nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Ground Shadows in Outdoor Consumer Photographs

Detecting shadows from images can significantly improve the performance of several vision tasks such as object detection and tracking. Recent approaches have mainly used illumination invariants which can fail severely when the qualities of the images are not very good, as is the case for most consumer-grade photographs, like those on Google or Flickr. We present a practical algorithm to automatically detect shadows cast by objects onto the ground, from a single consumer photograph. Our key hypothesis is that the types of materials constituting the ground in outdoor scenes is relatively limited, most commonly including asphalt, brick, stone, mud, grass, concrete, etc. As a result, the appearances of shadows on the ground are not as widely varying as general shadows and thus, can be learned from a labelled set of images. Our detector consists of a three-tier process including (a) training a decision tree classifier on a set of shadow sensitive features computed around each image edge, (b) a CRF-based optimization to group detected shadow edges to generate coherent shadow contours, and (c) incorporating any existing classifier that is specifically trained to detect grounds in images. Our results demonstrate good detection accuracy (85%) on several challenging images. Since most objects of interest to vision applications (like pedestrians, vehicles, signs) are attached to the ground, we believe that our detector can find wide applicability.
ACTORS - a model of concurrent computation in distributed systems

Gul A. Agha’s Actors: A Model of Concurrent Computation in Distributed Systems (The MIT Press,  1987, 144 pages, $25.00, ISBN O-262 010925) is part of the MIT Press Series in Artificial Intelligence. This volume is edited by Patrick Winston, Michael Brady, and Daniel Bobrow. In the actor formalism, pioneered by Carl Hewitt (1977), one perceives abstract computational agents, called actors, that are distributed in space. Each actor has a mailbox (and a mail address) and associated with each actor is a behavior. One actor can influence the actions of another actor only by sending it a communication. An actor can send another actor a communication only when it knows the address of the second actor. An actor can (should be able to) receive more than one message simultaneously (in some sense). In case of synchronous communication in nonactor paradigms, this simultaneous communication requires the sender to wait until the recipient of the communication is ready to accept it. Once the recipient starts processing a given communication, it is blocked from accepting any other messages until after it is done with the message-all for the sake of maintaining the integrity of communications, no doubt. The actor model achieves the same effect through buffering. A recipient’s mailbox is always ready to receive messages. Furthermore, in the actor framework, there are no restrictions on freely communicating mail addresses; thus, the interconnections of the actors can be dynamically modified, and resource management is more flexibly done. Should the reader begin pondering the difference between actors and objects (as found in object-oriented programming), I would like to make a few clarifying observations.
Governance as theory : five propositions

Science in the Department of Government, University of Strathclyde, Glasgow, G1 1XQ, UK, email: G.stoker K strathclyde.ac.uk His main research interests are in local government, urban politics, and cross-national policy transfer. Between 1992 and 1997 he was Director of the ESRC Local Governance Research Programme. He has authored or edited over a dozen books. His two most recent publications are: Rethinking Local Democracy, 1996 (edited with D. King) and The Privatisation of Urban Services in Europe, 1997 (edited with D. Lorrain). Governance as theory: five propositions
Standards-Based DevOps Automation and Integration Using TOSCA

DevOps is an emerging paradigm to tightly integrate developers with operations personnel. This is required to enable fast and frequent releases in the sense of continuously delivering software. Users and customers of today's Web applications and mobile apps running in the Cloud expect fast feedback to problems and feature requests. Thus, it is a critical competitive advantage to be able to respond quickly. Beside cultural and organizational changes that are necessary to implement DevOps in practice, tooling is required to implement end-to-end automation of deployment processes. Automation is the key to efficient collaboration and tight integration between development and operations. The DevOps community is constantly pushing new approaches, tools, and open-source artifacts to implement such automated processes. However, as all these proprietary and heterogeneous DevOps automation approaches differ from each other, it is hard to integrate and combine them to deploy applications in the Cloud. In this paper we present a systematic classification of DevOps artifacts and show how different kinds of artifacts can be transformed toward TOSCA, an emerging standard in this field. This enables the seamless and interoperable orchestration of arbitrary artifacts to model and deploy application topologies. We validate the presented approach by a prototype implementation, show its practical feasibility by a detailed case study, and evaluate its performance.
Fast End-to-End Trainable Guided Filter

