Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling for Cooperative Energy Harvesting Sensor Networks

Scheduling for Cooperative Energy Harvesting Sensor Networks Ahmed M. Abuseta Ammar In cooperative communication networks, the source node transmits its data to the destination either directly or cooperatively with a cooperating node. When using energy harvesting technology, where nodes collect their energy from the environment, the energy availability at the nodes becomes unpredictable due to the stochastic nature of energy harvesting processes. As a result, when the source has a transmission, it cannot immediately transmit its data cooperatively with the cooperating node. It first needs to determine whether the cooperating node has sufficient energy to forward its transmission or not. Otherwise, its transmitted data may get lost. Therefore, when using energy harvesting, the challenge is for the source to schedule its transmissions whether directly or cooperatively, such that the fraction of its events (sensed data) that are successfully reported to the destination is maximized. Hence, in this dissertation, we address the problem of cooperating node scheduling in energy harvesting sensor networks. We consider the problem for the case of a single cooperating node and the case of multiple cooperating nodes, as well as the scenarios of one-way and two-way cooperative communications. We propose a simple scheduling scheme, called feedback scheme, which enables the source to optimally schedule its transmissions whether directly or cooperatively. We show that the feedback scheme maximizes the system performance, but does not require auxiliary parameter optimization as does the-state-of-the-art scheme, i.e., the threshold-based scheme. However, the feedback scheme has the problem of overhead caused by transmitting the energy status of the cooperating node to the source. To overcome this burden, we introduce a statistical model that enables the source to estimate the energy status of the cooperating node. Because cooperation may result in the cooperating node performing worse than the source, we address this problem through fairness in the performance between the nodes in the network. In addition, we address the problem of scheduling for throughput maximization in a wireless energy harvesting uplink. We propose centralized and distributed algorithms that find the optimal solution, and we address complexity issues. Our algorithms are shown to have a linear or quadratic complexity compared to the exponential complexity of the brute force approach. Compared with cooperative transmission, our approach maximizes the network throughput such that no node’s throughput is adversely affected.
Decentralized control mechanism for body–limb coordination in quadruped running

As a mechanism for survival, quadrupeds have obtained skills involving coordination between limbs and the body (i.e. body–limb coordination), providing fast and adaptive locomotion compared with motion using only limbs. Several bio-inspired robotics studies have resulted in the development of legged robots that utilize a flexible spine, similar to cheetahs. However, the control principle of body–limb coordination has not been established to date. From the perspective of a decentralized control scheme, a minimal body–limb coordination mechanism is proposed in this study, in which body parts aid each other via a sensory feedback mechanism. The two-dimensional simulation and hardware experiments reveal that bilateral sensory feedback between limbs and body is essential for the robot to adaptively generate a body–limb coordination pattern and achieve faster locomotion speed than that by only limbs in efficient manner.
VinceptionC3D: a 3D convolutional neural network for retinal OCT image classification

In order to make further and more accurate automatic analysis and processing of optical coherence tomography (OCT) images, such as layer segmentation, disease region segmentation, registration, etc, it is necessary to screen OCT images first. In this paper, we propose an efficient multi-class 3D retinal OCT image classification network named as VinceptionC3D. VinceptionC3D is a 3D convolutional neural network which is improved from basic C3D by adding improved 3D inception modules. Our main contributions are: (1) Demonstrate that a fine-tuned C3D which is pretrained on nature action video datasets can be applied for the classification of 3D retinal OCT images; (2) Improve the network by employing 3D inception module which can capture multi-scale features. The proposed method is trained and tested on 873 3D OCT images with 6 classes. The average accuracy of the C3D with random initialization weights, the C3D with pre-trained weights, and the proposed VinceptionC3D with pre-trained weights are 89.35%, 92.09% and 94.04%, respectively. The result shows that the proposed VinceptionC3D is effective for the 6-class 3D retinal OCT image classification.
D4: Distributed Direct Digital Democracy: a Remote Electronic Voting Protocol

