nils-herrmann/clean_scirepeval huggingface dataset:
Sugar alcohols—their role in the modern world of sweeteners: a review

observed. It was mainly due to the developments in biological studies, the change of a population lifestyle and the increase in the consumer awareness concerning food products. The health quality of food depends mainly on nutrients, but also on foreign substances such as food additives. The presence of foreign substances in the food can be justified, allowed or tolerated only when they are harmless to our health. Epidemic obesity and diabetes encouraged the growth of the artificial sweetener industry. There are more and more people who are trying to lose weight or keeping the weight off; therefore, sweeteners can be now found in almost all food products. There are two main types of sweeteners, i.e., nutritive and artificial ones. The latter does not provide calories and will not influence blood glucose; however, some of nutritive sweeteners such as sugar alcohols also characterize with lower blood glucose response and can be metabolized without insulin, being at the same time natural compounds. Sugar alcohols (polyols or polyhydric alcohols) are low digestible carbohydrates, which are obtained by substituting and aldehyde group with a hydroxyl one [1, 2]. As most of sugar alcohols are produced from their corresponding aldose sugars, they are also called alditols . Among sugar alcohols can be listed hydrogenated monosaccharides (sorbitol, mannitol), hydrogenated disaccharides (isomalt, maltitol, lactitol) and mixtures of hydrogenated mono-diand/or oligosaccharides (hydrogenated starch hydrolysates) [1, 2, 4]. Polyols are naturally present in smaller quantities in fruits as well as in certain kinds of vegetables or mushrooms, and they are also regulated as either generally recognized as safe or food additives [5–7]. Food additives are substances that are added intentionally to foodstuffs in order to perform certain technological functions such as to give color, to sweeten or to help in food preservation.  Epidemic obesity and diabetes encouraged the changes in population lifestyle and consumers’ food products awareness. Food industry has responded people’s demand by producing a number of energy-reduced products with sugar alcohols as sweeteners. These compounds are usually produced by a catalytic hydrogenation of carbohydrates, but they can be also found in nature in fruits, vegetables or mushrooms as well as in human organism. Due to their properties, sugar alcohols are widely used in food, beverage, confectionery and pharmaceutical industries throughout the world. They have found use as bulk sweeteners that promote dental health and exert prebiotic effect. They are added to foods as alternative sweeteners what might be helpful in the control of calories intake. Consumption of low-calorie foods by the worldwide population has dramatically increased, as well as health concerns associated with the consequent high intake of sweeteners. This review deals with the role of commonly used sugar alcohols such as erythritol, isomalt, lactitol, maltitol, mannitol, sorbitol and xylitol as sugar substitutes in food industry.
Three Kinds of Network Security Situation Awareness Model Based on Big Data

In this paper, we have proposed three kinds of network security situation awareness (NSSA) models. In the era of big data, the traditional NSSA methods cannot analyze the problem effectively. Therefore, the three models are designed for big data. The structure of these models are very large, and they are integrated into the distributed platform. Each model includes three modules: network security situation detection (NSSD), network security situation understanding (NSSU), and network security situation projection (NSSP). Each module comprises different machine learning algorithms to realize different functions. We conducted a comprehensive study of the safety of these models. Three models compared with each other. The experimental results show that these models can improve the efficiency and accuracy of data processing when dealing with different problems. Each model has its own advantages and disadvantages.
FISM: factored item similarity models for top-N recommender systems

The effectiveness of existing top-N recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-N recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, wherein the value being estimated is not used for its own estimation. A comprehensive set of experiments on multiple datasets at three different sparsity levels indicate that the proposed methods can handle sparse datasets effectively and outperforms other state-of-the-art top-N recommendation methods. The experimental results also show that the relative performance gains compared to competing methods increase as the data gets sparser.
Dynamic Modeling of the 4 DoF BioRob Series Elastic Robot Arm for Simulation and Control

This paper presents the modeling of the light-weight BioRob robot arm with series elastic actuation for simulation and controller design. We describe the kinematic coupling introduced by the cable actuation and the robot arm dynamics including the elastic actuator and motor and gear model. We show how the inverse dynamics model derived from these equations can be used as a basis for a position tracking controller that is able to sufficiently damp the oscillations caused by the high, nonlinear joint elasticity. We presents results from simulation and briefly describe the implementation for a real world application.
Deep AutoRegressive Networks

We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling. We derive an efficient approximate parameter estimation method based on the minimum description length (MDL) principle, which can be seen as maximising a variational lower bound on the log-likelihood, with a feedforward neural network implementing approximate inference. We demonstrate state-of-the-art generative performance on a number of classic data sets: several UCI data sets, MNIST and Atari 2600 games.
Adaptive load shedding based on combined frequency and voltage stability assessment using synchrophasor measurements

Under frequency load shedding (UFLS) and under voltage load shedding (UVLS) are attracting more attention, as large disturbances occur more frequently than in the past. Usually, these two schemes work independently from each other, and are not designed in an integrated way to exploit their combined effect on load shedding. Besides, reactive power is seldom considered in the load shedding process. To fill this gap, we propose in this paper a new centralized, adaptive load shedding algorithm, which uses both voltage and frequency information provided by phasor measurement units (PMUs). The main contribution of the new method is the consideration of reactive power together with active power in the load shedding strategy. Therefore, this method addresses the combined voltage and frequency stability issues better than the independent approaches. The new method is tested on the IEEE 39-Bus system, in order to compare it with other methods. Simulation results show that, after large disturbance, this method can bring the system back to a new stable steady state that is better from the point of view of frequency and voltage stability, and loadability.
Gut/brain axis and the microbiota.

