nils-herrmann/clean_scirepeval huggingface dataset:
Attribute-based data access control in mobile cloud computing: Taxonomy and open issues

With the thriving growth of the cloud computing, the security and privacy concerns of outsourcing data have been increasing dramatically. However, because of delegating the management of data to an untrusted cloud server in data outsourcing process, the data access control has been recognized as a challenging issue in cloud storage systems. One of the preeminent technologies to control data access in cloud computing is Attribute-based Encryption (ABE) as a cryptographic primitive, which establishes the decryption ability on the basis of a user’s attributes. This paper provides a comprehensive survey on attribute-based access control schemes and compares each scheme’s functionality and characteristic. We also present a thematic taxonomy of attribute-based approaches based on significant parameters, such as access control mode, architecture, revocation mode, revocation method, revocation issue, and revocation controller. The paper reviews the state-of-the-art ABE methods and categorizes them into three main classes, such as centralized, decentralized, and hierarchal, based on their architectures. We also analyzed the different ABE techniques to ascertain the advantages and disadvantages, the significance and requirements, and identifies the research gaps. Finally, the paper presents open issues and challenges for further investigations.
angiogenesis in mouse skeletal muscle Voluntary running induces fiber type-specific

, November 1, 2007; 103 (5): 1815-1823.  M. J. Lyon, L. M. Steer and L. T. Malmgren human posterior cricoarytenoid muscle Stereological estimates indicate that aging does not alter the capillary length density in the [PDF]  [ , July 1, 2008; 295 (1): C288-C292.  promoter in response to [PDF]  [ , December 15, 2008; 586 (24): 6021-6035.  F. Holmes and T. P. Gavin necessary for the angiogenic response to exercise AMPK regulates basal skeletal muscle capillarization and VEGF expression, but is not [PDF]  [ , May 1, 2009; 106 (5): 1660-1667. J Appl Physiol L. E. Wong, T. Garland Jr., S. L. Rowan and R. T. Hepple mice Anatomic capillarization is elevated in the medial gastrocnemius muscle of mighty mini [PDF]  [ , June 1, 2009; 94 (6): 749-760. Exp Physiol M. H. Malek and I. M. Olfert exercise capacity in mice Global deletion of thrombospondin-1 increases cardiac and skeletal muscle capillarity and
Evaluation of lightweight wheelchairs using ANSI/RESNA testing standards.

Lightweight wheelchairs are characterized by their low cost and limited range of adjustment. Our study evaluated three different folding lightweight wheelchair models using the American National Standards Institute/Rehabilitation Engineering Society of North America (ANSI/RESNA) standards to see whether quality had improved since the previous data were reported. On the basis of reports of increasing breakdown rates in the community, we hypothesized that the quality of these wheelchairs had declined. Seven of the nine wheelchairs tested failed to pass the multidrum test durability requirements. An average of 194,502 +/- 172,668 equivalent cycles was completed, which is similar to the previous test results and far below the 400,000 minimum required to pass the ANSI/RESNA requirements. This was also significantly worse than the test results for aluminum ultralight folding wheelchairs. Overall, our results uncovered some disturbing issues with these wheelchairs and suggest that manufacturers should put more effort into this category to improve quality. To improve the durability of lightweight wheelchairs, we suggested that stronger regulations be developed that require wheelchairs to be tested by independent and certified test laboratories. We also proposed a wheelchair rating system based on the National Highway Transportation Safety Administration vehicle crash ratings to assist clinicians and end users when comparing the durability of different wheelchairs.
The NAIP-NLRC4 inflammasome in innate immune detection of bacterial flagellin and type III secretion apparatus.

Bacterial flagella and type III secretion system (T3SS) are evolutionarily related molecular transport machineries. Flagella mediate bacterial motility; the T3SS delivers virulence effectors to block host defenses. The inflammasome is a cytosolic multi-protein complex that activates caspase-1. Active caspase-1 triggers interleukin-1β (IL-1β)/IL-18 maturation and macrophage pyroptotic death to mount an inflammatory response. Central to the inflammasome is a pattern recognition receptor that activates caspase-1 either directly or through an adapter protein. Studies in the past 10 years have established a NAIP-NLRC4 inflammasome, in which NAIPs are cytosolic receptors for bacterial flagellin and T3SS rod/needle proteins, while NLRC4 acts as an adapter for caspase-1 activation. Given the wide presence of flagella and the T3SS in bacteria, the NAIP-NLRC4 inflammasome plays a critical role in anti-bacteria defenses. Here, we review the discovery of the NAIP-NLRC4 inflammasome and further discuss recent advances related to its biochemical mechanism and biological function as well as its connection to human autoinflammatory disease.
ROC curve analyses of eyewitness identification decisions: An analysis of the recent debate

