Miracle-dz/UniFAR-FTUs huggingface dataset:
A 1.17-Megapixel CMOS Image Sensor With 1.5 A/D Conversions per Digital CDS Pixel Readout and Four In-Pixel Gain Steps

This paper presents a low-power <inline-formula> <tex-math notation="LaTeX">$1232\,\,\times \,\, 952$ </tex-math></inline-formula> pixel back side-illuminated CMOS image sensor with a pixel pitch of 2.8 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>. It is implemented in a 90-nm CMOS image sensor technology. We propose a method to reduce the pixel readout energy consumption by computing two digital correlated double-sampling (DCDS) results with three analog–digital (A/D) conversions, thereby using an average of 1.5 A/D conversions per pixel. The conventional DCDS readout requires two A/D conversions per pixel. The DCDS operation uses 27.3 pJ/pixel and achieves a figure-of-merit (FoM) of 0.064<inline-formula> <tex-math notation="LaTeX">$\,\,\text {nJ}\cdot \text {e}^{-}$ </tex-math></inline-formula>, which is the lowest published to date. A pixel is designed with four gain steps that allow 175 <inline-formula> <tex-math notation="LaTeX">$\mu \text{V}/\text{e}^{-}$ </tex-math></inline-formula> conversion gain and 2.33 e<sup>−</sup> read noise at the highest pixel gain setting and 33.400 e<sup>−</sup> full well capacity at the lowest pixel gain setting. The pixel design avoids the need for dedicated capacitors in the pixel and instead uses capacitance already existing in the current readout row and the previous row.
Deep Reinforcement Learning for Autonomous Inspection System

The objective of the group is to investigate the application of Deep Reinforcement Learning to create a drone that autonomously inspects damages on buildings after natural disasters. Deep learning is a technique through which computers can identify complex patterns like detecting cracks in large amounts of data like a video stream. By applying Reinforcement Learning and Deep Convolutional Neural Networks, the drone will be trained to perform building inspections without scanning the whole structure. The need for such an autonomous system comes from: • Safety issues that prevent a human inspector from investigating the damaged state of an impacted building • Demand for reliable and cost-effective systems to perform building inspections in settings dangerous to humans. System • A reward branch focuses on pixel control. For the pixel control function we wish to detect changes in the stream, indicating building issues being detected. In order to learn in parallel, we will train off-policy by Reinforcement Learning. • A task branch focuses on the navigation of our drone. It is taught to fly off-policy using negative data since there are no large data-sets for learning to fly [2]. We will employ deep learning networks here with multiple convolutional layers with RELU applied. This branch will make predictions based off the most recent previous frames as a sequence [3]. Future Work As of now, we have a design for our system. Our future goals include: • Training the reward branch of our system on image streams including building inspection by a drone. • Once the reward branch has been trained to identify building damage we will invest in a drone and teach it to fly autonomously by applying a Deep Learning Network. • Building the two branches in parallel, we will attempt to train the task branch by flying and processing images. This would improve the drones ability to locate damage in an optimal manner. • Finally, we will conduct a thorough testing and improvement phase to optimize our networks • Once the control algorithm is finalized, a metric will be developed for quantifying or classifying the structural integrity of buildings based on severity of the cracks • A proposal for full scale deployment in disaster areas will be discussed Architecture
LEARNING MANAGEMENT SYSTEMS: AN APPLICATION FOR COST ACCOUNTING TEACHING

Learning Management Systems (LMS) offer interesting opportunities as e-learning software when different types of teaching activities are developed. Nowadays, masterclasses share space with other technics that enable the student a better understanding about the studied subject. Financial economy and accounting, as a teaching field, requires new technics that allow the student a better knowledge about his/her environment and profession. For this reason, the participation of external speakers or professionals through seminars is an interesting resource. However, this participation needs time, effort and economic resources for both the speaker and the students. This experience took place in the subject Cost Accounting in the Public University of Navarre. In this case, we chose students belonged to the degree in Business Administration. This subject is taught in the third year. We decided to introduce the ZOOM tool in the development of the third lesson related to the development of the business budget. Business budget has a different structure depending on the analysed entity. We opted to explain the case of a manufacturing company following the traditional scheme of a masterclass and we introduced the new technic in relation to a company oriented to the provision of a service. This second structure was explained by a professional. This professional was contacted by the teaching staff and expressed her will to participate in this subject, although she had not availability in terms of time and she was working in a different location. As a consequence, we planned the possibility of using this tool as a way to solve this limitation. The obtained results show an intense potential of this tool for both students and external speakers. The new technologies offer opportunities for development in Financial Economy and Accounting. In this sense, the results obtained in this experience give a good account of the transversal competences developed and the interest in the research career aroused in the students.
Domain Adaptation in Dependency Parsing via Transformation Based Error Driven Learning