Tremendous progress has been made in characterizing the bidirectional interactions between the central nervous system, the enteric nervous system, and the gastrointestinal tract. A series of provocative preclinical studies have suggested a prominent role for the gut microbiota in these gut-brain interactions. Based on studies using rodents raised in a germ-free environment, the gut microbiota appears to influence the development of emotional behavior, stress- and pain-modulation systems, and brain neurotransmitter systems. Additionally, microbiota perturbations by probiotics and antibiotics exert modulatory effects on some of these measures in adult animals. Current evidence suggests that multiple mechanisms, including endocrine and neurocrine pathways, may be involved in gut microbiota-to-brain signaling and that the brain can in turn alter microbial composition and behavior via the autonomic nervous system. Limited information is available on how these findings may translate to healthy humans or to disease states involving the brain or the gut/brain axis. Future research needs to focus on confirming that the rodent findings are translatable to human physiology and to diseases such as irritable bowel syndrome, autism, anxiety, depression, and Parkinson's disease.
Security Application of Failure Mode and Effect Analysis (FMEA)

Increasingly complex systems lead to an interweaving of security, safety, availability and reliability concerns. Most dependability analysis techniques do not include security aspects. In order to include security, a holistic risk model for systems is needed. In our novel approach, the basic failure cause, failure mode and failure effect model known from FMEA is used as a template for a vulnerability cause-effect chain, and an FMEA analysis technique extended with security is presented. This represents a unified model for safety and security cause-effect analysis. As an example the technique is then applied to a distributed industrial measurement system.
Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging

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Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search

We present a learning-based approach to computing solutions for certain NPhard problems. Our approach combines deep learning techniques with useful algorithmic elements from classic heuristics. The central component is a graph convolutional network that is trained to estimate the likelihood, for each vertex in a graph, of whether this vertex is part of the optimal solution. The network is designed and trained to synthesize a diverse set of solutions, which enables rapid exploration of the solution space via tree search. The presented approach is evaluated on four canonical NP-hard problems and five datasets, which include benchmark satisfiability problems and real social network graphs with up to a hundred thousand nodes. Experimental results demonstrate that the presented approach substantially outperforms recent deep learning work, and performs on par with highly optimized state-of-the-art heuristic solvers for some NP-hard problems. Experiments indicate that our approach generalizes across datasets, and scales to graphs that are orders of magnitude larger than those used during training.
Factorization of Latent Variables in Distributional Semantic Models

This paper discusses the use of factorization techniques in distributional semantic models. We focus on a method for redistributing the weight of latent variables, which has previously been shown to improve the performance of distributional semantic models. However, this result has not been replicated and remains poorly understood. We refine the method, and provide additional theoretical justification, as well as empirical results that demonstrate the viability of the proposed approach.
Accelerated Methods for Deep Reinforcement Learning

Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs. We confirm that both policy gradient and Q-value learning algorithms can be adapted to learn using many parallel simulator instances. We further find it possible to train using batch sizes considerably larger than are standard, without negatively affecting sample complexity or final performance. We leverage these facts to build a unified framework for parallelization that dramatically hastens experiments in both classes of algorithm. All neural network computations use GPUs, accelerating both data collection and training. Our results include using an entire DGX-1 to learn successful strategies in Atari games in mere minutes, using both synchronous and asynchronous algorithms.
graph2vec: Learning Distributed Representations of Graphs

Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered by problems such as poor generalization. To address this limitation, in this work, we propose a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs. graph2vec’s embeddings are learnt in an unsupervised manner and are task agnostic. Hence, they could be used for any downstream task such as graph classification, clustering and even seeding supervised representation learning approaches. Our experiments on several benchmark and large real-world datasets show that graph2vec achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels.
Topical Key Concept Extraction from Folksonomy

Concept extraction is a primary subtask of ontology construction. It is difficult to extract new concepts from traditional text corpus. Moreover, building a single ontology for multiple-topic corpus may lead to misconception. To deal with these problems, this paper proposes a novel framework to extract topical key concepts from folksonomy. Folksonomy is a valuable data source due to real-time update and rich user-generated contents. We first identify topics from folksonomy using topic models. Next the tags are ranked according to their importance for a certain topic by applying topic-specific random walk methods. The top-ranking tags are extracted as topical key concepts. Especially, a novel link weight function which combines the local structure information and global semantic similarity is proposed in importance score propagation. From the perspectives of qualitative and quantitative investigation, our method is feasible and effective.
Multimodal Biometric Identification System Based On Iris & Fingerprint

