nils-herrmann/clean_scirepeval huggingface dataset:
Symbolic Interpretation of Artificial Neural Networks

Hybrid Intelligent Systems that combine knowledge-based and artificial neural network systems typically have four phases involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training, and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule-extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by
2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning

Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.
Detecting Deception from Emotional and Unemotional Cues

Encoders were video recorded giving either truthful or deceptive descriptions of video footage designed to generate either emotional or unemotional responses. Decoders were asked to indicate the truthfulness of each item, what cues they used in making their judgements, and then to complete both the Micro Expression Training Tool (METT) and Subtle Expression Training Tool (SETT). Although overall performance on the deception detection task was no better than chance, performance for emotional lie detection was significantly above chance, while that for unemotional lie detection was significantly below chance. Emotional lie detection accuracy was also significantly positively correlated with reported use of facial expressions and with performance on the SETT, but not on the METT. The study highlights the importance of taking the type of lie into account when assessing skill in deception detection.
Similarity network fusion for aggregating data types on a genomic scale

Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e.g., patients) for each available data type and then efficiently fusing these into one network that represents the full spectrum of underlying data. For example, to create a comprehensive view of a disease given a cohort of patients, SNF computes and fuses patient similarity networks obtained from each of their data types separately, taking advantage of the complementarity in the data. We used SNF to combine mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets. SNF substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.
Factors associated with underutilization of antenatal care services in Indonesia: results of Indonesia Demographic and Health Survey 2002/2003 and 2007

Antenatal care aims to prevent maternal and perinatal mortality and morbidity. In Indonesia, at least four antenatal visits are recommended during pregnancy. However, this service has been underutilized. This study aimed to examine factors associated with underutilization of antenatal care services in Indonesia.



We used data from Indonesia Demographic and Health Survey (IDHS) 2002/2003 and 2007. Information of 26,591 singleton live-born infants of the mothers' most recent birth within five years preceding each survey was examined. Twenty-three potential risk factors were identified and categorized into four main groups, external environment, predisposing, enabling, and need factors. Logistic regression models were used to examine the association between all potential risk factors and underutilization of antenatal services. The Population Attributable Risk (PAR) was calculated for selected significant factors associated with the outcome.



Factors strongly associated with underutilization of antenatal care services were infants from rural areas and from outer Java-Bali region, infants from low household wealth index and with low maternal education level, and high birth rank infants with short birth interval of less than two years. Other associated factors identified included mothers reporting distance to health facilities as a major problem, mothers less exposed to mass media, and mothers reporting no obstetric complications during pregnancy. The PAR showed that 55% of the total risks for underutilization of antenatal care services were attributable to the combined low household wealth index and low maternal education level.



Strategies to increase the accessibility and availability of health care services are important particularly for communities in rural areas. Financial support that enables mothers from poor households to use health services will be beneficial. Health promotion programs targeting mothers with low education are vital to increase their awareness about the importance of antenatal services.
Training deep neural-networks using a noise adaptation layer

The availability of large datsets has enabled neural networks to achieve impressive recognition results. However, the presence of inaccurate class labels is known to deteriorate the performance of even the best classifiers in a broad range of classification problems. Noisy labels also tend to be more harmful than noisy attributes. When the observed label is noisy, we can view the correct label as a latent random variable and model the noise processes by a communication channel with unknown parameters. Thus we can apply the EM algorithm to find the parameters of both the network and the noise and estimate the correct label. In this study we present a neural-network approach that optimizes the same likelihood function as optimized by the EM algorithm. The noise is explicitly modeled by an additional softmax layer that connects the correct labels to the noisy ones. This scheme is then extended to the case where the noisy labels are dependent on the features in addition to the correct labels. Experimental results demonstrate that this approach outperforms previous methods.
Intellectual Property Protection of 3D Print Supply Chain with Blockchain Technology

Within “Industrie 4.0” approach 3D printing technology is characterized as one of the disruptive innovations. Conventional supply chains are replaced by value-added networks. The spatially distributed development of printed components, e.g. for the rapid delivery of spare parts, creates a new challenge when differentiating between “original part”, “copy” or “counterfeit” becomes necessary. This is especially true for safety-critical products. Based on these changes classic branded products adopt the characteristics of licensing models as we know them in the areas of software and digital media. This paper describes the use of digital rights management as a key technology for the successful transition to Additive Manufacturing methods and a key for its commercial implementation and the prevention of intellectual property theft. Risks will be identified along the process chain and solution concepts are presented. These are currently being developed by an 8-partner project named SAMPL (Secure Additive Manufacturing Platform).
Thermal properties of graphene and nanostructured carbon materials.

