nils-herrmann/clean_scirepeval huggingface dataset:
Microservices approach for the internet of things
Venue: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

The microservice approach has created a hype in the domain of cloud and enterprise application business. Before, grown, monolithic, software has been pushed to the limits of maintainability and scalability. The microservice architecture approach utilizes the service oriented architecture together with best practices and recent developments in software virtualization to overcome those issues. One monolithic application is split up into a set of distributed services. Those are strongly decoupled to enable high maintainability and scalability. In this case an application is split up in a top down manner. In the internet of things, applications need to be put together from a set of small and independent services. Thus, creating value added services would require to freely combine services of different vendors to fully make use of the IoT's heterogeneity. Even though the direction is different, many of the requirements in microservices are similar to those of the internet of things. This paper investigates patterns and best practices that are used in the microservices approach and how they can be used in the internet of things. Since the companies using microservices have made considerations on how services have to be designed to work together properly, IoT applications might adopt several of these design decisions to improve the ability to create value added applications from a multitude of services.
Accelerated deep neural networks for enhanced Intrusion Detection System
Venue: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

Network based communication is more vulnerable to outsider and insider attacks in recent days due to its wide spread applications in many fields. Intrusion Detection System (IDS) a software application or a hardware is a security mechanism that is able to monitor network traffic and find abnormal activities in the network. Machine learning techniques which have an important role in detecting the attacks were mostly used in the development of IDS. Due to huge increase in network traffic and different types of attacks, monitoring each and every packet in the network traffic is time consuming and computational intensive. Deep learning acts as a powerful tool by which thorough packet inspection and attack identification is possible. The parallel computing capabilities of the neural network make the Deep Neural Network (DNN) to effectively look through the network traffic with an accelerated performance. In this paper an accelerated DNN architecture is developed to identify the abnormalities in the network data. NSL KDD dataset is used to compute the training time and to analyze the effectiveness of the detection mechanism.
Basic emotion processing and the adolescent brain: Task demands, analytic approaches, and trajectories of changes
Venue: Developmental Cognitive Neuroscience

Early neuroimaging studies suggested that adolescents show initial development in brain regions linked with emotional reactivity, but slower development in brain structures linked with emotion regulation. However, the increased sophistication of adolescent brain research has made this picture more complex. This review examines functional neuroimaging studies that test for differences in basic emotion processing (reactivity and regulation) between adolescents and either children or adults. We delineated different emotional processing demands across the experimental paradigms in the reviewed studies to synthesize the diverse results. The methods for assessing change (i.e. analytical approach) and cohort characteristics (e.g. age range) were also explored as potential factors influencing study results. Few unifying dimensions were found to successfully distill the results of the reviewed studies. However, this review highlights the potential impact of subtle methodological and analytic differences between studies, need for standardized and theory driven experimental paradigms, and necessity of analytic approaches that are can adequately test the trajectories of developmental change that have recently been proposed. Recommendations for future research highlight connectivity analyses and non linear developmental trajectories, which appear to be promising approaches for measuring change across adolescence. Recommendations are made for evaluating gender and biological markers of development beyond chronological age.
Accelerated Generative Models for 3D Point Cloud Data
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Finding meaningful, structured representations of 3D point cloud data (PCD) has become a core task for spatial perception applications. In this paper we introduce a method for constructing compact generative representations of PCD at multiple levels of detail. As opposed to deterministic structures such as voxel grids or octrees, we propose probabilistic subdivisions of the data through local mixture modeling, and show how these subdivisions can provide a maximum likelihood segmentation of the data. The final representation is hierarchical, compact, parametric, and statistically derived, facilitating run time occupancy calculations through stochastic sampling. Unlike traditional deterministic spatial subdivision methods, our technique enables dynamic creation of voxel grids according the application's best needs. In contrast to other generative models for PCD, we explicitly enforce sparsity among points and mixtures, a technique which we call expectation sparsification. This leads to a highly parallel hierarchical Expectation Maximization (EM) algorithm well suited for the GPU and real time execution. We explore the trade offs between model fidelity and model size at various levels of detail, our tests showing favorable performance when compared to octree and NDT based methods.
Goblet cells of the conjunctiva: A review of recent findings
Venue: Progress in Retinal and Eye Research

