Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

The utility of Artificial Intelligence (AI) in healthcare strongly depends upon the quality of the data used to build models, and the confidence in the predictions they generate. Access to sufficient amounts of high-quality data to build accurate and reliable models remains problematic owing to substantive legal and ethical constraints in making clinically relevant research data available offsite. New technologies such as distributed learning offer a pathway forward, but unfortunately tend to suffer from a lack of transparency, which undermines trust in what data are used for the analysis. To address such issues, we hypothesized that, a novel distributed learning that combines sequential distributed learning with a blockchain-based platform, namely Chained Distributed Machine learning C-DistriM, would be feasible and would give a similar result as a standard centralized approach. C-DistriM enables health centers to dynamically participate in training distributed learning models. We demonstrate C-DistriM using the NSCLC-Radiomics open data to predict two-year lung-cancer survival. A comparison of the performance of this distributed solution, evaluated in six different scenarios, and the centralized approach, showed no statistically significant difference (AUCs between central and distributed models), all DeLong tests yielded $p$ -val >0.05. This methodology removes the need to blindly trust the computation in one specific server on a distributed learning network. This fusion of blockchain and distributed learning serves as a proof-of-concept to increase transparency, trust, and ultimately accelerate the adoption of AI in multicentric studies. We conclude that our blockchain-based model for sequential training on distributed datasets is a feasible approach, provides equivalent performance to the centralized approach.
IoT Based Street Lighting Using Dual Axis Solar Tracker and Effective Traffic Management System Using Deep Learning: Bangladesh Context

With the rapid increase of population, nonrenewable energy loss has become one of the prior concerns worldwide in recent years. Researchers are working on the proper utilization of renewable energy to save the rest of the non-renewable energy sources for future generations. One of the purposes of this research is to develop an intelligent automation system that can decide luminous control operation based on light intensity. Here, LDR sensor is installed in such a way that it can be able to identify Night and Day mode using a certain intensity value, and as well as IR sensors take over the control of street light. The solar cell will be used for power supply here and DC current will work as a secondary backup. The article also involves automatic monitoring of traffic controls through a video system. The camera incorporates automatic text retrieval from video data utilizing lip reading by decoding facial expression utilizing deep learning methods. The proposed solution includes a test data collection, an interpretation of the picture frame, and a text production of the specified words. The test data collection will be structured in the proposed methodology by integrating all potential facial expressions of different words.
Isolation and screening of thermophilic and thermotolerant fungi for production of hemicellulases from heated environments

ABSTRACT Thermostable hemicellulases have potential to improve the quality of products of various industries including pulp and paper, food and feed, textile, etc. This study was aimed to isolate, screen, and identify potential xylanase and mannanase producers from heated environments. Sixty-eight thermophilic and thermotolerant fungi were isolated from various self-heated habitats based on their ability to grow at 45°C. With the aid of cultural and morphological observations, the fungi were identified and grouped into 19 fungal species. The enzyme production was evaluated by cultivating isolated fungi in submerged fermentation using wheat bran as carbon source. Screening of these isolates for hydrolysis of xylan and mannan was confirmed by Congo red plate assay method followed by assessment of xylanase and mannanase activity. Based on experimental analysis, we have found that all the isolates have exhibited xylanase activity, whereas only 22 isolates have found positive for mannanase activity. The highest xylanase and mannanase production was obtained by the cultivation of Malbranchea cinnamomea NFCCI 3724 (242 and 27 nkat/ml) followed by Melanocarpus albomyces (195 and 24 nkat/ml), Aspergillus terreus (165 and 21 nkat/ml), and Myceliophthora thermophila NFCCI 3725 (130 and 18 nkat/ml), respectively.
Catchment, morphometric, and water quality characteristics differ between reservoirs and naturally formed lakes on a latitudinal gradient in the conterminous United States

