nils-herrmann/clean_scirepeval huggingface dataset:
Facial topography: clinical anatomy of the face.

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Caffeine & Health : Clarifying The Controversies IFIC

is one of the most comprehensively studied ingredients in the food supply. Yet, despite our considerable knowledge of caffeine and centuries of safe consumption in foods and beverages, questions and misperceptions about the potential health effects associated with caffeine persist. This Review provides up-to-date information on caffeine, examines its safety and summarizes the most recent key research conducted on caffeine and health.  Caffeine is added to soft drinks as a flavoring agent; it imparts a bitterness that modifies the flavors of other components, both sour and sweet. Although there has been controversy as to its effectiveness in this role, a review of the literature suggests that caffeine does, in fact, contribute to the sensory appeal of soft drinks.  Moderate intake of 300 mg/day (about three cups of coffee per day) of caffeine does not cause adverse health effects in healthy adults, although some groups, including those with hypertension and the elderly, may be more vulnerable. Also, regular consumers of coffee and other caffeinated beverages may experience some undesirable, but mild, short-lived symptoms if they stop consuming caffeine , particularly if the cessation is abrupt. However, there is little evidence of health risks of caffeine consumption. In fact, some evidence of health benefits exists for adults who consume moderate amounts of caffeine. Caffeine consumption may help reduce the risk of several chronic diseases, including diabetes, Parkinson's disease, liver disease, and colorectal cancer, as well as improve immune function. Large prospective cohort studies in the Netherlands, Finland, Sweden, and the United States have found caffeine consumption is associated with reduced risk of developing type 2 diabetes, although the mechanisms are unclear. Several other cohort studies have found that caffeine consumption from coffee and other beverages decreases the risk of Parkinson's Disease in men, as well as in women who have never used post-menopausal hormone replacement therapy. Epidemiological studies also suggest that coffee consumption may decrease the risk of liver injury, cirrhosis and hepatocellular carcinoma (liver cancer), although the reasons for these results have not been determined. In addition, coffee consumption appears to reduce the risk of colorectal cancer, but this has not generally been confirmed in prospective cohort studies. An anti-inflammatory effect has also been observed in a number of studies on caffeine's impact on the immune system. Most studies have found that caffeine consumption does not significantly increase the risk of coronary heart disease (CHD) or stroke. …
Experiments with security and privacy in IoT networks

We explore the risks to security and privacy in IoT networks by setting up an inexpensive home automation network and performing a set of experiments intended to study attacks and defenses. We focus on privacy preservation in home automation networks but our insights can extend to other IoT applications. Privacy preservation is fundamental to achieving the promise of IoT, Industrial Internet and M2M. We look at both simple cryptographic techniques and information manipulation to protect a user against an adversary inside the IoT network or an adversary that has compromised remote servers. We show how user data can be masked or selectively leaked and manipulated. We provide a blueprint for inexpensive study of IoT security and privacy using COTS products and services.
Video to Text Summary: Joint Video Summarization and Captioning with Recurrent Neural Networks

Video summarization and video captioning are considered two separate tasks in existing studies. For longer videos, automatically identifying the important parts of video content and annotating them with captions will enable a richer and more concise condensation of the video. We propose a general neural network configuration that jointly considers two supervisory signals (i.e., an image-based video summary and text-based video captions) in the training phase and generates both a video summary and corresponding captions for a given video in the test phase. Our main idea is that the summary signals can help a video captioning model learn to focus on important frames. On the other hand, caption signals can help a video summarization model to learn better semantic representations. Jointly modeling both the video summarization and the video captioning tasks offers a novel end-to-end solution that generates a captioned video summary enabling users to index and navigate through the highlights in a video. Moreover, our experiments show the joint model can achieve better performance than state-of-the-art approaches in both individual tasks.
Untangling Emoji Popularity Through Semantic Embeddings

Emojis have gone viral on the Internet across platforms and devices. Interwoven into our daily communications, they have become a ubiquitous new language. However, little has been done to analyze the usage of emojis at scale and in depth. Why do some emojis become especially popular while others don’t? How are people using them among the words? In this work, we take the initiative to study the collective usage and behavior of emojis, and specifically, how emojis interact with their context. We base our analysis on a very large corpus collected from a popular emoji keyboard, which contains a full month of inputs from millions of users. Our analysis is empowered by a state-of-the-art machine learning tool that computes the embeddings of emojis and words in a semantic space. We find that emojis with clear semantic meanings are more likely to be adopted. While entity-related emojis are more likely to be used as alternatives to words, sentimentrelated emojis often play a complementary role in a message. Overall, emojis are significantly more prevalent in a senti-
Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach

Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast wholeslide-images of extreme digital resolution (100, 000 pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions. The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models. We propose a method for disease localization in the context of weakly supervised learning, where only image-level labels are available during training. Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge. We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique from the field of semantic segmentation and object detection.
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference

This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. Secondly, we adopt factorization layers for efficient and expressive compression of alignment vectors into scalar features, which are then used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving competitive performance on all. A lightweight parameterization of our model also enjoys a≈ 3 times reduction in parameter size compared to the existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining competitive performance. Additionally, visual analysis shows that our propagated features are highly interpretable.
The Form of Referential Expressions in Discourse

Most instances of real-life language use involve discourses in which several sentences or utterances are coherently linked through the use of repeated references. Repeated reference can take many forms, and the choice of referential form has been the focus of much research in several related fields. In this article we distinguish between three main approaches: one that addresses the ‘why’ question – why are certain forms used in certain contexts; one that addresses the ‘how’ question – how are different forms processed; and one that aims to answer both questions by seriously considering both the discourse function of referential expressions, and the cognitive mechanisms that underlie their processing cost. We argue that only the latter approach is capable of providing a complete view of referential processing, and that in so doing it may also answer a more profound ‘why’ question – why does language offer multiple referential forms. Coherent discourse typically involves repeated references to previously mentioned referents, and these references can be made with different forms. For example, a person mentioned in discourse can be referred to by a proper name (e.g., Bill), a definite description (e.g., the waiter), or a pronoun (e.g., he). When repeated reference is made to a referent that was mentioned in the same sentence, the choice and processing of referential form may be governed by syntactic constraints such as binding principles (Chomsky 1981). However, in many cases of repeated reference to a referent that was mentioned in the same sentence, and in all cases of repeated reference across sentences, the choice and processing of referential form reflects regular patterns and preferences rather than strong syntactic constraints. The present article focuses on the factors that underlie these patterns. Considerable research in several disciplines has aimed to explain how speakers and writers choose which form they should use to refer to objects and events in discourse, and how listeners and readers process different referential forms (e.g., Chafe 1976; Clark & Wilkes 1986; Kintsch 1988; Gernsbacher 1989; Ariel 1990; Gordon, Grosz & Gilliom 1993; Gundel, Hedberg & Zacharski 1993; Garrod & Sanford 1994; Gordon & Hendrick 1998; Almor 1999; Cowles & Garnham 2005). One of the central observations in this research is that there exists an inverse relation between the specificity of the referential
The Minutiae Based Latent Fingerprint Recognition System

The emergence in the field of fingerprint recognition witness several efficient techniques that propose matching and recognition in less time. The latent fingerprints posed a challenge for such efficient techniques that may deviates results from ideal to worse. The minutiae are considered as a discriminative feature of finger patterns which is assessed in almost every technique for recognition purpose. But in latent patterns such minutiae may be missed or may have contaminated noise. In this paper, we presents such work that demonstrate the solution for latent fingerprints recognition but in ideal time. We also gathered the description about the techniques that have been evaluated on standard NIST Special Dataset (SD)27 of latent fingerprint.
On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results

We study in this paper the rate of convergence for learning distributions with the Generative Adversarial Networks (GAN) framework, which subsumes Wasserstein, Sobolev and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions, under a collection of objective evaluation metrics. On the nonparametric end, we investigate the minimax optimal rates and fundamental difficulty of the density estimation under the adversarial framework. On the parametric end, we establish theory for neural network classes, that characterizes the interplay between the choice of generator and discriminator. We investigate how to improve the GAN framework with better theoretical guarantee through the lens of regularization. We discover and isolate a new notion of regularization, called the generator/discriminator pair regularization, that sheds light on the advantage of GAN compared to classic parametric and nonparametric approaches for density estimation.
Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence.

Numerous theories in social and health psychology assume that intentions cause behaviors. However, most tests of the intention- behavior relation involve correlational studies that preclude causal inferences. In order to determine whether changes in behavioral intention engender behavior change, participants should be assigned randomly to a treatment that significantly increases the strength of respective intentions relative to a control condition, and differences in subsequent behavior should be compared. The present research obtained 47 experimental tests of intention-behavior relations that satisfied these criteria. Meta-analysis showed that a medium-to-large change in intention (d = 0.66) leads to a small-to-medium change in behavior (d = 0.36). The review also identified several conceptual factors, methodological features, and intervention characteristics that moderate intention-behavior consistency.
Collaborative Variational Autoencoder for Recommender Systems

Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their methods are very limited in current multimedia scenario. This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and rating. Unlike previous works with denoising criteria, the proposed CVAE learns a latent distribution for content in latent space instead of observation space through an inference network and can be easily extended to other multimedia modalities other than text. Experiments show that CVAE is able to significantly outperform the state-of-the-art recommendation methods with more robust performance.
DeepNeuro: an open-source deep learning toolbox for neuroimaging

