nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating the Multifactor Structure of the Long and Short Versions of the Self-Compassion Scale in a Clinical Sample.

There has been a growing interest in the concept of self-compassion in Eastern psychology. The aim of the present study was to explore the dimensionality of the widely used Self-Compassion Scale (SCS; long and short versions) in both clinical and nonclinical samples



Several confirmatory factor analyses (CFAs) were computed in a mixed clinical (n = 316) and a nonclinical sample (n = 1128) from the Portuguese population. Also, differences were tested between the groups in the SCS 6 factors.



The CFA supported both a 6-factor model and a hierarchical model in both samples. The SCS also showed good psychometric properties, with good internal consistency, test-retest reliability, and convergent validity. Our study further suggests that individuals with several psychopathological disorders showed significantly lower self-compassionate abilities.



The SCS (long and short versions) is thus a reliable instrument to assess self-compassion and is useful for research and, in particular, clinical practice.
Image In painter Mask Generator Object Classifier Real / Fake ? Is there a person ?

While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that co-operate to remove objects, and a novel GAN based prior for the mask generator that allows us to flexibly incorporate knowledge about object shapes. We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only.
Frustratingly Short Attention Spans in Neural Language Modeling

Neural language models predict the next token using a latent representation of the immediate token history. Recently, various methods for augmenting neural language models with an attention mechanism over a differentiable memory have been proposed. For predicting the next token, these models query information from a memory of the recent history which can facilitate learning midand long-range dependencies. However, conventional attention mechanisms used in memoryaugmented neural language models produce a single output vector per time step. This vector is used both for predicting the next token as well as for the key and value of a differentiable memory of a token history. In this paper, we propose a neural language model with a key-value attention mechanism that outputs separate representations for the key and value of a differentiable memory, as well as for encoding the next-word distribution. This model outperforms existing memoryaugmented neural language models on two corpora. Yet, we found that our method mainly utilizes a memory of the five most recent output representations. This led to the unexpected main finding that a much simpler model based only on the concatenation of recent output representations from previous time steps is on par with more sophisticated memory-augmented neural language models.
A survey on combine approach of low level features extraction in CBIR

Nowadays, digital story is for the most part around the place. Images noticeably are such of the virtually important categories bounded by the accessible data. Image processing is a way of doing thing to interpret an thought into digital comprise and plow some operations on it, in term to win an enhanced brain wave or to dig in to the past some enjoyable reference from it. Image Processing forms essential probe area within engineering and personal digital assistant science disciplines. Digital Processing techniques bolster in back rub of the digital images by for computers. The common three phases that all types of story interim using digital course are preprocessing, enhancement and prove, impression extraction. Improvements in data storage and thought acquisition skills have enabled the mint of enormous image datasets. In this how things stack up, it is unavoidable to when push comes to shove appropriate information systems to efficiently did a bang up job these collections. The mean approaches manage it called CBIR which serve Content-Based Image Retrieval systems.
Double Multiple Stream Tube Model and Numerical Analysis of Vertical Axis Wind Turbine

The present paper contributes to the modeling of unsteady flow analysis of vertical axis wind turbine (VAWT). Double multiple stream tube (DSMT) model was applied for the performance prediction of straight bladed fixed pitch VAWT using NACA0018 airfoil at low wind speed. A moving mesh technique was used to investigate two-dimensional unsteady flow around the same VAWT model with NACA0018 airfoil modified to be flexible at 15 from the main blade axis of the turbine at the trailing edge located about 70% of the blade chord length using fluent solving Reynolds average Navier-strokes equation. The results obtained from DMST model and the simulation results were then compared. The result shows that the CFD simulation with airfoil modified has shown better performance at low tip speed ratios for the modeled turbine.
Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set.
SUMMARIZATION : AN OVERVIEW ∗

This paper presents an overview of Text Summarization. Text Summarization is a challenging problem these days. Due to the great amount of information we are provided with and thanks to the development of Internet technologies, needs of producing summaries have become more and more widespread. Summarization is a very interesting and useful task that gives support to many other tasks as well as it takes advantage of the techniques developed for related Natural Language Processing tasks. The paper we present here may help us to have an idea of what Text Summarization is and how it can be useful for.
An improved deep learning architecture for person re-identification

In this work, we propose a method for simultaneously learning features and a corresponding similarity metric for person re-identification. We present a deep convolutional architecture with layers specially designed to address the problem of re-identification. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. Novel elements of our architecture include a layer that computes cross-input neighborhood differences, which capture local relationships between the two input images based on mid-level features from each input image. A high-level summary of the outputs of this layer is computed by a layer of patch summary features, which are then spatially integrated in subsequent layers. Our method significantly outperforms the state of the art on both a large data set (CUHK03) and a medium-sized data set (CUHK01), and is resistant to over-fitting. We also demonstrate that by initially training on an unrelated large data set before fine-tuning on a small target data set, our network can achieve results comparable to the state of the art even on a small data set (VIPeR).
Self-Driving Car Steering Angle Prediction Based on Image Recognition

