nils-herrmann/clean_scirepeval huggingface dataset:
Software Measurement: A Necessary Scientific Basis

AbstructSoftware measurement, like measurement in any other discipline, must adhere to the science of measurement if it is to gain widespread acceptance and validity. The observation of some very simple, but fundamental, principles of measurement can have an extremely beneficial effect on the subject. Measurement theory is used to highlight both weaknesses and strengths of software metrics work, including work on metrics validation. We identify a problem with the well-known Weyuker properties, but also show that a criticism of these properties by Cherniavsky and Smith is invalid. We show that the search for general software complexity measures is doomed to failure. However, the theory does help us to define and validate measures of specific complexity attributes. Above all, we are able to view software measurement in a very wide perspective, rationalising and relating its many diverse activities.
Requirements for Data Quality Metrics

Data quality and especially the assessment of data quality have been intensively discussed in research and practice alike. To support an economically oriented management of data quality and decision making under uncertainty, it is essential to assess the data quality level by means of well-founded metrics. However, if not adequately defined, these metrics can lead to wrong decisions and economic losses. Therefore, based on a decision-oriented framework, we present a set of five requirements for data quality metrics. These requirements are relevant for a metric that aims to support an economically oriented management of data quality and decision making under uncertainty. We further demonstrate the applicability and efficacy of these requirements by evaluating five data quality metrics for different data quality dimensions. Moreover, we discuss practical implications when applying the presented requirements.
Towards Unified Programmability of Cloud and Carrier Infrastructure

The rise of cloud services poses considerable challenges on the control of both cloud and carrier network infrastructures. While traditional telecom network services rely on rather static processes (often involving manual steps), the wide adoption of mobile devices including tablets, smartphones and wearables introduce previously unseen dynamics in the creation, scaling and withdrawal of new services. These phenomena require optimal flexibility in the characterization of services, as well as on the control and orchestration of both carrier and cloud infrastructure. This paper proposes a unified programmability framework addressing: the unification of network and cloud resources, the integrated control and management of cloud and network, the description for programming networked/cloud services, and the provisioning processes of these services. In addition proofs-of-concept are provided based on existing open source control software components.
Review of Action Recognition and Detection Methods

In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter.
Federated Access Control in Heterogeneous Intercloud Environment: Basic Models and Architecture Patterns

This paper presents on-going research to define the basic models and architecture patterns for federated access control in heterogeneous (multi-provider) multi-cloud and inter-cloud environment. The proposed research contributes to the further definition of Intercloud Federation Framework (ICFF) which is a part of the general Intercloud Architecture Framework (ICAF) proposed by authors in earlier works. ICFF attempts to address the interoperability and integration issues in provisioning on-demand multi-provider multi-domain heterogeneous cloud infrastructure services. The paper describes the major inter-cloud federation scenarios that in general involve two types of federations: customer-side federation that includes federation between cloud based services and customer campus or enterprise infrastructure, and provider-side federation that is created by a group of cloud providers to outsource or broker their resources when provisioning services to customers. The proposed federated access control model uses Federated Identity Management (FIDM) model that can be also supported by the trusted third party entities such as Cloud Service Broker (CSB) and/or trust broker to establish dynamic trust relations between entities without previously existing trust. The research analyses different federated identity management scenarios, defines the basic architecture patterns and the main components of the distributed federated multi-domain Authentication and Authorisation infrastructure.
Citation count prediction as a link prediction problem

The citation count is an important factor to estimate the relevance and significance of academic publications. However, it is not possible to use this measure for papers which are too new. A solution to this problem is to estimate the future citation counts. There are existing works, which point out that graph mining techniques lead to the best results. We aim at improving the prediction of future citation counts by introducing a new feature. This feature is based on frequent graph pattern mining in the so-called citation network constructed on the basis of a dataset of scientific publications. Our new feature improves the accuracy of citation count prediction, and outperforms the state-of-the-art features in many cases which we show with experiments on two real datasets.
Domain-size pooling in local descriptors: DSP-SIFT

We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor, which we call DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training.
Hybrid model predictive flight mode conversion control of unmanned Quad-TiltRotors

In this paper the autonomous flight mode conversion control scheme for a Quad-TiltRotor Unmanned Aerial Vehicle is presented. This convertible UAV type has the capability for flying both as a helicopter as well as a fixed-wing aircraft type, by adjusting the orientation of its tilt-enabled rotors. Thus, a platform combining the operational advantages of two commonly distinct aircraft types is formed. However, its autonomous mid-flight conversion is an issue of increased complexity. The approach presented is based on an innovative control scheme, developed based on hybrid systems theory. Particularly, a piecewise affine modeling approximation of the complete nonlinear dynamics is derived and serves as the model for control over which a hybrid predictive controller that provides global stabilization, optimality and constraints satisfaction is computed. The effectiveness of the proposed control scheme in handling the mode conversion from helicopter to fixed-wing (and conversely) is demonstrated via a series of simulation studies. The proposed control scheme exceeds the functionality of the aforementioned flight-mode conversion and is also able to handle the transition to intermediate flight-modes with rotors slightly tilted forward in order to provide a forward force component while flying in close to helicopter-mode.
A billion keys, but few locks: the crisis of web single sign-on