Recent years have seen a rapid growth of interest by the scientific and engineering communities in the thermal properties of materials. Heat removal has become a crucial issue for continuing progress in the electronic industry, and thermal conduction in low-dimensional structures has revealed truly intriguing features. Carbon allotropes and their derivatives occupy a unique place in terms of their ability to conduct heat. The room-temperature thermal conductivity of carbon materials span an extraordinary large range--of over five orders of magnitude--from the lowest in amorphous carbons to the highest in graphene and carbon nanotubes. Here, I review the thermal properties of carbon materials focusing on recent results for graphene, carbon nanotubes and nanostructured carbon materials with different degrees of disorder. Special attention is given to the unusual size dependence of heat conduction in two-dimensional crystals and, specifically, in graphene. I also describe the prospects of applications of graphene and carbon materials for thermal management of electronics.
An Exploration of Style Transfer Using Deep Neural Networks

Convolutional Neural Networks and Graphics Processing Units have been at the core of a paradigm shift in computer vision research that some researchers have called “the algorithmic perception revolution.” This thesis presents the implementation and analysis of several techniques for performing artistic style transfer using a Convolutional Neural Network architecture trained for large-scale image recognition tasks. We present an implementation of an existing algorithm for artistic style transfer in images and video. The neural algorithm separates and recombines the style and content of arbitrary images. Additionally, we present an extension of the algorithm to perform weighted artistic style transfer.
The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges

Progress in Information and Communication Technologies (ICTs) is shaping more and more the healthcare domain. ICTs adoption provides new opportunities, as well as discloses novel and unforeseen application scenarios. As a result, the overall health sector is potentially benefited, as the quality of medical services is expected to be enhanced and healthcare costs are reduced, in spite of the increasing demand due to the aging population. Notwithstanding the above, the scientific literature appears to be still quite scattered and fragmented, also due to the interaction of scientific communities with different background, skills, and approaches. A number of specific terms have become of widespread use (e.g., regarding ICTs-based healthcare paradigms as well as at health-related data formats), but without commonly-agreed definitions. While scientific surveys and reviews have also been proposed, none of them aims at providing a holistic view of how today ICTs are able to support healthcare. This is the more and more an issue, as the integrated application of most if not all the main ICTs pillars is the most agreed upon trend, according to the Industry 4.0 paradigm about ongoing and future industrial revolution. In this paper we aim at shedding light on how ICTs and healthcare are related, identifying the most popular ICTs-based healthcare paradigms, together with the main ICTs backing them. Studying more than 300 papers, we survey outcomes of literature analyses and results from research activities carried out in this field. We characterize the main ICTs-based healthcare paradigms stemmed out in recent years fostered by the evolution of ICTs. Dissecting the scientific literature, we also identify the technological pillars underpinning the novel applications fueled by these technological advancements. Guided by the scientific literature, we review a number of application scenarios gaining momentum thanks to the beneficial impact of ICTs. As the evolution of ICTs enables to gather huge and invaluable data from numerous and highly varied sources in easier ways, here we also focus on the shapes that this healthcare-related data may take. This survey provides an up-to-date picture of the novel healthcare applications enabled by the ICTs advancements, with a focus on their specific hottest research challenges. It helps the interested readership (from both technological and medical fields) not to lose orientation in the complex landscapes possibly generated when advanced ICTs are adopted in application scenarios dictated by the critical healthcare domain.
Antioxidant activity of pomegranate juice and its relationship with phenolic composition and processing.