Translating neural networks from theory to clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a deep learning framework that is best-suited to putting deep learning algorithms for neuroimaging in practical usage with a minimum of friction. We show how this framework can be used to both design and train neural network architectures, as well as modify state-of-the-art architectures in a flexible and intuitive way. We display the preand postprocessing functions common in the medical imaging community that DeepNeuro offers to ensure consistent performance of networks across variable users, institutions, and scanners. And we show how pipelines created in DeepNeuro can be concisely packaged into shareable Docker containers and command-line interfaces using DeepNeuros pipeline resources.
Improving scalability of apache spark-based scale-up server through docker container-based partitioning

We propose an Apache Spark-based scale-up server architecture using Docker container-based partitioning method to improve performance scalability. The performance scalability problem of Apache Spark-based scale-up servers is due to garbage collection(GC) and remote memory access overheads when the servers are equipped with significant number of cores and Non-Uniform Memory Access(NUMA). The proposed method minimizes the problems using Docker container-based architecture effectively partitioning the original scale-up server into small logical servers. Our evaluation study based on benchmark programs revealed that the partitioning method showed performance improvement by ranging from 1.1x through 1.7x on a 120 core scale-up system. Our proof-of-concept scale-up server architecture provides the basis towards complete and practical design of partitioning-based scale-up servers showing performance scalability.
Mobile cloud computing: advantage, disadvantage and open challenge

With modern smart phones and powerful mobile devices, Mobile apps provide many advantages to the community but it has also grown the demand for online availability and accessibility. Cloud computing is provided to be widely adopted for several applications in mobile devices. However, there are many advantages and disadvantages of using mobile applications and cloud computing. This paper focuses in providing an overview of mobile cloud computing advantages, disadvantages. The paper discusses the importance of mobile cloud applications and highlights the mobile cloud computing open challenges
Understanding and Representing the Semantics of Large Structured Documents

Understanding large, structured documents like scholarly articles, requests for proposals or business reports is a complex and difficult task. It involves discovering a document’s overall purpose and subject(s), understanding the function and meaning of its sections and subsections, and extracting low level entities and facts about them. In this research, we present a deep learning based document ontology to capture the general purpose semantic structure and domain specific semantic concepts from a large number of academic articles and business documents. The ontology is able to describe different functional parts of a document, which can be used to enhance semantic indexing for a better understanding by human beings and machines. We evaluate our models through extensive experiments on datasets of scholarly articles from arxiv and Request for Proposal documents.
A local expansion propagation algorithm for social link identification

Social link identification, that is to identify accounts across different online social networks that belong to the same user, is an important task in social network applications. Most existing methods to solve this problem directly applied machine learning classifiers on features extracted from user’s rich information. In practice, however, only some limited user information can be obtained because of privacy concerns. In addition, we observe that the existing methods cannot handle huge amount of potential account pairs from different online social networks. In this paper, we propose an effective method to address the above two challenges by expanding known anchor links (seed account pairs belonging to the same person). In particular, we leverage potentially useful information possessed by the existing anchor link and then develop a local expansion propagation model to identify new social links, which are taken as a generated anchor link to be used for iteratively identifying additional new social link. We evaluate our method on two most popular Chinese social networks. Experimental results show our proposed method can quickly find most of identity account pairs across different online social networks.
Factors Affecting Adoption of Electronic Banking System in Ethiopian Banking Industry

The aim of this paper is to identify factors that affect adoption of E-banking in the Ethiopian banking industry. The study was conducted based on the data gathered from four banks in Ethiopia; three private banks (Dashen bank, Zemen bank and Wegagen bank) and one state owned bank (commercial bank of Ethiopia). A mixed research approach was used to answer the research questions that emerge through the review of existing literature and the experiences of the researcher in respect of the E-banking system in Ethiopia. The study statistically analyzes data obtained from the survey questionnaire. A research framework developed based on technology-organizationenvironment model (TOE) developed by Tornatzky and Fleischer. The result of the study indicated that, the major barriers Ethiopian banking industry faces in the adoption of Electronic banking are: security risk, lack of trust, lack of legal and regulatory frame work, Lack of ICT infrastructure and absence of competition between local and foreign banks. The study suggests a series of measures which could be taken by the banking industry and by government to address various challenges identified. These measures include: Establishing a clear set of legal framework on the use of technology in banking industry, supporting banking industry by investing on ICT infrastructure and banks needs to be focused on technological innovation competition rather than traditional bases of retail bank competition.
Grating Coupler Design for Reduced Back-Reflections

A grating coupler having asymmetric grating trenches for low back reflections is experimentally demonstrated. Conventional and asymmetric-trench grating couplers have been fabricated on a silicon nitride waveguide platform. Both grating couplers have fully etched trenches, which normally result in higher back reflections than shallow-etched trenches. For evaluating the back reflection characteristics, test structures based on a 3-dB multimode interference power splitter have been measured and the backreflection has been extracted from each grating coupler using an equivalent optical circuit. The designed grating coupler has no critical penalty (<0.2 dB) in coupling efficiency and ~5 dB lower back reflections than a conventional grating coupler design. Using ray transfer matrix modeling, further improvements to the back reflection characteristics of the asymmetric grating coupler are expected.