nils-herrmann/clean_scirepeval huggingface dataset:
Actor-Action Semantic Segmentation with Region Masks

In this paper, we study the actor-action semantic segmentation problem, which requires joint labeling of both actor and action categories in video frames. One major challenge for this task is that when an actor performs an action, different body parts of the actor provide different types of cues for the action category and may receive inconsistent action labeling when they are labeled independently. To address this issue, we propose an end-to-end region-based actor-action segmentation approach which relies on region masks from an instance segmentation algorithm. Our main novelty is to avoid labeling pixels in a region mask independently instead we assign a single action label to these pixels to achieve consistent action labeling. When a pixel belongs to multiple region masks, max pooling is applied to resolve labeling conflicts. Our approach uses a two-stream network as the front-end (which learns features capturing both appearance and motion information), and uses two region-based segmentation networks as the back-end (which takes the fused features from the two-stream network as the input and predicts actor-action labeling). Experiments on the A2D dataset demonstrate that both the region-based segmentation strategy and the fused features from the two-stream network contribute to the performance improvements. The proposed approach outperforms the state-of-the-art results by more than 8% in mean class accuracy, and more than 5% in mean class IOU, which validates its effectiveness.  The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. The research was partially supported by the ST Engineering NTU Corporate Lab through the NRF corporate lab@university scheme. The research was partially supported by start-up funds of University at Buffalo. ar X iv :1 80 7. 08 43 0v 1 [ cs .C V ] 2 3 Ju l 2 01 8 2 DANG ET AL.: ACTOR-ACTION SEMANTIC SEGMENTATION WITH REGION MASKS bird-flying babywalking birdnone bird-flying
Fast and robust absolute camera pose estimation with known focal length

Some 3D computer vision techniques such as structure from motion (SFM) and augmented reality (AR) depend on a specific perspective-n-point (PnP) algorithm to estimate the absolute camera pose. However, existing PnP algorithms are difficult to achieve a good balance between accuracy and efficiency, and most of them do not make full use of the internal camera information such as focal length. In order to attack these drawbacks, we propose a fast and robust PnP (FRPnP) method to calculate the absolute camera pose for 3D compute vision. In the proposed FRPnP method, we firstly formulate the PnP problem as the optimization problem in the null space that can avoid the effects of the depth of each 3D point. Secondly, we can easily get the solution by the direct manner using singular value decomposition. Finally, the accurate information of camera pose can be obtained by optimization strategy. We explore four ways to evaluate the proposed FRPnP algorithm with synthetic dataset, real images, and apply it in the AR and SFM system. Experimental results show that the proposed FRPnP method can obtain the best balance between computational cost and precision, and clearly outperforms the state-of-the-art PnP methods.
Genetics-Based Machine Learning

This is a survey of the field of Genetics-based Machine Learning (GBML): the application of evolutionary algorithms to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimisation problems, but not necessarily with machine learning. We briefly outline the scope of machine learning, introduce the more specific area of supervised learning, contrast it with optimisation and present arguments for and against GBML. Next we introduce a framework for GBML which includes ways of classifying GBML algorithms and a discussion of the interaction between learning and evolution. We then review the following areas with emphasis on their evolutionary aspects: GBML for sub-problems of learning, genetic programming, evolving ensembles, evolving neural networks, learning classifier systems, and genetic fuzzy systems.
Characterizing Online Rumoring Behavior Using Multi-Dimensional Signatures

This study offers an in-depth analysis of four rumors that spread through Twitter after the 2013 Boston Marathon Bombings. Through qualitative and visual analysis, we describe each rumor's origins, changes over time, and relationships between different types of rumoring behavior. We identify several quantitative measures-including temporal progression, domain diversity, lexical diversity and geolocation features-that constitute a multi-dimensional signature for each rumor, and provide evidence supporting the existence of different rumor types. Ultimately these signatures enhance our understanding of how different kinds of rumors propagate online during crisis events. In constructing these signatures, this research demonstrates and documents an emerging method for deeply and recursively integrating qualitative and quantitative methods for analysis of social media trace data.
Predicting Parameters Importance in Higher Education Using Data Analysis: A Regression Model

