nils-herrmann/clean_scirepeval huggingface dataset:
A palliative approach for heart failure end-of-life care

The current review discusses the integration of guideline and evidence-based palliative care into heart failure end-of-life (EOL) care.



North American and European heart failure societies recommend the integration of palliative care into heart failure programs. Advance care planning, shared decision-making, routine measurement of symptoms and quality of life and specialist palliative care at heart failure EOL are identified as key components to an effective heart failure palliative care program. There is limited evidence to support the effectiveness of the individual elements. However, results from the palliative care in heart failure trial suggest an integrated heart failure palliative care program can significantly improve quality of life for heart failure patients at EOL.



Integration of a palliative approach to heart failure EOL care helps to ensure patients receive the care that is congruent with their values, wishes and preferences. Specialist palliative care referrals are limited to those who are truly at heart failure EOL.
Learning SO(3) Equivariant Representations with Spherical CNNs

We address the problem of 3D rotation equivariance in convolutional neural networks. 3D rotations have been a challenging nuisance in 3D classification tasks requiring higher capacity and extended data augmentation in order to tackle it. We model 3D data with multivalued spherical functions and we propose a novel spherical convolutional network that implements exact convolutions on the sphere by realizing them in the spherical harmonic domain. Resulting filters have local symmetry and are localized by enforcing smooth spectra. We apply a novel pooling on the spectral domain and our operations are independent of the underlying spherical resolution throughout the network. We show that networks with much lower capacity and without requiring data augmentation can exhibit performance comparable to the state of the art in standard retrieval and classification benchmarks.
Difflog: Learning Datalog Programs by Continuous Optimization

An important problem in automated reasoning involves learning logical patterns from structured data. Existing approaches to this task of inductive logic programming either involve solving computationally difficult combinatorial problems or performing parameter estimation in complex statistical relational models. In this paper, we present DIFFLOG, a simple extension of the popular logic programming language Datalog to the continuous domain. By attaching real-valued weights to individual rules, we naturally extend the traditional Boolean semantics of Datalog to additionally associate numerical values with individual conclusions. Rule learning may then be cast as the problem of determining the values of the weights which cause the best agreement between training labels and induced values of output tuples. We propose a novel algorithmic framework to efficiently evaluate DIFFLOG programs with provenance information, which in turn makes it feasible to employ standard numerical optimization techniques such as gradient descent and Newton’s method to the synthesis of logic programs. On a suite of 10 benchmark problems from different domains, DIFFLOG can learn complex programs with recursive rules and relations of arbitrary arity, even with small amounts of noise in the training data.
Ubiquitous Commerce: Theories, Technologies, and Applications

With the rapid development of ubiquitous computing and mobile communication technologies, the traditional business model will change drastically. As a logical extension of e-commerce and m-commerce, ubiquitous commerce (u-commerce) research and application are currently under transition with a history of numerous tried and failed solutions, and a future of promising but yet uncertain possibilities with potential new technology innovations. At this point of the development, we propose a suitable framework and organize the u-commerce research under the proposed classification scheme. The current situation outlined by the scheme has been addressed by exploratory and early phase studies. We hope the findings of this research will provide useful insights for anyone who is interested in u-commerce. The paper also provides some future directions for research.
Word Embeddings for the Construction Domain

We introduce word vectors for the construction domain. Our vectors were obtained by running word2vec on an 11M-word corpus that we created from scratch by leveraging freely-accessible online sources of construction-related text. We first explore the embedding space and show that our vectors capture meaningful constructionspecific concepts. We then evaluate the performance of our vectors against that of ones trained on a 100B-word corpus (Google News) within the framework of an injury report classification task. Without any parameter tuning, our embeddings give competitive results, and outperform the Google News vectors in many cases. Using a keyword-based compression of the reports also leads to a significant speed-up with only a limited loss in performance. We release our corpus and the data set we created for the classification task as publicly available, in the hope that they will be used by future studies for benchmarking and building on our work.
An innovative neural network approach for stock market prediction

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the development of word vector in deep learning, we demonstrate the concept of “stock vector.” The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market. In these two models, we use the embedded layer and the automatic encoder, respectively, to vectorize the data, in a bid to forecast the stock via long short-term memory neural network. The experimental results show that the deep LSTM with embedded layer is better. Specifically, the accuracy of two models is 57.2 and 56.9%, respectively, for the Shanghai A-shares composite index. Furthermore, they are 52.4 and 52.5%, respectively, for individual stocks. We demonstrate research contributions in IMMT for neural network-based financial analysis.
Tweeting is believing?: understanding microblog credibility perceptions

Twitter is now used to distribute substantive content such as breaking news, increasing the importance of assessing the credibility of tweets. As users increasingly access tweets through search, they have less information on which to base credibility judgments as compared to consuming content from direct social network connections. We present survey results regarding users' perceptions of tweet credibility. We find a disparity between features users consider relevant to credibility assessment and those currently revealed by search engines. We then conducted two experiments in which we systematically manipulated several features of tweets to assess their impact on credibility ratings. We show that users are poor judges of truthfulness based on content alone, and instead are influenced by heuristics such as user name when making credibility assessments. Based on these findings, we discuss strategies tweet authors can use to enhance their credibility with readers (and strategies astute readers should be aware of!). We propose design improvements for displaying social search results so as to better convey credibility.
Machine translation using deep learning: An overview

