Miracle-dz/UniFAR-FTUs huggingface dataset:
21st century witnessed great challenges, with continuing economic liberalization, noteworthy industrialization take-off and a highly favorable geographical location. During all those developments, Turkey became a promising country for foreign investments and international trade prospects (Kabasakal 

Paternalistic leadership is a unique leadership approach that creates a perfect balance between people orientedness and authoritativeness. It combines a strict authority with a fatherly benevolence. In fact, paternalism encompasses two controversial leadership attitudes simultaneously. Namely, a paternalistic leader prefers to be both a caring and authoritative leader. According to the extant literature, paternalism and paternalistic leadership are culturally bounded approaches and collectivist cultures are more prone to necessiate paternalism. In this study, Sabri Ülker, founder of one of the most successful family owned businesses in Turkey will be taken under spotlight as an example of paternalistic leadership in Turkish culture, which is also known as T-type leadership. This work will try to explain paternalist leadership in Turkish business culture through Sabri Ülker's example. And Hofstede’s cultural paradigm will be used in understanding Turkish business atmosphere, and the so called T-type leadership which is the equavalent of benevolent paternalistic leadership.
www.econstor.eu Exchange rate predictability and dynamic Bayesian learning

This paper considers how an investor in foreign exchange markets might exploit predictive information in macroeconomic fundamentals by allowing for switching between multivariate time series regression models. These models are chosen to reflect a wide array of established empirical and theoretical stylized facts. In an application involving monthly exchange rates for seven countries, we find that an investor using our methods to dynamically allocate assets achieves significant gains relative to benchmark strategies. In particular, we find strong evidence for fast model switching, with most of the time only a small set of macroeconomic fundamentals being relevant for forecasting.
Exploring the Contextual Renewal of Conditioned Attitudes After Counterconditioning

Research on contextualized attitude change suggests that, even when coun-terattitudinal information effectively influences evaluations in the context in which this information was learned, previously formed attitudes sometimes continue to determine evaluations in any other context (contextual renewal). Expanding on evidence for contextual renewal in attitude change based on verbal information, five experiments tested the emergence of contextual renewal in evaluative conditioning, involving pairings of a conditioned stimulus with a valenced unconditioned stimulus. Counter to the notion of contextual renewal, counterconditioning changed initially conditioned attitudes to the same extent irrespective of the context. Verbal information presented with the same procedural parameters produced contextual renewal effects only when evaluations were not measured between the formation of initial attitudes and the learning of counterattitudinal information. The results suggest two previously unidentified boundary conditions of contextualized attitude change that need to be reconciled with extant theories of evaluative learning.
Approximate Bayesian Reinforcement Learning for System Identification Approximatives Bayesianisches Verstärkendes Lernen zur Systemidentifikation

The application of machine learning techniques to the field of robotics has led to considerable progress towards the aim of realising autonomous robots. An autonomous robot, when not having specific tasks to be engaged in, should not remain idle, but prepare for upcoming tasks. One natural form of preparation is to explore the environment and accumulate knowledge in the form of a model of the world. A robot can use such a model to predict consequences of future actions which comes in handy for efficiently solving subsequent tasks. The central question we consider in this thesis is which actions the robot should execute to learn new aspects of the world and to improve its model optimally. We review existing work on exploration strategies and optimal decision making for information gain from several subfields of machine learning. As core of this thesis, we present a novel approach based on insights from active learning. The world is modelled as a Bayesian model and the optimal actions are formulated as the solution of an optimisation problem. To plan actions that are expected to improve the model, the expected model variance and the uncertainty of the resulting trajectory are considered as objectives, leading to two different problem formulations. Since an optimal solution for these problems is not tractable, we consider an approximation that can be solved efficiently using gradient-based trajectory planning methods. The resulting algorithms are compared to state-of-the-art exploration methods. Thanks to the natural representation of model uncertainty and trajectory planning, our proposed method is significantly faster, leads to lower model error and can be used to solve future tasks more reliably.
Formally Verified Blockchain Consensus

We present Toychain, the first proven-correct implementation of a Nakamoto-style blockchain consensus protocol. We build upon our previous work, the first formalisation of a blockchain-based distributed consensus protocol with a mechanised proof of consistency. Our formalisation includes a reference implementation of the block forest data structure and provably-correct message handlers for the protocol logic. The formal model is parametric wrt. implementations of several security primitives, such as hash functions, a notion of a proof object, a Validator Acceptance Function, and a Fork Choice Rule. We improve our original model by removing several overly-strong assumptions, notably the assumption that hashing is injective. Then, we instantiate the model with a SHA256-based proof-of-work scheme and extract our proven-correct OCaml implementation of Nakamoto consensus. Finally, we execute our implementation on a local area network to test its effectiveness. All our results are formalised in the Coq interactive theorem prover.
Development of ILP Marker to Assess Genetic Diversity and Population Structure of Jatropha curcas L

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Data mining based cyber-attack detection Tianfield, Huaglory

Detecting cyber-attacks undoubtedly has become a big data problem. This paper presents a tutorial on data mining based cyber-attack detection. First, a data driven defence framework is presented in terms of cyber security situational awareness. Then, the process of data mining based cyber-attack detection is discussed. Next, a multi-loop learning architecture is presented for data mining based cyber-attack detection. Finally, common data mining techniques for cyber-attack detection are discussed.
Understanding the motivations for breaking changes in Java APIs

Bibliotecas e APIs sao comumente usadas no desenvolvimento de software, visto que permitem o reuso de codigo fonte e melhoram a produtividade. Assim como a maioria dos sistemas de software, elas tambem evoluem, o que pode quebrar contratos previamente estabelecidos com sistemas clientes, introduzindo breaking changes que podem afetar a compilacao dos projetos. No entanto, as principais razoes para introduzir breaking changes em APIs nao sao claras. Portanto, nesta dissertacao de mestrado, sao reportados os resultados de um longo estudo de campo de aproximadamente 4 meses com bibliotecas e frameworks Java populares. Foi configurada uma infraestrutura para observar todas as mudancas em 400 bibliotecas e para detectar breaking changes logo apos sua introducao no codigo. Detectou-se possiveis breaking changes em 61 projetos. Depois de identificar as alteracoes, os desenvolvedores foram contatados, com o objetivo principal de entender as razoes por tras das alteracoes nas APIs. Durante o estudo, identificou-se 59 breaking changes, confirmadas pelos desenvolvedores de 19 projetos. Analisando as respostas dos desenvolvedores, observou-se que (i) 39 % das alteracoes investigadas no estudo podem ter um impacto nos clientes, sendo porem mudancas que exigem pouco esforco na migracao para as novas versoes da API, (ii) breaking changes sao motivadas principalmente pela necessidade de implementar novos recursos, pelo desejo de tornar as APIs mais simples, e para melhorar a manutencao do codigo fonte, (iii) os desenvolvedores nao depreciam elementos antes de uma mudanca devido ao aumento no esforco de manutencao, e (iv) a maioria dos desenvolvedores planejam documentar as mudancas, geralmente por release notes e changelogs. Na dissertacao, tambem sao fornecidas sugestoes para projetistas, desenvolvedores de ferramentas, pesquisadores da area Engenharia de Software e desenvolvedores de APIs. Para concluir o trabalho, a ferramenta apidiff usada nesta dissertacao para identificar breaking changes, foi atualizada com recursos importantes, por exemplo, suporte a operacoes de refatoracao e integracao com o sistema de controle de versoes git.
Bayesian Optimization for Learning from Randomized Simulations

