Miracle-dz/UniFAR-FTUs huggingface dataset:
Recommendation models for dynamic spatiotemporal big data

Τα τελευταία χρόνια η εξελισσόμενη φύση των κοινωνικών δικτύων έχει οδηγήσει στην ταχεία ανάπτυξη του Διαδικτύου και παράλληλα στη ραγδαία αύξηση του όγκου των δεδομένων σε παγκόσμια κλίμακα. Η ανάγκη για πρόσβαση και ανάκτηση πληροφοριών σχετικά με τις προτιμήσεις των χρηστών είναι ένα ανοικτό πρόβλημα που εγείρει συνεχώς νέες προκλήσεις για την έρευνα. Η ερευνητική αυτή περιοχή ονομάζεται συστήματα συστάσεων και εστιάζει στη μοντελοποίηση και ανάλυση των δεδομένων ώστε να ανακτηθεί σχετική πληροφορία με βάση τις προτιμήσεις των χρηστών ώστε να προταθούν κάποιες νέες εναλλακτικές επιλογές. Η εξερεύνηση των μεγάλων όγκων δεδομένων από τα υπάρχοντα μοντέλα που αναφέρονται στη βιβλιογραφία δεν είναι επαρκής εξαιτίας της ακολουθούμενης κατανομής που κάνει την εξατομίκευση μια δύσκολη διαδικασία. Το πρόβλημα αυτό είναι γνωστό και ως “Cold start” κατά το οποίο γίνεται προσπάθεια να ανακτηθεί σχετική πληροφορία με βάση τις προτιμήσεις των χρηστών για τους οποίους η πληροφορία δεν είναι επαρκής ώστε να μπορέσουν τα μοντέλα να τους συσχετίσουν με άλλους χρήστες, τοποθεσίες ή προϊόντα. Στο πρόβλημα αυτό θα πρέπει να εξετάσουμε τη δυναμική εξέλιξη των προτιμήσεων των χρηστών μεταξύ των χρονικών περιόδων, η οποία εναλλάσσει συνεχώς την ακρίβεια των μοντέλων πρόβλεψης. Από την άλλη πλευρά, υπάρχουν μοντέλα που ενσωματώνουν τη χρονική διάσταση της εναλλαγής των προτιμήσεων των χρηστών, αλλά πάσχουν από την εξατομίκευση των αποτελεσμάτων τους, καθώς είναι λιγοστές οι πληροφορίες για το ιστορικό των περισσότερων χρηστών. Αυτές οι προσεγγίσεις εστιάζουν μόνο στη χρονική επιρροή και αγνοούν την άμεση ή έμμεση πληροφορία που σχετίζεται με τους χρήστες. Κατά συνέπεια δεν μπορούν να επιλύσουν επαρκώς το πρόβλημα της πρόβλεψης για χρήστες με μικρό ιστορικό. Τέλος, υπάρχουν προσεγγίσεις που κλιμακώνουν την ευρωστία των προβλέψεων τους με την ενσωμάτωση γεωγραφική πληροφορίας αλλά πάσχουν από την αγνόηση τόσο της διάστασης του χρόνου όσο και της διάστασης της παράπλευρης πληροφορίας που σχετίζονται με τη συμπεριφορά των χρηστών. Τα περισσότερα από τα αναγραφόμενα στη βιβλιογραφία μοντέλα αγνοούν αυτή τη δυναμική με αποτέλεσμα να μειώνεται η ικανότητά τους να προσωποποιήσουν τις συστάσεις τους αλλά και ταυτόχρονα αδυνατούν να ξεπεράσουν το πρόβλημα του cold start. Με αυτή τη διατριβή, διερευνούμε την επίδραση όλων των προαναφερθέντων θεμάτων σχετικά με την προβλεψιμότητα των μοντέλων κατά την ανάκτηση των σχετικών πληροφοριών με το ιστορικό των χρηστών εξειδικεύoντας τις συστάσεις μας για κάθε μεμονωμένο χρήση ανεξαρτήτως του μεγέθους του ιστορικού του. Σε αυτό το πλαίσιο παρουσιάζονται 5 καινοτόμα μοντέλα συστάσεων που βελτιώνουν τις υπάρχουσες προσεγγίσεις, καθώς και ένας καινοτόμος τρόπος ταξινόμησης των μοντέλων βασιζόμενος στα δίκτυα πληροφορίας που χρησιμοποιούν.
Daniel@FinTOC-2019 Shared Task : TOC Extraction and Title Detection

