nils-herrmann/clean_scirepeval huggingface dataset:
Data Analysis Project : Using Knowledge Graphs for Image Classification

One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.
A systematic review of built environment factors related to physical activity and obesity risk: implications for smart growth urban planning.

Smart growth is an approach to urban planning that provides a framework for making community development decisions. Despite its growing use, it is not known whether smart growth can impact physical activity. This review utilizes existing built environment research on factors that have been used in smart growth planning to determine whether they are associated with physical activity or body mass. Searching the MEDLINE, Psycinfo and Web-of-Knowledge databases, 204 articles were identified for descriptive review, and 44 for a more in-depth review of studies that evaluated four or more smart growth planning principles. Five smart growth factors (diverse housing types, mixed land use, housing density, compact development patterns and levels of open space) were associated with increased levels of physical activity, primarily walking. Associations with other forms of physical activity were less common. Results varied by gender and method of environmental assessment. Body mass was largely unaffected. This review suggests that several features of the built environment associated with smart growth planning may promote important forms of physical activity. Future smart growth community planning could focus more directly on health, and future research should explore whether combinations or a critical mass of smart growth features is associated with better population health outcomes.
5G-Enabled Cooperative Intelligent Vehicular (5GenCIV) Framework: When Benz Meets Marconi

As one of the most popular social media platforms today, Twitter provides people with an effective way to communicate and interact with each other. Through these interactions, influence among users gradually emerges and changes people's opinions. Although previous work has studied interpersonal influence as the probability of activating others during information diffusion, they ignore an important fact that information diffusion is the result of influence, while dynamic interactions among users produce influence. In this article, the authors propose a novel temporal influence model to learn users' opinion behaviors regarding a specific topic by exploring how influence emerges during communications. The experiments show that their model performs better than other influence models with different influence assumptions when predicting users' future opinions, especially for the users with high opinion diversity.
Analysis of earthquake body wave spectra for potency and magnitude values : implications for magnitude scaling relations

S U M M A R Y We develop a simple methodology for reliable automated estimation of the low-frequency asymptote in seismic body wave spectra of small to moderate local earthquakes. The procedure corrects individual Pand S-wave spectra for propagation and site effects and estimates the seismic potency from a stacked spectrum. The method is applied to >11 000 earthquakes with local magnitudes 0 < ML < 4 that occurred in the Southern California plate-boundary region around the San Jacinto fault zone during 2013. Moment magnitude Mw values, derived from the spectra and the scaling relation of Hanks & Kanamori, follow a Gutenberg–Richter distribution with a larger b-value (1.22) from that associated with the ML values (0.93) for the same earthquakes. The completeness magnitude for the Mw values is 1.6 while for ML it is 1.0. The quantity (Mw − ML) linearly increases in the analysed magnitude range as ML decreases. An average earthquake with ML = 0 in the study area has an Mw of about 0.9. The developed methodology and results have important implications for earthquake source studies and statistical seismology.
Internet of things: Applications and challenges

The keyword ‘Internet of Things’ (IoT) is used to refer to many concepts related to the extension of the Internet and the Web into the physical daily life. This is supported by means of the widespread of distributed devices with embedded identification, sensing and/or actuation capabilities. IoT envisions a future in which both digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a big range of applications and services. In this article, I will highlight some details of these technologies, applications and research challenges for IoT.
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network

In this article we describe a new convolutional neural network (CNN) to classify 3D point clouds of urban or indoor scenes. Solutions are given to the problems encountered working on scene point clouds, and a network is described that allows for point classification using only the position of points in a multi-scale neighborhood. On the reduced-8 Semantic3D benchmark [Hackel et al., 2017], this network, ranked second, beats the state of the art of point classification methods (those not using a regularization step).  Example of classified point cloud on Semantic3D test set (blue: man-made terrain, cerulean blue: natural terrain, green: high vegetation, light green: low vegetation, chartreuse green: buildings, yellow: hard scape, orange: scanning artefacts, red: cars).
Impact of landscape composition and configuration on forest specialist and generalist bird species in the fragmented Lacandona rainforest, Mexico