Goblet cells within the conjunctival epithelium are specialized cells that secrete mucins onto the surface of the eye. Recent research has demonstrated new characteristics of the cells, including factors influencing their differentiation, their gene products and their functions at the ocular surface. The following review summarizes the newly discovered aspects of the role of Spdef, a member of the Ets transcription factor family in conjunctival goblet cell differentiation, the newly discovered goblet cell products including claudin2, the Wnt inhibitor Frzb, and the transmembrane mucin Muc16. The current concepts of conjunctival goblet cell function, including debris removal and immune surveillance are reviewed, as are changes in the goblet cell population in ocular surface diseases. Major remaining questions regarding conjunctival cell biology are discussed.
mlrMBO: A Toolbox for Model Based Optimization of Expensive Black Box Functions

Many practical optimization tasks, such as finding best parameters for simulators in engineering or hyperparameter optimization in machine learning, are of a black box nature, i.e. neither formulas of the objective nor derivative information is available. Instead, we can only query the box for its objective value at a given point. If such a query is very time consuming, the optimization task becomes extremely challenging, as we have to operate under a severely constrained budget of function evaluations. A modern approach is sequential model based optimization, aka Bayesian optimization. Here, a surrogate regression model learns the relationship between decision variables and objective outcome. Sequential point evaluations are planned to simultaneously exploit the so far learnt functional landscape and to ensure exploration of the search space. A popular instance of this general principle is the EGO algorithm, which uses Gaussian processes coupled with the expected improvement criterion for point proposal. The mlrMBO package offers a rich interface to many variants of model based optimization. As it builds upon the mlr package for machine learning in R, arbitrary surrogate regression models can be applied. It offers a wide variety of options to tackle different black box scenarios: Optimization of pure continuous as well as mixed continuous categorical search spaces. Single criteria optimization or approximated Pareto fronts for multi criteria problems. Single point proposal or parallel batch point planning during optimization. The package is designed as a convenient, easy to use toolbox of popular state ofthe art algorithms, but can also be used as as a research framework for algorithm designers.
A Sequential Decision Formulation of the Interface Card Model for Interactive IR
Venue: SIGIR

The Interface Card model is a promising new theoretical framework for modeling and optimizing interactive retrieval interfaces, but how to systematically instantiate it to solve concrete interface optimization problems remains an open challenge. We propose a novel formulation of the Interface Card model based on sequential decision theory, leading to a general framework for formal modeling of user states and stopping actions. The proposed framework naturally connects optimization of interactive retrieval with Markov Decision Processes and Partially Observable Markov Decision Processes, and enables the use of reinforcement learning algorithms for optimizing interactive retrieval interfaces. Simulation and user study experiments demonstrate the effectiveness of the proposed model in automatically adjusting the interface layout in adaptation to inferred user stopping tendencies in addition to user interaction and screen size.
From Idea to Reality: Extensive and Executable Modeling Language for Mobile Learning Games
Venue: EC TEL

Mobile Learning Games (MLGs) show great potential for education, especially in fields that deal with outdoor learning activities such as archaeology or botany. However, the number of MLGs currently used remains insignificant. This is partly due to the fact that the current authoring tools are based on modeling languages that only allow creating very specific and rigid types of MLGs. In this paper, we therefore propose an extensive modeling language for MLGs. This model was designed, with the help of botanical experts, in order to cover the variety of MLG types they would like for their field trips. This modeling language uses high level concepts, such as game activities and points of interest on a map that can therefore be used by teachers in any domain. Finally, we discuss how scenarios, described with this language can be automatically transformed into executable web applications.
Bacteriocins of lactic acid bacteria: extending the family
Venue: Applied Microbiology and Biotechnology

Lactic acid bacteria (LAB) constitute a heterogeneous group of microorganisms that produce lactic acid as the major product during the fermentation process. LAB are Gram positive bacteria with great biotechnological potential in the food industry. They can produce bacteriocins, which are proteinaceous antimicrobial molecules with a diverse genetic origin, posttranslationally modified or not, that can help the producer organism to outcompete other bacterial species. In this review, we focus on the various types of bacteriocins that can be found in LAB and the organization and regulation of the gene clusters responsible for their production and biosynthesis, and consider the food applications of the prototype bacteriocins from LAB. Furthermore, we propose a revised classification of bacteriocins that can accommodate the increasing number of classes reported over the last years.
Salient object detection based on boundary contrast with regularized manifold ranking
Venue: 2016 12th World Congress on Intelligent Control and Automation (WCICA)