Data Analysis is key to understand the importance of accumulated data over a period of time. The importance of the accumulated data is understood by the data analyst over period of time. The authors had shown the importance of the data collected of the higher education by finding the new and un-identified facts using the statistically techniques. The authors found that the regression techniques could be a boom for the analysis of the data of the higher education and is being analysed to understand the significance and relationship between key parameters of the higher education setup.
Multi-Task Learning for Machine Reading Comprehension

We propose a multi-task learning framework to jointly train a Machine Reading Comprehension (MRC) model on multiple datasets across different domains. Key to the proposed method is to learn robust and general contextual representations with the help of out-domain data in the multi-task framework. Empirical study of multi-task MRC model shows that the proposed approach is orthogonal to the existing pretrained representation models, such as word embedding and language models. Experiments on the Stanford Question Answering Dataset (SQuAD), the Microsoft MAchine Reading COmprehension Dataset (MS MARCO), NewsQA and other datasets show that our multi-task learning approach achieves significant improvement over state-of-the-art models in most existing MRC tasks.
Multiscale Hierarchical Convolutional Networks

Deep neural network algorithms are difficult to analyze because they lack structure allowing to understand the properties of underlying transforms and invariants. Multiscale hierarchical convolutional networks are structured deep convolutional networks where layers are indexed by progressively higher dimensional attributes, which are learned from training data. Each new layer is computed with multidimensional convolutions along spatial and attribute variables. We introduce an efficient implementation of such networks where the dimensionality is progressively reduced by averaging intermediate layers along attribute indices. Hierarchical networks are tested on CIFAR image data bases where they obtain comparable precisions to state of the art networks, with much fewer parameters. We study some properties of the attributes learned from these databases.
Design of an urban driverless ground vehicle

This paper presents the design and implementation of a driverless car for populated urban environments. We propose a system that explicitly map the static obstacles, d etects and track the moving obstacle, consider the unobserved area s, provide a motion plan with safety guarantees and executes it . All of it was implemented and integrated into a single comput er maneuvering on real time an electric vehicle into an unvisit ed area with moving obstacles. The overview of the algorithms a nd some experimental results are presented.
Design and construction of axial flux permanent magnet generator for wind turbine generated DC voltage at rated power 1500 W

This paper presents a design of permanent magnet machines (PM), such as the permanent magnet axial flux generator for wind turbine generated direct current voltage base on performance requirements. However recent developments in rare earth permanent magnet materials and power electronic devices has awakened interest in alternative generator topologies that can be used to produce direct voltage from wind energy using rectifier circuit convert alternating current to direct current. In preliminary tests the input mechanical energy to drive the rotor of the propose generator. This paper propose a generator which can change mechanical energy into electrical energy with the generator that contains bar magnets move relative generated flux magnetic offset winding coils in stator component. The results show that the direct current output power versus rotor speed of generator in various applications. These benefits present the axial flux permanent magnet generator with generated direct voltage at rated power 1500 W.
Identifying a Computer Forensics Expert: A Study to Measure the Characteristics of Forensic Computer Examiners

The usage of digital evidence from electronic devices has been rapidly expanding within litigation, and along with this increased usage, the reliance upon forensic computer examiners to acquire, analyze, and report upon this evidence is also rapidly growing. This growing demand for forensic computer examiners raises questions concerning the selection of individuals qualified to perform this work. While courts have mechanisms for qualifying witnesses that provide testimony based on scientific data, such as digital data, the qualifying criteria covers a wide variety of characteristics including, education, experience, training, professional certifications, or other special skills. In this study, we compare task performance responses from forensic computer examiners with an expert review panel and measure the relationship with the characteristics of the examiners to their quality responses. The results of this analysis provide insight into identifying forensic computer examiners that provide high-quality responses.
Correlates of academic procrastination: discomfort, task aversiveness, and task capability.

