nils-herrmann/clean_scirepeval huggingface dataset:
Modeling multimodal cues in a deep learning-based framework for emotion recognition in the wild

In this paper, we propose a multimodal deep learning architecture for emotion recognition in video regarding our participation to the audio-video based sub-challenge of the Emotion Recognition in the Wild 2017 challenge. Our model combines cues from multiple video modalities, including static facial features, motion patterns related to the evolution of the human expression over time, and audio information. Specifically, it is composed of three sub-networks trained separately: the first and second ones extract static visual features and dynamic patterns through 2D and 3D Convolutional Neural Networks (CNN), while the third one consists in a pretrained audio network which is used to extract useful deep acoustic signals from video. In the audio branch, we also apply Long Short Term Memory (LSTM) networks in order to capture the temporal evolution of the audio features. To identify and exploit possible relationships among different modalities, we propose a fusion network that merges cues from the different modalities in one representation. The proposed architecture outperforms the challenge baselines (38.81 % and 40.47 %): we achieve an accuracy of 50.39 % and 49.92 % respectively on the validation and the testing data.
Text Knowledge Mining: An Alternative to Text Data Mining

In this paper we introduced an alternative view of text mining and we review several alternative views proposed by different authors. We propose a classification of text mining techniques into two main groups: techniques based on inductive inference, that we call text data mining (TDM, comprising most of the existing proposals in the literature), and techniques based on deductive or abductive inference, that we call text knowledge mining (TKM). To our knowledge, the TKM view of text mining is new though, as we shall show, several existing techniques could be considered in this group. We discuss about the possibilities and challenges of TKM techniques. We also discuss about the application of existing theories in possible future research in this field.
An optimal power management system for automatic connection of DC and AC resources of hybrid-microgrid systems

Recently, the necessity of hybrid-microgrid system has been proved as a modern power structure. This paper studies a power management system (PMS) in a hybrid network to control the power-flow procedure between DC and AC buses. The proposed architecture for PMS is designed to eliminate the power disturbances and manage the automatic connection among multiple sources. In this paper, PMS benefits from a 3-phase proportional resonance (PR) control ability to accurately adjust the inverter operation. Also, a Photo-Voltaic (PV) unit and a distributed generator (DG) are considered to supply the load demand power. Compared to the previous studies, the applied scheme has sufficient capability of quickly supplying the load in different scenarios with no network failures. The validity of implemented method is verified through the simulation results.
TIP SHEET Providing Dental Care to Children with Autism Spectrum Disorders

Defining Autism The term autism is often used to describe pervasive developmental disorders that are neurologically based and of an unknown cause. Autism spectrum disorders include: autistic disorder; Asperger’s syndrome; Rett’s disorder; childhood disintegrative disorder; and pervasive developmental disorder, not otherwise specified (PDD NOS). While there are differences between the disorders, they are typically characterized by atypical social, communication, coping and play skills.
A 210 nW 29.3 ppm/°C 0.7 V voltage reference with a temperature range of −50 to 130 °C in 0.13 µm CMOS

A low-voltage, low-power CMOS voltage reference with high temperature stability in a wide temperature range is presented. The temperature dependence of mobility and oxide capacitance is removed by employing transistors in saturation and triode regions and the temperature dependence of threshold voltage is removed by exploiting the transistors in weak inversion region. Implemented in 0.13um CMOS, the proposed voltage reference achieves temperature coefficient of 29.3ppm/°C against temperature variation of −50 – 130°C and line sensitivity of 337ppm/V against supply variation of 0.7–1.8V, while consuming 210nW from 0.7V supply and occupying 0.023mm2.
Automatic Detection and Categorization of Election-Related Tweets

With the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary. As a consequence, and aided by the public nature of Twitter, political scientists now potentially have the means to analyse and understand the narratives that organically form, spread and decline among the public in a political campaign. However, the volume and diversity of the conversation on Twitter, combined with its noisy and idiosyncratic nature, make this a hard task. Thus, advanced data mining and language processing techniques are required to process and analyse the data. In this paper, we present and evaluate a technical framework, based on recent advances in deep neural networks, for identifying and analysing election-related conversation on Twitter on a continuous, longitudinal basis. Our models can detect election-related tweets with an F-score of 0.92 and can categorize these tweets into 22 topics with an F-score of 0.90.
Novel Criteria to Measure Performance of Time Series Segmentation Techniques

