Miracle-dz/UniFAR-FTUs huggingface dataset:
Pure color object extraction from a noisy state using quantum version parallel self organizing neural network

The classical techniques used for noisy object extraction recline that some a priori information concerning the noise characteristics is required during the extraction process. The proposed quantum version parallel self-organizing neural network (QVPSONN) architecture uses the quantum characteristics like superposition, coherence, decoherence, entanglement, etc. of quantum principle for its operation. The extraction of object achieved is better as well as the extraction time is reduced. QVPSONN architecture uses the phase shifting property of qubits to extract the objects from the noisy environment. At first the pure color input image is separated into three color components into the source layer. Then, the three color components are fed to the three parallel architectures of QMLSONNs for processing which are finally fused in the sink layer. Each of the processing layers of QVPSONN comprises qubit-based neurons. The weights between the network layers are demonstrated by single qubit rotation gates. Quantum measurement is applied for processing the information at the output layers, whereby the quantum states are destroyed using the decoherence property. When the system becomes stable, the sink layer fuses it and generates the output. Results of application on a synthetic image and a real-life reveal its efficacy.
How much data may be safely processed on one key in different modes?

Объем информации, который может быть обработан без смены ключа алгоритмом блочного шифрования в некотором режиме работы, - ключевая характеристика многих информационных систем и протоколов. В данной работе рассматриваются два подхода к оценке этой характеристики и приводятся обоснованные оценки, которые могут быть использованы при создании различных систем и разработке правил пользования подобными системами.
MPEG-I Coding performance in Immersive VR/AR applications

After decennia of developing leading-edge 2D video compression technologies, MPEG is currently working on the new era of coding for Immersive applications, referred to as MPEG-I. It ranges from 360-degree video with head-mounted displays to free navigation in 3D space, with head-mounted and 3D light field displays. Two families of coding approaches, covering typical industrial workflows, are currently considered for standardisation – Multiview + Depth Video Coding and Point Cloud Coding – both supporting high-quality rendering at bitrates of up to a couple of hundreds of Mbps. This paper provides a technical/historical overview of the acquisition, coding and rendering technologies considered in the MPEG-I standardization activities.
Deep Neural Architecture for Recovering Dropped Pronouns in Korean

Pronouns are frequently dropped in Korean sentences, especially in text messages in the mobile phone environment. Restoring dropped pronouns can be a beneficial preprocessing task for machine translation, information extraction, spoken dialog systems, and many other applications. In this work, we address the problem of dropped pronoun recovery by resolving two simultaneous subtasks: detecting zero‐pronoun sentences and determining the type of dropped pronouns. The problems are statistically modeled by encoding the sentence and classifying types of dropped pronouns using a recurrent neural network (RNN) architecture. Various RNN‐based encoding architectures were investigated, and the stacked RNN was shown to be the best model for Korean zero‐pronoun recovery. The proposed method does not require any manual features to be implemented; nevertheless, it shows good performance.
Brain-Inspired Models and Systems for Distributed Computation

Not only do biological nervous systems clearly outshine modern computing technology in terms of intelligence, but also in terms of energy requirements, speed, and compactness. Such capabilities do not seem obvious given that brain cells transmit signals at average rates of as low as 1 Hz, are noisy and imprecise, and long-range connectivity is very sparse. Thus, the governing design principles of brains must be such that high performance processing is achieved despite noise and imprecision inherent to the computing elements, while power and space requirements are minimized. Today’s computing technology is limited by constraints similar to those faced by biological systems, namely, restrictions in the number of transistors that can be accommodated per unit area, the resources and power required for communication between those transistors, and the increasing impact of noise as device dimensions shrink. Therefore, it is likely that modern information technology would benefit enormously from exploiting certain design principles loosely based on the ones found in biological systems. The purpose of this thesis is to investigate key characteristics of biological nervous systems and suggest ways of incorporating these principles in artificial systems. The presented work is structured into three core categories, corresponding different aspects that are central to neural information processing. Specifically, aspects concerning the connectivity structure (topology), the style of communication and processing, and the underlying physical substrate are investigated, and are divided into parts I-III as follows: I computational roles of structural constraints in neural and distributed processing systems, II the event-based processing and communication style of biological neurons, III exploiting and tolerating noise and imprecision in the underlying physical substrate implementing the computation. It is shown that by taking into account these principles, or by actively enforcing them, improvements can be achieved in artificial systems in terms of accuracy, speed, power, or space requirements. In part I, it is demonstrated how a non-trivial network topology, featuring predominantly local connectivity, can
Design and Implementation of a Proof-of-Stake Consensus Algorithm for Blockchain