The antioxidant activity of pomegranate juices was evaluated by four different methods (ABTS, DPPH, DMPD, and FRAP) and compared to those of red wine and a green tea infusion. Commercial pomegranate juices showed an antioxidant activity (18-20 TEAC) three times higher than those of red wine and green tea (6-8 TEAC). The activity was higher in commercial juices extracted from whole pomegranates than in experimental juices obtained from the arils only (12-14 TEAC). HPLC-DAD and HPLC-MS analyses of the juices revealed that commercial juices contained the pomegranate tannin punicalagin (1500-1900 mg/L) while only traces of this compound were detected in the experimental juice obtained from arils in the laboratory. This shows that pomegranate industrial processing extracts some of the hydrolyzable tannins present in the fruit rind. This could account for the higher antioxidant activity of commercial juices compared to the experimental ones. In addition, anthocyanins, ellagic acid derivatives, and hydrolyzable tannins were detected and quantified in the pomegranate juices.
Dispositional factors predicting use of online dating sites and behaviors related to online dating

Although prior research has examined how individual difference factors are related to relationship initiation and formation over the Internet (e.g., online dating sites, social networking sites), little research has examined how dispositional factors are related to other aspects of online dating. The present research therefore sought to examine the relationship between several dispositional factors, such as Big-Five personality traits, self-esteem, rejection sensitivity, and attachment styles, and the use of online dating sites and online dating behaviors. Rejection sensitivity was the only dispositional variable predictive of use of online dating sites whereby those higher in rejection sensitivity are more likely to use online dating sites than those lower in rejection sensitivity. We also found that those higher in rejection sensitivity, those lower in conscientiousness, and men indicated being more likely to engage in potentially risky behaviors related to meeting an online dating partner face-to-face. Further research is needed to further explore the relationships between these dispositional factors and online dating behaviors.
Explaining the decline in coronary heart disease mortality in England and Wales between 1981 and 2000.

Coronary heart disease mortality rates have been decreasing in the United Kingdom since the 1970s. Our study aimed to examine how much of the decrease in England and Wales between 1981 and 2000 could be attributed to medical and surgical treatments and how much to changes in cardiovascular risk factors.



The IMPACT mortality model was used to combine and analyze data on uptake and effectiveness of cardiological treatments and risk factor trends in England and Wales. The main data sources were published trials and meta-analyses, official statistics, clinical audits, and national surveys. Between 1981 and 2000, coronary heart disease mortality rates in England and Wales decreased by 62% in men and 45% in women 25 to 84 years old. This resulted in 68 230 fewer deaths in 2000. Some 42% of this decrease was attributed to treatments in individuals (including 11% to secondary prevention, 13% to heart failure treatments, 8% to initial treatments of acute myocardial infarction, and 3% to hypertension treatments) and 58% to population risk factor reductions (principally smoking, 48%; blood pressure, 9.5%; and cholesterol, 9.5%). Adverse trends were seen for physical activity, obesity and diabetes.



More than half the coronary heart disease mortality decrease in Britain between 1981 and 2000 was attributable to reductions in major risk factors, principally smoking. This emphasizes the importance of a comprehensive strategy that promotes primary prevention, particularly for tobacco and diet, and that maximizes population coverage of effective treatments, especially for secondary prevention and heart failure. These findings may be cautiously generalizable to the United States and other developed countries.
Staphylococcus lipolyticus sp. nov., a new cold-adapted lipase producing marine species

