nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation

Software-defined network (SDN) has become one of the most important architectures for the management of largescale complex networks, which may require repolicing or reconfigurations from time to time. SDN achieves easy repolicing by decoupling the control plane from data plane. Thus, the network routers/switches just simply forward packets by following the flow table rules set by the control plane. Currently, OpenFlow is the most popular SDN protocol/standard and has a set of design specifications. Although SDN/OpenFlow is a relatively new area, it has attracted much attention from both academia and industry. In this paper, we will conduct a comprehensive survey of the important topics in SDN/OpenFlow implementation, including the basic concept, applications, language abstraction, controller, virtualization, quality of service, security, and its integration with wireless and optical networks. We will compare the pros and cons of different schemes and discuss the future research trends in this exciting area. This survey can help both industry and academia R&D people to understand the latest progress of SDN/OpenFlow designs.
RFID in textile and clothing manufacturing: technology and challenges

The rapid pace at which the technological innovations are being introduced in the world poses a potential challenge to the retailer, supplier, and enterprises. In the field of Information Technology (IT) there is a rapid growth in the last 30 years (Want 2006; Landt 2005). One of the most promising technological innovations in IT is radio frequency identification (RFID) (Dutta et al. 2007; Whitaker et al. 2007; Bottani et al. 2009). The RFID technology was evolved in 1945 as an espionage tool invented by Leon Theremin for the Soviet Government (Nikitin et al. 2013, Tedjini et al. 2012). At that time it was mainly used by the military. The progress in microchip design, antenna technology and radio spread spectrum pushed it into various applications like supply chain management, retail, automatic toll collection by tunnel companies, animal tracking, ski lift access, tracking library books, theft prevention, vehicle immobilizer systems, railway rolling stock identification, movement tracking, security, healthcare, printing, textiles and clothing (Weinstein 2005; Liu and Miao 2006; Rao et al. 2005; Wu et al. 2009; Tan 2008). RFID can make the companies more competitive by changing the related processes in supply chain, manufacturing and retailing.
Pixelated Semantic Colorization

While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art. Jiaojiao Zhao Universiteit van Amsterdam, Amsterdam, the Netherlands E-mail: j.zhao3@uva.nl Jungong Han Lancaster University, Lancaster, UK E-mail: jungonghan77@gmail.com Ling Shao Inception Institute of Artificial Intelligence, Abu Dhabi, UAE E-mail: ling.shao@ieee.org Cees G. M. Snoek Universiteit van Amsterdam, Amsterdam, the Netherlands E-mail: cgmsnoek@uva.nl
3D Human Pose Estimation with Relational Networks

In this paper, we propose a novel 3D human pose estimation algorithm from a single image based on neural networks. We adopted the structure of the relational networks in order to capture the relations among different body parts. In our method, each pair of different body parts generates features, and the average of the features from all the pairs are used for 3D pose estimation. In addition, we propose a dropout method that can be used in relational modules, which inherently imposes robustness to the occlusions. The proposed network achieves state-of-the-art performance for 3D pose estimation in Human 3.6M dataset, and it effectively produces plausible results even in the existence of missing joints.
Morphometric Analysis of Wadi Aurnah Drainage System , Western Arabian Peninsula

The analysis of drainage morphometry is usually a prerequisite to the assessment of hydrological characteristics of surface water basin. In this study, the western region of the Arabian Peninsula was selected for detailed morphometric analysis. In this region, there are a large number of drainage systems that are originated from the mountain chains of the Arabian Shield to the east and outlet into the Red Sea. As a typical type of these drainage systems, the morphometry of Wadi Aurnah was analyzed. The study performed manual and computerized delineation and drainage sampling, which enables applying detailed morphological measures. Topographic maps in combination with remotely sensed data, (i.e. different types of satellite images) were utilized to delineate the existing drainage system, thus to identify precisely water divides. This was achieved using Geographic Information System (GIS) to provide computerized data that can be manipulated for different calculations and hydrological measures. The obtained morhpometric analysis in this study tackled: 1) stream behavior, 2) morphometric setting of streams within the drainage system and 3) interrelation between connected streams. The study introduces an imperial approach of morphometric analysis that can be utilized in different hydrological assessments (e.g., surface water harvesting, flood mitigation, etc). As well as, the applied analysis using remote sensing and GIS can be followed in the rest drainage systems of the Western Arabian Peninsula.
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

