Miracle-dz/UniFAR-FTUs huggingface dataset:
Object Detection without Color Feature: Case Study Autonomous Robot

—Object detection is a major area of concern and such method has been extensively used in numerous applications whereas the success of using color information has been continuously reported with less attention given to several issues, such as the lengthy process to calibrate color, color fading, and others. Nonetheless, the need of such application that does not necessarily rely on color information has seen a hike due to the mentioned issues. In fact, some of the desirable solutions are those that take less computation time, as well as those that provide higher accuracy and scalability for a large number of objects in a scene. One application that requires such solution is in a game playing by autonomous robot. This paper suggests a novel patch carried by autonomous robots with relevant detection algorithm using contour detection and geometric moment without using the color feature. 
On the Robustness of Domain-Independent Planning Engines: The Impact of Poorly-Engineered Knowledge

Recent advances in automated planning are leading towards the use of planning engines in a wide range of real-world applications. As the exploitation of planning techniques in applications increases, it becomes imperative to assess the robustness of planning engines with regards to poorly-engineered (or maliciously modified) knowledge models provided as input for the reasoning process. In this work, to understand the impact of poorly-engineered knowledge on planning engines, we consider the perspective of a hypothetical attacker that is interested in subtly manipulating such knowledge to introduce unnecessary overheads that consequently slow down the planning process. This narrative ploy allows us to describe different types of knowledge engineering issues that cannot be detected via validation of the models, and to measure their impact on the performance of a range of planning engines exploiting very different approaches for steps like pre-processing and search.
Rapid Method for Modeling Thermal Power Unit Fluid Network

Improving the accuracy of thermal power plant simulators is the primary goal of simulator developers. There are many fluid networks in a thermal power plant. The accuracy of the simulator is entirely dependent on the quality of the simulation of these fluid networks. Fluid network theory is a method that combines grid with fluid mechanics to study the transport state and transience of fluids. This study focused on designing a rapid modeling method to model the fluid dynamics in thermal power plants using a data driven mechanism analysis. First, the mechanism model structure of the fluid network was established based on mechanism analysis of the key devices. The mechanism model was devised using the concept of node and branch in the signal flow graph. Next, the undetermined parameters in the structure model were identified using historical data and Particle Swarm Optimization (PSO). In this way, the fluid network model could be developed quickly. The modeling process has been described herein, considering two specific examples.
Updating in transplant of hematopoietic progenitor cells in pediatric patients during the past 15 years

En el pasado año 2010 se conmemoró el 25o aniversario de la introducción en Cuba del trasplante de médula ósea, y su desarrollo ha seguido la secuencia de la historia universal del trasplante hematopoyético. En este trabajo nos referimos a los logros más importantes que se han alcanzado en los últimos 15 años, como ha sido la introducción del trasplante con células movilizadas hacia la sangre periférica. Se exponen los resultados parciales de un estudio comparativo de 2 grupos de pacientes pediátricos, uno que recibió células progenitoras hematopoyéticas obtenidas de médula ósea y otro con células movilizadas hacia la sangre periférica mediante factores de crecimiento hematopoyéticos. Otros avances han sido: la introducción del trasplante no mieloablativo en el año 2002, la aplicación de factores recombinantes producidos en Cuba en el manejo de los pacientes trasplantados, y la introducción de técnicas de quimerismo. Se analizan diferentes aspectos relacionados con la histocompatibilidad y los requerimientos para mejorar los resultados del trasplante. Se señala la contribución que ha tenido la experiencia obtenida con este proceder, para el desarrollo de la medicina regenerativa.
The Opinion of Practice Nurses and Dietitians on Implementing Diabetes Self-Management Education (DSME) in Africa and the Caribbean

Several reports have shown that diabetes self-management education (DSME) assists in preventing diabetes complications. This study assessed the opinions of practice nurses and dietitians on implementing DSME in Africa and the Caribbean. A total of 1,574 practice nurses and dietitians (1,057 Caribbean, 517 Nigerians) employed in the Ministries of Health in the two populations completed the self-administered research questionnaire previously pre-tested on a cohort of student nurses. The study was conducted in 2014 and 2012 in Nigeria and Trinidad & Tobago respectively. One research assistant was engaged for each study site to facilitate the distribution and collection of completed questionnaires from the volunteers. The anonymous questionnaires were distributed to nurses and dietitians in public hospitals and health centres in the two study populations. The majority of all the 1574 participants agreed that intensification of DSME is a good idea that will be helpful to the patients (94.5%) and assist to reduce long-term diabetes complications (89.7%). However, remarkable proportions of all participants agreed that their establishments do not have enough qualified health personnel (68.4%), educational facilities (63.6%) and economic resources (61.3%) for DSME. In all, only 29% of all participants believe that their work places were prepared to implement DSME. The expressions of inadequate qualified healthcare personnel, economic resources and educational facilities for DSME in these two populations constitute significant barriers to implementing effective DSME and may well reflect the situation in the remaining developing countries. Healthcare professionals working in the developing countries should intensify lobbying towards government's investment in human and material resources for DSME.
Wind Speed Prediction Research Considering Wind Speed Ramp and Residual Distribution

The random fluctuation of wind leads to the instability of wind power, particularly wind power intermittency is a critical event in case of grid connected power plants, leading to severe consequences like low system reliability, high reserve capacity and high operational costs. This paper proposes a novel wind speed prediction model based on wind speed ramp (WSR) and residual distribution. Firstly, the variational mode decomposition (VMD) is used to decompose original wind speed series to extract different fluctuation characteristics, and several sub-sequences are obtained. Then, we use 3 different neural networks to predict the main part of decomposition result and use auto-regressive moving average (ARMA) model to predict the rest fluctuation part of decomposition results. Next, WSR optimized by particle swarm optimization (PSO) is used to modify the prediction results of LSTM neural network to decrease the prediction errors caused by one-step lag, the kernel density estimation (KDE) is used to fit the distribution of VMD residuals and sample randomly from the distribution to get residual series. Finally, the final prediction results of V-PSOR-LSTM-KDE are obtained by adding prediction results of WSR modified LSTM, ARMA and random sampling based on KDE. This study decomposes wind speed into the main trend part, fluctuation part and residual part, analyzing each part and makes predictions with different models according to their characteristics, which provides a new thought for wind speed prediction and contributes to the construction of smart grid.
A 243-mW 1.25–56-Gb/s Continuous Range PAM-4 42.5-dB IL ADC/DAC-Based Transceiver in 7-nm FinFET

This article presents a compact analog-to-digital converter (ADC)/digital-to-analog converter (DAC) digital signal processing (DSP)-based long reach (LR) transceiver in 7-nm FinFET technology that operates seamlessly from 3.5—56 Gb/s in pulse-amplitude modulation (PAM-4) [from 1.25 to 28 Gb/s in non-return to zero (NRZ) mode] and consumes only 243 mW at 56 Gb/s. The receiver (RX) front end consists of a two-stage continuous-time linear equalizer (CTLE), a 40-way time-interleaved (TI) successive approximation register (SAR)-ADC, a DSP equalizer containing a 17-tap feed-forward equalizer (FFE) working concurrently with a one-tap speculative decision feedback equalizer (DFE) and a reflection canceling FFE, which implements four individually roaming taps. Clock recovery is achieved on a dedicated low latency path consisting of a five-tap FFE, slicer, time error detector (TED), and loop filter driving a dedicated LC—digital-controlled oscillator (DCO). The transmit section consists of a variety of pattern generators, a five-tap finite impulse response (FIR) section, and a terminated DAC as an analog transmitter. When working on a 42.5-dB-LR channel at 56 Gb/s PAM-4, the transceiver consumes 243 mW from the 0.9-V (analog) and 0.75-V (digital) supplies, corresponding to an efficiency of 4.3 pJ/b.
Sleep Duration in Children and Its Influence on Glucose Homeostasis, Ingestive Behavior and Primary Examination Performance

Sleep is a natural periodic state of rest essential for normal psychophysiologic responses. This study revealed the influence of sleep on ingestive behavior, glucose homeostasis and primary examination performance. Six hundred children between 10 to 12 years of age were sampled. The study design included 4 groups, a to d, comprising 150 samples each. Sleep duration was 8pm to 6am, 10pm to 6am, 12am to 6am and 2am to 6am for groups a, b, c and d respectively. The results were collected after careful observation and scientific experimentation. Statistical analysis revealed values to be significantly different (P≤0.05) compared to group a. There was an inverse relationship between duration of sleep, quantity of food ingested and blood glucose level. As the duration of sleep was increased, there was a corresponding improvement in primary examination performance and blood glucose homeostasis. From the outcome of this study, sleep duration may influence ingestive behavior, blood glucose level and primary examination performance.
Thérapie cellulaire myocardique

Objectif : a partir d'un sujet de DEA, avec le soutien de toute l'equipe INSERM U633, elaborer une strategie de recherche et progresser sur un theme pour permettre le developpement de la therapie cellulaire. Ce travail de recherche fondamentale permettant l'ecriture et l'application d'un protocole de recherche chez l'homme. Quatre protocoles differents ont ete realises : 1) Ameliorer la resistance du greffon cellulaire en appliquant un choc thermique ; 2) Ameliorer la survie des myoblastes squelettiques a l'aide d'un gene de survie cellulaire ; 3) Etudier la survie et l'efficacite des cellules progenitrices adultes multipotentes derivees de la moelle osseuse en greffon myocardique ; 4) Ameliorer les resultats de la transplantation de myoblastes squelettiques par l'apport d'erythropoietine. Un PHRC local sur la therapie cellulaire par des cellules mononuclees medullaires autologues lors de la revascularisation myocardique a ete elabore et accepte. Actuellement un tiers de l'effectif a ete inclus. Une modification des criteres d'inclusion permet actuellement un recrutement plus important de patients.
A methodology for evaluation and licensing of nuclear power plant control rooms

