nils-herrmann/clean_scirepeval huggingface dataset:
Online sequential extreme learning machine with kernels for nonstationary time series prediction

In this paper, an online sequential extreme learning machine with kernels (OS-ELMK) has been proposed for nonstationary time series prediction. An online sequential learning algorithm, which can learn samples one-by-one or chunk-by-chunk, is developed for extreme learning machine with kernels. A limited memory prediction strategy based on the proposed OS-ELMK is designed to model the nonstationary time series. Performance comparisons of OS-ELMK with other existing algorithms are presented using artificial and real life nonstationary time series data. The results show that the proposed OS-ELMK produces similar or better accuracies with at least an order-of-magnitude reduction in the learning time. & 2014 Elsevier B.V. All rights reserved.
Adding the missing time-dependent layout dependency into device-circuit-layout co-optimization - New findings on the layout dependent aging effects

In this paper, a new class of layout dependent effects (LDE)-the time-dependent layout dependency due to device aging, is reported for the first time. The BTI and HCI degradation in nanoscale HKMG devices are experimentally found to be sensitive to layout configurations, even biased at the same stress condition. This new effect of layout dependent aging (LDA) can significantly mess the circuit design, which conventionally only includes the static LDE modeled for time-zero performance. Further studies at circuit level indicate that, for resilient device-circuit-layout co-design, especially to ensure enough design margin near the end of life, LDA cannot be neglected. The results are helpful to guide the cross-layer technology/design co-optimization.
Privacy, Security and Trust Issues Arising from Cloud Computing

Cloud computing is an emerging paradigm for large scale infrastructures. It has the advantage of reducing cost by sharing computing and storage resources, combined with an on-demand provisioning mechanism relying on a pay-per-use business model. These new features have a direct impact on the budgeting of IT budgeting but also affect traditional security, trust and privacy mechanisms. Many of these mechanisms are no longer adequate, but need to be rethought to fit this new paradigm. In this paper we assess how security, trust and privacy issues occur in the context of cloud computing and discuss ways in which they may be addressed.
Measuring and Managing the Smartness of Cities: A Framework for Classifying Performance Indicators

Due to the continuous increase of the world population living in cities, it is crucial to identify strategic plans and perform associated actions to make cities smarter, i.e., more operationally efficient, socially friendly, and environmentally sustainable, in a cost effective manner. To achieve these goals, emerging smart cities need to be optimally and intelligently measured, monitored, and managed. In this context the paper proposes the development of a framework for classifying performance indicators of a smart city. It is based on two dimensions: the degree of objectivity of observed variables and the level of technological advancement for data collection. The paper shows an application of the presented framework to the case of the Bari municipality (Italy).
The effects of font type and size on the legibility and reading time of online text by older adults

This study examined passages containing two serif and sans serif fonts at 12 and 14-point sizes for differences in legibility, reading time, and general preference when read by an older population. A significant main effect of size was found for font legibility in that 14-point fonts were more legible to read than 12-point fonts. A marginal interaction was also found for reading time in that participants read 12-point serif fonts significantly slower than 14-point serif or sans serif fonts. Moreover, participants significantly preferred the 14-point to the 12-point font size. Font recommendations are discussed.
A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use

Original citation: Kardefelt-Winther, Daniel (2014) A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. LSE has developed LSE Research Online so that users may access research output of the School.  Internet addiction Compulsive internet use Problematic internet use Compensatory internet use Motivations for internet use a b s t r a c t Internet addiction is a rapidly growing field of research, receiving attention from researchers, journalists and policy makers. Despite much empirical data being collected and analyzed clear results and conclusions are surprisingly absent. This paper argues that conceptual issues and methodological shortcomings surrounding internet addiction research have made theoretical development difficult. An alternative model termed compensatory internet use is presented in an attempt to properly theorize the frequent assumption that people go online to escape real life issues or alleviate dysphoric moods and that this sometimes leads to negative outcomes. An empirical approach to studying compensatory internet use is suggested by combining the psychological literature on internet addiction with research on motivations for internet use. The theoretical argument is that by understanding how motivations mediate the relationship between psychosocial well-being and internet addiction, we can draw conclusions about how online activities may compensate for psychosocial problems. This could help explain why some people keep spending so much time online despite experiencing negative outcomes. There is also a method-ological argument suggesting that in order to accomplish this, research needs to move away from a focus on direct effects models and consider mediation and interaction effects between psychosocial well-being and motivations in the context of internet addiction. This is key to further exploring the notion of internet use as a coping strategy; a proposition often mentioned but rarely investigated. Internet addiction 1 is typically described as a state where an individual has lost control of the internet use and keeps using inter-net excessively to the point where he/she experiences problematic outcomes that negatively affects his/her life (Young & Abreu, 2011). Examples …
Source Task Creation for Curriculum Learning

