nils-herrmann/clean_scirepeval huggingface dataset:
Impact of increased penetration of photovoltaic generation on power systems

Present renewable portfolio standards are changing power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. With the increase in penetration of PV resources, power systems are expected to experience a change in dynamic and operational characteristics. This paper studies the impact of increased penetration of PV systems on static performance as well as transient stability of a large power system, in particular the transmission system. Utility scale and residential rooftop PVs are added to the aforementioned system to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The studied system is a large test system representing a portion of the Western U.S. interconnection. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance.
Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction

Two new, cost-effective thresholding algorithms for use in extracting binary images of characters from machineor hand-printed documents are described. The creation of a binary representation from an analog image requires such algorithms to determine whether a point is converted into a binary one because it falls within a character stroke or a binary zero because it does not. This thresholding is a critical step in Optical Character Recognition (OCR). I t is also essential for other Character Image Extraction (CIEJ applications, such as the processing of machine-printed or handwritten characters from carbon copy forms or bank checks, where smudges and scenic backgrounds, for example, may have to be suppressed. The first algorithm, a nonlinear, adaptive procedure, is implemented with a minimum of hardware and is intended for many CIE applications. The second is a more aggressive approach directed toward specialized, high-volume applications which justify extra complexity.
An Interaction Design Framework for Social Robots

We present a novel design framework enabling the development of social robotics applications by cross-disciplinary teams of programmers and interaction designers. By combining a modular back-end software architecture with an easy-to-use graphical interface for developing interaction sequences, this system enables programmers and designers to work in parallel to develop robot applications and tune the subtle details of social behaviors. In this paper, we describe the structure of our design framework, and we present an experimental evaluation of our system showing that it increases the effectiveness of programmerdesigner teams developing social robot applications. Keywords-human-robot interaction; software development; social robotics; programming interfaces;
Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time

Recently a few methods have been proposed in the literature for non-invasive cuff-less estimation of systolic and diastolic blood pressures. One of the most prominent methods is to use the Pulse Transit Time (PTT). Although it is proven that PTT has a strong correlation with the systolic and diastolic blood pressures, this relation is highly dependent to each individuals physiological properties. Therefore, it requires per person calibration for accurate and reliable blood pressure estimation from PTT, which is a big drawback. To alleviate this issue, in this paper, a novel method is proposed for accurate and reliable estimation of blood pressure that is calibration-free. This goal is accomplished by extraction of several physiological parameters from Photoplethysmography (PPG) signal as well as utilizing signal processing and machine learning algorithms. The results show that the accuracy of the proposed method achieves grade B for the estimation of the diastolic blood pressure and grade C for the estimation of the mean arterial pressure under the standard British Hypertension Society (BHS) protocol.
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predicting their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes. Our method is significantly faster than the previous image-based method and generates result that outperforms state-of-the-art generative scene models in terms of faithfulness to training data and perceived visual quality.
FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning

Emerging Deep Learning (DL) applications introduce heavy I/O workloads on computer clusters. The inherent long lasting, repeated, and random file access pattern can easily saturate the metadata and data service and negatively impact other users. In this paper, we present FanStore, a transient runtime file system that optimizes DL I/O on existing hardware/software stacks. FanStore distributes datasets to the local storage of compute nodes, and maintains a global namespace. With the techniques of system call interception, distributed metadata management, and generic data compression, FanStore provides a POSIX-compliant interface with native hardware throughput in an efficient and scalable manner. Users do not have to make intrusive code changes to use FanStore and take advantage of the optimized I/O. Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90% scaling efficiency.
Transfer Learning for Cross-Dataset Recognition: A Survey

This paper summarises and analyses the cross-dataset recognition transfer learning techniques with the emphasis on what kinds of methods can be used when the available source and target data are presented in different forms for boosting the target task. This paper for the first time summarises several transferring criteria in details from the concept level, which are the key bases to guide what kind of knowledge to transfer between datasets. In addition, a taxonomy of cross-dataset scenarios and problems is proposed according the properties of data that define how different datasets are diverged, thereby review the recent advances on each specific problem under different scenarios. Moreover, some real world applications and corresponding commonly used benchmarks of cross-dataset recognition are reviewed. Lastly, several future directions are identified.
Anonymizing Social Networks

