<.05 H0 was rejected. The study concluded that there was a significant influence of political risks on the performance of PPP renewable energy projects in Kenya. Based on this finding the study recommends sustainable political stability.
Reporting bias and knowledge acquisition
Venue: AKBC '13
Year: 2013

Much work in knowledge extraction from text tacitly assumes that the frequency with which people write about actions, outcomes, or properties is a reflection of real world frequencies or the degree to which a property is characteristic of a class of individuals. In this paper, we question this idea, examining the phenomenon of reporting bias and the challenge it poses for knowledge extraction. We conclude with discussion of approaches to learning commonsense knowledge from text despite this distortion.
Improved criteria for sampled data synchronization of chaotic Lur'e systems using two new approaches
Year: 2017

In this paper, the problem of the sampled data synchronization of two identical chaotic Lur'e systems is studied in which the controller is designed by using sampled output of the systems with variable sampling rates. Two novel approaches, sampling instant to present time fragmentation and free matrix based time dependent discontinuous Lyapunov approach, are proposed for the first time. The first one makes us to utilize more information of inner sampling behavior and later one helps handling a hybrid feature of sampled data control systems. Based on the presented two new approaches, novel synchronization conditions are developed in the form of linear matrix inequalities. The obtained conditions ensure the master slave synchronization of chaotic Lur'e systems under larger sampling period than remarkable existing works which can be found by two numerical examples.
DEIXIS IN INAUGURATION SPEECH OF PRESIDENT SUSILO BAMBANG YUDHOYONO
Year: 2015

This research deals with the English deixis. The problems of this research are to explain the types of deixis is most dominat found in Inauguration Speech of President Susilo Bambang Yudhoyono to find out the process of deixis in inauguration speech and to find out the reason of deixis in inauguration speech of President Susilo Bambang Yudhoyono are in such a way. The objectives of this research are to find out the dominant types of deixis, to know the process of deixis and to know the reason of using deixis in the speech of President Susilo Bambang Yudhoyono. This research uses the content analysis qualitative research design The source of the data is the inauguration speech of President Susilo Bambang Yudhoyono. The technique for collecting data is documentary technique The instrument for collecting data is documentary sheet. And the technique for analysing data is content analysis. The findings show five types of deixis in the first and the second inauguration speech. From the analysis of the inauguration speech tends to use personal deixis as the dominant type of deixis In the first inauguration speech, President Susilo Bambang Yudhoyono is dominant uses plural pronoun "kita(we)" while in the second inauguration speech, President Susilo Bambang Yudhoyono uses first Personal Pronoun "saya (I)" The reasons of using deixis in inauguration speech of President Susilo Bambang Yudhoyono are to point out or to indicate someone or thing to get information clearly from the inauguration speech Key words Deixis,
PENGARUH WORD OF MOUTH DAN SERVICE QUALITY TERHADAP CUSTOMER LOYALTY DENGAN CUSTOMER SATISFACTION SEBAGAI VARIABEL INTERVENING PADA MADU TIGA BEACH AND RESORT
Year: 2020

Broadly speaking, this study aims to determine the effect of word of mouth and service quality partially on loyalty or customer loyalty, the effect of word of mouth and service quality partially on satisfaction or customer satisfaction, the effect of word of mouth on customer loyalty through customer satisfaction, influence the effect of service quality on customer loyalty through customer satisfaction and the influence of customer satisfaction on customer loyalty. The population in this study were all customers of Madu Tiga Beach and Bintan Resort. Determination of the sample using random incidental sampling method, with sampling techniques using iteration formula to obtain 100 respondents. Data collection methods used were questionnaire questionnaire. The analytical methods used in this study are descriptive test, data quality test, classic assumption test, path analysis and hypothesis testing. The results of the analysis in this study indicate that word of mouth partially significantly influences customer loyalty, service quality partially does not significantly influence customer loyalty. Customer satisfaction is able to mediate between word of mouth and service quality on customer loyalty and customer satisfaction partially affect customer loyalty. Based on research results, Madu Tiga Beach and Resort Bintan is expected to continue to increase customer loyalty by taking into account the factors that support and support service quality and the effect of word of mouth itself.
Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint Loss Convolutional Neural Network
Venue: IEEE Transactions on Industrial Informatics
Year: 2020

