nils-herrmann/clean_scirepeval huggingface dataset:
Experience Report: Design and Implementation of a Component-Based Protection Architecture for ASP.NET Web Services

This report reflects, from a software engineering perspective, on the experience of designing and implementing protection mechanisms for ASP.NET Web services. The limitations of Microsoft ASP.NET container security mechanisms render them inadequate for hosting enterprise-scale applications that have to be protected according to diverse and/or complex application-specific security policies. In this paper we report on our experience of designing and implementing a component-based architecture for protecting enterprise-grade Web service applications hosted by ASP.NET. Due to its flexibility and extensibility, this architecture enables the integration of ASP.NET into the organizational security infrastructure with less effort by Web service developers. The architecture has been implemented in a real-world security solution. This paper also contributes a best practice on constructing flexible and extensible authentication and authorization logic for Web services by using Resource Access Decision and Attribute Function (AF) architectural styles. Furthermore, the lessons learned from our design and implementation experiences are discussed throughout the paper.
A study on brain tumor segmentation using convolution neural network

Computer vision is playing important role in the field of human health care. This role is growing day by day. The application of computer vision techniques in health care has one of the aim to reduce human judgement in diagnosis. Thus, human error in judgement may be reduced. Brain related diagnosis demands at most care and a minute error in judgment may be disastrous. This makes medical imaging very important field. Various imaging methods like CT Scans, X-Ray, and MRI are available but MRI is the most reliable and safe. Even the smallest aberrances in the human body can be identified using imaging techniques. More preferred contrast information about brain tissues is provided by Magnetic Resonance imaging (MRI). Image segmentation is an important problem in medical imaging, which involves separating the tumor and organisms out of the medical data. Machine learning (ML) has gained enormous application with innovation in hardware requirements for computing. Convolutional neural networks (CNN) is one of the most effective techniques in ML. CNN has find applications in almost every field of research. CNN also find effective applications in brain MRI segmentation. In this paper, we present a study on CNN based MRI segmentation.
An Empirical Comparison of Knowledge Graph Embeddings for Item Recommendation

In the past years, knowledge graphs have proven to be beneficial for recommender systems, efficiently addressing paramount issues such as new items and data sparsity. At the same time, several works have recently tackled the problem of knowledge graph completion through machine learning algorithms able to learn knowledge graph embeddings. In this paper, we show that the item recommendation problem can be seen as a specific case of knowledge graph completion problem, where the “feedback” property, which connects users to items that they like, has to be predicted. We empirically compare a set of state-of-the-art knowledge graph embeddings algorithms on the task of item recommendation on the Movielens 1M dataset. The results show that knowledge graph embeddings models outperform traditional collaborative filtering baselines and that TransH obtains the best performance.
Supporting change request assignment in open source development

Software repositories, such as CVS and Bugzilla, provide a huge amount of data regarding, respectively, source code and change request history. In this paper we propose a study on how change requests have been assigned to developers involved in an open source project and a method to suggest the set of best candidate developers to resolve a new change request. The method is based on the hypothesis that, given a new change request, developers that have resolved similar change requests in the past are the best candidates to resolve the new one. The suggestion can be useful for project managers in order to choose the best candidate to resolve a particular change request and/or to construct a competence database of developers working on software projects. We use the textual description of change requests stored in software repositories to index developers as documents in an information retrieval system. An Information Retrieval method is then applied to retrieve the candidate developers using the textual description of a new change request as a query.Case and evaluation study of the analysis and the methods introduced in this paper has been conducted on two large open source projects, Mozilla and KDE.
Detection of Information Leaks via Reflection in Android Apps