Dependency parsers generally perform well when they are trained and tested on data from the same domain. However, if the data set on which we use the parser is different from the sentences on which it is trained, results tend to be low. Addressing this problem via domain adaptation is still a challenging problem and has achieved only limited improvements in the target domain. One problem that has been ignored to date concerns the differences in annotation schemes between corpora from different domains, even when the annotations are based on the same underlying annotation guidelines. In the most extreme case, the target annotations may contain labels that do not occur in the source domain. This significantly affects the overall performance of the parser. This paper presents an approach of applying transformation based error driven learning (TBL) for domain adaptation of dependency parsing. We use TBL to learn dependency label corrections in the target domain, based on errors made by the source domain parser. We show that this method can reduce dependency label errors significantly. A major advantage of this method is that we can address all types of errors with this method. The method can also be easily applied to any domain without any major change to the rule templates.
Contributions to Nonparametric Predictive Inference for Bernoulli Data with Applications in Finance

Imprecise probability is a more general probability theory which has many advantages over precise probability theory in uncertainty quantification. Many statistical methodologies within imprecise probability framework have been developed today, one of which is nonparametric predicted inference (NPI). NPI has been developed to handle various data types and has many successful applications in different fields. This thesis firstly further developed NPI for Bernoulli data to address two current challenging issues, the computation of imprecise expectation for a general function of multiple future stages observations and handling of imprecise Bernoulli data. To achieve the former, we introduce the concept of the mass function from Weichselberger's axiomatization of imprecise probability theory and Dempster-Shafer's notion of basic probability assignment. Based on the concept of mass function, an algorithm to find the imprecise expectation measure for a general function of a finite random variable is proposed. We then construct mass functions of single and multiple future stages observations in NPI for Bernoulli data by its underlying latent variable representation, which leads to the applicability of the proposed algorithm in NPI for Bernoulli data. To achieve the latter, we extend the original NPI path counting method in its underlying lattice representation. This leads to the development of mass function and the imprecise probabilities of NPI for imprecise Bernoulli data. The property of NPI for imprecise Bernoulli data is illustrated with a numerical example. Subsequently, under the binomial tree model, NPI for Bernoulli data and imprecise data are applied to asset and European options trading and NPI for Bernoulli data is applied to portfolio assessment. The performances of both applications are evaluated via simulations. The predictive nature and ability of noise recognition of NPI for precise and imprecise Bernoulli data are validated. The viability for application of NPI in portfolio assessment is confirmed.
Exploration of Urban Intelligent Bus Management System based on Big Data

To discuss urban intelligent bus management system based on big data, an intelligent urban public transport management system is designed and developed around the traditional urban public transport management business. The system has the functions of intelligent bus dispatching, passenger travel service, and data statistical analysis, and has important value for fully mining the value of bus data and improving the level of intelligent bus management. Based on the analysis of the operation and management requirements of urban public transport industry in China, the overall framework of urban intelligent public transport management system based on big data is designed. The framework is built around the collection, transmission, storage, analysis and display of bus data, covering the functions of intelligent bus dispatching, arrival time prediction, and passenger flow prediction.
Autocatalysis in a Hierarchically Organized Inorganic Chemical Network

One of the salient features of living systems is presence of autocatalytic chemical reaction networks. Here we present a stochastic model of an inorganic autocatalyst, which is derived directly from empirical results. Using the model, we can explore the emergence of autocatalysis and its consequences on the larger, hierarchical, chemical network. This model provides a useful tool to study the emergence and organization of autocatalytic chemical networks and the effect autocatalysis has on the global system dynamics.
Exploração Dinâmica em Android

We live in a time where smartphones are indispensable for our daily life. In fact, with the evolution of these devices, and with the facilities that are given to the developers of applications, a rapid expansion of the number of available applications for them is being verified. Included in this number are critical applications, such as payment applications. For these reasons, it is increasingly important to properly test these applications. However, manual testing is complicated since there are many different devices. Automating the testing of mobile applications becomes, therefore, increasingly relevant. Model-Based Testing is one of the techniques to automate the generation of test cases from a model. However, this technique has some problems, such as the time-consuming process that is the manual creation of the model. To solve this problem, one of the possible solutions is the existence of a dynamic reverse engineering process that is sustained by an exploration of the application that can extract part of the model for the MBT technique. There are dynamic approaches to explore mobile applications, but these approaches have some challenges that need to be overcome in order to achieve good results, such as overcoming blocking points (e. g. an authentication system), choosing appropriate input data, testing dynamic behaviour of the application, executing the different existing interaction gestures, among others. This document presents a dynamic exploration approach of Android mobile applications that aims to overcome some of the identified problems. During the exploration process, the algorithm builds a Finite State Machine in which each state represents an identified screen and each transition between these states describes an event that allows moving from one screen to another. This approach is implemented as an extension of the iMPAcT tool. In order to validate the implemented algorithm, two experiments were performed with real applications available on Google Play Store. In these experiments, code and event coverage were analysed to assess the performance of the new algorithm. After analysing these results, it was possible to verify that the developed algorithm presents better performance than two other algorithms existent in the state of the art.
Joint Motion Classification and Person Identification via Multitask Learning for Smart Homes

In a smart home environment, assisted living has been a topic of great research over the past decade. Human motion analysis is considered as a key technology for living states recognition in an assisted living system. Recent research has proved that rich information can be obtained from human movements, such as the motion category, moving patterns, and human identity. In this paper, a nonintrusive human movement sensing system is established with a mono-static ultrawide bandwidth radar. Then, we propose a well-designed joint motion classification (MCL) and person identification (PID) convolutional neural network (named as “JMI-CNN”). To recognize human motions and identities simultaneously, the network employs a multitask learning scheme as well as the attention mechanism and the hierarchical feature reuse strategies. We report the experimental result on the data from 15 individuals, each performing six motions. It shows that the model achieves a promising performance of 80.57% on the joint task, while the accuracy for MCL and PID are 98.50% and 80.92%, respectively. Moreover, we carry out ablation studies to evaluate the design principles of the proposed method. Discussions on the impact of signal noise ratio and slow-time window length are also conducted.
Spatial Representation of Self and Other by Superposition Neural Network Model

The sense of self-location is necessary for individuals to perform adaptive behavior like navigation in the environment. The evidence of the sense of self-location is identified as the place cells which was firstly found in the hippocampus of rats’ brain (O’Keefe and Dostrovsky, 1971). Recently, it was reported that the locations of other individuals are also represented in the hippocampus of bats and rats (Danjo et al., 2018; Omer et al., 2018). Even some hippocampus cells represent their location without distinction between self and other, and these cells can be considered to be related to empathy mechanisms similar to the mirror neurons (Rizzolatti and Sinigaglia, 2016). However, how such spatial representation that associates self and other’s locations is developed is not understood as well as the mirror neurons. One of the popular explanation of representation of the other in self is simulation theory that individuals internally simulate the other’s internal recognition. However, the simulation of the other requires another module for inferring the other’s internal recognition, which is called model of others. Such simulation module should be different from the module that deal with self internal recognition, and the place cells shared between self and other cannot emerge. We considered the other’s location is represented in the same module as self location rather than in the different module that simulates other’s internal recognition. To realize shared representation of self and other’s locations like found by (Danjo et al., 2018; Omer et al., 2018), we propose superposition mechanism that two different representations are parallelly processed by the same module at the same time. We implement the superposition mechanism by using two same modules in our proposed neural network model as described later. Through the prediction learning of subjective vision of simulated mobile agent where another agent existed, our proposed network developed shared representation of self and other agent’s locations.
Technical risk synthesis and mitigation strategies of distributed energy resources integration with wireless sensor networks and internet of things – review

As electrical power operations tend to be smart grid with strong penetration of renewable energy resources, the evolution of operational technology brings on board another dimension of vulnerabilities with the participation of cyber-physical systems (CPSs). Thus, it becomes essential to perform technical risk assessment and CPS prognosis to envisage the integrity of the system's operational resilience. The communication network elements such as cyber signal transmission, wireless sensor networks (WSNs) and the internet of things (IoTs) converges as a superhighway hub. This seeks to improve on network operations for efficient power delivery of distributed energy resources (DERs) integration grid. It then demands system configuration to be firewalled to avoid operational intrusions and malfunctions. The paper examines the technical threats involved in the paradigm of schemes and mitigation strategies. The areas of study are the vulnerabilities of the DERs integration with CPS, WSNs and IoTs layers. The risk aversion strategies of DERs integration with CPS are also discussed. The study aims to ascertain the intrusion level and responses of the integrated DERs grid in terms of power management. The paper's objective is to present an overview of current developments in the integrated cyber-power systems evolution and the implementation strategies with DERs.
Functional Rotation Axis Based Approach for Estimating Hip Joint Angles Using Wearable Inertial Sensors: Comparison to Existing Methods

