nils-herrmann/clean_scirepeval huggingface dataset:
Predictive Direct Power Control of Doubly Fed Induction Generators Under Unbalanced Grid Voltage Conditions for Power Quality Improvement

This paper proposes a new control strategy of doubly fed induction generators (DFIGs) under unbalanced grid voltage conditions. The proposed controller includes a model predictive direct power control (MPDPC) method and a power compensation scheme. In MPDPC, the appropriate voltage vector is selected according to an optimization cost function, hence the instantaneous active and reactive powers are regulated directly in the stator stationary reference frame without the requirement of coordinate transformation, PI regulators, switching table, or PWM modulators. In addition, the behavior of the DFIG under unbalanced grid voltage is investigated. Next, a power compensation scheme without the need of extracting negative stator current sequence is developed. By combining the proposed MPDPC strategy and the power compensation scheme, distorted currents injected into the power grid by the DFIGs can be eliminated effectively.
GIS Cloud Computing Methodology

Geographic Information System (GIS) is the processes of managing, manipulating, analyzing, updating and presenting metadata according to its geographic location, to be effectively used in different aspects of life . Cloud Computing allowed the utilization of all computing resources and software as required through the web in a virtual computing environment [2], . Different Application software and data are provided at the (virtual) server side to be used. GIS Cloud is the future of Web GIS, with capabilities of easy and fast: collecting, processing, analyzing, updating, rectifying, publishing geospatial data through the internet. It is a web-based- GIS Application to enable all users fast, easy informed decision-making at fair price. It provides the power of desktop GIS on a web-based platform at fair cost anywhere. It offers a JavaScript Application Programming Interface (API) and a REST API, to provide GIS functionality into an application or website that can be hosted by GIS Cloud or by a third-party. In this work we are presenting a GIS-Cloud application in AL-Kamaliah region, small town of Amman suburbs to demonstrate its practicality and functionality.
World Models

We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io
How Software Project Risk Affects Project Performance: An Investigation of the Dimensions of Risk and an Exploratory Model

To reduce the high failure rate of software projects, managers need better tools to assess and manage software project risk. In order to create such tools, however, information systems researchers must first develop a better understanding of the dimensions of software project risk and how they can affect project performance. Progress in this area has been hindered by: (1) a lack of validated instruments for measuring software project risk that tap into the dimensions of risk that are seen as important by software project managers, and (2) a lack of theory to explain the linkages between various dimensions of software project risk and project performance. In this study, six dimensions of software project risk were identified and reliable and valid measures were developed for each. Guided by sociotechnical systems theory, an exploratory model was developed and tested. The results show that social subsystem risk influences technical subsystem risk, which, in turn, influences the level of project management risk, and ultimately, project performance. The implications of these findings for research and practice are discussed.  Sociotechnical Systems Theory, Software Project Risk, and Structural Equation Modeling. ∗The authors would like to thank the Project Management Institute’s Information Systems Special Interest Group (PMI-ISSIG) for supporting this research. We would also like to thank Georgia State University for their financial support through the PhD research grant program. The authors gratefully acknowledge Al Segars and Ed Rigdon for their insightful comments and assistance at various stages of this project. †Corresponding author.
Why Enforcing Corporate Social Responsibility ( CSR ) is Morally Questionable

Docketed literature on Corporate Social Responsibility (CSR) exhibits heated contentions on the nature of business-society relations. This paper seeks to explore this contentious issue in the light of contemporary incorporation of CSR in business strategic plans. It notes that enforcing CSR on business might lead to its manipulation to advance corporate organisations’ purely self-interested ends rather than pursuing intrinsic philanthropic activities for the good of society. An insight into the undesirable consequences of enforcing CSR is given. Using Kantian ethics, the paper notes that the instrumental use of CSR by corporate organisations is immoral because it does not treat CSR as an end in itself in the same way as the profit maximisation is conceptualized in business circles. The paper, therefore, concludes that the integration of CSR in corporate organisations’ strategic plans is seen as instrumental to the realisation of their profit motives other than a genuine show
A Novel Method for Decentralised Peer-to-Peer Software License Validation Using Cryptocurrency Blockchain Technology

