nils-herrmann/clean_scirepeval huggingface dataset:
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition

In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions. Since the choices of selecting representative frames are multitudinous for each video, we model the frame selection as a progressive process through deep reinforcement learning, during which we progressively adjust the chosen frames by taking two important factors into account: (1) the quality of the selected frames and (2) the relationship between the selected frames to the whole video. Moreover, considering the topology of human body inherently lies in a graph-based structure, where the vertices and edges represent the hinged joints and rigid bones respectively, we employ the graph-based convolutional neural network to capture the dependency between the joints for action recognition. Our approach achieves very competitive performance on three widely used benchmarks.
Imputation of Missing Values When the Probability of Response Depends On the Variable Being Imputed

A method is developed for imputing missing values when the probability of response depends upon the variable being imputed. The missing data problem is viewed as one of parameter estimation in a regression model with stochastic ensoring of the dependent variable. The prediction approach to imputation is used to solve this estimation problem. Wages and salaries are imputed to nonrespondents in the Current Population Survey and the results are compared to the nonrespondents' IRS wage and salary data. The stochastic ensoring approach gives improved results relative to a prediction approach that ignores the response mechanism.
Creating a Multitrack Classical Music Performance Dataset for Multimodal Music Analysis: Challenges, Insights, and Applications

We introduce a dataset for facilitating audio-visual analysis of music performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground-truth annotation files including frame-level and note-level transcriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches to address the challenges involved in maintaining synchronization and expressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset with existing widely used music audio datasets. We anticipate that the dataset will be useful for the development and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multimodal tasks. We benchmark two existing MIR tasks (multipitch analysis and score-informed source separation) on the dataset and compare them with other existing music audio datasets. In addition, we consider two novel multimodal MIR tasks (visually informed multipitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measurements and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.
Single Image Dehazing via Multi-scale Convolutional Neural Networks

The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multiscale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.
Security in Building Automation Systems

Building automation systems are traditionally concerned with the control of heating, ventilation, and air conditioning, as well as lighting and shading, systems. They have their origin in a time where security has been considered as a side issue at best. Nowadays, with the rising desire to integrate security-critical services that were formerly provided by isolated subsystems, security must no longer be neglected. Thus, the development of a comprehensive security concept is of utmost importance. This paper starts with a security threat analysis and identifies the challenges of providing security in the building automation domain. Afterward, the security mechanisms of available standards are thoroughly analyzed. Finally, two approaches that provide both secure communication and secure execution of possibly untrusted control applications are presented.
Basic suturing training using Microsoft HoloLens

Suturing is an essential skill across specialties. The most common method of teaching basic suturing is workshop format with a faculty instructor. However, many medical students do not reach proficiency due to lack of exposure. It can be expensive and time-consuming to supply these sessions with a sufficient number of faculty-experts, thus there has been increased interest in self-directed learning. This often entails the use of computer-based video instruction. A video-tutorial based curriculum for suturing training has been shown to be effective for training to proficiency. Furthermore, augmented reality is an emerging tool for effective medical education. This involves the projection of virtual instructional material in the user’s real environment. The usage of a voice-commanded headset with projected virtual content can help trainee’s maintain their flow of practice when interacting with instructional content. Our aim was to combine augmented reality with video-based instruction using Microsoft HoloLens to determine if the two methods combined would be accepted as an educational tool and if it is an effective method of training.  Suture Tutor was developed using Unity for use on the HoloLens. This module includes four videos, outlining the steps of the running locking suturing skill. Voice commands are used to play, pause, skip, and slow videos. Thirty six participants in their second year of medicine were split into two groups, the HoloLens group or the control group. The HoloLens group practiced the suturing technique using the Suture Tutor module while the control group used the same four videos on a laptop . Each participant’s first and final attempts at the technique were video recorded and assessed by experts using a previously validated global rating scale. The participants were given seven minutes to practice between the first and last attempts. The time spent interacting with the video during practice was recorded. At the end of the session, the HoloLens group was given a survey evaluating the usability and realism of Suture Tutor.  85% of the participants who used Suture Tutor agreed or strongly agreed that it is useful in the training of medical students. Furthermore, 95% of respondents agreed or strongly agreed that the instructional material used was realistic. We were unable to determine the effectiveness of Suture Tutor. However, the HoloLens group watched the instructional videos significantly more than the control group (7 [5.75 – 9.25] views vs 4.5 [3.75 – 6] views, P = 0.0175).  Participants found the Suture Tutor to be a user friendly and helpful adjunct. The study suggests that Suture Tutor improved accessibility of training material.
Decreased cortical representation of genital somatosensory field after childhood sexual abuse.