Advances in technology have made it possible to collect data about individuals and the connections between them, such as email correspondence and friendships. Agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data. However, in many cases the data describes relationships that are private (e.g., email correspondence) and sharing the data in full can result in unacceptable disclosures. In this paper, we present a framework for assessing the privacy risk of sharing anonymized network data. This includes a model of adversary knowledge, for which we consider several variants and make connections to known graph theoretical results. On several real-world social networks, we show that simple anonymization techniques are inadequate, resulting in substantial breaches of privacy for even modestly informed adversaries. We propose a novel anonymization technique based on perturbing the network and demonstrate empirically that it leads to substantial reduction of the privacy threat. We also analyze the effect that anonymizing the network has on the utility of the data for social network analysis.
Data Distillation: Towards Omni-Supervised Learning

We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lower-bounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we propose data distillation, a method that ensembles predictions from multiple transformations of unlabeled data, using a single model, to automatically generate new training annotations. We argue that visual recognition models have recently become accurate enough that it is now possible to apply classic ideas about self-training to challenging real-world data. Our experimental results show that in the cases of human keypoint detection and general object detection, state-of-the-art models trained with data distillation surpass the performance of using labeled data from the COCO dataset alone.
Employing automatic approach and avoidance tendencies for the assessment of implicit personality self-concept: The implicit association procedure (IAP).

A new chronometric procedure, the Implicit Association Procedure (IAP), was adapted to assess the implicit personality self-concept of shyness. A sample of 300 participants completed a shyness-inducing role play and, before or after the role play, a shyness IAP, a shyness Implicit Association Test (IAT), and direct self-ratings. The experimental group was instructed to fake nonshyness. The control group did not receive this instruction. IAT and IAP were unaffected by position effects, and were less susceptible to faking than direct self-ratings with regard to mean levels and correlates. Under faking, correlations between direct and indirect measures decreased, and direct but not indirect measures showed higher correlations with social desirability and lower correlations with observed shyness. Despite many similarities, the true correlation between IAT and IAP was estimated only .61, indicating high method-specific variance in both procedures. The findings suggest that indirect measures are more robust against faking than traditional self-ratings but do not yet meet psychometric criteria for practical assessment purposes.
A Dataset for Web-Scale Knowledge Base Population

For many domains, structured knowledge is in short supply, while unstructured text is plentiful. Knowledge Base Population (KBP) is the task of building or extending a knowledge base from text, and systems for KBP have grown in capability and scope. However, existing datasets for KBP are all limited by multiple issues: small in size, not open or accessible, only capable of benchmarking a fraction of the KBP process, or only suitable for extracting knowledge from title-oriented documents (documents that describe a particular entity, such as Wikipedia pages). We introduce and release CC-DBP, a web-scale dataset for training and benchmarking KBP systems. The dataset is based on Common Crawl as the corpus and DBpedia as the target knowledge base. Critically, by releasing the tools to build the dataset, we enable the dataset to remain current as new crawls and DBpedia dumps are released. Also, the modularity of the released tool set resolves a crucial tension between the ease that a dataset can be used for a particular subtask in KBP and the number of different subtasks it can be used to train or benchmark.
Fair Two-Party Computations via the BitCoin Deposits

We show how the Bitcoin currency system (with a small modification) can be used to obtain fairness in any two-party secure computation protocol in the following sense: if one party aborts the protocol after learning the output then the other party gets a financial compensation (in bitcoins). One possible application of such protocols is the fair contract signing: each party is forced to complete the protocol, or to pay to the other one a fine. We also show how to link the output of this protocol to the Bitcoin currency. More precisely: we show a method to design secure two-party protocols for functionalities that result in a “forced” financial transfer from one party to the other. Our protocols build upon the ideas of our recent paper “Secure Multiparty Computations on Bitcoin” (Cryptology ePrint Archive, Report 2013/784). Compared to that paper, our results are more general, since our protocols allow to compute any function, while in the previous paper we concentrated only on some specific tasks (commitment schemes and lotteries). On the other hand, as opposed to “Secure Multiparty Computations on Bitcoin”, to obtain security we need to modify the Bitcoin specification so that the transactions are “non-malleable” (we discuss this concept in more detail in the paper).
Implementation research: a synthesis of the literature.

