Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the Mode Collapse Problem in GANS

—It is easier for someone to identify Picasso’s painting than drawing one. Generative models, which are known for generating or creating data, are considered much more difﬁcult to build and train when compared to discriminative models, which are known for processing the data. We will understand the working of GANs and discuss the process involved in training. We will also try to understand why training a GAN is hard and how many of the GAN models suffer from major problems such as non convergence, mode collapse, diminished gradient to name a few. In this paper, we will address the problem of mode collapse and look at some of the ways in which we can overcome this problem. We will discuss the working of PacGAN and Unrolled GANS in depth as solutions to the mode collapse problem.
Highly Integrated Multiplexing and Buffering Electronics for Large Aperture Ultrasonic Arrays

Large aperture ultrasonic arrays can be implemented by tiling together multiple pretested modules of high-density acoustic arrays with closely integrated multiplexing and buffering electronics to form a larger aperture with high yield. These modular arrays can be used to implement large 1.75D array apertures capable of focusing in elevation for uniform slice thickness along the axial direction which can improve image contrast. An important goal for large array tiling is obtaining high yield and sensitivity while reducing extraneous image artifacts. We have been developing tileable acoustic-electric modules for the implementation of large array apertures utilizing Application Specific Integrated Circuits (ASICs) implemented using 0.35 μm high voltage (50 V) CMOS. Multiple generations of ASICs have been designed and tested. The ASICs were integrated with high-density transducer arrays for acoustic testing and imaging. The modules were further interfaced to a Verasonics Vantage imaging system and were used to image industry standard ultrasound phantoms. The first-generation modules comprise ASICs with both multiplexing and buffering electronics on-chip and have demonstrated a switching artifact which was visible in the images. A second-generation ASIC design incorporates low switching injection circuits which effectively mitigate the artifacts observed with the first-generation devices. Here, we present the architecture of the two ASIC designs and module types as well imaging results that demonstrate reduction in switching artifacts for the second-generation devices.
EVALUATION OF DIALKYL ARYL PHOSPHATES AS SELECTIVE INHIBITORS OF BUTYRYLCHOLINESTERASE

EVALUATION OF DIALKYL ARYL PHOSPHATES AS SELECTIVE INHIBITORS OF BUTYRYLCHOLINESTASE By Analisa S. Garcia May 2016 Cholinesterases are a ubiquitous group of enzymes that serve to hydrolyze choline esters. There are two types of cholinesterases: acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). The study of cholinesterase enzymes is a point of interest in the medical field due to its correlation with Alzheimer’s disease (AD). People who suffer from AD possess low levels of acetylcholine neurotransmitters in the brain in comparison to people who do not harbor the disease. This decrease in acetylcholine can be tied to the loss of memory and cognitive functions that define the disease itself. Thus, one established treatment strategy in the fight against AD is the use of cholinesterase inhibitors. By hindering the activity of cholinesterase enzymes, the level of acetylcholine neurotransmitters in the brain can increase back to normal levels. Organophosphates and carbamates are well-known inhibitors of AChE. However, the inhibition of BChE is far less understood. Therefore, dialkyl phenyl phosphates (DAPP’s) with various alkyl chain R groups were used as selective inhibitors of BChE. Analyzing these different interactions can provide insight on this unknown inhibition process of BChE. Understanding the dynamics of the enzyme’s binding pocket is essential in designing new treatment strategies for neurodegenerative disorders such as Alzheimer’s disease.
SMART DETECTION SYSTEM FOR FRUIT RIPENESS

-With the increasing growth of world population, the agricultural industry is demanding technological solutions focused on growth of food production and farming to enhance the farm yield. Nowadays, technology is constantly improving to solve human problems more easily. The manual experimentation and assessment through trial and error etc are no longer practicable. The intelligent system can be established for fruits that can permit the quality analysis. The proposed system will provide a much better and convenient way for farmers to get the maturity of fruit. This includes a series of steps from capturing the image of fruit to identify whether the fruit is fully ripened or moderately ripened or immature through the implementation in Raspberry Pi. The camera module is interfaced with Raspberry pi and the captured images are examined by various steps like preprocessing and segmentation. The status of the fruit has been sent by GSM module through SMS and displaying the status on the monitor display of the owner of the system. This in turn reduces the need for labor in fruit farms. This smart way of analyzing the fruit will enable growers and farmers and helps them to understand the potential of IoT market for agriculture by installing smart technologies to increase competitions and sustainability in their production. This will further help for the automatic fruit harvesting system using robotic arm.
EFFECTIVENESS OF IMAGE MATCHING ALGORITHMS IN DIGITAL IMAGE PROCESSING