Sexual dysfunction is a common clinical symptom in women who were victims of childhood sexual abuse. The precise mechanism that mediates this association remains poorly understood. The authors evaluated the relationship between the experience of childhood abuse and neuroplastic thinning of cortical fields, depending on the nature of the abusive experience.



The authors used MRI-based cortical thickness analysis in 51 medically healthy adult women to test whether different forms of childhood abuse were associated with cortical thinning in areas critical to the perception and processing of specific behavior implicated in the type of abuse.



Exposure to childhood sexual abuse was specifically associated with pronounced cortical thinning in the genital representation field of the primary somatosensory cortex. In contrast, emotional abuse was associated with cortical thinning in regions relevant to self-awareness and self-evaluation.



Neural plasticity during development appears to result in cortical adaptation that may shield a child from the sensory processing of the specific abusive experience by altering cortical representation fields in a regionally highly specific manner. Such plastic reorganization may be protective for the child living under abusive conditions, but it may underlie the development of behavioral problems, such as sexual dysfunction, later in life.
Towards a science of user engagement ( Position Paper )

User engagement is a key concept in designing user-centred web applications. It refers to the quality of the user experience that emphasises the positive aspects of the interaction, and in particular the phenomena associated with being captivated by technology. This definition is motivated by the observation that successful technologies are not just used, but they are engaged with. Numerous methods have been proposed in the literature to measure engagement, however, little has been done to validate and relate these measures and so provide a firm basis for assessing the quality of the user experience. Engagement is heavily influenced, for example, by the user interface and its associated process flow, the user’s context, value system and incentives. In this paper we propose an approach to relating and developing unified measures of user engagement. Our ultimate aim is to define a framework in which user engagement can be studied, measured, and explained, leading to recommendations and guidelines for user interface and interaction design for front-end web technology. Towards this aim, in this paper, we consider how existing user engagement metrics, web analytics, information retrieval metrics, and measures from immersion in gaming can bring new perspective to defining, measuring and explaining user engagement.
Filter Before You Parse: Faster Analytics on Raw Data with Sparser

Exploratory big data applications often run on raw unstructured or semi-structured data formats, such as JSON files or text logs. These applications can spend 80–90% of their execution time parsing the data. In this paper, we propose a new approach for reducing this overhead: apply filters on the data’s raw bytestream before parsing. This technique, which we call raw filtering, leverages the features of modern hardware and the high selectivity of queries found in many exploratory applications. With raw filtering, a user-specified query predicate is compiled into a set of filtering primitives called raw filters (RFs). RFs are fast, SIMD-based operators that occasionally yield false positives, but never false negatives. We combine multiple RFs into an RF cascade to decrease the false positive rate and maximize parsing throughput. Because the best RF cascade is datadependent, we propose an optimizer that dynamically selects the combination of RFs with the best expected throughput, achieving within 10% of the global optimum cascade while adding less than 1.2% overhead. We implement these techniques in a system called Sparser, which automatically manages a parsing cascade given a data stream in a supported format (e.g., JSON, Avro, Parquet) and a user query. We show that many real-world applications are highly selective and benefit from Sparser. Across diverse workloads, Sparser accelerates state-of-the-art parsers such as Mison by up to 22× and improves end-to-end application performance by up to 9×. PVLDB Reference Format: S. Palkar, F. Abuzaid, P. Bailis, M. Zaharia. Filter Before You Parse: Faster Analytics on Raw Data with Sparser. PVLDB, 11(11): xxxx-yyyy, 2018.
The Networked Physical World System

