Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Exposure to Electromagnetic Fields : Measurement Problems and Correction Techniques

Riassunto: Lo studio dei rischi provocati dall’esposizione ai campi elettromagnetici è un problema di notevole interesse in epidemiologia ambientale. In quest’ambito si colloca uno studio caso-controllo, attualmente in corso in Italia, noto come studio SETIL (Studio Epidemiologico Tumori Infantili Linfoemopoietici). Esso ammette la possibilità che l’esposizione sia misurata con errore. Il presente lavoro valuta l’applicabilità di alcune tecniche di correzione per gli errori di misura in questo contesto tramite simulazione. Le tecniche considerate sono la Regression-Calibration, le variabili strumentali e il SIMEX. Ci riferiamo a scenari in cui gli errori verificano o meno le assunzioni alla base dei metodi. Nelle situazioni esaminate, i metodi migliorano, per distorsione e copertura degli intervalli di confidenza, i risultati di un'analisi che ignora la presenza di errore di misura.
Fully Automatic Intervertebral Disc Segmentation Using Multimodal 3D U-Net

Intervertebral discs (IVDs), as small joints lying between adjacent vertebrae, have played an important role in pressure buffering and tissue protection. The fully-automatic localization and segmentation of IVDs have been discussed in the literature for many years since they are crucial to spine disease diagnosis and provide quantitative parameters in the treatment. Traditionally hand-crafted features are derived based on image intensities and shape priors to localize and segment IVDs. With the advance of deep learning, various neural network models have gained great success in image analysis including the recognition of intervertebral discs. Particularly, U-Net stands out among other approaches due to its outstanding performance on biomedical images with a relatively small set of training data. This paper proposes a novel convolutional framework based on 3D U-Net to segment IVDs from multi-modality MRI images. We first localize the centers of intervertebral discs in each spine sample and then train the network based on the cropped small volumes centered at the localized intervertebral discs. A detailed comprehensive analysis of the results using various combinations of multi-modalities is presented. Furthermore, experiments conducted on 2D and 3D U-Nets with augmented and non-augmented datasets are demonstrated and compared in terms of Dice coefficient and Hausdorff distance. Our method has proved to be effective with a mean segmentation Dice coefficient of 89.0% and a standard deviation of 1.4%.
Human Activity Recognition in Smart Homes

There is an increasing interest in activity recognition analysis due to the tremendous growth of sensors and devices that have recently brought significant attention to smart homes research which promotes inhabitants’ comfort, security, and safety. Therefore, there is a necessity to have a comprehensive activity recognition approach which is not only limited to recognise human activities and to detect unknown/abnormal behaviors, but also could update a stream learning model in a real-time setting with sequential sensor data. As answers to the aforementioned problems, a set of approaches is developed in this thesis to fulfill these requirements with results that are comparable with or better than the state-of-the-art approaches. By recognising activities from streaming sensor data, a new classification method, Adaptive Cluster-Based Ensemble Learning of Streaming sensor data (ACBEstreaming), is proposed. This method includes desired features, namely adaptive windowing and detecting, relevant sensor events, preserving past sensor information in its current window, and forming online clusters of streaming sensor data. ACBEstreaming improves the representation of sensor events, and learns and recognises activities in a stream fashion. However, there is still the challenge of the multi-class imbalance issue in the stream learning mode that causes misleading classification outcomes due to the presence of an inadequate representation of sensor data and class distribution skews. Conversely, we propose a new multi-class stream imbalance ensemble method where the base learner is a Näıve Bayesian (NB) classifier. In this approach, the training instances from any of the classes involved in learning satisfy the median prior probability threshold to aid in balancing the classes.
Understanding the calculation of the kappa statistic: A measure of inter-observer reliability

It is common practice to assess the consistency of diagnostic ratings in terms of “agreement beyond chance. ” The kappa coefficient is a popular index of agreement for binary and categorical ratings. This article focuses on the unweighted kappa statistic calculation by providing a stepwise approach that is supplemented with an example. The aim is that health care personnel may better understand the purpose of the kappa statistic and how to calculate it. The following core competencies are addressed in this article: Medical knowledge.
Highly resistant multicoloring with 3 attackers and 1 malfunctioning vertex

