nils-herrmann/clean_scirepeval huggingface dataset:
Metal halide perovskite light emitters.

Twenty years after layer-type metal halide perovskites were successfully developed, 3D metal halide perovskites (shortly, perovskites) were recently rediscovered and are attracting multidisciplinary interest from physicists, chemists, and material engineers. Perovskites have a crystal structure composed of five atoms per unit cell (ABX3) with cation A positioned at a corner, metal cation B at the center, and halide anion X at the center of six planes and unique optoelectronic properties determined by the crystal structure. Because of very narrow spectra (full width at half-maximum ≤20 nm), which are insensitive to the crystallite/grain/particle dimension and wide wavelength range (400 nm ≤ λ ≤ 780 nm), perovskites are expected to be promising high-color purity light emitters that overcome inherent problems of conventional organic and inorganic quantum dot emitters. Within the last 2 y, perovskites have already demonstrated their great potential in light-emitting diodes by showing high electroluminescence efficiency comparable to those of organic and quantum dot light-emitting diodes. This article reviews the progress of perovskite emitters in two directions of bulk perovskite polycrystalline films and perovskite nanoparticles, describes current challenges, and suggests future research directions for researchers to encourage them to collaborate and to make a synergetic effect in this rapidly emerging multidisciplinary field.
Number 693 December 2000 ON THE EFFECT OF THE INTERNET ON INTERNATIONAL TRADE

The Internet stimulates trade. Using a gravity equation of trade among 56 countries, we find no evidence of an effect of the Internet on total trade flows in 1995 and only weak evidence of an effect in 1996. However, we find an increasing and significant impact from 1997 to 1999. Specifically, our results imply that a 10 percent increase in the relative number of web hosts in one country would have led to about 1 percent greater trade in 1998 and 1999. Surprisingly, we find that the effect of the Internet on trade has been stronger for poor countries than for rich countries, and that there is little evidence that the Internet has reduced the impact of distance on trade. The evidence is consistent with a model in which the Internet creates a global exchange for goods, thereby reducing market-specific sunk costs of exporting.
Connectionist speech recognition of Broadcast News

This paper describes connectionist techniques for recogni ti n of Broadcast News. The fundamental difference between connectionist systems and more convent ional mixture-of-Gaussian systems is that connectionist models directly estimate posterior probabi lities as opposed to likelihoods. Access to posterior probabilities has enabled us to develop a number of no vel approaches to confidence estimation, pronunciation modelling and search. In addition we have inv stigated a new feature extraction technique based on the modulation-filtered spectrogram, and methods f or combining multiple information sources. We have incorporated all of these techniques into a system fo r the transcription of Broadcast News, and we present results on the 1998 DARPA Hub-4E Broadcast News ev aluation data.
Personality and Learning Motivation

In this study I investigated the relationship between personality traits and learning motivations by correlating Big Five model of personality, Core Self-evaluation, achievement and affiliation motivation, and intrinsic and extrinsic motivations for leaning. Data were collected from 298 participants using a questionnaire. Regression analysis results indicated that extraversion, agreeableness, openness to experience and conscientiousness were positively associated with intrinsic motivation, but neuroticism was positively associated with extrinsic motivation. Core self-evaluation was also positively related with intrinsic motivation and negatively related to extrinsic motivation. Furthermore, intrinsic motivation and extrinsic motivation are two concept that was mutually exclusive. Implications and further research directions are then discussed.
LSTM for dynamic emotion and group emotion recognition in the wild

In this paper, we describe our work in the fourth Emotion Recognition in the Wild (EmotiW 2016) Challenge. For video based emotion recognition sub-challenge, we extract acoustic features, LBPTOP, Dense SIFT and CNN-LSTM features to recognize the emotions of film characters. For group level emotion recognition sub-challenge, we use LSTM and GEM model. We train linear SVM classifiers for these kinds of features on the AFEW6.0 and HAPPEI dataset, and use a fusion network we proposed to combine all the extracted features at the decision level. The final achievements we have gained are 51.54% accuracy on the AFEW testing set and 0.836 RMSE on the HAPPEI testing set.
Finding Structure in Time

