Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Leukemia Identification System Using Otsu Image segmentation and MSER Approach for Microscopic Smear Image Database

In the current era of medical image processing, identification of blood disorder through visual observation is the most difficult job. The observation based disorder identification has been the approximation task. The haematological disorders of white blood cells (WBC) are really frequent in medical practices. From the recognition of blood disorders, it can contribute to the categorization of certain diseases related to blood. With automated leukemia identification, medical expert can keep off the complexity of the environment and focus along the important medical information that this image provides. We aim for development of automated identification of leukemia using microscopic blood smear image database. The computer based identification for leukemia detection has reduced the chances of error. The early identification of leukemia dieses plays a significant role because monitoring and prevention is possible in concern patients. This proposed scheme uses the most significant steps of image processing such as pre-processing, segmentation and matching. The leukemia smear image database is segmented using Otsu image segmentation and Maximally Stable Extremely Regions (MSER) technique employed for image pattern matching. The performance of the system is tested using false acceptance rate (FAR) and false rejection Rate (FRR). The proposed system reported accuracy of 95.12% where FAR is 5.0% and FRR is 4.75%. The author recommended the Otsu image segmentation and MSER image matching is the robust and dynamic approach for leukemia identification.
Circularly Polarized C-Band Lens-Horn Antenna for Radar Remote Sensing Calibration

In this paper the design and optimization process of a radar remote sensing calibration antenna is presented. The solution consists in a septum polarizer and a high gain Potter horn with exceptionally low axial ratio (0.2 dB) and low SLL at 5.3 GHz. To obtain a compact design, a dielectric lens has been designed to correct the horn phase error and reduce the final length. As the lens degrades the return loss, impedance matching transformers are designed and simulated in order to fulfill the system specifications. Finally measurements of the septum polarizer are shown and discussed.
Enhancement of LVRT Capability of DFIG-based Wind Turbines by Superconducting Fault Current Limiter

In this paper, a resistive-type superconducting fault current limiter (SFCL) besides an efficient control strategy and optimal parameter design approach is suggested to improve low voltage ride-through (LVRT) capability of DFIG wind turbines. The proposed structure is different from conventional hardware-based LVRT solutions from two aspects: the FCL type and installation location. Implementation of the SFCL inside the DFIG wind turbine in series with the rotor side converter (RSC) leads to the rotor current reduction, DC link voltage alleviation and consequently, LVRT enhancement. Analysis and simulation results are presented to validate the effectiveness of proposed approach during symmetrical and asymmetrical fault conditions. They show that by using the proposed method, LVRT capability of DFIG wind turbines is significantly improved.
Multimedia recommender systems

This tutorial introduces multimedia recommender systems (MMRS), in particular, recommender systems that leverage multimedia content to recommend different media types. In contrast to the still most frequently adopted collaborative filtering approaches, we focus on content-based MMRS and on hybrids of collaborative filtering and content-based filtering. The target recommendation domains of the tutorial are movies, music and images. We present state-of-the-art approaches for multimedia feature extraction (text, audio, visual), including deep learning methods, and recommendation approaches tailored to the multimedia domain. Furthermore, by introducing common evaluation techniques, pointing to publicly available datasets specific to the multimedia domain, and discussing the grand challenges in MMRS research, this tutorial provides the audience with a profound introduction to MMRS and an inspiration to conduct further research.
Adaptive collaborative topic modeling for online recommendation

Collaborative filtering (CF) mainly suffers from rating sparsity and from the cold-start problem. Auxiliary information like texts and images has been leveraged to alleviate these problems, resulting in hybrid recommender systems (RS). Due to the abundance of data continuously generated in real-world applications, it has become essential to design online RS that are able to handle user feedback and the availability of new items in real-time. These systems are also required to adapt to drifts when a change in the data distribution is detected. In this paper, we propose an adaptive collaborative topic modeling approach, CoAWILDA, as a hybrid system relying on adaptive online Latent Dirichlet Allocation (AWILDA) to model newly available items arriving as a document stream and incremental matrix factorization for CF. The topic model is maintained up-to-date in an online fashion and is retrained in batch when a drift is detected using documents automatically selected by an adaptive windowing technique. Our experiments on real-world datasets prove the effectiveness of our approach for online recommendation.
Secukinumab efficacy in the treatment of nail psoriasis: a case series

