nils-herrmann/clean_scirepeval huggingface dataset:
A Survey on Contextual Multi-armed Bandits

4 Stochastic Contextual Bandits 6 4.1 Stochastic Contextual Bandits with Linear Realizability Assumption . . . . 6 4.1.1 LinUCB/SupLinUCB . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.1.2 LinREL/SupLinREL . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1.3 CofineUCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1.4 Thompson Sampling with Linear Payoffs . . . . . . . . . . . . . . . . 11 4.1.5 SpectralUCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2 Kernelized Stochastic Contextual Bandits . . . . . . . . . . . . . . . . . . . 14 4.2.1 GP-UCB/CGP-UCB . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.2.2 KernelUCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Stochastic Contextual Bandits with Arbitrary Set of Policies .  19 4.3.1 Epoch-Greedy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.3.2 RandomizedUCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.3.3 ILOVETOCONBANDITS . . . . . . . . . . . . . . . . . . . . . . . .
Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data

The generalized Poisson regression model has been used to model dispersed count data. It is a good competitor to the negative binomial regression model when the count data is over-dispersed. Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for the situations where the data generating process results into too many zeros. In this paper, we propose a zero-inflated generalized Poisson (ZIGP) regression model to model domestic violence data with too many zeros. Estimation of the model parameters using the method of maximum likelihood is provided. A score test is presented to test whether the number of zeros is too large for the generalized Poisson model to adequately fit the domestic violence data.
Translingual Mining from Text Data

Like full-text translation, cross-language information retrieval (CLIR) is a task that requires some form of knowledge transfer across languages. Although robust translation resources are critical for constructing high quality translation tools, manually constructed resources are limited both in their coverage and in their adaptability to a wide range of applications. Automatic mining of translingual knowledge makes it possible to complement hand-curated resources. This chapter describes a growing body of work that seeks to mine translingual knowledge from text data, in particular, data found on the Web. We review a number of mining and filtering strategies, and consider them in the context of statistical machine translation, showing that these techniques can be effective in collecting large quantities of translingual knowledge necessary
Diagnosis of Skin Lesions Based on Dermoscopic Images Using Image Processing Techniques

Great effort has been put into the development of diagnosis methods for the most dangerous type of skin diseases Melanoma. This paper aims to develop a prototype capable of segment and classify skin lesions in dermoscopy images based on ABCD rule. The proposed work is divided into four distinct stages: 1) Pre-processing, consists of filtering and contrast enhancing techniques. 2) Segmentation, thresholding and statistical properties are computed to localize the lesion. 3) Features extraction, Asymmetry is calculated by averaging the calculated results of the two methods: Entropy and Bi-fold. Border irregularity is calculated by accumulate the statistical scores of the eight segments of the segmented lesion. Color feature is calculated among the existence of six candidate colors: white, black, red, light-brown, dark-brown, and blue-gray. Diameter is measured by the conversion operation from the total number of pixels in the greatest diameter into millimeter (mm). 4) Classification, the summation of the four extracted feature scores multiplied by their weights to yield a total dermoscopy score (TDS); hence, the lesion is classified into benign, suspicious, or malignant. The prototype is implemented in MATLAB and the dataset used consists of 200 dermoscopic images from Hospital Pedro Hispano, Matosinhos. The achieved results shows an acceptable performance rates, an accuracy 90%, sensitivity 85%, and specificity 92.22%.
Luke, I am Your Father: Dealing with Out-of-Domain Requests by Using Movies Subtitles

Even when the role of a conversational agent is well known users persist in confronting them with Out-of-Domain input. This often results in inappropriate feedback, leaving the user unsatisfied. In this paper we explore the automatic creation/enrichment of conversational agents’ knowledge bases by taking advantage of natural language interactions present in the Web, such as movies subtitles. Thus, we introduce Filipe, a chatbot that answers users’ request by taking advantage of a corpus of turns obtained from movies subtitles (the Subtle corpus). Filipe is based on Say Something Smart, a tool responsible for indexing a corpus of turns and selecting the most appropriate answer, which we fully describe in this paper. Moreover, we show how this corpus of turns can help an existing conversational agent to answer Out-of-Domain interactions. A preliminary evaluation is also presented.
Simple and Effective Question Answering with Recurrent Neural Networks

