Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpreting Word Embeddings with Eigenvector Analysis

Dense word vectors have proven their values in many downstream NLP tasks over the past few years. However, the dimensions of such embeddings are not easily interpretable. Out of the d-dimensions in a word vector, we would not be able to understand what high or low values mean. Previous approaches addressing this issue have mainly focused on either training sparse/non-negative constrained word embeddings, or post-processing standard pre-trained word embeddings. On the other hand, we analyze conventional word embeddings trained with Singular Value Decomposition, and reveal similar interpretability. We use a novel eigenvector analysis method inspired from Random Matrix Theory and show that semantically coherent groups not only form in the row space, but also the column space. This allows us to view individual word vector dimensions as human-interpretable semantic features.
The effect of adaptive performance support system on learning achievements of students.

The study compares the effectiveness of two performance support systems, adaptive and non-adaptive, on learning achievements of engineering students. In addition, the research design controls for a possible effect of learning style. The analysis reveals that students working with an adaptive performance support system score significantly higher than students using a non-adaptive performance system on a performance test across different learning styles. The only variation in the two experimental conditions, manipulated in the study, is embedded adaptive arrangement based on learning style. Embedded adaptation mode accommodates learning preferences of students through the structure of learning content as an association between types of learning content and different learning styles is assumed. Learning style does not produce a significant difference in the performance achievements of students and there is no indication for an interaction effect between performance support system as a method of instruction and learning style. These results are explained by two theoretical positions introduced in the background of the study, namely coping behaviour and the distinction between level and style type of cognitive constructs.
Constructing a High-Dimensional kNN-Graph Using a Z-Order Curve

Although many fast methods exist for constructing a kNN-graph for low-dimensional data, it is still an open question how to do it efficiently for high-dimensional data. We present a new method to construct an approximate kNN-graph for medium- to high-dimensional data. Our method uses one-dimensional mapping with a Z-order curve to construct an initial graph and then continues to improve this using neighborhood propagation. Experiments show that the method is faster than the compared methods with five different benchmark datasets, the dimensionality of which ranges from 14 to 784. Compared to a brute-force approach, the method provides a speedup between 12.7:1 and 414.2:1 depending on the dataset. We also show that errors in the approximate kNN-graph originate more likely from outlier points; and, it can be detected during runtime, which points are likely to have errors in their neighbors.
Enhancing Greedy Policy Techniques for Complex Cost-Sensitive Problems

One of the most prominent domains of application for machine learning techniques is data mining, which focuses on the discovery of novel, structured and potentially useful patterns in data. Each machine learning algorithm makes assumptions regarding the underlying problems it needs to solve. These assumptions and the search strategy employed constitute the bias of the learning algorithm. This bias restricts the area of successful application of the algorithm. Also, recent research in machine learning has revealed the necessity to consider more complex evaluation measures for learning algorithms, in some problem domains. One such novel measure is the cost. There are various types of costs involved in inductive concept learning, but, for the domains we focus on, the most important are test costs and misclassification costs. This chapter presents ProICET (Vidrighin et al, 2007), a hybrid system for solving complex cost-sensitive problems. We focus both on the theoretical principles of the approach, as well as highlight some implementation aspects and comparative evaluations on benchmark data (using other prominent learning algorithms). The remainder of the chapter is structured as follows: section 2 reviews the search problem, and a few fundamental strategies, and provides basic definitions for data mining and decision trees. Section 3 presents the cost-sensitive problem, together with a brief survey of the most prominent cost-sensitive learners present in literature. Section 4 presents the theory behind ProICET, followed by the enhancements considered and an overview of the implementation. Section 5 presents the experimental work performed in order to validate the approach. The chapter summary is presented in the final section.
Concepts for Developing Physical Gels of Chitosan and of Chitosan Derivatives

Chitosan macro- and micro/nano-gels have gained increasing attention in recent years, especially in the biomedical field, given the well-documented low toxicity, degradability, and non-immunogenicity of this unique biopolymer. In this review we aim at recapitulating the recent gelling concepts for developing chitosan-based physical gels. Specifically, we describe how nowadays it is relatively simple to prepare networks endowed with different sizes and shapes simply by exploiting physical interactions, namely (i) hydrophobic effects and hydrogen bonds—mostly governed by chitosan chemical composition—and (ii) electrostatic interactions, mainly ensured by physical/chemical chitosan features, such as the degree of acetylation and molecular weight, and external parameters, such as pH and ionic strength. Particular emphasis is dedicated to potential applications of this set of materials, especially in tissue engineering and drug delivery sectors. Lastly, we report on chitosan derivatives and their ability to form gels. Additionally, we discuss the recent findings on a lactose-modified chitosan named Chitlac, which has proved to form attractive gels both at the macro- and at the nano-scale.
Improving Video Quality in DASH Systems by Proposing Adaptive Bitrate Scheme based on Variable Segment Size Approach

Dynamic adaptive streaming via HTTP (DASH) has been widely disseminated over the Internet especially under the circumstances of the time varying network, which it is still the biggest challenge for providing smoothly video streaming with high quality. In DASH system, after the downloading process of a segment is completed, the player estimates the available network bandwidth by calculating the download throughput and adapting the video bitrate level based on its estimations. At client side, the DASH player uses an Adaptive Bitrate Algorithm (ABR) to choose the suitable bitrate for next segment based on current conditions. However, these adaptive algorithms discard the fact that the segment sizes greatly vary for a given video bitrate. Hence they may fail to predict the time needed for downloading the next segment. In this paper, an adaptive bitrate algorithm is proposed based on the video segment size as well as the network bandwidth estimation and the current buffer occupancy in order to accurately predict the time needed to download the next segment. Simulation results show that the proposed scheme is able to predict the download time. Also we compared the proposed scheme with other conventional schemes, we found that our proposed scheme outperforms others in
Molecular analysis of Continuous Plankton Recorder samples, an examination of echinoderm larvae in the North Sea

Analysis of the biological time series of plankton samples collected by the Continuous Plankton Recorder (CPR) in the North Atlantic and North Sea has shown a regime shift in the plankton in this region. Both the distributions of planktonic organisms and their timing of occurrence in the seasonal cycle have changed and these changes appear to reflect global warming. In the North Sea the planktonic larvae of echinoderms have shown a recent dramatic increase in both relative and absolute abundance and their seasonal peak of occurrence has advanced by 47 days. The identity of the echinoderm larvae involved in this change has, however, remained equivocal. The small size of many organisms like echinoderm larvae combined with incomplete taxonomic keys hinders their visual identification and their fragility often means that useful morphological features are damaged during sampling by the CPR. Here, using new molecular methods applied to CPR samples, we show that planktonic larvae of the benthic Echinocardium cordatum dominate the North Sea plankton. We argue that since this species benefits from mild winters and warmer waters their numerical increase in the plankton is consistent with recent climatic changes that appear to be affecting the wider ecology of this region.
Influence of Random Pinning on the Crystallization Process in Suspensions of Hard Spheres

We discuss crystal formation in supersaturated suspensions of monodisperse hard spheres with a concentration of hard spheres randomly pinned in space and time. The pinning procedure introduces an external length scale and an external time scale that restrict the accessible number of configureurations and ultimately the number of pathways leading to crystallization. We observe a significant drop in the nucleation rate density at a characteristic pinning concentration that can be directly related to the structure of the critical nucleus and the dynamics of its formation in the unpinned system.
MaPS: A framework to aid the development of collaborative applications for ubiquitous environments

A research topic of growing interest is the convergence between Collaborative Systems and Ubiquitous Computing, where context awareness is becoming a tool for enhancing collaboration processes. The application of Ubiquitous Computing concepts in the improvement of collaboration strategies created a research front called Ubiquitous Collaboration. This article proposes a framework to aid the development of collaborative applications for ubiquitous environments, called MaPS. MaPS works at one relevant stage of the collaboration. It uses context information and user profiles to improve the search for peers and the selection of communication channels. The article proposes the framework, its requirements and its architecture. Moreover, we describe a prototype and two applications which were developed with it. The framework was evaluated considering software development, based on the experience got in the implementation of the applications and aspects of functionalities. It was made through a scenario involving active participants. The results of both evaluations show the potential for using MaPS. Keywords: collaboration, collaborative applications, ubiquitous computing, ubiquitous collaboration, ubiquitous environments, context awareness.
Illustrating Training Opportunities Related to Manpower Facility Protection through the Example of Máv Co.