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction.
A Connected E-Shape and U-Shape Dual-Band Patch Antenna for Different Wireless Applications

In this paper, dual operation E-shape and U-shape Patch Antenna feed by transmission line is presented and study the effect of antenna dimensions length (L), width (W) and substrate parameters relative dielectric constant(Er), substrate thickness on radiation parameters of Band width. The proposed antenna is designed on two-layer, one RT/Duroid 6006 laminate substrate and another ground plane with an area of 33 mm by 42 mm. This paper contains designing a Connected E-Shape and U-Shape Dual Band Patch Antenna for different wireless applications except for its narrow band width. The dual operation frequencies are 2.46 GHz and 4.9 GHz. A (-8 dB) bandwidths of return loss S characteristic for the dual band are 13.02 % and 3.28 % respectively. This connected U-shape & E-shape patch antenna is mainly applicable to wireless local area net-works (WLAN).This paper suggests an alternative approach in enhancing the band width of microstrip antenna for the wireless application operating at a dual frequencies 2.46 GHz and 4.9 GHz. The measured results have been compared with the simulated results using software GEMS version-7.0.
Computational systems biology

To understand complex biological systems requires the integration of experimental and computational research — in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.
3D Graph Neural Networks for RGBD Semantic Segmentation

RGBD semantic segmentation requires joint reasoning about 2D appearance and 3D geometric information. In this paper we propose a 3D graph neural network (3DGNN) that builds a k-nearest neighbor graph on top of 3D point cloud. Each node in the graph corresponds to a set of points and is associated with a hidden representation vector initialized with an appearance feature extracted by a unary CNN from 2D images. Relying on recurrent functions, every node dynamically updates its hidden representation based on the current status and incoming messages from its neighbors. This propagation model is unrolled for a certain number of time steps and the final per-node representation is used for predicting the semantic class of each pixel. We use back-propagation through time to train the model. Extensive experiments on NYUD2 and SUN-RGBD datasets demonstrate the effectiveness of our approach.
Heterogeneous Model Integration for Multi-Source Urban Infrastructure Data

Data-driven modeling usually suffers from data sparsity, especially for large-scale modeling for urban phenomena based on single-source urban-infrastructure data under fine-grained spatial-temporal contexts. To address this challenge, we motivate, design, and implement UrbanCPS, a cyber-physical system with heterogeneous model integration, based on extremely-large multi-source infrastructures in the Chinese city Shenzhen, involving 42,000 vehicles, 10 million residents, and 16 million smartcards. Based on temporal, spatial, and contextual contexts, we formulate an optimization problem about how to optimally integrate models based on highly diverse datasets under three practical issues, that is, heterogeneity of models, input data sparsity, or unknown ground truth. We further propose a real-world application called Speedometer, inferring real-time traffic speeds in urban areas. The evaluation results show that, compared to a state-of-the-art system, Speedometer increases the inference accuracy by 29% on average.
Knowledge management infrastructure and processes on effectiveness of nursing care

The purpose of this study is to investigate the influence of knowledge management infrastructure and process on nursing care effectiveness in selected teaching hospitals in Southwest Nigeria. The organization of nursing knowledge resources is critical to health care organizations for providing safe and high quality care for patients. Therefore, it is necessary to study the role of knowledge management in clinical nursing practice and the outcomes on the effectiveness of nursing care. Despite the critical role of nursing care in defining high-performing health care delivery, knowledge management in this area is still at an early stage of development. Based on organizational capability theory and employing concurrent mixed-method research design, this study seeks to identify the components of knowledge infrastructure and processes that influence the efficiency and effectiveness of nursing care in clinical floor nursing of selected teaching hospitals in Nigeria, a developing country in Africa. The output from the research is expected to contribute to the domain of literature/knowledge; provide awareness of the knowledge management practices in nursing care in Nigeria; assist nursing administrators, health policy makers and hospital management to exploit and make effective use of knowledge-based resources to enhance nursing care which can improve the productivity of health care organizations.
Visual Privacy by Context: Proposal and Evaluation of a Level-Based Visualisation Scheme