Recently we have been witnesses to a string of controversial elections: the 2016 US Presidential election, the 2016 Brexit referendum, the 2018 Russian presidential election, the 2018 Zimbabwe elections. The controversies surrounding elections are seemingly endless. Why are these examples relevant? In the 21st century, we are still conducting most of our voting with paper and pencil. Electronic voting methods could resolve much of the controversies and difficulties regarding conventional elections. It would eliminate the uncertainty of counting and be directly verifiable by anyone, not just by a limited number of officials. Electronic voting also has the benefit of being easier to set up and run periodically, which could be implemented to increase the participation of the population further. This work focuses on how to utilise blockchain in an electronic voting scheme. The blockchain is perfect for voting application for two crucial aspects. First and foremost, due to its inherent public nature, everyone can verify the facts on the blockchain. Verifiability should be a critical requirement for any modern voting system. Secondly, the consensus mechanism allows anyone to participate in the validation and recording of the election process. Furthermore, being distributed, no single entity has full control over the infrastructure. In the context of voting, verifiability is as valuable as privacy. If the body of literature indicates that verifiability is an inherent property of the blockchain, this is not true for privacy. We propose a scheme that satisfies the strong privacy requirement of receipt-freeness. Validation has shown that the scheme is suitable for large-scale elections and that it performs well on consumer grade hardware.
Corner-point criterion for assessing nonlinear image processing imagers

Range performance modeling of optronics imagers attempts to characterize the ability to resolve details in the image. Today, digital image processing is systematically used in conjunction with the optoelectronic system to correct its defects or to exploit tiny detection signals to increase performance. In order to characterize these processing having adaptive and non-linear properties, it becomes necessary to stimulate the imagers with test patterns whose properties are similar to the actual scene image ones, in terms of dynamic range, contours, texture and singular points. This paper presents an approach based on a Corner-Point (CP) resolution criterion, derived from the Probability of Correct Resolution (PCR) of binary fractal patterns. The fundamental principle lies in the respectful perception of the CP direction of one pixel minority value among the majority value of a 2×2 pixels block. The evaluation procedure considers the actual image as its multi-resolution CP transformation, taking the role of Ground Truth (GT). After a spatial registration between the degraded image and the original one, the degradation is statistically measured by comparing the GT with the degraded image CP transformation, in terms of localized PCR at the region of interest. The paper defines this CP criterion and presents the developed evaluation techniques, such as the measurement of the number of CP resolved on the target, the transformation CP and its inverse transform that make it possible to reconstruct an image of the perceived CPs. Then, this criterion is compared with the standard Johnson criterion, in the case of a linear blur and noise degradation. The evaluation of an imaging system integrating an image display and a visual perception is considered, by proposing an analysis scheme combining two methods: a CP measurement for the highly non-linear part (imaging) with real signature test target and conventional methods for the more linear part (displaying). The application to color imaging is proposed, with a discussion about the choice of the working color space depending on the type of image enhancement processing used.
Using a Design Ontology to Identify the Terms that Represent the Design Results Across Research Communities

This paper is contextualized in a research project that aims to create a new paradigm to support the design process, substituting the sequential nature of design process models by a flexible structure. To implement this paradigm, we must identify the final and intermediate results of the design process, such as documents, models, artefacts, among others. However, design research is wide and multidisciplinary, resulting in non-uniformity of the terminology across research communities, what hinders the results identification by means of a literature review. This paper aims to identify the terms employed by different research communities to refer to the intermediate and final results of the design process, structuring synonym terms across research communities and establishing how those terms interrelate in the design ontology. Using literature review, the following terms were analysed: design objects, elements, deliverables, entities, information, components, data, and artefacts. The results provide a holistic view of how the terms are employed throughout research communities, supporting the creation of search strings and pointing out opportunities for improving the design ontology.
An Analysis of Translation Techniques in Translating Cultural Words in Booklet “Jogja The Real Java”

This study aimed to identify cultural words found in English Translation Booklet “Jogja The Real Java” and describe translation techniques used by the translator in translating the booklet. The method applied was descriptive qualitative research by collecting and analyzing both the Indonesian and English versions of the booklet “Jogja The Real Java”. This study starts by finding the problem, collecting data, classifying data, analyzing data, and drawing the conclusion. The cultural words classified based on Newmark’s theory, then the selected data analyzed by using Molina and Albir’s theory of translation techniques. The data of the research shows there are 106 cultural words with specific category. Cultural words are divided into five categories and the amount of cultural words is categorized: there are 8 cultural words in ecology, 59 cultural words in artifact culture, 7 cultural words in social culture, 28 cultural words in organization, 4 cultural words in habits and gesture. Therefore, result of this study shows that eight translation techniques are applied by the translator in translating those cultural words. The translation techniques which mostly applied by the translator is pure borrowing. Because not every cultural word in the booklet “Jogja The Real Java” have the equivalent in English. Keywords—translation; cultural words; translation techniques
Optimal network implementable controllers for networked systems