Image processing and pixel-wise dense prediction have been advanced by harnessing the capabilities of deep learning. One central issue of deep learning is the limited capacity to handle joint upsampling. We present a deep learning building block for joint upsampling, namely guided filtering layer. This layer aims at efficiently generating the high-resolution output given the corresponding low-resolution one and a high-resolution guidance map. The proposed layer is composed of a guided filter, which is reformulated as a fully differentiable block. To this end, we show that a guided filter can be expressed as a group of spatial varying linear transformation matrices. This layer could be integrated with the convolutional neural networks (CNNs) and jointly optimized through end-to-end training. To further take advantage of end-to-end training, we plug in a trainable transformation function that generates task-specific guidance maps. By integrating the CNNs and the proposed layer, we form deep guided filtering networks. The proposed networks are evaluated on five advanced image processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that the proposed approach runs 10-100Ã— faster and achieves the state-of-the-art performance. We also show that the proposed guided filtering layer helps to improve the performance of multiple pixel-wise dense prediction tasks. The code is available at https://github.com/wuhuikai/DeepGuidedFilter.
Inter-vehicle sensor fusion for accurate vehicle localization supported by V2V and V2I communications

Cooperative driving system techniques aim to minimize accidents, traffic congestion and consequently the environmental costs of road traffic. An accurate vehicle's pose is of extreme importance for the inner working of the traffic management systems. An agent based traffic simulator was developed integrating typical automotive sensors, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, vehicle's agent and infrastructure agent. In this paper, we propose to further enhance a multi-sensor localization algorithm in environments where V2V and V2I communication is possible. This paper describes an inter-vehicle sensor fusion for an accurate vehicle positioning, where each vehicle is modeled by an agent, and each agent provides information depending on its vehicle sensors. In the first fusion stage, data from four wheel encoders and one steering encoder are fused by means of an EKF, providing robust odometric information, namely in face of undesirable effects of wheels slippage. Next, a second fusion stage is processed for integrating odometric and inter-vehicle absolute positioning data.
Detecting Duplicate Posts in Programming QA Communities via Latent Semantics and Association Rules

Programming community-based question-answering (PCQA) websites such as Stack Overflow enable programmers to find working solutions to their questions. Despite detailed posting guidelines, duplicate questions that have been answered are frequently created. To tackle this problem, Stack Overflow provides a mechanism for reputable users to manually mark duplicate questions. This is a laborious effort, and leads to many duplicate questions remain undetected. Existing duplicate detection methodologies from traditional community based question-answering (CQA) websites are difficult to be adopted directly to PCQA, as PCQA posts often contain source code which is linguistically very different from natural languages. In this paper, we propose a methodology designed for the PCQA domain to detect duplicate questions. We model the detection as a classification problem over question pairs. To extract features for question pairs, our methodology leverages continuous word vectors from the deep learning literature, topic model features and phrases pairs that co-occur frequently in duplicate questions mined using machine translation systems. These features capture semantic similarities between questions and produce a strong performance for duplicate detection. Experiments on a range of real-world datasets demonstrate that our method works very well; in some cases over 30% improvement compared to state-of-the-art benchmarks. As a product of one of the proposed features, the association score feature, we have mined a set of associated phrases from duplicate questions on Stack Overflow and open the dataset to the public.
Delving into Transferable Adversarial Examples and Black-box Attacks

An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack Clarifai.com, which is a black-box image classification system.
RESTful Web Services: Principles, Patterns, Emerging Technologies

RESTful Web services are software services which are published on the Web, taking full advantage and making correct use of the HTTP protocol. This chapter gives an introduction to the REST architectural style and how it can be used to design Web service APIs. We summarize the main design constraints of the REST architectural style and discuss how they impact the design of so-called RESTful Web service APIs. We give examples on how the Web can be seen as a novel kind of software connector, which enables the coordination of distributed, stateful and autonomous software services. We conclude the chapter with a critical overview of a set of emerging technologies which can be used to support the development and operation of RESTful Web services.
SDD: A skin detection dataset for training and assessment of human skin classifiers