Abstract Nail involvement is frequent in patients with psoriasis, especially those with psoriatic arthritis (PsA), and can significantly impair quality of life (QoL). It is typically difficult to treat compared with skin lesions, although several conventional treatment options are available. The aim of this article is to describe our experience in the treatment of nail psoriasis with secukinumab in a case series. Fifteen patients (11 males and 4 females), with moderate-severe plaque psoriasis and nail psoriasis, eligible for systemic therapy, and received secukinumab. The Psoriasis Area and Severity Index (PASI) and body surface area (BSA) assessed cutaneous severity. Nail Psoriasis Severity Index (NAPSI) was used to evaluate nail involvement. Starting from 6 weeks after initiation of treatment with secukinumab 300 mg, a clinically significant response was observed, with progressive reduction of both skin and nail disease indexes. Average reduction of PASI was 75%, of BSA 70%, and of NAPSI 50%, at week 6. At week 12, NAPSI reduction was by 80%, of PASI 90%, and of BSA 97%. Effective treatment of both skin and nail psoriasis was obtained with secukinumab, a new approach to psoriatic patients resistant to topical therapy.
Self-Limited and Forming-Free CBRAM Device With Double Al2O3 ALD Layers

Self-limited and forming-free Cu-based CBRAM devices with a double Al<sub>2</sub>O<sub>3</sub> atomic layer deposition layer (D-ALD) structure were developed. The proposed structure offers desirable properties such as forming free (<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {forming}}={V}_{\textsf {SET}}$ </tex-math></inline-formula>), self-limited resistive switching with very low programming current ~10 nA), high ON/ OFF ratio (> 100), and nonlinear <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> (NL<inline-formula> <tex-math notation="LaTeX">$_{\textsf {read}}$ </tex-math></inline-formula> and NL<inline-formula> <tex-math notation="LaTeX">$_{\textsf {SET}}$ </tex-math></inline-formula> ~ 10) at low resistance state. The outstanding performance of D-ALD devices was due to the HT-ALD layer that operates as both a diffusion barrier preventing over injection of Cu ions and a tunneling barrier enabling self-limited programming.
User preference learning in multi-criteria recommendations using stacked auto encoders

Recommender System (RS) is an essential component of many businesses, especially in e-commerce domain. RS exploits the preference history (rating, purchase, review, etc.) of users in order to provide the recommendations. A user in traditional RS can provide only one rating value about an item. Deep Neural Networks have been used in this single rating system to improve recommendation accuracy in the recent times. However, the single rating systems are inadequate to understand the usersfi preferences about an item. On the other hand, business enterprises such as tourism, e-learning, etc. facilitate users to provide multiple criteria ratings about an item, thus it becomes easier to understand users' preference over single rating system. In this paper, we propose an extended Stacked Autoencoders (a Deep Neural Network technique) to utilize the multi-criteria ratings. The proposed network is designed to learn the relationship between each user's criteria and overall rating efficiently. Experimental results on real world datasets (Yahoo! Movies and TripAdvisor) demonstrate that the proposed approach outperforms state-of-the-art single rating systems and multi-criteria approaches on various performance metrics.
Mode-Coupling Theory of the Glass Transition: A Primer

Understanding the physics of glass formation remains one of the major unsolved challenges of condensed matter science. As a material solidifies into a glass, it exhibits a spectacular slowdown of the dynamics upon cooling or compression, but at the same time undergoes only minute structural changes. Among the numerous theories put forward to rationalize this complex behavior, Mode-Coupling Theory (MCT) stands out as a unique framework that provides a fully first-principles-based description of glass phenomenology. This review outlines the key physical ingredients of MCT, its predictions, successes, and failures, as well as recent improvements of the theory. We also discuss the extension and application of MCT to the emerging field of non-equilibrium active soft matter.
Module advisor: a hybrid recommender system for elective module exploration

