nils-herrmann/clean_scirepeval huggingface dataset:
Ketogenic Diet for Obesity: Friend or Foe?

Obesity is reaching epidemic proportions and is a strong risk factor for a number of cardiovascular and metabolic disorders such as hypertension, type 2 diabetes, dyslipidemia, atherosclerosis, and also certain types of cancers. Despite the constant recommendations of health care organizations regarding the importance of weight control, this goal often fails. Genetic predisposition in combination with inactive lifestyles and high caloric intake leads to excessive weight gain. Even though there may be agreement about the concept that lifestyle changes affecting dietary habits and physical activity are essential to promote weight loss and weight control, the ideal amount and type of exercise and also the ideal diet are still under debate. For many years, nutritional intervention studies have been focused on reducing dietary fat with little positive results over the long-term. One of the most studied strategies in the recent years for weight loss is the ketogenic diet. Many studies have shown that this kind of nutritional approach has a solid physiological and biochemical basis and is able to induce effective weight loss along with improvement in several cardiovascular risk parameters. This review discusses the physiological basis of ketogenic diets and the rationale for their use in obesity, discussing the strengths and the weaknesses of these diets together with cautions that should be used in obese patients.
Data Structures for Statistical Computing in Python

In this paper we are concerned with the practical issues of working with data sets common to finance, statistics, and other related fields. pandas is a new library which aims to facilitate working with these data sets and to provide a set of fundamental building blocks for implementing statistical models. We will discuss specific design issues encountered in the course of developing pandas with relevant examples and some comparisons with the R language. We conclude by discussing possible future directions for statistical computing and data analysis using Python.
Automatic Question Generation from Sentences

Question Generation (QG) and Question Answering (QA) are some of the many challenges for natural language understanding and interfaces. As humans need to ask good questions, the potential benefits from automated QG systems may assist them in meeting useful inquiry needs. In this paper, we consider an automatic Sentence-to-Question generation task, where given a sentence, the Question Generation (QG) system generates a set of questions for which the sentence contains, implies, or needs answers. To facilitate the question generation task, we build elementary sentences from the input complex sentences using a syntactic parser. A named entity recognizer and a part of speech tagger are applied on each of these sentences to encode necessary information. We classify the sentences based on their subject, verb, object and preposition for determining the possible type of questions to be generated. We use the TREC-2007 (Question Answering Track) dataset for our experiments and evaluation. Mots-clés : Génération de questions, Analyseur syntaxique, Phrases élémentaires, POS Tagging.
DRN: A Deep Reinforcement Learning Framework for News Recommendation

In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation. Online personalized news recommendation is a highly challenging problem due to the dynamic nature of news features and user preferences. Although some online recommendation models have been proposed to address the dynamic nature of news recommendation, these methods have three major issues. First, they only try to model current reward (e.g., Click Through Rate). Second, very few studies consider to use user feedback other than click / no click labels (e.g., how frequent user returns) to help improve recommendation. Third, these methods tend to keep recommending similar news to users, which may cause users to get bored. Therefore, to address the aforementioned challenges, we propose a Deep Q-Learning based recommendation framework, which can model future reward explicitly. We further consider user return pattern as a supplement to click / no click label in order to capture more user feedback information. In addition, an effective exploration strategy is incorporated to find new attractive news for users. Extensive experiments are conducted on the offline dataset and online production environment of a commercial news recommendation application and have shown the superior performance of our methods.
Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition

The representation of 3D pose plays a critical role for 3D action and gesture recognition. Rather than representing a 3D pose directly by its joint locations, in this paper, we propose a Deformable Pose Traversal Convolution Network that applies one-dimensional convolution to traverse the 3D pose for its representation. Instead of fixing the receptive field when performing traversal convolution, it optimizes the convolution kernel for each joint, by considering contextual joints with various weights. This deformable convolution better utilizes the contextual joints for action and gesture recognition and is more robust to noisy joints. Moreover, by feeding the learned pose feature to a LSTM, we perform end-to-end training that jointly optimizes 3D pose representation and temporal sequence recognition. Experiments on three benchmark datasets validate the competitive performance of our proposed method, as well as its efficiency and robustness to handle noisy joints of pose.
Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)

Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a “target” domain when the only available training data belongs to a different “source” domain. In this extended abstract we briefly describe a new DA method called Distributional Correspondence Indexing (DCI) for sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. The experiments we have conducted show that DCI obtains better performance than current state-of-theart techniques for cross-lingual and cross-domain sentiment classification.
Lessons and Challenges from Mining Retail E-Commerce Data

The architecture of Blue Martini Software's e-commerce suite has supported data collection, data transformation, and data mining since its inception. With clickstreams being collected at the application-server layer, high-level events being logged, and data automatically transformed into a data warehouse using meta-data, common problems plaguing data mining using weblogs (e.g., sessionization and conflating multi-sourced data) were obviated, thus allowing us to concentrate on actual data mining goals. The paper briefly reviews the architecture and discusses many lessons learned over the last four years and the challenges that still need to be addressed. The lessons and challenges are presented across two dimensions: business-level vs. technical, and throughout the data mining lifecycle stages of data collection, data warehouse construction, business intelligence, and deployment. The lessons and challenges are also widely applicable to data mining domains outside retail e-commerce.
Latent Semantic Analysis: five methodological recommendations

The recent influx in generation, storage and availability of textual data presents researchers with the challenge of developing suitable methods for their analysis. Latent Semantic Analysis (LSA), a member of a family of methodological approaches that offers an opportunity to address this gap by describing the semantic content in textual data as a set of vectors, was pioneered by researchers in psychology, information retrieval, and bibliometrics. LSA involves a matrix operation called singular value decomposition, an extension of principal component analysis. LSA generates latent semantic dimensions that are either interpreted, if the researcher’s primary interest lies with the understanding of the thematic structure in the textual data, or used for purposes of clustering, categorisation and predictive modelling, if the interest lies with the conversion of raw text into numerical data, as a precursor to subsequent analysis. This paper reviews five methodological issues that need to be addressed by the researcher who will embark on LSA. We examine the dilemmas, present the choices, and discuss the considerations under which good methodological decisions are made. We illustrate these issues with the help of four small studies, involving the analysis of abstracts for papers published in the European Journal of Information Systems.
Active Image Segmentation Propagation

We propose a semi-automatic method to obtain foreground object masks for a large set of related images. We develop a stagewise active approach to propagation: in each stage, we actively determine the images that appear most valuable for human annotation, then revise the foreground estimates in all unlabeled images accordingly. In order to identify images that, once annotated, will propagate well to other examples, we introduce an active selection procedure that operates on the joint segmentation graph over all images. It prioritizes human intervention for those images that are uncertain and influential in the graph, while also mutually diverse. We apply our method to obtain foreground masks for over 1 million images. Our method yields state-of-the-art accuracy on the ImageNet and MIT Object Discovery datasets, and it focuses human attention more effectively than existing propagation strategies.
Gender and sexual orientation differences in sexual response to sexual activities versus gender of actors in sexual films.

In this study, the authors investigated the hypothesis that women's sexual orientation and sexual responses in the laboratory correlate less highly than do men's because women respond primarily to the sexual activities performed by actors, whereas men respond primarily to the gender of the actors. The participants were 20 homosexual women, 27 heterosexual women, 17 homosexual men, and 27 heterosexual men. The videotaped stimuli included men and women engaging in same-sex intercourse, solitary masturbation, or nude exercise (no sexual activity); human male-female copulation; and animal (bonobo chimpanzee or Pan paniscus) copulation. Genital and subjective sexual arousal were continuously recorded. The genital responses of both sexes were weakest to nude exercise and strongest to intercourse. As predicted, however, actor gender was more important for men than for women, and the level of sexual activity was more important for women than for men. Consistent with this result, women responded genitally to bonobo copulation, whereas men did not. An unexpected result was that homosexual women responded more to nude female targets exercising and masturbating than to nude male targets, whereas heterosexual women responded about the same to both sexes at each activity level.
Cloud forensics-Tool development studies & future outlook

In this work, we describe our experiences in developing cloud forensics tools and use them to support three main points: First, we make the argument that cloud forensics is a qualitatively different problem. In the context of SaaS, it is incompatible with long-established acquisition and analysis techniques, and requires a new approach and forensic toolset. We show that client-side techniques, which are an extension of methods used over the last three decades, have inherent limitations that can only be overcome by working directly with the interfaces provided by cloud service providers. Second, we present our results in building forensic tools in the form of three case studies: kumoddea tool for cloud drive acquisition, kumodocsea tool for Google Docs acquisition and analysis, and kumofsea tool for remote preview and screening of cloud drive data. We show that these tools, which work with the public and private APIs of the respective services, provide new capabilities that cannot be achieved by examining client-side
Platform Envelopment