Protecting software copyright has been an issue since the late 1970’s, and software license validation has been a primary method employed in an attempt to minimise software piracy and protect software copyright. This paper presents a novel method for decentralised peer-topeer software license validation using cryptocurrency blockchain technology to ameliorate software piracy, and to provide a mechanism for all software developers to protect their copyrighted works.
Fault analysis in solar PV arrays under: Low irradiance conditions and reverse connections

Fault analysis in solar photovoltaic (PV) arrays is a fundamental task to protect PV modules from damages and to eliminate the risks of safety hazards. This paper examines two types of unique faults found in photovoltaic (PV) array installations that have not been studied in the literature. One is a fault that occurs under low irradiance conditions. In some circumstances, fault current protection devices are unable to detect certain types of faults so that the fault may remain hidden in the PV system, even after irradiance increases. The other type of fault occurs when a string of PV modules is reversely connected, caused by inappropriate installation. This fault type brings new challenges for overcurrent protection devices because of the high rating voltage requirement. In both cases, these unique PV faults may subsequently lead to unexpected safety hazards, reduced system efficiency and reduced reliability.
Intent Detection through Text Mining and Analysis

This work investigated using n-grams, parts-ofspeech and support vector machines for detecting the customer intents in the user generated contents. The work demonstrated a system of categorization of customer intents that is concise and useful for business purposes. We examined possible sources of text posts to be analyzed using three text mining algorithms. We presented the three algorithms and the results of testing them in detecting different six intents. This work established that intent detection can be performed on text posts with approximately 61% accuracy. Intent detection; text mining; support vector machines; N-grams; parts of speech
Reputation , Corporate Social Responsibility and Market Regulation

The paper investigates the role of the government and self-regulatory reputation mechanisms to internalise externalities of market operation. If it pays off for companies to invest in a good reputation by an active policy of corporate social responsibility (CSR), external effects of the market will be (partly) internalised by the market itself. The strength of the reputation mechanism depends on the functioning of non governmental organisations (NGOs), the transparency of the company, the time horizon of the company, and on the behaviour of employees, consumers and investors. On the basis of an extensive study of the empirical literature on these topics, we conclude that in general the working of the reputation mechanism is rather weak. Especially the transparency of companies is a bottleneck. If the government would force companies to be more transparent, it could initiate a self-enforcing spiral that would improve the working of the reputation mechanism. We also argue that the working of the reputation mechanism will be weaker for smaller companies and for both highly competitive and monopolistic markets. We therefore conclude that government regulation is still necessary, especially for small companies. Tijdschrift voor Economie en Management Vol. XLIX, 2, 2004
A survey of activity recognition in egocentric lifelogging datasets

With the appearance of many devices that everyday captured a large number of images. The rapid access to these huge collections of images and automatically characterizing an activity or an experience from this huge collection of unlabeled and unstructured egocentric data presents major challenges and requires novel and efficient algorithmic solutions. One of the big challenges of egocentric vision and lifelogging is to develop automatic algorithms to automatically characterize everyday activities. Such information is of high interest to predict migraines attacks or assure healthy behavior of patients and individuals of high healthy risk. In this work, we first conduct a comprehensive survey of existing egocentric datasets and we will present our future contribution to automatically characterize everyday activities.
Coaching mothers of children with autism: a qualitative study for occupational therapy practice.

The purpose of this study was to understand the perceptions of mothers of children with autism spectrum disorder (ASD) who participated in 10 one-hour coaching sessions. Coaching occurred between an occupational therapist and mother and consisted of information sharing, action, and reflection. Researchers asked 10 mothers six open-ended questions with follow-up probes related to their experiences with coaching. Themes were identified, labeled, and categorized. Themes emerged related to relationships, analysis, reflection, mindfulness, and self-efficacy. Findings indicate that parents perceive the therapist-parent relationship, along with analysis and reflection, as core features that facilitate increased mindfulness and self-efficacy. The findings suggest that how an intervention is provided can lead to positive outcomes, including increased mindfulness and self-efficacy.
Pension Reform in China.