Reflection is a language feature which allows to analyze and transform the behavior of classes at the runtime. Reflection is used for software debugging and testing. Malware authors can leverage reflection to subvert the malware detection by static analyzers. Reflection initializes the class, invokes any method of class, or accesses any field of class. But, instead of utilizing usual programming language syntax, reflection passes classes/methods etc. as parameters to reflective APIs. As a consequence, these parameters can be constructed dynamically or can be encrypted by malware. These cannot be detected by state-of-the-art static tools. We propose EspyDroid, a system that combines dynamic analysis with code instrumentation for a more precise and automated detection of malware employing reflection. We evaluate EspyDroid on 28 benchmark apps employing major reflection categories. Our technique show improved results over FlowDroid via detection of additional undetected flows. These flows have potential to leak sensitive and private information of the users, through various sinks.
Human Resource Information Management Model based on Blockchain Technology

The authenticity of human resource information has become an important factor that affects the cost and efficiency of human resource management. With the rapid development of mobile devices and Internet technology, various human resource risks caused by information asymmetry constantly bring economic loss, and even a fatal blow, to enterprises. Based on Blockchain(BC), this paper aims to combine traditional encryption technology with Internet-distributed technology, to establish a human resource information management model that reduces the risk of authenticity of human resource information. This model aims to solve the lack of discrimination of the authenticity of human resource information, and to provide authentic and effective decision support information to the human resource management of an organization. The operation method, such as Bitcoin, is used to certify the human resource documentations, as well as to bind the information and the documentation. Furthermore, human resource information is recorded in an accounting book based on BC technology; thus, modifying and validating may be difficult in the entire net work. Consensus mechanism, smart contract, accounting, and payment functions of BC can provide the basic support for human resource information management. Moreover, decentralization of the protection mechanism can be achieved to achieve low cost and high efficiency of information transfer, and to ensure a high-performance work system in the human resource management of enterprises.
Probiotic isolates from unconventional sources: a review

The use of probiotics for human and animal health is continuously increasing. The probiotics used in humans commonly come from dairy foods, whereas the sources of probiotics used in animals are often the animals' own digestive tracts. Increasingly, probiotics from sources other than milk products are being selected for use in people who are lactose intolerant. These sources are non-dairy fermented foods and beverages, non-dairy and non-fermented foods such as fresh fruits and vegetables, feces of breast-fed infants and human breast milk. The probiotics that are used in both humans and animals are selected in stages; after the initial isolation of the appropriate culture medium, the probiotics must meet important qualifications, including being non-pathogenic acid and bile-tolerant strains that possess the ability to act against pathogens in the gastrointestinal tract and the safety-enhancing property of not being able to transfer any antibiotic resistance genes to other bacteria. The final stages of selection involve the accurate identification of the probiotic species.
The glomerular basement membrane as a barrier to albumin

The glomerular basement membrane (GBM) is the central, non-cellular layer of the glomerular filtration barrier that is situated between the two cellular components—fenestrated endothelial cells and interdigitated podocyte foot processes. The GBM is composed primarily of four types of extracellular matrix macromolecule—laminin-521, type IV collagen α3α4α5, the heparan sulphate proteoglycan agrin, and nidogen—which produce an interwoven meshwork thought to impart both size-selective and charge-selective properties. Although the composition and biochemical nature of the GBM have been known for a long time, the functional importance of the GBM versus that of podocytes and endothelial cells for establishing the glomerular filtration barrier to albumin is still debated. Together with findings from genetic studies in mice, the discoveries of four human mutations affecting GBM components in two inherited kidney disorders, Alport syndrome and Pierson syndrome, support essential roles for the GBM in glomerular permselectivity. Here, we explain in detail the proposed mechanisms whereby the GBM can serve as the major albumin barrier and discuss possible approaches to circumvent GBM defects associated with loss of permselectivity.
Software defined mobile networks: concept, survey, and research directions