We present different methods for the two tasks of the 2019 FinTOC challenge: Title Detection and Table of Contents Extraction. For the Title Detection task we present different approaches using various features : visual characteristics, punctuation density and character n-grams. Our best approach achieved an ofﬁ-cial F-measure score of 94.88%, ranking 6 on this task. For the TOC extraction task, we presented a method combining visual characteristics of the document layout. With this method we ranked ﬁrst on this task with 42.72%.
Sparse Projection Oblique Randomer Forests

Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many recent extensions to decision forests are based on axis-oblique splits. Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency. We introduce yet another decision forest, called "Sparse Projection Oblique Randomer Forests" (SPORF). SPORF uses very sparse random projections, i.e., linear combinations of a small subset of features. SPORF significantly improves accuracy over existing state-of-the-art algorithms on a standard benchmark suite for classification with >100 problems of varying dimension, sample size, and number of classes. To illustrate how SPORF addresses the limitations of both axis-aligned and existing oblique decision forest methods, we conduct extensive simulated experiments. SPORF typically yields improved performance over existing decision forests, while mitigating computational efficiency and scalability and maintaining interpretability. SPORF can easily be incorporated into other ensemble methods such as boosting to obtain potentially similar gains.
Stata User's Guide: Release 15.

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Efficient Topic Evolution Discovery in a Corpus using Semantic Similarity and Citation Graph

A topic evolution graph enables one to get a quick overview of the knowledge body and its growth. Hence, discovery of topic evolution over time in a scientific corpus is an important problem. We propose a technique that uses the content of documents to discover topics as well as to link them in order to represent evolution. In addition to depicting the growth of knowledge body as a topic evolution chart, we mine influential documents and extract representative documents for topics. Previous work includes citation-aware approaches that go beyond considering documents as bags-of-words but which are computationally expensive. We discover topic labels (n-grams) using semantic relevance measures and discover influential nodes by applying the PageRank algorithm on the citation network. We evaluate our method on an arXiv corpus of ~29,000 research papers in physics. While producing meaningful results, our technique is also found to offer improvement on existing methods in terms of computational complexity and scalability. Specifically, our method runs in time linear in the number of documents and words in the corpus. Keywords—Topic evolution, corpus, citation graph, influential documents.
Convolutional Neural Network (CNN) based Gait Recognition System using Microsoft Kinect Skeleton Features

Biometric identification systems have recently made exponential advancements in term of complexity and accuracy in recognition for security purposes and a variety of other application. In this paper, a Convolutional Neural Network (CNN) based gait recognition system using Microsoft Kinect skeletal joint data points is proposed for human identification. A total of 23 subjects were used for the experiments. The subjects were positioned 45 degrees (oblique view) from Kinect. A CNN based on the modified AlexNet structure was used to fit the different input data size. The results indicate that the training and testing accuracies were 100% and 69.6% respectively.
SCREW SWIRLING EFFECTS ON FIBER ORIENTATION DISTRIBUTION IN LARGE-SCALE POLYMER COMPOSITE ADDITIVE MANUFACTURING

Large-Scale Additive Manufacturing (LSAM) polymer deposition employs a single screw extruder to melt and deliver the pelletized feedstock resulting in significantly higher flow rates as compared to conventional filament-extrusion AM processes. Single screw swirling motion in the melt flow during processing generates a unique pattern of flow-induced fiber alignment when fiber-filled polymer feedstock is processed. This paper investigates the effect of the single screw swirling motion on the fiber orientation and predicted elastic properties of a printed extrudate. A finite element extruder nozzle flow is created, where the extruder screw tip, the extrusion nozzle, and a short section of free extrudate compose the melt flow domain. The IRD-RSC fiber orientation diffusion model is applied to capture the slow orientation kinetics of short fibers in the concentrated fiber suspension. The results indicate that the swirling motion of the flow has a direct effect on predicted fiber orientation distribution and the associated averaged elastic properties in the extruded composite bead.
Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization

Unmanned aerial vehicle-based aerial base stations (BSs) can provide rapid communication services to ground users and are thus promising for future communication systems. In this paper, we consider a scenario where no functional terrestrial BSs are available and the aim is deploying multiple aerial BSs to cover a maximum number of users within a certain target area. To this end, we first propose a naive successive deployment method, which converts the non-convex constraints in the involved optimization into a combination of linear constraints through geometrical relaxation. Then, we investigate a deployment method based on <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means clustering. The method divides the target area into <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> convex subareas, where within each subarea, a mixed integer non-linear problem is solved. An iterative power efficient technique is further proposed to improve coverage probability with reduced power. Finally, we propose a robust technique for compensating the loss of coverage probability in the existence of inaccurate user location information. Our simulation results show that the proposed techniques achieve an up to 30% higher coverage probability when users are not distributed uniformly. In addition, the proposed simultaneous deployment techniques, especially the one using iterative algorithm, improve power-efficiency by up to 15% compared with the benchmark circle packing theory.
POLICY APPLICATIONS OF AMOD IN THE MULTI-MODAL ACTIVITY-DRIVEN AGENT-BASED URBAN SIMULATOR SIMMOBILITY

Among the new transportation services made possible by the introduction of Automated Vehicles (AVs), Automated Mobility on Demand (AMoD) has attracted a lot of attention from both industries and researchers. AMoD provides a service similar to taxi or ride-sharing services, while being driver-less. It is expected to attract a huge fraction of travelers currently using mass transit or private vehicles and will have a disruptive effect on urban transportation. While most studies have focused on the operational efficiency of the technology itself, our work aims to investigate its impact on urban mobility. Our contribution is two-fold. First, we present a flexible AMoD modeling and simulation framework developed within a multi-modal agent-based urban simulation platform (SimMobility). The framework allows the detailed simulation and assessment of different AMoD operations together with an activity-based framework that accounts for changes in demand, such as activity participation, trip making, mode, destination or route choice decisions. Second, we focus our attention on the role of mass transit in a futuristic urban system where AMoD is widely available. Mass transit is already challenged by current ride-sharing services, e.g., Uber and Lyft, which provide comparatively better and cheaper services. This trend will plausibly be exacerbated with the introduction of AMoD, which may indirectly act as a replacement to mass transit. Our simulation results show that mass transit is irreplaceable, despite the high efficiency of AMoD, in order to avoid congestion and maintain a sustainable urban transportation system with acceptable levels of service.
Radio resource management and protocol solutions for full-duplex systems

This deliverable provides description of algorithms and protocols for full-duplex operation in small area wireless networks. Different wireless network scenarios are covered, including point-to-point connection, single radio cell, multi-cell network, relaying and mobile ad-hoc network. Developed solutions for hybrid full-duplex and half-duplex operation are also reported. Keyword list: full-duplex, scheduling, power control, radio resource management, protocols Ref. Ares(2015)2277212 01/06/2015
How regulatory capital requirement affects banks’ productivity: An application on emerging economies banks

This paper presents a novel approach to measure efficiency and productivity decomposition in the banking systems of emerging economies with a special focus on the role of equity capital. We model the requirement to hold levels of a fixed input, i.e. equity, above the long run equilibrium level or, alternatively, to achieve a target equity-asset ratio. To capture the effect of this under-leveraging, we allow the banking system to operate in an uneconomic region of the technology. Productivity decomposition is developed to include exogenous factors such as policy constraints. We use a panel data set of banks in emerging economies during the financial upheaval period of 2005–2008 to analyse these ideas. Results indicate the importance of the capital constraint in the decomposition of productivity. JEL classification: C23, D24, G21
Fractional Diffusion in Finance : Basic Theory

In this paper we present a rather general phenomenological theory of tick-bytick dynamics in financial markets, based on the continuous time random walk (CTRW) model. The theory can take into account the possibility of the nonMarkovian character of financial time series by means of a generalized master equation with a time fractional derivative. We present predictions on the behaviour of the waiting-time probability density whose decay interpolates from a stretched exponential at small times to a power-law for long times. A proper transition to the so-called diffusion or hydrodynamic limit is also discussed by using scaling arguments. It turns out that the probability density function obeys a generalized diffusion equation of fractional order both in space and in time. Finally, a general representation of the fundamental solution of the fractional diffusion equation is given, which leads to a general scaling property for the the probability density function, henceforth to a statistical self-similarity for the limiting process. Mathematics Subject Classification: 26A33, 44A20, 33E12, 45E10, 45K05, 60G50, 91B28, 91B70. ∗ This review paper is an enlarged version of the talk given by F. Mainardi at MDEF2000 Workshop ”Modelli Dinamici in Economia e Finanza”, held in Urbino (Italy), September 28-30, 2000. 2 R. Gorenflo, F. Mainardi, M. Raberto, E. Scalas
Learned large field-of-view imaging with thin-plate optics

Typical camera optics consist of a system of individual elements that are designed to compensate for the aberrations of a single lens. Recent computational cameras shift some of this correction task from the optics to post-capture processing, reducing the imaging optics to only a few optical elements. However, these systems only achieve reasonable image quality by limiting the field of view (FOV) to a few degrees - effectively ignoring severe off-axis aberrations with blur sizes of multiple hundred pixels. In this paper, we propose a lens design and learned reconstruction architecture that lift this limitation and provide an order of magnitude increase in field of view using only a single thin-plate lens element. Specifically, we design a lens to produce spatially shift-invariant point spread functions, over the full FOV, that are tailored to the proposed reconstruction architecture. We achieve this with a mixture PSF, consisting of a peak and and a low-pass component, which provides residual contrast instead of a small spot size as in traditional lens designs. To perform the reconstruction, we train a deep network on captured data from a display lab setup, eliminating the need for manual acquisition of training data in the field. We assess the proposed method in simulation and experimentally with a prototype camera system. We compare our system against existing single-element designs, including an aspherical lens and a pinhole, and we compare against a complex multielement lens, validating high-quality large field-of-view (i.e. 53°) imaging performance using only a single thin-plate element.
Speech Recognition and Scholarly Research: Usability and Sustainability

For years we have been working on speech recognition (ASR) as a tool for scholarly research. The current state-of-the-art can be useful for many scholarly use cases focusing on audiovisual content, but practically applying ASR is often not so straightforward. In the CLARIAH Media Suite, a secured online portal for scholarly research for audiovisual media, we solved the most important hurdles for the practical deployment of ASR by focusing on usability and sustainability aspects.
AgamottoEye: Recovering Request Flow for Cloud Systems via Log Analysis

Cloud applications are implemented on top of different distributed systems to provide online service. A service request is decomposed into multiple sub-tasks, which are dispatched to different distributed systems components. For cloud providers, monitoring the execution of a service request is crucial to promptly find problems that may compromise cloud availability. In this paper, we present AgamottoEye, to automatically construct request flow from existing logs. AgamottoEye addresses the challenges of analyzing interleaved log instances, and can successfully extract request flow spread across multiple distributed systems. Our experiments with Hadoop2/YARN show that AgamottoEye can analyze 25,050 log instances in 57.4s, and the extracted request flow information is helpful with error detection and diagnosis.
Large-scale acquisition of entailment pattern pairs クロエツェー ジュリアン

This paper presents a supervised method for recognizing entailment between patterns such as 病気 に効く 薬 (“Xdrug efficient against Ydisease”). Using distributional similarity features as well as surface features and lexical resources, we were able to acquire 185 million such pairs in Japanese with a precision of 80% from our 600 million pages web archive.
GIANT FRESHWATER PRAWN , MACROBRACHIUM ROSENBERGII ( DE MAN 1879 ) : A REVIEW

