nils-herrmann/clean_scirepeval huggingface dataset:
Aneka Cloud Application Platform and Its Integration with Windows Azure

Aneka is an Application Platform-as-a-Service (Aneka PaaS) for Cloud Computing. It acts as a framework for building customized applications and deploying them on either public or private Clouds. One of the key features of Aneka is its support for provisioning resources on different public Cloud providers such as Amazon EC2, Windows Azure and GoGrid. In this chapter, we will present Aneka platform and its integration with one of the public Cloud infrastructures, Windows Azure, which enables the usage of Windows Azure Compute Service as a resource provider of Aneka PaaS. The integration of the two platforms will allow users to leverage the power of Windows Azure Platform for Aneka Cloud Computing, employing a large number of compute instances to run their applications in parallel. Furthermore, customers of the Windows Azure platform can benefit from the integration with Aneka PaaS by embracing the advanced features of Aneka in terms of multiple programming models, scheduling and management services, application execution services, accounting and pricing services and dynamic provisioning services. Finally, in addition to the Windows Azure Platform we will illustrate in this chapter the integration of Aneka PaaS with other public Cloud platforms such as Amazon EC2 and GoGrid, and virtual machine management platforms such as Xen Server. The new support of provisioning resources on Windows Azure once again proves the adaptability, extensibility and flexibility of Aneka.
Generative networks as inverse problems with Scattering transforms

Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be optimized with a discriminator or an encoder. The embedding is Lipschitz continuous to deformations so that generators transform linear interpolations between input white noise vectors into deformations between output images. This embedding is computed with a wavelet Scattering transform. Numerical experiments demonstrate that the resulting Scattering generators have similar properties as GANs or VAEs, without learning a discriminative network or an encoder.
uRank: Exploring Document Recommendations through an Interactive User-Driven Approach

Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.
Variational Message Passing

Bayesian inference is now widely established as one of the pr inci al foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniques have been developed, one of the most widely used being a deter ministic framework called variational inference. In this paper we introduce Variational Me ssage Passing (VMP), a general purpose algorithm for applying variational inference to Bayesian N etworks. Like belief propagation, VMP proceeds by sending messages between nodes in the network an d updating posterior beliefs using local operations at each node. Each such update increase s a lower bound on the log evidence (unless already at a local maximum). In contrast to belief pr opagation, VMP can be applied to a very general class of conjugate-exponential models becaus it es a factorised variational approximation. Furthermore, by introducing additional variatio nal parameters, VMP can be applied to models containing non-conjugate distributions. The VMP fr amework also allows the lower bound to be evaluated, and this can be used both for model compariso n and for detection of convergence. Variational message passing has been implemented in the for m of a general purpose inference engine called VIBES (‘Variational Inference for BayEsian net workS’) which allows models to be specified graphically and then solved variationally withou t recourse to coding.
Best practice recommendations for data screening

Survey respondents differ in their levels of attention and effort when responding to items. There are a number of methods researchers may use to identify respondents who fail to exert sufficient effort in order to increase the rigor of analysis and enhance the trustworthiness of study results. Screening techniques are organized into three general categories, which differ in impact on survey design and potential respondent awareness. Assumptions and considerations regarding appropriate use of screening techniques are discussed along with descriptions of each technique. The utility of each screening technique is a function of survey design and administration. Each technique has the potential to identify different types of insufficient effort. An example dataset is provided to illustrate these differences and familiarize readers with the computation and implementation of the screening techniques. Researchers are encouraged to consider data screening when designing a survey, select screening techniques on the basis of theoretical considerations (or empirical considerations when pilot testing is an option), and report the results of an analysis both before and after employing data screening techniques.
A Methodology for Collecting Valid Software Engineering Data

An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. The data to be collected are based on the changes made to the software during development, and are obtained when the changes are made. To ensure accuracy of the data, validation is performed concurrently with software development and data collection. Validation is based on interviews with those people supplying the data. Results from using the methodology show that data validation is a necessary part of change data collection. Without it, as much as 50 percent of the data may be erroneous. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments. The application showed that the methodology was both feasible and useful.
Digital Material Fabrication Using Mask-Image-Projection-based Stereolithography

