Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Detection and Counting of Red Blood Cell using Image Processing Techniques

The major issue in clinical laboratory is to produce a precise result for every test especially in the area of Red Blood Cell (RBC) count. Red blood cell (RBC) count in a blood test used to evaluate the overall health and diagnose a wide range of disorders, including anaemia, infection and malaria etc. This blood cell count infers about the disorders against normal healthy blood cell count. The traditional method of manual counting under a microscope yields inaccurate results and put an intolerable amount of stress to medical laboratory technicians. Due to high vulnerability in human error and large time consumption, better and more effective image processing software is needed. As a solution to this problem, this project proposes an image processing technique for counting the number of blood cells. This paper introduces a cost effective automatic RBC counting method using image analysis technique and specifically aims at improving the results using Hough circle detection. Removing the unnecessary circles created during the Hough Circle Detection will yield more accurate results
A systematic literature review of open data quality in practice

Context: The main objective of open data initiatives is to make information freely available through easily accessible mechanisms and facilitate exploitation. In practice openness should be accompanied with a certain level of trustworthiness or guarantees about the quality of data. Traditional data quality is a thoroughly researched field with several benchmarks and frameworks to grasp its dimensions. However, quality assessment in open data is a complicated process as it consists of stakeholders, evaluation of datasets as well as the publishing platform. Objective: In this work, we aim to identify and synthesize various features of open data quality approaches in practice. We applied thematic synthesis to identify the most relevant research problems and quality assessment methodologies. Method: We undertook a systematic literature review to summarize the state of the art on open data quality. The review process starts by developing the review protocol in which all steps, research questions, inclusion and exclusion criteria and analysis procedures are included. The search strategy retrieved 9323 publications from four scientific digital libraries. The selected papers were published between 2005 and 2015. Finally, through a discussion between the authors, 63 paper were included in the final set of selected papers. Results: Open data quality, in general, is a broad concept, and it could apply to multiple areas. There are many quality issues concerning open data hindering their actual usage for real-world applications. The main ones are unstructured metadata, heterogeneity of data formats, lack of accuracy, incompleteness and lack of validation techniques. Furthermore, we collected the existing quality methodologies from selected papers and synthesized under a unifying classification schema. Also, a list of quality dimensions and metrics from selected paper is reported. Conclusion: In this research, we provided an overview of the methods related to open data quality, using the instrument of systematic literature reviews. Open data quality methodologies vary depending on the application domain. Moreover, the majority of studies focus on satisfying specific quality criteria. With metrics based on generalized data attributes a platform can be created to evaluate all possible open dataset. Also, the lack of methodology validation remains a major problem. Studies should focus on validation techniques.
Sentiment analysis and opinion mining: Keynote address

Sentiment analysis and opinion mining are closely related tasks that recently have received great attention from both research community and industry. Opinion mining is aimed to turn a huge amount of user-contributed context in Internet, such as tweets, product reviews, and blogs into useful information, which allows the companies and the governmental bodies to improve their product and services, and, on the other hand, allows the consumers to make informed buying decisions. Sentiment analysis, apart from its use in opinion mining, finds other important applications in security, healthcare, and education, among others. In this paper, I briefly discuss the motivation behind these tasks and outline some techniques recently developed by our group, mainly based on specially tailored deep-learning architectures or other machine-learning methods.
Distribution Statement A : Approved for public release Initial Test of MITA / DIMM with an Operational CBP System

The MITA (Motion Imagery Task Analyzer) project was conceived by CBP OA (Customs and Border Protection Office of Acquisition) and executed by JHU/APL (Johns Hopkins University/Applied Physics Laboratory) and CERDEC NVESD MSD (Communications and Electronics Research Development Engineering Command Night Vision and Electronic Sensors Directorate Modeling and Simulation Division). The intent was to develop an efficient methodology whereby imaging system performance could be quickly and objectively characterized in a field setting. The initial design, development, and testing spanned a period of approximately 18 months with the initial project coming to a conclusion after testing of the MITA system in June 2017 with a fielded CBP system. The NVESD contribution to MITA was thermally heated target resolution boards deployed to support a range close to the sensor and, when possible, at range with the targets of interest. JHU/APL developed a laser DIMM (Differential Image Motion Monitor) system designed to measure the optical turbulence present along the line of sight of the imaging system during the time of image collection. The imagery collected of the target board was processed to calculate the in situ system resolution. This in situ imaging system resolution and the time-correlated turbulence measured by the DIMM system were used in NV-IPM (Night Vision Integrated Performance Model) to calculate the theoretical imaging system performance. Overall, this proves the MITA concept feasible. However, MITA is still in the initial phases of development and requires further verification and validation to ensure accuracy and reliability of both the instrument and the imaging system performance predictions.
Developing board game as learning media about waste sorting for fourth grade students of elementary school

The research aimed to develop a learning media in form of board game about waste sorting for fourth grade students due to the lack of students’ awareness about the purpose of waste sorting. The research used descriptive method to make a description or representation about a fact from real phenomenon, which was about waste sorting that considered less important in society and must be informed since early stage. Strategy of the research was linear strategy that consisted of some phases: (1) Data Collection Phase, (2) Data Analysis and Processing Phase, (3) Product Design Phasee, and (4) Testing Phase. The approach of the research was mix method, combination of qualitative and quantitative. Research subjects at testing phase were 42 students of fourth grade of St. Theresia Marsudirini 77 Salatiga, an Adiwiyata Elementary School. The result of the research showed that, (1) 95 percent of students more interested in learning using a game, (2) “Pilah Sampah” board game was well successfully delivered the learning material about organic and non organic waste, (3) using of board game as learning media alternative was rated good both by teacher and board game expert, based on product questionnaire.
The Effect of Student Team Achievement Division Cooperative Learning on The Concept Understanding Ability of Mathematic

The learning model of cooperative learning type Student Team Achievement Division (STAD) is one of the most simple cooperative learning models. The main advantages of this model lies in activity and interaction among students to motivate and help each other in mastering the subject matter, understanding the concepts, discussing in solving problems and tasks given by teacher. The purpose of this study is to test the effect of the STAD learning model on the student’s concept understanding ability. This study is a quasi-experimental research using posttest only control design. The research population is all student of fifth grade and measurement instruments using essay test. The result of measurements of the concept understanding ability of mathematic in the form of data and analyzed using t-test. Based on the data analysis, average the concept understanding ability of mathematic in the classroom experiment that is equal to 91.25, while the control class at 64.55. The results of the t-test analysis shows that t count > t table or 2,65 greater than 2,02. This mean that H0 refused and H1 accepted So there is the effect of STAD learning model on the concept understanding ability of mathematic. Keywords—concept understanding ability of mathematic; quasi-experimental research; student team achievement division
Bayerische Landesanstalt für Landwirtschaft

Landwirte füttern bei Getreideknappheit (Ernteausfällen, hohe Zukaufspreise, Planungsfehler .) immer wieder die „neue Ernte frisch vom Mähdrescher“ an ihre Schweine. Aufgrund der „ungünstigen“ Erfahrungen bei der Verfütterung von Frischgetreide sollte aber ein Einsatz an die empfindlichen Ferkel erst nach 4-wöchiger Lagerzeit erfolgen (Flachowsky 1993). Begründung: Enzymatische Umsetzungen sowie der hohe Oberflächenkeimbesatz bei „Schwitzgetreide“ (Wasserabgabe bei Entquellungsvorgang) verursachen Verdauungsstörungen und Leistungseinbußen. Die Höhe der damit verbundenen Minderzunahmen (ca. 10-15% bei roggenbetonten Rationen, Jeroch 1993) ist für gängige Hofmischungen mit den Hauptbestandteilen Weizen und Gerste nicht bekannt. Deshalb wurde ein Ferkelaufzuchtversuch mit hohen Anteilen erntefrischer Gerste und/oder Weizen aus 2007 im Vergleich zu abgelagertem Getreide aus der Vorjahresernte 2006 durchgeführt.
NARCISSISM AND EMPATHY 1 I don ’ t feel ya : How narcissism shapes empathy

Those who tend towards a self-absorbed personality are less likely to “feel others. ” Indeed, subclinical narcissism has been linked to decreased empathy: Individuals high in narcissism seem to neglect what other people are thinking and feeling and are less likely to emotionally share others’ mental states. Three studies (N = 1,008) extend the literature on narcissism and empathy in some important ways. We suggest that the empathy deficit among narcissists does not make an exception for close friends, that it manifests not only in less, but also in discordant affect, and that it is mainly driven by the antagonistic dimension of narcissism (Studies 1 and 2). Moreover, employing an experimental manipulation, the present findings offer a novel way of attenuating this empathy deficit among narcissists: the experience of trust (Study 2). Finally, a pre-registered laboratory study documents a “bright” consequence of narcissists’ empathy deficit with respect to parochial altruism (Study 3). Hence, as unfortunate as narcissists’ empathy deficit might be, it is not set in stone. Additionally, a narcissistic spotlight that shines exclusively on the self can reduce some of the parochialism that empathy for specific, often close others entails.
Machine learning methods in electronic nose analysis

The main existent tool to monitor chemical environ- ments in a continuous mode is gas sensor arrays, which have been popularized as electronic noses (enoses). To design and validate these monitoring systems, it is necessary to make use of machine learning techniques to deal with large amounts of heterogeneous data and extract useful information from them. Therefore, enose data present several challenges for each of the steps involved in the design of a machine learning system. Some of the machine learning tasks involved in this area of research include generation of operational patterns, detection anomalies, or classification and discrimination of events. In this work, we will review some of the machine learning approaches adopted in the literature for enose data analysis, and their application to three different tasks: single gas classification under tightly-controlled operating conditions, gas binary mixtures classification in a wind tunnel with two independent gas sources, and human activity monitoring in a NASA spacecraft cabin simulator.
Translation Analysis on Civil Engineering Text Produced by Machine Translator

Translation is extremely needed in communication since people have serious problem in the language used. Translation activity is done by the person in charge for translating the material. Translation activity is also able to be done by machine. It is called machine translation, reflected in the programs developed by programmer. One of them is Transtool. Many people used Transtool for helping them in solving the problem related with translation activities. This paper wants to deliver how important is the Transtool program, how effective is Transtool program and how is the function of Transtool for human business. This study applies qualitative research. The sources of data were document and informant. This study used documentation and in dept-interviewing as the techniques for collecting data. The collected data were analyzed by using interactive analysis. The results of the study show that, first; Transtool program is helpful for people in translating the civil engineering text and it functions as the aid or helper, second; the working of Transtool software program is effective enough and third; the result of translation produced by Transtool is good for short and simple sentences and not readable, not understandable and not accurate for long sentences (compound, complex and compound complex) thought the result is informative. The translated material must be edited by the professional translator.
Handwritten Character Detection Using Optical Character Recognition Method

: The handwritten character recognition using MATLAB converts the handwritten notes, printed documents, author books, papers etc into the editable word format. Using OCR(Optical Character Recognition) technique this can be possible with high speed and accuracy within a short interval of time. Now-a-days this technique serves a key factor and create the paperless environment. This paper presents a details of Handwritten Character Recognition.
Power Analysis and Fault Attacks against Secure CAN: How Safe Are Your Keys?

Designers of automotive systems find themselves pulled in an impossible number of directions. Systems must use the most advanced security features, but at the same time run on low-cost and resource-constrained hardware. Ultimately, an engineering trade-off will eventually be made regarding how encryption and key management is used on these systems, potentially leaving them vulnerable to attack. In this paper, we detail the applicability of side-channel power analysis and fault injection on automotive electronic systems, showing how these dangerous techniques can be used to break an otherwise secure system. We build a small example network using AES-CCM to implement an encrypted, authenticated CAN protocol. We demonstrate how open-source hardware and software can easily recover the encryption keys from some of these nodes with side-channel power analysis, and we recover a full firmware image from one device with a fault-injection attack using the same tools. We also discuss how these attacks can be improved to bypass some common countermeasures and be applied against devices in the real world, bypassing security on in-vehicle communication or over-the-air firmware updates. With these demonstrations in mind, we emphasize the importance of using strong encryption and authentication keys with proper key management and distribution methods. We discuss methods for mitigating these side-channel and fault attacks, and we use these methods to provide guidelines for creating a system architecture that is secure against these hardware attacks. History Received: 02 Aug 2017 Revised: 10 Oct 2017 Accepted: 29 Nov 2017 e-Available: 14 Feb 2018
Evolving Hierarchical Structures for Convolutional Neural Networks using Jagged Arrays

vii List of Abbreviations Used . viii Acknowledgements . ix Chapter
Exploring the role of hydraulic conductivity on the contribution of the hyporheic zone to in‐stream nitrogen uptake

Nitrogen uptake (N‐uptake) within the hyporheic zone provides key ecological services, such as nutrient removal, of stream ecosystems. We hypothesize that the hydraulic conductivity (Kf) of the hyporheic sediments governs nutrient uptake rates through effects on the (a) surface and subsurface flow (i.e., hyporheic flow) and (b) hyporheic N‐uptake. Here, we worked at two hierarchical spatial scales (reach and hyporheic scale) to disentangle the role of Kf on N‐uptake. At the reach scale, we performed coinjected N‐NH4+ and Cl– additions in six reaches with contrasting reach Kf (10−1–10−5 m/s) and simultaneously determined (a) in‐stream N‐uptake (hyporheic+benthic N‐uptake) and (b) hyporheic flow, and (c) N‐uptake and microbial community abundance at the hyporheic scale. Results suggest that Kf determines the contribution of the hyporheic zone to hydrological exchange but that its role varies between scales to determine in‐stream N‐uptake. At the reach scale, Kf variability seems to determine the extent at which the hyporheic zone contributes to hyporheic flow and, thus, to N‐uptake velocity. At the hyporheic scale, Kf seems to indirectly determine hyporheic N‐uptake through the proportion of surface water that enters the hyporheic zone (i.e., relative connectivity) and the abundance of the microbial community. These results suggest an interplay between Kf at both scales and its spatial heterogeneity, which will ultimately drive in‐stream N‐uptake at reach scale. In this sense, we found that Kf can be considered as a unifying variable for stream biogeochemical processes and as an important variable to derive the contribution of hyporheic zone to in‐stream nutrient removal capacity.
Electrosprayed polymeric nanobeads and nanofibers of modafinil: preparation, characterization, and drug release studies

Introduction: Modafinil (MDF) is used orally for the treatment of attention-deficit/hyperactivity disorder and narcolepsy. It holds low solubility and high permeability; therefore, improving its dissolution properties by preparing nanoformulations can be a promising approach to enhance its oral absorption. Our aims were to prepare and characterize MDF-Eudragit® RS100 (MDF-ERS) nanoparticles by electrospray technique. Methods: Electrosprayed nanoparticles were fabricated by varying MDF to ERS ratios and concentrations. The formulations were characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD), and Fourier-transform infrared spectroscopy (FTIR). Release studies were performed on nanoparticles, physical mixtures, and raw MDF. The release data were fitted to different models to understand the mechanism of the drug release. Results: Electrospraying of MDF and ERS solution resulted in the preparation of nonobeads or nanofibers, and the particulate characteristics of the obtained products were largely controlled by the polymer amount in the solution. PXRD and thermal analyses showed that MDF was an amorphous phase in the structures of nanoparticles. Using FTIR, no interaction was observed between MDF and ERS in nanoparticles. Nanoparticles showed biphasic release profiles and the order of dissolution rates was: nanofibers>MDF>nanobeads. The well-fitted model was Weibull model, indicating a Fickian diffusion as the main mechanism of release. Conclusion: The results suggest that by optimization of variables such as solution concentration of MDF-ERS nanofibers and nanobeads with higher dissolution rates can be made by electrospray. Electrospray deposition as a simple, continuous, and surfactant free method is an excellent choice for preparation of drug loaded polymeric nanoparticles.
Effects of Surface Treatments on the Fatigue Response of High Strength Fasteners

The aim of the present work was the assessment of the impact of deep-rolling and shot-peening performed in the underhead and in the unthreaded shank of two high strength screws (36 NiCrMo and 42 CrMoV) for fatigue life enhancement. The experimental campaign consisted of six combinations, including the non-treated state. The aforementioned treatments were evaluated alone or with shot-peening performed after deep-rolling in the underhead fillet of the screws. Deep rolling was carried out at the optimal rolling force, whereas two shot diameters were considered for shot-peening (Z100 and UFS70). The results have been evaluated in terms of fatigue limits and factor effects have been assessed by marginal mean plots.
Age-Related Differences in the Cognitive, Visual, and Temporal Demands of In-Vehicle Information Systems

In-vehicle information systems (IVIS) refer to a collection of features in vehicles that allow motorists to complete tasks (often unrelated to driving) while operating the vehicle. These systems may interfere, to a greater extent, with older drivers’ ability to attend to the visual and cognitive demands of the driving environment. The current study sought to examine age-related differences in the visual, cognitive and temporal demands associated with IVIS interactions. Older and younger drivers completed a set of common tasks using the IVIS of a representative sample of six different vehicles while they drove along a low-density residential street. Evaluation measures included a Detection Response Task (DRT), to assess both cognitive and visual attention, and subjective measures following each condition using the NASA Task Load Index (TLX). Two age cohorts were evaluated: younger drivers between 21 and 36 years of age, and older drivers between 55 and 75 years of age. Participants completed experimental tasks involving interactions with the IVIS to achieve a specific goal (i.e., using the touch screen to tune the radio to a station; using voice commands to find a specified navigation destination, etc.). Performance of tasks varied according to different modes of interaction available in the vehicles. Older drivers took longer to complete tasks, were slower to react to stimuli, and reported higher task demand when interacting with IVIS. Older drivers stand to benefit the most from advancements in-vehicle technology, but ironically may struggle the most to use them. The results document significant age-related costs in the potential for distraction from IVIS interactions on the road.
Technology Gaps, Trade and Income

This paper quantifies the contribution of technology gaps to international income inequality. I develop an endogenous growth model where cross-country differences in R&D efficiency and cross-industry differences in innovation and adoption opportunities together determine equilibrium technology gaps, trade patterns, and income inequality. Countries with higher R&D efficiency are richer and have comparative advantage in more innovation-dependent industries. I calibrate R&D efficiency by country and innovation dependence by industry using R&D, patent, and bilateral trade data. Counterfactual analysis implies technology gaps account for one-quarter to one-third of nominal wage variation within the OECD. (JEL D21, D24, D31, F14, O31, O33, O47)