First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 35%–65% accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. Based on this assumption of the structure, our simple yet effective approach trains two recurrent neural networks to outperform state of the art by significant margins — relative improvement reaches 16% for WebQuestions, and surpasses 38% for SimpleQuestions.
Object-Part Attention Model for Fine-Grained Image Classification

Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is highly challenging due to large variance in the same subcategory and small variance among different subcategories. Existing methods generally first locate the objects or parts and then discriminate which subcategory the image belongs to. However, they mainly have two limitations: 1) relying on object or part annotations which are heavily labor consuming; and 2) ignoring the spatial relationships between the object and its parts as well as among these parts, both of which are significantly helpful for finding discriminative parts. Therefore, this paper proposes the object-part attention model (OPAM) for weakly supervised fine-grained image classification and the main novelties are: 1) object-part attention model integrates two level attentions: object-level attention localizes objects of images, and part-level attention selects discriminative parts of object. Both are jointly employed to learn multi-view and multi-scale features to enhance their mutual promotion; and 2) Object-part spatial constraint model combines two spatial constraints: object spatial constraint ensures selected parts highly representative and part spatial constraint eliminates redundancy and enhances discrimination of selected parts. Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories. Importantly, neither object nor part annotations are used in our proposed approach, which avoids the heavy labor consumption of labeling. Compared with more than ten state-of-the-art methods on four widely-used datasets, our OPAM approach achieves the best performance.
Dynamic Matrix Flight Control design for a nonlinear missile

Nonlinear control problem for a missile autopilot is quick adaptation and minimizing the desired acceleration to missile nonlinear model. For this several missile controllers are provided which are on the basis of nonlinear control or design of linear control for the linear missile system. In this paper a linear control of dynamic matrix type is proposed for the linear model of missile. In the first section, an approximate two degrees of freedom missile model, known as Horton model, is introduced. Then, the nonlinear model is converted into observable and controllable model base on the feedback linear rule of input-state mode type. Finally for design of control model, the dynamic matrix flight control, which is one of the linear predictive control design methods on the basis of system step response information, is used. This controller is a recursive method which calculates the development of system input by definition and optimization of a cost function and using system dynamic matrix. So based on the applied inputs and previous output information, the missile acceleration would be calculated. Unlike other controllers, this controller doesn’t require an interaction effect and accurate model. Although, it has predicting and controlling horizon, there isn’t such horizons in non-predictive methods.
Whisper to normal speech conversion using pitch estimated from spectrum

We can perceive pitch in whispered speech, although fundamental frequency (F0) does not exist physically or phonetically due to the lack of vocal-fold vibration. This study was carried out to determine how people generate such an unvoiced pitch. We conducted experiments in which speakers uttered five whispered Japanese vowels in accordance with the pitch of a guide pure tone. From the results, we derived a multiple regression function to convert the outputs of a mel-scaled filter bank of whispered speech into the perceived pitch value. Next, using this estimated pitch value as F0, we constructed a system for conversion of whispered speech to normal speech. Since the pitch varies with time according to the spectral shape, it was expected that the pitch accent would be kept by this conversion. Indeed, auditory experiments demonstrated that the correctly perceived rate of Japanese word accent was increased from 55.5% to 72.0% compared with that when a constant F0 was used.
Cellular Senescence: A Translational Perspective

