nils-herrmann/clean_scirepeval huggingface dataset:
Modeling of Interconnected Critical Infrastructure Systems Using Complex Network Theory

This paper reviews several complex network theory-based methodologies to model and analyze interconnected networks. To address the deficiencies reported in past studies, an advanced model of coupled electric power system (EPS) and information and communication technology (ICT) is developed. The new model is designed as a 3-D graph with edges and nodes, and it highlights the interaction and interdependency of both EPS and ICT. The criticality of each component within the interconnected system is investigated by analyzing the efficiency-weighted node degree of each node symbolizing the components. The model has been tested on an ICT supported 14-bus distribution network under three different loading conditions. Results show that the coupled infrastructure complies with the typical characteristics of scale-free network. It is also shown the ICT system, although smaller in scale, contains more critical hubs than the power distribution network. The results of the proposed approach can be used as a reference to analyze the criticality in other coupled infrastructure by incorporating the internal characteristics of coupled systems.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present COMMONSENSEQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from CONCEPTNET (Speer et al., 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge. We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines. Our best baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy, well below human performance, which is 89%.
Bone Fracture Detection Algorithms Based on Image Processing-A Survey

Fracture in bone occurs when an external force exercised upon the bone is more than what the bone can tolerate or bear. As, its consequence structure and muscular power of the bone is disturbed and bone becomes frail, which causes tormenting pain on the bone and ends up in the loss of functioning of bone. Accurate bone structure and fracture detection is achieved using various algorithms which removes noise, enhances image details and highlights the fracture region. Automatic detection of fractures from x-ray images is considered as an important process in medical image analysis by both orthopaedic and radiologic aspect. Manual examination of x-rays has multitude drawbacks. The process is time consuming and subjective. In this paper we discuss several digital image processing techniques applied in fracture detection of bone. This led us to study techniques that have been applied to images obtained from different modalities like x-ray, CT, MRI and ultrasound.  Fracture detection, Medical Imaging, Morphology, Tibia, X-ray image
An Ensemble Based Method for Predicting Emotion Intensity of Tweets

Recently, user generated contents have increased tremendously in social media. Twitter is a popular micro-blogging platform in which users share their feelings, opinions, feedback, etc. It has been observed that microblogs are often associated with emotions. Several studies have focused on assigning a given tweet to one of the available emotion categories (e.g., anger, fear, joy, sadness). It is often useful in applications to find the intensity of emotion in the tweets. The focus on identifying emotion intensity is less in the literature. In this paper, we focus on determining the level of emotion intensity in the tweets. We use an ensemble of three methods: Convolution Neural Networks (CNN) with word embedding features, XGBoost with word n-gram and char n-gram features, and Support Vector Regression (SVR) with lexicon and word embedding features. The final prediction of the given tweet is obtained by the average of predictions of individual methods in the ensemble. The performance of ensemble is better than the methods in the ensemble due to diverse features. Our experimental results outperform baseline methods.
From Numerical Sensor Data to Semantic Representations

Hadi Banaee (2018): From Numerical Sensor Data to Semantic Representations: A Data-driven Approach for Generating Linguistic Descriptions. Örebro Studies in Technology 78. In our daily lives, sensors recordings are becoming more and more ubiquitous. With the increased availability of data comes the increased need of systems that can represent the data in human interpretable concepts. In order to describe unknown observations in natural language, an artificial intelligence system must deal with several issues involving perception, concept formation, and linguistic description. These issues cover various subfields within artificial intelligence, such as machine learning, cognitive science, and natural language generation. The aim of this thesis is to address the problem of semantically modelling and describing numerical observations from sensor data. This thesis introduces data-driven approaches to perform the tasks of mining numerical data and creating semantic representations of the derived information in order to describe unseen but interesting observations in natural language. The research considers creating a semantic representation using the theory of conceptual spaces. In particular, the central contribution of this thesis is to present a datariven approach that automatically constructs conceptual spaces from labelled numerical data sets. This constructed conceptual space then utilises semantic inference techniques to derive linguistic interpretations for novel unknown observations. Another contribution of this thesis is to explore an instantiation of the proposed approach in a real-world application. Specifically, this research investigates a case study where the proposed approach is used to describe unknown time series patterns that emerge from physiological sensor data. This instantiation first presents automatic data analysis methods to extract time series patterns and temporal rules from multiple channels of physiological sensor data, and then applies various linguistic description approaches (includ ing the proposed semantic representation based on conceptual spaces) to generate human-readable natural language descriptions for such time series patterns and temporal rules. The main outcome of this thesis is the use of data-driven strategies that enable the system to reveal and explain aspects of sensor data which may otherwise be difficult to capture by knowledge-driven techniques alone. Briefly put, the thesis aims to automate the process whereby unknown observations of data can be 1) numerically analysed, 2) semantically represented, and eventually 3) linguistically described.
Teachers ’ Content Knowledge and Pedagogical Content Knowledge : The Role of Structural Differences in Teacher Education