Salient object detection via graph based manifold ranking, which exploits the boundary prior by using image boundaries as labelled background queries, always achieves impressive performance. However, when the salient object broadly touches the image boundary, this method is fragile and may fail. To address this issue, we present a novel approach which bases on boundary contrast with regularized manifold ranking. First, we compute the contrast saliency against the image boundary as ranking queries, instead of directly using the boundaries as background queries. Second, we use an affinity matrix with regularization for manifold ranking to infer saliency value. Third, we integrate saliency inference result with foregroundness based on boundary connectivity to improve the detection accuracy. Last, we adopt multiscale method to mitigate the object scale effect in saliency detection. Experimental results on three benchmark datasets show that the proposed method achieves comparable or better performance than stat of the art methods.
Cramer Rao Bounds for Underdetermined Source Localization
Venue: IEEE Signal Processing Letters

Although Cramer Rao Bounds (CRB) for direction of arrival (DOA) estimation have been extensively studied for decades, existing results are mainly applicable when there are fewer sources than sensors. In contrast, this letter considers an underdetermined signal model (more sources than sensors) and investigates conditions under which CRB exist. Necessary and sufficient conditions are derived for the associated Fisher information matrix to be nonsingular, which in turn, leads to closed form expressions for the CRBs for underdetermined DOA estimation. These conditions highlight crucial roles played by the array geometry, as well as the correlation between source signals. The CRB for different array geometries are numerically compared both in the overdetermined and underdetermined settings.
RAO* An Algorithm for Chance Constrained POMDP's
Venue: AAAI

Autonomous agents operating in partially observable stochastic environments often face the problem of optimizing expected performance while bounding the risk of violating safety constraints. Such problems can be modeled as chance constrained POMDP's (CC POMDP's) Our first contribution is a systematic derivation of execution risk in POMDP domains, which improves upon how chance constraints are handled in the constrained POMDP literature. Second, we present RAO* a heuristic forward search algorithm producing optimal, deterministic, finite horizon policies for CC POMDP's. In addition to the utility heuristic, RAO* leverages an admissible execution risk heuristic to quickly detect and prune overly risky policy branches. Third, we demonstrate the usefulness of RAO* in two challenging domains of practical interest: power supply restoration and autonomous science agents.
Implementation and performance evaluation of IEEE 802.15.4 unslotted CSMA/CA protocol on Contiki OS
Venue: Ann. des Telecommunications

In the wireless sensor networks domain, one of the most used standards is IEEE 802.15.4. This standard has been made available on many low power operating systems such as TinyOS and Contiki OS. It is crucial for the implementation to be compliant with the specifications of the standard. In the case of Contiki OS, the provided version of the main medium access algorithm, unslotted Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) presents many flaws. In this paper, we discuss the implementation of the provided CSMA/CA algorithm and we point out to the parts that do not respect the standard specifications. We also propose and implement a compliant version of this algorithm and show through simulation the correctness of the implementation.
Business Models for Interoperable IoT Ecosystems
Venue: InterOSS@IoT

The Internet of Things (IoT) is growing and more and more devices, so called "things" are being connected every day. IoT platforms provide access to those "things" and make them available for services and applications. Today, a broad range of such IoT platforms exist with differing functional foci, target domains, and interfaces. However, to fully exploit the economic impact of the IoT, it is essential to enable applications to interoperate with the various IoT platforms. The BIG IoT project aims at enabling this interoperability and supporting the creation of vibrant IoT ecosystems, which facilitate the development of cross platform and cross domain applications. While the value of interoperability for the overall economy is well understood and cannot be underestimated, some stakeholders may still need to find their business value in interoperable IoT ecosystems. Thus, this paper identifies the different stakeholders of such ecosystems, and analyses how these stakeholders can enhance their existing business models when taking part in an interoperable IoT ecosystem.
A Case Study of Formative Assessment to Support Teaching of Reading Comprehension for English Learners

