Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Dynamic Ideological Behavior in Political Networks

In this article, we model and analyze the dynamic behavior of political networks, both at the individual (party member) and ideological community levels. Our study relies on public data covering 15 years of voting sessions of the House of Representatives of two diverse party system, namely, Brazil and the United States. Whereas the former is an example of a highly fragmented party system, the latter illustrates the case of a highly polarized and non-fragmented system. We characterize the ideological communities, their member polarization and how such communities evolve over time. Also, we propose a temporal ideological space model, based on temporal vertice embeddings, which allows us to assess the individual changes in ideological behavior over time, as expressed by the party members' voting patterns. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.
Location-Based Discovery and Vertical Handover in Heterogeneous Low-Power Wide-Area Networks

Low-power wide-area network (LPWAN) multi-radio access technology (RAT) devices promise enabling Internet of Things (IoT) use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing is usually used for discovering if communication between a mobile terminal (MT) and a base station (BS) or a handover of the MT across LPWAN technologies should be initiated. Because of continuous probing, this procedure increases signaling overhead and energy consumption of the MT. Assuming that the location information of the MT is required for enabling an IoT use-case, this information can potentially also be used for enhancing the discovery and vertical handover procedures in heterogeneous LPWANs. Hence, we propose a location-based mechanism for making discovery and handover decisions in outdoor LPWANs. We do that under the assumption that the location of the MT can be estimated with a certain level of localization errors, while the perfectly accurate location information of the BSs are known to the MT. The mechanism grounds the decisions on the expected SNR between the MT and the BS, which removes the need for continuous probing. If the location information of the MT can be estimated with GPS-like accuracy, we demonstrate that the mechanism can achieve more than 90% correct discovery decisions. We also show that the mechanism is highly accurate in determining if a handover between technologies should be initiated. For an order of magnitude less accurate location information (e.g., for SigFox-based fingerprinting), we show that the mechanism can still make reasonable discovery decisions.
Enhancement of post-editing performance: introducing machine translation post-editing in translator training

The key objectives of this thesis were to explore the profile of translators involved in post-editing, to outline the scope of required competencies and skills and to suggest a valid training proposal that would enhance post-editing performance of novice post-editors in conformity with the European Higher Education Area requirements. The thesis integrated two sequential studies: a survey-based research that yielded authentic information concerning post-editors’ profiles and practices and an empirical-experimental research that put the suggested training model to the test and involved a total of 46 translation students in the final year of their Bachelor’s program. To collect conclusive evidence about the applicability of the proposal across different linguistic backgrounds, the study focused on 22 participants who were students at Kharkiv National Aerospace University (Ukraine) and specialized in English-Russian translation, and 24 participants who were students at Universitat Autonoma de Barcelona (Spain) and specialized in English-Spanish translation. The suggested training model pursued acquisition of conceptual and operational knowledge by the trainees and was incorporated in a pretest-posttest experimental study. The impact of such model was examined by the evaluation of the quality of post-edited segments and throughput rates demonstrated by the participants as well as the students’ attitudes to MTPE-related issues and self-evaluation of their post-editing performance before and after the training. The thesis ends with reflections upon the changes that might be brought to the proposal if neural machine translation systems were used to generate the training corpus. The dissertation contributes to the definition of the scope of post-editors’ professional expertise, offers a scalable training model and describes to what extent such model may enhance post-editing performance in undergraduate translation students.
Optimal load scheduling of Nigerian power system with valve point effect using Jaya optimization algorithm