Pedagogical content knowledge (PCK) and content knowledge (CK) are key components of teacher competence that affect student progress. However, little is known about how teacher education affects the development of CK and PCK. To address this question, our research group constructed tests to directly assess mathematics teachers’ CK and PCK. Based on these tests, we compared the PCK and CK of four groups of mathematics teachers at different points in their teaching careers in Germany. Confirmatory factor analyses showed that PCK and CK measurement was satisfactorily invariant across the teacher populations considered. As expected, the largest differences in CK and PCK were found between the beginning and the end of initial teacher education. Differences in the structures of teacher education were reasonably well reflected in participants’ CK and PCK.
Leveraging Rich Communication Tools: Evidence of Online Trust and Guanxi in China

Driven by the evolution of consumer-to-consumer (C2C) online marketplaces, we examine the role of communication tools (i.e., an instant messenger, internal message box and a feedback system), in facilitating dyadic online transactions in the Chinese C2C marketplace. Integrating the Chinese concept of guanxi with theories of social translucence and social presence, we introduce a structural model that explains how rich communication tools influence a website’s interactivity and presence, subsequently building trust and guanxi among buyers and sellers, and ultimately predicting buyers’ repurchase intentions. The data collected from 185 buyers in TaoBao, China’s leading C2C online marketplace, strongly support the proposed model. We believe that this research is the first formal study to show evidence of guanxi in online C2C marketplaces, and it is attributed to the role of communication tools to enhance a website’s interactivity and presence.
A New Feedback Method for PR Current Control of LCL-Filter-Based Grid-Connected Inverter

For a grid-connected converter with an LCL filter, the harmonic compensators of a proportional-resonant (PR) controller are usually limited to several low-order current harmonics due to system instability when the compensated frequency is out of the bandwidth of the system control loop. In this paper, a new current feedback method for PR current control is proposed. The weighted average value of the currents flowing through the two inductors of the LCL filter is used as the feedback to the current PR regulator. Consequently, the control system with the LCL filter is degraded from a third-order function to a first-order one. A large proportional control-loop gain can be chosen to obtain a wide control-loop bandwidth, and the system can be optimized easily for minimum current harmonic distortions, as well as system stability. The inverter system with the proposed controller is investigated and compared with those using traditional control methods. Experimental results on a 5-kW fuel-cell inverter are provided, and the new current control strategy has been verified.
Thompson Sampling for Contextual Bandits with Linear Payoffs

Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have better empirical performance compared to the state-of-theart methods. However, many questions regarding its theoretical performance remained open. In this paper, we design and analyze a generalization of Thompson Sampling algorithm for the stochastic contextual multi-armed bandit problem with linear payoff functions, when the contexts are provided by an adaptive adversary. This is among the most important and widely studied version of the contextual bandits problem. We prove a high probability regret bound of Õ( 2 √ T 1+ ) in time T for any 0 < < 1, where d is the dimension of each context vector and is a parameter used by the algorithm. Our results provide the first theoretical guarantees for the contextual version of Thompson Sampling, and are close to the lower bound of Ω(d √ T ) for this problem. This essentially solves a COLT open problem of Chapelle and Li [COLT 2012].   W&CP volume 28.
Ask Your Neurons: A Deep Learning Approach to Visual Question Answering

