Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Frequent and Maximal Periodic Pattern Mining Algorithms in Spatiotemporal Databases

Detecting regular and efficient cyclic models is the demanding activity for data analysts due to unstructured, vigorous and enormous raw information produced from web. Many existing approaches generate large candidate patterns in the occurrence of huge and complex databases. In this work, two novel algorithms are proposed and a comparative examination is performed by considering scalability and performance parameters. The first algorithm is, EFPMA (Extended Regular Model Detection Algorithm) used to find frequent sequential patterns from the spatiotemporal dataset and the second one is, ETMA (Enhanced Tree-based Mining Algorithm) for detecting effective cyclic models with symbolic database representation. EFPMA is an algorithm grows models from both ends (prefixes and suffixes) of detected patterns, which results in faster pattern growth because of less levels of database projection compared to existing approaches such as Prefixspan and SPADE. ETMA uses distinct notions to store and manage transactions data horizontally such as segment, sequence and individual symbols. ETMA exploits a partition-and-conquer method to find maximal patterns by using symbolic notations. Using this algorithm, we can mine cyclic models in full-series sequential patterns including subsection series also. ETMA reduces the memory consumption and makes use of the efficient symbolic operation. Furthermore, ETMA only records time-series instances dynamically, in terms of character, series and section approaches respectively. The extent of the pattern and proving efficiency of the reducing and retrieval techniques from synthetic and actual datasets is a really open & challenging mining problem. These techniques are useful in data streams, traffic risk analysis, medical diagnosis, DNA sequence Mining, Earthquake prediction applications. Extensive investigational outcomes illustrates that the algorithms outperforms well towards efficiency and scalability than ECLAT, STNR and MAFIA approaches.
On-Device Federated Learning via Second-Order Optimization with Over-the-Air Computation

Federated learning becomes a promising approach for preserving privacy by keeping user data locally. The basic idea is that a central server iteratively aggregates distributed local models trained directly on mobile usersâ€™ local datasets to form a high-quality global model by computing the weighted sum of the locally updated models. However, the communication cost becomes the main bottleneck as a large number of communication rounds are involved in the federated learning procedure. We propose to update local models by second- order optimization methods with fast convergence rates, thereby significantly reducing the communication rounds for global model updates. Furthermore, the over-the-air computation technique is adopted to improve communication efficiency for model aggregation by utilizing the superposition property of wireless channels. A nonconvex low-rank beamforming approach is then developed to support over- the-air computation via difference-of-convexfunctions (DC) programming. Through extensive experiments, we reveal that the proposed DC algorithm is able to significantly minimize the aggregation error, and the second- order methods are quite robust to the model aggregation errors.
Computer-mediated communication (CMC) and social media corpora: Introduction

This issue brings together language-centered studies on computer-mediated communication (CMC) and social media corpora. They are illustrative of contemporary research interests in a very extensive research field that has strongly evolved over the past two decades: In the early days of CMC, much of the research was quite “anecdotal and speculative, rather than empirically grounded” (Herring 2004: 338) and in language-focused studies there was a predominant interest in the detection and description of prototypical features of the new genres (e. g. Crystal 2001). It took some time before the social and contextual embedding of these features and of CMC discourse in general was operationalized systematically as part of the research design. Androutsopoulos (2006: 430) discussed the reductive focus on the idiosyncrasies of the genre and argued that the time was ripe for “a user and community-centered approach, which is promising for a more complex theorizing of the social and contextual diversity of language use on the internet. ” Since then the field hasmatured. CMC research has not onlywitnessed a boost, it got firmly embedded in linguistic disciplines like sociolinguistics, pragmatics, discourse analysis, and obviously computational linguistics. Furthermore, the
Privacy-Preserving Data Sharing on Multi-Layer Blockchain: Case Study on Healthcare

This thesis designs a data exchange system architecture and applies it to healthcare cases, and will solve the following two problems. First, children forgets whether the type vaccines that has been vaccinated or not. Then, he/she has to vaccinate again in case. Second, we implement the medical record exchange between hospitals. The same concept can also be applied to the following situations. If someone sees a doctor in different hospitals, the medicines from different hospital may have drug interactions, which may cause side effects after taking them at the same time. Doctors can avoid this situation with the information through the blockchain. Recently, the applications of the medical blockchain are mostly implemented by Ethereum. This thesis, unlike other Ethereum applications, also proposed a new multi-layer blockchain architecture and replaced the consensus mechanism in order to speed up and make the system more reasonable in use. Keywords—Blockchain, Tendermint, Ethereum, Multi-Layers, Smart Contract, Data Exchange
Brain Image Segmentation with Gradient Information

