nils-herrmann/clean_scirepeval huggingface dataset:
Design of Greenhouse Control System Based on Wireless Sensor Networks and AVR Microcontroller

In order to accurately determine the growth of greenhouse crops, the system based on AVR Single Chip microcontroller and wireless sensor networks is developed, it transfers data through the wireless transceiver devices without setting up electric wiring, the system structure is simple. The monitoring and management center can control the temperature and humidity of the greenhouse, measure the carbon dioxide content, and collect the information about intensity of illumination, and so on. In addition, the system adopts multilevel energy memory. It combines energy management with energy transfer, which makes the energy collected by solar energy batteries be used reasonably. Therefore, the self-managing energy supply system is established. The system has advantages of low power consumption, low cost, good robustness, extended flexible. An effective tool is provided for monitoring and analysis decision-making of the greenhouse environment.
Structured Attentions for Visual Question Answering

Visual attention, which assigns weights to image regions according to their relevance to a question, is considered as an indispensable part by most Visual Question Answering models. Although the questions may involve complex rela- tions among multiple regions, few attention models can ef- fectively encode such cross-region relations. In this paper, we demonstrate the importance of encoding such relations by showing the limited effective receptive field of ResNet on two datasets, and propose to model the visual attention as a multivariate distribution over a grid-structured Con- ditional Random Field on image regions. We demonstrate how to convert the iterative inference algorithms, Mean Field and Loopy Belief Propagation, as recurrent layers of an end-to-end neural network. We empirically evalu- ated our model on 3 datasets, in which it surpasses the best baseline model of the newly released CLEVR dataset [13] by 9.5%, and the best published model on the VQA dataset [3] by 1.25%. Source code is available at https://github.com/zhuchen03/vqa-sva.
ADVANTAGE-WEIGHTED INFORMATION MAXIMIZA-

Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task. In this paper, we propose an HRL method that learns a latent variable of a hierarchical policy using mutual information maximization. Our approach can be interpreted as a way to learn a discrete and latent representation of the state-action space. To learn option policies that correspond to modes of the advantage function, we introduce advantage-weighted importance sampling. In our HRL method, the gating policy learns to select option policies based on an option-value function, and these option policies are optimized based on the deterministic policy gradient method. This framework is derived by leveraging the analogy between a monolithic policy in standard RL and a hierarchical policy in HRL by using a deterministic option policy. Experimental results indicate that our HRL approach can learn a diversity of options and that it can enhance the performance of RL in continuous control tasks.
3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores.
Bone tissue regeneration: the role of scaffold geometry.

The geometry of porous scaffolds that are used for bone tissue engineering and/or bone substitution has recently been shown to significantly influence the cellular response and the rate of bone tissue regeneration. Most importantly, it has been shown that the rate of tissue generation increases with curvature and is much larger on concave surfaces as compared to convex and planar surfaces. In this work, recent discoveries concerning the effects of geometrical features of porous scaffolds such as surface curvature, pore shape, and pore size on the cellular response and bone tissue regeneration process are reviewed. In addition to reviewing the recent experimental observations, we discuss the mechanisms through which geometry affects the bone tissue regeneration process. Of particular interest are the theoretical models that have been developed to explain the role of geometry in the bone tissue regeneration process. We then follow with a section on the implications of the observed phenomena for geometrical design of porous scaffolds including the application of predictive computational models in geometrical design of porous scaffolds. Moreover, some geometrical concepts in the design of porous scaffolds such as minimal surfaces and porous structures with geometrical gradients that have not been explored before are suggested for future studies. We especially focus on the porous scaffolds manufactured using additive manufacturing techniques where the geometry of the porous scaffolds could be precisely controlled. The paper concludes with a general discussion of the current state-of-the-art and recommendations for future research.
CAPJA - A Connector Architecture for Prolog and Java

Modern software often relies on the combination of several software modules that are developed independently. There are use cases where different software libraries from different programming languages are used, e.g., embedding DLL files in JAVA applications. Even more complex is the case when different programming paradigms are combined like within applications with database connections, for instance PHP and SQL. Such a diversification of programming languages and modules in just one software application is becoming more and more important, as this leads to a combination of the strengths of different programming paradigms. But not always, the developers are experts in the different programming languages or even in different programming paradigms. So, it is desirable to provide easy to use interfaces that enable the integration of programs from different programming languages and offer access to different programming paradigms. In this paper we introduce a connector architecture for two programming languages of different paradigms: JAVA as a representative of object oriented programming languages and PROLOG for logic programming. Our approach provides a fast, portable and easy to use communication layer between JAVA and PROLOG. The exchange of information is done via a textual term representation which can be used independently from a deployed PROLOG engine. The proposed connector architecture allows for Object Unification on the JAVA side. We provide an exemplary connector for JAVA and SWI-PROLOG, a well-known PROLOG implementation.
Feeling bad on Facebook: depression disclosures by college students on a social networking site.

Depression is common and frequently undiagnosed among college students. Social networking sites are popular among college students and can include displayed depression references. The purpose of this study was to evaluate college students' Facebook disclosures that met DSM criteria for a depression symptom or a major depressive episode (MDE).



