nils-herrmann/clean_scirepeval huggingface dataset:
The Future of Scientific Simulations: from Artificial Life to Artificial Cosmogenesis.

This philosophical paper explores the relation between modern scientific simulations and the future of the universe. We argue that a simulation of an entire universe will result from future scientific activity. This requires us to tackle the challenge of simulating open-ended evolution at all levels in a single simulation. The simulation should encompass not only biological evolution, but also physical evolution (a level below) and cultural evolution (a level above). The simulation would allow us to probe what would happen if we would “replay the tape of the universe” with the same or different laws and initial conditions. We also distinguish between real-world and artificial-world modelling. Assuming that intelligent life could indeed simulate an entire universe, this leads to two tentative hypotheses. Some authors have argued that we may already be in a simulation run by an intelligent entity. Or, if such a simulation could be made real, this would lead to the production of a new universe. This last direction is argued with a careful speculative philosophical approach, emphasizing the imperative to find a solution to the heat death problem in cosmology. The reader is invited to consult Annex 1 for an overview of the logical structure of this paper.
User Experience Design and Agile Development : From Theory to Practice

We used existing studies on the integration of user experience design and agile methods as a basis to develop a framework for integrating UX and Agile. We performed a field study in an ongoing project of a medium-sized company in order to check if the proposed framework fits in the real world, and how some aspects of the integration of UX and Agile work in a real project. This led us to some conclusions situating contributions from practice to theory and back again. The framework is briefly described in this paper and consists of a set of practices, artifacts, and techniques derived from the literature. By combining theory and practice we were able to confirm some thoughts and identify some gaps—both in the company process and in our proposed framework—and drive our attention to new issues that need to be addressed. We believe that the most important issues in our case study are: UX designers cannot collaborate closely with developers because UX designers are working on multiple projects and that UX designers cannot work up front because they are too busy with too many projects at the same time.
The Relationship Between Mobile Phone Use , Metacognitive Awareness and Academic Achievement

Mobile phones are getting smarter and the usage through university students becoming more popular. University students using mobile phones for talking, for texting message, for Internet search, for listening music, watching videos, playing games, using social media etc... Mobile phones are not accessory any more, they are integrated like our clothes. There are studies examining the effects of using mobile phones frequently. The aim of this study was to determine the relationship between mobile phone usage, satisfaction with life, academic achievement and metacognitive awareness. Metacognitive awareness inventory used to measure the awareness. Total GPA was used to measure the academic achievement and the mobile phone using time, the number of text messaging and callings are used for indicating the mobile phone usage. 250 total university students attended voluntarily to the study. The study carried out in private university in the southeast region of Turkey. The results indicated the usage of mobile phones are very frequent through university students. Many of the students are using mobile phones heavily. There was a positive relationship between mobile phone usage and academic achievement, also between mobile phone usage and metacognitiwe awareness.
A Deep Reinforcement Learning Algorithm with Expert Demonstrations and Supervised Loss and its application in Autonomous Driving

In this paper, we propose a deep reinforcement learning(DRL) algorithm which combines Deep Deterministic Policy Gradient (DDPG) with expert demonstrations and supervised loss for decision making for autonomous driving. Training DRL agent with supervised learning is adopted to accelerate the exploration process and increase the stability. A supervised loss function is introduced in the algorithm to update the actor networks. In addition, reward construction is combined to make the training process more stable and efficient. The proposed algorithm is applied to a popular autonomous driving simulator called TORCS. The experimental results show that the training efficiency and stability are improved by utilizing our algorithm in autonomous driving.
SNP Markers and Their Impact on Plant Breeding

The use of molecular markers has revolutionized the pace and precision of plant genetic analysis which in turn facilitated the implementation of molecular breeding of crops. The last three decades have seen tremendous advances in the evolution of marker systems and the respective detection platforms. Markers based on single nucleotide polymorphisms (SNPs) have rapidly gained the center stage of molecular genetics during the recent years due to their abundance in the genomes and their amenability for high-throughput detection formats and platforms. Computational approaches dominate SNP discovery methods due to the ever-increasing sequence information in public databases; however, complex genomes pose special challenges in the identification of informative SNPs warranting alternative strategies in those crops. Many genotyping platforms and chemistries have become available making the use of SNPs even more attractive and efficient. This paper provides a review of historical and current efforts in the development, validation, and application of SNP markers in QTL/gene discovery and plant breeding by discussing key experimental strategies and cases exemplifying their impact.
From Modeling to Implementation of Virtual Sensors in Body Sensor Networks

Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy.
Media Framing and Public Attitudes Toward Biofuels