Due to network effects and switching costs, platform providers often become entrenched. To dislodge them, entrants generally must offer revolutionary products. We explore a second path to platform leadership change that does not rely on Schumpeterian creative destruction: platform envelopment. By leveraging common components and shared user relationships, one platform provider can move into another’s market, combining its own functionality with the target’s in a multi-platform bundle. Dominant firms otherwise sheltered from entry by standalone rivals may be vulnerable to an adjacent platform provider’s envelopment attack. We analyze conditions under which envelopment strategies are likely to succeed.
Randomized Trial of Verubecestat for Mild-to-Moderate Alzheimer's Disease.

Alzheimer's disease is characterized by the deposition of amyloid-beta (Aβ) plaques in the brain. Aβ is produced from the sequential cleavage of amyloid precursor protein by β-site amyloid precursor protein-cleaving enzyme 1 (BACE-1) followed by γ-secretase. Verubecestat is an oral BACE-1 inhibitor that reduces the Aβ level in the cerebrospinal fluid of patients with Alzheimer's disease.



We conducted a randomized, double-blind, placebo-controlled, 78-week trial to evaluate verubecestat at doses of 12 mg and 40 mg per day, as compared with placebo, in patients who had a clinical diagnosis of mild-to-moderate Alzheimer's disease. The coprimary outcomes were the change from baseline to week 78 in the score on the cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-cog; scores range from 0 to 70, with higher scores indicating worse dementia) and in the score on the Alzheimer's Disease Cooperative Study Activities of Daily Living Inventory scale (ADCS-ADL; scores range from 0 to 78, with lower scores indicating worse function).



A total of 1958 patients underwent randomization; 653 were randomly assigned to receive verubecestat at a dose of 12 mg per day (the 12-mg group), 652 to receive verubecestat at a dose of 40 mg per day (the 40-mg group), and 653 to receive matching placebo. The trial was terminated early for futility 50 months after onset, which was within 5 months before its scheduled completion, and after enrollment of the planned 1958 patients was complete. The estimated mean change from baseline to week 78 in the ADAS-cog score was 7.9 in the 12-mg group, 8.0 in the 40-mg group, and 7.7 in the placebo group (P=0.63 for the comparison between the 12-mg group and the placebo group and P=0.46 for the comparison between the 40-mg group and the placebo group). The estimated mean change from baseline to week 78 in the ADCS-ADL score was -8.4 in the 12-mg group, -8.2 in the 40-mg group, and -8.9 in the placebo group (P=0.49 for the comparison between the 12-mg group and the placebo group and P=0.32 for the comparison between the 40-mg group and the placebo group). Adverse events, including rash, falls and injuries, sleep disturbance, suicidal ideation, weight loss, and hair-color change, were more common in the verubecestat groups than in the placebo group.



Verubecestat did not reduce cognitive or functional decline in patients with mild-to-moderate Alzheimer's disease and was associated with treatment-related adverse events.
Effective hate-speech detection in Twitter data using recurrent neural networks

This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users’ tendency towards racism or sexism. This data is fed as input to the above classifiers along with the word frequency vectors derived from the textual content. We evaluate our approach on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state-of-the-art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
1 GNU Radio

GNU radio is a free/open-source software toolkit for building software radios, in which software defines the transmitted waveforms and demodulates the received waveforms. Software radio is the technique of getting code as close to the antenna as possible. It turns radio hardware problems into software problems. GNU Radio provides functions to support implementing spectrum analyzer, an oscilloscope, concurrent multichannel receiver and an ever-growing collection of modulators and demodulators. In this academic semester, we will build the environment of GNU radio, be familiar with it, and learn how to use the existing libraries to transmit a jpeg file with Differential Binary Phase Shift Keying (DBPSK)/ Differential Quadrature Phase Shift Keying (DQPSK) modulation between two end systems by TCP connection. The future objective of this project is to build the video/audio transmission over wireless network, including error correction (CRC) and QoS (Quality of Service) by using GNU Radio.
Deep expectation for estimation of fingerprint orientation fields