This paper is related to the development of an innovative multimodal biometric identification system. Unimodal biometric systems often face significant limitations due to sensitivity to noise intraclass variability and other factors. Multimodal biometric identification systems aim to fuse two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejection Rate (FRR), thus improving system accuracy and dependability. In greater detail, a Multimodal Biometric Identification System Based On Iris & Fingerprint. Both biometric traits (Iris & Fingerprint) are processed individually through all steps like segmentation ,feature extraction & matching. The multimodal system is fused using match level fusion at the verification stage on obtained matching score of iris and fingerprint. The performance of the biometric system shows improvement in the False Acceptance Rate (FAR) and False Reject Rate (FRR) .The proposed multimodal system achieves interesting results with several commonly used databases.
Deep Multi-instance Learning with Dynamic Pooling

End-to-end optimization of multi-instance learning (MIL) using neural networks is an important problem with many applications, in which a core issue is how to design a permutation-invariant pooling function without losing much instance-level information. Inspired by the dynamic routing in recent capsule networks, we propose a novel dynamic pooling function for MIL. It is an adaptive scheme for both key instance selection and modeling the contextual information among instances in a bag. The dynamic pooling iteratively updates the instance contribution to its bag. It is permutation-invariant and can interpret instance-to-bag relationship. The proposed dynamic pooling based multi-instance neural network has been validated on many MIL tasks and outperforms other MIL methods.
Operator Variational Inference

Variational inference is an umbrella term for algorithms which cast Bayesian inference as optimization. Classically, variational inference uses the Kullback-Leibler divergence to define the optimization. Though this divergence has been widely used, the resultant posterior approximation can suffer from undesirable statistical properties. To address this, we reexamine variational inference from its roots as an optimization problem. We use operators, or functions of functions, to design variational objectives. As one example, we design a variational objective with a Langevin-Stein operator. We develop a black box algorithm, operator variational inference (opvi), for optimizing any operator objective. Importantly, operators enable us to make explicit the statistical and computational tradeoffs for variational inference. We can characterize different properties of variational objectives, such as objectives that admit data subsampling—allowing inference to scale to massive data—as well as objectives that admit variational programs—a rich class of posterior approximations that does not require a tractable density. We illustrate the benefits of opvi on a mixture model and a generative model of images.
Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment Supervision

Sentiment lexicon is an important tool for identifying the sentiment polarity of words and texts. How to automatically construct sentiment lexicons has become a research topic in the field of sentiment analysis and opinion mining. Recently there were some attempts to employ representation learning algorithms to construct a sentiment lexicon with sentiment-aware word embedding. However, these methods were normally trained under documentlevel sentiment supervision. In this paper, we develop a neural architecture to train a sentiment-aware word embedding by integrating the sentiment supervision at both document and word levels, to enhance the quality of word embedding as well as the sentiment lexicon. Experiments on the SemEval 2013-2016 datasets indicate that the sentiment lexicon generated by our approach achieves the state-of-the-art performance in both supervised and unsupervised sentiment classification, in comparison with several strong sentiment lexicon construction methods.
Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images

Object detection plays an active role in remote sensing applications. Recently, deep convolutional neural network models have been applied to automatically extract features, generate region proposals, and predict corresponding object class. However, these models face new challenges in VHR remote sensing images due to the orientation and scale variations and the cluttered background. In this letter, we propose an end-to-end multiscale visual attention networks (MS-VANs) method. We use skip-connected encoder–decoder model to extract multiscale features from a full-size image. For feature maps in each scale, we learn a visual attention network, which is followed by a classification branch and a regression branch, so as to highlight the features from object region and suppress the cluttered background. We train the MS-VANs model by a hybrid loss function which is a weighted sum of attention loss, classification loss, and regression loss. Experiments on a combined data set consisting of Dataset for Object Detection in Aerial Images and NWPU VHR-10 show that the proposed method outperforms several state-of-the-art approaches.
Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods

The task of paraphrasing is inherently familiar to speakers of all languages. Moreover, the task of automatically generating or extracting semantic equivalences for the various units of language—words, phrases, and sentences—is an important part of natural language processing (NLP) and is being increasingly employed to improve the performance of several NLP applications. In this article, we attempt to conduct a comprehensive and application-independent survey of data-driven phrasal and sentential paraphrase generation methods, while also conveying an appreciation for the importance and potential use of paraphrases in the field of NLP research. Recent work done in manual and automatic construction of paraphrase corpora is also examined. We also discuss the strategies used for evaluating paraphrase generation techniques and briefly explore some future trends in paraphrase generation.
Deep Reinforcement Learning for Mention-Ranking Coreference Models

Coreference Resolution September 2014 Present Kevin Clark, Christopher Manning Stanford University Developed coreference systems that build up coreference chains with agglomerative clustering. These models are more accurate than the mention-pair systems commonly used in prior work. Developed neural coreference systems that do not require the large number of complex hand-engineered features commonly found in statistical coreference systems. Applied imitation and reinforcement learning to directly optimize coreference systems for evaluation metrics instead of relying on hand-tuned heuristic loss functions. Made substantial advancements to the current state-of-the-art for English and Chinese coreference. Publicly released all models through Stanford’s CoreNLP.