Aquest document analitza i descriu la implementacio d'un algorisme de consens que sera una part fonamental en un prototip de blockchain on el principal objec- tiu es el de assignar, delegar i vincular direccions IP de forma descentralitzada y segura.
Taxonomy of Older Driver Behaviors and Crash Risk

This project’s objectives were to identify risky behaviors, driving habits, and exposure patterns that have been shown to increase the likelihood of crash involvement among older drivers; and to classify these crash-contributing factors according to a set of underlying functional deficits specific to, or more prevalent among, the older driver population. Such deficits may result from normal aging, age-related medical conditions, or medication use. A further goal was to identify and critically examine behavioral countermeasures with the potential to mitigate functional loss and/or diminish the occurrence of risky behaviors, and thus ameliorate crashes among older drivers. The first task was an analysis of older driver injuries and fatalities using national databases (FARS, GES), to identify driving patterns, driving tasks, and contributing factors associated with crashes by older drivers; more details are available in a separate document, Report No. DOT HS 811 093, “Identifying Behaviors and Situations Associated With Increased Crash Risk for Older Drivers. ” Additional project tasks included a review of the literature describing age-related functional changes, and evaluations of existing behavioral countermeasures to reduce older drivers’ crash risk; an expert panel meeting to supplement the information from the database analyses and literature review; and unstructured interviews with older drivers who have had crashes within the previous three years, and an age-matched group who have not had crashes within that period, to determine whether these groups differ in factors such as exposure or use of countermeasures. The outcomes of these project activities were used to develop and refine a taxonomy table that captures critical relationships between topics and subtopics highlighted in the literature review and crash database analyses. This table identifies critical performance errors that underlie crash types where older drivers are most strongly over represented; the functional deficits that are implicated in causing such performance errors; and the countermeasure strategies that presently appear to hold the greatest promise to ameliorate or to accommodate those (age-related) deficits. The taxonomy table is a resource that provides at-a-glance, state-of-the-knowledge information to assist researchers, health care practitioners, and others concerned about older driver safety to identify particular risk factors, and what can be done to reduce the risk.
Development and pilot testing of a model to translate risk assessment data for Great Lakes Native American communities using mobile technology

ABSTRACT The Chippewa Ottowa Resource Authority monitors fish contaminants in Anishinaabe (Great Lake Native American) tribal fisheries. A software program (app) was programmed to translate and operationalize these data for the first time in an interactive, personalized, and culturally tailored format. This article reports the process for creation, and preliminary community reception, of interactive fish consumption advisory using software that can be accessed via mobile phones and/or the internet. Response data to previous advisories guided the app presentation and aesthetics. The app “Gigiigoo'inaan’ [Our Fish] presents personalized recommendations based on back-end calculated reference doses as well as a novel risk/benefit quantification originally presented by Ginsberg et al. (Human Ecol Risk Assess, Vol. 21, 2015, pp. 810–39) and Ginsberg and Toal (Environ Health Perspect, Vol. 117, 2009, pp. 267–75) to characterize nutritional benefits. The partnering organization, Inter-Tribal Council of Michigan recruited focus groups to pilot test the app for usability, influence on dietary behavior, and cultural appropriateness. The general concept of the app and the presentation of the data were deemed culturally acceptable and pleasing to the participants. The results suggest that risk-benefit data is valuable to end-users but meal frequency recommendations, typically based on federal guidelines, should also be clearly communicated. How these guidelines are selected and presented will likely impact the reception and adoption of consumption advice.
A Study of the Texture Measurement Definition Problem (Image Analysis).

The problem of texture measurement definition is studied. A theory is presented in order to overcome the problem. This theory includes a conceptual framework for the general measurement definition problem. This framework is based on the concepts of a perceptual transform, a perceptual space, a measurement transform and a simila­ rity transform. Using this conceptual framework and a particular choice for the similarity transform, a set of requirements are defined. These requirements can be used to create a formal method for defining measurements off the GLC matrices. The formal procedure is based on preserving perceptual relationships among textures. To apply this technique a perceptual norm, a least-squares procedure and a synthesis procedure are needed. Each of these components are investigated and the motivation for selecting each of the methods used is presented. Individually, these components are tested in order to see if they are appropriate for use with our technique. Finally, a feasibility study is discussed to demonstrate the possibility of using this technique to solve for measurements. The problem was to define measurements given a limited number of textures. The defined measurements are studied in order to establish their contributions. It is shown that the newly defined measurement are gauging the periodicity and the symmetry of patterns, perceptual enti­ ties that have been demonstrated to be important in the human vision
Methods to improve applicability and efficiency of distributed data-centric compute frameworks