An important task in signal processing and temporal data mining is time series segmentation. In order to perform tasks such as time series classification, anomaly detection in time series, motif detection, or time series forecasting, segmentation is often a pre-requisite. However, there has not been much research on evaluation of time series segmentation techniques. The quality of segmentation techniques is mostly measured indirectly using the least-squares error that an approximation algorithm makes when reconstructing the segments of a time series given by segmentation. In this article, we propose a novel evaluation paradigm, measuring the occurrence of segmentation points directly. The measures we introduce help to determine and compare the quality of segmentation algorithms better, especially in areas such as finding perceptually important points (PIP) and other user-specified points.
Overview of Small Scale Electric Energy Storage Systems suitable for dedicated coupling with Renewable Micro Sources

Renewable Micro Sources (RENMSs) will strongly contribute to the accelerating electrification trend currently ongoing. Furthermore, the upcoming mass electrification of automotive, with the related pulverized, but numerically important, electricity storage potential, suggests to start considering how to guarantee a stable and sustainable grid power. In this frame, it is interesting to consider the possibility to couple to each RENMS a dedicated Small Scale Electrical Energy Storage System (SS-EESS), so to be able to dispatch out of the RENMS a grid-compliant RENMS-produced power. An overview of SS-EESSs is hence hereby given, under the points of view of their current main technical features and their prospected costs. It is found that mechanical-based systems like Small Scale Compressed Energy Storage and Flywheels are interesting options for RENMS/SS-EESS dedicated coupling, although fast technological progress in the field of SS-EESSs and the emergence of a clear trend towards joining more energy storage principles (like batteries-supercapacitors assemblies) will likely change the landscape of this field in the next years. In this view, further studies over dedicated coupling of SS-EESSs and RENMSs could help to avoid difficulties in dealing with exploding electricity storage problems in the next years.
Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention

Previous approaches to training syntaxbased sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English. Thus, we propose the use of tree-structured Long Short-Term Memory with an attention mechanism that pays attention to each subtree of the parse tree. Experimental results indicate that our model achieves the stateof-the-art performance in a Japanese sentiment classification task.
Efficient placement of multi-component applications in edge computing systems

Mobile Edge Computing (MEC) is a new paradigm which has been introduced to solve the inefficiencies of mobile cloud computing technologies. The key idea behind MEC is to enhance the capabilities of mobile devices by forwarding the computation of applications to the edge of the network instead of to a cloud data-center. One of the main challenges in MEC is determining an efficient placement of the components of a mobile application on the edge servers that minimizes the cost incurred when running the application. In this paper, we address the problem of multi-component application placement in edge computing by designing an efficient heuristic on-line algorithm that solves it. We also present a Mixed Integer Linear Programming formulation of the multi-component application placement problem that takes into account the dynamic nature of users' location and the network capabilities. We perform extensive experiments to evaluate the performance of the proposed algorithm. Experimental results indicate that the proposed algorithm has very small execution time and obtains near optimal solutions.
The relationship of service failure severity, service recovery justice and perceived switching costs with customer loyalty in the context of e-tailing

Given that e-tailing service failure is inevitable, a better understanding of how service failure and recovery affect customer loyalty represents an important topic for academics and practitioners. This study explores the relationship of service failure severity, service recovery justice (i.e., interactional justice, procedural justice, and distributive justice), and perceived switching costs with customer loyalty; as well, the moderating relationship of service recovery justice and perceived switching costs on the link between service failure severity and customer loyalty in the context of e-tailing are investigated. Data collected from 221 erceived switching costs ustomer loyalty useful respondents are tested against the research model using the partial least squares (PLS) approach. The results indicate that service failure severity, interactional justice, procedural justice and perceived switching costs have a significant relationship with customer loyalty, and that interactional justice can mitigate the negative relationship between service failure severity and customer loyalty. These findings provide several important theoretical and practical implications in terms of e-tailing service failure and
Comparing explicit and predictive distributional semantic models endowed with syntactic contexts

In this article, we introduce an explicit count-based strategy to build word space models with syntactic contexts (dependencies). A filtering method is defined to reduce explicit word-context vectors. This traditional strategy is compared with a neural embedding (predictive) model also based on syntactic dependencies. The comparison was performed using the same parsed corpus for both models. Besides, the dependency-based methods are also compared with bag-of-words strategies, both count-based and predictive ones. The results show that our traditional countbased model with syntactic dependencies outperforms other strategies, including dependency-based embeddings, but just for the tasks focused on discovering similarity between words with the same function (i.e. near-synonyms).
Strength and power training: physiological mechanisms of adaptation.

