Miracle-dz/UniFAR-FTUs huggingface dataset:
Switching Costs in the Finnish Retail Deposit Market

I calibrate the switching cost for the Finnish retail deposit market by using the approach developed by Oz Shy (2002). It turns out that switching costs faced by deposit customers of the main Finnish banks manifest large variation and are high, ranging from 200 euros to nearly 1,400 euros. Over a 20-year period, switching costs have increased by roughly 50% in real terms, but in relation to average account balance, switching costs have not essentially changed. Changes and differences in the banks’ competitive strategies might explain the variation in switching costs across time and banks.
A Distributed Iterative Learning Framework for DC Microgrids: Current Sharing and Voltage Regulation

With the penetration of computation intelligence, an increasing number of learning methods are developed into power engineering, such as dc microgrid applications. This paper establishes a distributed iterative learning framework to solve the current sharing and voltage regulation problem in an islanded dc microgrid from the perspective of game theory. The control objectives of dc microgrid include not only achieving the desired output current dispatch, but also regulating the voltage of dc bus to its rated value. Based on the two objectives, local performance indexes are established and an N-player game is formulated. Each source aims to minimize its own performance index and to achieve the current sharing objective simultaneously. Under the framework of game theory, a distributed iterative learning algorithm is designed based on the Bellman optimality principle and subsequently carried out using the approximate dynamic programming technique. The proposed algorithm is data based where it does not require to have the accurate model parameters of the dc microgrid and it ensures that the dc microgrid falls into a Nash equilibrium. Furthermore, a rigorous convergence analysis of the proposed algorithm is given. To demonstrate the effectiveness of the proposed method, simulation examples are presented on a tested dc microgrid.
CREATIVE DESIGN THINKING BY WALKING THROUGH VIRTUAL CONCEPT SPACE

In order to confirm how the visual understanding of the structure of concepts by walking through a virtual concept space can support design thinking, a virtual concept space is constructed based on a concept dictionary and is then visualized in a 3D virtual space. Then, two experiments are conducted to find the centre of three certain concepts in, first, virtual concept space and second, in ordinary space. The results show that new centredconcepts are formed by associating three certain concepts by the subjects as they are walking through the virtual concept space. In addition, the results indicate that the subjects experience no sensory discomfort as they perform the task. Hence, it is suggested that walking through a virtual concept space might support creative design thinking.
Magnesium-Based Materials for Hydrogen Storage: Microstructural Properties

Magnesium (Mg) is hydrogenated as core-shell-type hydride. Therefore, increase of absorption capacity to the theoretical hydrogen capacity is still one of the most important issues for the hydrogen storage materials. In this study, the procedure of the core-shell structure as well as effect of Al concentration in Mg on the growth MgH2 in Mg were investigated. MgH2 was formed on the surface as well as inside of unreacted Mg core. The inside MgH2 was formed in a granular form on Mg grain boundary and its size increased by applying plastic deformation. Thickness of the surface MgH2 and size of the internal MgH2 increased with an increase in hydrogenation time until the hydride surface was completely covered with MgH2. However, the growth of the surface and internal MgH2 came to a halt after the surface was covered with MgH2. From these results, supplying H from metal side was dominantly contributed for growth of the surface and internal MgH2 because diffusion rate of H in Mg was much higher than that in MgH2. In addi tion, the growth of internal MgH2 as well as control of surface MgH2 can contribute to the promotion of the complete hydrogenation of Mg-based hydrogen storage materials.
s-WIM : a scalable web information mining tool.

Web mining can be seen as the process of discovering patterns from the Web by means of data mining techniques. Web mining is a computation-intensive task and most mining software is developed ad-hoc, which makes scalability and reusability difficult for other mining tasks. Web mining is an iterative process and prototyping plays an essential role in experimenting with different alternatives, as well as in incorporating knowledge acquired in previous iterations of the process. Web Information Mining (WIM) is a model for fast Web mining prototyping. The main motivation behind WIM development was the fact that its conceptual model provides its users with a high level of abstraction, appropriate for prototyping and experimenting during the mining tasks. WIM is composed by a data model and an algebra. The WIM data model is a relational view of Web data. The three types of existing Web data, namely Web content, Web structure and Web usage, are represented by relations. The main input components for the WIM data model are the Web pages, the hyperlink structure linking Web pages and the query logs obtained from Web users’ navigation. WIM materializes a declarative programming language from its algebra. The WIM programming language is based on dataflows, where sequences of operations are applied to relations. The operations are defined by the WIM algebra, which contains operators for data manipulation and for data mining. The objective of this work is the software design and development of the Scalable Web Information Mining (s-WIM), given the data model and the algebra originally presented by WIM. In order to provide s-WIM operators with the intended scalability capabilities – and consequently the programs generated by them – the s-WIM operators were developed on top of Apache’s Hadoop and HBase, which provide linear scalability for both, data storage and processing, by the addition of hardware resources. The main motivation for s-WIM development is the lack of a free platform offering both, the same high level of abstraction provided by the WIM algebra, and the scalability necessary for the operation on huge data volumes. Furthermore, the high level of abstraction provided by the WIM algebra allows users without expertise in programming languages such as Java or C++ to effectively use s-WIM. The design and the architecture of s-WIM on top of Hadoop and HBase are presented in this work, as well as details on the implementation of the most complex s-WIM operators. This work also presents several experiments performed on s-WIM and their results, that ascertain s-WIM scalability, and consequently, its support for the mining of huge data volumes, including Web data sets.
Evaluations of variant models for stacking fault energy based on γ-TiAl