We propose a Deep Learning approach to the visual question answering task, where machines answer to questions about real-world images. By combining latest advances in image representation and natural language processing, we propose Ask Your Neurons, a scalable, jointly trained, end-to-end formulation to this problem. In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language inputs (image and question). We evaluate our approaches on the DAQUAR as well as the VQA dataset where we also report various baselines, including an analysis how much information is contained in the language part only. To study human consensus, we propose two novel metrics and collect additional answers which extend the original DAQUAR dataset to DAQUAR-Consensus. Finally, we evaluate a rich set of design choices how to encode, combine and decode information in our proposed Deep Learning formulation.
Discovering Internet Marketing Intelligence through Online Analytical Web Usage Mining

This article describes a novel way of combining data mining techniques on Internet data in order to discover actionable marketing intelligence in electronic commerce scenarios. The data that is considered not only covers various types of server and web meta information, but also marketing data and knowledge. Furthermore, heterogeneity resolution thereof and Internet- and electronic commerce-specific pre-processing activities are embedded. A generic web log data hypercube is formally defined and schematic designs for analytical and predictive activities are given. From these materialised views, various online analytical web usage data mining techniques are shown, which include marketing expertise as domain knowledge and are specifically designed for electronic commerce purposes.
The Graph Neural Network Model

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.
Localizing Moments in Video with Natural Language

We consider retrieving a specific temporal segment, or moment, from a video given a natural language text description. Methods designed to retrieve whole video clips with natural language determine what occurs in a video but not when. To address this issue, we propose the Moment Context Network (MCN) which effectively localizes natural language queries in videos by integrating local and global video features over time. A key obstacle to training our MCN model is that current video datasets do not include pairs of localized video segments and referring expressions, or text descriptions which uniquely identify a corresponding moment. Therefore, we collect the Distinct Describable Moments (DiDeMo) dataset which consists of over 10,000 unedited, personal videos in diverse visual settings with pairs of localized video segments and referring expressions. We demonstrate that MCN outperforms several baseline methods and believe that our initial results together with the release of DiDeMo will inspire further research on localizing video moments with natural language.
Reading Proficiency, Reading Strategies, Metacognitive Awareness and L2 Readers

__________________________ This paper provides an overview of the research related to second language learners and reading strategies. It also considers the more recent research focusing on the role of metacognitive awareness in the reading comprehension process. The following questions are addressed: 1) How can the relationship between reading strategies, metacognitive awareness, and reading proficiency be characterized? 2) What does research in this domain indicate about the reading process? 3) What research methodologies can be used to investigate metacognitive awareness and reading strategies? 4) What open questions still remain from the perspective of research in this domain, and what are some of the research and methodological concerns that need to be addressed in this area in order to advance the current conceptual understanding of the reading process in an L2. Since so much of second language research is grounded in first language research, findings from both L1 and L2 contexts are discussed. _________________________ Introduction The current explosion of research in second language reading has begun to focus on readers’ strategies. Reading strategies are of interest for what they reveal about the way readers manage their interaction with written text and how these strategies are related to text comprehension. Research in second language reading suggests that learners use a variety of strategies to assist them with the acquisition, storage, and retrieval of information (Rigney, 1978). Strategies are defined as learning techniques, behaviors, problem-solving or study skills which make learning more effective and efficient (Oxford and Crookall, 1989). In the context of second language learning, a distinction can be made between strategies that make learning more effective, versus strategies that improve comprehension. The former are generally referred to as learning strategies in the second language literature. Comprehension or reading strategies on the other hand, indicate how readers conceive of a task, how they make sense of what they read, and
Occluded Pedestrian Detection Through Guided Attention in CNNs