Smart towns plan several progressive practical innovations with the aim of ensuring the convenience of its inhabitants and that of commuters or visitors. These innovations aim at not only ensuring convenience but also at increasing the living standards based on sustainable results with the help of smart tools and logical methods. The foundation of smart towns is to ensure the security of its inhabitants. Hence, it is a vital task to maintain the operation under normal conditions of critical infrastructure. According to the Act 1997. year CLIX. critical infrastructure is also related to the railway activity of MAV-group. The Act concerns armed security sentry, conservationist and agrarian sentry. Hence safeguarding activity is performed by both security guards and armed security guards at MAV groups. Their duties do not only consist of preventive security tasks but also defence in certain unique cases in accordance with the regulations of their field of work. In order for them to perform their duties properly it is vital that an extensive, regular and practice-oriented training of security guards to happen. This publication aims at illustrating the current training opportunities applied in the field of safeguarding that ensure the functioning of the MAV group. Also, some potential improvements of training opportunities are shed a light on. We outline the internal training opportunities security professionals - employed by MAV Co. - can participate at. Besides that, we illustrate the possibilities of employee retraining and courses professionals employed by MAV Co. are offered in order for them to become well-trained professionals that can fit the complex system of the concept of a smart town.
Mechanism of high-resolution STM/AFM imaging with functionalized tips

High resolution Atomic Force Microscopy (AFM) and Scanning Tunnelling Microscopy (STM) imaging with functionalized tips is well established, but a detailed understanding of the imaging mechanism is still missing. We present a numerical STM/AFM model, which takes into account the relaxation of the probe due to the tip-sample interaction. We demonstrate that the model is able to reproduce very well not only the experimental intra- and intermolecular contrasts, but also their evolution upon tip approach. At close distances, the simulations unveil a significant probe particle relaxation towards local minima of the interaction potential. This effect is responsible for the sharp sub-molecular resolution observed in AFM/STM experiments. In addition, we demonstrate that sharp apparent intermolecular bonds should not be interpreted as true hydrogen bonds, in the sense of representing areas of increased electron density. Instead they represent the ridge between two minima of the potential energy landscape due to neighbouring atoms.
eview dvances in crop insect modelling methods — Towards a whole system pproach

A wide range of insects affect crop production and cause considerable yield losses. Difficulties reside on the development and adaptation of adequate strategies to predict insect pests for their timely management to ensure enhanced agricultural production. Several conceptual modelling frameworks have been proposed, and the choice of an approach depends largely on the objective of the model and the availability of data. This paper presents a summary of decades of advances in insect population dynamics, phenology models, distribution and risk mapping. Existing challenges on the modelling of insects are listed; followed by innovations in the field. New approaches include artificial neural networks, cellular automata (CA) coupled with fuzzy logic (FL), fractal, multi-fractal, percolation, synchronization and individual/agentbased approaches. A concept for assessing climate change impacts and providing adaptation options for agricultural pest management independently of the United Nations Intergovernmental Panel on Climate Change (IPCC) emission scenarios is suggested. A framework for estimating losses and optimizing yields within crop production system is proposed and a summary on modelling the economic impact of pests control is presented. The assessment shows that the majority of known insect modelling approaches are not holistic; they only concentrate on a single component of the system, i.e. the pest, rather than the whole crop production system. We suggest system thinking as a possible approach for linking crop, pest, and environmental conditions to provide a more comprehensive assessment of agricultural crop
How do we characteristically measure and analyze intergenerational mobility ?

This paper reviews the sociological and economic literature on intergenerational mobility. The author reviews mobility analysis in terms of social class, occupational status, earnings, and income. The conceptual foundations and empirical strategies to analyze these different types of mobility are examined, and factors accounting for discrepancies between them are discussed. The review also discusses the assessment of non-linearities in the intergenerational association; variation in mobility across advanced industrial countries; and recent mobility trends in the US. While the review focuses on the parent-children associations, it briefly describes sibling correlations as a measure of mobility.
Medium Voltage Single-Stage Dual Active Bridge Based Solid State Transformer (DABSST)

This paper introduces a single-stage medium voltage (MV) solid state transformer (SST) based on dual active bridge topology and recently developed high voltage Super-cascode device. A closed loop control system of the output voltage is designed and verified by simulation results. A fully functional SST based on the proposed concept has been developed and tested with an input voltage of 3.6kV and experimental results show unity power factor operation with zero voltage switching of both primary and secondary devices.
Formation of secondary aerosols over Europe: comparison of two gas-phase chemical mechanisms