Economic load dispatch is a non-linear optimization problem which is of great importance in power systems. This paper proposes the optimal scheduling of the Nigeria Integrated Power System considering valve point loading effect using Jaya Optimization Algorithm (JOA). Data was collected from national control centre Osogbo and was used to develop cost function for the thermal stations actively contributing power to the national grid. M file was written in MatLab R2012a environment using Jaya Optimization Algorithm, Pattern Search Algorithm (PSA) and Genetic Algorithm (GA) to optimally schedule power generation by the power system. Result obtained showed that scheduling with JOA produced a cost of 210.8092N/hr, PSA produced a cost of 211.9579N/hr and GA produced a cost of 212.6687N/hr. The simulation result shows the advantage of the proposed method for reducing the total cost of power generation by the system over the other methods. Keywords : load scheduling, value point, Jaya optimization, pattern search, genetic algorithm
A Frame Work for Hospital Readmission Based on Deep Learning Approach and Naive Bayes Classification Model

Received: 18 October 2018 Accepted: 3 February 2019 From the last few years has seen an eruption in the quantity of digital data stored in electronic health records (EHR). Presently the most difficult thing in world that was facing by patients and doctors was when to join hospital. And how to start treatment for patent. If a person was sick, he wants to go which hospital and met which kind of doctor for his problem. When a doctor diagnoses a person for some decease he needs his past medical history. Previously, these medical prescriptions were written on papers. But these papers are no longer available. Presently, the better choice is Electronic health records (EHR). But EHR data is a heterogeneous in nature, in order to process heterogeneous data we use deep learning based feature extraction method, and for predation we use navie basian classifier to make prediction. Here in this paper we propose a frame work based on VAR and Skipgram method to take features using those feature we use basian classification to predict the readmission into hospital.
Application specific performance measure optimization using deep learning

In this thesis, we address the action retrieval and the object category segmentation problems by directly optimizing application specific performance measures using deep learning. Most deep learning methods are designed to optimize simple loss functions (e.g., cross-entropy or hamming loss). These loss functions are suitable for applications where the performance of the application is measured by overall accuracy. But for many applications, the overall accuracy is not an appropriate performance measure. For example, applications like action retrieval often use the area under the Receiver Operating Characteristic curve (ROC curve) to measure the performance of a retrieval algorithm. Likewise, in object category segmentation from images, the intersection-over-union (IoU) is the standard performance measure. In this thesis, we propose approaches to directly optimize these complex performance measures in deep learning framework.
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data

Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such significant portion results in insufficient training data. Training with under-represented data leads to biased classifiers in conventionally-trained deep networks. In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples. A Gaussian prior of the variance is assumed across all subjects and the variance from regular ones are transferred to the under-represented ones. This encourages the under-represented distribution to be closer to the regular distribution. Further, an alternating training regimen is proposed to simultaneously achieve less biased classifiers and a more discriminative feature representation. We conduct ablative study to mimic the under-represented datasets by varying the portion of under-represented classes on the MS-Celeb-1M dataset. Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. Moreover, our feature transfer successfully presents smooth visual interpolation, which conducts disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations such as pose and lighting.
Identification of process models using relay feedback response

Relay based identification is widely used for the identification of process dynamics. The dynamic behavior of the process can be obtained from the limit cycle output. The process models are affected during abnormal conditions such as load disturbance and parameter variations. In this thesis, the dynamic model of first order plus dead-time (FOPDT) and second order plus dead-time (SOPDT) are identified during load and no-load disturbances and a controller is designed to reject the load disturbances. Due to the ease of implementation, relay identification is used in many practical applications. Here, the identification methods are implemented on DC-DC buck converter. An ideal relay is used during the identification. The relay is approximated as Dual-inputdescribing-function (DIDF) which is used for the identification during load and no-load disturbances. During the load disturbance, the relay settings are not modified to restore the symmetric limit cycle. Instead, the asymmetric limit cycle information is used to observe the variation in process parameters during the load disturbance. The DIDF approximation is applied to identify the process parameters of a DC-DC buck converter. A systematic procedure is developed to induce limit cycle oscillations. Utilizing the limit cycle measurements, the control to output transfer function of the converter is extracted. To show the effectiveness of the identified model, it is evaluated with the closed-loop response. As DIDF is an approximated method, state-space approach is used to obtain better accuracy in the identification. Required analytical expressions are derived to estimate the unknown process parameters. Half-limit cycle data is sufficient for identification. The performance of the buck converter model obtained from the proposed state-space method is analyzed. Further, a model-based controller (PID) is designed for the control of FOPDT and SOPDT processes. The controller parameters are tuned using direct-synthesis method with a single tuning parameter. The best value of the tuning parameter is obtained using particle TH-1900_126102002 swarm optimization (PSO) with optimal performance measure. The objective of the controller design is to achieve better performance during load disturbance. A set-point filter is used for satisfactory set-point tracking. The method is tested on standard benchmark examples available in the literature.
Spatiotemporal Representation Learning for Driving Behavior Analysis: A Joint Perspective of Peer and Temporal Dependencies

Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, braking, and turning). Analyzing driving behaviors can help us assess driver performances, improve traffic safety, and, ultimately, promote the development of intelligent and resilient transportation systems. While some efforts have been made for analyzing driving behaviors, existing methods can be improved via representation learning by jointly exploring the peer and temporal dependencies of driving behaviors. To that end, in this paper, we develop a Peer and Temporal-Aware Representation Learning based framework (PTARL) for driving behavior analysis with GPS trajectory data. Specifically, we first detect the driving operations and states of each driver from their GPS traces. Then, we derive a sequence of multi-view driving state transition graphs from the driving state sequences, in order to characterize a driver's driving behaviors that vary over time. In addition, we develop a peer and temporal-aware representation learning method to learn a sequence of time-varying yet relational vectorized representations from the driving state transition graphs. The proposed method can simultaneously model both the graph-graph peer dependency and the current-past temporal dependency in a unified optimization framework. Also, we provide two effective solutions for the optimization problem: (i) a joint optimization solution of representation learning and prediction; and (ii) a step-by-step solution of representation learning and prediction. Besides, we explore two strategies to fuse the learned representations from multi-view transition graphs: (i) simple alignment and (ii) collective fusion. Moreover, we apply the developed framework to the two applications of quantitative transportation safety: (i) scoring of driving performances, and (ii) detection of dangerous regions. Finally, we present extensive experimental results with big trajectory data to demonstrate the enhanced performances of the proposed method for quantitative transportation safety.
Ubiquitous Filtering for Nonlinear Problems

This chapter develops and extends the general theoretical results, previously published in the chapter “ Nonlinear filtering of weak chaotic signals ” , and presents detailed implementations of a computationally simple, robust (filtering fidelity almost insensitive to changes of the desired input signal properties) and rather precise approach for the filtering of weak signals of different physical nature (biological, seismic, voice, etc.) in presence of white Gaussian noise. The implementations rely on non-linear filtering techniques that in general can be considered as either one-moment or multi-moment, in the sense that they operate with a single sample (instantaneous fashion) or with various adjacent samples (non-instantaneous fashion). Chaotic modeling of the real input signals allows achieving an almost ubiquitous filtering approach with a computationally simple implementation. Application of the linearization strategies (for both one and two-moment filtering) provide, additionally, “ invariance ” of the processing algorithms to variations on the nature and statistics of the input signals.
Planning under Uncertainty with Multiple Heuristics