We describe the Networked Physical World system, a ubiquitous computing system that integrates the physical world with the virtual world. The instrumentation of non-electronic devices, such as cans of soda and boxes of detergent, with inexpensive, low functionality computing devices enables the virtual world to sense and identify physical objects. The ability to accurately and definitively identify physical objects without human intervention is paramount to many applications including home automation and supply chain management. However, a system that networks all physical objects requires a careful, deliberate system design to support the huge scale of a quadrillion node network. We have implemented a prototype of the Networked Physical World system and are evaluating its performance and capabilities within a large-scale real-world supply chain application, the Field Trial. We describe the building b lock system components of the Networked Physical World system and present some preliminary results from the Field Trial.
Epidemiology and Risk Factors of Melasma

Melasma is an acquired increased pigmentation of the skin , a symmetric hypermelanosis, characterized by irregular light to gray brown macules. Melasma comes from the Greek word melas [= black color), formerly known as Chloasma, another Greek word meaning green color, even though the term was more often used for melasma cases during pregnancy. It is considered to be part of a large group of facial melanosis, such as Riehl’s melanosis, Lichen planuspigmentous, erythema dyschromicumperstans, erythrosis and poikiloderma of Civatte [2]. Hyperpigmented macules and patches are most commonly developed in the sun-exposed areas of the skin . Melasma is considered to be a chronic acquired hypermelanosis of the skin [4], with poorly understood pathogenesis . The increased pigmentation and the photo damaged features that characterize melasma include solar elastosis, even though the main pathogenesis still remains unknown [6].
A deep learning framework for financial time series using stacked autoencoders and long-short term memory

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
Knowledge Distillation by On-the-Fly Native Ensemble

Knowledge distillation is effective to train small and generalisable network models for meeting the low-memory and fast running requirements. Existing offline distillation methods rely on a strong pre-trained teacher, which enables favourable knowledge discovery and transfer but requires a complex two-phase training procedure. Online counterparts address this limitation at the price of lacking a highcapacity teacher. In this work, we present an On-the-fly Native Ensemble (ONE) learning strategy for one-stage online distillation. Specifically, ONE trains only a single multi-branch network while simultaneously establishing a strong teacher onthe-fly to enhance the learning of target network. Extensive evaluations show that ONE improves the generalisation performance a variety of deep neural networks more significantly than alternative methods on four image classification dataset: CIFAR10, CIFAR100, SVHN, and ImageNet, whilst having the computational efficiency advantages.
Online and Social Media Suicide Prevention Interventions for Young People: A Focus on Implementation and Moderation.

Suicide remains a major global public health issue for young people. The reach and accessibility of online and social media-based interventions herald a unique opportunity for suicide prevention. To date, the large body of research into suicide prevention has been undertaken atheoretically. This paper provides a rationale and theoretical framework (based on the interpersonal theory of suicide), and draws on our experiences of developing and testing online and social media-based interventions.



The implementation of three distinct online and social media-based intervention studies, undertaken with young people at risk of suicide, are discussed. We highlight the ways that these interventions can serve to bolster social connectedness in young people, and outline key aspects of intervention implementation and moderation.



Insights regarding the implementation of these studies include careful protocol development mindful of risk and ethical issues, establishment of suitably qualified teams to oversee development and delivery of the intervention, and utilisation of key aspects of human support (i.e., moderation) to encourage longer-term intervention engagement.



