nils-herrmann/clean_scirepeval huggingface dataset:
FitNets: Hints for Thin Deep Nets

While depth tends to improve network performances, it also m akes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed a t obtaining small and fast-to-execute models, and it has shown that a student netw ork could imitate the soft output of a larger teacher network or ensemble of networ ks. In this paper, we extend this idea to allow the training of a student that is d eeper and thinner than the teacher, using not only the outputs but also the inte rmediate representations learned by the teacher as hints to improve the traini ng process and final performance of the student. Because the student intermedia te hidden layer will generally be smaller than the teacher’s intermediate hidde n layer, additional parameters are introduced to map the student hidden layer to th e prediction of the teacher hidden layer. This allows one to train deeper studen s that can generalize better or run faster, a trade-off that is controlled by the ch osen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teache r network.
Personality Traits Predict Music Taxonomy Preferences

Music streaming services increasingly incorporate additional music taxonomies (i.e., mood, activity, and genre) to provide users different ways to browse through music collections. However, these additional taxonomies can distract the user from reaching their music goal, and influence choice satisfaction. We conducted an online user study with an application called "Tune-A-Find," where we measured participants' music taxonomy choice (mood, activity, and genre). Among 297 participants, we found that the chosen taxonomy is related to personality traits. We found that openness to experience increased the choice for browsing music by mood, while conscientiousness increased the choice for browsing music by activity. In addition, those high in neuroticism were most likely to browse for music by activity or genre. Our findings can support music streaming services to further personalize user interfaces. By knowing the user's personality, the user interface can adapt to the user's preferred way of music browsing.
Learning-Aided Network Association for Hybrid Indoor LiFi-WiFi Systems

Given the scarcity of spectral resources in traditional wireless networks, it has become popular to construct visible light communication (VLC) systems. They exhibit high energy efficiency, wide unlicensed communication bandwidth as well as innate security; hence, they may become part of future wireless systems. However, considering the limited coverage and dense deployment of light-emitting diode (LED) lamps, traditional network association strategies are not readily applicable to VLC networks. Hence, by exploiting the power of online learning algorithms, we focus our attention on sophisticated multi-LED access point selection strategies conceived for hybrid indoor LiFi-WiFi communication systems. We formulate a multi-armed bandit model for supporting the decisions on beneficially selecting LED access points. Moreover, the ‘exponential weights for exploration and exploitation’ algorithm and the ‘exponentially weighted algorithm with linear programming’ algorithm are invoked for updating the decision probability distribution, followed by determining the upper bound of the associated accumulation reward function. Significant throughput gains can be achieved by the proposed network association strategies.
A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification

We consider the problem of developing privacypreserving machine learning algorithms in a distributed multiparty setting. Here different parties own different parts of a data set, and the goal is to learn a classifier from the entire data set without any party revealing any information about the individual data points it owns. Pathak et al [7] recently proposed a solution to this problem in which each party learns a local classifier from its own data, and a third party then aggregates these classifiers in a privacy-preserving manner using a cryptographic scheme. The generalization performance of their algorithm is sensitive to the number of parties and the relative fractions of data owned by the different parties. In this paper, we describe a new differentially private algorithm for the multiparty setting that uses a stochastic gradient descent based procedure to directly optimize the overall multiparty objective rather than combining classifiers learned from optimizing local objectives. The algorithm achieves a slightly weaker form of differential privacy than that of [7], but provides improved generalization guarantees that do not depend on the number of parties or the relative sizes of the individual data sets. Experimental results corroborate our theoretical findings.
Multi-level Residual Networks from Dynamical Systems View

Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks. However, the theoretical principles for designing and training ResNets are still not fully understood. Recently, several points of view have emerged to try to interpret ResNet theoretically, such as unraveled view, unrolled iterative estimation and dynamical systems view. In this paper, we adopt the dynamical systems point of view, and analyze the lesioning properties of ResNet both theoretically and experimentally. Based on these analyses, we additionally propose a novel method for accelerating ResNet training. We apply the proposed method to train ResNets and Wide ResNets for three image classification benchmarks, reducing training time by more than 40% with superior or on-par accuracy.
APE-GAN: Adversarial Perturbation Elimination with GAN

Although neural networks could achieve state-of-the-art performance while recongnizing images, they often suffer a tremendous defeat from adversarial examples–inputs generated by utilizing imperceptible but intentional perturbation to clean samples from the datasets. How to defense against adversarial examples is an important problem which is well worth researching. So far, very few methods have provided a significant defense to adversarial examples. In this paper, a novel idea is proposed and an effective framework based Generative Adversarial Nets named APE-GAN is implemented to defense against the adversarial examples. The experimental results on three benchmark datasets including MNIST, CIFAR10 and ImageNet indicate that APE-GAN is effective to resist adversarial examples generated from five attacks.
Institutional Repository Roman Hoffmann and Raya Muttarak Learn from the Past , Prepare for the Future : Impacts of Education and Experience on Disaster Preparedness in the Philippines and Thailand