Cellular senescence entails essentially irreversible replicative arrest, apoptosis resistance, and frequently acquisition of a pro-inflammatory, tissue-destructive senescence-associated secretory phenotype (SASP). Senescent cells accumulate in various tissues with aging and at sites of pathogenesis in many chronic diseases and conditions. The SASP can contribute to senescence-related inflammation, metabolic dysregulation, stem cell dysfunction, aging phenotypes, chronic diseases, geriatric syndromes, and loss of resilience. Delaying senescent cell accumulation or reducing senescent cell burden is associated with delay, prevention, or alleviation of multiple senescence-associated conditions. We used a hypothesis-driven approach to discover pro-survival Senescent Cell Anti-apoptotic Pathways (SCAPs) and, based on these SCAPs, the first senolytic agents, drugs that cause senescent cells to become susceptible to their own pro-apoptotic microenvironment. Several senolytic agents, which appear to alleviate multiple senescence-related phenotypes in pre-clinical models, are beginning the process of being translated into clinical interventions that could be transformative.
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distributed vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue that the graph representation of geometric objects allows for additional structure, which should be leveraged for enhanced reconstruction. Thus, we propose a system which properly benefits from the advantages of the geometric structure of graphencoded objects by introducing (1) a graph convolutional update preserving vertex information; (2) an adaptive splitting heuristic allowing detail to emerge; and (3) a training objective operating both on the local surfaces defined by vertices as well as the global structure defined by the mesh. Our proposed method is evaluated on the task of 3D object reconstruction from images with the ShapeNet dataset, where we demonstrate state of the art performance, both visually and numerically, while having far smaller space requirements by generating adaptive meshes.
Fast Generation for Convolutional Autoregressive Models

Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial for their success, generation is typically implemented in a naı̈ve fashion where redundant computations are unnecessarily repeated. This results in slow generation, making such models infeasible for production environments. In this work, we describe a method to speed up generation in convolutional autoregressive models. The key idea is to cache hidden states to avoid redundant computation. We apply our fast generation method to the Wavenet and PixelCNN++ models and achieve up to 21× and 183× speedups respectively.
Character-level Convolutional Networks for Text Classification

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several largescale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.
Mining Significant Microblogs for Misinformation Identification: An Attention-Based Approach

With the rapid growth of social media, massive misinformation is also spreading widely on social media, e.g., Weibo and Twitter, and brings negative effects to human life. Today, automatic misinformation identification has drawn attention from academic and industrial communities. Whereas an event on social media usually consists of multiple microblogs, current methods are mainly constructed based on global statistical features. However, information on social media is full of noise, which should be alleviated. Moreover, most of the microblogs about an event have little contribution to the identification of misinformation, where useful information can be easily overwhelmed by useless information. Thus, it is important to mine significant microblogs for constructing a reliable misinformation identification method. In this article, we propose an attention-based approach for identification of misinformation (AIM). Based on the attention mechanism, AIM can select microblogs with the largest attention values for misinformation identification. The attention mechanism in AIM contains two parts: content attention and dynamic attention. Content attention is the calculated-based textual features of each microblog. Dynamic attention is related to the time interval between the posting time of a microblog and the beginning of the event. To evaluate AIM, we conduct a series of experiments on the Weibo and Twitter datasets, and the experimental results show that the proposed AIM model outperforms the state-of-the-art methods.
Improving Data Governance in Large Organizations through Ontology and Linked Data

In the past decade, the role of data has increased exponentially from something that is queried or reported on, to becoming a true corporate asset. The same time period has also seen marked growth in corporate structural complexity. This combination has lead to information management challenges, as the data moving across a multitude of systems lends itself to a higher likelihood of impacting dependent processes and systems, should something go wrong or be changed. Many enterprise data projects are faced with low success rates and consequently subject to high amounts of scrutiny as senior leadership struggles to identify return on investment. While there are many tools and methods to increase a companies' ability to govern data, this research is based on the premise that you can not govern what you do not know. This lack of awareness of the corporate data landscape impacts the ability to govern data, which in turn impacts overall data quality within organizations. This paper seeks to propose a tools and techniques for companies to better gain an awareness of the landscape of their data, processes, and organizational attributes through the use of linked data, via the Resource Description Framework (RDF) and ontology. The outcome of adopting such techniques is an increased level of data awareness within the organization, resulting in improved ability to govern corporate data assets, and in turn increased data quality.
Work engagement and Machiavellianism in the ethical leadership process

