nils-herrmann/clean_scirepeval huggingface dataset:
Recent Advances in Treatment of Coronary Artery Disease: Role of Science and Technology

Coronary artery disease (CAD) is one of the most common causes of death worldwide. In the last decade, significant advancements in CAD treatment have been made. The existing treatment is medical, surgical or a combination of both depending on the extent, severity and clinical presentation of CAD. The collaboration between different science disciplines such as biotechnology and tissue engineering has led to the development of novel therapeutic strategies such as stem cells, nanotechnology, robotic surgery and other advancements (3-D printing and drugs). These treatment modalities show promising effects in managing CAD and associated conditions. Research on stem cells focuses on studying the potential for cardiac regeneration, while nanotechnology research investigates nano-drug delivery and percutaneous coronary interventions including stent modifications and coatings. This article aims to provide an update on the literature (in vitro, translational, animal and clinical) related to these novel strategies and to elucidate the rationale behind their potential treatment of CAD. Through the extensive and continued efforts of researchers and clinicians worldwide, these novel strategies hold the promise to be effective alternatives to existing treatment modalities.
Mobile Learning to Support Teaching English as a Second Language

Technology utilization in distance education has demonstrated its significance in the transfer of knowledge for both the instructors and the learners. This is also made possible through the use of the Internet which helps change the traditional teaching approaches into more modern methods when integrated with the pedagogical instruction. Mobile devices together with other forms of technology-based tools in education have established their potential in language teaching. In this regards, the Teaching of English as a Second Language (TESL) has become easier and more attractive via mobile learning. The aim of this study is to review the mobile-based teaching and learning in the English language classroom. Such integration of mobile learning with English language teaching may offer great innovations in the pedagogical delivery.
Deep Architectures for Face Attributes

We train a deep convolutional neural network to perform identity classification using a new dataset of public figures annotated with age, gender, ethnicity and emotion labels, and then fine-tune it for attribute classification. An optimal sharing pattern of computational resources within this network is determined by experiment, requiring only 1 G flops to produce all predictions. Rather than fine-tune by relearning weights in one additional layer after the penultimate layer of the identity network, we try several different depths for each attribute. We find that prediction of age and emotion is improved by fine-tuning from earlier layers onward, presumably because deeper layers are progressively invariant to non-identity related changes in the input.
Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.
Providing Data Protection as a Service in Cloud Computing

Our object oriented programming approach have great ability to improve the programming behavior for modern system and software engineering but it does not give the proper interaction of real world .In real world , programming required powerful interlinking among properties and characteristics towards the various objects. Basically this approach of programming gives the better presentation of object with real world and provide the better relationship among the objects. I have explained the new concept of my neuro object oriented approach .This approach contains many new features like originty , new concept of inheritance , new concept of encapsulation , object relation with dimensions , originty relation with dimensions and time , category of NOOPA like high order thinking object and low order thinking object , differentiation model for achieving the various requirements from the user and a rotational model .
Deep learning for financial time series forecasting in A-Trader system

The paper presents aspects related to developing methods for financial time series forecasting using deep learning in relation to multi-agent stock trading system, called A-Trader. On the basis of this model, an investment strategies in A-Trader system can be build. The first part of the paper briefly discusses a problem of financial time series on FOREX market. Classical neural networks and deep learning models are outlined, their performances are analyzed. The final part presents deployment and evaluation of a deep learning model implemented using H20 library as an agent of A-Trader system.
INFLUENCE OF THE EARTHQUAKE DIRECTION ON SEISMIC RESPONSE OF PLAN IRREGULAR RC FRAME BUILDINGS

