nils-herrmann/clean_scirepeval huggingface dataset:
State Monitoring in Cloud Datacenters

Monitoring global states of a distributed cloud application is a critical functionality for cloud datacenter management. State monitoring requires meeting two demanding objectives: high level of correctness, which ensures zero or low error rate, and high communication efficiency, which demands minimal communication cost in detecting state updates. Most existing work follows an instantaneous model which triggers state alerts whenever a constraint is violated. This model may cause frequent and unnecessary alerts due to momentary value bursts and outliers. Countermeasures of such alerts may further cause problematic operations. In this paper, we present a WIndow-based StatE monitoring (WISE) framework for efficiently managing cloud applications. Window-based state monitoring reports alerts only when state violation is continuous within a time window. We show that it is not only more resilient to value bursts and outliers, but also able to save considerable communication when implemented in a distributed manner based on four technical contributions. First, we present the architectural design and deployment options for window-based state monitoring with centralized parameter tuning. Second, we develop a new distributed parameter tuning scheme enabling WISE to scale to much more monitoring nodes as each node tunes its monitoring parameters reactively without global information. Third, we introduce two optimization techniques, including their design rationale, correctness and usage model, to further reduce the communication cost. Finally, we provide an in-depth empirical study of the scalability of WISE, and evaluate the improvement brought by the distributed tuning scheme and the two performance optimizations. Our results show that WISE reduces communication by 50-90 percent compared with instantaneous monitoring approaches, and the improved WISE gains a clear scalability advantage over its centralized version.
Challenges for efficient communication in underwater acoustic sensor networks

Ocean bottom sensor nodes can be used for oceanographic data collection, pollution monitoring, offshore exploration and tactical surveillance applications. Moreover, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with sensors, will find application in exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater Networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the underwater channel is characterized. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed at all layers of the protocol stack. Furthermore, open research issues are discussed and possible solution approaches are outlined.
Discovery of convoys in trajectory databases

As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for reallife applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the wellknown filter-refinement framework. In the filter step, we apply linesimplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper’s proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.
A Theory of Destructive Entrepreneurship October 2007

Policy interest since the early 1980s has focused in different ways on the creation of a large, productive, taxable economy – in which entrepreneurship plays a role for employment, income growth and innovation. The current understanding of various forms of entrepreneurship remains incomplete, focusing largely on productive and unproductive entrepreneurship. However, destructive entrepreneurship plays an important role in many, if not most, economies. This paper addresses the conceptual gap in the allocation of entrepreneurship by proposing a theory of destructive entrepreneurship.
On Poisson Graphical Models

Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables. These standard instances, however, are ill-suited to modeling count data, which are increasingly ubiquitous in big-data settings such as genomic sequencing data, user-ratings data, spatial incidence data, climate studies, and site visits. Existing classes of Poisson graphical models, which arise as the joint distributions that correspond to Poisson distributed node-conditional distributions, have a major drawback: they can only model negative conditional dependencies for reasons of normalizability given its infinite domain. In this paper, our objective is to modify the Poisson graphical model distribution so that it can capture a rich dependence structure between count-valued variables. We begin by discussing two strategies for truncating the Poisson distribution and show that only one of these leads to a valid joint distribution. While this model can accommodate a wider range of conditional dependencies, some limitations still remain. To address this, we investigate two additional novel variants of the Poisson distribution and their corresponding joint graphical model distributions. Our three novel approaches provide classes of Poisson-like graphical models that can capture both positive and negative conditional dependencies between count-valued variables. One can learn the graph structure of our models via penalized neighborhood selection, and we demonstrate the performance of our methods by learning simulated networks as well as a network from microRNA-sequencing data.
How to learn a graph from smooth signals