The purpose of this paper is to present a mask-image-projection-based Stereolithography process that can combine two base materials with various concentrations and structures to produce a solid object with desired material characteristics. Stereolithography is an additive manufacturing process in which liquid photopolymer resin is cross-linked and converted to solid. The fabrication of digital material requires frequent resin changes during the building process. The process presented in this paper attempts to address the related challenges in achieving such fabrication capability.  A two-channel system design is presented for the multi-material mask-imageprojection-based Stereolithography process. In such a design, a coated thick film and linear motions in two axes are used to reduce the separation force of a cured layer. The material cleaning approach to thoroughly remove resin residue on built surfaces is presented for the developed process. Based on a developed testbed, experimental studies were conducted to verify the effectiveness of the presented process on digital material fabrication.  The proposed two-channel system can reduce the separation force of a cured layer by an order of magnitude in the bottom-up projection system. The developed two-stage cleaning approach can effectively remove resin residue on built surfaces. Several multi-material designs have been fabricated to highlight the capability of the developed multi-material mask-image-projection-based Stereolithography process.  A proof-of-concept testbed has been developed. Its building speed and accuracy can be further improved. Our tests were limited to the same type of liquid resins. In addition, the removal of trapped air is a challenge in the presented process.  This paper presents a novel and a pioneering approach towards digital material fabrication based on the Stereolithography process. This research contributes to the additive manufacturing development by significantly expanding the selection of base materials in fabricating solid objects with desired material characteristics.
DFuse: a framework for distributed data fusion

Simple in-network data aggregation (or fusion) techniques for sensor networks have been the focus of several recent research efforts, but they are insufficient to support advanced fusion applications. We extend these techniques to future sensor networks and ask two related questions: (a) what is the appropriate set of data fusion techniques, and (b) how do we dynamically assign aggregation roles to the nodes of a sensor network. We have developed an architectural framework, DFuse, for answering these two questions. It consists of a data fusion API and a distributed algorithm for energy-aware role assignment. The fusion API enables an application to be specified as a coarse-grained dataflow graph, and eases application development and deployment. The role assignment algorithm maps the graph onto the network, and optimally adapts the mapping at run-time using role migration. Experiments on an iPAQ farm show that, the fusion API has low-overhead, and the role assignment algorithm with role migration significantly increases the network lifetime compared to any static assignment.
Animal-assisted therapy for persons with aphasia: A pilot study.

This study explored the effects and effectiveness of animal-assisted therapy (AAT) for persons with aphasia. Three men with aphasia from left-hemisphere strokes participated in this study. The men received one semester of traditional therapy followed by one semester of AAT. While both therapies were effective, in that each participant met his goals, no significant differences existed between test results following traditional speech-language therapy versus AAT. Results of a client-satisfaction questionnaire, however, indicated that each of the participants was more motivated, enjoyed the therapy sessions more, and felt that the atmosphere of the sessions was lighter and less stressed during AAT compared with traditional therapy.
Prediction Policy Problems.

Empirical policy research often focuses on causal inference. Since policy choices seem to depend on understanding the counterfactual–what happens with and without a policy–this tight link of causality and policy seems natural. While this link holds in many cases, we argue that there are also many policy applications where causal inference is not central, or even necessary. Consider two toy examples. One policy maker facing a drought must decide whether to invest in a rain dance to increase the chance of rain. Another seeing clouds must deciding whether to take an umbrella to work to avoid getting wet on the way home? Both decisions could benefit from an empirical duty of rain. But each has different requirements of the estimator. One requires causality: do rain dances cause rain? The other does not, needing only prediction: is the chance of rain high enough to merit an umbrella? We often focus on rain dance like policy problems. But there are also many umbrella-like policy problems. Not only are these prediction problems neglected, machine learning can help us solve them more effectively. In this paper, we (i) provide a simple
Development of working memory maintenance.