This article analyzes China's pension arrangement and notes that China has recently established a universal non-contributory pension plan covering urban non-employed workers and all rural residents, combined with the pension plan covering urban employees already in place. Further, in the latest reform, China has discontinued the special pension plan for civil servants and integrated this privileged welfare class into the urban old-age pension insurance program. With these steps, China has achieved a degree of universalism and integration of its pension arrangement unprecedented in the non-Western world. Despite this radical pension transformation strategy, we argue that the current Chinese pension arrangement represents a case of "incomplete" universalism. First, its benefit level is low. Moreover, the benefit level varies from region to region. Finally, universalism in rural China has been undermined due to the existence of the "policy bundle." Additionally, we argue that the 2015 pension reform has created a situation in which the stratification of Chinese pension arrangements has been "flattened," even though it remains stratified to some extent.
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems

Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chatbots, help users achieve a predefined goal (e.g. book a movie ticket) within a closed domain. A first step is to understand the user’s goal by using natural language understanding techniques. Once the goal is known, the bot must manage a dialogue to achieve that goal, which is conducted with respect to a learnt policy. The success of the dialogue system depends on the quality of the policy, which is in turn reliant on the availability of high-quality training data for the policy learning method, for instance Deep Reinforcement Learning. Due to the domain specificity, the amount of available data is typically too low to allow the training of good dialogue policies. In this master thesis we introduce a transfer learning method to mitigate the effects of the low in-domain data availability. Our transfer learning based approach improves the bot’s success rate by 20% in relative terms for distant domains and we more than double it for close domains, compared to the model without transfer learning. Moreover, the transfer learning chatbots learn the policy up to 5 to 10 times faster. Finally, as the transfer learning approach is complementary to additional processing such as warm-starting, we show that their joint application gives the best outcomes.
Accurate and Diverse Recommendation based on Users' Tendencies toward Temporal Item Popularity

Popularity bias is a phenomenon associated with collaborative filtering algorithms, in which popular items tend to be recommended over unpopular items. As the appropriate level of item popularity differs depending on individual users, a user-level modification approach can produce diverse recommendations while improving the recommendation accuracy. However, there are two issues with conventional user-level approaches. First, these approaches do not isolate users’ preferences from their tendencies toward item popularity clearly. Second, they do not consider temporal item popularity, although item popularity changes dynamically over time in reality. In this paper, we propose a novel approach to counteract the popularity bias, namely, matrix factorization based collaborative filtering incorporating individual users’ tendencies toward item popularity. Our model clearly isolates users’ preferences from their tendencies toward popularity. In addition, we consider the temporal item popularity and incorporate it into our model. Experimental results using a real-world dataset show that our model improve both accuracy and diversity compared with a baseline algorithm in both static and time-varying models. Moreover, our model outperforms conventional approaches in terms of accuracy with the same diversity level. Furthermore, we show that our proposed model recommends items by capturing users’ tendencies toward item popularity: it recommends popular items for the user who likes popular items, while recommending unpopular items for those who don’t like popular items.
Maturity assessment models : a design science research approach

In order to ascertain and measure dedicated aspects of social and technical systems ‘maturity’, a wide range of maturity assessment models have been developed by both, practitioners and academics over the past years. In spite of its broad proliferation, the concept has not been untroubled by criticism. Unnecessary bureaucracy, poor theoretical foundation, and the impression of a falsified certainty to achieve success are just a few examples for that. As there is still a significant lack of knowledge on how to design theoretically sound and widely accepted maturity assessment models, it is the aim of this paper to discuss the typical phases of maturity model development and application by taking a design science research perspective. Given that both, development and application are intimately connected, different decision parameters are identified that are relevant in respect to rigour and relevance of the maturity assessment model.
Strategic management and entrepreneurship : Friends or foes ?

The objective of this article is to create a better understanding of the intersection of the academic fields of entrepreneurship and strategic management, based on an aggregation of the extant literature in these two fields. The article structures and synthesizes the existing scholarly works in the two fields, thereby generating new knowledge. The results can be used to further enhance fruitful integration of these two overlapping but separate academic fields. The article attempts to integrate the two fields by first identifying apparent interrelations, and then by concentrating in more detail on some important intersections, including strategic management in small and medium-sized enterprises and start-ups, acknowledging the central role of the entrepreneur. The content and process sides of strategic management are discussed as well as their important connecting link, the business plan. To conclude, implications and future research directions for the two fields are proposed.
Home Automation System via Internet using Android Phone