In digital image processing, image matching is one of the major parts used in various health care applications. The usage of correct matching algorithm will modularize the effectiveness in image matching process. The quality of image matching algorithms is based on various measures such as accuracy, processing speed, flexibility to use for various data sets, invariance to rotation, scale and illumination, etc. Mapping these parameters of various algorithms with sample image set is used to find out the effectiveness of image matching algorithm. So, in this article the three cross domain algorithms called as SURF (Speeded Up Robust Feature), SIFT(Scale Invariant Feature Transform) and ORB (Oriented fast and Rotated BRIEF) are taken to suggest the best image matching algorithm based on match time, match rate and other qualities of algorithm.
Relationship of Patients’ Perception of Malocclusion Related Quality of Life and Orthodontic Treatment Need in Adults

The objective of the study was to find the relationship among patients’ perception of malocclusion, quality of life, and orthodontic treatment need. A total of 128 orthodontic patients participated in this study. Patients were interviewed by using Index of Orthodontic Treatment Need Aesthetic Component (IOTN_AC) and Oral Impacts on Daily Performance (OIDP) in order to determine their perspectives. For orthodontists’ examination, a dental model was analyzed and measured by Dental Aesthetic Index (DAI). ANOVA and Pearson correlation were used to find the relationship of patients’ perception of malocclusion, quality of life, and malocclusion measure by dentist. The result shows there was a relationship between the degree of orthodontic treatment need with DAI. In terms of malocclusion related quality of life, malocclusion affected participants the most by getting embarrassed when they smile, followed by difficulty in cleaning their teeth, and avoiding social interaction, respectively. In conclusion, the study shows the relationship between the degree of orthodontic treatment need from patients’ perception on malocclusion (IOTN_AC) and DAI. This finding emphasizes that IOTN_AC and DAI are alternative tools for evaluating the severity of malocclusion and reflect the degree of orthodontic treatment need. Clinical article (J Int Dent Med Res 2022; 15(1): 223-229)
From employability to ‘complexability’

Higher education students and employers in the creative industries dismiss the prevailing skills-focused concept of ‘employability’ as inadequate. Entry into the creative world and subsequent survival require access to contact networks, confidence and the adaptability to cope with uncertainty and with changing contexts for business, partnerships and innovative opportunities. ‘Complexability’, it is suggested, better describes such interactive abilities. In close work with undergraduates, graduates and practitioners in two contrasting disciplines, architecture and dance, a set of interlocking briefs – the ‘creatour’ construct – emerges for the development of the necessary abilities and perspectives. A dance student’s passion for parkour inspired the construct. The article sets out the rationale behind the development of creatour and the methodology through which it was achieved. Its key stages are explained in detail and initial evaluations are reviewed. Although initially developed for the creative arts, unexpectedly creatour has also been seen as applicable to law, business and medicine.
Electric Vehicle Battery Modelling Methods Based on State of Charge – Review

Battery behavior prognostic is an important concern in most of the applications especially in electric vehicles which are using lithium ion batteries. Battery performance estimation methods will improve the life span by preventing over-discharge and overcharge, yielding increased battery life. It allows the battery user to make out the available energy in the battery stack. Therefore an accurate, easy to use battery model should be established to estimate the battery parameters. In this paper five important lithium-ion battery models such as Empirical, Equivalent circuit, Electro chemical, Reduced-order and Data driven models are discussed based on the State of charge estimation and compared along with their advantages and disadvantages. Battery model parameters are obtained by using Electrochemical Impedance Spectroscopy (EIS) test and the implementation details are presented.
Appendix of Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery

In GNCD, base and novel sets contain different training targets — for base set we have accurate and consistent ground-truth labels, while for novel set the training targets (generated pseudo labels) are less accurate and may change during training. We aim to build a model able to handle both base and novel classes, which is challenging due to (1) the ambiguity between base and novel classes, and (2) the lack of supervision on novel data. To address (1), we propose to model base and novel data using two groups of compositional experts with complementary specialties from both batch-wise and class-wise perspectives. For (2), we propose a Local Aggregation strategy to refine the pseudo labels by encouraging local consistency among novel data. Our idea of the compositional structure comes from the observations towards GNCD output space, which can be clearly characterized with a batch-class view (cf. Fig. 1b in our paper). From this view the GNCD output space exhibits a compositional nature in both batch-wise and classwise perspectives. We propose to model this compositional nature using a group of batch-wise experts (Fig. 1c) and another group of class-wise experts (Fig. 1d). Each group of experts is capable of characterizing the whole dataset, yet presents different specialties — with batch-wise experts capturing separability between base and novel sets, and class-wise experts modeling discriminability within each set of classes. Considering the challenges of GNCD, we keep both groups of experts to make the most of their learning abilities by allowing complementary collaborations. We further propose to address the aforementioned inconsistent training targets using local aggregation for novel samples, which complement the global-to-local pseudolabeling with local-to-local regularization. Extensive experiments have validated the effectiveness of the above ideas, which compose our proposed ComEx and contribute to its superior performance in GNCD.
A DEVELOPMENT OF LOW-COST ACTIVE PROSTHESIS HAND PRINTED BY 3D PRINTER

