nils-herrmann/clean_scirepeval huggingface dataset:
Image Segmentation by Clustering Methods: Performance Analysis

Image segmentation plays a significant role in computer vision. It aims at extracting meaningful objects lying in the image. Generally there is no unique method or approach for image segmentation. Clustering is a powerful technique that has been reached in image segmentation. The cluster analysis is to partition an image data set into a number of disjoint groups or clusters. The clustering methods such as k means, improved k mean, fuzzy c mean (FCM) and improved fuzzy c mean algorithm (IFCM) have been proposed. K means clustering is one of the popular method because of its simplicity and computational efficiency. The number of iterations will be reduced in improved K compare to conventional K means. FCM algorithm has additional flexibility for the pixels to belong to multiple classes with varying degrees of membership. Demerit of conventional FCM is time consuming which is overcome by improved FCM. The experimental results exemplify that the proposed algorithms yields segmented gray scale image of perfect accuracy and the required computer time reasonable and also reveal the improved fuzzy c mean achieve better segmentation compare to others. The quality of segmented image is measured by statistical parameters: rand index (RI), global consistency error (GCE), variations of information (VOI) and boundary displacement error (BDE).
Global supply chain design : A literature review and critique

In this paper, we review decision support models for the design of global supply chains, and assess the fit between the research literature in this area and the practical issues of global supply chain design. The classification scheme for this review is based on ongoing and emerging issues in global supply chain management and includes review dimensions for (1) decisions addressed in the model, (2) performance metrics, (3) the degree to which the model supports integrated decision processes, and (4) globalization considerations. We conclude that although most models resolve a difficult feature associated with globalization, few models address the practical global supply chain design problem in its entirety. We close the paper with recommendations for future research in global supply chain modeling that is both forward-looking and practically oriented.
Compliance by design for artifact-centric business processes

Compliance to legal regulations, internal policies, or best practices is becoming a more and more important aspect in business processes management. Compliance requirements are usually formulated in a set of rules that can be checked during or after the execution of the business process, called compliance by detection. If noncompliant behavior is detected, the business process needs to be redesigned. Alternatively, the rules can be already taken into account while modeling the business process to result in a business process that is compliant by design. This technique has the advantage that a subsequent verification of compliance is not required. This paper focuses on compliance by design and employs an artifactcentric approach. In this school of thought, business processes are not described as a sequence of tasks to be performed (i. e., imperatively), but from the point of view of the artifacts that are manipulated during the process (i. e., declaratively). We extend the artifact-centric approach to model compliance rules and show how compliant business processes can be synthesized automatically.
Middleware to support sensor network applications

Current trends in computing include increases in both distribution and wireless connectivity, leading to highly dynamic, complex environments on top of which applications must be built. The task of designing and ensuring the correctness of applications in these environments is similarly becoming more complex. The unified goal of much of the research in distributed wireless systems is to provide higher-level abstractions of complex low-level concepts to application programmers, easing the design and implementation of applications. A new and growing class of applications for wireless sensor networks require similar complexity encapsulation. However, sensor networks have some unique characteristics, including dynamic availability of data sources and application quality of service requirements, that are not common to other types of applications. These unique features, combined with the inherent distribution of sensors, and limited energy and bandwidth resources, dictate the need for network functionality and the individual sensors to be controlled to best serve the application requirements. In this article, we describe different types of sensor network applications and discuss existing techniques for managing these types of networks. We also overview a variety of related middleware and argue that no existing approach provides all the management tools required by sensor network applications. To meet this need, we have developed a new middleware called MiLAN. MiLAN allows applications to specify a policy for managing the network and sensors, but the actual implementation of this policy is effected within MiLAN. We describe MiLAN and show its effectiveness through the design of a sensor-based personal health monitor.
Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources

We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer. The method constructs a textual representation of the semantic content of an image, and merges it with textual information sourced from a knowledge base, to develop a deeper understanding of the scene viewed. Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach. We are specifically able to answer questions posed in natural language, that refer to information not contained in the image. We demonstrate the effectiveness of our model on two publicly available datasets, Toronto COCO-QA [23] and VQA  and show that it produces the best reported results in both cases.
PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks

Similarity search is a primitive operation in database and Web search engines. With the advent of large-scale heterogeneous information networks that consist of multi-typed, interconnected objects, such as the bibliographic networks and social media networks, it is important to study similarity search in such networks. Intuitively, two objects are similar if they are linked by many paths in the network. However, most existing similarity measures are defined for homogeneous networks. Different semantic meanings behind paths are not taken into consideration. Thus they cannot be directly applied to heterogeneous networks. In this paper, we study similarity search that is defined among the same type of objects in heterogeneous networks. Moreover, by considering different linkage paths in a network, one could derive various similarity semantics. Therefore, we introduce the concept of meta path-based similarity, where a meta path is a path consisting of a sequence of relations defined between different object types (i.e., structural paths at the meta level). No matter whether a user would like to explicitly specify a path combination given sufficient domain knowledge, or choose the best path by experimental trials, or simply provide training examples to learn it, meta path forms a common base for a network-based similarity search engine. In particular, under the meta path framework we define a novel similarity measure called PathSim that is able to find peer objects in the network (e.g., find authors in the similar field and with similar reputation), which turns out to be more meaningful in many scenarios compared with random-walk based similarity measures. In order to support fast online query processing for PathSim queries, we develop an efficient solution that partially materializes short meta paths and then concatenates them online to compute top-k results. Experiments on real data sets demonstrate the effectiveness and efficiency of our proposed paradigm.
Trait-based Language for Smart Cyber-Physical Systems

The problem this paper aims to target is how to hoist the cooperation of software components, acting as autonomous agents and forming coalitions, at the architectural level in smart cyber-physical systems (sCPS). This is a hard problem as coalitions can be overlapping, nested, and dynamically formed and dismantled based on several criteria. To target this issue, we propose and implement an architecture description language (TCOF-ADL) based on Scala internal DSL, that describes architecture and formation of dynamic coalitions of components. To raise the level of expressivity, we introduce the concept of domain-specific extensions (traits) of the core TCOF-ADL to reflect different concerns—such as movement in a 2D map, state-space modeling of physical processes, statistical reasoning about uncertainty. This allows configuring the ADL for the needs of a specific application case and facilitates reuse. To evaluate our approach, we show how it can be beneficially used in addressing the coordination of agents within the RoboCup Rescue Simulation League. This work was partially supported by the project no. LD15051 from COST CZ (LD) programme by the Ministry of Education, Youth and Sports of the Czech Republic. Trait-based Language for Smart Cyber-Physical Systems Tomas Bures1, Ilias Gerostathopoulos2, Petr Hnetynka1, Frantisek Plasil1, Filip Krijt1, Jiri Vinarek1, Jan Kofron1 1 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic {surname}@d3s.mff.cuni.cz 2 Fakultät für Informatik, Technische Universität München, Munich, Germany
Health consequences of obesity in youth: childhood predictors of adult disease.

Obesity now affects one in five children in the United States. Discrimination against overweight children begins early in childhood and becomes progressively institutionalized. Because obese children tend to be taller than their nonoverweight peers, they are apt to be viewed as more mature. The inappropriate expectations that result may have an adverse effect on their socialization. Many of the cardiovascular consequences that characterize adult-onset obesity are preceded by abnormalities that begin in childhood. Hyperlipidemia, hypertension, and abnormal glucose tolerance occur with increased frequency in obese children and adolescents. The relationship of cardiovascular risk factors to visceral fat independent of total body fat remains unclear. Sleep apnea, pseudotumor cerebri, and Blount's disease represent major sources of morbidity for which rapid and sustained weight reduction is essential. Although several periods of increased risk appear in childhood, it is not clear whether obesity with onset early in childhood carries a greater risk of adult morbidity and mortality. Obesity is now the most prevalent nutritional disease of children and adolescents in the United States. Although obesity-associated morbidities occur more frequently in adults, significant consequences of obesity as well as the antecedents of adult disease occur in obese children and adolescents. In this review, I consider the adverse effects of obesity in children and adolescents and attempt to outline areas for future research. I refer to obesity as a body mass index greater than the 95th percentile for children of the same age and gender.
Understanding persistence in the use of Online Fitness Communities: Comparing novice and experienced users