This study aims at understanding the role of education in promoting disaster preparedness. Strengthening resilience to climate-related hazards is an urgent target of Goal 13 of the Sustainable Development Goals. Preparing for a disaster such as stockpiling of emergency supplies or having a family evacuation plan can substantially minimize loss and damages from natural hazards. However, the levels of household disaster preparedness are often low even in disaster-prone areas. Focusing on determinants of personal disaster preparedness, this paper investigates: (1) pathways through which education enhances preparedness; and (2) the interplay between education and experience in shaping preparedness actions. Data analysis is based on face-to-face surveys of adults aged 15 years in Thailand (N = 1,310) and the Philippines (N = 889, female only). Controlling for socio-demographic and contextual characteristics, we find that formal education raises the propensity to prepare against disasters. Using the KHB method to further decompose the education effects, we find that the effect of education on disaster preparedness is mainly mediated through social capital and disaster risk perception in Thailand whereas there is no evidence that education is mediated through observable channels in the Philippines. This suggests that the underlying mechanisms explaining the education effects are highly context-specific. Controlling for the interplay between education and disaster experience, we show that education raises disaster preparedness only for those households that have not been affected by a disaster in the past. Education improves abstract reasoning and anticipation skills such that the better educated undertake preventive measures without needing to first experience the harmful event and then learn later. In line with recent efforts of various UN agencies in promoting education for sustainable development, this study provides a solid empirical evidence showing positive externalities of education in disaster risk reduction. 2017TheAuthors.PublishedbyElsevierLtd.
Hidden Markov Model and Genetic Algorithm Based Credit Card Fraud Detection

Credit card is one of the electronic payment mode and the fraud is committing of use of credit card in a fraudulent way either using credit or debit card. The purpose can be solved by purchasing the accessories without paying and giving unauthorized way of payment from account. In this paper, we are proposing the algorithm with the combined approach of hidden markov model and gentic algorithm of the data mining techniques and get the better result with respective of the individual approaches. Furthermore, there performance in term of precision, recall, F-measure has also increased comparative to the the state-of-the-art papers included in
Intelligent defense using pretense against targeted attacks in cloud platforms

Cloud-hosted services are being increasingly used in online businesses in e.g., retail, healthcare, manufacturing, entertainment due to benefits such as scalability and reliability. These benefits are fueled by innovations in orchestration of cloud platforms that make them programmable as Software Defined everything Infrastructures (SDxI). At the same time, sophisticated targeted attacks such as Distributed Denial-of-Service (DDoS) and Advanced Persistent Threats (APTs) are growing on an unprecedented scale threatening the availability of online businesses. In this paper, we present a novel defense system called Dolus to mitigate the impact of targeted attacks launched against high-value services hosted in SDxI-based cloud platforms. Our Dolus system is able to initiate a ‘pretense’ in a scalable and collaborative manner to deter the attacker based on threat intelligence obtained from attack feature analysis. Using foundations from pretense theory in child play, Dolus takes advantage of elastic capacity provisioning via ‘quarantine virtual machines’ and SDxI policy co-ordination across multiple network domains to deceive the attacker by creating a false sense of success. We evaluate the efficacy of Dolus using a GENI Cloud testbed and demonstrate its real-time capabilities to: (a) detect DDoS and APT attacks and redirect attack traffic to quarantine resources to engage the attacker under pretense, (b) coordinate SDxI policies to possibly block attacks closer to the attack source(s).
Joint Learning from Labeled and Unlabeled Data for Information Retrieval

Recently, a significant number of studies have focused on neural information retrieval (IR) models. One category of works use unlabeled data to train general word embeddings based on term proximity. The general embeddings can be integrated into traditional IR models. The other category employs labeled data (e.g. click-through data) to train end-to-end neural IR models consisting of layers for target-specific representation learning. The latter idea accounts better for the IR task and is favored by recent research works, which is the one we will follow in this paper. We hypothesize that general semantics learned from unlabeled data can complement task-specific representation learned from labeled data of limited quality, and that a combination of the two is favorable. To this end, we propose a learning framework which can benefit from both labeled and more abundant unlabeled data for representation learning in the context of IR. Through a joint learning fashion in a single neural framework, the learned representation is optimized to minimize both the supervised loss on query-document matching and the unsupervised loss on text reconstruction. Standard retrieval experiments on TREC collections indicate that the joint learning methodology leads to significant better performance of retrieval over several strong baselines for IR.
Risk Management for Digital Transformation in Architecture Board: A Case Study on Global Enterprise

This study investigates the solutions incorporated by the Architecture Board in global healthcare enterprise (GHE) to mitigate architecture risks especially in Digital IT areas while proposing and implementing the Adaptive Integrated EA framework, which can be applied in companies promoting IT strategy with Cloud/Mobile IT. The study revealed the distribution of solutions across the architecture domains in Enterprise Architecture covering applications and technologies with Cloud/Mobile IT/Digital IT to mitigate risks. An in-depth analysis of this distribution resulted in practical guidance for companies that consider Risk Management for Digital Transformation while starting up the Architecture Board in Enterprise Architecture with IT strategy covering Digital IT related elements.
Project management aided by verbal decision analysis approaches: a case study for the selection of the best SCRUM practices

Agile methodologies, in particular the framework SCRUM, are popular in software development companies. Most of the time, however, it is not feasible for these companies to apply every characteristic of the framework. This paper presents a hybrid application of verbal decision analysis methodologies in order to select some of the most relevant SCRUM approaches to be applied by a company. A questionnaire was developed and a group of experienced ScrumMasters was selected to answer it, aiming at characterizing every SCRUM approach into criteria values. The hybrid application consists in dividing the SCRUM practices into groups (stage supported by the ORCLASS method application), using the ORCLASSWEB tool. Then, the rank of the preferred practices will be generated by the application of the ZAPROS-LM method.
Analyzing Data Sets with Missing Data: An Empirical Evaluation of Imputation Methods and Likelihood-Based Methods

ÐMissing data are often encountered in data sets used to construct effort prediction models. Thus far, the common practice has been to ignore observations with missing data. This may result in biased prediction models. In this paper, we evaluate four missing data techniques (MDTs) in the context of software cost modeling: listwise deletion (LD), mean imputation (MI), similar response pattern imputation (SRPI), and full information maximum likelihood (FIML). We apply the MDTs to an ERP data set, and thereafter construct regression-based prediction models using the resulting data sets. The evaluation suggests that only FIML is appropriate when the data are not missing completely at random (MCAR). Unlike FIML, prediction models constructed on LD, MI and SRPI data sets will be biased unless the data are MCAR. Furthermore, compared to LD, MI and SRPI seem appropriate only if the resulting LD data set is too small to enable the construction of a meaningful regression-based prediction model.
Assessing social responsibility : A quantitative analysis of Appraisal in BP ’ s and IKEA ’ s social reports

A growing public awareness of the potential negative impacts of corporate activities on the natural environment and society compels large companies to invest increasing resources in the communication of their responsible conduct. This paper employs Appraisal theory in a comparative analysis of BP’s and IKEA’s 2009 social reports, each company’s record of their nonfinancial performance. The main objective is to explore how, through Appraisal resources, BP and IKEA construct their corporate identity and relationship with their stakeholders. The analysis reveals two markedly different approaches to the construction of a responsible corporate identity. While BP deploys interpersonal resources to portray itself as a trustworthy and authoritative expert, IKEA discloses itself as a sensitive and caring corporation, engaged in a continual effort to improve. These differences are interpreted in light of the legitimisation challenges the two companies face.  discourse analysis, Appraisal, evaluation, stance, corpus-based approaches to evaluation, corporate social reports, corporate identity, legitimacy theory, corporate social responsibility This	  is	  a	  pre-­‐print	  version	  of	  an	  article	  that	  has	  been	  published	  in	  the journal	  Discourse	  &	  Communication,	  volume	  6,	  issue	  1.	  It	  can	  be	  cited as:	  Fuoli,	  M.	  2012.	  ‘Assessing	  social	  responsibility:	  A	  quantitative analysis	  of	  Appraisal	  in	  BP’s	  and	  IKEA’s	  social	  reports’,	  Discourse	  and Communication	  6	  (1),	  pp.	  55–81.	  For	  direct	  quotations	  and	  page numbers,	  please	  check	  the	  published	  version,	  which	  can	  be downloaded	  from	  here: http://dcm.sagepub.com/content/6/1/55.abstract
Limits to the sharing economy

There has been much interest in the Sharing Economy in recent years, accompanied with the hope that it will change and specifically make better use of existing resources. It intuitively makes sense, from a sustainability point of view, that the sharing of resources is good. It could even be said that the Sharing Economy ought to align well with Computing within Limits and its underlying premises. In this paper however, we take a critical stance and will elaborate on the intersection between the Sharing Economy and Limits (including pinpointing potential conflicts) so as to identify and discuss a 'Limits-compliant Sharing Economy'. We argue that even though there are limits to the Sharing Economy today, it still has potential benefits for a future of scarcity---but only if the practice of sharing is approached with a dual focus on sharing and on limits at the same time. Finally we conclude that even though we have begun to explore the future of sharing, there is still a need to further develop ideas of how the underlying infrastructure for this movement will look.
GET_PHYLOMARKERS, a Software Package to Select Optimal Orthologous Clusters for Phylogenomics and Inferring Pan-Genome Phylogenies, Used for a Critical Geno-Taxonomic Revision of the Genus Stenotrophomonas