This paper proposes the development of a low-cost active prosthesis hand-printed by a 3D printer. The bend sensor connected to the patient elbow is used in this research to control the operation of the prototype. The limitations of this research are the handicap of the patient that needs to have a wrist to the elbow which weighs under 0.5 kilograms. The experiments are divided into 3 sections (i) squeezing force measurement, (ii) object holding experiment, and (iii) battery consumption experiment. The results showed that the low-cost active prosthetic hand has the stability of squeezing force. The average squeezing force at the center of fingers is found to be 16.25 N, and the average squeezing force at the end of fingers (baby finger) is 12.55 N. It also has 1.25 and 1.37 of standard deviation (SD) respectively. Besides, the prototype can pick and hold huge and small objects which has a mass of less than 0.5 kg weight. Moreover, the prototype has low energy consumption around 48.96 Watt-hour. The cost of the equipment without a 3D printer is less than 100 US dollars.
IRIT at TREC Temporal Summarization 2015

This paper describes the IRIT lab participation to the TREC 2015 Temporal Summarization track. The goal of the Temporal Summarization track is to develop systems that allow users to efficiently monitor information about events over time. To tackle this task, we proposed three different methods. Obtained results are presented and discussed.
AUTOMATIC DETECTION OF TUBERCULOSIS BY ENHANCING X-RAY IMAGE USING DEEP LEARNING

-Tuberculosis is one in all the infectious diseases that cause inflammation, the formation of TB and alternative growths inside tissue will cause death of tissue. As per survey of World Health Organization(WHO) a worldwide case detection decreases by a mean of 25% over a amount of few months, in comparison with the detection before pandemic which is able to cause extra 190,000 TB deaths. This may increase total foretold deaths from TB in 2021 to quite 1.66 million. The chest x rays can show advanced tuberculosis. Chest tuberculosis(CTB) could be a widespread drawback, particularly in our country where it’s one in all the leading causes of mortality. Since Deep Learning is earning exceptional leads to several applications, we have a tendency to use decilitre for the study of Tuberculosis(TB). As several researchers have an interest in victimisation of computer Aided Diagnosis(CAD), we have a tendency for using MATLAB for the study of Tuberculosis(TB). The abnormality detection is predicated on two-level of hierarchy to reason healthy and unhealthy teams. 1st level can extract handcrafted geometrical options like form, size, eccentricity, perimeter etc. and in level a pair of ancient 2nd order applied math options like energy, entropy, contrast, correlation etc. are extracted from metameric lungfields. Further, a supervised classification approach is used on the extracted options to observe traditional and abnormal CXR pictures. Since our work additionally includes enhancing for prime quality pictures ,here chiefly 2 image sweetening techniques Unsharp masking and High-frequency Emphasis Filtering
The stabilizing effects of economic policies in Spain in times of COVID-19

Purpose – This paper aims to analyse the stabilizing macroeconomic effects of economic policies during the COVID-19 crisis in Spain. Design/methodology/approach – The contribution of the structural shocks that explain the behaviour of the main macroeconomic aggregates during 2020 are estimated, and the effects of economic policies are simulated using a dynamic stochastic general equilibrium (DSGE) model estimated for the Spanish economy. Findings – The results highlight the importance of supply and demand shocks in explaining the COVID-19 crisis. The annual fall in gross domestic product (GDP) moderates at least by 7.6 points in the most intense period of the crisis, thanks to these stabilizing policies. Finally, the potential effects of Next Generation EU in the Spanish economy are estimated. Assuming that Spain may receive from the EU between 1.5 and 2.25 percentage points (pp) of GDP, activity could increase to between 2 and 3 pp in 2024. Originality/value – To the best of the authors’ knowledge, the exercises and findings are original. All these results show the usefulness of a DSGE model, such as the estimated rational expectation model for © J.E. Bosc a, R. Doménech, J. Ferri, J.R. García and C. Ulloa. Published in Applied Economic Analysis. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http:// creativecommons.org/licences/by/4.0/legalcode JEL classification – E30, E32, E43, E51, E52, E62 This article has been carried out within the joint research project developed by BBVA Research, FEDEA, Rafael del Pino Foundation, Ministry of Economy and Digital Transformation and Ministry of Finance. The authors thank J. Cubero and P. Mas for their helpful comments. The support from the CICYT SEC ECO2017-84632-R and the Generalitat Valenciana PROMETEO2016-097 projects is gratefully acknowledged. AEA 29,85
A new circuit topology using Z-source resonant inverter for high power contactless power transfer applications