Mobile and wearable technologies facilitate physiological data collection for health and wellness purposes. Users typically access these data via Online Fitness Community (OFC) platforms (e.g., Fitbit, Strava, RunKeeper). These platforms present users with functionalities centered on self-monitoring, social networking and enjoyment. In order to fully benefit from these functionalities, users need to make a habit out of integrating OFC use into their everyday workout routines. However, research suggests that users often fail to use OFCs over a longer period of time. This study sheds light on the factors that explain persisted OFC use. To that end, the study compares novice and experienced users in terms of their OFC use motives and how these motives contribute to the habitual integration of OFCs into everyday workout routines. Based on the survey responses of 394 OFC users, a multi-sample structural equation model indicates that self-regulatory and social motives directly predict habitual OFC use, and that enjoyment and self-regulatory motives indirectly predict habitual OFC use, by driving the perceived usefulness of OFCs. Moderation analysis revealed that, for novice users, self-regulatory motives are the prime drivers of habitual OFC use, while social motives and enjoyment are more important for experienced users.
Feature-based brain MRI retrieval for Alzheimer disease diagnosis

In this paper we consider the application of the feature-based approach to medical image retrieval, particularly brain MRI scans for early Alzheimer's disease diagnosis. The key idea is to provide the doctor with the images which have similar visual properties and have full case record, giving the ability to make more informed decision in the prodromal phase of the disease. With regard to the state-of-the art SIFT features in a Bag-of-Visual-Words approach we propose to use the Laguerre Circular Harmonic Functions coefficients as feature vectors. An additional pre-classification step based on estimation of Alzheimer's disease early image abnormalities is proposed to improve overall precision.
HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection

We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON. The algorithm works by inducing the Markov Blanket of the variable to be classified or predicted. A wide variety of biomedical tasks with different characteristics were used for an empirical evaluation. Namely, (i) bioactivity prediction for drug discovery, (ii) clinical diagnosis of arrhythmias, (iii) bibliographic text categorization, (iv) lung cancer diagnosis from gene expression array data, and (v) proteomics-based prostate cancer detection. State-of-the-art algorithms for each domain were selected for baseline comparison.



(1) HITON reduces the number of variables in the prediction models by three orders of magnitude relative to the original variable set while improving or maintaining accuracy. (2) HITON outperforms the baseline algorithms by selecting more than two orders-of-magnitude smaller variable sets than the baselines, in the selected tasks and datasets.
Ridge Regression, Hubness, and Zero-Shot Learning

This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step. Assuming a simple data model, we prove that the proposed approach indeed reduces hubness. This was verified empirically on the tasks of bilingual lexicon extraction and image labeling: hubness was reduced with both of these tasks and the accuracy was improved accordingly.
An Empirical Study on Detecting and Fixing Buffer Overflow Bugs

Buffer overflow is one of the most common types of software security vulnerabilities. Although researchers have proposed various static and dynamic techniques for buffer overflow detection, buffer overflow attacks against both legacy and newly-deployed software systems are still quite prevalent. Compared with dynamic detection techniques, static techniques are more systematic and scalable. However, there are few studies on the effectiveness of state-of-the-art static buffer overflow detection techniques. In this paper, we perform an in-depth quantitative and qualitative study on static buffer overflow detection. More specifically, we obtain both the buggy and fixed versions of 100 buffer overflow bugs from 63 real-world projects totalling 28 MLoC (Millions of Lines of Code) based on the reports in Common Vulnerabilities and Exposures (CVE). Then, quantitatively, we apply Fortify, Checkmarx, and Splint to all the buggy versions to investigate their false negatives, and also apply them to all the fixed versions to investigate their false positives. We also qualitatively investigate the causes for the false-negatives and false-positives of studied techniques to guide the design and implementation of more advanced buffer overflow detection techniques. Finally, we also categorized the patterns of manual buffer overflow repair actions to guide automated repair techniques for buffer overflow. The experiment data is available at
Dimensionality Reduction by Learning an Invariant Mapping

Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar" points in input space are mapped to nearby points on the manifold. We present a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE.
Detecting Singleton Review Spammers Using Semantic Similarity

Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews. Prior works on the opinion spam problem mostly considered classifying fake reviews using behavioral user patterns. They focused on prolific users who write more than a couple of reviews, discarding one-time reviewers. The number of singleton reviewers however is expected to be high for many review websites. While behavioral patterns are effective when dealing with elite users, for one-time reviewers, the review text needs to be exploited. In this paper we tackle the problem of detecting fake reviews written by the same person using multiple names, posting each review under a different name. We propose two methods to detect similar reviews and show the results generally outperform the vectorial similarity measures used in prior works. The first method extends the semantic similarity between words to the reviews level. The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases. The experiments were conducted on reviews from three different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset (800 reviews).
Analysis and implementation of software rejuvenation in cluster systems

Several recent studies have reported the phenomenon of "software aging", one in which the state of a software system degrades with time. This may eventually lead to performance degradation of the software or crash/hang failure or both. "Software rejuvenation" is a pro-active technique aimed to prevent unexpected or unplanned outages due to aging. The basic idea is to stop the running software, clean its internal state and restart it. In this paper, we discuss software rejuvenation as applied to cluster systems. This is both an innovative and an efficient way to improve cluster system availability and productivity. Using Stochastic Reward Nets (SRNs), we model and analyze cluster systems which employ software rejuvenation. For our proposed time-based rejuvenation policy, we determine the optimal rejuvenation interval based on system availability and cost. We also introduce a new rejuvenation policy based on prediction and show that it can dramatically increase system availability and reduce downtime cost. These models are very general and can capture a multitude of cluster system characteristics, failure behavior and performability measures, which we are just beginning to explore. We then briefly describe an implementation of a software rejuvenation system that performs periodic and predictive rejuvenation, and show some empirical data from systems that exhibit aging
Low cost high performance uncertainty quantification

Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost which quickly becomes intractable with the current explosion of data sizes. In this work we reduce this complexity to quadratic with the synergy of two algorithms that gracefully complement each other and lead to a radically different approach. First, we turned to stochastic estimation of the diagonal. This allowed us to cast the problem as a linear system with a relatively small number of multiple right hand sides. Second, for this linear system we developed a novel, mixed precision, iterative refinement scheme, which uses iterative solvers instead of matrix factorizations. We demonstrate that the new framework not only achieves the much needed quadratic cost but in addition offers excellent opportunities for scaling at massively parallel environments. We based our implementation on BLAS 3 kernels that ensure very high processor performance. We achieved a peak performance of 730 TFlops on 72 BG/P racks, with a sustained performance 73% of theoretical peak. We stress that the techniques presented in this work are quite general and applicable to several other important applications.
A Computational Framework for Modelling and Analyzing Ice Storms

Ice storms are extreme weather events that can have devastating implications for the sustainability of natural ecosystems as well as man made infrastructure. Ice storms are caused by a complex mix of atmospheric conditions and are among the least understood of severe weather events. Our ability to model ice storms and characterize storm features will go a long way towards both enabling support systems that offset storm impacts and increasing our understanding of ice storms. In this paper, we present a holistic computational framework to answer key questions of interest about ice storms. We model ice storms as a function of relevant surface and atmospheric variables. We learn these models by adapting and applying supervised and unsupervised machine learning algorithms on data with missing or incorrect labels. We also include a knowledge representation module that reasons with domain knowledge to revise the output of the learned models. Our models are trained using reanalysis data and historical records of storm events. We evaluate these models on reanalyis data as well as Global Climate Model (GCM) data for historical and future climate change scenarios. Furthermore, we discuss the use of appropriate bias correction approaches to run such modeling frameworks with GCM data.