In the safe operation of nuclear power plants the performance of the control room crews plays an important role. In this respect, a well-designed control room and human-system interfaces (HSI) are crucial for safe and efficient operation of the plant. It is therefore essential that the design, development and evaluation of nuclear control rooms are conducted in a well-structured way, applying human factors principles, ergonomics requirements and guidelines in all phases. The aim of this paper is to propose a new methodology to evaluate nuclear power plant control rooms that includes the ergonomic approach, based on the operator activity analysis, together with human factors standards and guidelines already used. We describe a case study in which this methodology has been applied in a nuclear power plant control room. We show that the operator activity analysis uncovers a series of important safety related features in control room operation that are not detected by using the traditional methodology, based only on the human factors standards and human factors guidelines. The information gathered through this methodology make possible the generation of a series of recommendations for the adequacy of the control room to the legal requirements of the regulating agency, assisting the licensing process.
Alteration of normal gastric flora in neonates receiving ranitidine.

OBJECTIVES To determine if the administration of ranitidine to neonates leads to an increase in gastric pH to > or = 4 and if this increase in gastric pH correlates with gastric colonization. STUDY DESIGN 628 pH measurements and 276 gastric cultures were obtained from 86 neonates. Twenty-three patients received ranitidine and 63 patients served as controls. RESULTS Treated patients had a mean gastric pH of 5.6 compared with a control mean pH of 4.4 (p < 0.0001). Gastric pH was significantly affected by feeding and postnatal age. 54 patients were colonized with pathogenic bacteria and/or yeast (n = 20 treated, n = 34 control). Length of hospitalization (p < 0.0001), increase in gastric pH (p < 0.01), days of antibiotics before culture (p < 0.0001), and ranitidine use (p < 0.0001) were associated with an increased rate of colonization. CONCLUSIONS The use of ranitidine did lead to a significant increase in gastric pH and with this increase in gastric pH gastric colonization rates increased. No increased frequency of infection was found in ranitidine-treated infants.
Three essays in labor economics and the economics of networks

This thesis is about the in uence of social interactions and network structure on various economic outcomes. Speci cally, the thesis presents new ndings explaining how social interactions shape individual outcomes like their e ort, performance and productivity in the workplace, as well as their beliefs on miscellaneous social matters. Speci cally, Chapter 1 gives new empirical results on some variables a ecting the e ort, quality of healthcare provided, and performance of maternal and child health (MCH) workers from a developing country (Benin). The results are obtained in a context of xed salaries irrespective of workers' performance. In addition, Chapter 2 complements the results in Chapter 1, by explaining some of its main results on workers' productivity, in light of their bargaining power in the workplace. As for Chapter 3, it stands in the theory of opinion formation in a network. This chapter gives new results on the convergence of individual beliefs and reaching a consensus within a network when we consider a few cognitive biases in individuals' behavior. More speci cally, the results of this thesis are summarized as follows. Chapter 1 uses a non-cooperative game approach to bring to light the existence of strategic substitutability in the workplace of MCH workers in Benin. Particularly, the paper suggests that, to provide collectively a certain quality of healthcare in their health facility, some workers (altruists) increase their e ort to compensate for the failure of their peers in o ering a good quality of care. Moreover, using some relevant information in the data, the chapter also proposes a simple probability-based method to account for some variability in the strength of interactions among colleagues. Chapter 2 on the other hand, focuses on the same MCH workers, and proposes a new theory to understand better some mechanisms behind the equilibrium expressed by the strategic substitutability obtained in Chapter 1. More speci cally, the chapter presents a simple Nashbargaining approach to establish how individual characteristics mold their bargaining power and consequently their workload share. The results show that workers social characteristics like their education, experience and number of children determine their bargaining power in the workplace, and thus their productivity. Finally, Chapter 3 explores how some cognitive biases a ect convergence and consensus prop-
Towards Bilingual Lexicon Discovery From Visually Grounded Speech Audio

In this paper, we present a method for the discovery of word-like units and their approximate translations from visually grounded speech across multiple languages. We first train a neural network model to map images and their spoken audio captions in both English and Hindi to a shared, multimodal embedding space. Next, we use this model to segment and cluster regions of the spoken captions which approximately correspond to words. Finally, we exploit between-cluster similarities in the embedding space to associate English pseudo-word clusters with Hindi pseudo-word clusters, and show that many of these cluster pairings capture semantic translations between English and Hindi words. We present quantitative cross-lingual clustering results, as well as qualitative results in the form of a bilingual picture dictionary.
Making Digital Money "Work" for Low-Income Users: Critical Reflections for HCI