Online and social media-based interventions provide an opportunity to enhance feelings of connectedness in young people, a key component of the interpersonal theory of suicide. Our experience has shown that such interventions can be feasibly and safely conducted with young people at risk of suicide. Further studies, with controlled designs, are required to demonstrate intervention efficacy.
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation

Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based decoder deforms a canonical 2D grid onto the underlying 3D object surface of a point cloud, achieving low reconstruction errors even for objects with delicate structures. The proposed decoder only uses about 7% parameters of a decoder with fully-connected neural networks, yet leads to a more discriminative representation that achieves higher linear SVM classification accuracy than the benchmark. In addition, the proposed decoder structure is shown, in theory, to be a generic architecture that is able to reconstruct an arbitrary point cloud from a 2D grid. Our code is available at
SPATIO-SEMANTIC COHERENCE IN THE INTEGRATION OF 3D CITY MODELS

An increasing number of applications rely on 3D geoinformation. In addition to 3D geometry, these applications particularly require complex semantic information. In the context of spatial data infrastructures the needed data are drawn from distributed sources and often are thematically and spatially fragmented. Straight forward joining of 3D objects would inevitably lead to geometrical inconsistencies such as cracks, permeations, or other inconsistencies. Semantic information can help to reduce the ambiguities for geometric integration, if it is coherently structured with respect to geometry. The paper discusses these problems with special focus on virtual 3D city models and the semantic data model CityGML, an emerging standard for the representation and the exchange of 3D city models based on ISO 191xx standards and GML3. Different data qualities are analyzed with respect to their semantic and spatial structure leading to the distinction of six categories regarding the spatio-semantic coherence of 3D city models. Furthermore, it is shown how spatial data with complex object descriptions support the integration process. The derived categories will help in the future development of automatic integration methods for complex 3D geodata.
2017 AHA/ACC/HRS Guideline for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death: Executive Summary.

WRITING COMMITTEE MEMBERS Sana M. Al-Khatib, MD, MHS, FACC, FAHA, FHRS, Chair William G. Stevenson, MD, FACC, FAHA, FHRS,  Michael J. Ackerman, MD, PhD*† Anne M. Gillis, MD, FHRS*† William J. Bryant, JD, LLM† Mark A. Hlatky, MD, FACC, FAHA† David J. Callans, MD, FACC, FHRS*‡ Christopher B. Granger, MD, FACC, FAHA*† Anne B. Curtis, MD, FACC, FAHA, FHRS*† Stephen C. Hammill, MD, FACC, FHRS‡ Barbara J. Deal, MD, FACC, FAHA† José A. Joglar, MD, FACC, FAHA, FHRS║ Timm Dickfeld, MD, PhD, FHRS*† G. Neal Kay, MD† Michael E. Field, MD, FACC, FAHA, FHRS† Daniel D. Matlock, MD, MPH† Gregg C. Fonarow, MD, FACC, FAHA, FHFSA*§ Robert J. Myerburg, MD, FACC† Richard L. Page, MD, FACC, FAHA, FHRS‡
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks. Different from many existing toolkits that are specialized for specific applications (e.g., neural machine translation), Texar is designed to be highly flexible and versatile. This is achieved by abstracting the common patterns underlying the diverse tasks and methodologies, creating a library of highly reusable modules and functionalities, and enabling arbitrary model architectures and various algorithmic paradigms. The features make Texar particularly suitable for technique sharing and generalization across different text generation applications. The toolkit emphasizes heavily on extensibility and modularized system design, so that components can be freely plugged in or swapped out. We conduct extensive experiments and case studies to demonstrate the use and advantage of the toolkit.
Topic Extraction and Sentiment Classification by using Latent Dirichlet Markov Allocation and SentiWordNet

Now days, the power of internet is having an immense impact on human life and helps one to make important decisions. Since plenty of knowledge and valuable information is available on the internet therefore many users read review information given on web to take decisions such as buying products, watching movies, going to restaurants etc. Reviews contain user opinion about the product, service, event or topic. It is difficult for web users to read and understand the contents from large number of reviews. Whenever any detail is required in the document, this can be achieved by many probabilistic topic models. A topic model provides a generative model for documents and it defines a probabilistic scheme by which documents can be achieved. Topic model is an Integration of acquaintance and these acquaintances are blended with theme, where a theme is a fusion of terms. We describe Latent Dirichlet Markov Allocation 4 level hierarchical Bayesian Model (LDMA), planted on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which highlights on extracting multiword topics from text data. To retrieve the sentiment of the reviews, along with LDMA we will be using SentiWordNet and will compare our result to LDMA with feature extraction of baseline method of sentiment analysis.