The neural circuitry supporting mature visual spatial working memory (VSWM) has been well delineated in nonhuman primates and in human adults. However, we still have limited understanding about developmental change through adolescence in this network. We present results from a fast event-related functional MRI (fMRI) study aimed at characterizing developmental changes in brain mechanisms supporting VSWM across different delay periods. Forty-three healthy subjects (17 adults, 18-30 yr; 13 adolescents, 13-17 yr; 13 children, 8-12 yr) were scanned as they performed an oculomotor delayed response (ODR) task with short (2.5 s) and long (10 s) delay period trials. Results showed that all age groups recruited a common network of regions to support both delay trials, including frontal, parietal, and temporal regions, indicative of a core circuitry needed to perform the task. Several age-related differences were found in the recruitment of regions, supporting short delay trials, including fronto-caudal areas, which could contribute to known differences in initial memory-guided saccade precision. To support extended delay trials, adults primarily recruited additional posterior parietal cortex (PPC), whereas children and adolescents recruited a considerably more extensive distributed circuitry. Our findings indicate that brain processes supporting basic aspects of working memory across cortex are established by childhood. We also find evidence for continued immaturities in systems supporting working memory precision, reflected by differences in the circuitry recruited by children and by continued refinement of fronto-insular-temporal regions recruited by adolescents. Taken together, these results suggest distinct developmental changes in the circuitry supporting visual spatial working memory.
Challenges and prospects of lithium-sulfur batteries.

Electrical energy storage is one of the most critical needs of 21st century society. Applications that depend on electrical energy storage include portable electronics, electric vehicles, and devices for renewable energy storage from solar and wind. Lithium-ion (Li-ion) batteries have the highest energy density among the rechargeable battery chemistries. As a result, Li-ion batteries have proven successful in the portable electronics market and will play a significant role in large-scale energy storage. Over the past two decades, Li-ion batteries based on insertion cathodes have reached a cathode capacity of ∼250 mA h g(-1) and an energy density of ∼800 W h kg(-1), which do not meet the requirement of ∼500 km between charges for all-electric vehicles. With a goal of increasing energy density, researchers are pursuing alternative cathode materials such as sulfur and O2 that can offer capacities that exceed those of conventional insertion cathodes, such as LiCoO2 and LiMn2O4, by an order of magnitude (>1500 mA h g(-1)). Sulfur, one of the most abundant elements on earth, is an electrochemically active material that can accept up to two electrons per atom at ∼2.1 V vs Li/Li(+). As a result, sulfur cathode materials have a high theoretical capacity of 1675 mA h g(-1), and lithium-sulfur (Li-S) batteries have a theoretical energy density of ∼2600 W h kg(-1). Unlike conventional insertion cathode materials, sulfur undergoes a series of compositional and structural changes during cycling, which involve soluble polysulfides and insoluble sulfides. As a result, researchers have struggled with the maintenance of a stable electrode structure, full utilization of the active material, and sufficient cycle life with good system efficiency. Although researchers have made significant progress on rechargeable Li-S batteries in the last decade, these cycle life and efficiency problems prevent their use in commercial cells. To overcome these persistent problems, researchers will need new sulfur composite cathodes with favorable properties and performance and new Li-S cell configurations. In this Account, we first focus on the development of novel composite cathode materials including sulfur-carbon and sulfur-polymer composites, describing the design principles, structure and properties, and electrochemical performances of these new materials. We then cover new cell configurations with carbon interlayers and Li/dissolved polysulfide cells, emphasizing the potential of these approaches to advance capacity retention and system efficiency. Finally, we provide a brief survey of efficient electrolytes. The Account summarizes improvements that could bring Li-S technology closer to mass commercialization.
Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area?