Adaptations in resistance training are focused on the development and maintenance of the neuromuscular unit needed for force production [97, 136]. The effects of training, when using this system, affect many other physiological systems of the body (e.g., the connective tissue, cardiovascular, and endocrine systems) [16, 18, 37, 77, 83]. Training programs are highly specific to the types of adaptation that occur. Activation of specific patterns of motor units in training dictate what tissue and how other physiological systems will be affected by the exercise training. The time course of the development of the neuromuscular system appears to be dominated in the early phase by neural factors with associated changes in the types of contractile proteins. In the later adaptation phase, muscle protein increases, and the contractile unit begins to contribute the most to the changes in performance capabilities. A host of other factors can affect the adaptations, such as functional capabilities of the individual, age, nutritional status, and behavioral factors (e.g., sleep and health habits). Optimal adaptation appears to be related to the use of specific resistance training programs to meet individual training objectives.
No Evidence Left Behind: Understanding Semantics in Dialogs using Relational Evidence Based Learning

We describe a new structural learning approach to semantic analysis of utterances from conversational dialogs of low-resource domains. Typically an utterance is represented with a multi-layered semantic tag schema: a higher level global context (tag) defines the user’s intent, and associated arguments or slot tags define the local context. To deal with the low resource domains, the existing models encode prior information on either the global or the local context, but not on both. Because these components are highly correlated given the domain, we argue that paired priors on both components is more beneficial for semantic analysis of utterances. We introduce a new multi-layer structural learning approach, which integrates paired prior information about the global and local components of the utterances. Specifically we encode inter-correlations between the multilayered components into the joint learner by way of lexicons of paired tags provided by domain experts. Secondly, we introduce systematic ways to extend the paired tag lexicons for low-resource domains from Web-scale data. Across real dialogs from different domains, our approach results in an average improvement of 12%on intent classification and 3% on slot tagging over the baselines.
Beneficial effects of Lactobacillus paracasei subsp. paracasei NTU 101 and its fermented products

It is well-known that probiotics have a number of beneficial health effects in humans and animals, including the reduction of symptoms in lactose intolerance and enhancement of the bioavailability of nutrients. Probiotics have showed to possess antimutagenic, anticarcinogenic and hypocholesterolemic properties. Further, they were also observed to have antagonistic actions against intestinal and food-borne pathogens, to decrease the prevalence of allergies in susceptible individuals and to have immunomodulatory effects. Typically, the bacteria colonise the intestinal tract first and then reinforce the host defence systems by inducing a generalised mucosal immune response, balanced T-helper cell response, self-limited inflammatory response and secretion of polymeric IgA. Scientific reports showed that the Taiwan native lactic acid bacterium from newborn infant faeces identified as Lactobacillus paracasei subsp. paracasei NTU 101 and its fermented products proved to be effective for the management of blood cholesterol and pressure, prevention of gastric mucosal lesion development, immunomodulation and alleviation of allergies, anti-osteoporosis and inhibition the fat tissue accumulation. This review article describes that the beneficial effects of this Lactobacillus strains and derivative products may be suitable for human and animals.
Common Spatial Patterns in a few channel BCI interface

One source of EEG data quality deterioration is noise. The others are artifacts, such as the eye blinking, oculogyration, heart beat, or muscle activity. All these factors mentioned above contribute to the disappointing and poor quality of EEG signals. There are some solutions which allow increase of this signals quality. One of them is Common Spatial Patterns. Some scientific papers report that CSP can only be effectively used if there are many electrodes available. The aim of this paper is to use CSP method applied in the process of creating a brain computer interface in order to find out if there are any benefits of using this method in 3 channels BCI system.
Non-invasive tracking and monitoring glucose content using near infrared spectroscopy

Diabetes is deliberate as one of the major contributors of crystallize infirmity and death in non-infectious diseases. Common method for determination of blood glucose concentration is using a self-monitoring glucose meter. This process requires pricking the finger and pry out the blood from the forearm and doing the chemical analysis with the help of disposable test strips. The pain and difficulty caused by this method have lead to the evolution of a noninvasive method. This method makes use of a near infrared sensor for transmission and reception of rays from fingertip. Near-infrared (NIR) is passing through the fingertip, before and after obstructing the flow of blood. By explicate the variation in received signal intensity achieved after reflection in both the cases, glucose present in blood can be predicted and wirelessly transmitted to a remote PC. The results show the potential of glucose measurement using Near-infrared.
Unsupervised domain adaptation with joint supervised sparse coding and discriminative regularization term

Domain adaptation (DA) attempts to enhance the generalization capability of classifier through narrowing the gap of the distributions across domains. This paper focuses on unsupervised domain adaptation where labels are not available in target domain. Most existing approaches explore the domaininvariant features shared by domains but ignore the discriminative information of source domain. To address this issue, we propose a discriminative domain adaptation method (DDA) to reduce domain shift by seeking a common latent subspace jointly using supervised sparse coding (SSC) and discriminative regularization term. Particularly, DDA adapts SSC to yield discriminative coefficients of target data and further unites with discriminative regularization term to induce a common latent subspace across domains. We show that both strategies can boost the ability of transferring knowledge from source to target domain. Experiments on two real world datasets demonstrate the effectiveness of our proposed method over several existing state-of-the-art domain adaptation methods.
Scalable Database Logging for Multicores