ABSTRACT The stacking fault energy (SFE) is of great significance to understand deformation behaviour of materials. Calculated by different models, the SFEs show differences and influence our understanding. Several typical models have been applied to γ-TiAl and discussed with Ab initio calculation systematically. Different models have a little effect on the calculated intrinsic stacking fault energy (ISFE), while affect the calculated unstable stacking fault energy (USFE) a lot. An alias-to-affine deformation transform is being found in 6-layer model. The local energy analysis indicates it is caused by the intrinsic properties of the materials. Combined with experiment observation and theoretical prediction, deformation patterns are compared and discussed.
Voltage Scaling Based Energy Efficient FIR Filter Design on FPGA

In this paper Voltage Scaling is used to design energy efficient Gaussian FIR Filter. This design is implemented on Kintex-7 FPGA, XC7K70T device, -3 speed grade and FBG676 package. Among all powers in FPGA, it is observed that Logic Power have maximum Power reduction of 100% at 5GHz and IO power have minimum power reduction of 2.25% at 1 THz, while the voltage is scaled from 1.0 to 0.2V. Clock power is reduced up to 86.11%, 87.14%, 87.10% and 87.26% and Leakage power is reduced to up to 50.39%, 66%, 82.78% and 82.78%, when the FIR filter is operated at 5GHz, 50GHz, 500GHz and 1 THz frequencies respectively and voltage is scaled down from 1.0V to 0.2V.
A Comparative Study of Oversampling Techniques on Binary Classification for Detecting Duplicate Advertisement

The online marketplace has become a great platform for conducting business. Not only does it allow the users to find and buy desirable items easily, but also stages an area where the user can upload their refurbished products in search of a potential buyer. Due to ever increasing competition within the market, competitive sellers go to great lengths to ensure that their products are noticed. This results in sellers posting the same advertisement several times, using near-duplicate titles or using slightly altered descriptions. This study proposes to build a dichotomous classifier that would spot such duplicate commercial advertisements that feature the same product. A Russian dataset of 3 million records was translated into English, for the better understanding of the results. The dataset was imbalanced with data samples for duplicate class less than the non-duplicate class. This study compares the six oversampling techniques, Random oversampling, SMOTE, SMOTE-Borderline 1, SMOTE-Borderline 2, SVM SMOTE and ADA- SYN, used to achieve class balance in the dataset. Four classification models, Gradient Boosting Tree, Logistic Regression, Naive Bayes and SVM, are built, on top of the oversampling techniques, to identify the duplicate advertisements. This study finds that the performance of classifiers improves with an increase in the sample size of the training data. The best performing model was SVM when paired with Borderline-SMOTE 2, with an F1 score of 0.9151 The proposed model will prevent the buyers from sifting through the dozens of deceptively identical advertisements, thereby expediting the search process. With more accurate duplicate ad detection, the model will enable the buyers to easily find a desirable product.
The Heuristic of Representativeness and Overconfidence Bias in Entrepreneurs

Abstract This study investigated the existence of the representativeness heuristic and the manifestation of overconfidence in entrepreneurs. The use of representativeness and the manifestation of overconfidence were measured with an online questionnaire answered by 62 entrepreneurs; the results were compared to a control group of 63 business managers. The respondents were randomly chosen from the business network of a business incubator at the Federal University of Rio de Janeiro. The data were analyzed with statistical tests and their respective confidence curves. The results of the research indicate that entrepreneurs and business managers demonstrate representativeness and overconfidence in their judgment processes. No significant differences were identified, but the evidence was somewhat stronger in the group of entrepreneurs. The results of this study prompt reflections on the use of heuristics and biases in the judgment and decision-making processes regarding business creation and management. More specifically, angel investors and venture capitalists who consider the results of this research in their business models may improve their rates of success.
A comparison of the efficacy of nonweight-bearing and weight-bearing exercise programmes on function and pain pressure thresholds in knee osteoarthritis: a randomised study