Haxby et al.  recently argued that category-related responses in the ventral temporal (VT) lobe during visual object identification were overlapping and distributed in topography. This observation contrasts with prevailing views that object codes are focal and localized to specific areas such as the fusiform and parahippocampal gyri. We provide a critical test of Haxby's hypothesis using a neural network (NN) classifier that can detect more general topographic representations and achieves 83% correct generalization performance on patterns of voxel responses in out-of-sample tests. Using voxel-wise sensitivity analysis we show that substantially the same VT lobe voxels contribute to the classification of all object categories, suggesting the code is combinatorial. Moreover, we found no evidence for local single category representations. The neural network representations of the voxel codes were sensitive to both category and superordinate level features that were only available implicitly in the object categories.
A robust adaptive stochastic gradient method for deep learning

Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose an adaptive learning rate algorithm, which utilizes stochastic curvature information of the loss function for automatically tuning the learning rates. The information about the element-wise curvature of the loss function is estimated from the local statistics of the stochastic first order gradients. We further propose a new variance reduction technique to speed up the convergence. In our experiments with deep neural networks, we obtained better performance compared to the popular stochastic gradient
Statistical compression-based models for text classification

Text classification is the task of assigning predefined categories to text documents. It is a common machine learning problem. Statistical text classification that makes use of machine learning methods to learn classification rules are particularly known to be successful in this regard. In this research project we are trying to re-invent the text classification problem with a sound methodology based on statistical data compression technique-the Minimum Message Length (MML) principle. To model the data sequence we have used the Probabilistic Finite State Automata (PFSAs). We propose two approaches for text classification using the MML-PFSAs. We have tested both the approaches with the Enron spam dataset and the results of our empirical evaluation has been recorded in terms of the well known classification measures i.e. recall, precision, accuracy and error. The results indicate good classification accuracy that can be compared with the state of art classifiers.
How Does Cyber Crime Affect Firms? The Effect of Information Security Breaches on Stock Returns

A widely debated issue in recent years is cyber crime. Breaches in security of accessibility, integrity and confidentiality of information involve potentially high explicit and implicit costs for firms. This paper investigates the impact of information security breaches on stock returns. Using event-study methodology, we provide empirical evidence on the effect of announcements of cyber attacks on the market value of firms from 1995 to 2015. We show that substantial negative market returns occur following announcements of cyber attacks. We find that financial entities often suffer greater negative effects than other companies. We also find that non-confidential cyber attacks are the most dangerous, especially for the financial sector. Our results seem to show a link between cyber crime and insider trading.
Problem Frames - Analysing and Structuring Software Development Problems

pptx File Size: 436 kb Problem Frames : Analysing and Structuring Software Development Problems File Type: pptx Download File. bundles lying on the floor Problem Frames : Analysing and Structuring Software Development Problems next to him. 9  Analysing and Structuring Software Development Problems DecemberFirst Baptist Church of Alexandria's annual Special Friends. Alexandra Burke teams skintight trousers with snakeskin print top after arriving home Problem Frames : Analysing and Structuring Software Development Problems I think the white community should Problem Frames : Analysing and Structuring Software Development Problems take about 3 percent of the white male population and disguise them as black men. 01 Gopinath, Munisamy 01 Gopal, Ram D. Problem Frames : Analysing and Structuring Software Development Problems Alfa Romeo Spyder 1971-1994 Convertible top Problem Frames : Analysing and Structuring Software Development Problems OEM Cloth Non Padded Quarters. Farage thinks the EU is headed for a centralized dictatorship in Europe with little Problem Frames : Analysing and Structuring Software Development Problems
Multi-source Neural Automatic Post-Editing: FBK's participation in the WMT 2017 APE shared task

Previous phrase-based approaches to Automatic Post-editing (APE) have shown that the dependency of MT errors from the source sentence can be exploited by jointly learning from source and target information. By integrating this notion in a neural approach to the problem, we present the multi-source neural machine translation (NMT) system submitted by FBK to the WMT 2017 APE shared task. Our system implements multi-source NMT in a weighted ensemble of 8 models. The n-best hypotheses produced by this ensemble are further re-ranked using features based on the edit distance between the original MT output and each APE hypothesis, as well as other statistical models (n-gram language model and operation sequence model). This solution resulted in the best system submission for this round of the APE shared task for both en-de and de-en language directions. For the former language direction, our primary submission improves over the MT baseline up to -4.9 TER and +7.6 BLEU points. For the latter, where the higher quality of the original MT output reduces the room for improvement, the gains are lower but still significant (-0.25 TER and +0.3 BLEU).