Wearable sensors are at the heart of the digital health revolution. Integral to the use of these sensors for monitoring conditions impacting balance and mobility are accurate estimates of joint angles. To this end a simple and novel method of estimating hip joint angles from small wearable magnetic and inertial sensors is proposed and its performance is established relative to optical motion capture in a sample of human subjects. Improving upon previous work, this approach does not require precise sensor placement or specific calibration motions, thereby easing deployment outside of the research laboratory. Specific innovations include the determination of sensor to segment rotations based on functionally determined joint centers, and the development of a novel filtering algorithm for estimating the relative orientation of adjacent body segments. Hip joint angles and range of motion determined from the proposed approach and an existing method are compared to those from an optical motion capture system during walking at a variety of speeds and tasks designed to exercise the hip through its full range of motion. Results show that the proposed approach estimates flexion/extension angle more accurately (RMSE from 7.08 to 7.29◦) than the existing method (RMSE from 11.64◦ to 14.33◦), with similar performance for the other anatomical axes. Agreement of each method with optical motion capture is further characterized by considering correlation and regression analyses. Mean ranges of motion for the proposed method are not largely different from those reported by motion capture, and showed similar values to the existing method. Results indicate that this algorithm provides a promising approach for estimating hip joint angles using wearable inertial sensors, and would allow for use outside of constrained research laboratories. Table of
Real time intraoperative functional brain mapping using a RGB camera

We demonstrate that a RGB camera and a continuous wave white light illumination is a suitable approach to intraoperatively localize in real time the sensory and motor areas of the patient brain. The analysis of the reflectance spectra of the acquired light and Monte Carlo simulations allow us to measure the concentration changes of oxygenated and deoxygenated hemoglobin during the video acquisition. These measures are compared to the expected hemodynamic response to precisely localize the functional areas of the patient brain.
Location-aware convolutional neural networks based breast tumor detection

Breast cancer is one of the most common types of cancer affecting the lives of millions. Early detection and localization of the breast cancer tissues are vital for prevention and cure. Recently, there have been a number of developments on this front, particularly in the direction of automated image analysis. Although they are instrumental in expediting the process, such approaches lack the localization information and hence still demand substantial involvement of clinicians to deliver conclusive results. In this paper, we propose a novel approach for detecting and localizing cancer tissues from mammograms. In particular, we rely on Convolutional Neural Networks for exploiting the spatial relationship of the cancer tissues for detection and localization. Our evaluations on real datasets show that the proposed method is able to classify normal and tumor tissues with the classification accuracy of 90.8%. Furthermore, our approach achieves the sensitivity of 86.1% in detection with 1.4 false positives per image on the localization. In comparison to the state-of-the-art approaches
POLARIS: Probabilistic and Ontological Activity Recognition in Smart-Homes

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. Most activity recognition systems rely on supervised learning to extract activity models from labeled datasets. A problem with that approach is the acquisition of comprehensive activity datasets, which is an expensive task. The problem is particularly challenging when focusing on complex ADLs characterized by large variability of execution. Moreover, several activity recognition systems are limited to offline recognition, while many applications claim for online activity recognition. In this paper, we propose POLARIS, a framework for unsupervised activity recognition. POLARIS can recognize complex ADLs exploiting the semantics of activities, context data, and sensors. Through ontological reasoning, our algorithm derives semantic correlations among activities and sensor events. By matching observed events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Our system supports online recognition, thanks to a novel segmentation algorithm. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of supervised approaches. Moreover, the online version of our system achieves essentially the same accuracy of the offline version.
Computational Approaches for Heart Disease Prediction-A Review

The data mining techniques can be primarily used to extract the potentially useful hidden knowledge from the large volume of health care industry databases for predicting the diseases. It is also assist to locate the relationships and patterns among the extracted data. In this paper, detailed survey of various computational techniques used in health care industry for predicting the heart diseases is presented. The issues facing by the computational models are identified while predicting the heart diseases and the same is also presented with future possible research directions.