In this process of Home Automation System via Internet using Android Phone, the demonstration of the strategy which makes every local home appliance to incorporate the working of lighting installations, fans, music systems, washing machines and dryers, doors and ventilation system is totally initiated and controlled by using the Arduino Ethernet web server and advanced Android smart phones.This technology allows: 1. designing embedded machine based remote tracking gadget using a cell phone at low value. 2. Far flung tracking is applied in any cell phone model in order to deliver flexibility. 3. At times when messaging fails, the mechanism for communication incorporates the opportunity.  Automation, Internet, Arduino Ethernet web server, Android smart phone, Advanced home automation
Peer to Peer Hate: Hate Speech Instigators and Their Targets

While social media has become an empowering agent to individual voices and freedom of expression, it also facilitates anti-social behaviors including online harassment, cyberbullying, and hate speech. In this paper, we present the first comparative study of hate speech instigators and target users on Twitter. Through a multi-step classification process, we curate a comprehensive hate speech dataset capturing various types of hate. We study the distinctive characteristics of hate instigators and targets in terms of their profile selfpresentation, activities, and online visibility. We find that hate instigators target more popular and high profile Twitter users, and that participating in hate speech can result in greater online visibility. We conduct a personality analysis of hate instigators and targets and show that both groups have eccentric personality facets that differ from the general Twitter population. Our results advance the state of the art of understanding online hate speech engagement.
Increasing Deep Neural Network Acoustic Model Size for Large Vocabulary Continuous Speech Recognition

Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recognition systems. Part of the promise of DNNs is their ability to represent increasingly complex functions as the number of DNN parameters increases. This paper investigates the performance of DNNbased hybrid speech recognition systems as DNN model size and training data increase. Using a distributed GPU architecture, we train DNN acoustic models roughly an order of magnitude larger than those typically found in speech recognition systems. DNNs of this scale achieve substantial reductions in final system word error rate despite training with a loss function not tightly coupled to system error rate. However, training word error rate improvements do not translate to large improvements in test set word error rate for systems trained on the 300 hour Switchboard conversational speech corpus. Scaling DNN acoustic model size does prove beneficial on the Fisher 2,000 hour conversational speech corpus. Our results show that with sufficient training data, increasing DNN model size is an effective, direct path to performance improvements. Moreover, even smaller DNNs benefit from a larger training corpus.
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach

This paper investigates a number of computational intelligence techniques in the detection of heart disease. Particularly, comparison of six well known classifiers for the well used Cleveland data is performed. Further, this paper highlights the potential of an expert judgment based (i.e., medical knowledge driven) feature selection process (termed as MFS), and compare against the generally employed computational intelligence based feature selection mechanism. Also, this article recognizes that the publicly available Cleveland data becomes imbalanced when considering binary classification. Performance of classifiers, and also the potential of MFS are investigated considering this imbalanced data issue. The experimental results demonstrate that the use of MFS noticeably improved the performance, especially in terms of accuracy, for most of the classifiers considered and for majority of the datasets (generated by converting the Cleveland dataset for binary classification). MFS combined with the computerized feature selection process (CFS) has also been investigated and showed encouraging results particularly for NaiveBayes, IBK and SMO. In summary, the medical knowledge based feature selection method has shown promise for use in heart disease diagnostics. 2012
Middle-Status Conformity : Theoretical Restatement and Empirical Demonstration in Two Markets 1

This article aims to reestablish the long-standing conjecture that conformity is high at the middle and low at either end of a status order. On a theoretical level, the article clarifies the basis for expecting such an inverted U-shaped curve, taking care to specify key scope conditions on the social-psychological orientations of the actors, the characteristics of the status structure, and the nature of the relevant actions. It also validates the conjecture in two settings that both meet such conditions and allow for the elimination of confounding effects: the Silicon Valley legal services market and the market for investment advice. These results inform our understanding of how an actor’s status interacts with her role incumbency to produce differential conformity in settings that meet the specified scope conditions.
Fingerprint classification techniques: a review

A biometric is the automatic identification of an individual that is based on physiological or behavioral characteristics. Due to its security-related applications and the current world political climate, biometric is currently the subject of intense research by both private and academic institutions. Fingerprint is emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive researches that have been done on fingerprint classification over the last four decades. In particular, it discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem.