Miracle-dz/UniFAR-FTUs huggingface dataset:
Commitment in the unknown: An innovative use of the Profit-with-Purpose corporate framework to ensure responsible innovation

On attend de plus en plus des entreprises qu'elles mobilisent leur pouvoir d'action et leur capacite d'innovation pour faire face aux « grands defis » sociaux, environnementaux et economiques actuels. Cependant, on manque encore de cadres de « gouvernance responsable » permettant de s'assurer que les entreprises protegent leurs capacites d'innovation et les utilisent de maniere responsable. A cet effet, de nouveaux outils juridiques sont regulierement experimentes. Recemment, des formes d'entreprise dites « Profit-with-Purpose » ou « a mission » telles que la Benefit Corporation aux Etats-Unis ont ete introduites dans le droit des societes de plusieurs pays dans l'espoir de fournir un dispositif de gouvernance favorisant les comportements responsables des entreprises. Il reste cependant a etudier dans quelle mesure ces nouvelles formes juridiques peuvent effectivement contribuer a une « innovation responsable ». Cet article vise a explorer les mecanismes de gouvernance originaux deployes par une entreprise a mission pour faire face aux defis de l'innovation responsable. Il s'appuie sur une etude de cas longitudinale d'une entreprise francaise qui a experimente ce statut depuis 2015. Nous montrons que les entreprises a mission fournissent un cadre utile pour l'innovation responsable si certains outils specifiques sont ajoutees aux exigences juridiques telles que la formulation de la mission sous la forme d'un « engagement dans l'inconnu », qui vise a proteger les capacites d'innovation de l'entreprise et a susciter un avenir souhaitable auquel l'entreprise s'engage dans son developpement; et la creation d'un comite de gouvernance charge non seulement d'evaluer cet objectif, mais aussi d'evaluer la qualite de l'exploration par l'entreprise de diverses strategies novatrices pour assumer ses responsabilites futures. Nous discutons enfin de certaines implications pour le management et les legislateurs.
With a Little Help from My Friends: Peer Coaching for Refugee Adolescents and the Role of Social Media

This intervention study investigated how much impact a specific peer-coaching (Peer2Peer) for refugee adolescents has on different factors of well-being for both sides: refugee adolescents (peers, N = 16) and their local peer coaches (buddies, N = 16). Next to pre- and post-tests, four buddies reflected on the process via weekly media diaries. We found that higher peer-loneliness and lower self-esteem was reported for peers in the beginning but these differences disappeared. These results were confirmed by buddies’ media diaries: language and communication barriers reduced and friendships between buddies and peers grew. Buddies also reported high feelings of responsibilities in their media diaries which led to worries about their peer, but also to pride due to peers’ improvement. Online communication was used on an almost daily basis to stay in contact each other. Snapchat was found to influence emotional and affectionate support. In sum, Peer2Peer as a program showed positive effects for both sides. Future Peer2Peer programs should include trainings on social media as well, as most apps are able to be used independent of own language skills. Thus, social media can help to overcome language barriers and intensifies the feeling of being supported.
ONTOLOGY LIBRARY: A MODERN METHOD FOR THE DISTRIBUTION AND SHARING OF ONTOLOGIES

ONTOLOGY LIBRARY: A MODERN METHOD FOR THE DISTRIBUTION AND SHARING OF ONTOLOGIES Daniel Kotowski University of Guelph, 2016 Advisor: Professor Deborah Stacey. The backbone of semantic web technologies is the ontology. This is a powerful structure, which allows for the capture, reasoning and storing of expert knowledge across various domains. Ideally these structures should be developed and implemented by experts in a set domain as well as designed with re-usability in mind. However, often due to the lack of availability and difficulties of discovering ontologies, these structures are repeatedly recreated. Current methods for storing, discovering and sharing ontologies employ similar techniques as to those used for software source code or static web pages. These are exposed to the limitations inherent with keyword-based searches, such as ambiguity with the keywords themselves and therefore, the most relevant ontology may not be discovered. This paper will examine some of the existing techniques used for the storing and sharing of ontologies. It will offer a contrasting method analogous to software libraries to develop a standard to store, share, discover, and distribute common ontologies.
A Data-Driven Framework Based on Sparse Representation of Ultrasonic Guided-Waves for Online Damage Detection of Pipelines

ii iii To Mother Earth. iv v Acknowledgement This thesis would not have been possible without the guidance and support of many people who accompanied me throughout my Ph.D. I would like to thank my adviser Dr. Mario Berges, who never withheld his support. He indeed has had an immeasurable contribution in my journey in CMU, both academically and personally. His inquisitive and curious attitude developed a researcher's approach in me, which I have no doubt I will benefit from throughout my life. I would like to thank him for his friendship, unconditional guidance (even if it takes him to come to the lab on a Saturday to help me fixing a broken equipment), and his efforts in making the Ph.D. an enjoyable process for his students. I would like to thank my co-adviser Dr. Haeyoung Noh, who gladly agreed to be part of this journey. This thesis is indebted to her guidance, challenging questions (answering each of which was a step forward in my thesis), and valuable insights. She always encouraged me, and was always there when I needed her. I am grateful to my committee members, Dr. Irving Oppenheim and Dr. Joel Harley for their time and insightful discussions. I feel fortunate to have the privilege of learning from them throughout my Ph.D. Other people I owe thanks to are Dr. David Greve and Dr. Warren Junker for their valuable feedback on my work, and Dr. Chang Liu for providing the field data that I used for part of the experiments in this dissertation. vi I would also like to express my sincere gratitude to Dr. James H. Garrett. Regardless of how busy he was, he never stopped caring. His unconditional support and encouragement have had a significant impact on my experience in CMU. I am also grateful to kWantera Inc. for their generous contribution in my semester at CMU. I gained incredible friends in Pittsburgh, without whom this Ph.D. would have been very difficult and much less enjoyable. Emre, thank you for going through every ups and downs of this path with me, and never giving up on being a real friend. Sasan, thanks for always (always) being there for me. Your ability in making me laugh aloud at any time deserves a Nobel Prize. Laleh, Shadi and Azarin, thank you for being the girlfriends that every Ph.D. student would need. I cannot imagine finishing my Ph.D. …
Clustering over Evolving Data Stream with Mixed Attributes

Data stream has become an important source of data in many research and application fields. In this paper, a novel algorithm is presented for clustering evolving data stream with mixed attributes. It uses mirco-clusters to store the clustering statistical information, and then combines these mirco-clusters into macro-clusters and the final clusters by iterations. Periodically pruning outdated data and outliers with the filtering method will greatly save the memory space. Empirical evidence of the proposed algorithm shows its superiority over HCluStream [4] and CluStream [5] algorithms on the real data sets.
Application of Network Intrusion Based on Rough Set and Neural Network

Research on network intrusion detection problem.Because network intrusion data samples have incomplete,uncertainty and redundant information,traditional methods cannot deal with these data effectively,resulting in low intrusion detection accuracy.In order to improve the accuracy of network intrusion detection,this paper presents network intrusion detection method based on rough set and neural network,using rough sets theory to reduce the sample data attribute,eliminate redundant information,then the data is used as the BP neural network input.Through the BP neural network training,non-linear relation between input and output is established.Simulation results show that,compared with traditional BP neural network,the proposed method accelerates the network intrusion detection speed,improves the detection accuracy,reduces the rate of false positives.It is an effective network intrusion detection model.
Scalable and adaptive evolutionary clustering for noisy and dynamic data

In this dissertation a new evolutionary clustering algorithm, called ECSAGO from Evolutionary Clustering with Self Adaptive Genetic Operators, was developed. ECSAGO is a generalization an extension of Unsupervised Niche Clustering algorithm (UNC) that, while maintaining the desirable qualities of flexibility for a variety of applications, accuracy, and robustness to noise, is able to avoid UNC limitations related to representation, parameter dependency, and scalability in the face of large data sets. In particular, the proposed genetic clustering algorithm is able to deal with several encoding schemes, adapt the genetic operator rates automatically through the evolution, and is scalable and adaptable to different clustering problems. Extensions were done in order to deal with encoding schemes that can better represent complex search spaces, such as real and sparse spaces. Also, the UNC mating restriction condition was eliminated by introducing an appropriated encoding scheme and specialized genetic operators. The self-daptation of genetic operator rates was defined based on a hybrid parameter adaptation technique called Hybrid Adaptive Evolutionary Algorithm (HAEA), and it is performed while searching for the cluster prototypes. The scalable, ECSAGO module was defined based on a new interpretation of the mechanisms used in UNC within the context of kernel density estimation, incremental learning, data summarization, non-stationary function optimization, and memorization factor. The ECSAGO and scalable ECSAGO were used for solving real clustering problems where it is desired to discover the embedded categories in a data set without any prior knowledge about its class labels, and also used for classification problems, where an accurate clustering can form a summarized model of the data, that is later used for classification of new data and for anomaly detection. The data sets that were used in the experimental results stem from real life applications, including unsupervised classification and organization of text documents and network intrusion detection.
Special Measures for Children in Court: Law in Action in a Multi-agency Committee

ABSTRACT Approximately 25 years ago, special measures were adopted in many jurisdictions to support child sexual assault complainants in legal proceedings. This study tracked activities of a multi-agency committee of criminal justice professionals, including social workers, in enacting those provisions. We manually reviewed 99 sets of minutes of meetings of the New South Wales Sexual Assault Review Committee, spanning over two decades. Findings identified numerous gaps between legislation and practice, and the actions of this group, led by prosecutors, to narrow those gaps. Qualitative analyses revealed more focus on legislative issues and research in the early years, when closed-circuit television (CCTV) use commenced. Key evidence-based recommendations of the Committee were: (1) prerecorded evidence-in-chief of complainants; (2) cross-examination via CCTV; and (3) witness intermediaries. Investigation of and responses to stakeholder feedback on practice issues was an integral and consistent function of the Committee. In later years, the focus shifted to technological and administrative difficulties with prerecorded police interviews, CCTV, and court closure. IMPLICATIONS Longitudinal analyses revealed longstanding and robust issues in implementing special measures to improve experiences of vulnerable complainants in giving evidence. Proactive prosecution leadership and multidisciplinary membership contributed to a well-informed, evidence-based focus on the needs of vulnerable witnesses. Stakeholders from multiple agencies collaborated efficiently to resolve many practical issues, but improvements to certain systemic issues require resources beyond their capacity.
Research on the Order Picking Optimization Problem of the Automated Warehouse

Optimizing the order picking is effective on advancing the working efficiency of automatic warehouse.According to the characters of picking working in automated warehouse,a new mathematic model was proposed with capacity constrains and multiple objective.A genetic algorithm was presented to solve the problem with the initial population based on unfeasible degree and work times.Simulation and practice shows that this model and method is useful and effective.
Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network

ABSTRACT In this article, a novel dual-channel convolutional neural network (DC-CNN) framework is proposed for accurate spectral-spatial classification of hyperspectral image (HSI). In this framework, one-dimensional CNN is utilized to automatically extract the hierarchical spectral features and two-dimensional CNN is applied to extract the hierarchical space-related features, and then a softmax regression classifier is used to combine the spectral and spatial features together and predict classification results eventually. To overcome the problem of the limited available training samples in HSIs, we propose a simple data augmentation method which is efficient and effective for improving HSI classification accuracy. For comparison and validation, we test the proposed method along with three other deep-learning-based HSI classification methods on two real-world HSI data sets. Experimental results demonstrate that our DC-CNN-based method outperforms the state-of-the-art methods by a considerable margin.
Can They Do it All? Fiscal Space in Low-Income Countries

According to U.N. estimates, low-income countries will have to increase their annual public spending by up to 30 percent of GDP to achieve the Sustainable Development Goals (SDGs), raising the question of whether they can do it all. This paper develops a new metric of fiscal space in low-income countries that accounts for macroeconomic uncertainty, allowing us to assess whether those spending needs can be accommodated. Illustrative simulations based on this methodology imply that, even under benign conditions, the fiscal space available in lowincome countries is likely insufficient to undertake the spending needed to achieve the SDGs. Improving public investment efficiency and domestic revenue mobilization can somewhat narrow the gap but it will require major efforts relative to recent trends.
Life-cycle earnings role of labor market sorting and Job mobility

This paper studies how the underlying factors that determine an individual’s wage changes over their life-cycle, how these factors change at different education levels, and when a worker makes job-to-job moves. Using a longitudinal matched employer-employee data set from the Portuguese Ministry of Labor; I find that human capital accumulation and job mobility play an important role in wage growth during the beginning of an individual’s career. More specifically, I find that after 20 years of work wages increase, on average 41%. A large fraction of the returns to labor market experience is rooted in the returns to general training (25%) but sorting into better paying firms and job/titles is responsible for a significant 19% and 10% of the returns to labor market experience, respectively. Job-to-job mobility also enhances life-cycle earning (14%), as does the ability of the worker (32%).
Human Migration in the Era of Climate Change