In this paper we explore a way of securing a secret inside a graph by observing pieces of the secret as colors assigned to the graph vertices. If a graph allows a highly (a, b)-resistant k-multicoloring then a secret can be divided into k parts and sets of those parts distributed to the vertices of the graph so that no a attackers can steal the secret, and when a attackers and b malfuntioning vertices leave the graph, the secret is still whole in the remaining graph. In this paper we explore how many vertices a graph must have in order to allow a highly (3, 1)-resistant k-multicoloring, and what is the minimal number of colors, for graphs that do allow such multicoloring.
Railway Transport Infrastructure Monitoring by UAVs and Satellites

Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing (ERS), permitting to obtain information from large areas with a sufficiently high resolution, can provide significant assistance in solving the mentioned problems. This paper discusses the possibility of using various means of remote sensing such as satellites and unmanned aerial vehicles (UAV), also known as drones, for receiving information in different ranges of the electromagnetic spectrum. The paper states that joint using of both these means gives new possibilities in improving railroad security.
Ecludian Distance based Partial Face Recognition

— Various approaches have been proposed to recognize complete faces. However, few have dealt with the problem of recognizing an arbitrary partial face. In real-world scenarios, human faces might easily be occluded by other objects. This made traditional face recognition algorithms, which heavily rely on face alignment and face normalization, infeasible. In this paper, we propose an approach for partial face recognition which will be based on matching feature set, and this approach will be able to match partial face patches to gallery data set faces automatically and is robust to occlusions as well as illumination changes.Then, we propose arobust point set matching method to discriminatively match thesetwo extracted local feature sets, where both the textural informationand geometrical information of local features are explicitlyused for matching simultaneously. Finally, the similarity of two faces is been extracted and distance is been calculated based on Euclidian distance formula.
Object instance segmentation using recurrent models

This work intends to expand the Polygon-RNN model for object segmentation, able to create a polygon outlining an object using a CNN and a RNN, by trying several modifications. Several models and approaches have been explored. Out of them, the one that has shown the best IOU has been a joint model combining CNNs and a RNN. While the accuracy when running the model in default mode prediction only reaches 55.14% (which in fact is quite high), when running in beam search mode the accuracy has increased up to 59,94%. Once the final model was chosen, it was implemented in the LabelMe annotation tool, a web tool that can be used for annotators to segment instances of objects. This way the annotation process is made in a semi-automatic manner, making the work much faster and easier for the annotator. The annotator has to select a bounding box around the object and just slightly correct the resultant prediction to obtain the final annotation. The qualitative results have proven to be very precise.
In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags

In-air gesture interaction enables a natural communication between a man and a machine with its clear semantics and humane mode of operation. In this paper, we propose a real-time recognition system on multiple gestures in the air. It uses the commodity off-the-shelf (COTS) reader with three antennas to detect the radio frequency (RF) signals of the passive radio frequency identification (RFID) Tags attached to the fingers. The idea derives from the crucial insight that the sequential phase profile of the backscatter RF signals is a reliable and well-regulated indicator insinuating space-time situation of the tagged object, which presents a close interdependency with tag’s movements and positions. The KL divergence is utilized to extract the dynamic gesture segment by confirming the endpoints of the data flow. To achieve the template matching and classification, we bring in the dynamic time warping (DTW) and k-nearest neighbors (KNN) for similarity scores calculation and appropriate gesture recognition. The experiment results show that the recognition rates for static and dynamic gestures can reach 85% and 90%, respectively. Moreover, it can maintain satisfying performance under different situations, such as diverse antenna-to-user distances and being hidden from view by nonconducting obstacles.
Vehicle Travel Time Estimation by Sparse Trajectories