Segmentation is the process of extracting structures within the images. The purpose is to simplify the representation of the image into something meaningful and easier to analyse. A magnetic resonance (MR) brain image can be represented as three main tissues, e.g. cerebrospinal fluid (CSF), grey matter and white matter. Although various segmentation methods have been developed, such images are generally segmented by modelling the intensity histogram by using a Gaussian Mixture Model (GMM). However, the standard use of 1D histogram sometimes fails to find the mean for Gaussians. We hence solved this by including gradient information in the 2D intensity and intensity gradient histogram. We applied our methods on real data of 2D MR brain images. We then compared the methods with the previous published method of Petrovic et al. on their dataset, as well as on our larger datasets extracted from the same database of 3D MR brain mages, where the ground-truth annotations are available. This shows that our method performs better than the previous method.
Adaptación del protocolo EDCA para sistemas en tiempo real

Resumen. El Control de Acceso al Medio (MAC) del estándar 802.11e, EDCA (Enhanced Distributed Channel Access), proporciona cuatro niveles diferenciados de calidad de servicio (QoS) que, si bien es una mejora importante de su mecanismo MAC, no garantiza un retardo limitado en el envío de paquetes. Los sistemas de control de red (NCS) en redes inalámbricas (WNCS) son muy sensibles a las características impredecibles tales como latencia, jitter y pérdida de paquetes. En este trabajo se propone una alternativa que limita la latencia de transmisión y la pérdida de paquetes haciendo un manejo adecuado de la ventana de contención (CW), contador backoff y arbitraje-inter-frame-espacio (AIFS) de EDCA. Las simulaciones fueron realizadas en ns-2.
AILiveSim: An Extensible Virtual Environment for Training Autonomous Vehicles

Virtualization technologies have become common-place both in software development as well as engineering in a more general sense. Using virtualization offers other benefits than simulation and testing as a virtual environment can often be more liberally configured than the corresponding physical environment. This, in turn, introduces new possibilities for education and training, including both for humans and artificial intelligence (AI). To this end, we are developing a simulation platform AILiveSim. The platform is built on top of the Unreal Engine (www.unrealengine.com) game development system, and it is dedicated to training and testing autonomous systems, their sensors and their algorithms in a simulated environment. In this paper, we describe the elements that we have built on top of the engine to realize a Virtual Environment (VE) useful for the design, implementation, application and analysis of autonomous systems. We present the architecture that we have put in place to transform our simulation platform from automotive specific to be domain agnostic and support two new domains of applications: autonomous ships and autonomous mining machines. We describe the important specificity of each domain in regard to simulation.In addition, we also report the challenges encountered when simulating those applications, and the decisions taken to overcome these challenges.
Automation of Waste Sorting with Deep Learning

The importance of recycling is well known, either for environmental or economic reasons, it is impossible to escape it and the industry demands efficiency. Manual labour and traditional industrial sorting techniques are not capable of keeping up with the objectives demanded by the international community. Solutions based in computer vision techniques have the potential automate part of the waste handling tasks. In this paper, we propose a hierarchical deep learning approach for waste detection and classification in food trays. The proposed two-step approach retains the advantages of recent object detectors (as Faster R-CNN) and allows the classification task to be supported in higher resolution bounding boxes. Additionally, we also collect, annotate and make available to the scientific community a new dataset, named Labeled Waste in the Wild, for research and benchmark purposes. In the experimental comparison with standard deep learning approaches, the proposed hierarchical model shows better detection and classification performance.
LEARNING ALSO NEEDS TO BE A LITTLE FUN: WHAT ACADEMIC LEARNING GOALS CAN STUDENTS ACHIEVE BY DEVELOPING GAMES?