Modern databases, guaranteeing atomicity and durability, store transaction logs in a volatile, central log buffer and then flush the log buffer to non-volatile storage by the write-ahead logging principle. Buffering logs in central log store has recently faced a severe multicore scalability problem, and log flushing has been challenged by synchronous I/O delay. We have designed and implemented a fast and scalable logging method, ELEDA, that can migrate a surge of transaction logs from volatile memory to stable storage without risking durable transaction atomicity. Our efficient implementation of ELEDA is enabled by a highly concurrent data structure, GRASSHOPPER, that eliminates a multicore scalability problem of centralized logging and enhances system utilization in the presence of synchronous I/O delay. We implemented ELEDA and plugged it to WiredTiger and Shore-MT by replacing their log managers. Our evaluation showed that ELEDA-based transaction systems improve performance up to 71ˆ, thus showing the applicability of ELEDA. PVLDB Reference Format: Hyungsoo Jung, Hyuck Han, and Sooyong Kang. Scalable Database Logging for Multicores. PVLDB, 11(2): 135 148, 2017.
Predicting Engagement Intensity in the Wild Using Temporal Convolutional Network

Engagement is the holy grail of learning whether it is in a classroom setting or an online learning platform. Studies have shown that engagement of the student while learning can benefit students as well as the teacher if the engagement level of the student is known. It is difficult to keep track of the engagement of each student in a face-to-face learning happening in a large classroom. It is even more difficult in an online learning platform where, the user is accessing the material at different instances. Automatic analysis of the engagement of students can help to better understand the state of the student in a classroom setting as well as online learning platforms and is more scalable. In this paper we propose a framework that uses Temporal Convolutional Network (TCN) to understand the intensity of engagement of students attending video material from Massive Open Online Courses (MOOCs). The input to the TCN network is the statistical features computed on 10 second segments of the video from the gaze, head pose and action unit intensities available in OpenFace library. The ability of the TCN architecture to capture long term dependencies gives it the ability to outperform other sequential models like LSTMs. On the given test set in the EmotiW 2018 sub challenge-"Engagement in the Wild", the proposed approach with Dilated-TCN achieved an average mean square error of 0.079.
Hypothyroidism: causes, killers, and life-saving treatments.

Hypothyroidism is a very common, yet often overlooked disease. It can have a myriad of signs and symptoms, and is often nonspecific. Identification requires analysis of thyroid hormones circulating in the bloodstream, and treatment is simply replacement with exogenous hormone, usually levothyroxine (Synthroid). The deadly manifestation of hypothyroidism is myxedema coma. Similarly nonspecific and underrecognized, treatment with exogenous hormone is necessary to decrease the high mortality rate.
US Consumers’ Understanding of Nutrition Labels in 2013: The Importance of Health Literacy

We examined US adults' understanding of a Nutrition Facts panel (NFP), which requires health literacy (ie, prose, document, and quantitative literacy skills), and the association between label understanding and dietary behavior.



Data were from the Health Information National Trends Survey, a nationally representative survey of health information seeking among US adults (N = 3,185) conducted from September 6, 2013, through December 30, 2013. Participants viewed an ice cream nutrition label and answered 4 questions that tested their ability to apply basic arithmetic and understanding of percentages to interpret the label. Participants reported their intake of sugar-sweetened soda, fruits, and vegetables. Regression analyses tested associations among label understanding, demographic characteristics, and self-reported dietary behaviors.



Approximately 24% of people could not determine the calorie content of the full ice-cream container, 21% could not estimate the number of servings equal to 60 g of carbohydrates, 42% could not estimate the effect on daily calorie intake of foregoing 1 serving, and 41% could not calculate the percentage daily value of calories in a single serving. Higher scores for label understanding were associated with consuming more vegetables and less sugar-sweetened soda, although only the association with soda consumption remained significant after adjusting for demographic factors.



Many consumers have difficulty interpreting nutrition labels, and label understanding correlates with self-reported dietary behaviors. The 2016 revised NFP labels may address some deficits in consumer understanding by eliminating the need to perform certain calculations (eg, total calories per package). However, some tasks still require the ability to perform calculations (eg, percentage daily value of calories). Schools have a role in teaching skills, such as mathematics, needed for nutrition label understanding.