Miracle-dz/UniFAR-FTUs huggingface dataset:
Running head: COMPARE CORRELATIONS Toward Using Confidence Intervals to Compare Correlations (will appear in Psychological Methods)

Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate procedures that can maintain coverage at the nominal level in a non-lopsided manner. The purpose of this paper is to present a general approach to constructing approximate confidence intervals for differences between: 1) two independent correlations; 2) two overlapping correlations; 3) two nonoverlapping correlations; and 4) two independent R-squares. The distinctive feature of this approach is its acknowledgment of the asymmetry of sampling distributions for single correlations. This approach requires only the availability of confidence limits for the separate correlations and, for correlated correlations, a method for taking into account the dependency between correlations. These closed-form procedures are shown by simulation studies to provide very satisfactory results in small to moderate sample sizes. The proposed approach is illustrated using worked examples.
Internship – Master M2 (2014/2015)

Direct approach vs. translation for non-classical logics In order to mechanize non-classical logics, there exist at least two main approaches. The direct approach consists in building specialized proof systems for the logics and requires building new theorem provers but, it has the advantage to design fine-tuned tools and to propose plenty of optimizations. The development of tableaux-based provers for description logics perfectly illustrates this trend. By contrast, the translation approach consists in reducing decision problems for the source logics to similar problems for target logics that have already well-established theorem provers. Its main advantage is to use existing tools and therefore to focus only on the translations, that are usually much simpler to implement.
Displacement Measurement of Structural Response using Matlab Image Processing and Object

This paper presents a non-contact measurement method to measure displacements of a structural response, which is based on digital image processing and object tracking techniques. This method utilises MATLAB programming, which has been developed through three phases; object detection, calibration, and object tracking. For the object detection and calibration algorithms, Circular Hough Transform (CHT) technique is adopted, while for the object tracking algorithm, the Kanade-Lucas-Tomasi (KLT) feature tracker is employed. Videos are acquired through a vision-based system using a smartphone camera, and then they are analysed and interpreted using the developed program to obtain measurement data. The relationship between parameters of video resolution, distance between objects to camera and radius of target is established for guideline purposes. In order to evaluate the accuracy and reliability of this approach, parametric studies were carried out and the obtained results were compared against the ones provided by the conventional approach using a dial gauge. In overall, the computed results show a good agreement with the actual results with relatively small differences. This makes the proposed alternative method as a simple and cost-effective method yet provides favourable characterisation outcomes.
Exerting Self-Control 61⁄4 Sacrificing Pleasure

Self-control is a prominent topic in consumer research, where it is often conceptualized as the abstinence from hedonic consumption. We examine whether this conceptualization accurately captures consumers’ experiences of self-control conflicts/failures in light of seminal self-control theories in economics and psychology. Rejecting that notion, we argue that self-control failures are choices in violation of superordinate long-term goals accompanied by anticipated regret, rather than choices of hedonic over utilitarian consumption. This conceptualization has important methodological, theoretical, and practical implications. Methodologically, it highlights the need for experimental paradigms with higher construct validity. Theoretically, it helps elucidate how self-control is distinct from impatience and self-regulation. Practically, it provides a rich set of implications for deducing interventions on the individual and public policy level to help consumers exert self-control.
SYNCHRONIZATION IN MULTILAYER NETWORKS: WHEN GOOD

Many complex biological and technological systems can be represented by multilayer 4 networks where the nodes are coupled via several independent networks. Despite its significance 5 from both the theoretical and application perspectives, synchronization in multilayer networks and 6 its dependence on the network topology remain poorly understood. In this paper, we develop a 7 universal connection graph-based method which opens up the possibility of explicitly assessing critical 8 multilayer-induced interactions which can hamper network synchronization. The method reveals 9 striking, counterintuitive effects caused by multilayer coupling. It demonstrates that a coupling 10 which is favorable to synchronization in single-layer networks can reverse its role and destabilize 11 synchronization when used in a multilayer network. This property is controlled by the traffic load on 12 a given edge when the replacement of a lightly loaded edge in one layer with a coupling from another 13 layer can promote synchronization, but a similar replacement of a highly loaded edge can break 14 synchronization, forcing a “good” link to go “bad. ” This method can be transformative in the highly 15 active research field of synchronization in multilayer engineering and social networks, especially in 16 regard to hidden effects not seen in single-layer networks. 17
Distributed Query Optimization Master’s Thesis

This thesis focuses on the query optimization of graph databases that are distributed over several compute nodes. Most of these distributed graph databases optimize the query centrally. The resulting optimized query plan is then executed on all compute nodes. Since the different compute nodes store different data items, the centrally optimized query plan may be inefficient on some compute nodes. To overcome this limitation, this thesis proposes and investigates a distributed optimization approach that optimizes an individual query plan for each compute node while considering the stored data. Thereby the presented approach aims to improve the query performance by speeding up the processing of the query plan on all slave nodes. A side effect of the distributed optimization approach is the duplication of certain intermediate results during query processing. The evaluation shows that the actual performance gain of the approach cannot be determined, given the additional workload caused by processing these duplicate intermediate results.
Multi-target feature selection through output space clustering

A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms: the relevancy of a feature with the output, the redundancy and the complementarity between groups of features. The challenge becomes more pronounced in multi-target problems, where the output space is multi-dimensional. Our work presents a generic algorithm that captures these three desirable terms and is suitable for the well-known multi-target prediction settings of multi-label/dimensional classiﬁcation and multivariate regression. We achieve this by combining two ideas: deriving low-order information theoretic approximations for the input space and using clustering for deriving low-dimensional approximations of the output space.
It’s a family business: Investigating organisation and values of family-owned wineries in Australia, Germany and Italy

Purpose Family businesses play a crucial role in the world wine sector. Little research has been conducted previously to investigate how organisational characteristics of wineries are influenced through family ownerand leadership of the business. This study aims to advance knowledge in this field by exploring family wineries in different wine-producing countries. Design/methodology/approach Qualitative semi-structured interviews have been conducted with 53 family-owned wineries in Australia, Germany and Italy. The sample shaped up by companies of different age and size. Data have been transcribed and aggregated to identify patterns within the sample. Findings The governance of family-owned wineries has shown great diversity among the sample. Size, country, age of the business, as well as expertise of the top management team have been identified to be key influencing variables. Implementation of governance structures has shown to become indispensable in family businesses as the company grows. Australian businesses have been found to formally address this issue. Ensuring that family values are carried through any level of the business supports governance from a social point of view. Practical implications In an increasingly fast-paced business environment in the agricultural sector, implementation of governance structures can become a strategic resource and support longevity of the family businesses.
Extending Narrative Planning Domains with Linguistic Resources

Interactive Narrative (IN) is an emerging application of planning for control of virtual character behaviours. Despite popularity in research, more widespread adoption of planning has been hindered by the difficulty of authoring planning domain models as INs require models which are robust to dynamic environments and capable of generating a diversity of solutions. To reduce this authoring burden we explored automated extension of partially developed planning domains to address robustness and diversity. We introduce a novel algorithm, thype, which proposes additional types of characters and objects to extend a domain model. The extensions increase robustness by enlarging the range of ways to enact planned behaviours. In the paper we embed thype within a modular and extensible framework that allows multiple off-line generated extensions to be combined. We present results of a user study that show thype suggestions are plausible. We also empirically demonstrate the ability of thype extensions to increase the robustness of the models, demonstrate the modular nature of our extension framework by combining thype with another extension approach dedicated to recovery from plan failure and present results which show that enhanced performance results from combining multiple extensions.
Some aspects of Lévy processes in finance

This thesis deals with two aspects of Lévy processes in finance, namely change of time to model dependency in logreturns of financial data, and option pricing where change of measure is necessary. It is shown that a change of time with the so called integrated Cox Ingersoll Ross process, can be used to model dependency of squared logreturns of financial data. Option prices are compared in different Lévy models, using the Esscher change of measure.
Low Phase Noise Local Oscillator and Clock Generation for Cavity Field Detection

The paper presents local oscillator (LO) and clock (CLK) generation module of ultra-low phase noise level. The LO signal is used in superheterodyne receiver of Low-Level RF (LLRF) field detector. The CLK signal is used as a low jitter sampling clock for analog-to-digital converters (ADC) of the LLRF digitizer. These high performance signals allow precise control of electromagnetic field in superconducting RF cavities that accelerate electrons in the new generation of Free-Electron Laser (FEL) machines. Both LO and CLK signals are generated in a single module based on PCB circuit. The module meets stringent electrical and mechanical requirements of the accelerator system. The paper discusses a few most promising scenarios of the signal generation. Design considerations, modelling issues, circuit prototypes and final realization of the module are described. Phase noise measurements proof excellent results achieved. The designed module produces up to 0.5 W of low phase noise 1354 MHz signal of single femtoseconds of integrated RMS time jitter, noise floor of -165 dBc/Hz and spurious-free range better than 90 dBc.
Measuring the impact of code quality analysis tools Work in Progress

While some university-level programming courses focus on software quality, often in introductory courses code quality is little touched upon due to time constraints. When students work on a programming assignment, they usually get feedback on the code quality after the assignment was graded. This feedback cannot be used on that same assignment, before the grade is determined. Better Code Hub is a service that checks code quality according to ten guidelines. We employ Better Code Hub as a formative assessment and feedback tool that students can use to monitor their progress on code quality. Our aim is to improve students’ skills for code quality during the evolution of a students’ programming assignment, while keeping the overhead low for teaching staﬀ as well as for students. Preliminary ﬁndings indicate that there is an improvement in the code quality of the students’ assignments over the period Better Code Hub was used. This research is performed at the Software Improvement Group as a thesis project for the Software Engineering master program of the University of Amsterdam.
Development of a low voltage Dielectric Electro-Active Polymer actuator

In the present paper, a low-voltage Dielectric Electro-active Polymer (DEAP) device is presented. It is based on the DEAP technology, together with a very compact driver, utilising piezoelectric transformer technology. A major limitation for the use of DEAP for actuator application is the high voltage required for operation. The device presented in this paper is only supplied by low voltage and control signals leading to volume and mass savings for the overall system as well as easier integration. The driver is based on a piezoelectric transformer, combining high step-up ratio, high efficiency and small size. Several prototypes have been produced and tested. Test results indicate that the mechanical integration between electronics and DEAP has some impact on performance. However, performance and reliability are still very good, so this technology is promising. New prototypes are being developed with even improved performance.
Learning To Forget: Design of Experiments for Line-Based Bayesian Optimization in Dynamic Environments

Various scientific and engineering fields rely on measurements in 2D spaces to generate a map or locate the global optimum. Traditional design of experiments methods determine the measurement locations upfront, while a sequential approach iteratively extends the design. Typically, the cost of traveling between sample locations can be ignored, for example in simulation experiments. In those cases, the experimental design is generated using a point-based method. However, if traveling towards the next sample location incurs an additional cost, line-based sampling methods are favored. In this setting, the sampling algorithm needs to generate a route of measurement locations. A common engineering problem is locating the global optimum. In certain cases, such as fire hotspot monitoring, the location of the optimum dynamically changes. In this work, an algorithm is proposed for sequentially locating dynamic optima in a line-based setting. The algorithm is evaluated on two dynamic optimization benchmark problems.
The deep structure of continents

The phase velocity of Rayleigh waves has been used to study the deep structure of continents for more than fifty years. In the last ten years the widespread installation of broad band digital seismometers has generated more than 10 seismograms that can be used for surface wave tomography, and so obtain maps of the thickness of the continental lithosphere. Such maps show that the tectonic architecture of continents is controlled by variations in lithospheric thickness, and that many regions of flat-lying phanerozoic sediment are underlain by thick lithosphere. Perhaps the most surprising discovery is that the thickest lithosphere that has yet been mapped lies beneath Tibet.
A Proposal for Holistic Assessment of Computational Thinking for Undergraduate: Content Validity

Studies have acknowledged computational thinking (CT) as an efficient approach for problem-solving particularly required in digital workplaces. This research aims to identify indicators for a holistic CT assessment instrument for undergraduate students. A three-round fuzzy Delphi study has been conducted to gain comprehensive opinions and consensus from undergraduate lecturers of computer science disciplines and experts from the information technology industry. In round 1, the experts judged a set of predefined indicators describing CT skills and attitudes identified from the literature, while rounds 2 and 3 focused on variables selection. The consensus was achieved on holistic CT, and the indicators are teamwork, communication, spiritual intelligence, generalization, problem-solving, algorithmic thinking, evaluation, abstraction, decomposition, and debugging. Results demonstrate the importance of attitudes in the process of solving a problem and suggest higher education institutions to consider holistic CT in preparing qualified future graduates. Many CT studies focused only on the skills of CT. This study outlines the assessment indicators that consider both CT skills and attitudes, particularly at the undergraduate level.
Progress Towards Graph Optimization: Efficient Learning of Vector to Graph Space Mappings

Optimization in vector space domains is well understood. However, in high dimensional settings or when dealing with structured data such as sequences and graphs, optimization becomes diﬃcult. A possible strategy is to map graphs to vector codes and use machine learning to learn a map from codes back to graphs. This in turn allows to employ standard optimization techniques over vectors to optimize graphs. Here we propose an approach to invert a vector mapping based on a combination of graph kernels and graph grammars. We evaluate the proposed approach in an artiﬁcial setup and on real molecular graphs.
On the Speedup of Deep Reinforcement Learning Deep Q-Networks (RL-DQNs)

Deep reinforcement learning (DRL) merges reinforcement (RL) and deep learning (DL). In DRL-based agents rely on high-dimensional imagery inputs to make accurate decisions. Such excessively high-dimensional inputs and sophisticated algorithms require very powerful computing resources and longer training times. To alleviate the need for powerful resources and reduce the training times, this paper proposes novel solutions to mitigate the curse-of-dimensionality without compromising the DRL agent performance. Using these solutions, the deep Q-network model (DQN) and its improved versions require less training times while achieving better performance.
Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines

Background Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse–related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes—abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). Conclusions Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.
PROOF COVER SHEET

This study addresses the gap in the research for sound multidimensional assessment of social capital and its relationship with risk-taking behaviour among youths living in disadvantaged communities. Social capital and adolescent risk-taking outcomes were studied cross-sectionally in 1371 secondary students living in two disadvantaged communities within Australia. First, a multidimensional measure of social capital was developed and tested using confirmatory factor analysis. Then, the associations between social capital and a range of youth risk-taking behaviours were examined using structural equation modelling across fiveyear groups (Grades 7–12). With a few exceptions, higher levels of social capital and belongingness within the school and community were generally associated with decreases in smoking, alcohol and drug consumption, and physical violence. Some outcomes were more strongly associated with family and peer social capital, while others associated more with neighbour and community social capital, indicating that attempts to build social capital need to be targeted across the whole community. This study supports the notion that social capital can be measured empirically and is beneficial in alleviating many of the detrimental health outcomes commonly associated with risk-taking behaviours during adolescence. ARTICLE HISTORY Received 22 April 2015 Accepted 18 September 2015