A cold-adapted lipase producing bacterium, designated SS-33T, was isolated from sea sediment collected from the Bay of Bengal, India, and subjected to a polyphasic taxonomic study. Strain SS-33T exhibited the highest 16S rRNA gene sequence similarity with Staphylococcus cohnii subsp. urealyticus (97.18 %), Staphylococcus saprophyticus subsp. bovis (97.16 %) and Staphylococcus cohnii subsp. cohnii (97.04 %). Phylogenetic analysis based on the 16S rRNA gene sequences showed that strain SS-33T belongs to the genus Staphylococcus. Cells of strain SS-33T were Gram-positive, coccus-shaped, non-spore-forming, non-motile, catalase-positive and oxidase-negative. The major fatty acid detected in strain SS-33T was anteiso-C15:0 and the menaquinone was MK-7. The genomic DNA G + C content was 33 mol%. The DNA-DNA hybridization among strain SS-33T and the closely related species indicated that strain SS-33T represents a novel species of the genus Staphylococcus. On the basis of the morphological, physiological and chemotaxonomic characteristics, the results of phylogenetic analysis and the DNA-DNA hybridization, a novel species is proposed for strain SS-33T, with the name Staphylococcus lipolyticus sp. nov. The strain type is SS-33T (=MTCC 10101T = JCM 16560T). Staphylococcus lipolyticus SS-33T hydrolyzed various substrates including tributyrin, olive oil, Tween 20, Tween 40, Tween 60, and Tween 80 at low temperatures, as well as mesophilic temperatures. Lipase from strain SS-33T was partially purified by acetone precipitation. The molecular weight of lipase protein was determined 67 kDa by SDS-PAGE. Zymography was performed to monitor the lipase activity in Native-PAGE. Calcium ions increased lipase activity twofold. The optimum pH of lipase was pH 7.0 and optimum temperature was 30 °C. However, lipase exhibited 90 % activity of its optimum temperature at 10 °C and became more stable at 10 °C as compared to 30 °C. The lipase activity and stability at low temperature has wide ranging applications in various industrial processes. Therefore, cold-adapted mesophilic lipase from strain SS-33T may be used for industrial applications. This is the first report of the production of cold-adapted mesophilic lipase by any Staphylococcus species.
Determinants of Foreign Direct Investment : A Sectoral and Institutional Approach

This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. Using a dataset which breaks down FDI flows into primary, secondary and tertiary sector investments and a GMM dynamic approach to address concerns about endogeneity, the paper analyzes various macroeconomic, developmental, and institutional/qualitative determinants of FDI in a sample of emerging market and developed economies. While FDI flows into the primary sector show little dependence on any of these variables, secondary and tertiary sector investments are affected in different ways by countries’ income levels and exchange rate valuation, as well as development indicators such as financial depth and school enrollment, and institutional factors such as judicial independence and labor market flexibility. Finally, we find that the effect of these factors often differs between advanced and emerging economies.
Neural Network Model for Email-Spam Detection

Email spam is a word that we come across in our daily life. The word spam means junk mails. The unsolicited emails that are received by any person in his/her mailbox are called spam. These junk mails are usually sent in bulk for advertising and marketing some products. This work presents a neural network approach to intrusion detection. A Multi-Layer Perceptron using Back Propagation Algorithm is used to classify the emails as a spam or normal mail in an email spam application.
IT Governance Hands-on : Using COBIT to Implement IT Governance

In the past, running an IT organization as a support function—a function separate and distinct from the business—was a common practice. Now, most IT infrastructure investments and new IT applications span business lines and functions. Some organizations even integrate partners and customers into their internal processes. Therefore, CEOs and CIOs increasingly feel the need for a tighter relationship between IT and the business. But how should they deal with this strategic challenge? The key questions are: • Is there a framework to guide business and technology management leaders in their efforts to change information technology’s role within the organization and to close the gap between IT and the business that IT is supposed to support and drive? • What are the responsibilities at the board and management levels? • Is this a governance issue?
Knowledge-Based Semantic Embedding for Machine Translation

In this paper, with the help of knowledge base, we build and formulate a semantic space to connect the source and target languages, and apply it to the sequence-to-sequence framework to propose a Knowledge-Based Semantic Embedding (KBSE) method. In our KBSE method, the source sentence is firstly mapped into a knowledge based semantic space, and the target sentence is generated using a recurrent neural network with the internal meaning preserved. Experiments are conducted on two translation tasks, the electric business data and movie data, and the results show that our proposed method can achieve outstanding performance, compared with both the traditional SMT methods and the existing encoder-decoder models.
Role of Big Data and Analytics in Smart Cities

The aim of this paper is to study the real potential of using Big Data Analytics in Smart Cities. In this work, we studied cases across the globe where decision maker are using Big Data Analytics as a tool for making Smart City. The paper covers how Internet of Things, Machine to machine, Big Data and Smart Cities Linkages can help in doing predictive analytics which can be helpful to human wellbeing. This paper focused on two main areas – Smart Grid and Traffic Congestion Management where Big Data Analytics can be useful for decision makers and city planner. The report includes various pilot project currently undergoing for making a city smarter along with benefits to human wellbeing. The report also considered various challenges that can be encountered while implementing Big Data solution in making Smart Cities.