This study presents an exploratory investigation of how learning through game development can contribute to students’ academic learning and how applying game making for learning as a teaching method can be linked to subject-specific learning goals. The paper describes a project where twenty-two second grade Danish students worked with problem-based assignments that supported them in developing digital games for learning with the Scratch tool. The students succeeded in creating digital games for learning and in embedding learning situations, learning activities and evaluation opportunities into their games. Analysis of the learning dynamics suggests that the students were involved in interactive communication and production processes and used the Danish language as a tool inside their small digital games when engaging in this problem-based and constructionist learning approach. According to the analysis, the students managed to work with specific academic learning goals and enjoyed learning through the development of games for learning.
Using Cartograms for Visualizing extended Floating Car Data (xFCD)

Abstract. This study assesses the usefulness of cartograms when visualizing extended Floating Car Data (xFCD). Cartograms deform regions in a map proportionally to assigned values. We apply this method for visualizing highresolution extended Floating Car Data (xFCD). Elaborating on this, we perform a case study in Mönchengladbach, Germany using 1.8 Million record points containing information about carbon dioxide (CO2) emissions based on an xFCD dataset. Utilizing a diffusion-based approach, we compute cartograms. Findings indicate a good suitability for identifying areas with a higher (or lower) average emission of CO2. We provide a documented workflow to compute cartograms based on parameters from an extended floating car dataset. The quality and spatial distribution of the basic dataset turns out to be important. Choosing the correct spatial subdivision of the research area as a basis for deforming areas is significant as it strongly influences the visual output.
Geo-Analysis of Compatibility Determinants for Data in the Land and Property Register (LPR)

The development of modern technologies and accessibility of data on space and the natural environment has led to their increasing use for socio-economic purposes. Data users believe that these systems reflect the reality in the field. This applies in particular to databases used for construction investment projects or as the basis for calculations of financial obligations, e.g., taxes. The Land and Property Register (LPR), which is part of the Land Administration System, serves a number of economic and legal purposes. This geo-system often contains low-quality information regarding the technical potential of modern data acquisition methods and is continuously updated. The authors propose a two-step analysis of data contained in the LPR. The first step identified the sources of discrepancies between data from the LPR and the reality in the field. The second step emphasises the importance of the factors under analysis, which include both a plot’s geometric parameters, the geo-location features (associated with the natural environment elements) and factors associated with the supplementary data acquisition methods. The results show that sufficient quality data play the main role in achieving compatibility between the data in the Land and Property Register and with reality. Studies conducted so far have dealt with data on a global scale and were based on in situ data and focused on the specific values of each plot under analysis.
Lecture notes on mathematical models of interconnected chemostats

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Lecture notes on mathematical models of interconnected chemostats Alain Rapaport
Review of Bayesian Analysis in Additive Hazards Model

In Survival Analysis, the focus of interest is a time T* until the occurrence of some event. A set of explanatory variables (denoted by a vector Z) is considered to analyze if there is a relationship between any of them and T*. Accordingly, the ``hazard function'' is defined: \[ \lambda(t,z) := \lim_{\Delta \downarrow 0} \frac{P[T\leq t+ \Delta \vert T >t,Z=z]}{\Delta} . \] Several models are defined based on this, as is the case of the additive model (among others). Bayesian techniques allow to incorporate previous knowledge or presumption information about the parameters into the model. This area grows extensively since the computationally techniques increase, giving rise to powerful Markov Chain Monte Carlo (MCMC) methods, which allow to generate random samples from the desired distributions. The purpose of this article is to offer a summary of the research developed in Bayesian techniques to approach the additive hazard models.
Robust and Universal Seamless Handover Authentication in 5G HetNets

The evolving fifth generation (5G) cellular networks will be a collection of heterogeneous and backward-compatible networks. With the increased heterogeneity and densification of 5G heterogeneous networks (HetNets), it is important to ensure security and efficiency of frequent handovers in 5G wireless roaming environments. However, existing handover authentication mechanisms still have challenging issues, such as anonymity, robust traceability and universality. In this paper, we address these issues by introducing RUSH, a Robust and Universal Seamless Handover authentication protocol for 5G HetNets. In RUSH, anonymous mutual authentication with key agreement is enabled for handovers by exploiting the trapdoor collision property of chameleon hash functions and the tamper-resistance of blockchains. RUSH achieves universal handover authentication for all the diverse mobility scenarios, as exemplified by the handover between 5G new radio and non-3GPP access regardless of the trustworthiness of non-3GPP access and the consistency of the core network. RUSH also achieves perfect forward secrecy, master key forward secrecy, known randomness secrecy, key escrow freeness and robust traceability. Our formal security proofs based on the BAN-logic and formal verification based on AVISPA indicate that RUSH resists various attacks. Comprehensive performance evaluation and comparisons show that RUSH outperforms other schemes in both computation and communication efficiencies.
A Human-Robot Interaction for a Mecanum Wheeled Mobile Robot with Real-Time 3D Two-Hand Gesture Recognition