Recently, skin segmentation has been utilized in wide variety of biometric applications including face detection, recognition, tracking, image filtering, archival and retrieval, etc. Along with its applications, different methods have been designed in order to segment pixels of an arbitrary image into skin and non-skin classes. However, there is no reliable, accurate, appropriate and applicatory dataset either to train or evaluate these algorithms. To this end, a comprehensive dataset, SDD, is introduced in this paper which addresses the limitations of former image libraries. SDD contains more than 20,000 color images accompanying with their manually annotated ground truth. It is suitable for assessment of skin classifiers since it is a very extensive database in which images are divided distinctively (very important from evaluation and training point of view) and it covers multifarious photos captured in all around the world in different conditions. In addition, unlike many other datasets with semi-automatic ground truth labeling, GTs in SDD are very precise thanks to the use of a professional graphical tool and more importantly, the idea of ternary division. The proposed database has been compared to SFA through which both qualitatively and quantitatively, the appealing features of the SDD are confirmed.
Transformer tests using MATLAB/Simulink and their integration into undergraduate electric machinery courses

This article describes MATLAB/Simulink realization of open-circuit and short-circuit tests of transformers that are performed to identify equivalent circuit parameters. These simulation models are developed to support and enhance electric machinery education at the undergraduate level. The proposed tests have been successfully integrated into electric machinery courses at Drexel University, Philadelphia, PA and Nigde University, Nigde, Turkey. 2006 Wiley Periodicals, Inc. Comput Appl Eng Educ 14: 142 150, 2006; ; DOI 10.1002/cae.20077
Automatic Moth Detection from Trap Images for Pest Management

Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort. It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required.
Intensionality was only alleged: On adjective-noun composition in distributional semantics

Distributional semantics has very successfully modeled semantic phenomena at the word level, and recently interest has grown in extending it to capture the meaning of phrases via semantic composition. We present experiments in adjective-noun composition which (1) show that adjectival modification can be successfully modeled with distributional semantics, (2) show that composition models inspired by the semantics of higher-order predication fare better than those that perform simple feature union or intersection, (3) contrary to what the theoretical literature might lead one to expect, do not yield a distinction between intensional and non-intensional modification, and (4) suggest that head noun polysemy and whether the adjective corresponds to a typical attribute of the noun are relevant factors in the distributional representation of adjective phrases.
Contextual Consent: Ethical Mining of Social Media for Health Research

Social media are a rich source of insight for data mining and u sercentred research, but the question of consent arises when st udying such data without the express knowledge of the creator. Case studies that mine social data from users of online services s uch as Facebook and Twitter are becoming increasingly common. Thi s has led to calls for an open discussion into how researchers c an best use these vast resources to make innovative findings whi le still respecting fundamental ethical principles. In this positi n paper we highlight some key considerations for this topic and argu e that the conditions of informed consent are often not being met, a nd that using social media data that some deem free to access and analyse may result in undesirable consequences, particula ly within the domain of health research and other sensitive topics. We posit that successful exploitation of online personal data, part icularly for health and other sensitive research, requires new and us able methods of obtaining consent from the user.
A new ultra-wideband, ultra-short monocycle pulse generator with reduced ringing

We introduce a new ultra-wideband (UWB), ultra-short, step recovery diode monocycle pulse generator. This pulse generator uses a simple RC high-pass filter as a differentiator to generate the monocycle pulse directly. The pulse-shaping network employs a resistive circuit to achieve UWB matching and substantial removal of the pulse ringing, and rectifying and switching diodes to further suppress the ringing. An ultra-short monocycle pulse of 300-ps pulse duration, -17-dB ringing level, and good symmetry has been demonstrated. Good agreement between the measured and calculated results was achieved.
A Rule-Based Strategy for the Semantic Annotation of Geodata