Nonlinear response of structures is usually evaluated considering two accelerograms acting simultaneously along orthogonal directions. In this paper the influence of the earthquake direction on the seismic response of buildings structures is examined. Three multi-story RC buildings, representing a very common structural typology in Italy, are assumed in the paper as case-studies for the evaluation. They are respectively a rectangular plan shape, a L plan shape and a rectangular plan shape with courtyard buildings. Nonlinear static and dynamic analyses are performed considering different seismic levels, characterized by a peak ground acceleration on stiff soil equal to 0.35 g, 0.25 g and 0.15 g. Nonlinear dynamic analyses are carried out considering twelve different earthquake directions, rotating the direction of both the orthogonal components by 30° for each analysis (from 0° to 330°). The survey is carried out on the L plan shape structure. The results show that the angle of the seismic input motion significantly influences the response of RC structures: the critical seismic angle, i.e. the incidence angle that produces the maximum demand, provides an increase up to 37% both in roof displacements and in terms of plastic hinge rotations.
Weak supervision for detecting object classes from activities

Weakly supervised learning for object detection has been gaining significant attention in the recent past. Visually similar objects are extracted automatically from weakly labelled videos hence bypassing the tedious process of manually annotating training data. However, the problem as applied to small or medium sized objects is still largely unexplored. Our observation is that weakly labelled information can be derived from videos involving human-object interactions. Since the object is characterized neither by its appearance nor its motion in such videos, we propose a robust framework that taps valuable human context and models similarity of objects based on appearance and functionality. Furthermore, the framework is designed such that it maximizes the utility of the data by detecting possibly multiple instances of an object from each video. We show that object models trained in this fashion perform between 86% and 92% of their fully supervised counterparts on three challenging RGB and RGB-D datasets.
Conceptualizing “Smart Cities”

“Smart City” has become a buzzword. Much is being written about smart cities as we speak, most of it promotional and uncritical [5, 14]. The goal of this article is not to criticize smart cities, nor is it to promote them. Rather, we would like to make a contribution to the conceptualization of smart cities and, by doing so, help the concept to become intellectually more solid. We think that this will also contribute to developing a more realistic and ultimately more practical view of what smart cities can achieve ... and what they cannot. In this article, we will proceed along the following five steps: in a first step, we will conceptualize cities as complex and dynamic socio-technical systems, a conceptualization without which “smart cities” – i. e., the penetration of cities by the information and communication technologies (a phenomenon also called digitalization) – cannot really be understood. In a second step, we will then define such digitalization much more precisely. Such a definition will be necessary in order to understand, in a third step, what digitalization exactly does to cities. In a fourth section, we will then discuss what such digitalization means for cities. In a fifth section, we will further elaborate on this and discuss the different perspectives on smart cities made possible by the digitalization of urban systems.
Searching for Activation Functions

The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose to leverage automatic search techniques to discover new activation functions. Using a combination of exhaustive and reinforcement learning-based search, we discover multiple novel activation functions. We verify the effectiveness of the searches by conducting an empirical evaluation with the best discovered activation function. Our experiments show that the best discovered activation function, f(x) = x · sigmoid(βx), which we name Swish, tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.
Anti-Inflammatory Properties and Chemical Characterization of the Essential Oils of Four Citrus Species

Citrus fruits have potential health-promoting properties and their essential oils have long been used in several applications. Due to biological effects described to some citrus species in this study our objectives were to analyze and compare the phytochemical composition and evaluate the anti-inflammatory effect of essential oils (EO) obtained from four different Citrus species. Mice were treated with EO obtained from C. limon, C. latifolia, C. aurantifolia or C. limonia (10 to 100 mg/kg, p.o.) and their anti-inflammatory effects were evaluated in chemical induced inflammation (formalin-induced licking response) and carrageenan-induced inflammation in the subcutaneous air pouch model. A possible antinociceptive effect was evaluated in the hot plate model. Phytochemical analyses indicated the presence of geranial, limonene, γ-terpinene and others. EOs from C. limon, C. aurantifolia and C. limonia exhibited anti-inflammatory effects by reducing cell migration, cytokine production and protein extravasation induced by carrageenan. These effects were also obtained with similar amounts of pure limonene. It was also observed that C. aurantifolia induced myelotoxicity in mice. Anti-inflammatory effect of C. limon and C. limonia is probably due to their large quantities of limonene, while the myelotoxicity observed with C. aurantifolia is most likely due to the high concentration of citral. Our results indicate that these EOs from C. limon, C. aurantifolia and C. limonia have a significant anti-inflammatory effect; however, care should be taken with C. aurantifolia.
Learning Community Embedding with Community Detection and Node Embedding on Graphs