Problems at the intersection of vision and language are of significant importance both as challenging research questions and for the rich set of applications they enable. However, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in models that ignore visual information, leading to an inflated sense of their capability. We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance the popular VQA dataset (Antol et al., ICCV 2015) by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset is publicly available at http://visualqa.org/ as part of the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA v2.0). We further benchmark a number of state-of-art VQA models on our balanced dataset. All models perform significantly worse on our balanced dataset, suggesting that these models have indeed learned to exploit language priors. This finding provides the first concrete empirical evidence for what seems to be a qualitative sense among practitioners. Finally, our data collection protocol for identifying complementary images enables us to develop a novel interpretable model, which in addition to providing an answer to the given (image, question) pair, also provides a counter-example based explanation. Specifically, it identifies an image that is similar to the original image, but it believes has a different answer to the same question. This can help in building trust for machines among their users.
Evaluation of initial cryptoasset offerings: the state of the practice

Initial Cryptoasset Offering (ICO), also often called Initial Coin Offering or Initial Token Offering (ITO) is a new means of fundraising through blockchain technology, which allows startups to raise large amounts of funds from the crowd in an unprecedented speed. However it is not easy for ordinary investors to distinguish genuine fundraising activities through ICOs from scams. Different websites that gather and evaluate ICOs at different stages have emerged as a solution to this issue. What remains unclear is how these websites are evaluating ICOs, and consequently how reliable and credible their evaluations are. In this paper we present the first findings of an analysis of a set of 28 ICO evaluation websites, aiming at revealing the state of the practice in terms of ICO evaluation. Key information about ICOs collected by these websites are categorised, and key factors that differentiate the evaluation mechanisms employed by these evaluation websites are identified. The findings of our study could help a better understanding of what entails to properly evaluate ICOs. It is also a first step towards discovering the key success factors of ICOs.
A Survey of Security Attacks in Information-Centric Networking

Information-centric networking (ICN) is a new communication paradigm that focuses on content retrieval from a network regardless of the storage location or physical representation of this content. In ICN, securing the content itself is much more important than securing the infrastructure or the endpoints. To achieve the security goals in this new paradigm, it is crucial to have a comprehensive understanding of ICN attacks, their classification, and proposed solutions. In this paper, we provide a survey of attacks unique to ICN architectures and other generic attacks that have an impact on ICN. It also provides a taxonomy of these attacks in ICN, which are classified into four main categories, i.e., naming, routing, caching, and other miscellaneous related attacks. Furthermore, this paper shows the relation between ICN attacks and unique ICN attributes, and that between ICN attacks and security requirements, i.e., confidentiality, integrity, availability, and privacy. Finally, this paper presents the severity levels of ICN attacks and discusses the existing ICN security solutions.
Knowledge sharing models: Do they really fit public organizations?

Literature in knowledge management reveals that there are a number of knowledge sharing (KS) models. A range of models exists as a result of differed view on the subject which is broad and subjective. Some of the models address the antecedents or factors influencing KS whilst the others address the relationship between KS and performance. The benefit of KS cannot be materialized if it does not contribute to organizational attribute. Despite the variety of models, only a few focuses on KS in public sectors. This could resulted from a situation whereby KS is at its infancy in public services.. This paper reviews the existing models of KS and upon a critical review, a KS model which is thought to be suitable for used by public sector in Malaysia is then proposed. The proposed model provides foundation for subsequent research to firstly investigate factors affecting KS in public sector and secondly, seek to identify the relationship between KS, organizational performance and service delivery.
Grit: perseverance and passion for long-term goals.

The importance of intellectual talent to achievement in all professional domains is well established, but less is known about other individual differences that predict success. The authors tested the importance of 1 noncognitive trait: grit. Defined as perseverance and passion for long-term goals, grit accounted for an average of 4% of the variance in success outcomes, including educational attainment among 2 samples of adults (N=1,545 and N=690), grade point average among Ivy League undergraduates (N=138), retention in 2 classes of United States Military Academy, West Point, cadets (N=1,218 and N=1,308), and ranking in the National Spelling Bee (N=175). Grit did not relate positively to IQ but was highly correlated with Big Five Conscientiousness. Grit nonetheless demonstrated incremental predictive validity of success measures over and beyond IQ and conscientiousness. Collectively, these findings suggest that the achievement of difficult goals entails not only talent but also the sustained and focused application of talent over time.
Stochastic Answer Networks for Natural Language Inference

We propose a stochastic answer network (SAN) to explore multi-step inference strategies in Natural Language Inference. Rather than directly predicting the results given the inputs, the model maintains a state and iteratively refines its predictions. Our experiments show that SAN achieves the state-of-the-art results on three benchmarks: Stanford Natural Language Inference (SNLI) dataset, MultiGenre Natural Language Inference (MultiNLI) dataset and Quora Question Pairs dataset.
Effect of nutrition education intervention based on Pender's Health Promotion Model in improving the frequency and nutrient intake of breakfast consumption among female Iranian students.

To determine the effectiveness of nutrition education intervention based on Pender's Health Promotion Model in improving the frequency and nutrient intake of breakfast consumption among female Iranian students.



The quasi-experimental study based on Pender's Health Promotion Model was conducted during April-June 2011. Information (data) was collected by self-administered questionnaire. In addition, a 3 d breakfast record was analysed. P < 0·05 was considered significant.



Two middle schools in average-income areas of Qom, Iran.



One hundred female middle-school students.



There was a significant reduction in immediate competing demands and preferences, perceived barriers and negative activity-related affect constructs in the experimental group after education compared with the control group. In addition, perceived benefit, perceived self-efficacy, positive activity-related affect, interpersonal influences, situational influences, commitment to a plan of action, frequency and intakes of macronutrients and most micronutrients of breakfast consumption were also significantly higher in the experimental group compared with the control group after the nutrition education intervention.



Constructs of Pender's Health Promotion Model provide a suitable source for designing strategies and content of a nutrition education intervention for improving the frequency and nutrient intake of breakfast consumption among female students.
A pooling based scene text proposal technique for scene text reading in the wild

Automatic reading texts in scenes has attracted increasing interest in recent years as texts often carry rich semantic information that is useful for scene understanding. In this paper, we propose a novel scene text proposal technique aiming for accurate reading texts in scenes. Inspired by the pooling layer in the deep neural network architecture, a pooling based scene text proposal technique is developed. A novel score function is designed which exploits the histogram of oriented gradients and is capable of ranking the proposals according to their probabilities of being text. An end-to-end scene text reading system has also been developed by incorporating the proposed scene text proposal technique where false alarms elimination and words recognition are performed simultaneously. Extensive experiments over several public datasets show that the proposed technique can handle multi-orientation and multi-language scene texts and obtains outstanding proposal performance. The developed end-to-end systems also achieve very competitive scene text spotting and reading performance.  2018 ar X iv :1 81 1. 10 00 3v 1 [ cs .C V ] 2 5 N ov 2 01 8
Internet addiction: coping styles, expectancies, and treatment implications

Internet addiction (IA) has become a serious mental health condition in many countries. To better understand the clinical implications of IA, this study tested statistically a new theoretical model illustrating underlying cognitive mechanisms contributing to development and maintenance of the disorder. The model differentiates between a generalized Internet addiction (GIA) and specific forms. This study tested the model on GIA on a population of general Internet users. The findings from 1019 users show that the hypothesized structural equation model explained 63.5% of the variance of GIA symptoms, as measured by the short version of the Internet Addiction Test. Using psychological and personality testing, the results show that a person's specific cognitions (poor coping and cognitive expectations) increased the risk for GIA. These two factors mediated the symptoms of GIA if other risk factors were present such as depression, social anxiety, low self-esteem, low self-efficacy, and high stress vulnerability to name a few areas that were measured in the study. The model shows that individuals with high coping skills and no expectancies that the Internet can be used to increase positive or reduce negative mood are less likely to engage in problematic Internet use, even when other personality or psychological vulnerabilities are present. The implications for treatment include a clear cognitive component to the development of GIA and the need to assess a patient's coping style and cognitions and improve faulty thinking to reduce symptoms and engage in recovery.
A Deep Reinforcement Learning Chatbot

