Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive finite element computations for deep learning inversion

We focus on the inversion of borehole resistivity measurements in real time. To perform this task, we propose the use of Deep Learning methods. One critical task on this endeavor is to produce a large database that can be used to train Deep Neural Networks. In this work, we explore different venues to obtain such database conforming the ground truth data via massive finite element computer simulations of the so-called forward problem. This consists of solving multiple Boundary Value Problems governed by a Partial Differential Equation with different material coefficients. After describing the Finite Element Method, we investigate a venue to achieve high performance for performing a large amount of simulations using a Fourier approximation based Finite Element Method. The idea is to exploit the orthogonality of Fourier basis functions under reasonable assumptions often satisfied in our geophysics applications to reduce the computational cost of building the corresponding systems of linear equations. Solving such systems requires the use of advanced iterative solvers, which will be analyzed during the Ph.D. studies of Carlos Uriarte.
The OSCE and EU Actions towards Georgian Separatist Conflicts: the Case of South Ossetia.

Both the OSCE and the EU got involved in the management of the Georgian-South Ossetian conflict considering it as a testing ground for their capacities to act as security actors and easier to deal with in comparison with the other unsolved confrontations in the Post-Soviet area. By this way, they disregarded the root causes of the conflict and then proved unable to deploy the necessary resources to respond to the security expectations of the two sides, especially since the regional geopolitical environment switched from cooperation to confrontation between Russia and an expanding NATO presence. Following the 2008 War, the EU is left as the only mediating player on the ground but is not recognised as such by the SO side supported by Russia while Georgia has thwarted the possibilities of conflict resolution adopting a punitive ‘Occupied Territories’ narrative.
Fiscal Policy Impacts on Growth : an OECD Cross-Country Study with an Emphasis on Capital Rates Accumulation

A growing body of literature tests the effects of different tax structures on long-run economic growth. The paper aims at testing if the composition of tax revenue affects economic growth in the long run via the capitals saving rates. To avoid heterogeneity and endogenous bias in the estimation process we use a multivariate finite mixture model with regressors measurement error. We argue that these tests do not properly account for endogeneity between supposedly independent variables. We run Dynamic Fixed Effect regression and finite mixture models with special attention to capital saving rates coefficients, and show how those parameters can be affected by different tax policies. Moreover we find that effects of taxation on capital accumulation rates and growth is different for different countries. keywords: Tax policy, Finite Mixture Models, Dynamic Fixed Effect Regression.
Dynamic Personalized Recommendation on Sparse Data

Recommendation techniques are very important in the areas of E-commerce and other Web-based services. One of the main difficulties is to provide high-quality recommendation on sparse data dynamically. Here in this paper, we proposed a novel dynamic personalized recommendation algorithm, in which data contained in both profile and ratings contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm is satisfying performance. Key Terms—dynamic recommendation, dynamic features, multiple phases of interest.
Parametric Investigation of Hull Shaped Fuselage for an Amphibious UAV

Performance of amphibious UAV’s (Unmanned Aerial Vehicles) that take off from and land on water, like seaplanes, greatly depend on hydrodynamic effects as well as aerodynamic effects, therefore their geometries need to be optimized. This study mainly investigates the effect of geometric parameters of a generic, hull-shaped fuselage that are constrained by hydrodynamic drivers, such as the step height needed to reduce hydrodynamic drag, and deadrise and sternpost angles needed for safe landing, on aerodynamic drag of the fuselage under cruise conditions by means of the commercial CFD code ANSYS Fluent. Study includes a comparison of the experimental [1] and numerical results obtained at angles of attack varying between 8° to 16° and with Spalart-Allmaras, k-ω and k-ε turbulence models.
Foundations of Collaborative Task-Oriented Dialogue: What’s in a Slot?