With the development of economy as well as the quality of people's life, the cars' number has increased very fast, and the condition of transportation is also becoming much more complex. Thus, it's necessary to come up with an efficient algorithm to estimate the travel time of cars. "Travel time estimation" is an important problem in intelligent transportation field, and is also pretty common in transportation monitoring and routing system. In this article, based on the work of Wang Y et al, we use data mining method to find a way to estimate the travel time of cars on any path efficiently. Firstly, considering the paths' condition, we use tensor decomposition to overcome the sparsity problem of trajectory data. Secondly we construct an evaluation function, and use dynamic programming with suffix tree optimization to choose the best combination of sub-paths. All the code are reproduced by Python language rather than Matlab, which is originally used in the work of Wang Y et al. The experiment suggests that this method can estimate travel time effectively. In this article, we investigate the study about this problem both domestically and internationally, and based on which improve the original algorithm by replacing SGD by Adam. We summarize the theory, provide detailed process of the algorithm and show the key code, after which we examine the effectiveness of it by conducting experiments. In the summary chapter, we analysis the pros and cons of the algorithm objectively, and come up with the direction of our study in the future.
FORMATION OF THE LEXEME’S CORPUS FOR THE LEVEL MODEL DEVELOPMENT OF LANGUAGE

Purpose: The article deals with the principles of selection of factual material on the problem of fixing language invariants and variants. Methodology: This is an analytic-dialectical research in which the data was obtained through the analysis of language and grammar in English texts. Main Findings: As a result of using the system-object method of knowledge representation, a model for securing language variants is constructed. The UFO model of the organizational system can act as a network, production, and frame models. In conclusion, only the internal flows are of interest at the level of the function description, since the algebra of images of the theory of patterns and the theory of graphs are involved in external flows. Applications: This article can be used by regional schools, cultural organizations and universities. Novelty/Originality: In this research, a level model of language invariants and variants was developed and presented. Statistical accounting methods, linguistic and computer modeling methods were used on this model.
Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At first, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixedwing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed.
Determining the Trajectory of Unmanned Aerial Vehicles by a Novel Approach for the Particle Filter

There are several applications of Unmanned Aerial Vehicles (UAV), such as environmental monitoring, surveillance, support to the engineering projects, entertainment, among others. The UAV autonomous navigation is one relevant research topic, and a key issue is to estimate the drone position. Here, the signal from a Global Navigation Satellite System (GNSS) is not used to estimate the UAV position. Two approaches are combined to determine the UAV positioning: a computer vision system (edge extraction in images by self-configurating supervised neural networks and correlation with georeferenced images), and visual odometry. The two techniques are employed by a data fusion process by using a new formulation for the particle filter. The particle filter is a Bayesian method, where we are applying the Tsallis’ distribution as the likelihood operator. The Tsallis’ distribution has been employed to derive a generalized statistical mechanics. The filtering process is able to address the fusion data procedure, and to address the uncertanty quatification associated to the UAV trajectory estimation. The methodology was successful in performing the data fusion, generating good results and allowing to compute a confidence interval.
Test case selection and prioritization in continuous integration environment

MARKUS MULKAHAINEN: Test case selection and prioritization in continuous integration environment Tampere University Master of Science Thesis, 60 pages, 4 Appendix pages March 2019 Master’s Degree Programme in Information Technology Major: Software Engineering Examiners: Professor Kari Systä and professor Hannu-Matti Järvinen
Semantic Stream Management Framework for Data Consistency in Smart Spaces

Semantic technology can provide a bridge between smart applications and Internet of Things (IoT) to enable possible integration and interoperability of data produced by heterogeneous devices. In IoT, data quality plays an important role when it comes to interfacing sensor readings with real-time applications at the basic atomic level. Popular techniques of machine learning and point-based calibrations are inadequate due to inability to perform semantic reasoning and interoperability on sensor streams even in real time. In this paper, a layered software framework based on semantic technologies is developed to maintain the consistency of data streams produced by physical sensors that interprets measurements as numeric values. The framework shows how semantic modelling and reasoning can be applied to validate the consistency of data streams while placing emphasis on the temporal characteristics of the stream. The evaluation of the approach involves analysing the effects of different Resource Description Format(RDF) data serializations on the response times of the reasoning engine and throughput of continuous semantic stream query execution. The outcome of experiments indicates the semantic framework as a promising approach for stream validation in Smart Spaces and other related IoT domains.
CONTROL OF UNDERACTUATED ACROBOT SYSTEM USING PARTIAL FEEDBACK LINEARIZATION