How should the accuracy of eyewitness identification decisions be measured, so that best practices for identification can be determined? This fundamental question is under intense debate. One side advocates for continued use of a traditional measure of identification accuracy, known as the diagnosticity ratio, whereas the other side argues that receiver operating characteristic curves (ROCs) should be used instead because diagnosticity is confounded with response bias. Diagnosticity proponents have offered several criticisms of ROCs, which we show are either false or irrelevant to the assessment of eyewitness accuracy. We also show that, like diagnosticity, Bayesian measures of identification accuracy confound response bias with witnesses' ability to discriminate guilty from innocent suspects. ROCs are an essential tool for distinguishing memory-based processes from decisional aspects of a response; simulations of different possible identification tasks and response strategies show that they offer important constraints on theory development.
The clinical outcome of childhood masturbation.

This study was performed to investigate the clinical outcome of childhood masturbation. For this purpose 50 children (mean age = 48.7 +/- 24.5 months, 34 girls females and 16 boys males) with masturbation symptoms were examined at first visit to the Department of Child Psychiatry and two years thereafter with psychiatric interviews. The mean masturbation frequency at the initial interview was significantly decreased after two years. It was noted that 39 children (78%) were completely recovered and 11 children (22%) continued to masturbate after two years. Children who did not recover were significantly younger, began to masturbate earlier and masturbated more frequently than others at the time of initial evaluation. It was concluded that the findings about the beneficial effect of sedative drugs in combination with parental guidance, education and means for behavior modification were promising.
Integrating AI Techniques In SDLC: Design Phase Perspective

Software Development Life Cycle (SDLC) is a process consisting of various phases like requirements analysis, designing, coding, testing and implementation & maintenance of a software system as well as the way, in which these phases are implemented. Research studies reveal that the initial two phases, viz. requirements and design are the skeleton of the entire development life cycle. Designing has several sub-activities such as Architectural, Function-Oriented and Object- Oriented design, which aim to transform the requirements into detailed specifications covering all facets of the system in a proper way, but at the same time, there exists various related challenges too. One of the foremost challenges is the minimum interaction between construction and design teams causing numerous problems during design such as: production delays, incomplete designs, rework, change orders, etc. Prior research studies reveal that Artificial Intelligence (AI) techniques may eliminate these problems by offering several tools/techniques to automate certain processes up to a certain extent. In this paper, our major aim is to identify the challenges in each of the stages of the design phase and possibility of AI techniques to overcome these identified issues. In addition, the paper also explores the relationship between these issues and their possible AI solution/s through Venn-Diagram. For some of the issues, there exist more than one AI techniques but for some issues, no AI technique/s have been found to overcome the same and accordingly, those issues are still open for further research.
Hetero-Manifold Regularisation for Cross-Modal Hashing

Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. Furthermore, a novel cumulative distance inequality defined on the hetero-manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes. By using the inequality, cross-modal hashing is transformed into a problem of hetero-manifold regularised support vector learning. Therefore, the performance of cross-modal search can be significantly improved by seamlessly combining the integrated information of the hetero-manifold and the strong generalisation of the support vector machine. Comprehensive experiments show that the proposed HMR achieve advantageous results over the state-of-the-art methods in several challenging cross-modal tasks.
On causal mediation analysis with a survival outcome.

Suppose that having established a marginal total effect of a point exposure on a time-to-event outcome, an investigator wishes to decompose this effect into its direct and indirect pathways, also known as natural direct and indirect effects, mediated by a variable known to occur after the exposure and prior to the outcome. This paper proposes a theory of estimation of natural direct and indirect effects in two important semiparametric models for a failure time outcome. The underlying survival model for the marginal total effect and thus for the direct and indirect effects, can either be a marginal structural Cox proportional hazards model, or a marginal structural additive hazards model. The proposed theory delivers new estimators for mediation analysis in each of these models, with appealing robustness properties. Specifically, in order to guarantee ignorability with respect to the exposure and mediator variables, the approach, which is multiply robust, allows the investigator to use several flexible working models to adjust for confounding by a large number of pre-exposure variables. Multiple robustness is appealing because it only requires a subset of working models to be correct for consistency; furthermore, the analyst need not know which subset of working models is in fact correct to report valid inferences. Finally, a novel semiparametric sensitivity analysis technique is developed for each of these models, to assess the impact on inference, of a violation of the assumption of ignorability of the mediator.
Rospeex: A cloud robotics platform for human-robot spoken dialogues