In this paper, we examine the foundations of task-oriented dialogues, in which systems are requested to perform tasks for humans. We argue that the way this dialogue task has been framed has limited its applicability to processing simple requests with atomic “slot-fillers”. However, real task-oriented dialogues can contain more complex utterances that provide non-atomic constraints on slot values. For example, in response to the system’s question “What time do you want me to reserve the restaurant? ”, a user should be able to say “the earliest time available,” which cannot be handled by classic “intent + slots” approaches that do not incorporate expressive logical form meaning representations. Furthermore, situations for which it would be desirable to build task-oriented dialogue systems, e.g., to engage in mixed-initiative, collaborative or multiparty dialogues, will require a more general approach. In order to overcome these limitations and to provide such an approach, we give a logical analysis of the “intent+slot” dialogue setting using a modal logic of intention and including a more expansive notion of “dialogue state”. Finally, we briefly discuss our program of research to build a next generation of plan-based dialogue systems that goes beyond “intent + slots”.
Covering the global threat landscape PROSECTING THE CITADEL BOTNET – REVEALING THE DOMINANCE OF THE ZEUS DESCENDENT

Recent years have seen a signifi cant rise in cybercriminal activities, and in particular the theft of online banking credentials. The majority of cybercriminals use automated exploitation frameworks to infect computers and exfi ltrate data. The most widely used weapons in this type of cybercrime are botnets. Botnets have been in existence for many years, but their design frameworks have changed over time. We are now seeing a third generation of botnets that are targeting the users of online fi nancial services. This era of targeted attacks started with the rival Zeus and SpyEye botnets and is evolving. In this paper, we look at the design and working details of the Citadel botnet. Citadel, which is believed to have European origins, is a sophisticated descendent of the Zeus botnet. Our analysis provides insight into the design components of Citadel, including its system infection and data exfi ltration tactics.
A Structure Based Drug Designing of Bioactive Compounds of Gracilaria edulis against Virulent Bacterial Enzyme Aureolysin

The bioactive compounds of Gracilaria edulis were determined by using Gas Chromatography Mass spectroscopy. The drug compounds were screened for analyzing the inhibition potential against the virulent bacterial enzyme. In this research, the protein responsible for bacterial infection was docked against the drug compounds of Gracilaria edulis. The data of the virulent enzymes are studied and retrieved from PDB. The bioactive compounds were screened by Lipinski rule of five and ADMET properties. Using Autodock 4.2. 6 the molecular docking analysis were done against virulent enzymes and was visualized by discovery studio 3.1. The bioactive compound eugenol with binding energy -4.42 Kcal/mol followed by 2 Heptene, 2,4,4,6 tetramethyl -3.89 Kcal/mol and 1, 2-Propanediol 2.77 Kcal/mol. The hydrogen and vanderwaals interaction of amino acids were studied. This research work mainly focuses on targeting the virulent enzymes that can reduce clinical costs by designing novel drug.
Cognitively Realistic Problem Solving through Causal Learning

Traditionally, search algorithms such as the A* algorithm have been used for general problem solving in AI, including looking for paths that circumvent obstacles. However, even with the assistance of (often hand-coded) heuristics, the search space is still often very large. The problem solving process is not cognitively realistic because natural intelligent systems such as human beings do not solve problems in this manner. Building on our previous work on effective causal learning, this paper demonstrates how an intelligent system can learn about the physical properties of objects in the environment through identifying the causes of, say obstruction, and use that knowledge to solve problems rapidly without having to resort to extensive and unintelligent search processes.
Study of Corporate Elements Culture in Effective Secondary Schools

The present research aimed to identify the elements of corporate culture in Farzanegan schools (schools for gifted and talented students) of Bandar Abbas in Iran. National Organization for Development of Exceptional Talents ( NODET ) are national selective schools in Iran developed specifically for the development of exceptionally talented students. In this study we employ a grounded theory approach and a systematic plan. The population of this study consists of all principals, teachers, and students of high schools for gifted students in the city of Bandar Abbas in Iran. Fifty-two principals, teachers, and students were selected through purposeful and theoretical sampling and were interviewed by a main question and a number of open questions. The reliability of research for data collection tools was determined by using generator questions. The data were analyzed using grounded theory. In the conceptualization stage, 33 concepts were extracted from the responses of the interviewees. The concepts were categorized in three dimensions of corporate culture and then the known dimensions were free coded with titles of organized organization, role model, and strategic initiatives. Then, code of strategic initiatives (actions) was selected as the key code and explained the other codes by paradigm pattern.
A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices

Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, each individual now expects to be delivered with state-of-the-art reports about his body. The most dominant wearables in the market often focus on general metrics such as the number of steps, distance walked, heart rate, oximetry, sleep quality, and sleep stage. But, for accurately identifying the well-being of an individual, another important metric needs to be analyzed, which is the state of mind. The state of mind is a metric of an individual that boils down to the activity of all other related metrics. But, the detection of the state of mind has formed a huge challenge for the researchers as a single biosignal cannot propose a particular decision threshold for detection. Therefore, in this work, multiple biosignals from different parts of the body are used to determine the state of mind of an individual. The biosignals, blood volume pulse (BVP), and accelerometer are intercepted from a wrist-worn wearable, and electrocardiography (ECG), electromyography (EMG), and respiration are intercepted from a chest-worn pod. For the classification of the biosignals to the multiple state-of-mind categories, a multichannel convolutional neural network architecture was developed. The overall model performed pretty well and pursued some encouraging results by demonstrating an average recall and precision of 97.238% and 97.652% across all the classes, respectively.
Engagement Capabilities for ECAs

Embodied Conversational Agents (ECAs) are being used more and more in applications involving interactions with users. One of the major problems these applications faced is to have the conversation last more than few second between the users and the ECAs. The reasons for such a short duration may be manifold: after being amused and intrigued by the ECAs, users may find rapidly the restrictions and limitations of the dialog systems; they may perceive the repetition of the ECAs animation; they may find the behaviors of ECAs to be inconsistent and implausible; etc. Research in several areas has been undertaken to overcome these shortcomings. But we believe that another aspect to consider is the creation of special links, or bonds, that could be established between users and ECAs. Building a relationship is linked to the notion of engagement in the conversation. Engagement is viewed, by Sidner et al. [25], as “the process by which two (or more) participants establish, maintain and end their perceived connection during interactions they jointly undertake”. In our terms [10, 21], it could be defined as “the value that a participant in an interaction attributes to the goal of being together with the other participant(s) and of continuing the interaction”. Our view is that cognitive and emotional involvement and commitment are key factors that underlie the notion of engagement. If this is the case, then for an ECA to be able to establish, maintain and end interactions, it must be endowed with mechanisms that allow it to perceive, adapt to and generate behaviors relating to attention and emotion. In this paper, we will discuss some important capabilities that we have been working on: we do not present a full speaker/listener model, but rather illustrate how the concepts may cluster together to form the core of such a model. In Section 2 we will consider some of the aspects required for a complete model. We will then present the steps involved in engagement 3. We will provide a definition of backchannels and their properties 4.2. We will also discuss those capabilities where we have thus far focused our research efforts in Section 4.
ANALYSIS OF THE IMPACT OF THE AGROSECTOR'S SHARE ON DIFFERENCES IN THE PERFORMANCE OF REGIONS AT NUTS-III LEVEL: A CASE STUDY OF THE SLOVAK REPUBLIC

There are differences between the territorial units of each economy, which can be easily quantified using the economic performance indicators of the region. The causes of regional disparities are the natural, geographical, environmental and demographic conditions of localization of economic activities. These specified types of conditions represent assumptions or limitations in the formation of the sectoral structure of economic activities in the territory. Opinions of theorists and empirical findings confirm that each of the sectors has a different ability to contribute to the formation of national product, gross fixed capital as well as added value. Due to the different economic effects of the represented sectors, there may be a gradual increase in differences in economic performance of territorial units or even loss of ability of independent development of the region. Accordingly, it can be stated the existence of regional disparities between the territorial units of the Slovak Republic at the regional level. Differences are identified in a different representation of sectors on the economic structure of the territory, in the value of GDP creation as well as the formation of fixed capital of individual regions. Agrosector is considered as the sector with the least contribution to the creation of a national product. Therefore, in the manuscript we analyze the existence and the strength of the relationship between the share of the agricultural sector in the structure of the economy and the indicators of the economic performance of the region as well as the existence or absence of a trend of increasing interregional differences. In order to identify the preservation of the development capacities of territorial units, we analyzed also the differences in the ability of regions with different range of agrosectors to form development capital.
Political discourse analysis and YouTube

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Development , Democratization , and Democratic Deepening ∗

Existing studies of the relationship between economic development and democracy – whether they find support for a positive association between development and democracy or not – overlook key conceptual and empirical issues. An overwhelming proportion of the literature fails to distinguish between the processes of democratization and democratic deepening. We argue that democratization and democratic deepening must be distinguished analytically and modeled empirically as distinct stages; by conflating the two processes, analysts can miss a significant relationship in one of the stages. We further show that prominent theories of the development-democracy link imply both a relationship between development and democratization and one between development and democratic deepening, although the latter relationship has largely been overlooked as a theoretical implication. We test for both associations empirically by developing a two-stage empirical strategy. Our results reveal a positive relationship between development and democratization, but no significant association between development and democratic deepening. These findings demonstrate the importance of not conflating the two processes and suggest that existing theories that link development and democracy should be revisited.
Automatic Chord Estimation Based on a Frame-wise Convolutional Recurrent Neural Network with Non-Aligned Annotations

This paper describes a weakly-supervised approach to Automatic Chord Estimation (ACE) task that aims to estimate a sequence of chords from a given music audio signal at the frame level, under a realistic condition that only non-aligned chord annotations are available. In conventional studies assuming the availability of time-aligned chord annotations, Deep Neural Networks (DNNs) that learn frame-wise mappings from acoustic features to chords have attained excellent performance. The major drawback of such frame-wise models is that they cannot be trained without the time alignment information. Inspired by a common approach in automatic speech recognition based on nonaligned speech transcriptions, we propose a two-step method that trains a Hidden Markov Model (HMM) for the forced alignment between chord annotations and music signals, and then trains a powerful frame-wise DNN model for ACE. Experimental results show that although the frame-level accuracy of the forced alignment was just under 90%, the performance of the proposed method was degraded only slightly from that of the DNN model trained by using the ground-truth alignment data. Furthermore, using a sufficient amount of easily collected non-aligned data, the proposed method is able to reach or even outperform the conventional methods based on ground-truth time-aligned annotations.
OF ECG-SIGNAL TRANSMISSION SYSTEM DEVELOPMENT

The article concerns the concept of medical cardiodiagnostical automated systems based on wireless technologies, Matlab application package and ECS PhysioBank ATM database. This article considers the principles of Holter monitoring systems to be used at home. It describes the methods of application of electrocardiographic signals archive for debugging and setting up of the system developed. The undertaken study showed that the proposed methods shall create effective systems of daily cardiac activity monitoring characterized by high speed and low energy consumption.
SMART GOVERNANCE IN THE CAPITAL CITY OF WARSAW WITH THE USE OF ICT AND GEOINFORMATION TECHNOLOGIES

Abstract. The article presents the results of work carried out as part of the “Objectives of the SMART CITY program in the capital city of Warsaw” project run at the request of the Digitisation Bureau of the Warsaw City Hall by a design team from the Warsaw University of Technology in cooperation with Comtegra S.A. company. The first part shows the analytical assessment of systems, services, and solutions used in Warsaw in September and October 2017 as well as a SWOT analysis of weaknesses, opportunities, and threats of transforming Warsaw into a smart city. The second part of the article presents reasons and objectives of the transformation, as well as a general conceptual framework for Warsaw as a smart city. The study focuses on describing the critical processes that should take place in the structures of the city; principles and future directions for the model of organisation: services and solutions, actions and assistance that are already in progress or will be undertaken in the first stages of transforming the capital city of Warsaw into a smart city. The solution proposed by the authors of the article below concerning the transformation of the Polish capital into a smart city is currently being implemented by the City Hall of Warsaw in cooperation with Deloitte.
A REVIEW OF DIGITAL FORENSICS METHODS FOR JPEG FILE CARVING

Digital forensics is an important field of cybersecurity and digital crimes investigation. It entails applying file recovery methods to analyze data from storage media and extract hidden, deleted or overwritten files. The recovery process might have accompanied with cases of unallocated partitions of blocks or clusters and the absence of file system metadata. These cases entail advance recovery methods that have carving abilities. The file carving methods include different types of techniques to identify, validate and reassemble the file. This paper presents a comprehensive study of data recovery, file carving, and file reassembling. It focuses on identifying and recovering JPEG Images as it is a wildly covered in the literature. It classifies the carving techniques into three types: signature-, structure-, and content-based carvers. Subsequently, the paper reviews seven advanced carving methods in the literature. Finally, the paper presents a number of research gaps and conclude a number of possible improvements. Generally, both the gaps and possible improvements are associated with the fragmentation problem of data files.