With accelerated land-use change throughout the tropics, an increasing proportion of global biodiversity is located in human-modified landscapes. Understanding the relative effects of landscape composition and configuration on biodiversity is needed to design effective conservation strategies. Yet this topic is poorly understood because most studies have been performed at the patch scale, and do not assess the impact of landscape structure on species. Using a multi-model inference approach, we assessed the relative effect of landscape composition (i.e. percentage of forest cover and matrix composition) and landscape configuration (i.e. number of forest patches and forest edge density) on aand b-diversity of birds in 17 forest fragments and three areas of continuous forest within the Lacandona rainforest, Mexico. We tested these impacts at two spatial scales (100 and 500 ha) for forest specialist and generalist birds. In general, forest specialist birds showed stronger responses to landscape characteristics than generalist species, particularly to variations in landscape composition at the 100-ha scale. The loss of forest cover represented the main threat to forest specialist birds, with a negative impact on a-diversity that was consistent across the two spatial scales. In contrast, at the two spatial scales generalist birds seemed to be favored by forest loss, as a-diversity of these birds increased in landscapes with lower forest cover and higher number of forest patches. If current deforestation rates continue, several forest specialists are likely to disappear. Future conservation and management initiatives should therefore prevent deforestation in this biodiversity-rich but vanishing tropical forest ecosystem.
Heart disease and stroke statistics--2015 update: a report from the American Heart Association.

Véronique L. Roger, MD, MPH, FAHA; Alan S. Go, MD; Donald M. Lloyd-Jones, MD, ScM, FAHA;  Benjamin, MD, ScM, FAHA; Jarett D. Berry, MD; William B. Borden, MD; Dawn M. Bravata, MD; Shifan Dai, MD, PhD*; Earl S. Ford, MD, MPH, FAHA*; Caroline S. Fox, MD, MPH; Heather J. Fullerton, MD; Cathleen Gillespie, MS*; Susan M. Hailpern, DPH, MS; John A. Heit, MD, FAHA; Virginia J. Howard, PhD, FAHA; Brett M. Kissela, MD; Steven J. Kittner, MD, FAHA; Daniel T. Lackland, DrPH, MSPH, FAHA; Judith H. Lichtman, PhD, MPH; Lynda D. Lisabeth, PhD, FAHA; Diane M. Makuc, DrPH*; Gregory M. Marcus, MD, MAS, FAHA; Ariane Marelli, MD, MPH; David B. Matchar, MD, FAHA; Claudia S. Moy, PhD, MPH; Dariush Mozaffarian, MD, DrPH, FAHA; Michael E. Mussolino, PhD; Graham Nichol, MD, MPH, FAHA; Nina P. Paynter, PhD, MHSc; Elsayed Z. Soliman, MD, MSc, MS; Paul D. Sorlie, PhD; Nona Sotoodehnia, MD, MPH; Tanya N. Turan, MD, FAHA; Salim S. Virani, MD; Nathan D. Wong, PhD, MPH, FAHA; Daniel Woo, MD, MS, FAHA; Melanie B. Turner, MPH; on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
oncentrated solar energy applications using Fresnel lenses : A review

Solar energy concentration technology using Fresnel lens is an effective way to make full use of sunlight. This paper makes a review about the recent development of the concentrated solar energy applications using Fresnel lenses. The ongoing research and development involves imaging systems and non-imaging systems. Compared with imaging systems, non-imaging systems have the merits of larger accept angles, higher concentration ratios with less volume and shorter focal length, higher optical efficiency, etc. Concentrated photovoltaics is a major application and the highest solar-to-electric conversion efficiency based on imaging Fresnel lens and non-imaging Fresnel lens are reported as over 30% and 31.5 ± 1.7%, respectively. Moreover, both kinds of systems are widely used in other fields such as hydrogen generation, photo-bio reactors as well as photochemical reactions, surface modification of metallic materials, solar lighting and solar-pumped laser. During the recent two decades, such applications have been built and tested successfully to validate the practicality of Fresnel lens solar concentration systems. Although the present application scale is small, the ongoing research and development works suggest that Fresnel lens solar concentrators, especially non-imaging Fresnel lenses, will bring a breakthrough of commercial solar energy concentration application technology in the near future. Finally, the advantages and disadvantages of two systems are also summarized.
A Review of Deep Learning Methods Applied on Load Forecasting