Abstract Constructed lakes and impoundments (reservoirs) likely exhibit substantial differences in their physics, chemistry, and biology from naturally formed lakes; however, because reservoirs and natural lakes generally have different latitudinal distributions, less is known about quantitative, generalized differences between the 2 waterbody types. We compared a suite of limnological variables among 1033 reservoirs and natural lakes across multiple size classes on a latitudinal gradient in the conterminous United States. In general, reservoirs had significantly greater perimeters, catchment areas, and catchment area:surface area ratios than natural lakes. Interestingly, several lakeshore land use, morphometric, and water quality response variables exhibited significant interactions between waterbody type and latitude. Southern reservoirs were deeper and had higher proportions of forested land and less agriculture and developed land use in their lakeshore than southern natural lakes, whereas northern reservoirs were shallower and had less forest and more agriculture and developed land in their lakeshore than northern natural lakes. Following the waterbody depth and land use data, natural lakes also had greater total phosphorus (TP) concentrations and shallower Secchi disk depths at lower latitudes, whereas reservoirs had greater TP concentrations and shallower Secchi disk depths at higher latitudes. Overall, natural lakes were more eutrophic than reservoirs, having greater total nitrogen and chlorophyll a concentrations, regardless of latitude. Our findings indicate that many physical, chemical, and lakeshore land use characteristics of reservoirs and natural lakes vary on a latitudinal gradient, which has implications for the water quality, ecology, and management of these waterbodies.
Design of a High-Performance 2-bit Magnitude Comparator Using Hybrid Logic Style

Design of a 2-bit binary Magnitude Comparator (MC) is presented in this research. The proposed MC has been designed using Conventional CMOS (CCMOS) logic, Pass Transistor Logic (PTL). The design is simulated along with 5 other existing MC designs in order to carry out evaluation and comparison. The proposed 2-bit MC displayed satisfactory level of improvement in speed and power. For this reason, significant enhancement in Power Delay Product (PDP) could have been attained. Due to the significant enhancement in performance, the proposed MC can be considered as a highly effective alternative to the existing MC designs.
Neural Networks for Indoor Human Activity Reconstructions

Low cost, ubiquitous, tagless, and privacy aware indoor monitoring is essential to many existing or future applications, such as assisted living of elderly persons. We explore how well different types of neural networks in basic configurations can extract location and movement information from noisy experimental data (with both high-pitch and slow drift noise) obtained from capacitive sensors operating in loading mode at ranges much longer than the diagonal of their plates. Through design space exploration, we optimize and analyze the location and trajectory tracking inference performance of multilayer perceptron (MLP), autoregressive feedforward, 1D Convolutional (1D-CNN), and Long-Short Term Memory (LSTM) neural networks on experimental data collected using four capacitive sensors with 16 cm $\times$ 16 cm plates deployed on the boundaries of a 3 m $\times$ 3 m open space in our laboratory. We obtain the minimum error using a 1D-CNN [0.251 m distance Root Mean Square Error (RMSE) and 0.307 m Average Distance Error (ADE)] and the smoothest trajectory inference using an LSTM, albeit with higher localization errors (0.281 m RMSE and 0.326 m ADE). 1D Convolutional and window-based neural networks have best inference accuracy and smoother trajectory reconstruction. LSTMs seem to infer best the person movement dynamics.
The mystery behind the mid-temperature transition(s) in vinylidenefluoride-based homo-, co- and terpolymers — has the puzzle been solved?