Kambatla, Karthik Shashank PhD, Purdue University, December 2016. Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks. Major Professor: Ananth Y. Grama. The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources – web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks. While data-centric models like MapReduce allow applications to process large datasets on thousands of nodes, the lack of inter-task communication and high synchronization costs limit their applicability to data-parallel computations. In this dissertation, we (1) enable inter-task communication through transactional execution of tasks over shared address space, and (2) lower the synchronization costs through relaxed synchronization techniques. These improvements extend the applicability of MapReduce and allow applications to exploit amorphous data parallelism and algorithmic asynchrony. We demonstrate this by improving the scalability and performance, by an order of magnitude, of many unstructured graph applications. The simplicity of data-centric models, that hides the complexity of distributed execution from end-users, is one of the main reasons for their widespread adoption and success. However, this makes it harder for end-users to efficiently utilize and share cluster resources. In this dissertation, we devise UBIS, a utilization-aware cluster scheduler, to improve cluster utilization and job throughput. UBIS demonstrates improvements of up to 30% for repre-
Performance evaluation of unimodal, bimodal and trimodal biometric identification system

Multimodal biometric systems that integrate the evidences presented by multiple sources of informationaddressed the limitation of the unimodal biometric systems with a variety of problems, namely, noisy data, intra-class variations, restricted degrees of freedom, non-universality and spoof attacks. The likelihood ratio based score fusion framework was developed specifically for the verification scenario where the goal is to decide whether an input sample belongs to the genuine or impostor class. Twenty users were randomly selected from the population. The various biometric traits, namely face, voice and signature of ten users were used to train the respective modules of the proposed identification system. As a general observation for three scenarios, the p-value in the ANOVA analysis conclude that the fusion of three modalities brings systematically a clear improvement of the results in comparison to either unimodal and bimodal.
Do visitors gaze and reproduce what destination managers wish to commercialise? Perceived and projected image in the UNESCO World Heritage area

Destination image plays a key role in helping people decide where to travel and affects satisfaction, likelihood of return visits and word of mouth. While photography is not the only way of projecting and perceiving an image, 'a picture paints a thousand words'. The rise of social media and user-generated content has made the image formation process more complex, and has reduced the extent of control that tourism suppliers can exert on the image they wish to project. It is thus necessary to further investigate whether tourists reproduce the commercialised image in what the literature calls the 'hermeneutic circle of representation' or capture and share their own impressions. This study constructs a categorisation scheme for conducting photography-based image analysis to compare images of two Norwegian destinations as projected by destination management organisations with those shared by Instagram users (perceived image). Results indicate that this circle of representation is not hermeneutic.
AUTOMAÇÃO DE DIAGNÓSTICO PARA MANUTENÇÃO PREDITIVA BASEADA EM ANÁLISE DE FLUIDOS DE EQUIPAMENTOS COM MACHINE LEARNING

Resumo Realizar manutenções preditivas de forma cada vez mais assertiva é um dos benefícios da atual evolução dos sistemas produtivos industriais conhecida como Indústria 4.0. A Sotreq revendedora Caterpillar oferece um serviço de análise de fluidos a partir da coleta periódica para máquinas operantes em campo. Com os resultados das medições, especialistas atribuem às amostras um nível de criticidade, justificativas e recomendações. Neste trabalho propõe-se uma solução que aumentou a eficiência desse processo através da utilização de modelos de Machine Learning treinados com as análises já realizadas que automatizam a classificação e a geração do texto de diagnóstico para novas amostras. As predições do sistema foram consideradas bem sucedidas, com métricas de sensibilidade e especificidade acima de 88%. Palavras-chave: Aprendizado de máquina; Manutenção baseada em condição; Análise de fluidos de equipamentos.
Etude de la faisabilité de l’évaluation de l’efficacité des mouvements de tracé du jeune apprenti-scripteur

