nils-herrmann/clean_scirepeval huggingface dataset:
Customer behavior in electronic commerce : The moderating effect of e-purchasing experience

a r t i c l e i n f o This study analyzes the perceptions which induce customers to purchase over the Internet, testing the moderating effect of e-purchasing experience. We distinguish between two groups: (1) potential e-customers, who are considering making their first e-purchase, and (2) experienced e-customers, who have made at least one e-purchase and are thinking about continuing to do so. The perceptions that induce individuals to purchase online for the first time may not be the same as those that produce repurchasing behavior. Our findings demonstrate that customer behavior does not remain stable because the experience acquired from past e-purchases means that perceptions evolve. The relationships between perceptions of e-commerce change with purchasing experience, whilst the influence of Internet experience is stable for all users. The implications are especially interesting for e-commerce providers whose business models depend on e-customer behavior. The analysis of consumer behavior is a key aspect for the success of an e-business. However, the behavior of consumers in the Internet market changes as they acquire e-purchasing experience (Gefen et al., 2003; Yu et al., 2005). The perceptions which induce them to make an initial e-purchase may have different effects on their subsequent decisions or repurchasing behavior because the use of the information technology (IT) may modify certain perceptions and attitudes (Thompson et al. Despite these differences, very little research carried out in the e-commerce field has conducted a separate analysis of the perceptions related to the adoption and to the " post-adoption " decisions (Karahanna et al., 1999; Vijayasarathy, 2004). Moreover, hardly any researchers have analyzed the behavior of e-customers as they gain experience (as Taylor and Todd, 1995; Vijayasarathy, 2004 state). Most studies have considered that the low level of development of this new channel meant that the differences between the two decisions were not yet significant, and their principal objective was, therefore, to determine the perceptions which led consumers to adopt the Internet as an alternative shopping channel to the offline market (Chen et al., 2002; Verhagen et al., 2006). Nevertheless, the growth of e-commerce has made it clear that customer behavior has evolved. As in other types of purchase situations (Sheth, 1968; Heilman et al., 2000), customer behavior does not necessarily remain stable over time since the experience acquired from past purchases means that perceptions change (Taylor and Todd, 1995; Yu et al., 2005). When customers repeat their behavior …
MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable

Dropout prediction in MOOCs is a well-researched problem where we classify which students are likely to persist or drop out of a course. Most research into creating models which can predict outcomes is based on student engagement data. Why these students might be dropping out has only been studied through retroactive exit surveys. This helps identify an important extension area to dropout prediction— how can we interpret dropout predictions at the student and model level? We demonstrate how existing MOOC dropout prediction pipelines can be made interpretable, all while having predictive performance close to existing techniques. We explore each stage of the pipeline as design components in the context of interpretability. Our end result is a layer which longitudinally interprets both predictions and entire classification models of MOOC dropout to provide researchers with in-depth insights of why a student is likely to dropout.
Learning descriptors for object recognition and 3D pose estimation

Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

Photorealistic frontal view synthesis from a single face image has a wide range of applications in the field of face recognition. Although data-driven deep learning methods have been proposed to address this problem by seeking solutions from ample face data, this problem is still challenging because it is intrinsically ill-posed. This paper proposes a Two-Pathway Generative Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by simultaneously perceiving global structures and local details. Four landmark located patch networks are proposed to attend to local textures in addition to the commonly used global encoderdecoder network. Except for the novel architecture, we make this ill-posed problem well constrained by introducing a combination of adversarial loss, symmetry loss and identity preserving loss. The combined loss function leverages both frontal face distribution and pre-trained discriminative deep face models to guide an identity preserving inference of frontal views from profiles. Different from previous deep learning methods that mainly rely on intermediate features for recognition, our method directly leverages the synthesized identity preserving image for downstream tasks like face recognition and attribution estimation. Experimental results demonstrate that our method not only presents compelling perceptual results but also outperforms state-of-theart results on large pose face recognition.
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs

The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal variations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial appearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures.
Pain catastrophizing and kinesiophobia: predictors of chronic low back pain.