We selected public Facebook profiles from sophomore and junior undergraduates and evaluated personally written text: "status updates." We applied DSM criteria to 1-year status updates from each profile to determine prevalence of displayed depression symptoms and MDE criteria. Negative binomial regression analysis was used to model the association between depression disclosures and demographics or Facebook use characteristics.



Two hundred profiles were evaluated, and profile owners were 43.5% female with a mean age of 20 years. Overall, 25% of profiles displayed depressive symptoms and 2.5% met criteria for MDE. Profile owners were more likely to reference depression, if they averaged at least one online response from their friends to a status update disclosing depressive symptoms (exp(B) = 2.1, P <.001), or if they used Facebook more frequently (P <.001).



College students commonly display symptoms consistent with depression on Facebook. Our findings suggest that those who receive online reinforcement from their friends are more likely to discuss their depressive symptoms publicly on Facebook. Given the frequency of depression symptom displays on public profiles, social networking sites could be an innovative avenue for combating stigma surrounding mental health conditions or for identifying students at risk for depression.
The role of the positive emotional attractor in vision and shared vision: toward effective leadership, relationships, and engagement

Personal and shared vision have a long history in management and organizational practices yet only recently have we begun to build a systematic body of empirical knowledge about the role of personal and shared vision in organizations. As the introductory paper for this special topic in Frontiers in Psychology, we present a theoretical argument as to the existence and critical role of two states in which a person, dyad, team, or organization may find themselves when engaging in the creation of a personal or shared vision: the positive emotional attractor (PEA) and the negative emotional attractor (NEA). These two primary states are strange attractors, each characterized by three dimensions: (1) positive versus negative emotional arousal; (2) endocrine arousal of the parasympathetic nervous system versus sympathetic nervous system; and (3) neurological activation of the default mode network versus the task positive network. We argue that arousing the PEA is critical when creating or affirming a personal vision (i.e., sense of one's purpose and ideal self). We begin our paper by reviewing the underpinnings of our PEA-NEA theory, briefly review each of the papers in this special issue, and conclude by discussing the practical implications of the theory.
Analysis in Amazon Reviews Using Probabilistic Machine Learning

Users of the online shopping site Amazon are encouraged to post reviews of the products that they purchase. Little attempt is made by Amazon to restrict or limit the content of these reviews. The number of reviews for different products varies, but the reviews provide accessible and plentiful data for relatively easy analysis for a range of applications. This paper seeks to apply and extend the current work in the field of natural language processing and sentiment analysis to data retrieved from Amazon. Naive Bayes and decision list classifiers are used to tag a given review as positive or negative. The number of stars a user gives a product is used as training data to perform supervised machine learning. A corpus contains 50,000 product review from 15 products serves as the dataset of study. Top selling and reviewed books on the site are the primary focus of the experiments, but useful features of them that aid in accurate classification are compared to those most useful in classification of other media products. The features, such as bag-of-words and bigrams, are compared to one another in their effectiveness in correctly tagging reviews. Errors in classification and general difficulties regarding the selection of features are analyzed and discussed.
The ecology of the microbiome: Networks, competition, and stability

The human gut harbors a large and complex community of beneficial microbes that remain stable over long periods. This stability is considered critical for good health but is poorly understood. Here we develop a body of ecological theory to help us understand microbiome stability. Although cooperating networks of microbes can be efficient, we find that they are often unstable. Counterintuitively, this finding indicates that hosts can benefit from microbial competition when this competition dampens cooperative networks and increases stability. More generally, stability is promoted by limiting positive feedbacks and weakening ecological interactions. We have analyzed host mechanisms for maintaining stability—including immune suppression, spatial structuring, and feeding of community members—and support our key predictions with recent data.
Learning to Rank with Selection Bias in Personal Search

Click-through data has proven to be a critical resource for improving search ranking quality. Though a large amount of click data can be easily collected by search engines, various biases make it difficult to fully leverage this type of data. In the past, many click models have been proposed and successfully used to estimate the relevance for individual query-document pairs in the context of web search. These click models typically require a large quantity of clicks for each individual pair and this makes them difficult to apply in systems where click data is highly sparse due to personalized corpora and information needs, e.g., personal search. In this paper, we study the problem of how to leverage sparse click data in personal search and introduce a novel selection bias problem and address it in the learning-to-rank framework. This paper proposes a few bias estimation methods, including a novel query-dependent one that captures queries with similar results and can successfully deal with sparse data. We empirically demonstrate that learning-to-rank that accounts for query-dependent selection bias yields significant improvements in search effectiveness through online experiments with one of the world's largest personal search engines.
Active video games to promote physical activity in children and youth: a systematic review.

To systematically review levels of metabolic expenditure and changes in activity patterns associated with active video game (AVG) play in children and to provide directions for future research efforts.



A review of the English-language literature (January 1, 1998, to January 1, 2010) via ISI Web of Knowledge, PubMed, and Scholars Portal using the following keywords: video game, exergame, physical activity, fitness, exercise, energy metabolism, energy expenditure, heart rate, disability, injury, musculoskeletal, enjoyment, adherence, and motivation.