A Synthesis of the Literature, by Dean Fixsen, Sandra Naoom, Karen Blase, Robert Friedman, and Frances Wallace was published in 2005 by the National Implementation Research Network at the Louis de la Parte Florida Mental Health Institute, University of South Florida and produced with support from the William T. Grant Foundation. This summary was created by the National Center for Mental Health Promotion and Youth Violence Prevention and is being distributed with permission from the National Implementation Research Network. The opinions expressed in the publication are those of the authors and do not necessarily reflect those of the William T. Grant Foundation. The complete version of Implementation Research: A Synthesis of the Literature is available online at http://nirn.fmhi.usf.edu/resources.
Cloud forensics: A research perspective

Cloud computing and digital forensics are both developing topics and researching these topics requires an understanding of the main aspects of both cloud computing and digital forensics. In cloud computing it is necessary not only to understand its characteristics and the different services and deployment models but also to survey the underpinning elements of cloud computing such as virtualization and the distributed computing which are important to identify its impact on current digital forensics guidelines and procedures. Unlike papers discussing the challenges and opportunities presented by cloud computing in relation to digital forensics, in this paper, we will discuss the underpinning cloud computing elements which are required to provide forensics friendly cloud services. Furthermore, we suggest a set of questions that will aid in the process of cloud forensics analysis.
Capability Maturity Model for Software

This paper provides an overview of the latest version of the Capability Maturity Model for Software, CMM v1.1. Based on over six years of experience with software process improvement and the contributions of hundreds of reviewers, CMM v1.1 describes the software engineering and management practices that characterize organizations as they mature their processes for developing and maintaining software. This paper stresses the need for a process maturity framework to prioritize improvement actions, describes the process maturity framework of five maturity levels and the associated structural components, and discusses future directions for the CMM.
Semantic Web User Interfaces : A Systematic Mapping Study and Review

Exploration of user interfaces has attracted a considerable attention in Semantic Web research in the last several years. However, the overall impact of these research efforts and the level of the maturity of their results are still unclear.  This study aims at surveying existing research on Semantic Web user interfaces in order to synthesise their results, assess the level of the maturity of the research results, and identify needs for future research.  This paper reports the results of a systematic literature review and mapping study on Semantic Web user interfaces published between 2003 and 2011. After collecting a corpus of 87 papers, the collected papers are analysed through four main dimensions: general information such as sources, types and time of publication; covered research topics; quality of evaluations; and finally, used research methods and reported rigour of their use.  Based on only the obtained data, the paper provides a high level overview of the works reviewed, a detailed quality assessment of papers involving human subjects and a discussion of the identified gaps in the analysed papers and draws promising venues for future research.  We arrive at two main conclusions: important gaps exist which hinder a wide adoption of Semantic Web technologies (e.g. lack user interfaces based on semantic technologies for social and mobile applications) and experimental research hardly used any proven theories and instruments specifically designed for investigating technology acceptance.
Evaluating Mobile Phones and Web Sites for Academic Information Needs

In the last decade, there has been an exponential growth in use of mobile phones among people. Smart phone invention has digitized life of a common man especially after introduction of 3G/4G technology. People are used to use Internet on the move because of this advancement in technology. This advancement has also motivated usability design researchers to propose more usable designs for both smart phones and web sites. This work focuses on evaluation of web usability of mobile phones as well as usability of university web sites. Evaluation is performed on the most popular mobile phones required by the most common mobile users. Selection of the most popular mobile devices, the most common mobile users and their web usage is done by conducting a very detailed survey in the local market. Survey concludes that students and labors are the most common buyers of mobile phones and we choose three mobiles phones from the category of most popular phones that are iPhone (iPhone 4 precisely), Q-Mobile (Q Mobile A35) and Windows phone (Lumia 535). Six participants (three male and three females) are selected for fully detailed and rigorous taskbased usability testing with “think aloud” technique. Task scenarios are defined to evaluate the usability of both i.e. smart phones and chosen university web sites. From results of usability testing, we find out that iPhone has better usability design as far as its response time is concerned while Q Mobile ranks second and Microsoft Windows phone takes last position in this ranking. Usability evaluation of university web sites on these mobile phones concludes that web site of Islamia university of Bahawalpur (I.U.B, Bahawalpur) has better mobile usability design and Bahauddin Zakariya university (BZU, Multan) and NFC Institute of Engineering and Technology NFCIET, Multan) second and third respectively and while web site of Institute of Southern Punjab (ISP, Multan) comes last when measured in terms of task completion time. All tests are performed on wireless network when internet download speed is between 3MBPS to 3.2MBPS. Usability Engineering; Smart Phones; Academic information need
A low cost implementation of MQTT using ESP8266

Technology is great growling engine of the change and Internet of Things (IoT) is the backbone of such revolutionary engines. Basically, in the real world the things having sensor capability, sufficient power supply and connectivity to internet makes field like Internet of Things (IoT) possible. For such rapid growing technology, it is the necessity to have very light, inexpensive and minimum bandwidth protocol like Message Queuing Telemetry Transport (MQTT) Protocol. Because of such non-established protocol it is easy for the clients to publish or/and subscribe the desire topic through the host acting as server of the network also known to be the broker. In this paper, it is shown that communication between the low power ESP8266 WiFi as client with the clients on smartphones and laptop using an MQTT protocol becomes easier and more reliable. The WiFi enabled ESP8266 board interfaces with DHT11 sensor and LDR sensor to monitor the ambient condition and according to the light intensity level the brightness level of 8∗8 Neopixel matrix is controlled. The adafruit.io is the MQTT server i.e, broker which also provides the facility of monitoring system through the dashboard. The clients on smartphone and laptop subscribes to the topics of temperature, humidity and light intensity level gets the respective update. The main objective of the paper is to implement the idea for the smart home appliance, street light system for the smart cities, fire alert systems, whether monitoring system and so.
cocor: A Comprehensive Solution for the Statistical Comparison of Correlations

A valid comparison of the magnitude of two correlations requires researchers to directly contrast the correlations using an appropriate statistical test. In many popular statistics packages, however, tests for the significance of the difference between correlations are missing. To close this gap, we introduce cocor, a free software package for the R programming language. The cocor package covers a broad range of tests including the comparisons of independent and dependent correlations with either overlapping or nonoverlapping variables. The package also includes an implementation of Zou's confidence interval for all of these comparisons. The platform independent cocor package enhances the R statistical computing environment and is available for scripting. Two different graphical user interfaces-a plugin for RKWard and a web interface-make cocor a convenient and user-friendly tool.
A FRAMEWORK FOR DETERMINING CRITICAL SUCCESS FACTORS INFLUENCING CONSTRUCTION BUSINESS PERFORMANCE

Recent reports by Latham (1994) and Egan (1998) have emphasized the need for the construction industry to increase its competitiveness, and have suggested the use of performance measurement as a tool for continuous improvement. Comprehensive measurement of a company’s performance and subsequent feedback to its managers is vital for business transformation. Measurement also enables businesses to be compared with each other on the basis of standardized information, allowing best practices to be identified and applied more widely. Historically, business performance has been determined principally through the use of financial performance criteria, but recently, it has been established that performance measurement needs to go beyond this. This paper reviews the various financial and non-financial factors that influence construction business. A methodology for defining the critical success factors relevant to construction businesses is further outlined. The critical success factors associated with construction business are also reviewed. Finally, it is concluded that more important than presenting a list of critical success factors (which are bound to change) is the need to have a holistic framework for identifying and implementing the required success factors.