Self-driving vehicles have expanded dramatically over the last few years. Udacity has release a dataset containing, among other data, a set of images with the steering angle captured during driving. The Udacity challenge aimed to predict steering angle based on only the provided images. We explore two different models to perform high quality prediction of steering angles based on images using different deep learning techniques including Transfer Learning, 3D CNN, LSTM and ResNet. If the Udacity challenge was still ongoing, both of our models would have placed in the top ten of all entries.
Promoting general metacognitive awareness

I describe two aspects of metacognition, knowledge of cognition and regulation of cognition, and how they are related to domain-specific knowledge and cognitive abilities. I argue that metacognitive knowledge is multidimensional, domain-general in nature, and teachable. Four instructional strategies are described for promoting the construction and acquisition of metacognitive awareness. These include promoting general awareness, improving selfknowledge and regulatory skills, and promoting learning environments that are conducive to the construction and use of metacognition. This paper makes three proposals: (a) metacognition is a multidimensional phenomenon, (b) it is domain-general in nature, and (c) metacognitive knowledge and regulation can be improved using a variety of instructional strategies. Let me acknowledge at the beginning that each of these proposals is somewhat speculative. While there is a limited amount of research that supports them, more research is needed to clarify them. Each one of these proposals is addressed in a separate section of the paper. The first makes a distinction between knowledge of cognition and regulation of cognition. The second summarizes some of the recent research examining the relationship of metacognition to expertise and cognitive abilities. The third section describes four general instructional strategies for improving metacognition. These include fostering construction of new knowledge, explicating conditional knowledge, automatizing a monitoring heuristic, and creating a supportive motivational environment in the classroom. I conclude with a few thoughts about general cognitive skills instruction. A framework for understanding metacognition Researchers have been studying metacognition for over twenty years. Most agree that cognition and metacognition differ in that cognitive skills are necessary to perform a task, while metacognition is necessary to understand how the task was performed (Garner, 1987). Most researchers also make a VICTORY: PIPS No.: 136750 LAWKAP truchh7.tex; 9/12/1997; 18:12; v.6; p.1
Accident Forecasting in CCTV Traffic Camera Videos

This paper presents a novel dataset for traffic accidents analysis. Our goal is to resolve the lack of public data for research about automatic spatio-temporal annotations for traffic safety in the roads. Through the analysis of the proposed dataset, we observed a significant degradation of object detection in pedestrian category in our dataset, due to the object sizes and complexity of the scenes. To this end, we propose to integrate contextual information into conventional Faster R-CNN using Context Mining (CM) and Augmented Context Mining (ACM) to complement the accuracy for small pedestrian detection. Our experiments indicate a considerable improvement in object detection accuracy: +8.51% for CM and +6.20% for ACM. Finally, we demonstrate the performance of accident forecasting in our dataset using Faster R-CNN and an Accident LSTM architecture. We achieved an average of 1.684 seconds in terms of Time-To-Accident measure with an Average Precision of 47.25%. Our Webpage for the paper is https:
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as software and system action templates. Compared to existing end-toend approaches, HCNs considerably reduce the amount of training data required, while retaining the key benefit of inferring a latent representation of dialog state. In addition, HCNs can be optimized with supervised learning, reinforcement learning, or a mixture of both. HCNs attain stateof-the-art performance on the bAbI dialog dataset (Bordes and Weston, 2016), and outperform two commercially deployed customer-facing dialog systems.
The challenges of mobile computing

The technical challenges that mobile computing must surmount to achieve its potential are hardly trivial. Some of the challenges in designing software for mobile computing systems are quite different from those involved in the design of software for today's stationary networked systems. The authors focus on the issues pertinent to software designers without delving into the lower level details of the hardware realization of mobile computers. They look at some promising approaches under investigation and also consider their limitations. The many issues to be dealt with stem from three essential properties of mobile computing: communication, mobility, and portability. Of course, special-purpose systems may avoid some design pressures by doing without certain desirable properties. For instance portability would be less of a concern for mobile computers installed in the dashboards of cars than with hand-held mobile computers. However, the authors concentrate on the goal of large-scale, hand-held mobile computing as a way to reveal a wide assortment of issues.<<ETX>>
A Perspective View and Survey of Meta-Learning

Different researchers hold different views of what the term meta-learning exactlymeans. The first part of this paper provides our own perspective view in which the goal isto build self-adaptive learners (i.e. learning algorithms that improve their bias dynamicallythrough experience by accumulating meta-knowledge). The second part provides a survey ofmeta-learning as reported by the machine-learning literature. We find that, despite differentviews and research lines, a question remains constant: how can we exploit knowledge aboutlearning (i.e. meta-knowledge) to improve the performance of learning algorithms? Clearlythe answer to this question is key to the advancement of the field and continues being thesubject of intensive research.
Augmented Chain of Ownership: Configuring IoT Devices with the Help of the Blockchain