Reading comprehension and related processes form the foundation for most of the academic work one does in school. To support the teaching and learning of reading skills for English learners (ELs) teachers seek guidance, resources and intervention programs (Callahan, 2013; Gandara, Maxwell Jolly, Driscoll, 2005; Rivera, Moughamian, Lesaux, Francis, 2008; Teachers of English to Speakers of Other Languages [TESOL] 2010; Walqui Heritage, 2012) Educators express a need for understanding how to measure EL reading comprehension progress, tailor successful reading instruction, support academic language and content learning, and support overall student success (TESOL, 2010) Reading assessments for formative purposes are argued to be a promising approach for addressing the challenges teachers face, and supporting EL reading in particular (Heritage, 2008, 2012) Though formative assessment is well supported in the literature, there has been little empirical work done on its use with ELs (Bailey Carroll, 2015; Black Wiliam, 1998, Heritage, 2008; Gijbels, Dochy, 2006;  2015; Walqui Heritage, 2012; Wei, 2010) This paper describes the design, development, and piloting of a formative measurement system to support EL reading comprehension in the middle grades, the English Learner Formative Assessment (ELFA) system. In particular, this paper reports on a small scale usability study where the ELFA system was used by eight middle school teachers supporting ELs. We aim to share the lessons we learned during the development and trial of ELFA formative assessment materials for future development and effective implementation of formative assessment for ELs. Our specific research questions and research design are described in the Current Study section.
Image Restoration Using Group based Sparse Representation Technique

This paper presents an efficient algorithm for solving restoration problem in the frame based image restoration. In image restoration, the patch based approach has been used for better results without degrading the image. Our proposed procedure for solving the stable optimal problem is based on a patched group strategy method. In this paper GSR (Group Based Sparse Representation) algorithm is proposed which is based on concept that group of patches are constructed which maintains the relationship among various patches of images and it is implemented for three image restoration problems i.e. DE noising, DE blurring and Image enhancement. Various parameters are taken into account like PSNR, SSIM, speed and time. The simulation experiments have been conducted in MATLAB and the results have been compared with existing scheme i.e. patch based sparse representation. Both standard and real time images have been included in simulation. It has been observed that after the reconstruction of an image values of parameters are increased which are more than existing scheme.
Group based sparse representation for low lighting image enhancement
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

The Group based Sparse Representation (GSR) is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self similarity of images simultaneously in a unified framework. And the GSR driven L0 minimization method for image restoration has been proposed. This paper expands the application of GSR from image restoration to low lighting image enhancement. The GSR is not used to represent the natural images anymore, but representing the transmission map of the haze image and recovering it. Because the transmission map is very important for the low lighting image enhancement, the dark channel prior based enhancement method with the enhanced transmission map can get a better enhanced results. Different from other methods, we evaluate the quality of the enhanced images not only by qualitative analysis but also by quantitative results. Extensive experiments on low lighting image show that the GSR based method gets a better enhancement result than many current state of the art ones.
The Study of Polysemy in the Framework of Cognitive Semantics in Azerbaijani Turkish

The aim of the present study is to investigate the role of conceptual metaphor in the polysemy of the visual verb of /baxmaq/ (look) in Azerbaijani Turkish within the framework of cognitive semantics. The research method is a descriptive analytic and the study tries to present the various meanings of the verb. To achieve this goal, the linguistic data was gathered by using Azerbaijani Turkish dictionaries, books, magazines, newspapers, and one of the authors' linguistic intuitions. The findings indicate that for visual verb /baxmaq/ there are 19 senses with regard to its classification and abstracting away of contextual variations. They also show that the polysemy is a systematic phenomenon and factor such as metaphor and metonymy are crucially involved in the formation and description of polysemy and this makes broad meaning and eventually leads to polysemy of verb. In this way, semantic extensive network of verb is formed and on the other hand, it is claimed that metaphor has stronger role than metonymy in the formation of polysemy and usage of verb in the different context. The findings also show that polysemy is not randomly and forms based on our experiences of different concepts and the meanings of verbs are different from each other because they are understood from different ways.
Improved Contacts to MoS2 Transistors by Ultra High Vacuum Metal Deposition.
Venue: Nano letters

The scaling of transistors to sub 10 nm dimensions is strongly limited by their contact resistance (RC) Here we present a systematic study of scaling MoS2 devices and contacts with varying electrode metals and controlled deposition conditions, over a wide range of temperatures (80 to 500 K) carrier densities (10(12) to 10(13) cm( 2) and contact dimensions (20 to 500 nm) We uncover that Au deposited in ultra high vacuum ~10( 9) Torr) yields three times lower RC than under normal conditions, reaching 740 O*mm and specific contact resistivity 3 x 10( 7) O*cm(2) stable for over four months. Modeling reveals separate RC contributions from the Schottky barrier and the series access resistance, providing key insights on how to further improve scaling of MoS2 contacts and transistor dimensions. The contact transfer length is ~35 nm at 300 K, which is verified experimentally using devices with 20 nm contacts and 70 nm contact pitch (CP) equivalent to the "14 nm" technology node.