Human interaction with mobile robot becomes a popular research area and its applications are widely used in industrial, commercial and military fields. A two-hand gesture recognition method with depth camera is presented for real-time controlling the mecanum wheeled mobile robot. Seven different gestures could be recognized from one hand for mobile robot navigation and three gestures could be recognized from the other hand for controlling the gripper installed on the robot. Under the proposed control scheme, the mobile robot system can be navigated and can be operated at the same time for achieving missions by two different groups of hand gestures. The accuracy of the gesture recognition is about 94%. During mobile robot control experiment, the system works timely, accurately and stably for certain tasks such as directional movement, grasping and cleaning obstacles.
Speech Recognition Using Convolutional Neural Networks

Automatic speech recognition (ASR) is the process of converting the vocal speech signals into text using transcripts. In the present era of computer revolution, the ASR plays a major role in enhancing the user experience, in a natural way, while communicating with the machines. It rules out the use of traditional devices like keyboard and mouse, and the user can perform an endless array of applications like controlling of devices and interaction with customer care. In this paper, an ASR based Airport enquiry system is presented. The system has been developed natively for telugu language. The database is created based on the most frequently asked questions in an airport enquiry. Because of its high performance, Convolutional Neural Network (CNN) has been used for training and testing of the database. The salient feature of weight connectivity, local connectivity and polling result is a through training of the system, thus resulting in a superior testing performance. Experiments performed on wideband speech signals results in significant improvement in the performance of the system in comparison to the traditional techniques.
Self-Replication in Neural Networks

The foundation of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. We argue that backpropagation is the natural way to navigate the space of network weights and show how it allows non-trivial self-replicators to arise naturally. We then extend the setting to construct an artificial chemistry environment of several neural networks.
D5.4.2. Revision and Extension of the NeOn Methodology for Building Contextualized Ontology Networks

This deliverable presents the second version of the NeOn methodology for building ontology networks. This version extends the previous one presented in D5.4. 1 [80] providing methodological guidelines for: developing and creating ontology networks, reusing and re-engineering non-ontological resources, reusing ontology design patterns, modularizing ontologies, evaluating ontologies, evolving ontologies, and localizing ontologies.
The Influence of Content and Device Awareness on QoE for Medical Video Streaming over Small Cells : subjective and objective quality evaluations

There is a significant increase in mobile internet usage resulting in demand for traffic volume, frequency efficiency and energy and cost reduction, for which small cell networks are expected to play a key role. Due to high bandwidth requirement, mobile health applications (m-health) and medical video streaming in particular will benefit from small cell networks in terms of Quality of Service (QoS) and Quality of Experience (QoE) enhancements. This paper presents content-aware and device-aware medical Quality of Experience evaluations in terms of subjective (e.g. MOS) and objective (e.g. PSNR and SSIM) quality metrics obtained over small cell networks. Furthermore, we address the following two main research questions: (1) How significant is ultrasound video content type in determining medical QoE? (2) How much of a role does the display device play in medical experts’ diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatio-temporal features and validating their significance through medical experts’ subjective ratings. The latter is answered by conducting a subjective experiment of the ultrasound video sequences across multiple devices, ranging in screen size and resolution.
New spatial-temporal patterns and the first programmable on-chip bifurcation test-bed. (Research report of the Analogical and Neural Computing Laboratory DNS-6-2001.)

In this paper, we introduce a new experimental tool, a real-time programmable, spatial-temporal bifurcation test bed. We present the experimental analysis of an antisymmetric template class. This class produces novel spatial-temporal patterns that exhibit complex dynamics. The character of these propagating patterns depends on the self-feedback and on the sign of the coupling below the self-feedback template element.
Text Modeling Based on the Analysis of Categorical Words

Recently the most essential research in machine learning is to discover correct topics from large scale of electronic documents, and many studies about text modeling have been made in natural language process with the environment of big data. This paper discusses a fundamental way for people to form words while talking and describing ideas, the document is considered as words convergence and a new method for text modeling based on the relational analysis about verbs, nouns, and modifiers is proposed. Additionally the paper introduces a new algorithm for estimating parameters in the generative probabilities of the model, which can alleviate the process about statistics-based text analysis from computational complexity. Experimental results show that the method improves the quality for learning a document.