nils-herrmann/clean_scirepeval huggingface dataset:
A resource-based view of competitive advantage at the Port of Singapore

The purpose of this paper is to discuss the resources, including operations and information technology that have contributed to the competitive position of the Port of Singapore. We present a detailed discussion of the Port and its resources, and analyze the case using the resource-based view of strategy. A firm with a competitive advantage excels in time, quality, or cost, or a combination of such over its competitors. We argue that a combination of resources including supportive government policies, ample investment, and well thought out operations and information technology along with location and a natural deep harbor to help create a sustainable advantage for the Port. We find that Singapore compensated for some of its natural disadvantages like small land area by successfully applying information technology in critical areas to increase the island’s capacity to handle shipping. q 2004 Elsevier B.V. All rights reserved.
Factors influencing students career choices among secondary school students in Kisumu municipality, Kenya

Career choice is a complex decision for students since it determines the kind of profession that they intend to pursue in life. As students try to make career choice while in secondary school, they face problem of matching their career choices with their abilities and school performance. In Kenya every year form four secondary school students make career choices before sitting for their final examination (Kenya Certificate of Secondary Education examinations) however, studies indicate that most students enter into careers that are totally different from the ones they chose while in secondary. The purpose of this study was to examine factors influencing career choice among form four secondary school students in Kisumu municipality, Kenya. The study was conducted using descriptive survey design with a population of 332 students. The data for this study was collected using questionnaire and interview schedules. The findings of this study indicate that availability of advancement opportunities and learning experiences are the most influential factors affecting career choices among students. While males reported learning experiences and career flexibility as the most influential factors, females however reported availability of advancement opportunity and opportunity to apply skills as the most influential factors. However, no variance was reported for persons influencing career choice by gender __________________________________________________________________________________________
Experience Marketing : Concepts , Frameworks and Consumer Insights

Experience is a new and exciting concept marketing academia and practice. This monograph reviews the various meanings of experience as the term is used in philosophy, psychology, and in consumer behavior and marketing. I will discuss the key concepts of experience marketing such as experiential value, different types of experiences, the distinction between ordinary and extraordinary experiences and experience touchpoints. I will also review the empirical findings that provide consumer insights on experiences — such as how experiences are remembered, whether positive and negative experiences can co-exist, how experiential attributes are processed and whether experiences are rational. Practical frameworks for managing and marketing experiences will be discussed. I will conclude with an exploration of how experience marketing can contribute to customer happiness.
Dynamic noise aware training for speech enhancement based on deep neural networks

We propose three algorithms to address the mismatch problem in deep neural network (DNN) based speech enhancement. First, we investigate noise aware training by incorporating noise information in the test utterance with an ideal binary mask based dynamic noise estimation approach to improve DNN’s speech separation ability from the noisy signal. Next, a set of more than 100 noise types is adopted to enrich the generalization capabilities of the DNN to unseen and non-stationary noise conditions. Finally, the quality of the enhanced speech can further be improved by global variance equalization. Empirical results show that each of the three proposed techniques contributes to the performance improvement. Compared to the conventional logarithmic minimum mean squared error speech enhancement method, our DNN system achieves 0.32 PESQ (perceptual evaluation of speech quality) improvement across six signal-tonoise ratio levels ranging from -5dB to 20dB on a test set with unknown noise types. We also observe that the combined strategies can well suppress highly non-stationary noise better than all the competing state-of-the-art techniques we have evaluated.
Facial Landmark Detection with Tweaked Convolutional Neural Networks

This paper concerns the problem of facial landmark detection. We provide a unique new analysis of the features produced at intermediate layers of a convolutional neural network (CNN) trained to regress facial landmark coordinates. This analysis shows that while being processed by the CNN, face images can be partitioned in an unsupervised manner into subsets containing faces in similar poses (i.e., 3D views) and facial properties (e.g., presence or absence of eye-wear). Based on this finding, we describe a novel CNN architecture, specialized to regress the facial landmark coordinates of faces in specific poses and appearances. To address the shortage of training data, particularly in extreme profile poses, we additionally present data augmentation techniques designed to provide sufficient training examples for each of these specialized sub-networks. The proposed Tweaked CNN (TCNN) architecture is shown to outperform existing landmark detection methods in an extensive battery of tests on the AFW, ALFW, and 300W benchmarks. Finally, to promote reproducibility of our results, we make code and trained models publicly available through our project webpage.
The Sharing Economy Meets the Semantic Web: An Ontology for the Matchmaking of Peers