Fault diagnosis and remaining useful life (RUL) prediction are always two major issues in modern industrial systems, which are usually regarded as two separated tasks to make the problem easier but ignore the fact that there are certain information of these two tasks that can be shared to improve the performance. Therefore, to capture common features between different relative problems, a joint loss convolutional neural network (JL CNN) architecture is proposed in this paper, which can implement bearing fault recognition and RUL prediction in parallel by sharing the parameters and partial networks, meanwhile keeping the output layers of different tasks. The JL CNN is constructed based on a CNN, which is a widely used deep learning method because of its powerful feature extraction ability. During optimization phase, a JL function is designed to enable the proposed approach to learn the diagnosis prognosis features and improve generalization while reducing the overfitting risk and computation cost. Moreover, because the information behind the signals of different problems has been shared and exploited deeper, the generalization and the accuracy of results can also be improved. Finally, the effectiveness of the JL CNN method is validated by run to failure dataset. Compared with support vector regression and traditional CNN, the mean square error of the proposed method decreases 82.7$ and 24.9$ respectively. Therefore, results and comparisons show that the proposed method can be applied for the intercrossed applications between fault diagnosis and RUL prediction.
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
Venue: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Year: 2020

Maneuvering in dense traffic is a challenging task for autonomous vehicles because it requires reasoning about the stochastic behaviors of many other participants. In addition, the agent must achieve the maneuver within a limited time and distance. In this work, we propose a combination of reinforcement learning and game theory to learn merging behaviors. We design a training curriculum for a reinforcement learning agent using the concept of level k behavior. This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies. We show that our approach learns more efficient policies than traditional training methods.
SLA aware Virtual Machine Scheduling in OpenStack based Private Cloud
Venue: 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS)
Year: 2018

Cloud computing is a distributed platform that offers dynamic provisioning of on demand resources to the users. The rapid growth of cloud computing is due to its adaptive nature that brings enormous benefits to the organizations. OpenStack is a cloud operating system used to build public and private clouds. OpenStack consists of a number of interconnected entities that are combined to govern the processing of compute, network and storage resources. Resource scheduling in OpenStack deals with provisioning of compute resources. Nova scheduler is a component of OpenStack Compute service which is responsible for resource scheduling. The default nova scheduler used in OpenStack is Filter Scheduler. The scheduling approach presently used is ineffective in terms of cost, resource usage and energy cost per server. To address these issues, we propose a SLA (Service Level Agreement) aware resource scheduling strategy which provides better gain to both customers and cloud service providers. We carry out the experiments using real time multinode private cloud setup. The results reveals the effectiveness of the designed resource scheduling model.
Ancient Egyptian Civilization and Archaeoastronomy
Year: 2013

The book contain three chapters: the first chapter deal with the Egypt's, of the Ancient Egyptian Civilization between Lies and Reality. The second chapter deals with the Sun Perpendicularity on the Great Temple of Abu Simbel Phenomenon. Beside the third chapter deal with the results of the Egyptian Spanish mission in archaeoastronomy in ancient Egypt (2004 2008) and the astronomy in the Ancient Egypt.
Information centric networking for connected vehicles: a survey and future perspectives
Venue: IEEE Communications Magazine
Year: 2016