This paper adds to the research on digitization and money in HCI. By presenting a case of rickshaw drivers in India and their use of Ola, an app-based taxi service like Uber, and Ola Money, an embedded m-wallet, this paper makes a threefold contribution. First, it shows how cash and digital money are not simply different manifestations of the ‘same' money for users. They provide distinct affordances and have different meanings and values, yielding rich insights for design. Second, it seeks to highlight the hidden work done by users around making digital money ‘work' for them. In doing so, it calls for a broader understanding of ‘moneywork' that goes beyond a temporal analysis, through the concept of ‘mobility work'. Finally, it highlights the role of ‘friction' in design. Friction is crucial to users' negotiation of the trade-off between consumption and saving, and can be leveraged to provoke reflection and user-awareness.
Multitask sparse Learning based Facial Expression Classification

— In today’s era, Facial Expression and recognition is a very challenging and fascinating subject with regards to field of AI and pattern recognition because of developmental psychology and human machine interface. For outward appearance designing classifiers with high reliability is a significant advance in this research. This paper represents a framework for person dependent expressions by combining all types of facial recognition types of facial by means of various multiple kernel learning in Support vector machines(SVM). We contemplated the impact of MKL for learning the piece loads and observationally assess the aftereffects of six fundamental expressions with impartial expression. included. In our investigations we have joined two mainstream facial element portrayals, dlib library and Multikernel SVM with polynomial kernel. Our experimental results on the cohn-Kanade face database as well as manually included database demonstrate that this framework out performs the state-of-arts, conventional techniques and straightforward MKL based multiclass SVM for facial expression recognition.
Investigating Weak Supervision in Deep Ranking

Abstract A number of deep neural networks have been proposed to improve the performance of document ranking in information retrieval studies. However, the training processes of these models usually need a large scale of labeled data, leading to data shortage becoming a major hindrance to the improvement of neural ranking models’ performances. Recently, several weakly supervised methods have been proposed to address this challenge with the help of heuristics or users’ interaction in the Search Engine Result Pages (SERPs) to generate weak relevance labels. In this work, we adopt two kinds of weakly supervised relevance, BM25-based relevance and click model-based relevance, and make a deep investigation into their differences in the training of neural ranking models. Experimental results show that BM25-based relevance helps models capture more exact matching signals, while click model-based relevance enhances the rankings of documents that may be preferred by users. We further proposed a cascade ranking framework to combine the two weakly supervised relevance, which significantly promotes the ranking performance of neural ranking models and outperforms the best result in the last NTCIR-13 We Want Web (WWW) task. This work reveals the potential of constructing better document retrieval systems based on multiple kinds of weak relevance signals.
Loss Function Considering Temporal Sequence for Feed-Forward Neural Network–Fundamental Frequency Case

This paper describes a novel loss function for training feed-forward neural networks (FFNNs), which can generate smooth speech parameter sequences without post-processing. In statistical parametric speech synthesis based on deep neural networks (DNNs), maximum likelihood parameter generation (MLPG) or recurrent neural networks (RNNs) are generally used to generate smooth speech parameter sequences. However, because the MLPG process requires utterance-level processing, it is not suitable for speech synthesis requiring low latency. Furthermore, networks such as long short-term memory RNNs (LSTM-RNNs) have high computational costs. As RNNs are not recommended in limited computational resource situations, we look at employing FFNNs as an alternative. One limitation of FFNNs is that they train to ignore relationships between speech parameters in adjacent frames. To overcome this limitation and generate smooth speech parameter sequences from FFNNs alone, we propose a novel loss function that uses long-and short-term features from speech parameters. We evaluated the proposed loss function with a focus on the fundamental frequency (F0) at found that, using the proposed loss function, an FFNN-only approach can generate F0 contours that are perceptually equal to or better in terms of naturalness than those generated by MLPG or LSTM-RNNs.
FINANCIAL INCLUSION, MOBILE PHONE DIFFUSION, AND ECONOMIC GROWTH; EVIDENCE FROM AFRICA

Whether finance enhances growth has continued to be a major debate among academics in the bids to drive economic growth and development. Hitherto, empirical studies focusing on Africa in addressing these issues for the expected regional sustainable growth are rare. This study applied structural equation modelling to simultaneously analyse mobile phones diffusion, financial inclusion, and economic growth in a panel of 32 African countries over the period from 2004 to 2016. The results provided evidence that financial inclusion affects economic growth via mobile phones. This study has implication for managing the deployment of mobile phones by for finance and growth relevance in Africa.