nils-herrmann/clean_scirepeval huggingface dataset:
LCARS: A Spatial Item Recommender System

Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge to the traditional collaborative filtering-based recommender systems. The problem becomes even more challenging when people travel to a new city where they have no activity information.
 In this article, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants and shopping malls) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item cooccurrence patterns and exploiting item contents. The online recommendation part takes a querying user along with a querying city as input, and automatically combines the learned interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up the online process, a scalable query processing technique is developed by extending both the Threshold Algorithm (TA) and TA-approximation algorithm. We evaluate the performance of our recommender system on two real datasets, that is, DoubanEvent and Foursquare, and one large-scale synthetic dataset. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency. Besides, the experimental analysis results also demonstrate the excellent interpretability of LCARS.
Visualizing Media Bias through

Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Therefore, tracking bias in everyday news and building a platform where people can receive balanced news information is important. We propose a model that maps the news media sources along a dimensional dichotomous political spectrum using the co-subscriptions relationships inferred by Twitter links. By analyzing 7 million follow links, we show that the political dichotomy naturally arises on Twitter when we only consider direct media subscription. Furthermore, we demonstrate a real-time Twitter-based application that visualizes an ideological map of various media sources.
A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges

The Internet of Things (IoT) is a promising technology which tends to revolutionize and connect the global world via heterogeneous smart devices through seamless connectivity. The current demand for machine-type communications (MTC) has resulted in a variety of communication technologies with diverse service requirements to achieve the modern IoT vision. More recent cellular standards like long-term evolution (LTE) have been introduced for mobile devices but are not well suited for low-power and low data rate devices such as the IoT devices. To address this, there is a number of emerging IoT standards. Fifth generation (5G) mobile network, in particular, aims to address the limitations of previous cellular standards and be a potential key enabler for future IoT. In this paper, the state-of-the-art of the IoT application requirements along with their associated communication technologies are surveyed. In addition, the third generation partnership project cellular-based low-power wide area solutions to support and enable the new service requirements for Massive to Critical IoT use cases are discussed in detail, including extended coverage global system for mobile communications for the Internet of Things, enhanced machine-type communications, and narrowband-Internet of Things. Furthermore, 5G new radio enhancements for new service requirements and enabling technologies for the IoT are introduced. This paper presents a comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT. The challenges and open research directions pertinent to the deployment of massive to critical IoT applications are also presented in coming up with an efficient context-aware congestion control mechanism.
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks

In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant. On the other hand, unsupervised deep learning approaches for localization and mapping in unknown environments from unlabelled data have received comparatively less attention in VO research. In this study, we propose a generative unsupervised learning framework that predicts 6-DoF pose camera motion and monocular depth map of the scene from unlabelled RGB image sequences, using deep convolutional Generative Adversarial Networks (GANs). We create a supervisory signal by warping view sequences and assigning the re-projection minimization to the objective loss function that is adopted in multi-view pose estimation and single-view depth generation network. Detailed quantitative and qualitative evaluations of the proposed framework on the KITTI  and Cityscapes  datasets show that the proposed method outperforms both existing traditional and unsupervised deep VO methods providing better results for both pose estimation and depth recovery.
Instance-Dependent PU Learning by Bayesian Optimal Relabeling

When learning from positive and unlabelled data, it is a strong assumption that the positive observations are randomly sampled from the distribution of X conditional on Y = 1, where X stands for the feature and Y the label. Most existing algorithms are optimally designed under the assumption. However, for many realworld applications, the observed positive examples are dependent on the conditional probability P (Y = 1|X) and should be sampled biasedly. In this paper, we assume that a positive example with a higher P (Y = 1|X) is more likely to be labelled and propose a probabilistic-gap based PU learning algorithms. Specically, by treating the unlabelled data as noisy negative examples, we could automatically label a group positive and negative examples whose labels are identical to the ones assigned by a Bayesian optimal classier with a consistency guarantee. e relabelled examples have a biased domain, which is remedied by the kernel mean matching technique. e proposed algorithm is model-free and thus do not have any parameters to tune. Experimental results demonstrate that our method works well on both generated and real-world datasets.  Sydney Articial Intelligence Centre and the School of Information Technologies, Faculty of Engineering and Information Technologies, e University of Sydney, Darlington, NSW 2008, ; ; dacheng.tao@sydney.edu.au 7 A ug 2 01 8
Sustainable Treatment of Aquaculture Effluents — What Can We Learn from the Past for the Future ?

Many aquaculture systems generate high amounts of wastewater containing compounds such as suspended solids, total nitrogen and total phosphorus. Today, aquaculture is imperative because fish demand is increasing. However, the load of waste is directly proportional to the fish production. Therefore, it is necessary to develop more intensive fish culture with efficient systems for wastewater treatment. A number of physical, chemical and biological methods used in conventional wastewater treatment have been applied in aquaculture systems. Constructed wetlands technology is becoming more and more important in recirculating aquaculture systems (RAS) because wetlands have proven to be well-established and a cost-effective method for treating wastewater. This review gives an overview about possibilities to avoid the pollution of water resources; it focuses initially on the use of systems combining aquaculture and plants with a historical review of aquaculture and the treatment of its effluents. It discusses the present state, taking into account the load of pollutants in wastewater such as nitrates and phosphates, and finishes with recommendations to prevent or at least reduce the pollution of water resources in the future.
Closed Kinetic Chain Upper Extremity Stability test (CKCUES test): a reliability study in persons with and without shoulder impingement syndrome

The Close Kinetic Chain Upper Extremity Stability Test (CKCUES test) is a low cost shoulder functional test that could be considered as a complementary and objective clinical outcome for shoulder performance evaluation. However, its reliability was tested only in recreational athletes' males and there are no studies comparing scores between sedentary and active samples. The purpose was to examine inter and intrasession reliability of CKCUES Test for samples of sedentary male and female with (SIS), for samples of sedentary healthy male and female, and for male and female samples of healthy upper extremity sport specific recreational athletes. Other purpose was to compare scores within sedentary and within recreational athletes samples of same gender.



A sample of 108 subjects with and without SIS was recruited. Subjects were tested twice, seven days apart. Each subject performed four test repetitions, with 45 seconds of rest between them. The last three repetitions were averaged and used to statistical analysis. Intraclass Correlation Coefficient ICC2,1 was used to assess intrasession reliability of number of touches score and ICC2,3 was used to assess intersession reliability of number of touches, normalized score, and power score. Test scores within groups of same gender also were compared. Measurement error was determined by calculating the Standard Error of the Measurement (SEM) and Minimum detectable change (MDC) for all scores.



The CKCUES Test showed excellent intersession reliability for scores in all samples. Results also showed excellent intrasession reliability of number of touches for all samples. Scores were greater in active compared to sedentary, with exception of power score. All scores were greater in active compared to sedentary and SIS males and females. SEM ranged from 1.45 to 2.76 touches (based on a 95% CI) and MDC ranged from 2.05 to 3.91(based on a 95% CI) in subjects with and without SIS. At least three touches are needed to be considered a real improvement on CKCUES Test scores.



Results suggest CKCUES Test is a reliable tool to evaluate upper extremity functional performance for sedentary, for upper extremity sport specific recreational, and for sedentary males and females with SIS.
Independent wheel torque control of 4WD electric vehicle for differential drive assisted steering

In-wheel motors have tremendous potential to create an advanced all-wheel drive system. In this paper, a novel power assisted steering technology and its torque distribution control system were proposed, due to the independent driving characteristics of four-wheel-independent-drive electric vehicle. The first part of this study deals with the full description of the basic theory of differential drive assisted steering system. After that, 4-wheel-drive (4WD) electric vehicle dynamics model as well as driver model were built. Furthermore, the differential drive assisted steering control system, as well as the drive torque distribution and compensation control system, was also presented. Therein, the proportional–integral (PI) feedback control loop was employed to track the reference steering effort by controlling the drive torque distribution between the two sides wheels of the front axle. After that, the direct yaw moment control subsystem and the traction control subsystem were introduced, which were both employed to make the differential drive assisted steering work as well as wished. Finally, the open-loop and closed-loop simulation for validation were performed. The results verified that, the proposed differential drive torque assisted steering system cannot only reduce the steering efforts significantly, as well as ensure a stiffer steering feel at high vehicle speed and improve the returnability of the vehicle, but also keep the lateral stability of the vehicle.
Plant Phenolics: Extraction, Analysis and Their Antioxidant and Anticancer Properties