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.
The Instrumented Sit-to-Stand Test (iSTS) Has Greater Clinical Relevance than the Manually Recorded Sit-to-Stand Test in Older Adults

The ability to rise from sitting to standing is critical to an individual's quality of life, as it is a prerequisite for functional independence. The purpose of the current study was to examine the hypothesis that test durations as assessed with the instrumented repeated Sit-To-Stand (STS) show stronger associations with health status, functional status and daily physical activity of older adults than manually recorded test durations.



In 63 older participants (mean age 83 ±6.9 years, 51 female), health status was assessed using the European Quality of Life questionnaire and functional status was assessed using the physical function index of the of the RAND-36. Physical performance was measured using a wearable sensor-based STS test. From this test, durations, sub-durations and kinematics of the STS movements were estimated and analysed. In addition, physical activity was measured for one week using an activity monitor and episodes of lying, sitting, standing and locomotion were identified. Associations between STS parameters with health status, functional status and daily physical activity were assessed.



The manually recorded STS times were not significantly associated with health status (p = 0.457) and functional status (p = 0.055), whereas the instrumented STS times were (both p = 0.009). The manually recorded STS durations showed a significant association to daily physical activity for mean sitting durations (p = 0.042), but not for mean standing durations (p = 0.230) and mean number of locomotion periods (p = 0.218). Furthermore, durations of the dynamic sit-to-stand phase of the instrumented STS showed more significant associations with health status, functional status and daily physical activity (all p = 0.001) than the static phases standing and sitting (p = 0.043-0.422).



As hypothesized, instrumented STS durations were more strongly associated with participant health status, functional status and physical activity than manually recorded STS durations in older adults. Furthermore, instrumented STS allowed assessment of the dynamic phases of the test, which were likely more informative than the static sitting and standing phases.
Highly integrated sensor-actuator-controller units for modular robot design

We present highly integrated sensor-actuator-controller units (SAC units), addressing the increasing need for easy to use components in the design of modern high-performance robotic systems. Following strict design principles and an electro-mechanical co-design from the beginning on, our development resulted in highly integrated SAC units. Each SAC unit includes a motor, a gear unit, an IMU, sensors for torque, position and temperature as well as all necessary embedded electronics for control and communication over a high-speed EtherCAT bus. Key design considerations were easy to use interfaces and a robust cabling system. Using slip rings to electrically connect the input and output side, the units allow continuous rotation even when chained along a robotic arm. The experimental validation shows the potential of the new SAC units regarding the design of humanoid robots.
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Stochastic neurons and hard non-linearities can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic or non-smooth neurons? I.e., can we “back-propagate” through these stochastic neurons? We examine this question, existing approaches, and compare four families of solutions, applicable in different settings. One of them is the minimum variance unbiased gradient estimator for stochatic binary neurons (a special case of the REINFORCE algorithm). A second approach, introduced here, decomposes the operation of a binary stochastic neuron into a stochastic binary part and a smooth differentiable part, which approximates the expected effect of the pure stochatic binary neuron to first order. A third approach involves the injection of additive or multiplicative noise in a computational graph that is otherwise differentiable. A fourth approach heuristically copies the gradient with respect to the stochastic output directly as an estimator of the gradient with respect to the sigmoid argument (we call this the straight-through estimator). To explore a context where these estimators are useful, we consider a small-scale version of conditional computation, where sparse stochastic units form a distributed representation of gaters that can turn off in combinatorially many ways large chunks of the computation performed in the rest of the neural network. In this case, it is important that the gating units produce an actual 0 most of the time. The resulting sparsity can be potentially be exploited to greatly reduce the computational cost of large deep networks for which conditional computation would be useful.