In India, 11 species of shrimp have been found to be suitable for culture among 23 species available. Litopenaeus vannamei, Penaeus monodonand Macrobrachium rosenbergii are the major three species contributing great economy in Indian aquaculture industry.Total freshwater aquaculture production increased in 2016 compared to previous year.Aquaculture production of M. rosenbergii increased from 8,729 tonnes in 2015 to 10,152 tonnes in 2016. Giant freshwater prawn M. rosenbergii is becoming an important targeted species, as its culture is considered to be the potential source of income among farmers. This study reviews the current production status of freshwater prawn, white tail diseaseand its diagnostic methods and potential drugs available to treat the prawn diseases.
AI_Blues at FinSBD Shared Task: CRF-based Sentence Boundary Detection in PDF Noisy Text in the Financial Domain

This paper reports the team AI Blues’s participation in the FinSBD 2019 shared Task on ‘Sentence Boundary Detection in PDF Noisy Text in the Financial Domain’. Sentence detection from noisy text is a challenging task. We modeled the sentence boundary detection problem as a sequence labeling problem using Conditional Random Field (CRF) approach for English and French language ﬁnancial texts. We proposed to use punctuation embeddings as an additional feature along with the basic language speciﬁc features and obtained 84.5%(F1) and 86.5%(F1) accuracies in the English and French language shared task datasets respectively.
Amelioration of salt stress tolerance in Solanum lycopersicum L . using salicylic acid

: Solanum lycopersicum L. is a glycophytic species and vegetable crop grown worldwide for its edible fruits. Laboratory experiments were conducted to determine the ameliorating effects of salicylic acid on salt tolerance. Total ten germplasm seeds were germinated and grown under 50 mM and 150 mM salt stress and 0.5 mM salicylic acid (SA). Morphological and physio-biochemical variables such as seed germination, biomass, relative water content, Proline, protein content, total chlorophyll content and peroxidase activity were evaluated to assess the amelioration of salt stress tolerance. At 150 mM NaCl stress the seed germination of the germplasms were improved under the exogenous application of salicylic acid. Similarly, application of salicylic acid helps to maintain the Relative Water Content (RWC), chlorophyll and peroxidase activity in comparison to salt stress condition. After the application of SA the NB6 (EC-514013) and NB7 (EC-514101) germplasms were showed appreciably enhanced salt tolerance. The SA application could play an important role to induce plant resistance against salt stress.
High-Quality Location Based Wireless Sensor Networks

Location monitoring refers to the system where the wireless sensor network odes counts the number of sensors which are capable of detecting the objects present in their sensing areas. Third party always monitor the personal location which is becoming a privacy threat. To over come this we have proposed a method by using a series of routers to hide the client’s IP address from the server. We propose a privacy-preserving location monitoring system for wireless sensor networks. In our system, we design two in-network location anonymization algorithms, namely, Cloaked Area Determination Algorithm and quality enhanced histogram algorithm that will help the system to enable and provide high-quality location monitoring services for system users, while preserving personal location privacy. The Cloaked Area determination algorithm aims to minimize communication and computational cost, A quality enhanced histogram approach is used that estimates the distribution of the monitored persons based on the gathered aggregate location information. Then, the estimated distribution is used to provide location monitoring services through answering range queries Keywords— monitoring algorithm, privacy threar, anonymization, histogram approach.
TESTING ADHESIVE BOND STRENGTH AND FRACTURE MECHANISMS OF THICKER AND POROUS PLASMA SPRAY COATINGS

For the functionality of metal products protected with plasma spray coatings critical is the adhesive strength of the bond of the coatings with the substrate surfaces. For the working life of the coatings the cohesive strength is also very important, which is directly related to the strength of the inter-lamellar connections, share of pores, presence of cracks and residual stresses in the deposited layers of coating and at the interface with the substrate. To test the adhesion / cohesion strength the most frequently used were the standardized test methods, ASTM C633 and Pratt & Whitney (PN 582005). Access to control of deposited plasma spray coatings allows the development, mastering and application of new quality materials, powders at the micro and nano level for more effective performance of metal products in service. For this paper, selected for testing were coating systems NiCrAlCoY2O3/ZrO2CeO2Y2O3 deposited by plasma spray process at atmospheric pressure (APS). Tests were performed according to standard Pratt & Whitney (PN 582005) and shown were assessments of adhesive bond strength and fracture mechanisms. The results showed that the method is relatively easy and reliable for the assessment of bond strength and fracture mechanisms of thicker and porous plasma spray coatings.