OpenID and InfoCard are two mainstream Web single sign-on (SSO) solutions intended for Internet-scale adoption. While they are technically sound, the business model of these solutions does not provide content-hosting and service providers (CSPs) with sufficient incentives to become relying parties (RPs). In addition, the pressure from users and identity providers (IdPs) is not strong enough to drive CSPs toward adopting Web SSO. As a result, there are currently over one billion OpenID-enabled user accounts provided by major CSPs, but only a few relying parties.
 In this paper, we discuss the problem of Web SSO adoption for RPs and argue that solutions in this space must offer RPs sufficient business incentives and trustworthy identity services in order to succeed. We suggest future Web SSO development should investigate and fulfill RPs' business needs, identify IdP business models, and build trust frameworks. Moreover, we propose that Web SSO technology should build identity support into browsers in order to facilitate RPs' adoption.
BoostCluster: boosting clustering by pairwise constraints

Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints. We present a boosting framework for data clustering,termed as BoostCluster, that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints. The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised. The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are, on the one hand, adapted to the clustering results at previous iterations by the given algorithm, and on the other hand consistent with the given side information. Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms (K-means, partitional SingleLink, spectral clustering), and its performance is comparable to the state-of-the-art algorithms for data clustering with side information.
Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.
Robust Stereo Matching Using Adaptive Normalized Cross-Correlation

A majority of the existing stereo matching algorithms assume that the corresponding color values are similar to each other. However, it is not so in practice as image color values are often affected by various radiometric factors such as illumination direction, illuminant color, and imaging device changes. For this reason, the raw color recorded by a camera should not be relied on completely, and the assumption of color consistency does not hold good between stereo images in real scenes. Therefore, the performance of most conventional stereo matching algorithms can be severely degraded under the radiometric variations. In this paper, we present a new stereo matching measure that is insensitive to radiometric variations between left and right images. Unlike most stereo matching measures, we use the color formation model explicitly in our framework and propose a new measure, called the Adaptive Normalized Cross-Correlation (ANCC), for a robust and accurate correspondence measure. The advantage of our method is that it is robust to lighting geometry, illuminant color, and camera parameter changes between left and right images, and does not suffer from the fattening effect unlike conventional Normalized Cross-Correlation (NCC). Experimental results show that our method outperforms other state-of-the-art stereo methods under severely different radiometric conditions between stereo images.
Multi-label Image Classification with A Probabilistic Label Enhancement Model

In this paper, we present a novel probabilistic label enhancement model to tackle multi-label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and occlusions. We propose to tackle these difficulties from a novel perspective by constructing auxiliary labels in the output space. Our idea is to exploit label combinations to enrich the label space and improve the label identification capacity in the original label space. In particular, we identify a set of informative label combination pairs by constructing a tree-structured graph in the label space using the maximum spanning tree algorithm, which naturally forms a conditional random field. We then use the produced label pairs as auxiliary new labels to augment the original labels and perform piecewise training under the framework of conditional random fields. In the test phase, max-product message passing is used to perform efficient inference on the tree graph, which integrates the augmented label pair classifiers and the standard individual binary classifiers for multi-label prediction. We evaluate the proposed approach on several image classification datasets. The experimental results demonstrate the superiority of our label enhancement model in terms of both prediction performance and running time comparing to the-stateof-the-art multi-label learning methods.
A Comparative Study of Cloud based ERP systems with Traditional ERP and Analysis of Cloud ERP implementation

Cloud based ERP system architecture provides solutions to all the difficulties encountered by conventional ERP systems. It provides flexibility to the existing ERP systems and improves overall efficiency. This paper aimed at comparing the performance traditional ERP systems with cloud base ERP architectures. The challenges before the conventional ERP implementations are analyzed. All the main aspects of an ERP systems are compared with cloud based approach. The distinct advantages of cloud ERP are explained. The difficulties in cloud architecture are also mentioned.
Mining Mid-level Visual Patterns with Deep CNN Activations