Transfer learning in reinforcement learning has been an active area of research over the past decade. In transfer learning, training on a source task is leveraged to speed up or otherwise improve learning on a target task. This paper presents the more ambitious problem of curriculum learning in reinforcement learning, in which the goal is to design a sequence of source tasks for an agent to train on, such that final performance or learning speed is improved. We take the position that each stage of such a curriculum should be tailored to the current ability of the agent in order to promote learning new behaviors. Thus, as a first step towards creating a curriculum, the trainer must be able to create novel, agent-specific source tasks. We explore how such a space of useful tasks can be created using a parameterized model of the domain and observed trajectories on the target task. We experimentally show that these methods can be used to form components of a curriculum and that such a curriculum can be used successfully for transfer learning in 2 challenging multiagent reinforcement learning domains.
Latent topic model for audio retrieval

Latent topic model such as Latent Dirichlet Allocation (LDA) has been designed for text processing and has also demonstrated success in the task of audio related processing. The main idea behind LDA assumes that the words of each document arise from a mixture of topics, each of which is a multinomial distribution over the vocabulary. When applying the original LDA to process continuous data, the wordlike unit need be first generated by vector quantization (VQ). This data discretization usually results in information loss. To overcome this shortage, this paper introduces a new topic model named GaussianLDA for audio retrieval. In the proposed model, we consider continuous emission probability, Gaussian instead of multinomial distribution. This new topic model skips the vector quantization and directly models each topic as a Gaussian distribution over audio features. It avoids discretization by this way and integrates the procedure of clustering. The experiments of audio retrieval demonstrate that GaussianLDA achieves better performance than other compared methods.
Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

The framework of dynamic movement primitives (DMPs) contains many favorable properties for the execution of robotic trajectories, such as indirect dependence on time, response to perturbations, and the ability to easily modulate the given trajectories, but the framework in its original form remains constrained to the kinematic aspect of the movement. In this paper, we bridge the gap to dynamic behavior by extending the framework with force/torque feedback. We propose and evaluate a modulation approach that allows interaction with objects and the environment. Through the proposed coupling of originally independent robotic trajectories, the approach also enables the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion and, thus, modifies the trajectory in accordance to the desired positions or external forces. A stability analysis and results of simulated and real-world experiments using two KUKA LWR arms for bimanual tasks and interaction with the environment are presented. By expanding on the framework of DMPs, we keep all the favorable properties, which is demonstrated with temporal modulation and in a two-agent obstacle avoidance task.
A 300khz-13.5ghz directional bridge

This paper presents a wideband directional bridge with a range of operating frequencies from 300 kHz to 13.5 GHz. The original topology of the directional bridge was designed, using the multilayer printed circuit board (PCB) technology, with the top layer of the laminated microwave dielectric Rogers RO4350, as resistive elements surface mounted (SMD) components are used. The circuit is designed for a nominal value of 16 dB coupling and an insertion loss of 1.6 dB.
Trust-aware recommender systems

Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.
AdaBoost.RT: a boosting algorithm for regression problems

A boosting algorithm, AdaBoost.RT, is proposed for regression problems. The idea is to filter out examples with a relative estimation error that is higher than the pre-set threshold value, and then follow the AdaBoost procedure. Thus it requires to select the sub-optimal value of relative error threshold to demarcate predictions from the predictor as correct or incorrect. Some experimental results using the M5 model tree as a weak learning machine for benchmark data sets and for hydrological modeling are reported, and compared to other boosting methods, bagging and artificial neural networks, and to a single M5 model tree. AdaBoost.Rt is proved to perform better on most of the considered data sets.
Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations

This paper introduces flash organizations: crowds structured like organizations to achieve complex and open-ended goals. Microtask workflows, the dominant crowdsourcing structures today, only enable goals that are so simple and modular that their path can be entirely pre-defined. We present a system that organizes crowd workers into computationally-represented structures inspired by those used in organizations - roles, teams, and hierarchies - which support emergent and adaptive coordination toward open-ended goals. Our system introduces two technical contributions: 1) encoding the crowd's division of labor into de-individualized roles, much as movie crews or disaster response teams use roles to support coordination between on-demand workers who have not worked together before; and 2) reconfiguring these structures through a model inspired by version control, enabling continuous adaptation of the work and the division of labor. We report a deployment in which flash organizations successfully carried out open-ended and complex goals previously out of reach for crowdsourcing, including product design, software development, and game production. This research demonstrates digitally networked organizations that flexibly assemble and reassemble themselves from a globally distributed online workforce to accomplish complex work.
Evaluating Bayesian Networks via Data Streams

Consider a stream of n-tuples that empirically define the joint distribution of n discrete random variables X1, . . . , Xn. Previous work of Indyk and McGregor [6] and Braverman et al. [1, 2] addresses the problem of determining whether these variables are n-wise independent by measuring the `p distance between the joint distribution and the product distribution of the marginals. An open problem in this line of work is to answer more general questions about the dependencies between the variables. One powerful way to express such dependencies is via Bayesian networks where nodes correspond to variables and directed edges encode dependencies. We consider the problem of testing such dependencies in the streaming setting. Our main results are: 1. A tight upper and lower bound of Θ̃(nk) on the space required to test whether the data is consistent with a given Bayesian network where k is the size of the range of each Xi and d is the max in-degree of the network. 2. A tight upper and lower bound of Θ̃(k) on the space required to compute any 2-approximation of the log-likelihood of the network. 3. Finally, we show space/accuracy trade-offs for the problem of independence testing using `1 and `2 distances.
The neural basis of romantic love.

The neural correlates of many emotional states have been studied, most recently through the technique of fMRI. However, nothing is known about the neural substrates involved in evoking one of the most overwhelming of all affective states, that of romantic love, about which we report here. The activity in the brains of 17 subjects who were deeply in love was scanned using fMRI, while they viewed pictures of their partners, and compared with the activity produced by viewing pictures of three friends of similar age, sex and duration of friendship as their partners. The activity was restricted to foci in the medial insula and the anterior cingulate cortex and, subcortically, in the caudate nucleus and the putamen, all bilaterally. Deactivations were observed in the posterior cingulate gyrus and in the amygdala and were right-lateralized in the prefrontal, parietal and middle temporal cortices. The combination of these sites differs from those in previous studies of emotion, suggesting that a unique network of areas is responsible for evoking this affective state. This leads us to postulate that the principle of functional specialization in the cortex applies to affective states as well.
Using ghost edges for classification in sparsely labeled networks

We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical relational learning have been shown to perform well on network classification tasks by exploiting dependencies between class labels of neighboring nodes. However, relational classifiers can fail when unlabeled nodes have too few labeled neighbors to support learning (during training phase) and/or inference (during testing phase). This situation arises in real-world problems when observed labels are sparse.
 In this paper, we propose a novel approach to within-network classification that combines aspects of statistical relational learning and semi-supervised learning to improve classification performance in sparse networks. Our approach works by adding "ghost edges" to a network, which enable the flow of information from labeled to unlabeled nodes. Through experiments on real-world data sets, we demonstrate that our approach performs well across a range of conditions where existing approaches, such as collective classification and semi-supervised learning, fail. On all tasks, our approach improves area under the ROC curve (AUC) by up to 15 points over existing approaches. Furthermore, we demonstrate that our approach runs in time proportional to L • E, where L is the number of labeled nodes and E is the number of edges.
A Language-Independent Feature Schema for Inflectional Morphology

This paper presents a universal morphological feature schema that represents the finest distinctions in meaning that are expressed by overt, affixal inflectional morphology across languages. This schema is used to universalize data extracted from Wiktionary via a robust multidimensional table parsing algorithm and feature mapping algorithms, yielding 883,965 instantiated paradigms in 352 languages. These data are shown to be effective for training morphological analyzers, yielding significant accuracy gains when applied to Durrett and DeNero’s (2013) paradigm learning framework.
Fingerprint Identification and Verification System using Minutiae Matching