The utility industry has invested widely in smart grid (SG) over the past decade. They considered it the future electrical grid while the information and electricity are delivered in two-way flow. SG has many Artificial Intelligence (AI) applications such as Artificial Neural Network (ANN), Machine Learning (ML) and Deep Learning (DL). Recently, DL has been a hot topic for AI applications in many fields such as time series load forecasting. This paper introduces the common algorithms of DL in the literature applied to load forecasting problems in the SG and power systems. The intention of this survey is to explore the different applications of DL that are used in the power systems and smart grid load forecasting. In addition, it compares the accuracy results RMSE and MAE for the reviewed applications and shows the use of convolutional neural network CNN with k-means algorithm had a great percentage of reduction in terms of RMSE.
Network security - private communication in a public world

No required textbook References: 1. Network Security Private Communication in a Public World Charlie Kaufman, Radia Perlman, Mike Speciner 2. Cryptography and Network Security William Stallings 3. Applied Cryptography Bruce Schneier 4. Hackers Beware Eric Cole 5.  Network Security Secrests & Solutions Stuart McClure, Joel Scambray, George Kurtz 6. Security in Computing Charles Pfleeger, Shari Pfleeger
Forecasting time series with complex seasonal patterns using exponential smoothing

A new innovations state space modeling framework, incorporating Box-Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods, high frequency seasonality, non-integer seasonality and dual-calendar effects. Our new modelling framework provides an alternative to existing exponential smoothing models, and is shown to have many advantages. The methods for initialization and estimation, including likelihood evaluation, are presented, and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensible approach to forecasting complex seasonal time series. Our trigonometric formulation is also presented as a means of decomposing complex seasonal time series, which cannot be decomposed using any of the existing decomposition methods. The approach is useful in a broad range of applications, and we illustrate its versatility in three empirical studies where it demonstrates excellent forecasting performance over a range of prediction horizons. In addition, we show that our trigonometric decomposition leads to the identification and extraction of seasonal components, which are otherwise not apparent in the time series plot itself.
Integrated Spatio-temporal Data Mining for Forest Fire Prediction

Forests play a critical role in sustaining the human environment. Most forest fires not only destroy the natural environment and ecological balance, but also seriously threaten the security of life and property. The early discovery and forecasting of forest fires are both urgent and necessary for forest fire control. This article explores the possible applications of Spatio-temporal Data Mining for forest fire prevention. The research pays special attention to the spatio-temporal forecasting of forest fire areas based upon historic observations. An integrated spatio-temporal forecasting framework – ISTFF – is proposed: it uses a dynamic recurrent neural network for spatial forecasting. The principle and algorithm of ISTFF are presented, and are then illustrated by a case study of forest fire area prediction in Canada. Comparative analysis of ISTFF with other methods shows its high accuracy in short-term prediction. The effect of spatial correlations on the prediction accuracy of spatial forecasting is also explored.
De-identification of patient notes with recurrent neural networks

Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of electronic health record databases, the limited number of researchers with access to non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value.



We introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset.



Our ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall of 97.38 and a precision of 98.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall of 99.25 and a precision of 99.21.



Our findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no manual feature engineering.
The role of information technology in supply chain integration

Outsourcing has increasingly been recognized as a source of great competitive advantage. By moving away from vertical integration and towards outsourcing, firms face a new challenge in integrating the various activities of the supply chain. The advent of novel information technologies, however, has made the integration of supply chain activities more manageable. In this study, we evaluate the effects of the usage of different information technologies on supplier and logistics integration in supply chains. A cross-sectional mail survey of ISM members in the United States was utilized to collect empirical data. ANOVA analysis was conducted to delineate the differences in the integration constructs across different levels of information technology usage. The results provide empirical evidence supporting the fact that information technology engenders supplier as well as logistics integration.
Spike-timing dependent plasticity in a transistor-selected resistive switching memory.