The purpose of mid-level visual element discovery is to find clusters of image patches that are representative of, and which discriminate between, the contents of the relevant images. Here we propose a pattern-mining approach to the problem of identifying mid-level elements within images, motivated by the observation that such techniques have been very effective, and efficient, in achieving similar goals when applied to other data types. We show that Convolutional Neural Network (CNN) activations extracted from image patches typical possess two appealing properties that enable seamless integration with pattern mining techniques. The marriage between CNN activations and a pattern mining technique leads to fast and effective discovery of representative and discriminative patterns from a huge number of image patches, from which mid-level elements are retrieved. Given the patterns and retrieved mid-level visual elements, we propose two methods to generate image feature representations. The first encoding method uses the patterns as codewords in a dictionary in a manner similar to the Bag-of-Visual-Words model. We thus label this a Bag-of-Patterns representation. The second relies on mid-level visual elements to construct a Bag-of-Elements representation. We evaluate the two encoding methods on object and scene classification tasks, and demonstrate that our approach outperforms or matches the performance of the state-of-the-arts on these tasks.
Body Parts Segmentation with Attached Props Using RGB-D Imaging

People detection is essential in a lot of different systems. Many applications nowadays tend to require people detection to achieve certain tasks. These applications come under many disciplines, such as robotics, ergonomics, biomechanics, gaming and automotive industries. This wide range of applications makes human body detection an active area of research. With the release of depth sensors or RGB-D cameras such as Micosoft Kinect, this area of research became more active, specially with their affordable price. Human body detection requires the adaptation of many scenarios and situations. Various conditions such as occlusions, background cluttering and props attached to the human body require training on custom built datasets. In this paper we present an approach to prepare training datasets to detect and track human body with attached props. The proposed approach uses rigid body physics simulation to create and animate different props attached to the human body. Three scenarios are implemented. In the first scenario the prop is closely attached to the human body, such as a person carrying a backpack. In the second scenario, the prop is slightly attached to the human body, such as a person carrying a briefcase. In the third scenario the prop is not attached to the human body, such as a person dragging a trolley bag. Our approach gives results with accuracy of 93% in identifying both the human body parts and the attached prop in all the three scenarios.
Automatically Constructing a Corpus of Sentential Paraphrases

An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters.
Textual analysis of stock market prediction using breaking financial news: The AZFin text system

Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the S&P 500 stocks during a five week period. We applied our analysis to estimate a discrete stock price twenty minutes after a news article was released. Using a support vector machine (SVM) derivative specially tailored for discrete numeric prediction and models containing different stock-specific variables, we show that the model containing both article terms and stock price at the time of article release had the best performance in closeness to the actual future stock price (MSE 0.04261), the same direction of price movement as the future price (57.1% directional accuracy) and the highest return using a simulated trading engine (2.06% return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.
Lead Iodide Perovskite Sensitized All-Solid-State Submicron Thin Film Mesoscopic Solar Cell with Efficiency Exceeding 9%

We report on solid-state mesoscopic heterojunction solar cells employing nanoparticles (NPs) of methyl ammonium lead iodide (CH(3)NH(3))PbI(3) as light harvesters. The perovskite NPs were produced by reaction of methylammonium iodide with PbI(2) and deposited onto a submicron-thick mesoscopic TiO(2) film, whose pores were infiltrated with the hole-conductor spiro-MeOTAD. Illumination with standard AM-1.5 sunlight generated large photocurrents (J(SC)) exceeding 17 mA/cm(2), an open circuit photovoltage (V(OC)) of 0.888 V and a fill factor (FF) of 0.62 yielding a power conversion efficiency (PCE) of 9.7%, the highest reported to date for such cells. Femto second laser studies combined with photo-induced absorption measurements showed charge separation to proceed via hole injection from the excited (CH(3)NH(3))PbI(3) NPs into the spiro-MeOTAD followed by electron transfer to the mesoscopic TiO(2) film. The use of a solid hole conductor dramatically improved the device stability compared to (CH(3)NH(3))PbI(3) -sensitized liquid junction cells.
A novel dual polarized aperture coupled patch element with a single layer feed network and high isolation

The aperture coupled patch geometry has been extended to dual polarization by several authors. In Tsao et al. (1988) a cross-shaped slot is fed by a balanced feed network which allows for a high degree of isolation. However, the balanced feed calls for an air-bridge which complicates both the design process and the manufacture. An alleviation to this problem is to separate the two channels onto two different substrate layers separated by the ground plane. In this case the disadvantage is increased cost. Another solution with a single layer feed is presented in Brachat and Baracco (1995) where one channel feeds a single slot centered under the patch whereas the other channel feeds two separate slots placed near the edges of the patch. Our experience is that with this geometry it is hard to achieve a well-matched broadband design since the slots near the edge of the patch present very low coupling. All the above geometries maintain symmetry with respect to the two principal planes if we ignore the small spurious coupling from feed lines in the vicinity of the aperture. We propose to reduce the symmetry to only one principal plane which turns out to be sufficient for high isolation and low cross-polarization. The advantage is that only one layer of feed network is needed, with no air-bridges required. In addition the aperture position is centered under the patch. An important application for dual polarized antennas is base station antennas. We have therefore designed and measured an element for the PCS band (1.85-1.99 GHz).