The Sharing Economy with the central theme of “access over ownership” gains popularity and is receiving a lot of coverage by the media. Companies like UBER, AIRBNB and BLABLACAR are valued in the billions and enable individuals to act as private taxi drivers, rent out their apartments or participate in ridesharing. Besides those prominent companies, there are platforms that facilitate the sharing of tools, miscellaneous types of equipment, 3D printers and even pets. A peer searching for a specific resource to borrow needs to at first identify relevant websites and then look through various offers to find an appropriate one. This laborious process can be eased by making use of Semantic Web technologies. Using the Design Science Research Methodology (DSRM), we synthesize the characteristics of various exemplary Sharing Economy websites with regard to the description of resources and services that can be shared including the transaction details and other relevant components. Following up, core concepts serving as a foundation for the conceptualization and the design of a description language in the form of an ontology are derived. The description language is based on Semantic Web technologies to enable efficient semantic search and matchmaking using established standards like RDF(S), OWL, and SPARQL. The underlying idea is to abstract from proprietary implementations in order to provide a common ground for service description in the domain of the Sharing Economy. Thus, a search against multiple websites could be facilitated to improve the discoverability and comparability of individual offers on different websites.
Constrained Nearest Neighbor Queries

In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed extensively in previous literature, the implications of constrained nearest neighbor queries have not been discussed. Due to their versatility, CNN queries are suitable to a wide range of applications from GIS systems to reverse nearest neighbor queries and multimedia applications. We develop methods for answering CNN queries with different properties and advantages. We prove the optimality (with respect to I/O cost) of one of the techniques proposed in this paper. The superiority of the proposed technique is shown by a performance analysis.
Socioeconomic factors and processes associated with domestic violence in rural Bangladesh.

Although the pervasiveness of domestic violence against women in Bangladesh is well documented, specific risk factors, particularly those that can be affected by policies and programs, are not well understood.



In 2001-2002, surveys, in-depth interviews and small group discussions were conducted with married women from six Bangladeshi villages to examine the types and severity of domestic violence, and to explore the pathways through which women's social and economic circumstances may influence their vulnerability to violence in marriage. Women's odds of experiencing domestic violence in the past year were assessed by logistic regression analysis.



Of about 1,200 women surveyed, 67% had ever experienced domestic violence, and 35% had done so in the past year. According to the qualitative findings, participants expected women with more education and income to be less vulnerable to domestic violence; they also believed (or hoped) that having a dowry or a registered marriage could strengthen a women's position in her marriage. Yet, of these potential factors, only education was associated with significantly reduced odds of violence; meanwhile, the odds were increased for women who had a dowry agreement or had personal earnings that contributed more than nominally to the marital household. Women strongly supported educating their daughters, but pressures remain to marry them early, in part to avoid high dowry costs.



In rural Bangladesh, women's social and economic circumstances may influence their risk of domestic violence in complex and contradictory ways. Findings also suggest a disconnect between women's emerging expectations and their current realities.
Using WordNet as a Knowledge Base for Measuring Semantic Similarity between Words

In this paper we propose the use of WordNet as a knowledge base in an information retrieval task. The application areas range from information filtering and document retrieval to multimedia retrieval and data sharing in large scale distributed database systems. The WordNet derived knowledge base makes semantic knowledge available which can be used in overcoming many problems associated with the richness of natural language. A semantic similarity measure is also proposed which can be used as an alternative to pattern matching in the comparison process.
Transportation mode detection using mobile phones and GIS information

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.
Marine debris in central California: quantifying type and abundance of beach litter in Monterey Bay, CA.

Monitoring beach litter is essential for reducing ecological threats towards humans and wildlife. In Monterey Bay, CA information on seasonal and spatial patterns is understudied. Central California's coastal managers require reliable information on debris abundance, distribution, and type, to support policy aimed at reducing litter. We developed a survey method that allowed for trained citizen scientists to quantify the types and abundance of beach litter. Sampling occurred from July 2009-June 2010. Litter abundance ranged from 0.03 to 17.1 items m(-2). Using a mixed model approach, we found season and location have the greatest effect on litter abundance. Styrofoam, the most numerically abundant item, made up 41% of the total amount of litter. Unexpected items included fertilizer pellets. The results of this study provide a baseline on the types and abundance of litter on the central coast and have directly supported policy banning Styrofoam take out containers from local municipalities.
Review of wireless big data in 5G: From physical layer to application layer

The 5th generation wireless communication system has become popular in recent industry and academic studies. The 5G wireless communication system has advantages in spectrum efficiency, energy efficiency, transmission speed, latency, etc. However, current key techniques could not satisfy the gradually increased service on-demand requirements. In this work, we summarize the utilization of physical layer, link layer, network layer and application layer (upper layer) using big data related technology, and propose the advantages and further possible advances in such areas. Big data technology will help the network to perform operations more precise and effectively, from historical data, environmental data, transmission and user behaviors, etc. Suggestions that how to make big data and 5G system together are concluded.
Accurate Approximations for Posterior Moments and Marginal Densities Author ( s ) :

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Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging.