Only studies involving youth (< or = 21 years) and reporting measures of energy expenditure, activity patterns, physiological risks and benefits, and enjoyment and motivation associated with mainstream AVGs were included. Eighteen studies met the inclusion criteria. Articles were reviewed and data were extracted and synthesized by 2 independent reviewers.  Energy expenditure during AVG play compared with rest (12 studies) and activity associated with AVG exposure (6 studies).



Percentage increase in energy expenditure and heart rate (from rest).



Activity levels during AVG play were highly variable, with mean (SD) percentage increases of 222% (100%) in energy expenditure and 64% (20%) in heart rate. Energy expenditure was significantly lower for games played primarily through upper body movements compared with those that engaged the lower body (difference, -148%; 95% confidence interval, -231% to -66%; P = .001).



The AVGs enable light to moderate physical activity. Limited evidence is available to draw conclusions on the long-term efficacy of AVGs for physical activity promotion.
An efficient lane detection algorithm for lane departure detection

In this paper, we propose an efficient lane detection algorithm for lane departure detection; this algorithm is suitable for low computing power systems like automobile black boxes. First, we extract candidate points, which are support points, to extract a hypotheses as two lines. In this step, Haar-like features are used, and this enables us to use an integral image to remove computational redundancy. Second, our algorithm verifies the hypothesis using defined rules. These rules are based on the assumption that the camera is installed at the center of the vehicle. Finally, if a lane is detected, then a lane departure detection step is performed. As a result, our algorithm has achieved 90.16% detection rate; the processing time is approximately 0.12 milliseconds per frame without any parallel computing.
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2 , for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on three different tasks: (1) object classification, (2) semantic segmentation, and (3) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-ofthe-art methods. Our network has better generalization properties than ShuffleNetv2 when tested on the MSCOCO multi-object classification task and the Cityscapes urban scene semantic segmentation task. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at https://github.com/sacmehta/ESPNetv2.
Inferring user interests in microblogging social networks: a survey

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.
Data and Hardware Efficient Design for Convolutional Neural Network

Hardware design of deep convolutional neural networks (CNNs) faces challenges of high computational complexity and data bandwidth as well as huge divergence in different CNN network layers, in which the throughput of the convolutional layer would be bounded by available hardware resource, and throughput of the fully connected layer would be bounded by available data bandwidth. Thus, a highly flexible and efficient design is desired to meet these needs. This paper presents an end-to-end CNN accelerator that maximizes hardware utilization with run-time configurations of different kernel sizes. It also minimizes data bandwidth with the output first strategy to improve the data reuse of the convolutional layers by up to  $300\times \sim 600\times $  compared with the non-reused case. The whole CNN implementation of the target network is generated optimally for both hardware and data efficiency under design resource constraints, which can be run-time reconfigured by the layer optimized parameters to achieve real-time and end-to-end CNN acceleration. An implementation example for AlexNet consumes a 1.783 M gate count for 216 MACs and a 142.64 kb internal buffer with TSMC 40 nm process, and achieves 99.7 and 61.6 f/s under 454 MHz clock frequency for the convolutional layers and the whole AlexNet, respectively.
Design of Blockchain-Based Apps Using Familiar Software Patterns to Address Interoperability Challenges in Healthcare

Since the inception of Bitcoin technology, its underlying data structureâĂŞ-the blockchainâĂŞ-has garnered much attention due to properties such as decentralization, transparency, and immutability. These properties make blockchains suitable for apps that require disintermediation through trustless exchange, consistent and incorruptible transaction records, and operational models beyond cryptocurrency. In particular, blockchain and its programmable smart contracts have the potential to address healthcare interoperability issues, such as enabling effective interactions between users and medical applications, delivering patient data securely to a variety of organizations and devices, and improving the overall efficiency of medical practice workflow. Despite the interest in using blockchain technology for healthcare interoperability, however, little information is available on the concrete architectural styles and recommendations for designing blockchain-based apps targeting healthcare. This paper provides an initial step in filling this gap by showing: (1) the features and implementation challenges in healthcare interoperability, (2) an end-to-end case study of a blockchain-based healthcare app that we are developing, and (3) how designing blockchain-based apps using familiar software patterns can help address healthcare specific challenges.
Chapter 6 Improving Flotation of Sphalerite-Effect of pH and Activating Potential on the Kinetics of Copper Uptake and Xanthate Adsorption on Sphalerite

The effect of pH and electrochemical potential on copper uptake, xanthate adsorption and the hydrophobicity of sphalerite were studied from flotation practice point of view using electrochemical and micro-flotation techniques. Voltammetric studies conducted using the combination of carbon matrix composite (CMC) electrode and surface conduction (SC) electrode show that the kinetics of activation increases with decreasing activating pH. Controlling potential contact angle measurements conducted on a copper-activated SC electrode in xanthate solution with different pHs show that, xanthate adsorption occurs at acidic and alkaline pHs and renders the mineral surface hydrophobic. At near neutral pH, although xanthate adsorbs on Cu:ZnS, the mineral surface is hydrophilic. Microflotation tests confirm this finding. Cleaning reagent was used to improve the flotation response of sphalerite at near neutral pH.