To build conversational robots, roboticists are required to have deep knowledge of both robotics and spoken dialogue systems. Although they can use existing cloud services that were built for other services, e.g., voice search, it will be difficult to share robotics-specific speech corpora obtained as server logs, because they will get buried in non-robotics-related logs. Building a cloud platform especially for the robotics community will benefit not only individual robot developers but also the robotics community since we can share the log corpus collected by it. This is challenging because we need to build a wide variety of functionalities ranging from a stable cloud platform to high-quality multilingual speech recognition and synthesis engines. In this paper, we propose “rospeex,” which is a cloud robotics platform for multilingual spoken dialogues with robots. We analyze the logs we have collected by operating rospeex for more than a year. Our key contribution lies in building a cloud robotics platform and allowing the robotics community to use it without payment or authentication.
Generalizing Top Trading Cycles for Housing Markets with Fractional Endowments

The housing market setting constitutes a fundamental model of exchange economies of goods. In most of the work concerning housing markets, it is assumed that agents own and are allocated discrete houses. The drawback of this assumption is that it does not cater for randomized assignments or allocation of time-shares. Recently, house allocation with fractional endowment of houses was considered by Athanassoglou and Sethuraman (2011) who posed the open problem of generalizing Gale’s Top Trading Cycles (TTC) algorithm to the case of housing markets with fractional endowments. In this paper, we address the problem and present a generalization of TTC called FTTC that is polynomialtime as well as core stable and Pareto optimal with respect to stochastic dominance even if there are indifferences in the preferences. We prove that if each agent owns one discrete house, FTTC coincides with a state of the art strategyproof mechanism for housing markets with discrete endowments and weak preferences. We show that FTTC satisfies a maximal set of desirable properties by proving two impossibility theorems. Firstly, we prove that with respect to stochastic dominance, core stability and no justified envy are incompatible. Secondly, we prove that there exists no individual rational, Pareto optimal and weak strategyproof mechanism, thereby answering another open problem posed by Athanassoglou and Sethuraman (2011). The second impossibility implies a number of results in the literature.
A Systematic Mapping Study of Software Development With GitHub

GitHub, nowadays the most popular social coding platform, has become the reference for mining Open Source repositories, a growing research trend aiming at learning from previous software projects to improve the development of new ones. In the last years, a considerable amount of research papers have been published reporting findings based on data mined from GitHub. As the community continues to deepen in its understanding of software engineering thanks to the analysis performed on this platform, we believe that it is worthwhile to reflect on how research papers have addressed the task of mining GitHub and what findings they have reported.  The main objective of this paper is to identify the quantity, topic, and empirical methods of research works, targeting the analysis of how software development practices are influenced by the use of a distributed social coding platform like GitHub.  A systematic mapping study was conducted with four research questions and assessed 80 publications from 2009 to 2016.  Most works focused on the interaction around coding-related tasks and project communities. We also identified some concerns about how reliable were these results based on the fact that, overall, papers used small data sets and poor sampling techniques, employed a scarce variety of methodologies and/or were hard to replicate.  This paper attested the high activity of research work around the field of Open Source collaboration, especially in the software domain, revealed a set of shortcomings and proposed some actions to mitigate them. We hope that this paper can also create the basis for additional studies on other collaborative activities (like book writing for instance) that are also moving to GitHub.
MOG: Mapper on Graphs for Relationship Preserving Clustering

The interconnected nature of graphs often results in difficult to interpret clutter. Typically techniques focus on either decluttering by clustering nodes with similar properties or grouping edges with similar relationship. We propose using mapper, a powerful topological data analysis tool, to summarize the structure of a graph in a way that both clusters data with similar properties and preserves relationships. Typically, mapper operates on a given data by utilizing a scalar function defined on every point in the data and a cover for scalar function codomain. The output of mapper is a graph that summarize the shape of the space. In this paper, we outline how to use this mapper construction on an input graphs, outline three filter functions that capture important structures of the input graph, and provide an interface for interactively modifying the cover. To validate our approach, we conduct several case studies on synthetic and real world data sets and demonstrate how our method can give meaningful summaries for graphs with various
Energy-Efficient Hash Join Implementations in Hardware-Accelerated MPSoCs