Fingerprints are the most widely used biometric feature for person identification and verification in the field of biometric identification. Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) global ridge and furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. This paper presents the implementation of a minutiae based approach to fingerprint identification and verification and serves as a review of the different techniques used in various steps in the development of minutiae based Automatic Fingerprint Identification Systems (AFIS). The technique conferred in this paper is based on the extraction of minutiae from the thinned, binarized and segmented version of a fingerprint image. The system uses fingerprint classification for indexing during fingerprint matching which greatly enhances the performance of the matching algorithm. Good results (~92% accuracy) were obtained using the FVC2000 fingerprint databases.
Program element matching for multi-version program analyses

Multi-version program analyses require that elements of one version of a program be mapped to the elements of other versions of that program. Matching program elements between two versions of a program is a fundamental building block for multi-version program analyses and other software evolution research such as profile propagation, regression testing, and software version merging.In this paper, we survey matching techniques that can be used for multi-version program analyses and evaluate them based on hypothetical change scenarios. This paper also lists challenges of the matching problem, identifies open problems, and proposes future directions.
Introducing the Trusted Virtual Environment Module: A New Mechanism for Rooting Trust in Cloud Computing

We introduce a new mechanism for rooting trust in a cloud computing environment called the Trusted Virtual Environment Module (TVEM). The TVEM helps solve the core security challenge of cloud computing by enabling parties to establish trust relationships where an information owner creates and runs a virtual environment on a platform owned by a separate service provider. The TVEM is a software appliance that provides enhanced features for cloud virtual environments over existing Trusted Platform Module virtualization techniques, which includes an improved application program interface, cryptographic algorithm flexibility, and a configurable modular architecture. We define a unique Trusted Environment Key that combines trust from the information owner and the service provider to create a dual root of trust for the TVEM that is distinct for every virtual environment and separate from the platform’s trust. This paper presents the requirements, design, and architecture of our approach.
Few-Shot Segmentation Propagation with Guided Networks

Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors. To remedy the rigidity and annotation burden of standard approaches, we address the problem of few-shot segmentation: given few image and few pixel supervision, segment any images accordingly. We propose guided networks, which extract a latent task representation from any amount of supervision, and optimize our architecture end-to-end for fast, accurate few-shot segmentation. Our method can switch tasks without further optimization and quickly update when given more guidance. We report the first results for segmentation from one pixel per concept and show real-time interactive video segmentation. Our unified approach propagates pixel annotations across space for interactive segmentation, across time for video segmentation, and across scenes for semantic segmentation. Our guided segmentor is state-of-the-art in accuracy for the amount of annotation and time. See http://github.com/shelhamer/revolver for code, models, and more details.
Knowledge Base Unification via Sense Embeddings and Disambiguation

We present KB-UNIFY, a novel approach for integrating the output of different Open Information Extraction systems into a single unified and fully disambiguated knowledge repository. KB-UNIFY consists of three main steps: (1) disambiguation of relation argument pairs via a sensebased vector representation and a large unified sense inventory; (2) ranking of semantic relations according to their degree of specificity; (3) cross-resource relation alignment and merging based on the semantic similarity of domains and ranges. We tested KB-UNIFY on a set of four heterogeneous knowledge bases, obtaining high-quality results. We discuss and provide evaluations at each stage, and release output and evaluation data for the use and scrutiny of the community1.
Facebook bullying: An extension of battles in school

This study examines the phenomenon of cyberbullying on Facebook and how it is related to school bullying among secondary school students in Singapore, aged 13–17. We also focus on generic use of Facebook and risky Facebook behaviors as the predictors of cyberbullying and victimization on Facebook. 1676 secondary students, from two secondary schools, participated in a pen and paper survey. The findings show that the intensity of Facebook use and engagement in risky Facebook behaviors were related to Facebook victimization and Facebook bullying, respectively. Moderately strong positive relationships between school bullying and Facebook bullying, as well as between school victimization and Facebook victimization, were also uncovered.