By using a population-based cohort of the general Dutch population, the authors studied whether an excessively negative orientation toward pain (pain catastrophizing) and fear of movement/(re)injury (kinesiophobia) are important in the etiology of chronic low back pain and associated disability, as clinical studies have suggested. A total of 1,845 of the 2,338 inhabitants (without severe disease) aged 25-64 years who participated in a 1998 population-based questionnaire survey on musculoskeletal pain were sent a second questionnaire after 6 months; 1,571 (85 percent) participated. For subjects with low back pain at baseline, a high level of pain catastrophizing predicted low back pain at follow-up (odds ratio (OR) = 1.7, 95% confidence interval (CI): 1.0, 2.8) and chronic low back pain (OR = 1.7, 95% CI: 1.0, 2.3), in particular severe low back pain (OR = 3.0, 95% CI: 1.7, 5.2) and low back pain with disability (OR = 3.0, 95% CI: 1.7, 5.4). A high level of kinesiophobia showed similar associations. The significant associations remained after adjustment for pain duration, pain severity, or disability at baseline. For those without low back pain at baseline, a high level of pain catastrophizing or kinesiophobia predicted low back pain with disability during follow-up. These cognitive and emotional factors should be considered when prevention programs are developed for chronic low back pain and related disability.
OPERATIONS OF A SHARED , AUTONOMOUS , ELECTRIC VEHICLE FLEET : IMPLICATIONS OF VEHICLE & CHARGING INFRASTRUCTURE DECISIONS

The nexus of autonomous vehicle (AV) and electric vehicle (EV) technologies has important potential impacts on our transportation systems, particularly in the case of shared-use vehicles. There are natural synergies between shared AV fleets and EV technology, since fleets of AVs resolve the practical limitations of today’s non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous (battery-only) electric vehicles (SAEVs) in a regional discrete-time, agent-based model. The simulation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas. Results indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. SAEVs can serve 96 to 98% of trip requests with average wait times between 7 and 10 minutes per trip. However, due to the need to travel while “empty” for charging and passenger pick-up, SAEV fleets are predicted to generate an additional 7.1 to 14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low mileage households, and thus be competitive with current manually-driven carsharing services and significantly cheaper than on-demand driver-operated transportation services. The availability of inductive (wireless) charging infrastructure allows SAEVs to be price-competitive with nonelectric SAVs (when gasoline prices are between $2.18 and $3.50 per gallon). However, charging SAEVs at attendant-operated stations with traditional corded chargers incurs an additional $0.08 per mile compared to wireless charging, and as such would only be price-competitive with SAVs when gasoline reaches $4.35 to $5.70 per gallon.
Kafka versus RabbitMQ: A comparative study of two industry reference publish/subscribe implementations: Industry Paper

Publish/subscribe is a distributed interaction paradigm well adapted to the deployment of scalable and loosely coupled systems.
 Apache Kafka and RabbitMQ are two popular open-source and commercially-supported pub/sub systems that have been around for almost a decade and have seen wide adoption. Given the popularity of these two systems and the fact that both are branded as pub/sub systems, two frequently asked questions in the relevant online forums are: how do they compare against each other and which one to use?
 In this paper, we frame the arguments in a holistic approach by establishing a common comparison framework based on the core functionalities of pub/sub systems. Using this framework, we then venture into a qualitative and quantitative (i.e. empirical) comparison of the common features of the two systems. Additionally, we also highlight the distinct features that each of these systems has. After enumerating a set of use cases that are best suited for RabbitMQ or Kafka, we try to guide the reader through a determination table to choose the best architecture given his/her particular set of requirements.
Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors

In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low-power write operations for the learning and implement short-term association using temporal coding and spike-timing-dependent plasticity–based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.
Team AnnieWAY's Autonomous System

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the DARPA Urban Challenge 2007 competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. A recent laser scanner plays the prominent role in the perception of the environment. It measures range and reflectivity for each pixel. While the former is used to provide 3D scene geometry, the latter allows robust lane marker detection. Mission and maneuver selection is conducted via a concurrent hierarchical state machine that specifically ascertains behavior in accordance with California traffic rules. We conclude with a report of the results achieved during the competition.
Contrast-Oriented Deep Neural Networks for Salient Object Detection

Deep convolutional neural networks (CNNs) have become a key element in the recent breakthrough of salient object detection. However, existing CNN-based methods are based on either patchwise (regionwise) training and inference or fully convolutional networks. Methods in the former category are generally time-consuming due to severe storage and computational redundancies among overlapping patches. To overcome this deficiency, methods in the second category attempt to directly map a raw input image to a predicted dense saliency map in a single network forward pass. Though being very efficient, it is arduous for these methods to detect salient objects of different scales or salient regions with weak semantic information. In this paper, we develop hybrid contrast-oriented deep neural networks to overcome the aforementioned limitations. Each of our deep networks is composed of two complementary components, including a fully convolutional stream for dense prediction and a segment-level spatial pooling stream for sparse saliency inference. We further propose an attentional module that learns weight maps for fusing the two saliency predictions from these two streams. A tailored alternate scheme is designed to train these deep networks by fine-tuning pretrained baseline models. Finally, a customized fully connected conditional random field model incorporating a salient contour feature embedding can be optionally applied as a postprocessing step to improve spatial coherence and contour positioning in the fused result from these two streams. Extensive experiments on six benchmark data sets demonstrate that our proposed model can significantly outperform the state of the art in terms of all popular evaluation metrics.
Consanguinity and its relevance to clinical genetics