Over the last half century, the existence of an additional thermal transition in between the glass transition and the Curie/melting transition has been frequently observed on vinylidenefluoride-based ferro-, pyro- and piezoelectric homo- and co-polymers. The transition has also been observed recently in some of the related relaxor-ferroelectric terpolymers. Despite its well-known existence and the rich history of its treatment in the literature, the origin(s) and a more or less complete picture of the mid-temperature transition have remained elusive until now. Over the years, several authors have put forth various explanations for the so-called mid-temperature transition — some complementary and some contradictory to each other. At the 17th IEEE International Symposium on Electrets (ISE-17) in Limerick, Ireland, in September 2019, the mysterious mid-temperature transition and its possible mechanism(s) became the subject of a panel discussion a) to mark the Golden Jubilee of the discovery of piezoelectricity in polyvinylidenefluoride (PVDF) by Heiji Kawai of Kobayashi Institute of Physical Research, Japan, as well as the Centennial of the first recognition of ferroelectricity in piezoelectric Seignette's or Rochelle salt. The panel put forward a new hypothesis that the mid-temperature transition is most likely a result of several interrelated processes that take place within the respective temperature range. The relevant processes include an upper glass transition or relaxation, a relaxation related to conformational disorder, possible imperfect/time-dependent structures formed as a result of thermal processing and secondary crystallization, as well as interface polarization effects at crystalline-amorphous boundaries. The article captures the essence of the panel discussion and the perspectives obtained therefrom to elucidate the complex mid-temperature transition in vinylidenefluoride-based ferro-, pyro- and piezoelectric homo-, co- and ter-polymers.
Road Accident Prediction and Feature Analysis By Using Deep Learning

With the growing number of road accidents in the city Kerala, it is authoritative to carry out road accident mitigation processes to prevent damage of many lives. Handling of information mining has usually encountered issues like noise, high dimensionality and imbalance data. High dimensionality denotes to the large quantity of attribute among the data set. On the off chance that the quantity of attributes in a datasets is huge, at that point it is in all likelihood that a large portion of these qualities are repetitive or futile for building an prediction model and better result can be accomplished if the redundant attributes from the dataset are removed. Feature selection techniques are used to select the subset of the features which is used in the learning process. The SMOTE technique is used to handle the imbalance of the Kerala road data set then $\delta$ – agree based boosting algorithm for stacked autoencoder techniques are used for feature selection as well as data classification analysis. From this research, we conclude about various causes of the accidents, then find out total accidents per annual in separate state in particular time interval and helps to find accident trend in each state in each time interval over the year in each state.
An Efficient Approach for Measuring Semantic Similarity Combining WordNet and Wikipedia

The measurement of semantic similarity between concepts is an important research topic in natural language processing. In the past, several approaches for measuring the semantic similarity between concepts have been proposed based on WordNet or Wikipedia. However, improvements in the measurement accuracy of most methods have led to a dramatic increase in time complexity, and the existing methods do not effectively integrate WordNet and Wikipedia. In this paper, we focus on designing an efficient semantic similarity method based on WordNet and Wikipedia. To improve the accuracy of WordNet edge-based measures, we propose an edge weight model for combining edge and density information, which assigns a weight to each edge adaptively based on the number of direct hyponyms of the subsumer. Second, to improve the computational efficiencies of the existing Wikipedia link vector-based measures, we propose a new Wikipedia link feature-based semantic similarity method that converts Wikipedia links into semantic knowledge and replaces the TF-IDF statistical weight model in the existing measures. In addition, we propose two new word disambiguation strategies to further improve the accuracy of Wikipedia link-based measures. Finally, to fully exploit the advantages of WordNet and Wikipedia, we propose two new aggregation schemas for combining WordNet “is-a” semantics and Wikipedia link semantics to replace the current aggregation schemas that combine WordNet “is-a” semantics with category semantics in Wikipedia. The experimental results show that our aggregation models are outstanding in terms of accuracy, efficiency and word coverage compared to state-of-the-art similarity measures.
Bit-Interleaved Coded Multiple Beamforming in Millimeter-Wave Massive MIMO Systems

An analysis of the diversity gain for bit-interleaved coded multiple beamforming (BICMB) method in millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems is carried out for both the single-user and multi-user scenarios. We show that full diversity gain and full spatial multiplexing order can be achieved in both scenarios. Also, we show that the diversity gain in the multi-user scenario is independent of the number of users in the system. The assumption here is that the channel state information (CSI) is known at both sides of the transmitter and the receiver and the number of antennas in each remote antenna unit (RAU) tends to infinity. Based on these assumptions, the diversity gain for the single-user scenario is <inline-formula> <tex-math notation="LaTeX">$\frac {\left ({\sum _{i,j}\beta _{ij}}\right)^{2}}{\sum _{i,j}\beta _{ij}^{2}L_{ij}^{-1}}$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$L_{ij}$ </tex-math></inline-formula> is the number of propagation paths and<inline-formula> <tex-math notation="LaTeX">$\beta _{ij}$ </tex-math></inline-formula> is the large scale fading coefficient between the <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>th RAU in the transmitter and the <inline-formula> <tex-math notation="LaTeX">$j$ </tex-math></inline-formula>th RAU in the receiver. The diversity gain in the multi-user scenario for the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>th user is <inline-formula> <tex-math notation="LaTeX">$\frac {M^{2}}{\sum _{j}L_{kj}^{-1}}$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> represents the number of RAUs at the transmitter. Simulation results show that when the perfect CSI assumption is satisfied, the use of BICMB results in the diversity gain values predicted by the analysis.
Multi-Attack Detection using Forensics and Neural Network based Prevention for Secure MANETs

This paper presents Forensic methods for detection and prevention of multiple attacks along with neural networks like Denial-of-Service (DoS), probe, vampire, and User-to-Root (U2R) attacks, in a Mobile Ad hoc Network (MANET). We accomplish attacker(s) detection and prevention percentage upto 99% in varied node density scenarios 50/100/150.
Gender Disparity in Computer Science Education in Bangladesh: A Study of Women’s Participation in Computer Science

Bangladesh is yet to produce adequate number of engineering professionals in the field of Computer Science.29 public, 54 private and 2 international universities aren’t enough to allow more students in engineering and technical education. On the other hand, Bangladesh is still economically struggling and eastern patriarchal social barriers dominate the socio-cultural condition. Females of Bangladesh has been facing many visible and inherent obstacles in the way of earning higher education in technological discipline. The gender ratio of computer science education in undergraduate level in Bangladesh is 1:4, while in the IT sector the percentage of females goes down to less than 10%. In this paper, we have carried out an explorative study to investigate the complexities of these phenomena through qualitative research of Bangladeshi female students enrolled in computer science undergraduate program. We studied on a mixed sample of students who are deeply involved with computer science related activities and competitive programming. The study reveals that social barriers and economic issues quite hinder the smooth progress of an enthusiastic and potential female student of Computer science, while strong determination and family support take students to achieve their desired learning curve in computer science.
Effective Use of LMS in Inculcating 21st Century Skills among University Graduates – A Conceptual Success Model

The last two decades have seen universities actively implementing ICT in teaching and learning, largely via the use of Learning Management Systems (LMS). With the increasing demands to respond to the needs of Industrial Revolution 4.0 (IR 4.0) and with Blended Learning (BL) becoming popular in the educational landscape, LMS' functionalities have been uplifted to include components that provide robust platforms for students’ 21st century skills development through online exchange of communication, assessment and multimedia elements. However, mixed outcomes have been reported as the result of this initiative. While some educational enthusiasts may have been successful in integrating LMS in their teaching, there are lecturers who merely use LMS as a place to ‘park’ the contents of their subjects, hence not realizing the many potentials that LMS has to offer in cultivating 21st century skills among students. This study identifies the factors contributing to lecturers’ limited usage of LMS in their teaching and learning activities and based on the factors identified, propose a success model which can be referred to as a guideline for effective implementation of LMS in higher education. The outcomes are hoped to contribute to the tertiary education’s initiatives in producing graduates who are ready to meet the demands of the 21st Century workforce through the use of ICT in education.
The Effect of Glucagon-like Peptide-2 on Insulin-like Growth Factor-1 in Murine Intestinal Subepithelial Myofibroblasts