In a neural network, neuron computation is achieved through the summation of input signals fed by synaptic connections. The synaptic activity (weight) is dictated by the synchronous firing of neurons, inducing potentiation/depression of the synaptic connection. This learning function can be supported by the resistive switching memory (RRAM), which changes its resistance depending on the amplitude, the pulse width and the bias polarity of the applied signal. This work shows a new synapse circuit comprising a MOS transistor as a selector and a RRAM as a variable resistance, displaying spike-timing dependent plasticity (STDP) similar to the one originally experienced in biological neural networks. We demonstrate long-term potentiation and long-term depression by simulations with an analytical model of resistive switching. Finally, the experimental demonstration of the new STDP scheme is presented.
Deep learning-based personality recognition from text posts of online social networks

Personality is an important psychological construct accounting for individual differences in people. Computational personality recognition from online social networks is gaining increased research attention in recent years. However, the majority of existing methodologies mainly focused on human-designed shallow statistical features and didn’t make full use of the rich semantic information in user-generated texts, while those texts are exactly the most direct way for people to translate their internal thoughts and emotions into a form that others can understand. This paper proposes a deep learning-based approach for personality recognition from text posts of online social network users. We first utilize a hierarchical deep neural network composed of our newly designed AttRCNN structure and a variant of the Inception structure to learn the deep semantic features of each user’s text posts. Then we concatenate the deep semantic features with the statistical linguistic features obtained directly from the text posts, and feed them into traditional regression algorithms to predict the real-valued Big Five personality scores. Experimental results show that the deep semantic feature vectors learned from our proposed neural network are more effective than the other four kinds of non-trivial baseline features; the approach that utilizes the concatenation of our deep semantic features and the statistical linguistic features as the input of the gradient boosting regression algorithm achieves the lowest average prediction error among all the approaches tested by us.
A Tour of Unsupervised Deep Learning for Medical Image Analysis

Interpretation of medical images for diagnosis and treatment of complex disease from highdimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical imaging and image analysis. Unlike supervised learning which is biased towards how it is being supervised and manual efforts to create class label for the algorithm, unsupervised learning derive insights directly from the data itself, group the data and help to make data driven decisions without any external bias. This review systematically presents various unsupervised models applied to medical image analysis, including autoencoders and its several variants, Restricted Boltzmann machines, Deep belief networks, Deep Boltzmann machine and Generative adversarial network. Future research opportunities and challenges of unsupervised techniques for medical image analysis have also been discussed.
Liposomal drug delivery systems: from concept to clinical applications.

The first closed bilayer phospholipid systems, called liposomes, were described in 1965 and soon were proposed as drug delivery systems. The pioneering work of countless liposome researchers over almost 5 decades led to the development of important technical advances such as remote drug loading, extrusion for homogeneous size, long-circulating (PEGylated) liposomes, triggered release liposomes, liposomes containing nucleic acid polymers, ligand-targeted liposomes and liposomes containing combinations of drugs. These advances have led to numerous clinical trials in such diverse areas as the delivery of anti-cancer, anti-fungal and antibiotic drugs, the delivery of gene medicines, and the delivery of anesthetics and anti-inflammatory drugs. A number of liposomes (lipidic nanoparticles) are on the market, and many more are in the pipeline. Lipidic nanoparticles are the first nanomedicine delivery system to make the transition from concept to clinical application, and they are now an established technology platform with considerable clinical acceptance. We can look forward to many more clinical products in the future.
Narcissism and childhood recollections: a quantitative test of psychoanalytic predictions.

Different psychotherapeutic theories provide contradictory accounts of adult narcissism as the product of either parental coldness or excessive parental admiration during childhood. Yet, none of these theories has been tested systematically in a nonclinical sample. The authors compared four structural equation models predicting overt and covert narcissism among 120 United Kingdom adults. Both forms of narcissism were predicted by both recollections of parental coldness and recollections of excessive parental admiration. Moreover, a suppression relationship was detected between these predictors: The effects of each were stronger when modeled together than separately. These effects were found after controlling for working models of attachment; covert narcissism was predicted also by attachment anxiety. This combination of childhood experiences may help to explain the paradoxical combination of grandiosity and fragility in adult narcissism.