nils-herrmann/clean_scirepeval huggingface dataset:
TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections

Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.
A critical review of the literature on social and leisure activity and wellbeing in later life

An engaged lifestyle is seen as an important component of successful ageing. Many older adults with high participation in social and leisure activities report positive wellbeing, a fact that fuelled the original activity theory and that continues to influence researchers, theorists and practitioners. This study’s purpose is to review the conceptualisation and measurement of activity among older adults and the associations reported in the gerontological literature between specific dimensions of activity and wellbeing. We searched published studies that focused on social and leisure activity and wellbeing, and found 42 studies in 44 articles published between 1995 and 2009. They reported from one to 13 activity domains, the majority reporting two or three, such as informal, formal and solitary, or productive versus leisure. Domains associated with subjective wellbeing, health or survival included social, leisure, productive, physical, intellectual, service and solitary activities. Informal social activity has accumulated the most evidence of an influence on wellbeing. Individual descriptors such as gender or physical functioning sometimes moderate these associations, while contextual variables such as choice, meaning or perceived quality play intervening roles. Differences in definitions and measurement make it difficult to draw inferences about this body of evidence on the associations between activity and wellbeing. Activity theory serves as shorthand for these associations, but gerontology must better integrate developmental and psychological constructs into a refined, comprehensive activity theory.
Hierarchical Temporal Memory Method for Time-Series-Based Anomaly Detection

Time-series-based anomaly detection is a quite important field that has been researched over years. Many techniques have been developed and applied successfully for certain application domains. However, there are still some challenges, such as continuously learning, tolerance to noise and generalization.  We have evaluated HTM on real and artificial datasets. The experiment results show that HTM can successfully discover anomalies in time-series data.
Speech and Language Processing. Information Extraction

I am the very model of a modern Major-General, I've information vegetable, animal, and mineral, I know the kings of England, and I quote the fights historical From Marathon to Waterloo, in order categorical... Imagine that you are an analyst with an investment firm that tracks airline stocks. You're given the task of determining the relationship (if any) between airline announcements of fare increases and the behavior of their stocks the next day. Historical data about stock prices is easy to come by, but what about the airline an-nouncements? You will need to know at least the name of the airline, the nature of the proposed fare hike, the dates of the announcement, and possibly the response of other airlines. Fortunately, these can be all found in news articles like this one: Citing high fuel prices, United Airlines said Friday it has increased fares by $6 per round trip on flights to some cities also served by lower-cost carriers. American Airlines, a unit of AMR Corp., immediately matched the move, spokesman Tim Wagner said. United, a unit of UAL Corp., said the increase took effect Thursday and applies to most routes where it competes against discount carriers, such as Chicago to Dallas and Denver to San Francisco. This chapter presents techniques for extracting limited kinds of semantic content from text. This process of information extraction (IE), turns the unstructured information extraction information embedded in texts into structured data, for example for populating a relational database to enable further processing. The first step in most IE tasks is to find the proper names or named entities mentioned in a text. The task of named entity recognition (NER) is to find each named entity recognition mention of a named entity in the text and label its type. What constitutes a named entity type is application specific; these commonly include people, places, and organizations but also more specific entities from the names of genes and proteins (Cohen and Demner-Fushman, 2014) to the names of college courses (McCallum, 2005). Having located all of the mentions of named entities in a text, it is useful to link, or cluster, these mentions into sets that correspond to the entities behind the mentions, for example inferring that mentions of United Airlines and United in the sample text refer to the same real-world entity. We'll defer discussion of this task of coreference resolution until Chapter 23. The task …
An Integrated Strategy for a Production Planning and Warehouse Layout Problem : Modeling and Solution Approaches

We study a real-world production warehousing case, where the company always faces the challenge to find available space for its products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and-fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported.
Combining Convolutional and Recurrent Neural Networks for Human Skin Detection

Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs, and etc. It is a challenging problem due to many factors such as variation in pigment melanin, uneven illumination, and differences in ethnicity geographics. Besides, age and gender introduce additional difficulties to the detection process. It is hard to determine whether a single pixel is skin or nonskin without considering the context. An efficient traditional hand-engineered skin color detection algorithm requires extensive work by domain experts. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have achieved great success in pixel-wise labeling tasks. However, CNN-based architectures are not sufficient for modeling the relationship between pixels and their neighbors. In this letter, we integrate recurrent neural networks (RNNs) layers into the fully convolutional neural networks (FCNs), and develop an end-to-end network for human skin detection. In particular, FCN layers capture generic local features, while RNN layers model the semantic contextual dependencies in images. Experimental results on the COMPAQ and ECU skin datasets validate the effectiveness of the proposed approach, where RNN layers enhance the discriminative power of skin detection in complex background situations.
Rethinking ImageNet Pre-training

We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pretrained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate 50.9 AP on COCO object detection without using any external data—a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of ‘pretraining and fine-tuning’ in computer vision.
Ultra-Fine Entity Typing

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced
EUCAST expert rules in antimicrobial susceptibility testing.

EUCAST expert rules have been developed to assist clinical microbiologists and describe actions to be taken in response to specific antimicrobial susceptibility test results. They include recommendations on reporting, such as inferring susceptibility to other agents from results with one, suppression of results that may be inappropriate, and editing of results from susceptible to intermediate or resistant or from intermediate to resistant on the basis of an inferred resistance mechanism. They are based on current clinical and/or microbiological evidence. EUCAST expert rules also include intrinsic resistance phenotypes and exceptional resistance phenotypes, which have not yet been reported or are very rare. The applicability of EUCAST expert rules depends on the MIC breakpoints used to define the rules. Setting appropriate clinical breakpoints, based on treating patients and not on the detection of resistance mechanisms, may lead to modification of some expert rules in the future.
Models and algorithms for social influence analysis

Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this tutorial, we survey the research on social influence analysis with a focus on the computational aspects. First, we introduce how to verify the existence of social influence in various social networks. Second, we present computational models for quantifying social influence. Third, we describe how social influence can help real applications. In particular, we will focus on opinion leader finding and influence maximization for viral marketing. Finally, we apply the selected algorithms of social influence analysis on different social network data, such as twitter, arnetminer data, weibo, and slashdot forum.
Monitoring and testing dairy herds for metabolic disease.

Clinical impressions of metabolic disease problems in dairy herds can be corroborated with herd-based metabolic testing. Ruminal pH should be evaluated in herds showing clinical signs associated with SARA (lame cows, thin cows, high herd removals or death loss across all stages of lactation, or milk fat depression). Testing a herd for the prevalence of SCK via blood BHB sampling in early lactation is useful in almost any dairy herd, and particularly if the herd is experiencing a high incidence of displaced abomasum or high removal rates of early lactation cows. If cows are experiencing SCK within the first 3 weeks of lactation, then consider NEFA testing of the prefresh cows to corroborate prefresh negative energy balance. Finally, monitoring cows on the day of calving for parturient hypocalcemia can provide early detection of diet-induced problems in calcium homeostasis. If hypocalcemia problems are present despite supplementing anionic salts before calving, then it may be helpful to evaluate mean urinary pH of a group of the prefresh cows. Quantitative testing strategies based on statistical analyses can be used to establish minimum sample sizes and interpretation guidelines for all of these tests.
Operating Room Personnel Viewpoints About Certified Registered Nurse Anesthetists.

The purpose of this project was to explore what attitudes physicians, nurses, and operating room technicians had about working with Certified Registered Nurse Anesthetists (CRNAs) to better understand practice barriers and facilitators. This Q methodology study used a purposive sample of operating room personnel from four institutions in the Midwestern United States. Participants completed a -4 to +4 rank-ordering of their level of agreement with 34 attitude statements representing a wide range of beliefs about nurse anesthetists. Centroid factor analysis with varimax rotation was used to analyze 24 returned Q sorts. Three distinct viewpoints emerged that explained 66% of the variance: favoring unrestricted practice, favoring anesthesiologist supervision, and favoring anesthesiologist practice. Research is needed on how to develop workplace attitudes that support autonomous nurse anesthetist practice and to understand preferences for restricted practice in team members other than physicians.
Grammatical Facial Expressions Recognition with Machine Learning

The automated analysis of facial expressions has been widely used in different research areas, such as biometrics or emotional analysis. Special importance is attached to facial expressions in the area of sign language, since they help to form the grammatical structure of the language and allow for the creation of language disambiguation, and thus are called Grammatical Facial Expressions (GFEs). In this paper we outline the recognition of GFEs used in the Brazilian Sign Language. In order to reach this objective, we have captured nine types of GFEs using a KinectTMsensor, designed a spatial-temporal data representation, modeled the research question as a set of binary classification problems, and employed a Machine Learning technique.
Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction.

Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule-based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10-accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions.
Meta-Learning for Semi-Supervised Few-Shot Classification

In this work, we advance the few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of labeled classes of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. To address this paradigm, we propose novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes. These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. We also propose a new split of ImageNet, consisting of a large set of classes, with a hierarchical structure. Our experiments confirm that our Prototypical Networks can learn to improve their predictions due to unlabeled examples, much like a semi-supervised algorithm would.
Cue-based oral feeding clinical pathway results in earlier attainment of full oral feeding in premature infants

To study whether a cue-based clinical pathway for oral feeding initiation and advancement of premature infants would result in earlier achievement of full oral feeding.Age of achievement of full oral intake was compared for two groups of preterm infants; a prospective study group vs historic cohort controls. Study infants had oral feedings managed by nurses using a clinical pathway that relied on infant behavioral readiness signs to initiate and advance oral feedings. Controls had oral feedings managed by physician orders.Fifty-one infants (n=28 study and n=23 control) were studied. Gender distribution, gestational age, birth weight and ventilator days were not different between groups. Study infants reached full oral feedings 6 days earlier than controls (36±1 3/7 weeks of postmenstrual age (PMA) vs 36 6/7±1 4/7 weeks of PMA, The cue-based clinical pathway for oral feeding initiation and advancement of premature infants resulted in earlier achievement of full oral feeding.
Don't be a phish: steps in user education

Phishing, e-mails sent out by hackers to lure unsuspecting victims into giving up confidential information, has been the cause of countless security breaches and has experienced in the last year an increase in frequency and diversity. While regular phishing attacks are easily thwarted, designing the attack to include user context information could potentially increase the user's vulnerability. To prevent this, phishing education needs to be considered. In this paper we provide an overview of phishing education, focusing on context aware attacks and introduce a new strategy for educating users by combining phishing IQ tests and class discussions. The technique encompasses displaying both legitimate and fraudulent e-mails to users and having them identify the phishing attempts from the authentic e-mails. Proper implementation of this system helps teach users what to look for in e-mails, and how to protect their confidential information from being caught in the nets of phishers. The strategy was applied in Introduction to Computing courses as part of the computer security component. Class assessment indicates an increased level of awareness and better recognition of attacks.
IoT-LAB tutorial : hands-on practice with a very large scale testbed tool for the Internet of Things

FIT IoT-LAB provides a very large scale infrastructure facility suitable for testing small wireless sensor devices and heterogeneous communicating objects. The testbed offers web-based reservation and tooling for applications development, along with direct command-line access to the platform. Sensor nodes firmwares can be built from source and deployed on reserved nodes, application activity can be controlled and monitored, power consumption or radio interference can be measured using the provided tools. The proposed tutorial is a hands-on training, with several practical exercises showing how to use the platform and demonstrating key features. We believe that researchers and engineers in fields different from the Wireless Sensor Network (WSN) community may as well benefit from using the tool, for instance parties interested in leveraging the facility for large scale ubiquitous application or large scale embedded software experimentations.
Brand Attitude Strength : Conceptual and Empirical Differentiation ofTwo Critical Brand Equity Drivers

Research has not verified the theoretical or practical value of the brand attachment construct in relation to alternative constructs, particularly brand attitude strength. The authors make conceptual, measurement, and managerial contributions to this research issue. Conceptually, they define brand attachment, articulate its defining properties, and differentiate it from brand attitude strength. From a measurement perspective, they develop and validate a parsimonious measure of brand attachment, test the assumptions that underlie it, and demonstrate that it indicates the concept of attachment. They also demonstrate the convergent and discriminant validity of this measure in relation to brand attitude strength. Managerially, they demonstrate that brand attachment offers value over brand attitude strength in predicting (1) consumers’ intentions to perform difficult behaviors (those they regard as using consumer resources), (2) actual purchase behaviors, (3) brand purchase share (the share of a brand among directly competing brands), and (4) need share (the extent to which consumers rely on a brand to address relevant needs, including those brands in substitutable product categories).