Abstract. The impact of two recent gas-phase chemical kinetic mechanisms (CB05 and RACM2) on the formation of secondary inorganic and organic aerosols is compared for simulations of PM 2.5 over Europe between 15 July and 15 August 2001. The host chemistry transport model is Polair3D of the Polyphemus air-quality platform. Particulate matter is modeled with a sectional aerosol model (SIREAM), which is coupled to the thermodynamic model ISORROPIA for inorganic species and to a module (MAEC) that treats both hydrophobic and hydrophilic species for secondary organic aerosol (SOA). Modifications are made to the gas-phase chemical mechanisms to handle the formation of SOA. In order to isolate the effect of the original chemical mechanisms on PM formation, the addition of reactions and chemical species needed for SOA formation was harmonized to the extent possible between the two gas-phase chemical mechanisms. Model performance is satisfactory with both mechanisms for speciated PM 2.5 . The monthly-mean difference of the concentration of PM 2.5 is less than 1 μg m −3 (6%) over the entire domain. Secondary chemical components of PM 2.5 include sulfate, nitrate, ammonium and organic aerosols, and the chemical composition of PM 2.5 is not significantly different between the two mechanisms. Monthly-mean concentrations of inorganic aerosol are higher with RACM2 than with CB05 (+16% for sulfate, +11% for nitrate, and +10% for ammonium), whereas the concentrations of organic aerosols are slightly higher with CB05 than with RACM2 (+22% for anthropogenic SOA and +1% for biogenic SOA). Differences in the inorganic and organic aerosols result primarily from differences in oxidant concentrations (OH, O 3 and NO 3 ). Nitrate formation tends to be HNO 3 -limited over land and differences in the concentrations of nitrate are due to differences in concentration of HNO 3 . Differences in aerosols formed from aromatic SVOC are due to different aromatic oxidation between CB05 and RACM2. The aromatic oxidation in CB05 leads to more cresol formation, which then leads to more SOA. Differences in the aromatic aerosols would be significantly reduced with the recent CB05-TU mechanism for toluene oxidation. Differences in the biogenic aerosols are due to different oxidant concentrations (monoterpenes) and different particulate organic mass concentrations affecting the gas-particle partitioning of SOA (isoprene). These results show that the formulation of a gas-phase chemical kinetic mechanism for ozone can have significant direct (e.g., cresol formation) and indirect (e.g., oxidant levels) effects on PM formation. Furthermore, the incorporation of SOA into an existing gas-phase chemical kinetic mechanism requires the addition of reactions and product species, which should be conducted carefully to preserve the original mechanism design and reflect current knowledge of SOA formation processes (e.g., NO x dependence of some SOA yields). The development of chemical kinetic mechanisms, which offer sufficient detail for both oxidant and SOA formation is recommended.
Determinants of Credit Allocation for Photovoltaic Projects: Research Outline and Preliminary Findings from Conjoint Experiments with German Financing Experts

An increasing concern for photovoltaic (PV) projects is a shortage of capital in the medium term. PV project financing is an urgent topic due to a fundamental lack of knowledge about how debt capital providers evaluate this class of infrastructure projects. Moreover, after recent turbulences on the financial markets it is unclear how the availability of capital for renewable energy projects will develop. In this context financing models are decisive especially for medium- and large-scale PV projects, since large shares of debt capital are needed: Practitioners assume debt ratios of up to 80% or even 90%. As there is only limited academic research on project characteristics and their impact on credit allocations, the focus is on lenders’ preferences for different project types. The explorative research approach therefore addresses the following question: Which types of PV projects do lenders prefer to finance? We try to answer this question by conducting an Adaptive Choice Based Conjoint experiment (ACBC) with German experts in renewable energy project financing (banks, savings banks, consultants, project developers). To get robust and relevant results we clearly define our research scope: medium- and large scale ground-mounted installations being subject to the German Renewable Energy Sources Act (EEG) as of 2009. Initially, we supposed that from a lender’s perspective two attributes might be of utmost importance: First, Debt Service Cover Ratio (DSCR), indicating the security of loan repayment. Second, a hardly discerned but assumingly important aspect is the project initiator who is the central stakeholder during project development. This attribute is conceptualized by referring to possible project initiators’ business models. Examples are vertically integrated PV manufacturers, financial investors, service providers, and regional or multinational utilities. To provide for an as authentic as possible conjoint experiment, six attributes with 22 attribute levels in total were developed through literature studies and expert interviews. The six attributes are: DSCR, system capacity, brand/low cost technology, project initiator, maintenance concept, and equity ratio. Our survey, taking place from January to March 2010, is still online as this working paper is being written; thus, results are preliminary. We report from a sample size of 1.240 choice tasks conducted by 30 interviewees; 40 to 50 experts are expected to participate in the end. So far, the empirical data do not support our assumptions regarding the outstanding importance of DSCR and project initiator. Instead, our preliminary results are suggestive of a “brand bias” in lenders’ decisions.