Phenolics are broadly distributed in the plant kingdom and are the most abundant secondary metabolites of plants. Plant polyphenols have drawn increasing attention due to their potent antioxidant properties and their marked effects in the prevention of various oxidative stress associated diseases such as cancer. In the last few years, the identification and development of phenolic compounds or extracts from different plants has become a major area of health- and medical-related research. This review provides an updated and comprehensive overview on phenolic extraction, purification, analysis and quantification as well as their antioxidant properties. Furthermore, the anticancer effects of phenolics in-vitro and in-vivo animal models are viewed, including recent human intervention studies. Finally, possible mechanisms of action involving antioxidant and pro-oxidant activity as well as interference with cellular functions are discussed.
The Mathematical Side of

W hile the mathematical side of Dutch graphic artist M. C. Escher (1898– 1972) is often acknowledged, few of his admirers are aware of the mathematical depth of his work. Probably not since the Renaissance has an artist engaged in mathematics to the extent that Escher did, with the sole purpose of understanding mathematical ideas in order to employ them in his art. Escher consulted mathematical publications and interacted with mathematicians. He used mathematics (especially geometry) in creating many of his drawings and prints. Several of his prints celebrate mathematical forms. Many prints provide visual metaphors for abstract mathematical concepts; in particular, Escher was obsessed with the depiction of infinity. His work has sparked investigations by scientists and mathematicians. But most surprising of all, for several years Escher carried out his own mathematical research, some of which anticipated later discoveries by mathematicians. And yet with all this, Escher steadfastly denied any ability to understand or do mathematics. His son George explains:
Digital image processing - principles and applications

In undergoing this life, many people always try to do and get the best. New knowledge, experience, lesson, and everything that can improve the life will be done. However, many people sometimes feel confused to get those things. Feeling the limited of experience and sources to be better is one of the lacks to own. However, there is a very simple thing that can be done. This is what your teacher always manoeuvres you to do this one. Yeah, reading is the answer. Reading a book as this digital image processing principles and applications and other references can enrich your life quality. How can it be?
Determinants of health policy impact: a theoretical framework for policy analysis

This paper addresses the role of policy and evidence in health promotion. The concept of von Wright’s “logic of events” is introduced and applied to health policy impact analysis. According to von Wright (1976), human action can be explained by a restricted number of determinants: wants, abilities, duties, and opportunities. The dynamics of action result from changes in opportunities (logic of events). Applied to the policymaking process, the present model explains personal wants as subordinated to political goals. Abilities of individual policy makers are part of organisational resources. Also, personal duties are subordinated to institutional obligations. Opportunities are mainly related to political context and public support. The present analysis suggests that policy determinants such as concrete goals, sufficient resources and public support may be crucial for achieving an intended behaviour change on the population level, while other policy determinants, e.g., personal commitment and organisational capacities, may especially relate to the policy implementation process. The paper concludes by indicating ways in which future research using this theoretical framework might contribute to health promotion practice for improved health outcomes across populations.
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems

This paper presents a large-scale corpus for non-task-oriented dialogue response selection, which contains over 27K distinct prompts more than 82K responses collected from social media.1 To annotate this corpus, we define a 5-grade rating scheme: bad, mediocre, acceptable, good, and excellent, according to the relevance, coherence, informativeness, interestingness, and the potential to move a conversation forward. To test the validity and usefulness of the produced corpus, we compare various unsupervised and supervised models for response selection. Experimental results confirm that the proposed corpus is helpful in training response selection models.
Deep Convolutional Neural Network for Natural Image Matting Using Initial Alpha Mattes

