nils-herrmann/clean_scirepeval huggingface dataset:
Love and the commitment problem in romantic relations and friendship.

On the basis of the proposition that love promotes commitment, the authors predicted that love would motivate approach, have a distinct signal, and correlate with commitment-enhancing processes when relationships are threatened. The authors studied romantic partners and adolescent opposite-sex friends during interactions that elicited love and threatened the bond. As expected, the experience of love correlated with approach-related states (desire, sympathy). Providing evidence for a nonverbal display of love, four affiliation cues (head nods, Duchenne smiles, gesticulation, forward leans) correlated with self-reports and partner estimates of love. Finally, the experience and display of love correlated with commitment-enhancing processes (e.g., constructive conflict resolution, perceived trust) when the relationship was threatened. Discussion focused on love, positive emotion, and relationships.
BoRF: Loop-closure detection with scale invariant visual features

In this paper, we present a novel method for visual loop-closure detection in autonomous robot navigation. Our method, which we refer to as bag-of-raw-features or BoRF, uses scale-invariant visual features (such as SIFT) directly, rather than their vector-quantized representation or bag-of-words (BoW), which is popular in recent studies of the problem. BoRF avoids the offline process of vocabulary construction, and does not suffer from the perceptual aliasing problem of BoW, thereby significantly improving the recall performance. To reduce the computational cost of direct feature matching, we exploit the fact that images in the case of robot navigation are acquired sequentially, and that feature matching repeatability with respect to scale can be learned and used to reduce the number of the features considered for matching. The proposed method is tested experimentally using indoor visual SLAM image sequences.
A kernel density estimation method for networks, its computational method and a GIS-based tool

We develop a kernel density estimation method for estimating the density of points on a network and implement the method in the GIS environment. This method could be applied to, for instance, finding 'hot spots' of traffic accidents, street crimes or leakages in gas and oil pipe lines. We first show that the application of the ordinary two-dimensional kernel method to density estimation on a network produces biased estimates. Second, we formulate a 'natural' extension of the univariate kernel method to density estimation on a network, and prove that its estimator is biased; in particular, it overestimates the densities around nodes. Third, we formulate an unbiased discontinuous kernel function on a network, and fourth, an unbiased continuous kernel function on a network. Fifth, we develop computational methods for these kernels and derive their computational complexity. We also develop a plug-in tool for operating these methods in the GIS environment. Sixth, an application of the proposed methods to the density estimation of bag-snatches on streets is illustrated. Lastly, we summarize the major results and describe some suggestions for the practical use of the proposed methods.
Deep Learning of Transferable Representation for Scalable Domain Adaptation

Domain adaptation generalizes a learning model across source domain and target domain that are sampled from different distributions. It is widely applied to cross-domain data mining for reusing labeled information and mitigating labeling consumption. Recent studies reveal that deep neural networks can learn abstract feature representation, which can reduce, but not remove, the cross-domain discrepancy. To enhance the invariance of deep representation and make it more transferable across domains, we propose a unified deep adaptation framework for jointly learning transferable representation and classifier to enable scalable domain adaptation, by taking the advantages of both deep learning and optimal two-sample matching. The framework constitutes two inter-dependent paradigms, unsupervised pre-training for effective training of deep models using deep denoising autoencoders, and supervised fine-tuning for effective exploitation of discriminative information using deep neural networks, both learned by embedding the deep representations to reproducing kernel Hilbert spaces (RKHSs) and optimally matching different domain distributions. To enable scalable learning, we develop a linear-time algorithm using unbiased estimate that scales linearly to large samples. Extensive empirical results show that the proposed framework significantly outperforms state of the art methods on diverse adaptation tasks: sentiment polarity prediction, email spam filtering, newsgroup content categorization, and visual object recognition.
On Coresets for k-Median and k-Means Clustering in Metric and Euclidean Spaces and Their Applications

We present new approximation algorithms for the k-median and k-means clustering problems. To this end, we obtain small coresets for k-median and k-means clustering in general metric spaces and in Euclidean spaces. In Rd, these coresets are of size with polynomial dependency on the dimension d. This leads to (1 + ε)-approximation algorithms to the optimal k-median and kmeans clustering in Rd, with running time O(ndk +2(k/ε) O(1) d2 log n), where n is the number of points. This improves over previous results. We use those coresets to maintain a (1+ ε)-approximate k-median and k-means clustering of a stream of points in Rd, using O(d2k2ε−2 log n) space. These are the first streaming algorithms, for those problems, that have space complexity with polynomial dependency on the dimension.
Learning from imbalanced data: open challenges and future directions

Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.
Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.
Sentiment Aggregation using ConceptNet Ontology

Sentiment analysis of reviews traditionally ignored the association between the features of the given product domain. The hierarchical relationship between the features of a product and their associated sentiment that influence the polarity of a review is not dealt with very well. In this work, we analyze the influence of the hierarchical relationship between the product attributes and their sentiments on the overall review polarity. ConceptNet is used to automatically create a product specific ontology that depicts the hierarchical relationship between the product attributes. The ontology tree is annotated with feature-specific polarities which are aggregated bottom-up, exploiting the ontological information, to find the overall review polarity. We propose a weakly supervised system that achieves a reasonable performance improvement over the baseline without requiring any tagged training data.
The appropriation and repurposing of social technologies in higher education

This paper presents some of the findings from a recent project that conducted a virtual ethnographic study of three formal courses in higher education that use ‘Web 2.0’or social technologies for learning and teaching. It describes the pedagogies adopted within these courses, and goes on to explore some key themes emerging from the research and relating to the pedagogical use of weblogs and wikis in particular. These themes relate primarily to the academy’s tendency to constrain and contain the possibly more radical effects of these new spaces. Despite this, the findings present a range of student and tutor perspectives which show that these technologies have significant potential as new collaborative, volatile and challenging environments for formal learning.
Information systems for sustainable competitive advantage

There has been an increase in the attention paid to the strategic potential of information systems and a new willingness to accept the possibility that information systems can be the source of strategic gains. This belief is reflected in a host of publications, from the popular press to respected journals. Much of this has been supported by a very limited set of prominent and well publicized success stories, principally involving marketing and distribution, financial services, and the airlines. Unfortunately, there has been little attempt at an analysis that abstracts from these experiences to determine factors that determine strategic success. This can be attributed in part to the absence of attention paid to unsuccessful ventures in the use of information technology for competitive advantage. Although this paper relies on the same anecdotes, it augments them with data on a few unsuccessful attempts to exploit information technology and with economic theory where appropriate. General conditions that appear necessary for sustainable competitive advantage are developed.
Web-Based Measure of Semantic Relatedness

Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality. In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching.
A Comparison and Analysis of Name Matching Algorithms

Names are important in many societies, even in technologically oriented ones which use e.g. ID systems to identify individual people. Names such as surnames are the most important as they are used in many processes, such as identifying of people and genealogical research. On the other hand variation of names can be a major problem for the identification and search for people, e.g. web search or security reasons. Name matching presumes a-priori that the recorded name written in one alphabet reflects the phonetic identity of two samples or some transcription error in copying a previously recorded name. We add to this the lode that the two names imply the same person. This paper describes name variations and some basic description of various name matching algorithms developed to overcome name variation and to find reasonable variants of names which can be used to further increasing mismatches for record linkage and name search. The implementation contains algorithms for computing a range of fuzzy matching based on different types of algorithms, e.g. composite and hybrid methods and allowing us to test and measure algorithms for accuracy. NYSIIS, LIG2 and Phonex have been shown to perform well and provided sufficient flexibility to be included in the linkage/matching process for optimising name searching. Data mining, name matching algorithm, nominal data, searching system.
Class point approach for software effort estimation using stochastic gradient boosting technique

The success of software development depends on the proper estimation of the effort required to develop the software. Project managers require a reliable approach for software effort estimation. It is especially important during the early stages of the software development life cycle. Accurate software effort estimation is a major concern in software industries. Stochastic Gradient Boosting (SGB) is one of the machine learning techniques that helps in getting improved estimated values. SGB is used for improving the accuracy of models built on decision trees. In this paper, the main goal is to estimate the effort required to develop various software projects using the class point approach. Then, optimization of the effort parameters is achieved using the SGB technique to obtain better prediction accuracy. Further- more, performance comparisons of the models obtained using the SGB technique with the Multi Layer Perceptron and the Radial Basis Function Network are presented in order to highlight the performance achieved by each method.
Anticipatory Driving for a Robot-Car Based on Supervised Learning

Prediction and Planning are essential elements of successful human driving, making them equally important for autonomously driving systems. Many approaches achieve planning based on built-in world-knowledge. However, we show how a learning-based system can be extended to planning, needing little a priori knowledge. A car-like robot is trained by a human driver by constructing a database, where look ahead sensory information is stored together with action sequences. From that we achieve a novel form of velocity control, based only on information in image coordinates. For steering we employ a two-level approach in which database information is combined with an additional reactive controller. The result is a trajectory planning robot running at real-time, issuing steering and velocity control commands in a human manner.
Provable Possession and Replication of Data over Cloud Servers