In this thesis, we study the problem of network implementable controllers for network distributed systems. Network distributed control problem gains importance by the increase in networked system applications in many areas which require network distributed control and estimation. By network implementable controller, we mean controller can be implemented over the given network with the predefined/given delay and sparsity constraints. We define all stabilizing controllers by re-interpreting plant and controller. We define a congruent stable plant of the original plant which is not necessarily stable, such that the controller of the congruent plant is linearly function of the original plant’s controller. When we put structural constraints on all stabilizing controllers of the stable congruent plant, these controllers embody controllers of the main plant. Therefore, all stabilizing controllers of the original plant are defined as all stabilizing controllers of the congruent plant with structural constraints. In the view of this problem, we obtain all stabilizing controller parametrization of the original plant wherein equality constraints are introduced on the Youla parameter. Moreover, we define a necessary and sufficient problem to attain a controller in the form of norm minimization problem benefiting formulated all stabilizing controller parametrization and provide a solution method for it. Moreover, we introduce a doubly-coprime factorization of blkdiag(Inx ,K) which allows us to have a network implementable state-space realization of a structured controller, K, which inherits sparsity and delay constraints introduced by the given network in z-domain, of a network distributed system with order nx. By network implementable state-space realization, we mean state-space realization can be expressed as a strictly causal interaction of some sub-systems over the given network. We call such structured controllers as network realizable controller, i.e. controllers whose network implementable state-space realization
Increasing protected data accessibility for age-related cataract research using a semi-automated honest broker

Ophthalmology researchers are becoming increasingly reliant on protected data sets to find new trends and enhance patient care. However, there is an inherent lack of trust in the current healthcare community ecosystem between the data custodians (i.e., health care organizations and hospitals) and data consumers (i.e., researchers and clinicians). This typically results in a manual governance approach that causes slow data accessibility for researchers due to concerns such as ensuring auditability for any authorization of data consumers, and assurance to ensure compliance with health data security standards. In this paper, we address this issue of long-drawn data accessibility by proposing a semi-automated “honest broker” framework that can be implemented in an online health application. The framework establishes trust between the data consumers and the custodians by: 1. improving the eiciency in compliance checking for data consumer requests using a risk assessment technique; 2. incorporating auditability for consumers to access protected data by including a custodian-in-the-loop only when essential; and 3. increasing the speed of large-volume data actions (such as view, copy, modify, and delete) using a popular common data model. Via an ophthalmology case study involving an age-related cataract research use case in a community cloud testbed, we demonstrate how our solution approach can be implemented in practice to improve timely data access and secure computation of protected data for ultimately achieving data-driven eye health insights.
Decision support systems classification in industry

This research has presented a discussion of related literature on decision support systems (DSS) and its role in production systems. The first part of the research presents DSS and how this has evolved until 2010. The next part of the research provides a discussion of the decision making process and the data mining phases as these relate to DSS. The last part of the research presents further discussion of DSS and various perspectives on the criteria that could be used for DSS. The criteria in the classification of DSS includes the following: user relationship; orientation; scope of use; type and frequency of decision making; mode of assistance; focus area; objective; capacity; degree of guidance; and, degree of non-procedurality. The research also included a discussion of industry 4.0, which is known as the fourth revolution in the industry.
Fast data acquisition for silicon tracking detectors at high rates

Silicon tracking detectors play a key role in many current high energy physics experiments. To enhance experimental sensitivities for searches for new physics, beam energies and event rates are constantly being increased, which leads to growing volumes of detector data that have to be processed. This thesis covers high-speed data acquisition for silicon tracking detectors in the context of the Mu3e experiment and future hadron collider experiments. For the Mu3e experiment, a vertical slice of the trigger-less readout system is realized as a beam telescope consisting of 8 layers of pixel sensors that are read out using a prototype of the Mu3e front-end board. The performance of the full readout system is studied during beam tests. Sensor hit rates of up to 5 MHz can be handled without significant losses. Hence, the system fulfils the requirements for the first phase of the experiment. To fully exploit the potential of silicon tracking detectors at future hadron collider experiments, the implementation of high-speed data links is mandatory. Wireless links operating at frequencies of 60 GHz and above present an attractive alternative to electrical and optical links, as they offer high bandwidth, small form factor and low power consumption. This thesis describes readout concepts for tracking detectors applying wireless data transfer and presents studies of wireless data transmission.
Using machine learning to perform early diagnosis of Autism Spectrum Disorder based on simple upper limb movements