Privacy in image and video data has become an important subject since cameras are being installed in an increasing number of public and private spaces. Specifically, in assisted living, intelligent monitoring based on computer vision can allow one to provide risk detection and support services that increase people's autonomy at home. In the present work, a level-based visualisation scheme is proposed to provide visual privacy when human intervention is necessary, such as at telerehabilitation and safety assessment applications. Visualisation levels are dynamically selected based on the previously modelled context. In this way, different levels of protection can be provided, maintaining the necessary intelligibility required for the applications. Furthermore, a case study of a living room, where a top-view camera is installed, is presented. Finally, the performed survey-based evaluation indicates the degree of protection provided by the different visualisation models, as well as the personal privacy preferences and valuations of the users.
A Game-like Application for Dance Learning Using a Natural Human Computer Interface

Game-based learning and gamification techniques are recently becoming a popular trend in the field of Technology Enhanced Learning. In this paper, we mainly focus on the use of game design elements for the transmission of Intangible Cultural Heritage (ICH) knowledge and, especially, for the learning of traditional dances. More specifically, we present a 3D game environment that employs an enjoyable natural human computer interface, which is based on the fusion of multiple depth sensors data in order to capture the body movements of the user/learner. In addition, the system automatically assesses the learner’s performance by utilizing a combination of Dynamic Time Warping (DTW) with Fuzzy Inference System (FIS) approach and provides feedback in a form of a score as well as instructions from a virtual tutor in order to promote self-learning. As a pilot use case, a Greek traditional dance, namely Tsamiko, has been selected. Preliminary small-scaled experiments with students of the Department of Physical Education and Sports Science at Aristotle University of Thessaloniki have shown the great potential of the proposed application.
Mining Frequency of Drug Side Effects over a Large Twitter Dataset Using Apache Spark

Despite clinical trials by pharmaceutical companies as well as current FDA reporting systems, there are still drug side effects that have not been caught. To find a larger sample of reports, a possible way is to mine online social media. With its current widespread use, social media such as Twitter has given rise to massive amounts of data, which can be used as reports for drug side effects. To process these large datasets, Apache Spark has become popular for fast, distributed batch processing. In this work, we have improved on previous pipelines in sentimental analysis-based mining, processing, and extracting tweets with drug-caused side effects. We have also added a new ensemble classifier using a combination of sentiment analysis features to increase the accuracy of identifying drug-caused side effects. In addition, the frequency count for the side effects is also provided. Furthermore, we have also implemented the same pipeline in Apache Spark to improve the speed of processing of tweets by 2.5 times, as well as to support the process of large tweet datasets. As the frequency count of drug side effects opens a wide door for further analysis, we present a preliminary study on this issue, including the side effects of simultaneously using two drugs, and the potential danger of using less-common combination of drugs. We believe the pipeline design and the results present in this work would have great implication on studying drug side effects and on big data analysis in general.
A Neural Autoregressive Approach to Collaborative Filtering

This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to improve the model by sharing parameters between different ratings. A factored version of CF-NADE is also proposed for better scalability. Furthermore, we take the ordinal nature of the preferences into consideration and propose an ordinal cost to optimize CF-NADE, which shows superior performance. Finally, CF-NADE can be extended to a deep model, with only moderately increased computational complexity. Experimental results show that CF-NADE with a single hidden layer beats all previous state-of-the-art methods on MovieLens 1M, MovieLens 10M, and Netflix datasets, and adding more hidden layers can further improve the performance.
Novelty and diversity metrics for recommender systems: choice, discovery and relevance