In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes. We find that community embedding is not only useful for community-level applications such as graph visualization, but also beneficial to both community detection and node classification. To learn such embedding, our insight hinges upon a closed loop among community embedding, community detection and node embedding. On the one hand, node embedding can help improve community detection, which outputs good communities for fitting better community embedding. On the other hand, community embedding can be used to optimize the node embedding by introducing a community-aware high-order proximity. Guided by this insight, we propose a novel community embedding framework that jointly solves the three tasks together. We evaluate such a framework on multiple real-world datasets, and show that it improves graph visualization and outperforms state-of-the-art baselines in various application tasks, e.g., community detection and node classification.
Automatic License Plate Detection Using Deep Learning Techniques

Automatic License Plate Recognition (ALPR) systems capture a vehicle‟s license plate and recognize the license number and other required information from the captured image. ALPR systems have numbers of significant applications: law enforcement, public safety agencies, toll gate systems, etc. The goal of these systems is to recognize the characters and state on the license plate with high accuracy. ALPR has been implemented using various techniques. Traditional recognition methods use handcrafted features for obtaining features from the image. Unlike conventional methods, deep learning techniques automatically select features and are one of the game changing technologies in the field of computer vision, automatic recognition tasks and natural language processing. Some of the most successful deep learning methods involve Convolutional Neural Networks. This technique applies deep learning techniques to the ALPR problem of recognizing the state and license number from the USA license plate. Existing ALPR systems include three stages of
Microstructures of superhydrophobic plant leaves - inspiration for efficient oil spill cleanup materials.

The cleanup of accidental oil spills in water is an enormous challenge; conventional oil sorbents absorb large amounts of water in addition to oil and other cleanup methods can cause secondary pollution. In contrast, fresh leaves of the aquatic ferns Salvinia are superhydrophobic and superoleophilic, and can selectively absorb oil while repelling water. These selective wetting properties are optimal for natural oil absorbent applications and bioinspired oil sorbent materials. In this paper we quantify the oil absorption capacity of four Salvinia species with different surface structures, water lettuce (Pistia stratiotes) and Lotus leaves (Nelumbo nucifera), and compare their absorption capacity to artificial oil sorbents. Interestingly, the oil absorption capacities of Salvinia molesta and Pistia stratiotes leaves are comparable to artificial oil sorbents. Therefore, these pantropical invasive plants, often considered pests, qualify as environmentally friendly materials for oil spill cleanup. Furthermore, we investigated the influence of oil density and viscosity on the oil absorption, and examine how the presence and morphology of trichomes affect the amount of oil absorbed by their surfaces. Specifically, the influence of hair length and shape is analyzed by comparing different hair types ranging from single trichomes of Salvinia cucullata to complex eggbeater-shaped trichomes of Salvinia molesta to establish a basis for improving artificial bioinspired oil absorbents.
Modelling and Analysis of User Behaviour in Online Communities

Understanding and forecasting the health of an online community is of great value to its owners and managers who have vested interests in its longevity and success. Nevertheless, the association between community evolution and the behavioural patterns and trends of its members is not clearly understood, which hinders our ability of making accurate predictions of whether a community is flourishing or diminishing. In this paper we use statistical analysis, combined with a semantic model and rules for representing and computing behaviour in online communities. We apply this model on a number of forum communities from Boards.ie to categorise behaviour of community members over time, and report on how different behaviour compositions correlate with positive and negative community growth in these forums.
PANAS-t: A Pychometric Scale for Measuring Sentiments on Twitter