We propose a deep convolutional neural network (CNN) method for natural image matting. Our method takes multiple initial alpha mattes of the previous methods and normalized RGB color images as inputs, and directly learns an end-to-end mapping between the inputs and reconstructed alpha mattes. Among the various existing methods, we focus on using two simple methods as initial alpha mattes: the closed-form matting and KNN matting. They are complementary to each other in terms of local and nonlocal principles. A major benefit of our method is that it can “recognize” different local image structures and then combine the results of local (closed-form matting) and nonlocal (KNN matting) mattings effectively to achieve higher quality alpha mattes than both of the inputs. Furthermore, we verify extendability of the proposed network to different combinations of initial alpha mattes from more advanced techniques such as KL divergence matting and information-flow matting. On the top of deep CNN matting, we build an RGB guided JPEG artifacts removal network to handle JPEG block artifacts in alpha matting. Extensive experiments demonstrate that our proposed deep CNN matting produces visually and quantitatively high-quality alpha mattes. We perform deeper experiments including studies to evaluate the importance of balancing training data and to measure the effects of initial alpha mattes and also consider results from variant versions of the proposed network to analyze our proposed DCNN matting. In addition, our method achieved high ranking in the public alpha matting evaluation dataset in terms of the sum of absolute differences, mean squared errors, and gradient errors. Also, our RGB guided JPEG artifacts removal network restores the damaged alpha mattes from compressed images in JPEG format.
Fully Convolutional Network for Liver Segmentation and Lesions Detection

In this work we explore a fully convolutional network (FCN) for the task of liver segmentation and liver metastases detection in computed tomography (CT) examinations. FCN has proven to be a very powerful tool for semantic segmentation. We explore the FCN performance on a relatively small dataset and compare it to patch based CNN and sparsity based classification schemes. Our data contains CT examinations from 20 patients with overall 68 lesions and 43 livers marked in one slice and 20 different patients with a full 3D liver segmentation. We ran 3-fold cross-validation and results indicate superiority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results.
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus

With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality.
Gender Role Portrayal and the Disney Princesses

The popular Disney Princess line includes nine films (e.g., Snow White, Beauty and the Beast) and over 25,000 marketable products. Gender role depictions of the prince and princess characters were examined with a focus on their behavioral characteristics and climactic outcomes in the films. Results suggest that the prince and princess characters differ in their portrayal of traditionally masculine and feminine characteristics, these gender role portrayals are complex, and trends towards egalitarian gender roles are not linear over time. Content coding analyses demonstrate that all of the movies portray some stereotypical representations of gender, including the most recent film, The Princess and the Frog. Although both the male and female roles have changed over time in the Disney Princess line, the male characters exhibit more androgyny throughout and less change in their gender role portrayals.
Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks

Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration. In this paper, we propose a machine learning approach to simulate ultrasound images at given 3D spatial locations (relative to the patient anatomy), based on conditional generative adversarial networks (GANs). In particular, we introduce a novel neural network architecture that can sample anatomically accurate images conditionally on spatial position of the (real or mock) freehand ultrasound probe. To ensure an effective and efficient spatial information assimilation, the proposed spatially-conditioned GANs take calibrated pixel coordinates in global physical space as conditioning input, and utilise residual network units and shortcuts of conditioning data in the GANs’ discriminator and generator, respectively. Using optically tracked B-mode ultrasound images, acquired by an experienced sonographer on a fetus phantom, we demonstrate the feasibility of the proposed method by two sets of quantitative results: distances were calculated between corresponding anatomical landmarks identified in the held-out ultrasound images and the simulated data at the same locations unseen to the networks; a usability study was carried out to distinguish the simulated data from the real images. In summary, we present what we believe are state-of-the-art visually realistic ultrasound images, simulated by the proposed GAN architecture that is stable to train and capable of generating plausibly diverse image samples.