In the connected vehicle ecosystem, a high volume of information rich and safety critical data will be exchanged by roadside units and onboard transceivers to improve the driving and traveling experience. However, poor quality wireless links and the mobility of vehicles highly challenge data delivery. The IP address centric model of the current Internet barely works in such extremely dynamic environments and poorly matches the localized nature of the majority of vehicular communications, which typically target specific road areas (e.g. in the proximity of a hazard or a point of interest) regardless of the identity/address of a single vehicle passing by. Therefore, a paradigm shift is advocated from traditional IP based networking toward the groundbreaking information centric networking. In this article, we scrutinize the applicability of this paradigm in vehicular environments by reviewing its core functionalities and the related work. The analysis shows that, thanks to features like named content retrieval, innate multicast support, and in network data caching, information centric networking is positioned to meet the challenging demands of vehicular networks and their evolution. Interoperability with the standard architectures for vehicular applications along with synergies with emerging computing and networking paradigms are debated as future research perspectives.
Looking Through a Different Lens: Patient Satisfaction With Telemedicine in Delivering Pediatric Fracture Care
Venue: Journal of the American Academy of Orthopaedic Surgeons. Global research reviews
Year: 2019

Telemedicine may transform health care by overcoming geographical and travel associated barriers to patient care. This study assesses patient satisfaction with telemedicine for fracture care.  Two groups of patients were compared from suburban/rural Pennsylvania. One group reported to a regional medical center for real time video consultation with a pediatric orthopaedic surgeon facilitated by a physician's assistant. The other group underwent conventional outpatient clinic visits at a tertiary care hospital. The distance between the tertiary care hospital and the regional medical center was 69 miles. New or follow up fracture patients not living in the vicinity of either medical center were included. A satisfaction survey and questionnaire were administered to both groups at the end of their visit.  One hundred sixty seven patients returned the questionnaires (66 conventional and 101 telemedicine) Telemedicine visits decreased indirect and direct costs (P 0.032) Travel costs and travel times were lower (P 0.001) in the telemedicine group. Patient satisfaction was similar. Only 8 of 101 patients in the telemedicine cohort preferred their next visit to be a conventional follow up.  Utilization of video consultation and trained physician assistants to provide pediatric orthopaedic care across suburban/rural areas can increase pediatric orthopaedic surgeon access and decrease travel costs while maintaining patient satisfaction.
Event abstraction in process mining: literature review and taxonomy
Year: 2020

The execution of processes in companies generates traces of event data, stored in the underlying information system(s) capturing the actual execution of the process. Analyzing event data, i.e. the focus of process mining, yields a detailed understanding of the process, e.g. we are able to discover the control flow of the process and detect compliance and performance issues. Most process mining techniques assume that the event data are of the same and/or appropriate level of granularity. However, in practice, the data are extracted from different systems, e.g. systems for customer relationship management, Enterprise Resource Planning, etc. record the events at different granularity levels. Hence, pre processing techniques that allow us to abstract event data into the right level of granularity are vital for the successful application of process mining. In this paper, we present a literature study, in which we assess the state of the art in the application of such event abstraction techniques in the field of process mining. The survey is accompanied by a taxonomy of the existing approaches, which we exploit to highlight interesting novel directions.
The praxis of studying interorganizational practices in B2B marketing and purchasing A critical literature review
Year: 2020

Practice has developed into a key concept in management research, including B2B marketing and purchasing studies. However, the adoption of the term in B2B marketing and purchasing is characterized by some difficulties. Research on B2B practices is growing, but seems marked by fragmentation, inconsistency, and lack of precision among others. Without conceptual consistency and integrity, B2B practice studies are at risk of becoming derailed, compromising the developments of future theory and practice within B2B. In this critical review paper, we therefore seek to create an overview of B2B practice research as perceived through a practice lens. Based on a review of 116 identified practice papers from key B2B journals, we map the topic areas where the practice concept has been applied for investigations, and we also investigate how well the applied practice conceptualization in these papers align with a recognized practice theory conceptualization. We find that the majority of B2B studies align poorly with the three elements of practice: managerial action, habitual behavior, and action structure duality. Since many of the alignment issues in the review are caused by methodological problems, we propose a series of methodological tools that can provide a more accurate understanding of B2B practices in future research.
Deep Unordered Composition Rivals Syntactic Methods for Text Classification
Venue: ACL
Year: 2015