This study investigates the relationship between media framing and public opinion on the issue of biofuels—transportation fuels made from plants, animal products, or organic waste. First, the paper investigates how media framing of biofuels has changed since the issue regained national prominence in the early 2000s. Through a detailed content analysis of newspaper coverage, the paper documents an increase in negative frames between 1999 and 2008, especially frames focusing on the negative economic effects of biofuels on consumers. Second, using data from a 2010 Internet survey of a random sample of the U.S. public, the paper analyzes the relative influence of these new media frames on public attitudes toward biofuels compared with other common predictors of public opinion, such as party ID, regional economic interests, and personal identity as an environmentalist. In general, the results confirm that public attitudes toward biofuels appear to be shaped by these new media frames, especially among those who indicate a high degree of attention to the media, suggesting the relative importance of framing effects on policy attitudes for environmental and energy policies in general.
Review on Classification and Grading of Wheat Granules using SVM and Naive Bayes Classifier

India is the second largest producer of wheat in the world. Specifying the quality of wheat manually is very time consuming and requires an expert judgment. The aim of this proposal is to suggest algorithm for quality analysis of wheat using image processing techniques. With the help of image processing techniques, an automated software system can be made to avoid the human inspection and related drawbacks. Analysis and Classification of wheat is done visually and manually by human inspectors. Images of wheat grains are captured using digital camera and thresholding is performed. Edges of wheat kernels will be detected by canny edge detection. Following this step, morphological and color features of wheat are extracted from these images. For classification and grading SVM and Naïve based classifiers will be used. Digital Image processing can classify the wheat grains with speed and accuracy. Review on Classification and Grading of Wheat Granules using SVM and Naive Bayes Classifier Paper ID IJIFR/ V2/ E7/ 024 Page No. 2073-2081 Research Area Image Processing
NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features

This paper describes our system submitted to Aspect Based Sentiment Analysis Task 5 of SemEval-2016. Our system consists of two components: binary classifiers trained using single layer feedforward network for aspect category classification (Slot 1), and sequential labeling classifiers for opinion target extraction (Slot 2). Besides extracting a variety of lexicon features, syntactic features, and cluster features, we explore the use of deep learning systems to provide additional neural network features. Our system achieves the best performances on the English datasets, ranking 1st for four evaluations (Slot 1 for both restaurant and laptop domains, Slot 2, and Slot 1 & 2).
1 Keyword extraction : a review of methods and approaches

Paper presents a survey of methods and approaches for keyword extraction task. In addition to the systematization of methods, the paper gathers a comprehensive review of existing research. Related work on keyword extraction is elaborated for supervised and unsupervised methods, with special emphasis on graphbased methods as well as Croatian keyword extraction. Selectivity-based keyword extraction method is proposed as a new unsupervised graph-based keyword extraction method which extracts nodes from a complex network as keyword candidates. The paper provides guidelines for future research and development of new graph-based approaches for keyword extraction.
Bipolar pulse generator for very high frequency (> 100 MHz) ultrasound applications

Design of a cost-effective and compact monocycle bipolar pulse generator for very high frequency (> 100 MHz) transducers and cell applications is presented. The designed pulse generator could provide a monocycle bipolar pulse with center frequency of 220 MHz, -6 dB bandwidth of 100 % and the peak-to-peak amplitude of 2 V. Moreover, it generates less ringing effect to improve the axial resolution of the ultrasound systems. For the transducers that triggered by the pulse generator, the -6 dB bandwidth of received echo signals was considerably improved by 122 % compared to the Panametrics 5900 transmitter. In addition, wire phantom measurements are presented.
Learning Discriminative Projections for Text Similarity Measures

Traditional text similarity measures consider each term similar only to itself and do not model semantic relatedness of terms. We propose a novel discriminative training method that projects the raw term vectors into a common, low-dimensional vector space. Our approach operates by finding the optimal matrix to minimize the loss of the pre-selected similarity function (e.g., cosine) of the projected vectors, and is able to efficiently handle a large number of training examples in the highdimensional space. Evaluated on two very different tasks, cross-lingual document retrieval and ad relevance measure, our method not only outperforms existing state-of-the-art approaches, but also achieves high accuracy at low dimensions and is thus more efficient.
An adaptive and adaptable learning platform with realtime eye-tracking support: lessons learned

For a learning platform to be adapti ve, it needs to monitor the user in r ealtime. At the ScienceCampus Tübingen, we conduct empirical research on learning strategies, in particular with regard to system adaptivity. Our experimental Webbased adaptiv e multimedia learning environment is able to communicate with an eyetracking system in real-time, and l inks gaze data to browser-based learning content. By processing simple evaluation scripts in the browser, we can control the learning process by adapting the learning content in response to predefined conditions and user inputs. The experimental system serves as a basis for a series of psyc hologi al studies in the learning context. We have already conducted some studies with our experimental learning platform, but its devel opment is still ongoing. In particular, we are adding new feature s required for future studies. In this paper, we desc ribe the actual state of our experimental learning environment, lessons learned from its a pplication in the studies, and our plans for future devel opment.
Towards work-integrated learning communities