Estimation of the orientation field is one of the key challenges during biometric feature extraction from a fingerprint sample. Many important processing steps rely on an accurate and reliable estimation. This is especially challenging for samples of low quality, for which in turn accurate preprocessing is essential. Regressional Convolutional Neural Networks have shown their superiority for bad quality samples in the independent benchmark framework FVC-ongoing. This work proposes to incorporate Deep Expectation. Options for further improvements are evaluated in this challenging environment of low quality images and small amount of training data. The findings from the results improve the new algorithm called DEX-OF. Incorporating Deep Expectation, improved regularization, and slight model changes DEX-OF achieves an RMSE of 7.52° on the bad quality dataset and 4.89° at the good quality dataset at FVC-ongoing. These are the best reported error rates so far.
Visual Semantic Planning Using Deep Successor Representations

A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment from an initial state to a goal state. Doing so entails knowledge about objects and their affordances, as well as actions and their preconditions and effects. We propose learning these through interacting with a visual and dynamic environment. Our proposed solution involves bootstrapping reinforcement learning with imitation learning. To ensure cross task generalization, we develop a deep predictive model based on successor representations. Our experimental results show near optimal results across a wide range of tasks in the challenging THOR environment.
BigData visualization: Parallel coordinates using density approach

Information visualization is a very important tool in BigData analytics. BigData, structured and unstructured data which contains images, videos, texts, audio and other forms of data, collected from multiple datasets, is too big, too complex and moves too fast to analyse using traditional methods. This has given rise to two issues; 1) how to reduce multidimensional data without the loss of any data patterns for multiple datasets, 2) how to visualize BigData patterns for analysis. In this paper, we have classified the BigData attributes into `5Ws' data dimensions, and then established a `5Ws' density approach that represents the characteristics of data flow patterns. We use parallel coordinates to display the `5Ws' sending and receiving densities which provide more analytic features for BigData analysis. The experiment shows that this new model with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.
Temporal-Difference Learning With Sampling Baseline for Image Captioning

The existing methods for image captioning usually train the language model under the cross entropy loss, which results in the exposure bias and inconsistency of evaluation metric. Recent research has shown these two issues can be well addressed by policy gradient method in reinforcement learning domain attributable to its unique capability of directly optimizing the discrete and non-differentiable evaluation metric. In this paper, we utilize reinforcement learning method to train the image captioning model. Specifically, we train our image captioning model to maximize the overall reward of the sentences by adopting the temporal-difference (TD) learning method, which takes the correlation between temporally successive actions into account. In this way, we assign different values to different words in one sampled sentence by a discounted coefficient when back-propagating the gradient with the REINFORCE algorithm, enabling the correlation between actions to be learned. Besides, instead of estimating a “baseline” to normalize the rewards with another network, we utilize the reward of another Monte-Carlo sample as the “baseline” to avoid high variance. We show that our proposed method can improve the quality of generated captions and outperforms the state-of-the-art methods on the benchmark dataset MS COCO in terms of seven evaluation metrics.
Decision Making for Predictive Maintenance in Asset Information Management

Asset management is a process of identification, design, construction, operation, and maintenance of physical assets (Wenzler, 2005). An asset-centric approach is vital for the success of an asset intensive organisation as the effective management of assets is a major determinant of organisational success. One key issue in asset information management is the availability of information at the right time, in the right format, before the right person, against the right query, and at the right level. This paper provides a comprehensive and in-depth critical analysis from literature which fulfils an identified need of fusing asset information for predictive maintenance so that decision making can be improved. The critical literature review included also highlights the need for an expert system which integrates reliable information with effective decision-support, under the umbrella of Asset Management. Various elements of asset management were critically reviewed, highlighting the need for more robust Predictive maintenance management for assets. We argue that this is best achieved by a system that, in particular, incorporates Expert System to enhance the quality of predictive maintenance through accurate decision analysis. In addition, it should have fuzzy logic reasoning ability that assists in the decision-making process. Our analysis leads us to propose that Expert System when combined with fuzzy logic provides a better way of decision making in predictive maintenance management of assets.
Optical Character Recognition

This paper describes two implementations in optical character recognition using template matching method and feature extraction method followed by support vector machine classification. With proper image preprocessing, the texts are segmented into isolated characters and the correlations between a single character and a given set of templates are computed to find the similarities and then identify the input character. In the second method, features extracted from the segmented characters are used to train the SVM classifiers, which are later, tested by a test set of handwritten digits. Optical character recognition; template matching; feature extraction; support vector machine.