Online social networks have become a major communication platform, where people share their thoughts and opinions about any topic real-time. The short text updates people post in these network contain emotions and moods, which when measured collectively can unveil the public mood at population level and have exciting implications for businesses, governments, and societies. Therefore, there is an urgent need for developing solid methods for accurately measuring moods from large-scale social media data. In this paper, we propose PANAS-t, which measures sentiments from short text updates in Twitter based on a well-established psychometric scale, PANAS (Positive and Negative Affect Schedule). We test the efficacy of PANAS-t over 10 real notable events drawn from 1.8 billion tweets and demonstrate that it can efficiently capture the expected sentiments of a wide variety of issues spanning tragedies, technology releases, political debates, and healthcare.
Poetry generation system with an emotional personality

We introduce a multiagent blackboard system for poetry generation with a special focus on emotional modelling. The emotional content is extracted from text, particularly blog posts, and is used as inspiration for generating poems. Our main objective is to create a system with an empathic emotional personality that would change its mood according to the affective content of the text, and express its feelings in the form of a poem. We describe here the system structure including experts with distinct roles in the process, and explain how they cooperate within the blackboard model by presenting an illustrative example of generation process. The system is evaluated considering the final outputs and the generation process. This computational creativity tool can be extended by incorporating new experts into the blackboard model, and used as an artistic enrichment of
Patterns as Formulas : Patterns in the Digital Humanities

During the last years, in particular due to the Digital Humanities, empirical processes, data capturing or data analysis got more and more popular as part of humanities research. In this paper, we want to show that even the complete scientific method of natural science can be applied in the humanities. By applying the scientific method to the humanities, certain kinds of problems can be solved in a confirmable and replicable manner. In particular, we will argue that patterns may be perceived as the analogon to formulas in natural science. This may provide a new way of representing solution-oriented knowledge in the humanities. Keywords-pattern; pattern languages; digital humanities;
Microbial Fuel Cells : Recent Advances , Bacterial Communities and Application Beyond Electricity Generation

The increasing demand for energy in the near future has created strong motivation for environmentally clean alternative energy resources. Microbial fuel cells (MFCs) have opened up new ways of utilizing renewable energy sources. MFCs are devices that convert the chemical energy in the organic compounds to electrical energy through microbial catalysis at the anode under anaerobic conditions, and the reduction of a terminal electron acceptor, most preferentially oxygen, at the cathode. Due to the rapid advances in MFC-based technology over the last decade, the currently achievable MFC power production has increased by several orders of magnitude, and niche applications have been extended into a variety of areas. Newly emerging concepts with alternative materials for electrodes and catalysts as well as innovative designs have made MFCs promising technologies. Aerobic bacteria can also be used as cathode catalysts. This is an encouraging finding because not only biofouling on the cathode is unavoidable in the prolonged-run MFCs but also noble catalysts can be substituted with aerobic bacteria. This article discusses some of the recent advances in MFCs with an emphasis on the performance, materials, microbial community structures and applications beyond electricity generation.
Sample Size and Saturation in PhD Studies Using Qualitative Interviews

A number of issues can affect sample size in qualitative research; however, the guiding principle should be the concept of saturation. This has been explored in detail by a number of authors but is still hotly debated, and some say little understood. A sample of PhD studies using qualitative approaches, and qualitative interviews as the method of data collection was taken from theses.com and contents analysed for their sample sizes. Five hundred and sixty studies were identified that fitted the inclusion criteria. Results showed that the mean sample size was 31; however, the distribution was non-random, with a statistically significant proportion of studies, presenting sample sizes that were multiples of ten. These results are discussed in relation to saturation. They suggest a pre-meditated approach that is not wholly congruent with the principles of qualitative research.
High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks

Fig. 1. Face completion results of our method on CelebA-HQ [Karras et al. 2017]. Images in the left most column of each group are masked with gray color, while the rest are synthesized faces. Top: our approach can complete face images at high resolution (1024 × 1024). Bottom: the attributes of completed faces can be controlled by conditional vectors. Attributes [“Male”, “Smiling”] are used in this example. The conditional vectors of column two to five are [0, 0], [1, 0], [0, 1], and [1, 1] in which “1” denotes the generated images have the particular attribute while “0” denotes not. Images are at 512 × 512 resolution. All images best viewed enlarged.