The blockchain, distributed and unalterable ledger, has lowered the cost of ownership records. This mechanism that required the involvement of notaries and was once reserved for expensive items can now be used for every type of assets. We propose to use it to keep track of the ownership of IoT devices. By registering devices and their transfer into the blockchain, we build a chain of ownership that can be used to guarantee ownership before a sell or to track and warn owners of security threats faced by their devices. Two extensions are proposed. The first one facilitates remote configuration and key management when a single owner must configure a great number of devices. The second one offers potential users information about an IoT device. That information can be used to create a discovery service, inform access decisions or keep track of a device’s state.
Daily life behaviour monitoring for health assessment using machine learning: bridging the gap between domains

Analysis of human behaviour for deducing health and well-being information is one of the contemporary challenges given the ageing in place. To this end, existing and newly developed machine learning methods are needed to be evaluated using annotated real-world data sets. However, the metrics used in performance evaluation are directly taken from the machine learning domain, and they do not necessarily consider the specific needs of human behaviour analysis such as recognizing the duration, start time and frequency of the activities. Moreover, the commonly used metrics such as accuracy or F-measure can be misleading in the presence of skewed class distributions as in the case of human behaviour recognition. In this study, we evaluate the performance of two machine learning methods, hidden Markov model and time windowed neural network on five different real-world data sets through human behaviour understanding for health assessment perspective. According to the experimental results, standard metrics fail to reveal the actual performance of the two compared machine learning methods in terms of behaviour recognition. On the other hand, the proposed evaluation mechanism which considers three different activity categories leads to a more realistic evaluation of the overall performance.
A comparison of a graph database and a relational database: a data provenance perspective

Relational databases have been around for many decades and are the database technology of choice for most traditional data-intensive storage and retrieval applications. Retrievals are usually accomplished using SQL, a declarative query language. Relational database systems are generally efficient unless the data contains many relationships requiring joins of large tables. Recently there has been much interest in data stores that do not use SQL exclusively, the so-called NoSQL movement. Examples are Google's BigTable and Facebook's Cassandra. This paper reports on a comparison of one such NoSQL graph database called Neo4j with a common relational database system, MySQL, for use as the underlying technology in the development of a software system to record and query data provenance information.
Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification

Recent years have witnessed the breakthrough success of deep convolutional neural networks (DCNNs) in image classification and other vision applications. Although freeing users from the troublesome handcrafted feature extraction by providing a uniform feature extraction-classification framework, DCNNs still require a handcrafted design of their architectures. In this paper, we propose the genetic DCNN designer, an autonomous learning algorithm can generate a DCNN architecture automatically based on the data available for a specific image classification problem. We first partition a DCNN into multiple stacked meta convolutional blocks and fully connected blocks, each containing the operations of convolution, pooling, fully connection, batch normalization, activation and drop out, and thus convert the architecture into an integer vector. Then, we use refined evolutionary operations, including selection, mutation and crossover to evolve a population of DCNN architectures. Our results on the MNIST, Fashion-MNIST, EMNISTDigit, EMNIST-Letter, CIFAR10 and CIFAR100 datasets suggest that the proposed genetic DCNN designer is able to produce automatically DCNN architectures, whose performance is comparable to, if not better than, that of stateof-the-art DCNN models.
Scientific Article Recommendation by using Distributed Representations of Text and Graph

Scientific article recommendation problem deals with recommending similar scientific articles given a query article. It can be categorized as a content based similarity system. Recent advancements in representation learning methods have proven to be effective in modeling distributed representations in different modalities like images, languages, speech, networks etc. The distributed representations obtained using such techniques in turn can be used to calculate similarities. In this paper, we address the problem of scientific paper recommendation through a novel method which aims to combine multimodal distributed representations, which in this case are: 1. distributed representations of paper’s content, and 2. distributed representation of the graph constructed from the bibliographic network. Through experiments we demonstrate that our method outperforms the state-of-the-art distributed representation methods in text and graph, by 29.6% and 20.4%, both in terms of precision and mean-average-precision respectively.
Open Mind Common Sense: Knowledge Acquisition from the General Public

Open Mind Common Sense is a knowledge acquisition system designed to acquire commonsense knowledge from the general public over the web. We describe and evaluate our first fielded system, which enabled the construction of a 400,000 assertion commonsense knowledge base. We then discuss how our second-generation system addresses weaknesses discovered in the first. The new system acquires facts, descriptions, and stories by allowing participants to construct and fill in natural language templates. It employs word-sense disambiguation and methods of clarifying entered knowledge, analogical inference to provide feedback, and allows participants to validate knowledge and in turn each other.
Land Surface Temperature Retrieval from Landsat 8 TIRS - Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method

Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.  2014, 6 9830