Universit g.2013.0 Abstract Consanguineous marriages have been practiced since the early existence of modern humans. Until now, consanguinity is widely practiced in several global communities with variable rates. The present study was undertaken to analyze the effect of consanguinity on different types of genetic diseases and child morbidity and mortality. Patients were grouped according to the types of genetic errors into four groups: Group I: Chromosomal and microdeletion syndromes. Group II: Single gene disorders. Group III: Multifactorial disorders. Group IV: Diseases of different etiologies. Consanguineous marriage was highly significant in 54.4% of the studied group compared to 35.3% in the control group (P < 0.05). Consanguineous marriages were represented in 31.4%, 7.1%, 0.8%, 6%, 9.1% among first cousins, one and a half cousins, double first cousins, second cousins and remote relatives respectively in the studied group. Comparison between genetic diseases with different modes of inheritance showed that recessive and multifactorial disorders had the highest values of consanguinity (78.8%, 69.8%, respectively), while chromosomal disorders had the lowest one (29.1%). Consanguineous marriage was recorded in 51.5% of our cases with autosomal dominant diseases and in 31% of cases with X linked diseases, all cases of mental retardation (100%) and in 92.6% of patients with limb anomalies (P < 0.001). Stillbirths, child deaths and recurrent abortions were significantly increased among consanguineous parents (80.6%, 80%, 67%) respectively than among non consanguineous parents. In conclusion, consanguineous marriage is significantly higher in many genetic diseases which suggests that couples may have deleterious lethal genes, inherited from common ancestor and when transmitted to their offsprings, they can lead to prenatal, neonatal, child morbidity or mortality. So public health education and genetic counseling are highly recommended in our community.
Performance Engineering of Software Systems

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Smart cities in the new service economy: building platforms for smart services

Recent changes in service environments have changed the preconditions of their production and consumption. These changes include unbundling services from production processes, growth of the information-rich economy and society, the search for creativity in service production and consumption and continuing growth of digital technologies. These contextual changes affect city governments because they provide a range of infrastructure and welfare services to citizens. Concepts such as ‘smart city’, ‘intelligent city’ and ‘knowledge city’ build new horizons for cities in undertaking their challenging service functions in an increasingly cost-conscious, competitive and environmentally oriented setting. What is essential in practically all of them is that they paint a picture of cities with smooth information processes, facilitation of creativity and innovativeness, and smart and sustainable solutions promoted through service platforms. This article discusses this topic, starting from the nature of services and the new service economy as the context of smart local public services. On this basis, we build an overall framework for understanding the basic forms and dimensions of smart public services. The focus is on conceptual systematisation of the key dimensions of smart services and the conceptual modelling of smart service platforms through which digital technology is increasingly embedded in social creativity. We provide examples of real-life smart service applications within the European context.
Geographic and Temporal Trends in Fake News Consumption During the 2016 US Presidential Election

We present an analysis of traffic to websites known for publishing fake news in the months preceding the 2016 US presidential election. The study is based on the combined instrumentation data from two popular desktop web browsers: Internet Explorer 11 and Edge. We find that social media was the primary outlet for the circulation of fake news stories and that aggregate voting patterns were strongly correlated with the average daily fraction of users visiting websites serving fake news. This correlation was observed both at the state level and at the county level, and remained stable throughout the main election season. We propose a simple model based on homophily in social networks to explain the linear association. Finally, we highlight examples of different types of fake news stories: while certain stories continue to circulate in the population, others are short-lived and die out in a few days.
Text Mining in Social Networks

Social networks are rich in various kinds of contents such as text and multimedia. The ability to apply text mining algorithms effectively in the context of text data is critical for a wide variety of applications. Social networks require text mining algorithms for a wide variety of applications such as keyword search, classi cation, and clustering. While search and classi cation are well known applications for a wide variety of scenarios, social networks have a much richer structure both in terms of text and links. Much of the work in the area uses either purely the text content or purely the linkage structure. However, many recent algorithms use a combination of linkage and content information for mining purposes. In many cases, it turns out that the use of a combination of linkage and content information provides much more effective results than a system which is based purely on either of the two. This paper provides a survey of such algorithms, and the advantages observed by using such algorithms in different scenarios. We also present avenues for future research in this area.
An adaptive color-based particle filter

Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non-linear and nonGaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Color distributions are applied, as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. As the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters, the target model is adapted during temporally stable image observations. An initialization based on an appearance condition is introduced since tracked objects may disappear and reappear. Comparisons with the mean shift tracker and a combination between the mean shift tracker and Kalman filtering show the advantages and limitations of the new approach. q 2002