OF THESIS The Effect of Glucagon-Like Peptide-2 on Insulin-Like Growth Factor-1 in Murine Intestinal Subepithelial Myofibroblasts Master of Science 2009 Jason Lam Shang Leen Graduate Department of Physiology University of Toronto Insulin-like growth factor-1 (IGF-1), a known secretory product of intestinal subepithelial myofibroblasts (ISEMF), is essential for the intestinotrophic effects of glucagon-like peptide-2 (GLP-2). I hypothesized that GLP-2 increases the production of IGF-1 by primary murine ISEMF in culture. Immunocytochemistry showed that the ISEMF stained appropriately for α smooth muscle actin and vimentin but not for desmin. The ISEMF also expressed GLP-2 receptor and IGF-1 mRNA transcripts. ISEMF treated with GLP-2 revealed a maximal increase in IGF-1 mRNA transcript levels at 10 M GLP-2 and 2hr. Interestingly, immunoblotting revealed an increase in P-AKT/T-AKT with GLP-2, but no changes in cAMP, P-ERK/T-ERK or calcium were detected. PI3K inhibition and kinase-dead AKT over-expression abrogated GLP-2induction of IGF-1 mRNA, and ISEMF from GLP-2R null mice demonstrated reductions in IGF1 mRNA and cellular IGF-1, but not in media IGF-1, vs. wild-type ISEMF. These findings suggest a possible mechanism by which GLP-2 increases intestinal growth in-vivo.
Comparison of Data Mining Classification Algorithms for Student Performance

Student performance has an important role to measure student quality. Student quality can be measured through predictions of student performance. Prediction can be done using data mining techniques. One technique that can be used is the classification method. The research aims to find out which classification model has the best performance related to student performance data. The data used is taken from UCI Machine Learning, namely student performance. The study used seven methods, namely K-nearest neighbor, classification and regression trees, naïve bayes, adaboost, extratree, bernaoulli naïve bayes, random forest. The technology used to compare the seven methods uses Python programming. Testing the performance of methods using cross validation. The results of this study are the comparison of student performance classification algorithms on student math, namely K-Nearest Neighboring of 86.52%, classification and regression tests of 86.08%, naïve bayes of 84.78%, adaboost of 88.04%, extratree of 81.30%, bernaoulli naïve bayes of 79.34%, random forest E of 87.82%, random forest G of 89.78%. Based on these results we know that the best classification method is the random forest G of 89.78%.
EVALUATION OF LITCHI VARIETIES SEEKING SOURCES RESISTANT TO Aceria litchi MITE

Desde o aparecimento do acaro-da-erinose Aceria litchii no Brasil, a produtividade das lichieiras (Litchi chinensis) vem diminuindo nas principais regioes produtoras. Os objetivos do trabalho foram avaliar variedades de lichieiras quanto as reacoes de alimentacao de A. litchii, bem como determinar os teores de umidade e de acucares redutores, visando a fontes de resistencia a este acaro. O experimento foi realizado no Banco Ativo de Germoplasma de Fruticultura e nos Laboratorios de Acarologia e de Biogeoquimica de Solo da FCAV/UNESP. As variedades estudadas foram: Americana, Bengal, Groff, Mauritius e Sweet Clift. Foram realizadas descricoes dos sintomas causados por A. litchii e determinaram-se os teores de umidade e de acucares redutores presentes nas folhas das variedades. Ha diferencas entre as variedades com relacao a sintomatologia causada por A. litchii quanto a formacao e a coloracao da erinose. A erinose final, formada na ‘Groff’, ‘Americana’ e ‘Bengal’, e semelhante quanto a coloracao e a densidade de erineos. Os maiores teores, de umidade e de acucares redutores foram verificados na ‘Bengal’, enquanto os menores teores na ‘Mauritius’. Visualmente no campo, ‘Bengal’, mostrou-se altamente suscetivel ao A. litchii, e ‘Mauritius’ foi a que se apresentou menos danificada pela erinose. ‘Sweet Clift’ apresentou-se suscetivel ao A. lichii semelhante a variedade Bengal. Os resultados obtidos sao pioneiros no Brasil e fornecem subsidios para programas de melhoramento de lichieiras resistentes ao acaro-da-erinose.