There is an increasing realization in the Recommender Systems (RS) field that novelty and diversity are fundamental qualities of recommendation effectiveness and added-value. We identify however a gap in the formalization of novelty and diversity metrics –and a consensus around them– comparable to the recent proposals in IR diversity. We study a formal characterization of different angles that RS novelty and diversity may take from the end-user viewpoint, aiming to contribute to a formal definition and understanding of different views and meanings of these magnitudes under common groundings. Building upon this, we derive metric schemes that take item position and relevance into account, two aspects not generally addressed in the novelty and diversity metrics reported in the RS literature.
Modelling the scaling properties of human mobility

Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.
Learned Primal-Dual Reconstruction

We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it does not depend on any initial reconstruction such as filtered back-projection (FBP). We compare performance of the proposed method on low dose computed tomography reconstruction against FBP, total variation (TV), and deep learning based post-processing of FBP. For the Shepp-Logan phantom we obtain >6 dB peak signal to noise ratio improvement against all compared methods. For human phantoms the corresponding improvement is 6.6 dB over TV and 2.2 dB over learned post-processing along with a substantial improvement in the structural similarity index. Finally, our algorithm involves only ten forward-back-projection computations, making the method feasible for time critical clinical applications.
VELNET (Virtual Environment for Learning Networking)

The problems of providing a real, physical specialist laboratory to teach computer networking such as, the lack of funding and physical space and the risks and threats to the network environment and infrastructure, can be solved by the use of a virtual learning environment. Velnet is such a virtual learning environment that we have developed and used successfully. Velnet consists of one or more host machines and operating systems, commercial virtual machine software, virtual machines and their operating systems, a virtual network connecting the virtual machines, and remote desktop display software. In order to be able to present more computernetworking concepts and to improve on our original version of Velnet we have been developing a virtual reality overlay. This virtual reality overlay allows students to build virtual networking topologies in a virtual lab. This paper describes Velnet our virtual environment for learning networking and the virtual reality overlay under development.
Understanding Design Fundamentals: How Synthesis and Analysis Drive Creativity, Resulting in Emergence

This paper presents results of an ongoing interdisciplinary study to develop a computational theory of creativity for engineering design. Human design activities are surveyed, and popular computer-aided design methodologies are examined. It is argued that semiotics has the potential to merge and unite various design approaches into one fundamental theory that is naturally interpretable and so comprehensible in terms of computer use. Reviewing related work in philosophy, psychology, and cognitive science provides a general and encompassing vision of the creativity phenomenon. Basic notions of algebraic semiotics are given and explained in terms of design. This is to define a model of the design creative process, which is seen as a process of semiosis, where concepts and their attributes represented as signs organized into systems are evolved, blended, and analyzed, resulting in the development of new concepts. The model allows us to formally describe and investigate essential properties of the design process, namely its dynamics and non-determinism inherent in creative thinking. A stable pattern of creative thought – analogical and metaphorical reasoning – is specified to demonstrate the expressive power of the modeling approach; illustrative examples are given. The developed theory is applied to clarify the nature of emergence in design: it is shown that while emergent properties of a product may influence its creative value, emergence can simply be seen as a by-product of the creative process. Concluding remarks summarize the research, point to some unresolved issues, and outline directions for future work.
Image-Processing Projects for an Algorithms Course

Courses on algorithm analysis often include little programming, and do not emphasize application of advanced techniques to practical problems. On the other hand, students usually prefer hands-on learning, and may lack motivation to study theory. We augmented an algorithms course with a series of programming tasks, which involved application of the course material to image processing. These tasks motivated the students, and led to better understanding and retention of theoretical material. They also enabled the students to learn the basics of representing and manipulating images, along with the algorithm theory.
Exercise motion classification from large-scale wearable sensor data using convolutional neural networks

The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.