We propose a framework that learns the graph structure underlying a set of smooth signals. Given X ∈ Rm×n whose rows reside on the vertices of an unknown graph, we learn the edge weights w ∈ R + under the smoothness assumption that tr ( X>LX ) is small. We show that the problem is a weighted `-1 minimization that leads to naturally sparse solutions. We point out how known graph learning or construction techniques fall within our framework and propose a new model that performs better than the state of the art in many settings. We present efficient, scalable primal-dual based algorithms for both our model and the previous state of the art, and evaluate their performance on artificial and real data.
Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.
Auxiliary Tasks in Multi-task Learning

Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust representation that generalizes well to different atomic tasks. We extend this concept by adding auxiliary tasks, which are of minor relevance for the application, to the set of learned tasks. As a kind of additional regularization, they are expected to boost the performance of the ultimately desired main tasks. To study the proposed approach, we picked vision-based road scene understanding (RSU) as an exemplary application. Since multi-task learning requires specialized datasets, particularly when using extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU, called synMT. More than 2.5 · 105 synthetic images, annotated with 21 different labels, were acquired from the video game Grand Theft Auto V (GTA V). Our proposed deep multi-task CNN architecture was trained on various combination of tasks using synMT. The experiments confirmed that auxiliary tasks can indeed boost network performance, both in terms of final results and training time.
Folded cascode OTA design for wide band applications

This paper deals with design and optimization of a folded cascode operational transconductance amplifier. First, a detailed description of an optimum OTA topology is done in order to optimize MOS transistor sizing. Second, the design of folded cascode OTA, which works for frequencies that lead to a base band circuit design for RF application, is based on transistor sizing methodology. Third, folded cascode OTAs generally find several applications that are well developed. Simulation results are performed using SPICE software and BSIM3V3 model for CMOS 0.35mum process, show that the designed folded cascode OTA has a 85dB DC gain and provides a gain bandwidth product of around 332MHz
Implementing deep neural networks for financial market prediction on the Intel Xeon Phi

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to financial market prediction has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. A critical step in the viability of the approach in practice is the ability to effectively deploy the algorithm on general purpose high performance computing infrastructure. Using an Intel Xeon Phi co-processor with 61 cores, we describe the process for efficient implementation of the batched stochastic gradient descent algorithm and demonstrate a 11.4x speedup on the Intel Xeon Phi over a serial implementation on the Intel Xeon.
Robust rank aggregation for gene list integration and meta-analysis

The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task.



Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings.


AVAILABILITY
All the methods are implemented as a GNU R package RobustRankAggreg, freely available at the Comprehensive R Archive Network http://cran.r-project.org/.
Gist: A Solver for Probabilistic Games

Gist is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides the first and efficient implementations of several reduction-based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications.
An IoT based reference architecture for smart water management processes

Water is a vital resource for life, and its management is a key issue nowadays. Information and communications technology systems for water control are currently facing interoperability problems due to the lack of support of standardization in monitory and control equipment. This problem affects various processes in water management, such as water consumption, distribution, system identification and equipment maintenance. OPC UA (Object Linking and Embedding for Process Control Unified Architecture) is a platform independent service-oriented architecture for the control of processes in the logistic and manufacturing sectors. Based on this standard we propose a smart water management model combining Internet of Things technologies with business processes coordination and decision support systems. We provide an architecture for sub-system interaction and a detailed description of the physical scenario in which we will test our implementation, allowing specific vendor equipment to be manageable and interoperable in the specific context of water management processes.
Spectral regression: a unified subspace learning framework for content-based image retrieval

Relevance feedback is a well established and effective framework for narrowing down the gap between low-level visual features and high-level semantic concepts in content-based image retrieval. In most of traditional implementations of relevance feedback, a distance metric or a classifier is usually learned from user's provided negative and positive examples. However, due to the limitation of the user's feedbacks and the high dimensionality of the feature space, one is often confront with the issue of the curse of the dimensionality. Recently, several researchers have considered manifold ways to address this issue, such as Locality Preserving Projections, Augmented Relation Embedding, and Semantic Subspace Projection. In this paper, by using techniques from spectral graph embedding and regression, we propose a unified framework, called spectral regression, for learning an image subspace. This framework facilitates the analysis of the differences and connections between the algorithms mentioned above. And more crucially, it provides much faster computation and therefore makes the retrieval system capable of responding to the user's query more efficiently.
The Fast-Forward Planning System

planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS’00) planning systems competition. Like the well-known HSP system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to HSP and evaluates their benefits in terms of run-time and solution-length behavior.
Measuring Urban Social Diversity Using Interconnected Geo-Social Networks

Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.
Question Asking During Tutoring

Whereas it is well documented that student question asking is infrequent in classroom environments, there is little research on questioning processes during tutoring. The present study investigated the questions asked in tutoring sessions on research methods (college students) and algebra (7th graders). Student questions were approximately 240 times as frequent in tutoring settings as classroom settings, whereas tutor questions were only slightly more frequent than teacher questions. Questions were classified by (a) degree of specification , (b) content, and (c) question-generation mechanism to analyze their quality. Student achievement was positively correlated with the quality of student questions after students had some experience with tutoring, but the frequency of questions was not correlated with achievement. Students partially self-regulated their learning by identifying knowledge deficits and asking questions to repair them, but they need training to improve these skills. We identified some ways that tutors and teachers might improve their question-asking skills. specializations are discourse processes and questioning mechanisms.
User experience design and agile development: managing cooperation through articulation work

Previous discussions of how User Experience (UX) designers and Agile developers can work together have focused on bringing the disciplines together by merging their processes or adopting specific techniques. This paper reports in detail on one observational study of a mature Scrum team in a large organization, and their interactions with the UX designers working on the same project. The evidence from our study shows that Agile development and UX design practice is not explained by rationalized accounts dealing with processes or techniques. Instead, understanding practice requires examining the wider organizational setting in which the Agile developers and UX designers are embedded. Our account focuses on the situatedness of the work by making reference to values and assumptions in the organizational setting, and the consequences that those values and assumptions had for practice. In this organizational setting, cooperation between the Agile developers and UX designers was achieved through ongoing articulation work by the developers, who were compelled to engage a culturally distinct UX design division. Based on this study, insights into culture, self-organization and purposeful work highlight significant implications for practice.
Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior

Work in behavior-based systems focuses on functional modeling, that is, the synthesis of life-like and/or biologically inspired behavior that is robust, repeatable and adaptive. Inspiration from cognitive science, neuroscience and biology drives the development of new methods and models in behavior-based robotics, and the results tie together several related fields including artificial life, evolutionary computation, and multi-agent systems. Ideas from artificial intelligence and engineering continue to be explored actively and applied to behavior-based robots as their role in animal modeling and practical applications is being developed.
Differential pattern of functional brain plasticity after compassion and empathy training.

Although empathy is crucial for successful social interactions, excessive sharing of others' negative emotions may be maladaptive and constitute a source of burnout. To investigate functional neural plasticity underlying the augmentation of empathy and to test the counteracting potential of compassion, one group of participants was first trained in empathic resonance and subsequently in compassion. In response to videos depicting human suffering, empathy training, but not memory training (control group), increased negative affect and brain activations in anterior insula and anterior midcingulate cortex-brain regions previously associated with empathy for pain. In contrast, subsequent compassion training could reverse the increase in negative effect and, in contrast, augment self-reports of positive affect. In addition, compassion training increased activations in a non-overlapping brain network spanning ventral striatum, pregenual anterior cingulate cortex and medial orbitofrontal cortex. We conclude that training compassion may reflect a new coping strategy to overcome empathic distress and strengthen resilience.
Different models for model matching: An analysis of approaches to support model differencing

Calculating differences between models is an important and challenging task in Model Driven Engineering. Model differencing involves a number of steps starting with identifying matching model elements, calculating and representing their differences, and finally visualizing them in an appropriate way. In this paper, we provide an overview of the fundamental steps involved in the model differencing process and summarize the advantages and shortcomings of existing approaches for identifying matching model elements. To assist potential users in selecting one of the existing methods for the problem at stake, we investigate the trade-offs these methods impose in terms of accuracy and effort required to implement each one of them.