Autism like other diseases requires early cure in order to magnify the remedy’s results. The impact of Autism Spectrum Disorder (ASD) is embodied in the following things: the inability for kids to interact with other people, the difﬁculty in socialize with others, speaking after a long time comparing with other kids, lack of eye contact with other. Such activities are utilized for the resolution regarding diagnosing ASD. For instance, kids shift their upper limb before other activities and such moving considers as an indicator to decide whether such children suffer from autism. The current paper checks diagnosing autism that simply depends on altering upper-limb for kids between two to four years old that depends on carrying out certain mechanisms and machine learning. Such study utilized a Linear Discriminant Analysis (LDA) method to elicit features and, the Support Vector Machines (SVM) in order to categorize thirty kids i.e. categorizing around ﬁfteen autistic kids out of ﬁfteen normal children by analyzing kinematic information that is collected from implementing simple task. However, such study achieved an optimal precision categorization of 100% as well as 93% of intermediate precision. Such ﬁndings provide more clues for simple upper-limb movement that can be utilized in order to precisely categorize the kids who suffer from low-functioning autism.
The Impact of Vivid Graphical Presentation of Financial Information in Digital Annual Reports on Investors' Impressions of Management and Firm Performance

This study examines the effect of graphical vividness on nonprofessional investors' impressions of management and firm performance when the financial performance news is either positive or negative. Conducting a 2 × 2 between-participants experiment with 470 participants from Amazon Mechanical Turk (M-Turk), I find that when the news is positive, nonprofessional investors have more positive impressions of management, which, in turn, leads to more positive impressions of firm performance when the graphical presentation is vivid versus pallid. In contrast, when the news is negative, presenting graphs vividly has little effect on nonprofessional investors' impressions. The study contributes to regulators and practice by demonstrating that allowing a high degree of presentation flexibility in digital annual reports has behavioral outcomes to nonprofessional investors' judgments and decisions. The study also contributes to the strategic disclosure literature by demonstrating the impact of graphical vividness in presenting financial performance information.
Force control of an uncertain series elastic actuator system via a fuzzy sliding mode controller and a nonlinear state estimator

Nowadays, series elastic actuators play an important role in actuation technology, especially for mechatronics and robotic applications. In this article, the accurate tracking of output force for a series elastic actuator is investigated. In order to achieve this aim, a novel robust control scheme is developed to overcome friction effects, backlash in the drivetrain, measurement noises (sensor inaccuracy) and uncertainties in the dynamic model. The control scheme consists of a fuzzy sliding mode controller and a nonlinear state estimator. The theory of Lyapunov stability is used for the formulation of the proposed controller and also the fuzzy logic strategy is used for tuning the sliding surface parameter in order to achieve accurate and chattering-free performance. Furthermore, a powerful estimator algorithm, unscented H-infinity filter, is added to the control scheme to improve the robustness of the system. Simulation results, as well as experimental analysis, approved the efficiency of the proposed approach in the presence of uncertainties and disturbances.
MetaCLAS: A Prototype Evolutionary Proposal to Automatically Suggest Clustering Methods and their Parameters

Resumen Uno de los principales problemas al que nos enfrentamos al momento de realizar agrupamiento de datos consiste en elegir cuál es el mejor método de clustering para clasificarlos, y cuál es la cantidad ideal (k) de grupos en los que se debeŕıan separar esos datos. En este trabajo presentamos una primera aproximación de un método que, a partir de un conjunto de datos estandarizados, sugiere el método de clustering y el valor de k que mejor los agrupa. Para esto considera cuatro ı́ndices de evaluación de la estructura final de clusters: Dunn, Silueta, Entroṕıa y Widestgap. El algoritmo está implementado como un algoritmo genético en el cual los individuos son posibles configuraciones de métodos de clustering y sus parámetros. En este primer prototipo, el algoritmo sugiere entre los métodos de partición K-means, PAM, CLARA y Fanny. Asimismo, además de sugerir el método que presentó mejor desempeño, también se obtiene como resultado el valor de los parámetros para ejecutarlo. El prototipo fue desarrollado en un entorno de R y se pudo corroborar que sus resultados son consistentes con una combinación de resultados provistos por otros métodos con objetivos similares. La idea de este trabajo es que sirva de base inicial para un desarrollo que incorpore opciones para reducción de la matriz de datos, evaluación de más métodos de agrupamiento y optimización de los operadores genéticos del algoritmo.