Physics simulators offer a powerful way to develop and test policies in robotics. Promising progress in learning difﬁcult robotic control tasks has recently been made using reinforcement learning, however primarily in simulations due to the high sample complexity of state-of-the-art reinforcement learning algorithms. Transferring learned policies to the real world often fails because simulations only approximate the real world and are inherently imprecise. In recent years, domain randomization gained a lot of momentum for learning policies in simulation that are more likely transferable to the real world. However, the success of domain randomization depends on the design of the randomization distribution. In this thesis, we argue that the distribution can be adapted in order to maximize real-world performance of learned policies. We propose, how Bayesian optimization can be used to iteratively adapt the domain randomization distribution using reward signals of the real-world system. Finally, we present empirical results on a Furuta pendulum that the likelihood of learning transferable policies can be substantially increased by optimizing the domain parameter distribution.
A Novel Approach to Identify Confidential Data in Data Leakage Prevention Systems

The popularization of mobile networks and Internet has increased the speed not only of disseminating information but also leaking confidential documents. Therefore, security engineers need a Data Leakage Prevention (DLP) system to ensure that the content of the material flowing out from their organizations does not reveal any secrets. However, these information may often be tampered with by interested parties, making it difficult for the system to judge whether the file is a confidential document. This paper develops the GEMINI (Generalized Epitome Message Inspection for Non-Releasable Information) method, which is a system comprising a summary algorithm, a feature extraction algorithm, and a classifier. Compared with common firewalls and DLP on the market that rely solely on keywords to block files, our system uses the digest algorithm to determine the focus of the file, remove inconsequential sentences, and help the network security manager prevent the leakage of confidential files. This system is helpful for protecting companies’ and governments’ confidential information.
AEB effective evaluations by accident reconstructions using videos of drive recorders in perpendicular and turning carto-cyclist collisions

A drive recorder provides useful information to analyze factors that caused accidents. The information includes the driving video, car velocity, acceleration data, brake/turning indicator data, and GPS data. In this research, 237 videos of accidents involving taxi-to-cyclist collisions at intersections were collected from drive recorders. The collision data were compared to near-miss incident data to identify the factors that made the difference between collisions and near-miss incidents. It was shown that collisions occurred when cyclists entered an area where a car deceleration over 0.55 G was necessary to stop the car from colliding with the cyclist. In left and right turn collisions, the car velocity was low and many drivers had already braked when entering the intersection. Therefore, the cause of the accident was likely related to the driver’s perception. To investigate the effectiveness of autonomous emergency braking (AEB) systems, car-to-cyclist collisions were reconstructed using PC-Crash simulations that were based on the videos from the drive recorders. Extending the field of view (FOV) of AEB system from 50° to 90° was effective in reducing the number of collisions in perpendicular and leftand right-turn collisions. With an ideal AEB with a 360° FOV and no sensor delay time, 52 of the 63 perpendicular collisions and 49 of 51 left and right turn collisions were avoidable.
Refinements to Indices for Perceived Humanness, Attractiveness, and Eeriness

Using a hypothetical graph, Masahiro Mori proposed in 1970 the relation between the human likeness of robots and other anthropomorphic characters and an observer’s affective or emotional appraisal of them. The relation is positive apart from a U-shaped region known as the uncanny valley. To measure the relation, we previously developed and validated indices for the perceptual-cognitive dimension humanness and three affective dimensions: interpersonal warmth, attractiveness, and eeriness. Nevertheless, the design of these indices was not informed by how the untrained observer perceives anthropomorphic characters categorically. As a result, scatter plots of humanness vs. eeriness show the stimuli cluster tightly into categories that are widely separated from each other. The present study applies a card sorting task, laddering interview, and adjective evaluation (N = 30) to revise the humanness, attractiveness, and eeriness indices and validate them via a representative survey (N =1,311). The revised eeriness index maintains its orthogonality to humanness (r = .04, p = .285), but the stimuli show much greater spread, reflecting the breadth of their range in human likeness and eeriness. The revised indices enable empirical relations among characters to be plotted similarly to Mori’s graph of the uncanny valley. Accurate measurement with these indices can be used to enhance the design of androids and 3D computer-animated characters.
Exploring the State-of-Receptivity for mHealth Interventions

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users' responsiveness towards generic phone notifications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors affecting users' receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach -Ally - which was available on Android and iOS platforms. We define several metrics to gauge receptivity towards the interventions, and found that (1) several participant-specific characteristics (age, personality, and device type) show significant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show significant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the effectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classifier.
A decentralized algorithm for network node counting

Node counting on a graph is subject to some fundamental theoretical limitations, yet a solution to such problems is necessary in many applications of graph theory to real-world systems, such as collective robotics and distributed sensor networks. Thus several stochastic and naive deterministic algorithms for distributed graph size estimation or calculation have been provided. Here we present a deterministic and distributed algorithm that allows every node of a connected graph to determine the graph size in finite time, if an upper bound on the graph size is provided. The algorithm consists in the iterative aggregation of information in local hubs which then broadcast it throughout the whole graph. The proposed node-counting algorithm is on average more efficient in terms of node memory and communication than its previous deterministic counterpart for node counting, and appears comparable or more efficient in terms of average-case time complexity. As well as node counting, the algorithm is more broadly applicable to problems such as summation over graphs, quorum sensing, and spontaneous hierarchy creation.
Faster and Simpler SNN Simulation with Work Queues

We present a clock-driven Spiking Neural Network simulator which is up to 3x faster than the state of the art while, at the same time, being more general and requiring less programming effort on both the user’s and maintainer’s side. This is made possible by designing our pipeline around "work queues" which act as interfaces between stages and greatly reduce implementation complexity. We evaluate our work using three well-established SNN models on a series of benchmarks.
Fuzzy Logic based Autonomous Parking Systems - Part I: An Integrated Multi-functional System

This paper presents an intelligent autonomous parking system with multi-functions. The integrated system consists of two sub-systems, namely the Fuzzy-Based Onboard System and control center. Unlike most current auto-parking experiments, this FBOS enables the car to perform both slot detection and parking under two different parking modes. Facilitated by the control center, real-time monitoring is also achieved to reduce chances of error.
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.
Joint Beamforming, User Association, and Power Control for Cellular-Enabled UAV Communications

Supporting reliable and seamless mobility for aerial users, such as unmanned aerial vehicles (UAVs), is a key challenge for the next-generation cellular systems. To tackle this challenge, we propose a joint beamforming, user association, and UAV-height control framework for cellular-connected multi-UAV networks with multiple antenna base stations (BSs). With the aim of maximizing the minimum achievable rate for UAVs subject to co-existed terrestrial user equipment's rate constraints, we devise a hierarchical bi-layer iterative algorithm to optimize BSs' beamforming vectors, UAV association matrix, and the height of UAVs jointly. With the aid of projection gradient method in inner layer iteration and geometric program modelling plus convex-concave procedure in outer layer iteration, our proposed algorithm is proved to converge to a local optimum. Taking mobility characteristics of UAVs into account, we also exploit our proposed algorithm for imperfect channel estimation scenario. Numerical results show that our proposed algorithm can achieve improved UAVs' minimum achievable rate compared with that of the conventional nearest association of UAVs for both perfect and imperfect channel estimation scenarios. Moreover, we also examine the trade-off between the UAVs' minimum achievable rate and the frequency for updating optimization variables with single moving UAV.
Brain-Inspired Inference on Missing Video Sequence

In this paper, we propose a novel end-to-end architecture that could generate a variety of plausible video sequences correlating two given discontinuous frames. Our work is inspired by the human ability of inference. Specifically, given two static images, human are capable of inferring what might happen in between as well as present diverse versions of their inference. We firstly train our model to learn the transformation to understand the movement trends within given frames. For the sake of imitating the inference of human, we introduce a latent variable sampled from Gaussian distribution. By means of integrating different latent variables with learned transformation features, the model could learn more various possible motion modes. Then applying these motion modes on the original frame, we could acquire various corresponding intermediate video sequence. Moreover, the framework is trained in adversarial fashion with unsupervised learning. Evaluating on the moving Mnist dataset and the 2D Shapes dataset, we show that our model is capable of imitating the human inference to some extent.