The relationships among five aspects of academic procrastination--behavioral delay, personal upset about the delay, task aversiveness, task capability, and the desire to reduce behavioral delay--were investigated in 10th-grade Israeli students (N = 195). Upset about delay was weakly related to delay itself, and--unlike delay--was strongly related to perceived capability to perform academic tasks and to the desire to change delaying behavior. Students delayed more on academic tasks labeled unpleasant than pleasant, were neutral in between, and were correspondingly more upset about the former than the latter. They more frequently acknowledged reasons for academic procrastination that were less threatening to their self-image (e.g., problems in time management) than reasons that were more threatening (e.g., lack of ability). Interest in reducing delay is related more to self-perceived ability to handle tasks than to time spent procrastinating or reasons given for procrastinating.
Compositional Distributional Models of Meaning

Distributional models of meaning (see Turney and Pantel (2010) for an overview) are based on the pragmatic hypothesis that meanings of words are deducible from the contexts in which they are often used. This hypothesis is formalized using vector spaces, wherein a word is represented as a vector of cooccurrence statistics with a set of context dimensions. With the increasing availability of large corpora of text, these models constitute a well-established NLP technique for evaluating semantic similarities. Their methods however do not scale up to larger text constituents (i.e. phrases and sentences), since the uniqueness of multi-word expressions would inevitably lead to data sparsity problems, hence to unreliable vectorial representations. The problem is usually addressed by the provision of a compositional function, the purpose of which is to prepare a vector for a phrase or sentence by combining the vectors of the words therein. This line of research has led to the field of compositional distributional models of meaning (CDMs), where reliable semantic representations are provided for phrases, sentences, and discourse units such as dialogue utterances and even paragraphs or documents. As a result, these models have found applications in various NLP tasks, for example paraphrase detection; sentiment analysis; dialogue act tagging; machine translation; textual entailment; and so on, in many cases presenting stateof-the-art performance. Being the natural evolution of the traditional and well-studied distributional models at the word level, CDMs are steadily evolving to a popular and active area of NLP. The topic has inspired a number of workshops and tutorials in top CL conferences such as ACL and EMNLP, special issues at high-profile journals, and it attracts a substantial amount of submissions in annual NLP conferences. The approaches employed by CDMs are as much as diverse as statistical machine leaning (Baroni and Zamparelli, 2010), linear algebra (Mitchell and Lapata, 2010), simple category theory (Coecke et al., 2010), or complex deep learning architectures based on neural networks and borrowing ideas from image processing (Socher et al., 2012; Kalchbrenner et al., 2014; Cheng and Kartsaklis, 2015). Furthermore, they create opportunities for interesting novel research, related for example to efficient methods for creating tensors for relational words such as verbs and adjectives (Grefenstette and Sadrzadeh, 2011), the treatment of logical and functional words in a distributional setting (Sadrzadeh et al., 2013; Sadrzadeh et al., 2014), or the role of polysemy and the way it affects composition (Kartsaklis and Sadrzadeh, 2013; Cheng and Kartsaklis, 2015). The purpose of this tutorial is to provide a concise introduction to this emerging field, presenting the different classes of CDMs and the various issues related to them in sufficient detail. The goal is to allow the student to understand the general philosophy of each approach, as well as its advantages and limitations with regard to the other alternatives.
The Neurological Examination in Aging , Dementia and Cerebrovascular Disease Part 3 : Coordination , Balance and Gait

This four-part series of articles provides an overview of the neurological examination of the elderly patient, particularly as it applies to patients with cognitive impairment, dementia or cerebrovascular disease.The focus is on the method and interpretation of the bedside physical examination; the mental state and cognitive examinations are not covered in this review.Part 1 (featured in the September issue of Geriatrics & Aging) began with an approach to the neurological examination in normal aging and in disease,and reviewed components of the general physical, head and neck, neurovascular and cranial nerve examinations relevant to aging and dementia. Part 2 (featured in the October issue) covered the motor examination with an emphasis on upper motor neuron signs and movement disorders. Part 3, featured here, reviews the assessment of coordination,balance and gait,and Part 4 will discuss the muscle stretch reflexes, pathological and primitive reflexes, sensory examination and concluding remarks. Throughout this series, special emphasis is placed on the evaluation and interpretation of neurological signs in light of findings considered normal in the
ThingPot: an interactive Internet-of-Things honeypot

The Mirai Distributed Denial-of-Service (DDoS) attack exploited security vulnerabilities of Internet-of-Things (IoT) devices and thereby clearly signaled that attackers have IoT on their radar. Securing IoT is therefore imperative, but in order to do so it is crucial to understand the strategies of such attackers. For that purpose, in this paper, a novel IoT honeypot called ThingPot is proposed and deployed. Honeypot technology mimics devices that might be exploited by attackers and logs their behavior to detect and analyze the used attack vectors.  is the first of its kind, since it focuses not only on the IoT application protocols themselves, but on the whole IoT platform. A Proof-of-Concept is implemented with XMPP and a REST API, to mimic a Philips Hue smart lighting system. ThingPot has been deployed for 1.5 months and through the captured data we have found five types of attacks and attack vectors against smart devices. The  source code is made available as open source.
Evolving the Theory of Waste Management – Implications to waste minimization