In this paper we raise the concept of Work-Integrated Learning (WIL) as a means of supporting activities of competence development and learning in organisations. In order to illustrate our conceptual understanding of WIL, the paper includes empirical results from a case study in a consultant firm, Ypsilon, which operates in the education sector and delivers courses for IT-professionals. Ypsilon is at present undergoing a transition from having one big customer to being forced into a more flexible way of meeting the different demands from many new clients. The results highlights three main considerations believed to be critical for WIL activities: (i) A shift from an individual teaching culture to a social and collaborative culture. (ii ) Extending the role of technology from codifying competencies to supporting negotiation of practice. (iii) Adoption of holistic and integrated strategy that can cope with a dynamic business context. We conclude the paper by pointing at several implications for how the understanding and advancement of work-integrated learning can be approached in the future.
The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in shortand long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.
Approximate Reverse Carry Propagate Adder for Energy-Efficient DSP Applications

In this paper, a reverse carry propagate adder (RCPA) is presented. In the RCPA structure, the carry signal propagates in a counter-flow manner from the most significant bit to the least significant bit; hence, the carry input signal has higher significance than the output carry. This method of carry propagation leads to higher stability in the presence of delay variations. Three implementations of the reverse carry propagate full-adder (RCPFA) cell with different delay, power, energy, and accuracy levels are introduced. The proposed structure may be combined with an exact (forward) carry adder to form hybrid adders with tunable levels of accuracy. The design parameters of the proposed RCPA implementations and some hybrid adders realized utilizing these structures are studied and compared with those of the state-of-the-art approximate adders using HSPICE simulations in a 45-nm CMOS technology. The results indicate that employing the proposed RCPAs in the hybrid adders may provide, on average, 27%, 6%, and 31% improvements in delay, energy, and energy-delay-product while providing higher levels of accuracy. In addition, the structure is more resilient to delay variation compared to the conventional approximate adder. Finally, the efficacy of the proposed RCPAs is investigated in the discrete cosine transform (DCT) block of the JPEG compression and finite-impulse response (FIR) filter applications. The investigation reveals 60% and 39% energy saving in the DCT of JPEG and FIR filter, respectively, for the proposed RCPAs.
Parameter estimation of q-Gaussian Radial Basis Functions Neural Networks with a Hybrid Algorithm for binary classification

A classification problem is a decision-making task that many researchers have studied. A number of techniques have been proposed to perform binary classification. Neural networks are one of the artificial intelligence techniques that has had the most successful results when applied to this problem. Our proposal is the use of q-Gaussian Radial Basis Function Neural Networks (q-Gaussian RBFNNs). This basis function includes a supplementary degree of freedom in order to adapt the model to the distribution of data. A Hybrid Algorithm (HA) is used to search for a suitable architecture for the qGaussian RBFNN. The use of this type of more flexible kernel could greatly improve the discriminative power of RBFNNs. In order to test performance, the RBFNN with the q-Gaussian basis functions is compared to RBFNNs with Gaussian, Cauchy and Inverse Multiquadratic RBFs, and to other recent neural networks approaches. An experimental study is presented on 11 binary-classification datasets taken from the UCI repository. Moreover, aerial imagery taken in mid-May, mid-June and mid-July was used to evaluate the potential of the methodology proposed for discriminating Ridolfia segetum patches (one of the most dominant and harmful weeds in sunflower crops) in two naturally infested fields in southern Spain. & 2011 Elsevier B.V. All rights reserved.
Deep Portrait Image Completion and Extrapolation

General image completion and extrapolation methods often fail on portrait images where parts of the human body need to be recovered a task that requires accurate human body structure and appearance synthesis. We present a twostage deep learning framework for tacking this problem. In the first stage, given a portrait image with an incomplete human body, we extract a complete, coherent human body structure through a human parsing network, which focuses on structure recovery inside the unknown region with the help of pose estimation. In the second stage, we use an image completion network to fill the unknown region, guided by the structure map recovered in the first stage. For realistic synthesis the completion network is trained with both perceptual loss and conditional adversarial loss. We evaluate our method on public portrait image datasets, and show that it outperforms other state-of-art general image completion methods. Our method enables new portrait image editing applications such as occlusion removal and portrait extrapolation. We further show that the proposed general learning framework can be applied to other types of images, e.g. animal images.
Addressing Function Approximation Error in Actor-Critic Methods

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.
What is a robot companion - friend, assistant or butler?

The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions.
Distributed Edge Cloud R-CNN for Real Time Object Detection

Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high-volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks, these performance improvements only focus on a single, contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN pipeline. By splitting the object detection pipeline into components and dynamically distributing these components in the cloud, we can achieve optimal performance to enable real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform including cloud servers and edge-embedded devices for real-time object detection on live video streams.