This Paper reveals the information about Deep Neural Network (DNN) and concept of deep learning in field of natural language processing i.e. machine translation. Now day's DNN is playing major role in machine leaning technics. Recursive recurrent neural network (R2NN) is a best technic for machine learning. It is the combination of recurrent neural network and recursive neural network (such as Recursive auto encoder). This paper presents how to train the recurrent neural network for reordering for source to target language by using Semi-supervised learning methods. Word2vec tool is required to generate word vectors of source language and Auto encoder helps us in reconstruction of the vectors for target language in tree structure. Results of word2vec play an important role in word alignment of the input vectors. RNN structure is very complicated and to train the large data file on word2vec is also a time-consuming task. Hence, a powerful hardware support (GPU) is required. GPU improves the system performance by decreasing training time period.
Towards an Understanding of Business Intelligence

) Given the wide recognition of business intelligence (BI) over the last 20 years, we performed a literature review on the concept from a managerial perspective. We analysed 103 articles related to BI in the period 1990 to 2010. We found that BI is defined as a process, a product, and as a set of technologies, or a combination of these, which involves data, information, knowledge, decision making, related processes and technologies that support them. Our findings show that the literature focuses mostly on data and information, and less on knowledge and decision making. Moreover, in relation to the processes there is a substantial amount of literature about gathering and storing data and information, but less about analysing and using information and knowledge, and almost nothing about acting (making decisions) based on intelligence. The research literature has mainly focused on technologies and neglecting the role of the decision maker. We conclude by synthesizing a unified definition of BI and identifying possible future research streams.
Combining Memory and Emotion With Dialog on Social Companion: A Review

In the coming era of social companions, many researches have been pursuing natural dialog interactions and long-term relations between social companions and users. With respect to the quick decrease of user interests after the first few interactions, various emotion and memory models are developed and integrated with social companions for better user engagement. This paper reviews related works in the effort of combining memory and emotion with natural language dialog on social companions. We separate these works into three categories: (1) Affective system with dialog, (2) Task-driven memory with dialog, (3) Chat-driven memory with dialog. In addition, we discussed limitations and challenging issues to be solved. Finally, we also introduced our framework of social companions.
Link prediction in social networks: the state-of-the-art

In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed. 对社交网络中的链接预测研究现状进行系统回顾、分析和讨论, 并指出未来研究挑战. 在动态社交网络中, 链接预测是挖掘和分析网络演化的一项重要任务, 其目的是预测当前未知的链接以及未来链接的变化. 过去十余年中, 在社交网络链接预测问题上已有大量研究工作. 本文旨在对该问题的研究现状和趋势进行全面回顾、分析和讨论. 提出一种分类法组织链接预测技术和问题. 详细分析和讨论了链接预测的技术、问题和应用. 介绍了该问题的活跃研究组. 分析和讨论了社交网络链接预测研究的未来挑战.
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term “outlier” can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework.
Ambient Assisted Living Healthcare Frameworks, Platforms, Standards, and Quality Attributes

Ambient Assisted Living (AAL) is an emerging multi-disciplinary field aiming at exploiting information and communication technologies in personal healthcare and telehealth systems for countering the effects of growing elderly population. AAL systems are developed for personalized, adaptive, and anticipatory requirements, necessitating high quality-of-service to achieve interoperability, usability, security, and accuracy. The aim of this paper is to provide a comprehensive review of the AAL field with a focus on healthcare frameworks, platforms, standards, and quality attributes. To achieve this, we conducted a literature survey of state-of-the-art AAL frameworks, systems and platforms to identify the essential aspects of AAL systems and investigate the critical issues from the design, technology, quality-of-service, and user experience perspectives. In addition, we conducted an email-based survey for collecting usage data and current status of contemporary AAL systems. We found that most AAL systems are confined to a limited set of features ignoring many of the essential AAL system aspects. Standards and technologies are used in a limited and isolated manner, while quality attributes are often addressed insufficiently. In conclusion, we found that more inter-organizational collaboration, user-centered studies, increased standardization efforts, and a focus on open systems is needed to achieve more interoperable and synergetic AAL solutions.
Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark

Recent deep learning based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition (HCCR) by learning discriminative representations directly from raw data. Nevertheless, we believe that the long-and-well investigated domain-specific knowledge should still help to boost the performance of HCCR. By integrating the traditional normalization-cooperated direction-decomposed feature map (directMap) with the deep convolutional neural network (convNet), we are able to obtain new highest accuracies for both online and offline HCCR on the ICDAR-2013 competition database. With this new framework, we can eliminate the needs for data augmentation and model ensemble, which are widely used in other systems to achieve their best results. This makes our framework to be efficient and effective for both training and testing. Furthermore, although directMap+convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective. A new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer. The adaptation process can be efficiently and effectively implemented in an unsupervised manner. By adding the adaptation layer into the pre-trained convNet, it can adapt to the new handwriting styles of particular writers, and the recognition accuracy can be further improved consistently and significantly. This paper gives an overview and comparison of recent deep learning based approaches for HCCR, and also sets new benchmarks for both online and offline HCCR.
Wheat grass juice in the treatment of active distal ulcerative colitis: a randomized double-blind placebo-controlled trial.