Abstract Aim To establish what type of intervention (nonweight-bearing [NWB] or weight-bearing [WB]) was more effective in enhancing muscle strength and knee function and in increasing pressure pain thresholds (PPT) points close and distant from the knee (hyperalgesia) in persons with mild and moderate knee osteoarthritis (KOA). Methods Thirty persons (62 ± 9 years) with mild or moderate KOA and knee pain for 6 months or more were randomly stratified according to sex and KOA severity to groups of intervention: WB or NWB. The intervention was performed three times a week over 8 weeks, focussing on progressive strength exercise. One-repetition maximum strength (1-RM) test, the Western Ontario and McMaster Universities (WOMAC) function scale and PPT points close and distant from the knee were evaluated before and after the intervention. Results Both interventions promoted improvement in 1-RM test (NWB exercise [IC 63–88], p = 0.001; WB exercise [IC 57–122], p = 0.007), functional capacity (NWB exercise [IC 10–29.4], p = 0.001; WB exercise [IC 10–45], p = 0.001), PPT points close to the knee (NWB exercise [IC 5.42–8.93], p = 0.001; WB exercise [IC 3.97–8.47], p = 0.001) and PPT points distant from the knee (NWB exercise [IC 4.38–7.70], p = 0.001; WB exercise [IC 3.75–7.31], p = 0.002). There were no statistical differences between them. Conclusions The WB and NWB interventions were effective in improving muscle strength, knee function, and PPT points close and distant from the knee (hyperalgesia) in persons with mild and moderate KOA, with no significant differences between them. Trial registration Clinical trial registration number: RBR-5t95n5; Date of registration: July 15, 2013.
Emerging and Disappearing Work, Thriving and Declining Firms

Using the text of vacancy postings from 1940 to 2000, we examine the characteristics of firms which hire for newly emerging and soon-to-be disappearing work practices. To do so, we classify job titles as emerging or disappearing according to the years in which they appear. We verify that emerging work involves higher skill levels and is closer to the technological frontier --- qualities inherent to innovation. We find that, among the set of publicly listed firms, those which post ads for emerging work tend to be younger, more R&D intensive, and have higher future sales growth. Among privately held firms, those which post ads for emerging work are more likely to go public in the future. We then explore heterogeneity in the job vintage-firm performance relationship according to jobs that are in product-related technical fields versus in business related non-technical fields. New technological work correlates with R&D intensity and future sales growth, while survival and publicly traded status is more closely related to having newer vintages of business-related non-technological jobs. Our measures of firm innovativeness can be constructed for all employers, and are not limited to publicly traded firms or to industries in which R&D spending and patenting are prevalent.
Input verification for deep neural networks

As deep learning systems are more frequently being applied to safety-critical domains, the protection against irrelevant and malicious data is of greater significance than ever before. By applying an algorithm, or supervisor, which removes this irrelevant data from the deep learning system, the credibility of the system is increased. The purpose of this project was to find and evaluate existing algorithms, and to develop a new method which can protect a neural network from irrelevant inputs. The thesis is centered on a hypothesis of capturing the learned domain of a CNN by using adversarial examples. A thorough literature review resulted in 18 analyzed methods of which five methods were implemented. The methods were evaluated on three scenarios: MNIST versus Omniglot; CIFAR-10 versus CIFAR-100; Retinal OCT-images with a held out class. Results of the study point out the difficulties in using adversarial examples to represent the infinite set of novelties. Rather than using adversarial examples to train algorithms, only utilizing the existing training data to define what is normal is better suited for the protection against undesired inputs. Methods that use the final layer activations of the neural network to detect abnormalities achieve the best results. A new supervisor, not only using the final layer but all layer activations to detect abnormalities, was created. The analysis found that valuable information can be found in the earlier layers of the network and that this information can be used for more than just novelty detection. The developed supervisor shows performance comparable to the better supervisors in the first two scenarios but show difficulties with the Retinal OCT scenario.
Existence of Equilibria in Procurement Auctions

This paper investigates symmetric equilibria in first- and second-price auctions with multidimensional types. The constructed model mirrors the information structure of actual procurement auctions. We demonstrate by a counterexample that symmetric and continuous type distribution is not a sufficient condition for the existence of a pure-strategy equilibrium, but it is guaranteed if distributions of all components are log-concave. We state a new Revenue Equivalence Theorem applied to first- and second-price auctions and conclude that the two standard auction formats yields the same expected price to the auctioneer.
Adhering to GDPR Codes of Conduct: A Possible Option for SMEs to GDPR Certification