Nous definissons l’efficacite des mouvements de trace comme la capacite a concilier des caracteristiques de rapidite, fluidite et regularite satisfaisant aux attentes des enseignements scolaires. L’equipe educative en charge, des l’entree a l’ecole maternelle, de l’accompagnement de l’acquisition de l’ecriture, ne dispose ni de moyens d’evaluation objectifs et fiables de l’efficacite des mouvements de trace adaptes aux profils des tres jeunes enfants, ni d’outils d’aide a la decision des exercices les plus appropries pour renforcer et controler la progression de chaque enfant compte-tenu de son deja-la psychomoteur notamment. Nous expliciterons en quoi les dispositifs interactifs d’entrainement graphomoteur orientes stylo sont inadaptes a constituer des aides a la decision et a l’accompagnement des acquisitions pour ces trois domaines. Nous verifierons ensuite experimentalement l’existence de pistes, a la fois d’un point de vue technologique et methodologique, pour doter ces dispositifs de capacite d’evaluation de l’efficacite des mouvements de trace du jeune apprenti-scripteur.
Prevalence of malocclusion among adolescent schoolchildren in Malaysia

Malocclusion is an appreciable deviation from the normal occlusion that may be considered functionally and esthetically unsatisfactory. [1] It is considered to be one of the most widespread oral health problems. [2] A well-aligned teeth not only contribute to the health of oral cavity and stomatognathic system but also influence the personality of the individual. Malocclusion compromises the health of tissues and can also lead to social and psychological problems. [3] It is known to have a tremendous social impact in terms of perceived attractiveness, school functioning, and employability, particularly when it is more evident in the anterior teeth.
Development of a method for calculating the safe position of military units by using artificial neural networks based on swarm algorithms

Запропоновано метод пошуку безпечної позиції військових одиниць під час ведення бойових дій шляхом використання нейронних мереж та алгоритму жаб. Розроблено математичну модель методу пошуку безпечної позиції військових одиниць шляхом використання штучних нейронних мереж. Побудовано гібридний алгоритм пошуку безпечної позиції на основі алгоритму ройового інтелекту. Визначені області потенційного застосування методу. ключові слова: нейронні мережі, безпечна позиція, прогнозування рішень, покрив кластерів. lytvyn V. , Uhryn d. , Iliiyuk o. , Klichuk o.
Remote based patient monitoring system using wearable sensors through online and offline mode for android based mobile platforms

People need health care services to be done and maintained on a regular basis. Complications arise as they wear the sensor-based health monitoring devices. Although Health monitoring devices are affordable, monitoring them complicates the equipment along with its usage. Multiple modalities such as the heart rate and pulse rate, body temperature, blood pressure also can be sensed with the help of sensors and those data's can also be stored for future monitoring and analysis process. Though the possibility is there in every aspect to monitor; there are complications which exist to be resolved. Few unresolved queries exist such as helping the blind people by themselves, capturing the high and low-level pressure and storing it in the database, initiating the person nearby to utilizing the first aid kid, advise the public to assist the patient, identifying and send an emergency alert tracking the location through GPS. The collected and stored data will help the patients to utilize for further usage. The means to resolve the problem are analyzed, and a methodology is proposed.
Knowledge sourcing in a traditional industry: prospects of peripheral regions

Despite its economic relevance, only a few studies focus on knowledge creation, diffusion and utilisation in traditional industries located in peripheral regions, and even fewer on the innovation interdependencies between industries and regions. The theory of differentiated knowledge bases is capable to explain both the industrial and spatial patterns of the knowledge flows. The present study aims to reveal the process of innovation-related knowledge sourcing in the printing industry located in the peripheral region of Kecskemet, Hungary. The research builds on three main questions: what are the main sources of new knowledge? Who are the main collaborative partners in knowledge acquisition and transfer? And what is the main spatial level of knowledge sourcing? The results indicate that in a peripheral region, innovative firms build on the combination of direct and indirect knowledge sources mostly external to the region, while the non-innovative ones typically rely on local, incomplex knowledge sources.
Approach to measure the degree of the unbalanced power in induction motor for energy-efficient use

This study utilises the power unbalance factors (PUFs) to determine the output precisely of the induction motor (IM) subjected to unbalanced supply voltage, and use them to analyse motor performance [i.e. copper losses, motor efficiency, power factor, input active and reactive powers, electromagnetic torque and derating factor (DF)]. The analysis of IM is applied using the approach of symmetrical component. Furthermore, MATLAB/Simulink is used to examine the behaviour of IM during balanced and unbalanced operations. Results illustrate that the reactive PUF ( K Q ) provides accurate assessment for the machine performance. Three cases of the unbalanced condition were selected to provide various scenarios, which may IM subjected to them. The influence of cases on electromagnetic torque, rotor speed, stator and rotor current, active and reactive input powers has been investigated. DF is investigated and tuned to improve energy efficiency during unbalanced operations.