The Theory of Waste Management is a unified body of knowledge about waste and waste management, and it is founded on the expectation that waste management is to prevent waste to cause harm to human health and the environment and promote resource use optimization. Waste Management Theory is to be constructed under the paradigm of Industrial Ecology as Industrial Ecology is equally adaptable to incorporate waste minimization and/or resource use optimization goals and values.
Noise in the animal shelter environment: building design and the effects of daily noise exposure.

Sound levels in animal shelters regularly exceed 100 dB. Noise is a physical stressor on animals that can lead to behavioral, physiological, and anatomical responses. There are currently no policies regulating noise levels in dog kennels. The objective of this study was to evaluate the noise levels dogs are exposed to in an animal shelter on a continuous basis and to determine the need, if any, for noise regulations. Noise levels at a newly constructed animal shelter were measured using a noise dosimeter in all indoor dog-holding areas. These holding areas included large dog adoptable, large dog stray, small dog adoptable, small dog stray, and front intake. The noise level was highest in the large adoptable area. Sound from the large adoptable area affected some of the noise measurements for the other rooms. Peak noise levels regularly exceeded the measuring capability of the dosimeter (118.9 dBA). Often, in new facility design, there is little attention paid to noise abatement, despite the evidence that noise causes physical and psychological stress on dogs. To meet their behavioral and physical needs, kennel design should also address optimal sound range.
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.
Visual Reverse Engineering of Binary and Data Files

The analysis of computer files poses a difficult problem for security researchers seeking to detect and analyze malicious content, software developers stress testing file formats for their products, and for other researchers seeking to understand the behavior and structure of undocumented file formats. Traditional tools, including hex editors, disassemblers and debuggers, while powerful, constrain analysis to primarily text based approaches. In this paper, we present design principles for file analysis which support meaningful investigation when there is little or no knowledge of the underlying file format, but are flexible enough to allow integration of additional semantic information, when available. We also present results from the implementation of a visual reverse engineering system based on our analysis. We validate the efficacy of both our analysis and our system with case studies depicting analysis use cases where a hex editor would be of limited value. Our results indicate that visual approaches help analysts rapidly identify files, analyze unfamiliar file structures, and gain insights that inform and complement the current suite of tools currently in use.
Reverse engineering the communications protocol of an RFID public transportation card

Radio Frequency Identification (RFID) security has not been properly handled in numerous applications, such as in public transportation systems. In this paper, a methodology to reverse engineer and detect security flaws is put into practice. Specifically, the communications protocol of an ISO/IEC 14443-B public transportation card used by hundreds of thousands of people in Spain was analyzed. By applying the methodology with a hardware tool (Proxmark 3), it was possible to access private information (e.g. trips performed, buses taken, fares applied…), to capture tag-reader communications, and even emulate both tags and readers.
Comparing anomaly-detection algorithms for keystroke dynamics

Keystroke dynamics-the analysis of typing rhythms to discriminate among users-has been proposed for detecting impostors (i.e., both insiders and external attackers). Since many anomaly-detection algorithms have been proposed for this task, it is natural to ask which are the top performers (e.g., to identify promising research directions). Unfortunately, we cannot conduct a sound comparison of detectors using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to collect a keystroke-dynamics data set, to develop a repeatable evaluation procedure, and to measure the performance of a range of detectors so that the results can be compared soundly. We collected data from 51 subjects typing 400 passwords each, and we implemented and evaluated 14 detectors from the keystroke-dynamics and pattern-recognition literature. The three top-performing detectors achieve equal-error rates between 9.6% and 10.2%. The results-along with the shared data and evaluation methodology-constitute a benchmark for comparing detectors and measuring progress.
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding

This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which is intended to be useful for the task of interest even though the training is done on unlabeled data. Our models achieve better results than previous approaches on sentiment classification and topic classification tasks.