Leaders who express an ethical identity are proposed to affect followers’ attitudes and work behaviors. In two multi-source studies, we first test a model suggesting that work engagement acts as a mediator in the relationships between ethical leadership and employee initiative (a form of organizational citizenship behavior) as well as counterproductive work behavior. Next, we focus on whether ethical leadership always forms an authentic expression of an ethical identity, thus in the second study, we add leader Machiavellianism to the model. For Machiavellian leaders, the publicly expressed identity of ethical leadership is inconsistent with the privately held unethical Machiavellian norms. Literature on surface acting suggests people can at least to some extent pick up on such inauthentic displays, making the effects less strong. We thus argue that the positive effects of ethical leader behavior are likely to be suppressed when leaders are highly Machiavellian. Support for this moderated mediation model was found: The effects of ethical leader behavior on engagement are less strong when ethical leaders are high as opposed to low on Machiavellianism.
Learning to Reason with a Scalable Probabilistic Logic NSH 1507 | Friday , August 28 , 2015 | 12 : 00 pm Proposal

In this thesis, we propose a new, scalable probabilistic logic called ProPPR to combine the best of the symbolic and statistical worlds. ProPPR has the rich semantic representation of Prolog, but we associate a feature vector to each clause, such that each clause has a weight vector that can be learned from the training data. Instead of searching over the entire graph for solutions, ProPPR uses a provably-correct approximate personalized PageRank to construct a subgraph for local grounding: the inference time is now independent of the size of the KB. We show that ProPPR can be viewed as a recursive extension to the path ranking algorithm (PRA), and outperforms PRA in the inference task with one million facts from NELL.
Inferring and Executing Programs for Visual Reasoning

Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, these black-box models often learn to exploit biases in the data rather than learning to perform visual reasoning. Inspired by module networks, this paper proposes a model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer. Both the program generator and the execution engine are implemented by neural networks, and are trained using a combination of backpropagation and REINFORCE. Using the CLEVR benchmark for visual reasoning, we show that our model significantly outperforms strong baselines and generalizes better in a variety of settings.
A critical view of driver behavior models: What do we know

There appears to be a lack of new ideas in driver behavior modeling. Although behavioral research is under some pressure, it seems too facile to attribute this deplorable state of affairs only to a lack of research funds. In my opinion the causal chain may well run in the opposite direction. An analysis of what is wrong has led me to the conclusion that human factors research in the area of driver behavior has hardly been touched by the “cognitive revolution” that ‘swept psychology in the past fifteen years. A more cognitive approach might seem advisable and the “promise of progress” of such an approach should be assessed.
The Effects of White Noise on Agitated Behaviors, Mental Status, and Activities of Daily Living in Older Adults With Dementia.

The aging of society is a global trend, and care of older adults with dementia is an urgent challenge. As dementia progresses, patients exhibit negative emotions, memory disorders, sleep disorders, and agitated behavior. Agitated behavior is one of the most difficult problems for family caregivers and healthcare providers to handle when caring for older adults with dementia.



The aim of this study was to investigate the effectiveness of white noise in improving agitated behavior, mental status, and activities of daily living in older adults with dementia.



An experimental research design was used to study elderly participants two times (pretest and posttest). Six dementia care centers in central and southern Taiwan were targeted to recruit participants. There were 63 participants: 28 were in the experimental group, and 35 were in the comparison group. Experimental group participants received 20 minutes of white noise consisting of ocean, rain, wind, and running water sounds between 4 and 5 P.M. daily over a period of 4 weeks. The comparison group received routine care. Questionnaires were completed, and observations of agitated behaviors were collected before and after the intervention.



Agitated behavior in the experimental group improved significantly between pretest and posttest. Furthermore, posttest scores on the Mini-Mental Status Examination and Barthel Index were slightly better for this group than at pretest. However, the experimental group registered no significant difference in mental status or activities of daily living at posttest. For the comparison group, agitated behavior was unchanged between pretest and posttest.



The results of this study support white noise as a simple, convenient, and noninvasive intervention that improves agitated behavior in older adults with dementia. These results may provide a reference for related healthcare providers, educators, and administrators who care for older adults with dementia.