Cloud Computing (CC) is an emerging computing paradigm that can potentially offer a number of important advantages. One of the fundamental advantages of CC is pay-as-you-go pricing model, where customers pay only according to their usage of the services. Currently, data generation is outpacing users’ storage availability, thus there is an increasing need to outsource such huge amount of data. Outsourcing data to a remote Cloud Service Provider (CSP) is a growing trend for numerous customers and organizations alleviating the burden of local data storage and maintenance. Moreover, customers rely on the data replication provided by the CSP to guarantee the availability and durability of their data. Therefore, Cloud Service Providers (CSPs) provide storage infrastructure and web services interface that can be used to store and retrieve an unlimited amount of data with fees metered in GB/month. The mechanisms used for data replication vary according to the nature of the data; more copies are needed for critical data that cannot easily be reproduced. This critical data should be replicated on multiple servers across multiple data centers. On the other hand, non-critical, reproducible data are stored at reduced levels of redundancy. The pricing model is related to the replication strategy. Therefore, it is of crucial importance to customers to have a strong evidence that they actually get the service they pay for. Moreover, they need to verify that all their data copies are not being tampered with or partially deleted over time. Consequently, the problem of Provable Data Possession (PDP) has been considered in many research papers. Unfortunately, previous PDP schemes focus on a single copy of the data and provide no guarantee that the CSP stores multiple copies of customers’ data. In this paper we address this challenging issue and propose Efficient Multi-Copy Provable Data Possession (EMC-PDP) protocols. We prove the security of our protocols against colluding servers. Through extensive performance analysis and experimental results, we demonstrate the efficiency of our protocols.
A hybrid approach for recommendation system with added feedback component

With the increasing E-Commerce and online shopping there is a need for recommendation systems which help the customers in decision making and to suggest potential goods of purchase. In domains such as automobiles there are many websites but most of them are not having enhanced recommendation systems to enable easy decision making. Thus we have taken the initiative of building a dataset with multiple parameters based on a survey of the communities needs using potential blogs and created a recommendation system using user based and item based collaborative filtering. In addition to the combined collaborative filtering techniques we propose a framework which includes a feedback analysis to improve the recommendation system. The enhanced model aids the customers in decision making. We have proposed the feedback system at two levels. One is external feedback where the comments are gathered from public platforms like social media and automobile websites. The other is internal feedback i.e. the feedback is taken from users who have been provided with recommended items. The opinions extracted from such varied comments broadens the system and results. Our proposed hybrid model with feedback analysis has improvised the current system by providing better suggestions to customers.
Medical images enhancement by using anisotropic filter and CLAHE

The purpose of image enhancement is to process an acquired image for better contrast and visibility of features of interest for visual examination as well as subsequent computer-aided analysis and diagnosis. Therefore, we have proposed an algorithm for medical images enhancement. In the study, we used top-hat transform, contrast limited histogram equalization and anisotropic diffusion filter methods. The system results are quite satisfactory for many different medical images like lung, breast, brain, knee and etc.
How Anywhere Computing Just Killed Your Phone-Based Two-Factor Authentication

Exponential growth in smartphone usage combined with recent advances in mobile technology is causing a shift in (mobile) app behavior: application vendors no longer restrict their apps to a single platform, but rather add synchronization options that allow users to conveniently switch from mobile to PC or vice versa in order to access their services. This process of integrating apps among multiple platforms essentially removes the gap between them. Current, state of the art, mobile phone-based two-factor authentication (2FA) mechanisms, however, heavily rely on the existence of such separation. They are used in a variety of segments (such as consumer online banking services or enterprise secure remote access) to protect against malware. For example, with 2FA in place, attackers should no longer be able to use their PC-based malware to instantiate fraudulent banking transactions. In this paper, we analyze the security implications of diminishing gaps between platforms and show that the ongoing integration and desire for increased usability results in violation of key principles for mobile phone 2FA. As a result, we identify a new class of vulnerabilities dubbed 2FA synchronization vulnerabilities. To support our findings, we present practical attacks against Android and iOS that illustrate how a Manin-the-Browser attack can be elevated to intercept One-Time Passwords sent to the mobile phone and thus bypass the chain of 2FA mechanisms as used by many financial services.
Virtualization vs Containerization to Support PaaS

PaaS vendors face challenges in efficiently providing services with the growth of their offerings. In this paper, we explore how PaaS vendors are using containers as a means of hosting Apps. The paper starts with a discussion of PaaS Use case and the current adoption of Container based PaaS architectures with the existing vendors. We explore various container implementations - Linux Containers, Docker, Warden Container, lmctfy and OpenVZ. We look at how each of this implementation handle Process, FileSystem and Namespace isolation. We look at some of the unique features of each container and how some of them reuse base Linux Container implementation or differ from it. We also explore how IaaSlayer itself has started providing support for container lifecycle management along with Virtual Machines. In the end, we look at factors affecting container implementation choices and some of the features missing from the existing implementations for the next generation PaaS.