The paper shows that adherence to a code of conduct (CoC) offers small and medium enterprises (SMEs) an interesting option to a certification obtained under Article 42 of the General Data Protection Regulation (GDPR). Adhering controllers or processors benefit from similar rights to the one attached to certification without having to demonstrate conformity with the content of the CoC. Moreover, CoCs offer a set of customised guidelines, approved by a data protection authority (DPA(s)) that are accessible for free and designed to facilitate GDPR implementation. The functional scope that might be covered by CoCs is already wider than the one offered by certification, allowing controllers and processors to demonstrate compliance with a broader range of GDPR requirements. Nevertheless, using a CoC instead of certification presents some disadvantages. CoCs have a sectoral coverage limiting availability to the covered sectors. The adherence to a CoC does not grant any seal to signal compliance to end users. The likely competition between national business representatives to draft their own CoC entails the risk of inconsistencies between one member state and another. This risk is fostered by the absence of mutual recognition between national CoCs and the absence of mechanisms to prevent duplicates at national and European levels. The option chosen by the European lawmaker to entrust the accreditation of monitoring bodies to the DPA leaves some questions open on the capacity of DPAs to handle that task. Many of them have already complained about the shortage of resources, and accreditation will require hiring additional specialised profiles. Nevertheless, adhering to a GDPR CoC, when available, offers advantages over certification that should be considered by SMEs when they seek to comply with the accountability requirement set by the GDPR.
ScrumFall: A Hybrid Software Process Model

— Every software project is unique in its own way. As a consequence, a single software process model cannot be suitable for all types of projects. In the real world, practitioners face different difficulties with the existing process models during development. Still, they cope up with the challenges by tailoring the software development lifecycle according to their needs. Most of these custom-tailored practices are kept inside the walls of the organizations. However, sharing these proven and tested practices as well as acquired knowledge and experience would be highly beneficial for other practitioners as well as researchers. So in this paper, we have presented a software process model which contains the characteristics of both Scrum and Waterfall model and named it “ScrumFall”. This model has been practicing in an Anonymous Software Development Company, Bangladesh to solve the shortcomings of Scrum and Waterfall models. Moreover, we have analyzed the performance and suitability for applying this process model. The result shows that this process model is highly effective for the certain projects.
Deformation Models for Human Shape Analysis. (Modèles de déformation pour l'analyse des formed humaines)

This manuscript presents the author’s most significant works conducted from 2011 to 2017 on the topic of processing and analyzing 3D geometric data, and in particular deformations of 3D human shapes and their accessories. Applications of such 3D data include product design, avatar creation, and recognition tasks. Inferring high-level information from raw scan data is challenging due to high levels of noise in the captured data and a high degree of variability in geometry both across different human subjects and across different poses. For this reason, to date, most of the 3D scan data used in applications are processed with the help of manual input. The goal of our work can be summarized as providing automatic methods for processing and analyzing raw geometric 3D data showing the shape and deformations of humans and their accessories. To achieve this goal, we take advantage of the fact that humans and their clothing allow for a limited set of deformations, and use deformation models to constrain the space of possible human 3D shapes. This document presents contributions based on three deformation models. The first part considers a near-isometric deformation model defined on partial regions that can be used to model loco-motions of humans as well as cloth deformations. The main novelty of this model is that it allows for processing that is robust to acquisition noise. The second and third parts consider generative models for human body and face shape that are learned from a training database of 3D scans. For both bodies and faces, we present automatic processing pipelines that allow to build generative models based on databases of thousands of raw scans. We further present applications of both the near-isometric deformation model and the generative model of human body shape.
Assessing the prospects for robust sub-diffraction limited super-resolution imaging with deep neural networks

High resolution images are critical for a wide variety of military detection, recognition and identification tasks. Super-resolution reconstruction algorithms aim to enhance the image resolution beyond the capability of the imaging system being used. Until recently, undersampling of the optical signal on the image sensor has been the key factor limiting the attainable resolution of visible and infrared imaging systems. Traditional SR algorithms aim to overcome this undersampling by combining data from multiple frames in a sequence. However, recent advances in manufacturing technologies have led to a steady increase in the number of pixels in an image sensor. Instead, image blur caused by optical diffraction is becoming an important limitation to the attainable image resolution. Here we investigate if image resolutions beyond the limitations posed by optical diffraction may be achieved using deep neural network based single image super-resolution algorithms. These networks learn a mapping from low resolution images to high resolution counterparts from pairs of training images. This could allow them to reconstruct high frequency information beyond the diffraction limit based on prior information about likely scene contents. We find that an average gain in image resolution of over 30% could be achieved by such networks on simulated diffraction limited imagery. In addition we investigate how robust these networks are to the presence of noise in the low resolution input imagery. We show that low noise levels can lead to poor reconstruction results with networks trained on noise free examples, but also that training on multiple noise levels can be used to mitigate this deterioration in performance.