Pedestrian detection has progressed significantly in the last years. However, occluded people are notoriously hard to detect, as their appearance varies substantially depending on a wide range of occlusion patterns. In this paper, we aim to propose a simple and compact method based on the FasterRCNN architecture for occluded pedestrian detection. We start with interpreting CNN channel features of a pedestrian detector, and we find that different channels activate responses for different body parts respectively. These findings motivate us to employ an attention mechanism across channels to represent various occlusion patterns in one single model, as each occlusion pattern can be formulated as some specific combination of body parts. Therefore, an attention network with self or external guidances is proposed as an add-on to the baseline FasterRCNN detector. When evaluating on the heavy occlusion subset, we achieve a significant improvement of 8pp to the baseline FasterRCNN detector on CityPersons and on Caltech we outperform the state-of-the-art method by 4pp.
How to Create a Malevolent Artificial Intelligence

Cybersecurity research involves publishing papers about malicious exploits as much as publishing information on how to design tools to protect cyber-infrastructure. It is this information exchange between ethical hackers and security experts, which results in a well-balanced cyber-ecosystem. In the blooming domain of AI Safety Engineering, hundreds of papers have been published on different proposals geared at the creation of a safe machine, yet nothing, to our knowledge, has been published on how to design a malevolent machine. Availability of such information would be of great value particularly to computer scientists, mathematicians, and others who have an interest in AI safety, and who are attempting to avoid the spontaneous emergence or the deliberate creation of a dangerous AI, which can negatively affect human activities and in the worst case cause the complete obliteration of the human species. This paper provides some general guidelines for the creation of a Malevolent Artificial Intelligence (MAI).
Guiding Principles and Recommendations for the Assessment of Competence

This article presents guiding principles for the assessment of competence developed by the members of the American Psychological Association’s Task Force on Assessment of Competence in Professional Psychology. These principles are applicable to the education, training, and credentialing of professional psychologists, and to practicing psychologists across the professional life span. The principles are built upon a review of competency assessment models, including practices in both psychology and other professions. These principles will help to ensure that psychologists reinforce the importance of a culture of competence. The implications of the principles for professional psychology also are highlighted.
Pyramid Methods in Image Processing

The data structure used to represent image information can be critical to the successful completion of an image processing task. One structure that has attracted considerable attention is the image pyramid This consists of a set of lowpass or bandpass copies of an image, each representing pattern information of a different scale. Here we describe a variety of pyramid methods that we have developed for image data compression, enhancement, analysis and graphics.  A digital image is initially encoded as an array of pixel intensities, but this raw format is not suited to most asks. Alternatively, an image may be represented by its Fourier transform, with operations applied to the transform coefficients rather than to the original pixel values. This is appropriate for some data compression and image enhancement tasks, but inappropriate for others. The transform representation is particularly unsuited for machine vision and computer graphics, where the spatial location of pattem elements is critical. Recently there has been a great deal of interest in representations that retain spatial localization as well as localization in the spatial—frequency domain. This is achieved by decomposing the image into a set of spatial frequency bandpass component images. Individual samples of a component image represent image pattern information that is appropriately localized, while the bandpassed image as a whole represents information about a particular fineness of detail or scale. There is evidence that the human visual system uses such a representation, 1 and multiresolution schemes are becoming increasingly popular in machine vision and in image processing in general. The importance of analyzing images at many scales arises from the nature of images themselves. Scenes in the world contain objects of many sizes, and these objects contain features of many sizes. Moreover, objects can be at various distances from the viewer. As a result, any analysis procedure that is applied only at a single scale may miss information at other scales. The solution is to carry out analyses at all scales simultaneously. Convolution is the basic operation of most image analysis systems, and convolution with large weighting functions is a notoriously expensive computation. In a multiresolution system one wishes to perform convolutions with kernels of many sizes, ranging from very small to very large. and the computational problems appear forbidding. Therefore one of the main problems in working with multiresolution representations is to develop fast and efficient techniques. Members of the Advanced Image Processing Research Group have been actively involved in the development of multiresolution techniques for some time. Most of the work revolves around a representation known as a "pyramid," which is versatile, convenient, and efficient to use. We have applied pyramid-based methods to some fundamental problems in image analysis, data compression, and image manipulation.