The massive accumulation of genome-sequences in public databases promoted the proliferation of genome-level phylogenetic analyses in many areas of biological research. However, due to diverse evolutionary and genetic processes, many loci have undesirable properties for phylogenetic reconstruction. These, if undetected, can result in erroneous or biased estimates, particularly when estimating species trees from concatenated datasets. To deal with these problems, we developed GET_PHYLOMARKERS, a pipeline designed to identify high-quality markers to estimate robust genome phylogenies from the orthologous clusters, or the pan-genome matrix (PGM), computed by GET_HOMOLOGUES. In the first context, a set of sequential filters are applied to exclude recombinant alignments and those producing anomalous or poorly resolved trees. Multiple sequence alignments and maximum likelihood (ML) phylogenies are computed in parallel on multi-core computers. A ML species tree is estimated from the concatenated set of top-ranking alignments at the DNA or protein levels, using either FastTree or IQ-TREE (IQT). The latter is used by default due to its superior performance revealed in an extensive benchmark analysis. In addition, parsimony and ML phylogenies can be estimated from the PGM. We demonstrate the practical utility of the software by analyzing 170 Stenotrophomonas genome sequences available in RefSeq and 10 new complete genomes of Mexican environmental S. maltophilia complex (Smc) isolates reported herein. A combination of core-genome and PGM analyses was used to revise the molecular systematics of the genus. An unsupervised learning approach that uses a goodness of clustering statistic identified 20 groups within the Smc at a core-genome average nucleotide identity (cgANIb) of 95.9% that are perfectly consistent with strongly supported clades on the core- and pan-genome trees. In addition, we identified 16 misclassified RefSeq genome sequences, 14 of them labeled as S. maltophilia, demonstrating the broad utility of the software for phylogenomics and geno-taxonomic studies. The code, a detailed manual and tutorials are freely available for Linux/UNIX servers under the GNU GPLv3 license at https://github.com/vinuesa/get_phylomarkers. A docker image bundling GET_PHYLOMARKERS with GET_HOMOLOGUES is available at https://hub.docker.com/r/csicunam/get_homologues/, which can be easily run on any platform.
Blockchain as Radical Innovation: A Framework for Engaging with Distributed Ledgers as Incumbent Organization

Blockchain is an emerging technology that is perceived as groundbreaking. However, blockchain presents incumbent organizations with significant challenges. How should they respond to the advent of this innovative technology, and how can they build the capabilities that are necessary to successfully engage with blockchain? In this case study, we analyze how an incumbent bank deals with the radical innovation of blockchain. We find that blockchain as an innovation is unique, because its transaction cost-lowering nature requires cooperation not only on an intra-organizational, but also on an inter-organizational level to fully leverage the technology. We develop a framework illustrating how the process of discovering, incubating, and accelerating with blockchain can look like. Our research is one of the first case studies in the area; shedding light on the organizational challenges of incumbents as they engage with blockchain. The paper provides a blueprint for business executives in their endeavor of embracing blockchain technology.
An Introduction to Multilevel Modeling for Social and Personality Psychology

Multilevel modeling is a technique that has numerous potential applications for social and personality psychology. To help realize this potential, this article provides an introduction to multilevel modeling with an emphasis on some of its applications in social and personality psychology. This introduction includes a description of multilevel modeling, a rationale for this technique, and a discussion of applications of multilevel modeling in social and personality psychological research. Some of the subtleties of setting up multilevel analyses and interpreting results are presented, and software options are discussed. Once you know that hierarchies exist, you see them everywhere.  at one level of analysis are nested within observations at another level. For example, in a study of classrooms or work groups, individuals are considered to be nested within groups. Similarly, in diary-style studies, observations (e.g., diary entries) are nested within persons. What is particularly important for present purposes is that when you have multilevel data, you need to analyze them using techniques that take into account this nesting. As discussed below (and in numerous places, including Nezlek, 2001), the results of analyses of multilevel data that do not take into account the multilevel nature of the data may (or perhaps will) be inaccurate. This article is intended to acquaint readers with the basics of multilevel modeling. For researchers, it is intended to provide a basis for further study. I think that a lack of understanding of how to think in terms of hierarchies and a lack of understanding of how to analyze such data inhibits researchers from applying the ‘multilevel perspective’ to their work. For those simply interested in understanding what multilevel