The use of wheat grass (Triticum aestivum) juice for treatment of various gastrointestinal and other conditions had been suggested by its proponents for more than 30 years, but was never clinically assessed in a controlled trial. A preliminary unpublished pilot study suggested efficacy of wheat grass juice in the treatment of ulcerative colitis (UC).



A randomized, double-blind, placebo-controlled study. One gastroenterology unit in a tertiary hospital and three study coordinating centers in three major cities in Israel. Twenty-three patients diagnosed clinically and sigmoidoscopically with active distal UC were randomly allocated to receive either 100 cc of wheat grass juice, or a matching placebo, daily for 1 month. Efficacy of treatment was assessed by a 4-fold disease activity index that included rectal bleeding and number of bowel movements as determined from patient diary records, a sigmoidoscopic evaluation, and global assessment by a physician.



Twenty-one patients completed the study, and full information was available on 19 of them. Treatment with wheat grass juice was associated with significant reductions in the overall disease activity index (P=0.031) and in the severity of rectal bleeding (P = 0.025). No serious side effects were found. Fresh extract of wheat grass demonstrated a prominent tracing in cyclic voltammetry methodology, presumably corresponding to four groups of compounds that exhibit anti-oxidative properties.



Wheat grass juice appeared effective and safe as a single or adjuvant treatment of active distal UC.
A new, fast, and efficient image codec based on set partitioning in hierarchical trees

Embedded zerotree wavelet EZW coding introduced by J M Shapiro is a very e ective and computationally simple technique for image compression Here we o er an alternative explanation of the principles of its operation so that the reasons for its excellent performance can be better understood These principles are partial ordering by magnitude with a set partitioning sorting algorithm ordered bit plane transmission and exploitation of self similarity across di erent scales of an image wavelet transform Moreover we present a new and di erent implementation based on set partitioning in hierarchical trees SPIHT which provides even better performance than our previosly reported extension of the EZW that surpassed the performance of the original EZW The image coding results calculated from actual le sizes and images reconstructed by the decoding algorithm are either compara ble to or surpass previous results obtained through much more sophisticated and computationally complex methods In addition the new coding and decoding pro cedures are extremely fast and they can be made even faster with only small loss in performance by omitting entropy coding of the bit stream by arithmetic code
Abstract feature extraction for text classification

feature extraction for text classification Göksel BİRİCİK∗, Banu DİRİ, Ahmet Coşkun SÖNMEZ Department of Computer Engineering, Yıldız Technical University, Esenler, İstanbul-TURKEY e-mails:  Feature selection and extraction are frequently used solutions to overcome the curse of dimensionality in text classification problems. We introduce an extraction method that summarizes the features of the document samples, where the new features aggregate information about how much evidence there is in a document, for each class. We project the high dimensional features of documents onto a new feature space having dimensions equal to the number of classes in order to form the abstract features. We test our method on 7 different text classification algorithms, with different classifier design approaches. We examine performances of the classifiers applied on standard text categorization test collections and show the enhancements achieved by applying our extraction method. We compare the classification performance results of our method with popular and well-known feature selection and feature extraction schemes. Results show that our summarizing abstract feature extraction method encouragingly enhances classification performances on most of the classifiers when compared with other methods.Feature selection and extraction are frequently used solutions to overcome the curse of dimensionality in text classification problems. We introduce an extraction method that summarizes the features of the document samples, where the new features aggregate information about how much evidence there is in a document, for each class. We project the high dimensional features of documents onto a new feature space having dimensions equal to the number of classes in order to form the abstract features. We test our method on 7 different text classification algorithms, with different classifier design approaches. We examine performances of the classifiers applied on standard text categorization test collections and show the enhancements achieved by applying our extraction method. We compare the classification performance results of our method with popular and well-known feature selection and feature extraction schemes. Results show that our summarizing abstract feature extraction method encouragingly enhances classification performances on most of the classifiers when compared with other methods.
RISE: Randomized Input Sampling for Explanation of Black-box Models

Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users. In this paper, we address the problem of Explainable AI for deep neural networks that take images as input and output a class probability. We propose an approach called RISE that generates an importance map indicating how salient each pixel is for the model’s prediction. In contrast to white-box approaches that estimate pixel importance using gradients or other internal network state, RISE works on blackbox models. It estimates importance empirically by probing the model with randomly masked versions of the input image and obtaining the corresponding outputs. We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments. Extensive experiments on several benchmark datasets show that our approach matches or exceeds the performance of other methods, including white-box approaches.