Recommender systems are omni-present in our every day lives, guiding us through the vast amount of information available. However, in the academic world, personalised recommendations are less prominent, leaving students to navigate through the typically large space of available courses and modules manually. Since it is crucial for students to make informed choices about their learning pathways, we aim to improve the way students discover elective modules by developing a hybrid recommender system prototype that is specifically designed to help students find elective modules from a diverse set of subjects. We can improve the discoverability of long-tail options and help students broaden their horizons by combining notions of similarity and diversity.
Extensive Contamination of Water with Saxitoxin Near the Dam of the Irkutsk Hydropower Station Reservoir (East Siberia, Russia)

An area of discolored water 50 m wide and 30 m long was found in September 2017 close to the dam of the Irkutsk hydroelectric power station. Water from this spot was sampled for investigation in the present study. Microscopic analysis revealed that the suspended matter in the sample was composed of clumps of filaments, vegetative cells, akinetes and heterocysts that formed short filaments and solitary cells. This matter was found to consist of partially degraded cells of the cyanobacterium Dolichospermum lemmermannii. Nucleotide sequencing of DNA isolated from the biomass revealed the presence of the sxtA gene which is involved in the synthesis of saxitoxin. Water from the polluted area contained 600 ± 100 μg L−1 saxitoxin as measured by HPLC-MS with pre-column modification of the toxin with 2,4-dinitrophenylhydrazine. Immunoassay analysis (ELISA) showed a concentration of saxitoxins in the water of 2900 ± 900 μg L−1. Hydrochemical and microbiological analyses suggested the contaminated area appeared as a result of a D. lemmermannii bloom, followed by its decay and release of saxitoxin and nutrients. The present paper describes the results of a case study. Better understanding of the phenomenon will depend on the possibility to perform implementation of a large-scale monitoring program.
Moving object detection and tracking using deep learning neural network and correlation filter

Object tracking is a key step in computer vision for video surveillance, public safety, and traffic analysis. Object detection and tracking are the two correlated components of Video Surveillance. Object detection in videos is the first step before performing complicated tasks such as tracking. Deep learning neural networks is a powerful programming paradigm which learns multiple levels of representation and abstraction of data such as images, sound, and text. In this paper Gaussian mixture model (GMM) based object detection, deep learning neural network-based recognition and tracking of objects using correlation filter is proposed, which can handle false detections, with improving the efficiency. The algorithm is designed to detect only cars and humans' while the performance is analyzed using True Positive Rate (TPR) and False Alarm Rate (FAR) as probabilistic metrics. The Experimental results of the proposed method are found to be better with an accuracy of 88%.
Diplogastrellus nematodes are sexually transmitted mutualists that alter the bacterial and fungal communities of their beetle host

Significance Many organisms, from plants to humans, have long evolutionary histories with nematode worms. Historically these relationships have been assumed to be detrimental—as with intestinal or blood-borne parasites—or at best innocuous, to their hosts. However, this paradigm has been challenged recently, for example, with findings in mammals that worms can modulate their host’s immune system and thereby thwart autoimmune disease. In our study, we describe a phenomenon wherein sexually transmitted and parentally provided worms benefit their insect hosts, possibly by “engineering” the microbiome present in the maternally constructed chamber where offspring develop. Given nematodes’ association with many insects, particularly those with parental care, this phenomenon may be a more widespread feature of insect health. A recent accumulation of studies has demonstrated that nongenetic, maternally transmitted factors are often critical to the health and development of offspring and can therefore play a role in ecological and evolutionary processes. In particular, microorganisms such as bacteria have been championed as heritable, symbiotic partners capable of conferring fitness benefits to their hosts. At the same time, parents may also pass various nonmicrobial organisms to their offspring, yet the roles of such organisms in shaping the developmental environment of their hosts remain largely unexplored. Here, we show that the nematode Diplogastrellus monhysteroides is transgenerationally inherited and sexually transmitted by the dung beetle Onthophagus taurus. By manipulating artificial chambers in which beetle offspring develop, we demonstrate that the presence of D. monhysteroides nematodes enhances the growth of beetle offspring, empirically challenging the paradigm that nematodes are merely commensal or even detrimental to their insect hosts. Finally, our research presents a compelling mechanism whereby the nematodes influence the health of beetle larvae: D. monhysteroides nematodes engineer the bacterial and fungal communities that also inhabit the beetle developmental chambers, including specific taxa known to be involved in biomass degradation, possibly allowing larval beetles better access to their otherwise recalcitrant, plant-based diet. Thus, our findings illustrate that nongenetic inheritance can include intermediately sized organisms that live and proliferate in close association with, and in certain cases enhance, the development of their hosts’ offspring.
Deep Recurrent Multi-instance Learning with Spatio-temporal Features for Engagement Intensity Prediction