The ability to represent geospatial semantics is of great importance when building geospatial applications for the Web. This ability will enhance discovery, retrieval and translation of geographic information as well as the reuse of geographic information in different contexts. The problem of generating semantic annotations has been recognized as one of the most serious obstacles for realizing the Geospatial Semantic Web vision. We present a rule-based strategy for the semantic annotation of geodata that combines Semantic Web and Geospatial Web Services technology. In our approach, rules are employed to partially automate the annotation process. Rules define conditions for identifying geospatial concepts. Based on these rules, spatial analysis procedures are implemented that allow for inferring whether or not a feature in a dataset represents an instance of a geospatial concept. This automated evaluation of features in the dataset generates valuable information for the creation and refinement of semantic annotations on the concept level. The approach is illustrated by a case study on annotating data sources containing representations of lowlands. The presented strategy lays the foundations for the specification of a semantic annotation tool for geospatial web services that supports data providers in annotating their sources according to multiple domain views.
BIDaaS: Blockchain Based ID As a Service

Blockchain technology has been known as the underlying technology of cryptocurrencies, but nowadays it is further considered as a functional technology for improving existing technologies and creating new applications previously never practical. In this paper, we are focused on utilizing blockchain technology to introduce a new ID as a service (IDaaS) for digital identity management. The proposed blockchain-based ID as a service (BIDaaS) is explained with one practical example that shows how the proposed BIDaaS works as an identity and authentication management infrastructure for mobile users of a mobile telecommunication company.
Machine learning based encrypted traffic classification: Identifying SSH and Skype

The objective of this work is to assess the robustness of machine learning based traffic classification for classifying encrypted traffic where SSH and Skype are taken as good representatives of encrypted traffic. Here what we mean by robustness is that the classifiers are trained on data from one network but tested on data from an entirely different network. To this end, five learning algorithms — AdaBoost, Support Vector Machine, Naïe Bayesian, RIPPER and C4.5 — are evaluated using flow based features, where IP addresses, source/destination ports and payload information are not employed. Results indicate the C4.5 based approach performs much better than other algorithms on the identification of both SSH and Skype traffic on totally different networks.
A Bio-Inspired Robot With Undulatory Fins and Its Control Methods

This paper proposes a bio-inspired robot with undulatory fins and summarizes its control methods. First, three basic motions, forward/backward swimming, diving/rising motion, and turning, are implemented and evaluated by experiments. Next, a hybrid control that combines active disturbance rejection control with a fuzzy strategy is presented to achieve closed-loop depth and course control according to the evaluation of the three basic motions. Finally, waypoint tracking with a line-of-sight guidance system based on a finite-state machine for this bio-inspired robot is presented. The results of swimming experiments are provided to illustrate the validity of the proposed methods.
TextOntoEx: Automatic ontology construction from natural English text

Most of existing ontologies construction tools support construction of ontological relations (e.g., taxonomy, equivalence, etc.) but they do not support construction of domain relations, non-taxonomic conceptual relationships (e.g., causes, caused by, treat, treated by, has-member, contain, material-of, operated-by, controls, etc.). Domain relations are found mainly in text sources. TextOntoEx constructs ontology from natural domain text using semantic pattern-based approach. TextOntoEx is a chain between linguistic analysis and ontology engineering. TextOntoEx analyses natural domain text to extract candidate relations and then maps them into meaning representation to facilitate constructing ontology. The paper explains this approach in more details and discusses some experiments on deriving ontology from natural text.
Impact of Capital Structure on Financial Performance of the Listed Trading Companies in Sri Lanka

Capital structure is a financial tool that helps to determine ‘how do firms choose their capital structure?’ a firms capital structure is then the composition or structure of its liabilities. In this study, an attempt has been made to analyze the Capital structure and financial performance during 2006 to 2010 (05 years) financial year of listed trading companies in Sri Lanka. For the purpose of this study, the data was extracted from the annual reports of sample companies. Correlation and multiple regression analysis are used for analysis. The results revealed there is positive relationship between capital structure and financial performance. And also capital structure is significantly impact on financial performance of the firm showed that debt asset ratio, debt equity ratio and long term debt correlated with gross profit margin(GPM), net profit margin(NPM), Return on Capital Employed(ROCE),Return on Asset (ROA) & Return on Equity(ROE )at significant level of 0.05 and 0.1