A magnetic resonance imaging method is presented for quantifying the degree to which water diffusion in biologic tissues is non-Gaussian. Since tissue structure is responsible for the deviation of water diffusion from the Gaussian behavior typically observed in homogeneous solutions, this method provides a specific measure of tissue structure, such as cellular compartments and membranes. The method is an extension of conventional diffusion-weighted imaging that requires the use of somewhat higher b values and a modified image postprocessing procedure. In addition to the diffusion coefficient, the method provides an estimate for the excess kurtosis of the diffusion displacement probability distribution, which is a dimensionless metric of the departure from a Gaussian form. From the study of six healthy adult subjects, the excess diffusional kurtosis is found to be significantly higher in white matter than in gray matter, reflecting the structural differences between these two types of cerebral tissues. Diffusional kurtosis imaging is related to q-space imaging methods, but is less demanding in terms of imaging time, hardware requirements, and postprocessing effort. It may be useful for assessing tissue structure abnormalities associated with a variety of neuropathologies.
Anomaly Detection and Localization in Crowded Scenes

The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.
Food image recognition with deep convolutional features

In this paper, we report the feature obtained from the Deep Convolutional Neural Network boosts food recognition accuracy greatly by integrating it with conventional hand-crafted image features, Fisher Vectors with HoG and Color patches. In the experiments, we have achieved 72.26% as the top-1 accuracy and 92.00% as the top-5 accuracy for the 100-class food dataset, UEC-FOOD100, which outperforms the best classification accuracy of this dataset reported so far, 59.6%, greatly.
Bayesian Active Learning-Based Robot Tutor for Children's Word-Reading Skills

Effective tutoring requires personalization of the interaction to each student. Continuous and efficient assessment of the student’s skills are a prerequisite for such personalization. We developed a Bayesian activelearning algorithm that continuously and efficiently assesses a child’s word-reading skills and implemented it in a social robot. We then developed an integrated experimental paradigm in which a child plays a novel story-creation tablet game with the robot. The robot is portrayed as a younger peer who wishes to learn to read, framing the assessment of the child’s wordreading skills as well as empowering the child. We show that our algorithm results in an accurate representation of the child’s word-reading skills for a large age range, 4-8 year old children, and large initial reading skill range. We also show that employing childspecific assessment-based tutoring results in an ageand initial reading skill-independent learning, compared to random tutoring. Finally, our integrated system enables us to show that implementing the same learning algorithm on the robot’s reading skills results in knowledge that is comparable to what the child thinks the robot has learned. The child’s perception of the robot’s knowledge is age-dependent and may facilitate an indirect assessment of the development of theory-of-mind.
A Fast Lock-Free Internal Binary Search Tree

We present a new lock-free algorithm for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations. It combines ideas from two recently proposed lock-free algorithms: one of them provides good performance for a read-dominated workload and the other one for a write-dominated workload. Specifically, it uses internal representation of a search tree (as in the first one) and is based on marking edges instead of nodes (as in the second one). Our experiments indicate that our new lock-free algorithm outperforms other lock-free algorithms in most cases providing up to 35% improvement in some cases over the next best algorithm.
Deep Coattention Networks for Reading Comprehension

Machine reading comprehension of text is an important task in Natural Language Processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD) formulates the problem as question answering, and it provides a large corpus of challenging, realistic questions. To address this task, we implement an end-to-end neural encoder/decoder model. The encoder consists of the coattention model proposed by [10], which intelligently interleaves both the question and context paragraph encodings into one co-dependent knowledge representation, and the decoder is a simple linear classifier. Our experiments show that this model achieves an F1 score of 59.37% and an exact match score of 42.4% on the validation dataset.
Ray tracing animated scenes using coherent grid traversal

We present a new approach to interactive ray tracing of moderate-sized animated scenes based on traversing frustum-bounded packets of coherent rays through uniform grids. By incrementally computing the overlap of the frustum with a slice of grid cells, we accelerate grid traversal by more than a factor of 10, and achieve ray tracing performance competitive with the fastest known packet-based kd-tree ray tracers. The ability to efficiently rebuild the grid on every frame enables this performance even for fully dynamic scenes that typically challenge interactive ray tracing systems.
A Taxi Order Dispatch Model based On Combinatorial Optimization

Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.
 We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.