This paper elaborates the winner approach for engagement intensity prediction in the EmotiW Challenge 2018. The task is to predict the engagement level of a subject when he or she is watching an educational video in diverse conditions and different environments. Our approach formulates the prediction task as a multi-instance regression problem. We divide an input video sequence into segments and calculate the temporal and spatial features of each segment for regressing the intensity. Subject engagement, that is intuitively related with body and face changes in time domain, can be characterized by long short-term memory (LSTM) network. Hence, we build a multi-modal regression model based on multi-instance mechanism as well as LSTM. To make full use of training and validation data, we train different models for different data split and conduct model ensemble finally. Experimental results show that our method achieves mean squared error (MSE) of 0.0717 in the validation set, which improves the baseline results by 28%. Our methods finally win the challenge with MSE of 0.0626 on the testing set.
Säilytettävän aineiston tautipuhdistus kryoterapialla

Suvuttomasti lisättävät viljelykasvit ja monivuotiset koristekasvit ovat erityisen uhanalaisia virustartunnoille, sillä lisäystapa ja monivuotisuus ovat omiaan lisäämään tartunnan riskiä. Perunan, ryväsja valkosipulin, vadelman ja monien muiden kasvien tuottavuus taantuu virusten takia, ellei viljelmiä uusita aika ajoin terveellä aineistolla. Koristekasvit puolestaan menettävät näyttävyytensä virusten aiheuttamien oireiden ja kitukasvuisuuden takia ja sietävät sairaina huonommin muitakin stressejä. Kylmäsäilytys nestetypessä tunnetaan geenivarojen ylläpitomenetelmänä, mutta se sopii myös kasvupisteiden puhdistamiseen viruksista, jolloin sitä kutsutaan kryoterapiaksi. Tämän työn tavoitteena oli käyttää kryoterapiaa tervetaimien tuottamiseen viroottisesta vadelman jalostulinjasta, joka on ominaisuuksiltaan lupaava mutta jota ei ole kuitenkaan pystytty puhdistamaan vadelman kääpiökasvuviruksesta (RBDV). Tämä on estänyt jalosteen tuonnin markkinoille. Aluksi kryomenetelmä optimoitiin useille vadelmalajikkeille, jotta mahdollisimman suuri osuus kasvupisteistä selviytyisi käsittelystä. Näillä olosuhteilla ei kuitenkaan saatu yhtään virusvapaata kasvia. Seuraavaksi kokeiltiin termoterapiaa, joka on jo kauan tunnettu terveiden kasvien saantoa lisäävänä menetelmänä. Sekään ei yksistään tuottanut terveitä taimia. Lopulta kokeiltiin kasvupisteiden käsittelyä kryoterapialla termoterapian jälkeen. Kryokäsittelyn jälkeen kasvuun lähteneistä kasvupisteistä kolmannes tuotti terveen taimen, kun termoterapia kesti 28-36 vrk. Kokeissa saatiin ensi kertaa viitteitä siitä, mihin termoterapian vaikutus viruspuhdistuksessa perustuu. Sen havaittiin kiihdyttävän virus-RNA:n hajotusta kasvupisteissä. Kryoterapia puolestaan tuhosi lämpökäsitellyistä kasvupisteistä kaikki muut paitsi vähiten erilaistuneet solut. Siten nämä menetelmät yhdistettyinä tuhosivat suurimman osan viroottisista soluista. Jäljelle jääneissäkin viroottisissa soluissa virus-RNA oli todennäköisesti suurelta osin hajonnut. Puhdistettua materiaalia voidaan nyt käyttää jatkokokeisiin ja uuden lajikkeen kaupallistamiseen. Materiaalia voidaan siirtää kryoterapiasta myös suoraan pitkäaikaissäilytykseen. Suomalaisesta tervetaimituotannosta on tullut edelläkävijä kryoterapian käytössä puhtaan lisäys-aineiston tuottamiseksi.
A New 10 Gb/s Optical Receiver Using Active Inductor in 90 nm CMOS Technology

This paper presents the design of a new low-power 10 Gb/s CMOS optical receiver in 90nm consisting of a transimpedance amplifier and two stages of a limiting amplifier. The transimpedance amplifier consists of two stages, and has a transimpedance gain of 54.8 $\mathbf{dB}\mathbf{\Omega}$ and −3 dB bandwidth of 8 GHz for 0.15 pF input capacitance while dissipating 4.64 mW. The two-stage limiting amplifier employs a differential topology with active feedback. The complete optical receiver has a gain of 82.6 $\mathbf{dB}\mathbf{\Omega}$, bandwidth of 8 GHz and consumes 13.64 mW from a 1-V supply.
A Streamlined Approach for Real-Time Data Analytics

Complex and diverse data is continuously being generated every microsecond because of computational intelligence in every field. Leveraging the huge amount of sensory information is a key issue to realize the IoT solutions in many areas. Traditional approaches reported in the literature for ingesting and processing data in real-time become in-efficient sometimes or may result in inadequate results. The faster one can manipulate information from data, the greater the value and cost of operational data become. This paper explains the cost-efficient method of processing and analyzing data in real time along with tools and techniques used for certain applications.
A Review on Machine Learning Big Data using R

Big Data becoming huge attention in the recent technological advancements due to electronic data and processing. Enormous data are collected or generated and stored in the electronic repository every day. Almost all the domains like Engineering, Technology, Science, Health, Commerce and Finance are emerged as huge data contributors and which includes variety of data sources. Big data offers a new space of opportunities from research and development to online transactions and services in many sectors. Analyzing this data can find unanticipated uses of data, correlations, new patterns and can be used for decision making and other related processes. Analyzing big data is a complex task. R programming is a tool used for analyzing the big data in efficient manner. This article introduces the R programming tool and a real world data set is analyzed and results are discussed.
An Efficient Recommender System Method Based on the Numerical Relevances and the Non-Numerical Structures of the Ratings

In this paper, we propose a collaborative filtering method designed to improve the current memory-based prediction times without worsening and even improving the existing accuracy results. The accuracy improvement is achieved by combining the numerical relevance of the ratings with non-numerical information based on the votes structure. The improvement of the prediction time is achieved by setting four actions: 1) simplification of the similarity measure design, in order to minimize the necessary calculations; 2) construction and maintenance of a model that simplifies the predictions processing; 3) optimization of the computation, using a set-based model and a bit-based processing implementation; and 4) switching between the bit processing and the numerical processing, depending on the density of the users’ ratings. Experimental results show the improvements both in the prediction time and the accuracy. Experiments have used a significant amount of state-of-the-art baselines and collaborative filtering public data sets.
Long-Range Diagnosis of and Support for Skin Conditions in Field Settings

Skin diseases are a significant cause of morbidity and mortality worldwide; however, access to dermatology services are critically limited, particularly in low- to middle-income countries (LMIC), where there is an overall shortage of physicians. Implementation of long-range technological support tools has been growing in an effort to provide quality dermatology care to even the most remote settings globally. eHealth strategies can provide realistic healthcare solutions if implemented in a feasible and sensitive way, customizing tools to address the unique needs and resource limitations of the local setting. This article summarizes the various types of telemedicine and mobile health (mHealth) tools and their practical applications and benefits for patient care. The challenges and barriers of teledermatology are discussed, as well as steps to consider when implementing a new teledermatology initiative. eHealth arguably offers one of the most flexible and realistic tools for providing critically needed access to dermatology skills in underserved LMICs.