Many existing deep learning models for natural language processing tasks focus on learning the compositionality of their inputs, which requires many expensive computations. We present a simple deep neural network that competes with and, in some cases, outperforms such models on sentiment analysis and factoid question answering tasks while taking only a fraction of the training time. While our model is syntactically ignorant, we show significant improvements over previous bag of words models by deepening our network and applying a novel variant of dropout. Moreover, our model performs better than syntactic models on datasets with high syntactic variance. We show that our model makes similar errors to syntactically aware models, indicating that for the tasks we consider, nonlinearly transforming the input is more important than tailoring a network to incorporate word order and syntax.
Review of Strength and Conditioning for Alpine Ski Racing
Year: 2013

A WIDE RANGE OF LOWER LIMB AND HIP MOBILITY. STRENGTH AND CONDITIONING SHOULD FOCUS ON HYPERTROPHY, MAXIMAL STRENGTH, POWER, BALANCE, DYNAMIC MOBILITY, AND ANAEROBIC METABOLIC SUPPORT. THE PURPOSE OF THIS ARTICLE IS TO PROVIDE THE NECESSARY BACKGROUND FOR A STRENGTH COACH TO IMPLEMENT AS MUCH EVIDENCE BASED COACHING AS POSSIBLE FOR ALPINE SKI RACING.
Interrater Reliability of the Record of Driving Errors (RODE)
Venue: The American journal of occupational therapy official publication of the American Occupational Therapy Association
Year: 2015

The Record of Driving Errors (RODE) is a novel standardized tool designed to quantitatively document the specific types of driving errors that occur during a standardized performance based road test. The purpose of this study was to determine interrater reliability between two occupational therapy driver rehabilitation specialists who quantitatively scored specific driving errors using the RODE in a sample of older adults diagnosed with dementia (n=24) Intraclass correlation coefficients of major driving error and intervention categories indicated almost perfect agreement between raters. Using raters with adequate training and similar professional backgrounds, it is possible to have good interrater reliability using the RODE on a standardized road test.
The SARS coronavirus papain like protease: Structure, function and inhibition by designed antiviral compounds
Venue: Antiviral Research
Year: 2015

Over 10 years have passed since the deadly human coronavirus that causes severe acute respiratory syndrome (SARS CoV) emerged from the Guangdong Province of China. Despite the fact that the SARS CoV pandemic infected over 8500 individuals, claimed over 800 lives and cost billions of dollars in economic loss worldwide, there still are no clinically approved antiviral drugs, vaccines or monoclonal antibody therapies to treat SARS CoV infections. The recent emergence of the deadly human coronavirus that causes Middle East respiratory syndrome (MERS CoV) is a sobering reminder that new and deadly coronaviruses can emerge at any time with the potential to become pandemics. Therefore, the continued development of therapeutic and prophylactic countermeasures to potentially deadly coronaviruses is warranted. The coronaviral proteases, papain like protease (PLpro) and 3C like protease (3CLpro) are attractive antiviral drug targets because they are essential for coronaviral replication. Although the primary function of PLpro and 3CLpro are to process the viral polyprotein in a coordinated manner, PLpro has the additional function of stripping ubiquitin and ISG15 from host cell proteins to aid coronaviruses in their evasion of the host innate immune responses. Therefore, targeting PLpro with antiviral drugs may have an advantage in not only inhibiting viral replication but also inhibiting the dysregulation of signaling cascades in infected cells that may lead to cell death in surrounding, uninfected cells. This review provides an up to date discussion on the SARS CoV papain like protease including a brief overview of the SARS CoV genome and replication followed by a more in depth discussion on the structure and catalytic mechanism of SARS CoV PLpro, the multiple cellular functions of SARS CoV PLpro, the inhibition of SARS CoV PLpro by small molecule inhibitors, and the prospect of inhibiting papain like protease from other coronaviruses. This paper forms part of a series of invited articles in Antiviral Research on "From SARS to MERS: 10years of research on highly pathogenic human coronaviruses."