Migration is one response to climatic stress and shocks. In this article we review the recent literature across various disciplines on the effects of climate change on migration. We explore key features of the relationship between climate change and migration, distinguishing between fast-onset and slow-onset climatic events and examining the causes of heterogeneity in migratory responses to climate events. We also seek to shed light on the interactions between different types of adaptations to climate events as well as the mechanisms underlying the relationship between climate change and migration. Based on our review of the existing literature, we identify gaps in the literature and present some general policy recommendations and priorities for research on climate-induced migration.
prepdat - An R Package for Preparing Experimental Data for Statistical Analysis

In many research fields the outcome of running an experiment is a raw data file for each subject, containing a table in which each row describes one trial conducted during the experiment. The next step is to merge all files into one big table, and then aggregate it into one finalized table in which each row corresponds (usually) to the averaged performance of each subject. prepdat - An R package- enables to easily perform these steps, including several possibilities for dependent measures and trimming procedures. prepdat helps researchers to optimize and speedup their analysis, and to better understand the results.
[PhD Thesis] Integrating distributed post-genomic data to infer the molecular basis of bacterial phenotypes

The aim of the project described in this thesis is to understand and predict the characteristics and behaviour of a family of bacteria through an analysis of genome wide data from a variety of sources. The focus of the research is a family of bacteria, Bacillus, whose members show a diverse range of phenotypes, from the non-pathogenic B. subtilis to B. anthrncis, the causative agent of anthrax. Specifically, the focus was on the genomic scale identification and characterisation of secreted proteins from Bacillus species. Firstly, the application of Grid-based computational approaches to problems in genomic analysis and annotation was investigated, applying mllGrid technology to a biological problem not previously addressed using this approach. e-Science workflows and a service-oriented approach were developed and applied to predict and characterise secreted proteins, and the results automatically integrated into a custom relational database. An associated Web portal was also developed to facilitate expert curation, results browsing and querying over the database. Workflow technology was also used to classify the putative secreted proteins into families and to study the relationships between and within these families. The design of the workflows, the architecture and the reasoning behind the approach used to build this system, called BaSPP, are discussed. Analysis of the putative Bacillus secretomes revealed clear distinctions between proteins present in the pathogens and those in the non-pathogens. The properties of the protein families present in all Bacillus secretomes, as well as those specific either to the pathogens or to the non-pathogens were investigated. Many of the protein families contained members of unknown function. In the iv second part of the project, these families were investigated in more depth, using additional data integration strategies not previously applied to these organisms. The secretomes were modelled at the system level, in the broader context of interactomes. A system called SubtilNet was therefore developed, using B. subtilis as the reference organism. As part of SubtilNet, a toolkit and architecture were developed and implemented for building and analysing probabilistic functional integrated networks (PFINs). The PFINs built for each Bacillus species using this system were subsequently used to delve further into the interactions specific to the secreted proteins by extracting and exploring the cross-species PFINs of these proteins. The cross-species PFINs for the protein families specific to the pathogens and non-pathogens were explored, with particular emphasis on the core PrsA-like protein family, which acted as a use case to show how the PFIN s can be used to shed light on protein function. The addition of orthologous links between species was demonstrated to facilitate network clustering and analysis, enabling putative annotations to be applied to proteins previously of unknown function.
A systematic review of climate change education: giving children and young people a ‘voice’ and a ‘hand’ in redressing climate change

ABSTRACT The reality of anthropogenic climate change has been established ‘beyond reasonable doubt’ by leading scientists worldwide. Applying a systematic literature review process, we analysed existing literature from 1993 to 2014 regarding climate change education for children and young people, with the aim of identifying key areas for further research. While a number of studies have indicated that young people’s understandings of climate change are generally limited, erroneous and highly influenced by mass media, other studies suggest that didactic approaches to climate change education have been largely ineffectual in affecting students’ attitudes and behaviour. The review identifies the need for participatory, interdisciplinary, creative, and affect-driven approaches to climate change education, which to date have been largely missing from the literature. In conclusion, we call for the development of new forms of climate change education that directly involve young people in responding to the scientific, social, ethical, and political complexities of climate change.
Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidationbased and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple