Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on Iris Recognition System

-Automatic identification of an individual based on a robust authentication biometric system is iris [1]. Iris is one of among all the biometrics which are possess unique features & characteristics of individual physiology and does not change with age. In Iris biometric system, firstly, a sample of features of an individual through a camera is taken for iris recognition. Then sample are pre-processed and some form of mathematical operations has to be done on samples to transform it in a template. The templates are normalized pre-processed form of sample. The biometric template possess high single out features, which are discriminate, it to other template and compare with other template to decide identity.
Review paper on sentiment analysis on social networking sites

— This paper describes the approach we take to social media analysis, combining the exploration of the opinion of text and centered on the recognition of entities and events. We examine a particular use case, which is to help archivists select materials for inclusion in a social media archive to preserve community memories, moving towards structured preservation around semantic categories. The textual approach we adopt is rule based and relies on a number of sub-components, taking into account issues inherent in social media such as noisy non-grammatical text, use of insulting words, short language popularly called as SLANG, and so on. In order to resolve the ambiguity and provide additional contextual information, we propose two major innovations in this work: first, the novel combination of tools for extracting texts and multimedia opinions; and second, the adaptation of NLP tools for the analysis of opinion specific to the problems of social media.
An Architecture-Level Energy and Area Estimator for Processing-In-Memory Accelerator Designs

Processing-in-memory (PIM) deep neural network (DNN) accelerators, which aim to improve energy/area efficiency of DNN processing by integrating computation into data storage, have gained popularity in recent years. Therefore, it is attractive to have a generally applicable framework that is able to quickly provide insights into the various trade-offs involved in PIM accelerator designs. We present an architecture-level design estimation framework for PIM accelerators that allows easy representations of the designs with provided architecture templates and component design templates, performs analytical runtime simulations, and produces technology-dependent area and energy estimations. We show that the framework can be easily used to evaluate state of the art PIM accelerator designs; it achieves 95% accurate total energy estimations and reproduces exact area breakdowns of the components in the design. Related open-source code is available at http://accelergy.mit. edu/.
A STUDY OF THE INNOVATIVE WORK BEHAVIOR OF ACADEMIC STAFF: FOCUS ON ETHIOPIAN HIGHER EDUCATION INSTITUTION

This main objective this study was to study the innovative work behavior of academic staff in some selected Ethiopian Public Universities. This research is descriptive in nature, which follows deductive approach with descriptive research design. The researchers used proportional stratified sampling, out of 340 respondents sampled, only 301 has returned the valid questionnaire. The respondents of this study were only academic staff selected from the selected universities. The data were analyzed by SPSS version 20 to show statistical out puts of descriptive statistical results. The major finding of this study indicated that there is upright initiation of innovative work behavior of academic staff in some selected Ethiopian Public Universities. The response of respondents on the positive questions asked were strong in positive manner. We strongly recommend other scholars to consider factors which can affect the innovative work behavior of employees in future.
MAP-MRF based Simultaneous Image Registration, Interpolation and Restoration for Super Resolution Image Reconstruction

:- Super-resolution (SR) is the process of combining a sequence of low resolution images in-order to producea higher resolution images. The goal of super resolution, as its name suggests, is to increase the resolutionof an image. Reduction of noise and blur in the super resolution process is a challenge task in reconstruction.Resolution of registered image depends on accurate feature extraction and matching. These factors reduce the quality of the image. In this paper we presented a novel approach for simultaneous image registration, interpolation and restoration is proposed with the help of affine transformation,RANdomSAmple Consensus, New Edge Directed Interpolation and MAP-MRF.The performance of the proposed is verified with quality metrics such as PSNR,SSIM and MSE values with existing methods.The results show that the proposed method generates high quality images.
Automated Machine Learning Tool: The First Stop for Data Science and Statistical Model Building

Machine learning techniques are designed to derive knowledge out of existing data. Increased computational power, use of natural language processing, image processing methods made easy creation of rich data. Good domain knowledge is required to build useful models. Uncertainty remains around choosing the right sample data, variables reduction and selection of statistical algorithm. A suitable statistical method coupled with explaining variables is critical for model building and analysis. There are multiple choices around each parameter. An automated system which could help the scientists to select an appropriate data set coupled with learning algorithm will be very useful. A freely available web-based platform, named automated machine learning tool (AMLT), is developed in this study. AMLT will automate the entire model building process. AMLT is equipped with all most commonly used variable selection methods, statistical methods both for supervised and unsupervised learning. AMLT can also do the clustering. AMLT uses statistical principles like R2 to rank the models and automatic test set validation. Tool is validated for connectivity and capability by reproducing two published works.
Performance Evaluation LoRa-GPRS Integrated Electricity usage Monitoring System for Decentralized Mini-Grids

The emerging Internet of Things (IoT) technologies such as Long-Range (LoRa), combined with traditional cellular communications technologies such as General Packet Radio Service (GPRS) offers decentralized mini-grid companies the opportunity to have cost-effective monitoring systems for the mini-grid resources. Nevertheless, most of the existing decentralized mini-grid companies still rely on traditional cellular networks to fully monitor electricity consumption information, which is not a feasible solution especially for the resource-constrained mini-grid systems. This paper presents the performance evaluation of the proposed LoRa-GPRS integrated power consumption monitoring system for decentralized mini-grid centers. Each mini-grid center consists of a network of custom designed smart meters equipped with LoRa modules for local data collection, while the GPRS gateway is used to transmit collected data from the local monitoring centre to the cloud server. Performance testing was conducted by using five electrical appliances whose power consumption data form the cloud server was compared to the same data collected by using a reference digital meter. The correlation between the two data sets was used as a key performance metric of the proposed system. The performance results show that the proposed system has a good accuracy hence providing a cost-effective framework for monitoring and managing of power resources in decentralized mini-grid centers.
Conscious Agents

Self-awareness is an aspect of consciousness that is highly developed in humans in comparison with other animals. A human being unaware of his or her personal characteristics, of what he or she knows and doesn’t know, can do and cannot do, wants and doesn’t want, has experienced and is experiencing, etc., would surely be difficult to communicate with naturally. Therefore we believe that consciousness plays a crucial role in building artificial dialog agents with human-level abilities. We will provide an overview of consciousness as viewed in philosophy, biology and artificial intelligence, and we will present relevant work on agents that show abilities related to consciousness. Moreover we will present our initial attempt to extend the architecture of a simple Epilogbased agent originally built by A.N. Kaplan in the direction of our goal of a human-like conscious dialog agent. keywords: self-aware agents, consciousness, episodic memory, dialog agents, knowledge representation.
Analysis of potential for Internet of Things (IoT) to act as an enabler for climate protection and the Energy Strategy 2050 Impacts of IoT on energy consumption and CO 2eq emissions in specific use cases

The goal of this study was to investigate in four Swiss specific use cases whether the implementation of IoT equipment can act as an enabler for climate protection and the Energy Strategy 2050 in terms of reducing CO 2 emissions. Further, the potential quantity of CO 2eq savings for Switzerland regarding the use cases for the year 2020 was analysed. The analysed use cases were remote PV monitoring, remote filling level monitoring of waste containers and heating optimisation, once for a service building and once for a multifamily house. The results clearly show that the grey energy of the IoT equipment is negligible in all investigated use cases compared to the avoided amount of CO 2eq emissions. The upscaling of the results was based on assumption and gives a possible order of magnitude of annual savings in the year 2020 for Switzerland: 2.4 kt for PV monitoring, 115 t for level monitoring of waste containers, 0.3 Mt in the heating optimisation of service buildings and 2.3 Mt in the heating optimisation of domestic space.
Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots

Annotated datasets are essential for supervised learning. However, annotating large datasets is a tedious and time-intensive task. This paper addresses active learning in the context of semantic segmentation with the goal of reducing the human labeling effort. Our application is agricultural robotics and we focus on the task of distinguishing between crop and weed plants from image data. A key challenge in this application is the transfer of an existing semantic segmentation CNN to a new field, in which growth stage, weeds, soil, and weather conditions differ. We propose a novel approach that, given a trained model on one field together with rough foreground segmentation, refines the network on a substantially different field providing an effective method of selecting samples to annotate for supporting the transfer. We evaluated our approach on two challenging datasets from the agricultural robotics domain and show that we achieve a higher accuracy with a smaller number of samples compared to random sampling as well as entropy based sampling, which consequently reduces the required human labeling effort.
Enhanced Bioretention Cell Modeling: Moving from Water Balances to Hydrograph Production Modélisation améliorée des cellules de biorétention: passer des bilans hydriques à la production d'hydrogrammes

Over the last decade, bioretention systems have become a leading stormwater control measure. Although these systems have performed well in many site-scale field studies, less is known about how these systems impact the watershed. Modelling of bioretention provides an avenue for such scaling to the larger watershed. However, hydrologic models must first be improved at the site scale to provide reliable estimations of their performance at the watershed scale. Currently, hydrologic models capable of simulating bioretention largely consist of lumped parameters and simplifications that do not fully account for fundamental hydrologic processes (in particular soil-water processes). DRAINMOD is an agricultural drainage model that has shown promise when applied to bioretention systems by using the soil-water characteristic curve to obtain detailed daily water balances over a continuous time-period (advances over most other models for bioretention). For this study, DRAINMOD has been recoded to develop DRAINMOD-Urban which allows high temporal resolution inputs and outputs, more closely matching the travel times of urban systems. Resulting modelling revealed: (1) if DRAINMOD-Urban can effectively produce hydrographs, (2) how parameters calibrated for the original DRAINMOD model translate to DRAINMOD-Urban, and (3) how the performance of this enhanced model, DRAINMODUrban, compares to simplistic, lumped-parameter bioretention models.
Synthesis of Musical Instrument Sounds: Physics-Based Modeling or Machine Learning?

Introduction Music (and sound) synthesis has become widespread and is found across many musical genres. Many members of the Acoustical Society of America (ASA) also use various types of synthesis to produce the sounds used in research projects, ranging from measurements of hearing to studies of sound propagation in the oceans. Although there is a long history of attempts to synthesize music and other sounds (e.g., Pejrolo and Metcalfe, 2017), this paper focuses on the most recent, and very exciting, approaches now being used to synthesize the sounds of musical instruments as well as for a wide range of other acoustic phenomena. Earlier methods for musical instrument sound synthesis used signal-processing effects such as frequency-modulated (FM) synthesis or wavetables (i.e., sampling), whereas the applications of physics-based modeling and machine learning have only been applied relatively recently because of their higher computational cost.
Mesenchymal stromal/stem cell-derived extracellular vesicles in tissue repair: challenges and opportunities

Mesenchymal stem/stromal cells (MSCs) are important players in tissue homeostasis and regeneration owing to their immunomodulatory potential and release of trophic factors that promote healing. They have been increasingly used in clinical trials to treat multiple conditions associated with inflammation and tissue damage such as graft versus host disease, orthopedic injuries and cardiac and liver diseases. Recent evidence demonstrates that their beneficial effects are derived, at least in part, from their secretome. In particular, data from animal models and first-in-man studies indicate that MSC-derived extracellular vesicles (MSC-EVs) can exert similar therapeutic potential as their cells of origin. MSC-EVs are membranous structures loaded with proteins, lipids, carbohydrates and nucleic acids, which play an important role in cell-cell communication and may represent an attractive alternative for cell-based therapy. In this article we summarize recent advances in the use of MSC-EVs for tissue repair. We highlight several isolation and characterization approaches used to enrich MSC-derived EVs. We discuss our current understanding of the relative contribution of the MSC-EVs to the immunomodulatory and regenerative effects mediated by MSCs and MSC secretome. Finally we highlight the challenges and opportunities, which come with the potential use of MSC-EVs as cell free therapy for conditions that require tissue repair.
Abstract 2826: Sustained melanoma regression is achieved with continuous palbociclib and PLX4720 treatment but not with intermittent or sequential dosing

In melanoma the development of resistance to BRAF inhibitors limits clinical responses. Thus the search for novel single agent and combination therapies as well as sequencing strategies that overcome or delay the emergence of resistance is needed. The p16-CDK4- cyclinD-RB1 pathway (CDK4 pathway) is deregulated in approximately 90% of melanomas and most melanoma cell lines are sensitive to palbociclib a specific CDK4/6 inhibitor. We hypothesized that dual targeting the MAPK/ERK and CDK4 pathways would lead to robust inhibition of the CDK4/Cyclin D complex and consequently induce greater tumor regression than single agent treatment. Proliferation and colony formation assays demonstrated that sequential and intermittent treatment with PLX4720 and palbociclib was not as effective as continuous combinational dosing. Only the combination of PLX4720 and palbociclib overcame the development of resistance leading to sustained inhibition of proliferation, cell death and induction of senescence. In A375 and HT144 human tumor xenograft models, palbociclib and PLX4720 initially induced tumor regression and tumor stasis, respectively, however resistance eventually developed to both agents. In contrast, the combination treatment induced rapid and sustained tumor regression. Biomarker studies indicate resistance to single agent PLX4720 was due to reactivation MAPK/ERK pathway and resistance to palbociclib to partial restoration of phosphorylated RB1. Tumor regression in the combination treatment was associated with the infiltration of leukocytes including activated natural killer cells indicating that these cells were involved in tumor clearance. These data demonstrate that dual targeting CDK4/6 and mutant BRAF evades resistance to single agents and leads to sustained tumor regression Citation Format: Karen E. Sheppard, Carleen Cullinane, Claire Martin, Laura Kirby, Nicole Haynes, Kelly Waldeck, Richard Young, Todd VanArsdale, Grant McArthur. Sustained melanoma regression is achieved with continuous palbociclib and PLX4720 treatment but not with intermittent or sequential dosing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2826.
Mapping nearly a century and a half of global marine fishing_ 1869–2015

Understanding global fisheries patterns contributes significantly to their management. By combining harmonized unmapped data sources with maps from satellite tracking data, regional tuna management organisations, the ranges of fished taxa, the access of fleets and the logistics of associated fishing gears the expansion and intensification of marine fisheries for nearly a century and half (1869–2015) is illustrated. Estimates of industrial, non-industrial reported, illegal/unreported (IUU) and discards reveal changes in country dominance, catch composition and fishing gear use. Catch of industrial and non-industrial marine fishing by year, fishing country, taxa and gear by 30-min spatial cell broken to reported, IUU and discards is available. Results show a historical increase in bottom trawl with corresponding reduction in the landings from seines. Though diverse, global landings are now dominated by demersal and small pelagic species.
Early Work on Optimization-Based Heuristics for the Sliding Tile Puzzle

Optimization-based heuristics may offer very good estimates. But, calculating them may be time consuming, especially if the optimization problem is intractable. This raises the question of their applicability. This paper summarizes early work from the year 2000 on optimization-based heuristics in the context of PDBs for the Tile-Puzzle. We show that an admissible heuristic based on Vertex-Cover (VC) can be calculated in reasonable time over a large collection of small PDBs. When larger PDBs are involved we suggest the idea of using another lookup table that precalculates and stores all possible relevant VC values. This table can be later looked up in a constant time during the search. We discuss the conditions under which this idea can be generalized. Experimental re-sults demonstrate the applicability of these two ideas on the 15-and 24-Puzzle. The ﬁrst idea appeared in (Felner, Korf, and Hanan 2004) but the second idea is presented here for the ﬁrst time.
Awareness And Attitude Towards Cryptocurrencies In Relation To Adoption Among College Students In A Private Tertiary Institution In Cagayan De Oro City, Philippines

The paramount need to understand the intricate details about cryptocurrency not only as a technology but also as an investment vehicle or a commodity has struck popularity even before it reached the top of its fame after the 2008 Financial Crisis. However the success of cryptocurrency will largely depend on the extent to which society is willing to adopt it. In this note, awareness and attitude tend to be the most common antecedent to explain adoption partly because of the of several technology adoption models that involve the mentioned factors. Thus, this study aimed at determining the level of awareness and attitude of college students towards cryptocurrency and how these may result to eventual decision to adopt. The study utilized a descriptive research design utilizing a researcher made questionnaire as research instrument. Logistics regression was used to identify the knowledge and attitude on cryptocurrency relates to adoption. The results of the study proved that Attitude is a significant factor, at 0.05 level of significance, in explaining possible adoption (p= 0.027) while Awareness is only significant (p=0.082) at 0.10 significance level. The over-all fit of the model was found significant at 5% with a x value of 7.72 (p=0.021).
Electrical Efficiency Improvement of PV Cells

Photovoltaic solar cell generates electricity by receiving solar irradiance. solar energy is becoming one of the most important energies in the future. Recently, there has been an enormous increase in the understanding of the operational principle of photovoltaic devices, which led to a rapid increase in the electrical efficiencies of such devices. The temperature of photovoltaic modules increases when it absorbs solar radiation, causing a decrease in efficiency. This undesirable effect can be partially avoided by applying a heat recovery unit with liquid circulation with the photovoltaic module. Such unit is called photovoltaic/thermal collector (PV/T) or hybrid (PV/T). The objective of the present work is to design a system for cooling the solar cell in order to increase its electrical efficiency and also to extract the heat energy. A hybrid solar system which generates both electricity and heat energy simultaneously is studied. This hybrid system consists of PV cells attached to an absorber plate with fins attached at the other side of the absorber surface.
Relationship Between Mannose-Binding Lectin and Febrile Neutropenia in Acute Leukemia Patients

Selcuk Akan1, Sule Mine Bakanay2, Yusuf Bilen3, MD Baran Balcan4, Fuat Erdem3 1Internal Medicine Department, Ankara Cıty Hospital, Ankara, 2Hematology Department, Ankara Yıldırım Beyazıt University Medical School, Ankara Cıty Hospital Ankara, 3Adıyaman University, Adıyaman Training and Research Hospital, Hematology, Adıyaman, 4Department of Chest Diseases, Baskent University Istanbul Hospital, Istanbul, Turkey Relationship between mannose-binding lectin and febrile neutropenia Relationship Between Mannose-Binding Lectin and Febrile Neutropenia in Acute Leukemia Patients
Drug treatment of HIV and AIDS

9 6 • H O S P I TA L P H A R M AC I S T Ms Davies is principal pharmacist,HIV and GUM, Dr Morlese is research registrar,HIV and GUM,Dr Bower is consultant physician,medical and HIV oncology and Dr Pozniak is consultant physician,HIV and GUM,all at the Chelsea and Westminster Hospital Healthcare NHS Trust By E. DAVIES, BSC, MRPHARMS, J. F. MORLESE, BSC, MBBS, M. BOWER, PHD, MRCP and A. POZNIAK, MD, FRCP