Underactuated mechanical systems are those have fewer actuators than degrees of freedom. They arise in applications, such as space robots, mobile robots, flexible robots, walking and gymnastic robots, such as the Acrobot. The swing-up control problem is studied for the Acrobot system; as one of the most important benchmark problem of the underactuated mechanical systems. The dynamic model based on Lagrange formulation is present. Then, the design of the swing up control provided. The design methodology is based on applying partial feedback linearization in a first stage to linearized the unactuate degree of freedom pushing the Acrobot as near as possible to its equilibrium point. Then, switch to a balancing controller linear quadratic regulator (LQR), which forces the Acrobot to reach its equilibrium upward position.
Hardware cost evaluation of the posit number system

The posit number system is proposed as a replacement of IEEE floats. It encodes floating-point values with tapered precision: numbers whose exponent is close to 0 have more precision than IEEE floats, while numbers with high-magnitude exponents have lower precision, because their encoding takes bits from the significand. In addition, the posit standard mandates the presence of the "quire", a Kulisch-like large accumulator able to perform exact sums of products. Several works have demonstrated that posit arithmetic can provide improved accuracy at the application level. However, the variable-length fields of posit encoding impacts the hardware cost of posit arithmetic. Existing comparisons of posit hardware versus float hardware are unconvincing, and the overhead of the exact accumulator has not been studied in detail so far. This work aims at filling this gap. To this purpose, it introduces an open-source tool to compare the respective costs of floats and posits on an application basis. A C++ templatized library compatible with Vivado HLS implements operators for custom size posits and their associated quire. These architectures are evaluated on recent FPGA hardware and compared to their IEEE-754 counterpart. The standard 32 bits posit adder is found to be twice as large as the corresponding floating-point adder. Posit multiplication requires about 7 times more LUTs and a few more DSPs for a latency which is 2x worst than the IEEE-754 32 bit multiplier. Furthermore , the cost of the posit 32 quire is shown to be the same as a 32 bits floating-point Kulisch accumulator.
Learning P2P Lending Credit Evaluation Bayesian Network from Missing Data

Credit evaluation is an important issue for investors in the financial field. However, there is a large amount of missing data in the P2P lending platform. To evaluate borrowers' credit from missing data, a credit evaluation Bayesian network model learning algorithm is proposed based on domain knowledge. Specifically, we first give a credit evaluation Bayesian network (CEBN) model to represent the borrowers' attributions and the relationships between attributions, and then we design the CEBN learning algorithm based on domain knowledge. Furthermore, we analyze and discuss the time complexity of the algorithm. Finally, the experimental results demonstrate that the CEBN model has good interpretability, learning performance, and evaluation performance by comparing it with other methods
A Heuristic Approach to Break Cycles for the Class Integration Test Order Generation

It is a general objective to minimize overall stubbing cost when performing class integration test order generation. Existing approaches are unable to obtain a cost-optimal class test order, this is largely due to the lack of a comprehensive analysis on the factors that affect overall stubbing cost, i.e., the number of required test stubs and the corresponding stubbing complexity. To address this issue, we propose an approach called HBCITO (Heuristic approach to Break Cycles for the class Integration Test Order generation). Given a set of removed dependencies, a heuristic algorithm is employed to search for a near ideal set of class dependencies. Such dependencies break the same or greater number of cycles as the initialized dependencies but attract less stubbing cost. The experimental results show that HBCITO is capable of generating class test orders with significantly lower stubbing cost compared with other approaches.
Discovering utterance fragment boundaries in small unsegmented texts

We propose an algorithm for inferring boundaries of utterance fragments in relatively small unsegmented texts. The algorithm looks for subsequent largest chunks that occur at least twice in the text. Then adjacent fragments below an arbitrary length bound are merged. In our pilot experiment three types of English text were segmented: mother-child language from the CHILDES database, excerpts from Gulliver's travels by Jonathan Swift, and Now We Are Six, a children’s poem by A. A. Milne. The results are interpreted in terms of four precision metrics: Inference Precision, Alignment Precision, Redundancy, and Boundary Variability. We find that i) Inference Precision grows with merge-length, whereas Alignment Precision decreases – i.e. the longer a segment is the more probable that its two boundaries are correct; ii) Redundancy and Boundary Variability also decrease with the merge-length bound – i.e. the less boundaries we insert, the closer they are to the ideal boundaries.