In this study, a new circuit topology using a Z-source resonant inverter (ZSRI) for high power applications in large-air-gap contactless power transfer (CPT) systems, has been investigated. The main shortcoming of a large-air-gap CPT system is the poor power transfer efficiency due to low magnetic coupling. In order to minimize this shortcoming and to improve the overall performance of the system by boosting the power transfer capability, in this paper a CPT system with the newly developed circuit topology using high frequency Z-source resonant inverter has been proposed. Using the newly developed circuit topology for the CPT system, it has been observed that the overall performance of the system has been improved to a reasonable level with a purely sinusoidal resonant current flowing through the primary side. Therefore, no harmonics will be injected into the source. The proposed CPT system with an air gap of 16 cm and a misalignment of 3 cm has been simulated using the Maxwell finite element tool and Simplorer circuit simulation software for an output power of 2 kW.
Flexible compartments IoT driven smart pill-box

The scenario today in taking care of elderlypeople and other groups of people who need continuous medical attention involves assisting them with the right medication at the right moment of time. To enable these set of people to take medication on their own without the assistance of another individual is the main aim of this proposed paper. The proposed system is said to have inculcated Assistive technology which provides means of assistance by use of rehabilitative devices to people with varies disabilities. Assistive technology(AT) aims at promoting independence of the people by actually enabling them to perform varies tasks with ease which they were unable to perform otherwise. Assistive technology provides means of enhancing the lifestyle of people by providing assistance. On a reality check today not everyone is able to access AT catering to reasons like high costs, less awareness and less availability of the varies assistive devices. But it has been estimated that by 2050, a major portion of the society will be running on AT devices. In this paper we have proposed an approach advancing towards using AT devices. There has been usage of open source technologies and gives a new organised manner of taking medication dosages. "Flexible compartment smart pillbox" basically allows the organization of several medication schedules that health disorders presented in elderly people and other groups of people require. The proposed system includes a buzzer along with an automatic pill dispensing system, user-friendly display interface and a notification system that uses a WIFI module. ARM-7 is taken as the principal controller. The development of this device is mainly focused on supporting elderly people and other groups of people who may have the needfor an assisted care.
WIP: Identifying Structural and Cultural Characteristics of Hispanic-Serving Institutions in Engineering Education – A Morphogenetic Approach

This work-in-progress (WIP) paper presents the methodological approach of Phase 1 of a larger study exploring how Hispanic-serving institutions (HSIs) can better “serve” Latinx engineering students, as illustrated through a particular case study institution. The main goal of Phase 1 is to examine the impacts of the HSI designation on institutional systems and identify the resulting structural and cultural characteristics that may influence the experiences of undergraduate Latinx engineering students. Ultimately, this study seeks to identify how colleges of engineering at HSIs can leverage their institutional systems to enable and sustain educational transformation as they pursue their goals of serving undergraduate Latinx engineering students. This qualitative case study combines Margaret Archer’s (1995) morphogenetic approach as a theoretical framework [1] and the Multidimensional Conceptual Framework of Servingness in HSIs [2] to reach the stated goals. It further employs the use of an underutilized form of case studies, the normative (value-laden) case study [3] to connect the results of the study to broader societal values about what it means to “serve” Latinx engineering students at HSIs. With the ever-increasing number of HSIs and a continued need to redefine the field of engineering towards diversity, equity, and inclusion, the need to explore how HSIs can better serve Latinx engineering students becomes imperative.
Modeling the Detection of Internet Addiction Disorder Using Bayesian Networks

Today’s life revolves around online activities and has made internet a necessity. The unregulated excessive use of internet interferes in daily life and may lead to addiction to it. In literature, Internet Addiction Disorder (IAD) is defined as compulsive online behaviour which dominates and becomes organizing principle of one’s life impairing physical, emotional and social well-being. The common conventional assessment tools available to detect IAD are Internet Addiction Test (IAT), Compulsive Internet Use Scale (CIUS), Problematic Internet Use Questionnaire (PIUQ). Recently researchers are adopting Machine Learning (ML) to automate the detection of IAD. This study proposes to develop graphical interface IAD models by harnessing the significant features of Bayesian Networks (BN), a powerful ML technique. The models are constructed with real data sets collected online through IAT, CIUS and PIUQ questionnaires as domain knowledge and we propose these models as IAT-BN, CIUS-BN and PIUQ-BN respectively. The graphical presentations of models provide an effective visualization of causes and symptoms of IAD and facilitate in the better explanation of its occurrence. The promising experimental results show superior performance of the IAT-BN model with 100% accuracy followed by CIUS-BN as 95% and PIUQ-BN model with 90% accuracy. The study also highlights the important factors which when controlled may substantially reduce the risk of IAD. The models can be an efficient psychiatric decision support system in predicting the occurrence of IAD, risk assessment and management.