Join is one of the most important operators in database query processing. Its research progressively focuses on hardware-conscious implementations since single-threaded performance improvements of general-purpose processors will slow down in the next years. SIMD extensions, multithreading as well as multi-core processors may further lead to performance advantages. Besides that, multiprocessor system-on-chips (MPSoCs) are a suitable platform to keep up with high-performance processors while providing an up to three orders of magnitude lower power consumption. In this paper, we study the implementation of hash join algorithms on MPSoCs and exemplarily employ the Tomahawk4 chip. Tomahawk4 integrates four processing modules each equipped with tightly-coupled SRAM as well as an instruction set extension tailored to hashing algorithms. An external DRAM serves as shared main memory and can be accessed by DMA transfers. We aim to best exploit the architecture and to adapt the algorithms to the MPSoC. Hence, we compare two hash table designs according to their memory accesses and investigate the performance impact of the additional hashing instructions. Furthermore, the MPSoC platform allows power measurements with different clock frequencies and supply voltages to find the configuration with highest energy savings. Our experiments on the MPSoC show that four database-specific cores outperform a standard RISC CPU by up to factor 5 while consuming less than 200 mW.
Topology optimization for galvanic coupled wireless intra-body communication

Implanted sensors and actuators in the human body promise in-situ health monitoring and rapid advancements in personalized medicine. We propose a new paradigm where such implants may communicate wirelessly through a technique called as galvanic coupling, which uses weak electrical signals and the conduction properties of body tissues. While galvanic coupling overcomes the problem of massive absorption of RF waves in the body, the unique intra-body channel raises several questions on the topology of the implants and the external (i.e., on skin) data collection nodes. This paper makes the first contributions towards (i) building an energy-efficient topology through optimal placement of data collection points/relays using measurement-driven tissue channel models, and (ii) balancing the energy consumption over the entire implant network so that the application needs are met. We achieve this via a two-phase iterative clustering algorithm for the implants and formulate an optimization problem that decides the position of external data-gathering points. Our theoretical results are validated via simulations and experimental studies on real tissues, with demonstrated increase in the network lifetime.
A new compact wide band 8-way SIW power divider at X-band

Ultra wideband components have been developed using SIW technology. The various components including a GCPW transition with less than 0.4dB insertion loss are developed. In addition to, T and Y-junctions are optimized with relatively wide bandwidth of greater than 63% and 40% respectively that have less than 0.6 dB insertion loss. The developed transition was utilized to design an X-band 8 way power divider that demonstrated excellent performance over a 5 GHz bandwidth with less than ±4º and ±0.9 dB phase and amplitude imbalance, respectively. The developed SIW power divider has a low profile and is particularly suitable for circuits' integration.
Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases

Kutato is a system that takes as input a database of cases and produces a belief network that captures many of the dependence relations represented by those data. This system incorporates a module for determining the entropy of a belief network and a module for constructing belief networks based on entropy calculations. Kutato constructs an initial belief network in which all variables in the database are assumed to be marginally independent. The entropy of this belief network is calculated, and that arc is added that minimizes the entropy of the resulting belief network. Conditional probabilities for an arc are obtained directly from the database. This process continues until an entropy-based threshold is reached. We have tested the system by generating databases from networks using the probabilistic logic-sampling method, and then using those databases as input to Kutato. The system consistently reproduces the original belief networks with high fidelity.
Occlusion-Aware 3D Morphable Models and an Illumination Prior for Face Image Analysis

Faces in natural images are often occluded by a variety of objects. We propose a fully automated, probabilistic and occlusion-aware 3D morphable face model adaptation framework following an analysis-by-synthesis setup. The key idea is to segment the image into regions explained by separate models. Our framework includes a 3D morphable face model, a prototype-based beard model and a simple model for occlusions and background regions. The segmentation and all the model parameters have to be inferred from the single target image. Face model adaptation and segmentation are solved jointly using an expectation–maximization-like procedure. During the E-step, we update the segmentation and in the M-step the face model parameters are updated. For face model adaptation we apply a stochastic sampling strategy based on the Metropolis–Hastings algorithm. For segmentation, we apply loopy belief propagation for inference in a Markov random field. Illumination estimation is critical for occlusion handling. Our combined segmentation and model adaptation needs a proper initialization of the illumination parameters. We propose a RANSAC-based robust illumination estimation technique. By applying this method to a large face image database we obtain a first empirical distribution of real-world illumination conditions. The obtained empirical distribution is made publicly available and can be used as prior in probabilistic frameworks, for regularization or to synthesize data for deep learning methods.