Many robotic tasks, such as mobile manipulation, often require interaction with unstructured environments and are subject to imperfect sensing and actuation. This brings substantial uncertainty into the problems. Reasoningunder this uncertainty can provide higher level of robustness but is computationally significantly more challenging. More specifically, sequential decision making under motion and sensing uncertainty can be formulated in a principled form as a Partially Observable Markov Decision Process (POMDP). Solving POMDPs exactly is computationally intractable due to their exponential complexity with the number of states and the depth of the planning horizon, so called, the curse of dimensionality and the curse ofhistory, respectively. In this work, we propose a novel search-based robust planning framework that sample-efficiently finds a solution with theoretical suboptimalitybounds by leveraging multiple heuristics that are designed using domain knowledge. The main contributions of this work can be summarized as follows: 1) it works with generative models, in the absence of mathematical models, similarly to the reinforcement learning paradigm, but 2) it still exhibits high sample-efficiency by bootstrapping with the domain knowledge in the form of heuristics, and 3) it is empowered with effective guidance of the search toward the goal through the systematic employment of multipleheuristics. Its solution can be returned in an anytime fashion, and converges to the bounded suboptimality with theoretical guarantees. We validate the proposed frameworks through simulation and robot experiments,which includes 2D rover navigation, PR2 parts assembly, and full size mobile manipulator truck unloading tasks. The truck unloading task is especially interesting, where the custom-built robot is to unload up to several thousands of boxes from a truck. It is a challenging real-world manipulation problem in continuous space with excessively high uncertainty, and we demonstrate the efficacy of our approach in such a domain.
A Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. We train and evaluate our approach over an independently labeled dataset comprised of 30 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall.
Identity Recognition using Facial Appearance and Shape Information

This thesis focus on identity recognition through RGBD images and for that effect two different methods will be used simultaneously: Constrained Local Models (CLM) and Sparse Coding. The CLMs are popular method designed to locate characteristics points in faces. They combine a set of local detectors (or patch experts), one for each landmark, with a global optimization strategy that maximize the score of all the detectors simultaneous. In this thesis, it was developed an extension of the Constrained Local Models algorithm that allows the use of multiples sources of information, like for example data from RGBD sensors. This type of information allow us to combine traditional RGB images with depth images with the intention of mitigating some drawbacks of traditional methods an augment his robustness, since the depth images are insensible to lightning conditions, reflect pure geometry and contours of the shape. In this context, it was introduced the possibility to use normal vectors, as depth descriptor, in the CLM formulation. In practice are use linear detectors which resort to the Histogram of Oriented Normal Vectors (HONV). Relatively to facial recognition, it will be used sparse coding, due to is rising popularity with the scientific community of Computers Vision, justified by its capabilities of both representation and classification, minimizing the computational overhead and its ability to deal with occlusion. The sparse coding is based in, using a prior leaned dictionary, finding the sparse linear combination of atoms, or elements, that most accurately recreate the original image. This will allow to validate the existence of an individual in the dictionary (database) thus making the sparse coding a potential candidate to perform the desired facial recognition. Both methods will be developed and tested in OpenCV/C++ along side with a Kinect camera, since it has the ability to capture both color images and depth images. To achieve the objective a photo database will be created, using nineteen individuals, where each one will be asked to make seven facial expressions and five different poses. This will be used in order to create the deformable face models, and in parallel, will originate a dictionary in order to enable the computer to make a face matching. This dissertation aims to verify if there is improvements in the alignment process compared with the traditional RGB images; see if it is possible to combine alignment methods with this kind of facial recognition; and finally, testing the feasibility of a developed system in real-time to perform both processes.
Integration and Boost of a Read-Modify-Write Module in Phase Change Memory System

Phase-change memory (PCM) is a non-volatile memory device with favorable characteristics such as persistence, byte-addressability, and lower latency when compared to flash memory. However, it comprises memory cells that have limited lifetime and higher access latency than DRAM. The row buffer size of a PCM is preferred to be larger than 128B to fill the latency gap between two memories and to reduce the metadata overhead incurred by wear leveling. As the cache line size in a general-purpose processor is 64B, a read-modify-write (RMW) module is required to be placed between the processor and the PCM, which in turn induces a performance degradation. To reduce such an overhead and enhance the reliability of a device, this paper presents a new RMW architecture. The proposed model introduces a DRAM cache in the RMW module, which minimizes redundant read operations for write operations by pre-fetching the entire transaction unit instead of merely caching the 64B requested data. Furthermore, a typeless merge operation is performed with the proposed cache by gathering multiple commands accessing consecutive addresses, irrespective of whether they are READ or WRITE. Simulation results indicate that the proposed method enhances the speed by 3.2 times and the reliability by 49 percent as compared to the baseline model.