This article provides a brief overview on the current development of software-defined mobile networks (SDMNs). Software defined networking is seen as a promising technology to manage the complexity in communication networks. The need for SDMN comes from the complexity of network management in 5G mobile networks and beyond, driven by increasing mobile traffic demand, heterogeneous wireless environments, and diverse service requirements. The need is strong to introduce new radio network architecture by taking advantage of software oriented design, the separation of the data and control planes, and network virtualization to manage complexity and offer flexibility in 5G networks. Clearly, software oriented design in mobile networks will be fundamentally different from SDN for the Internet, because mobile networks deal with the wireless access problem in complex radio environments, while the Internet mainly addresses the packet forwarding problem. Specific requirements in mobile networks shape the development of SDMN. In this article we present the needs and requirements of SDMN, with particular focus on the software-defined design for radio access networks. We analyze the fundamental problems in radio access networks that call for SDN design and present an SDMN concept. We give a brief overview on current solutions for SDMN and standardization activities. We argue that although SDN design is currently focusing on mobile core networks, extending SDN to radio access networks would naturally be the next step. We identify several research directions on SDN for radio access networks and expect more fundamental studies to release the full potential of software-defined 5G networks.
Impact of malaria morbidity on gross domestic product in Uganda

The burden of malaria is a key challenge to both human and economic development in malaria endemic countries. The impact of malaria can be categorized from three dimensions, namely: health, social and economic. The objective of this study was to estimate the impact of malaria morbidity on gross domestic product (GDP) of Uganda.



The impact of malaria morbidity on GDP of Uganda was estimated using double-log econometric model. The 1997-2003 time series macro-data used in the analysis were for 28 quarters, i.e. 7 years times 4 quarters per year. It was obtained from national and international secondary sources.



The slope coefficient for Malaria Index (M) was -0.00767; which indicates that when malaria morbidity increases by one unit, while holding all other explanatory variables constant, per capita GDP decreases by US$0.00767 per year. In 2003 Uganda lost US$ 49,825,003 of GDP due to malaria morbidity. Dividing the total loss of US$49.8 million by a population of 25,827,000 yields a loss in GDP of US$1.93 per person in Uganda in 2003.



Malaria morbidity results in a substantive loss in GDP of Uganda. The high burden of malaria leads to decreased long-term economic growth, and works against poverty eradication efforts and socioeconomic development of the country.
Hartstone Benchmark User's Guide, Version 1.0.

The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange. Use of any other trademarks in this report is not intended in any way to infringe on the rights of the trademark holder.  The Hartstone benchmark is a set of timing requirements for testing a system's ability to handle hard real-time applications. It is specified as a set of processes with well-defined workloads and timing constraints. The name Hartstone derives from HArd Real Time and the fact that the workloads are presently based on the well-known Whetstone benchmark. This report describes the structure and behavior of an implementation in the Ada programming language of one category of Hartstone requirements , the Periodic Harmonic (PH) Test Series. The Ada implementation of the PH series is aimed primarily at real-time embedded processors where the only executing code is the benchmark and the Ada runtime system. Guidelines for performing various Hartstone experiments and interpreting the results are provided. Also included are the source code listings of the benchmark, information on how to obtain the source code in machine-readable form, and some sample results for Version 1.0 of the Systems Designers XD Ada VAX/VMS-MC68020 cross-compiler.
COTS-Aware Requirements Engineering and Software

At the heart of a well-disciplined, systematic methodology that explicitly supports the use of COTS components is a clearly defined process for effectively using components that meet the needs of the system under development. In this paper, we present the CARE/SA approach which supports the iterative matching, ranking, and selection of COTS components, using a representation of COTS components as an aggregate of their functional and non-functional requirements and architecture. The approach is illustrated using a Digital Library System example. 1 This is an extended and improved version of ; this extension considers both functional and non-functional requirements as candidates for the matching, ranking, and selection process.
Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algorithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.
Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory

With the popularity of social network-based services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. Device-to-device (D2D) communication, as a promising solution to overcome wireless spectrum crisis, can enable fast content delivery based on user activities in social networks. In this paper, we address the content delivery problem related to optimization of peer discovery and resource allocation by combining both the social and physical layer information in D2D underlay networks. The social relationship, which is modeled as the probability of selecting similar contents and estimated by using the Bayesian nonparametric models, is used as a weight to characterize the impact of social features on D2D pair formation and content sharing. Next, we propose a 3-D iterative matching algorithm to maximize the sum rate of D2D pairs weighted by the intensity of social relationships while guaranteeing the quality of service requirements of both cellular and D2D links simultaneously. Moreover, we prove that the proposed algorithm converges to a stable matching and is weak Pareto optimal, and also provide the theoretical complexity. Simulation results show that the algorithm is able to achieve more than 90% of the optimum performance with a computation complexity 1000 times lower than the exhaustive matching algorithm. It is also demonstrated that the satisfaction performance of D2D receivers can be increased significantly by incorporating social relationships into the resource allocation design.
Resonant Switches - A Unified Approach to Improve Performances of Switching Converters

In searching for a general "zero-current-Switching" technique for DC-DC converters, the concept of resonant switches is developed. As a combination of switching device and LC network, the resonant switch offers advantages of quasi-sinusoidal current waveforms, zero switching stresses, zero switching losses, self-commutation, and reduced EMI. Furthermore, application of the resonant switch concept to conventional converters leads to the discovery of a host of new converter circuits.
EasyMKL: a scalable multiple kernel learning algorithm

The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a target kernel machine. State-of-the-art methods of MKL have the drawback that the time required to solve the associated optimization problem grows (typically more than linearly) with the number of kernels to combine. Moreover, it has been empirically observed that even sophisticated methods often do not significantly outperform the simple average of kernels. In this paper, we propose a time and space efficient MKL algorithm that can easily cope with hundreds of thousands of kernels and more. The proposed method has been compared with other baselines (random, average, etc.) and three state-of-the-art MKL methods showing that our approach is often superior. We show empirically that the advantage of using the method proposed in this paper is even clearer when noise features are added. Finally, we have analyzed how our algorithm changes its performance with respect to the number of examples in the training set and the number of kernels combined. & 2015 Elsevier B.V. All rights reserved.
First language acquisition differs from second language acquisition in prelingually deaf signers: Evidence from sensitivity to grammaticality judgement in British Sign Language

Age of acquisition (AoA) effects have been used to support the notion of a critical period for first language acquisition. In this study, we examine AoA effects in deaf British Sign Language (BSL) users via a grammaticality judgment task. When English reading performance and nonverbal IQ are factored out, results show that accuracy of grammaticality judgement decreases as AoA increases, until around age 8, thus showing the unique effect of AoA on grammatical judgement in early learners. No such effects were found in those who acquired BSL after age 8. These late learners appear to have first language proficiency in English instead, which may have been used to scaffold learning of BSL as a second language later in life.
Micro Actions and Deep Static Features for Activity Recognition

A complex activity is a temporal composition of sub-events, and a sub-event typically consists of several low level micro-actions, such as body movement of different actors. Extracting these micro actions explicitly is beneficial for complex activity recognition due to actor selectivity, higher discriminative power, and motion clutter suppression. Moreover, considering both static and motion features is vital for activity recognition. However, optimally controlling the contribution from static and motion features still remains uninvestigated. In this work, we extract motion features at micro level, preserving the actor identity, to later obtain a high-level motion descriptor using a probabilistic model. Furthermore, we propose two novel schemas for combining static and motion features: Cholesky-transformation based and entropy-based. The former allows to control the contribution ratio precisely, while the latter obtains the optimal ratio mathematically. The ratio given by the entropy based method matches well with the experimental values obtained by the Choleksy transformation based method. This analysis also provides the ability to characterize a dataset, according to its richness in motion information. Finally, we study the effectiveness of modeling the temporal evolution of sub-event using an LSTM network. Experimental results demonstrate that the proposed technique outperforms state-of-the-art, when tested against two popular datasets.