Miracle-dz/UniFAR-FTUs huggingface dataset:
Successful fabrication of a convex platform PMMA cell-counting slide using a high-precision perpendicular dual-spindle CNC machine tool

This study presents a novel approach to the fabrication of a biomedical-mold for producing convex platform PMMA (poly-methyl-meth-acrylate) slides for counting cells. These slides allow for the microscopic examination of urine sediment cells. Manufacturing of such slides incorporates three important procedures: (1) the development of a tabletop high-precision dual-spindle CNC (computerized numerical control) machine tool; (2) the formation of a boron-doped polycrystalline composite diamond (BD-PCD) wheel-tool on the machine tool developed in procedure (1); and (3) the cutting of a multi-groove-biomedical-mold array using the formed diamond wheel-tool in situ on the developed machine. The machine incorporates a hybrid working platform providing wheel-tool thinning using spark erosion to cut, polish, and deburr microgrooves on NAK80 steel directly. With consideration given for the electrical conductive properties of BD-PCD, the diamond wheel-tool is thinned to a thickness of 5 µm by rotary wire electrical discharge machining. The thinned wheel-tool can grind microgrooves 10 µm wide. An embedded design, which inserts a close fitting precision core into the biomedical-mold to create step-difference (concave inward) of 50 µm in height between the core and the mold, is also proposed and realized. The perpendicular dual-spindles and precision rotary stage are features that allow for biomedical-mold machining without the necessity of uploading and repositioning materials until all tasks are completed. A PMMA biomedical-slide with a plurality of juxtaposed counting chambers is formed and its usefulness verified.
Tabla Gharana Recognition from Audio music recordings of Tabla Solo performances

Tabla is a percussion instrument in Hindustani music tradition. Tabla learning and performance in the Indian subcontinent is based on stylistic schools called ghar¯an¯a-s . Each ghar¯an¯a is characterized by its unique style of playing technique, dynamics of tabla strokes, repertoire, compositions, and improvisations. Identifying the ghar¯an¯a from a tabla performance is hence helpful to characterize the performance. This paper addresses the task of automatic ghar¯an¯a recognition from solo tabla recordings. We motivate the problem and present different facets and challenges in the task. We present a comprehensive and diverse collection of over 16 hours of tabla solo recordings for the task. We propose an approach using deep learning models that use a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The CNNs are used to extract ghar¯an¯a discriminative features from the raw audio data. The LSTM networks are trained to classify the ghar¯an¯a-s by processing the sequence of extracted features from CNNs. Our experiments on ghar¯an¯a recognition include different lengths of audio data and comparison between various aspects of the task. An evaluation demonstrates promising results with the highest recognition accuracy of 93%.
Synthesis and Characterization of CoFe2O4 and CuFe2O4 Composited with Hematite by Impregnation Method for Removing Organic Pollutants

In this research, using impregnation method, spinel cobalt and copper ferrites nanoparticles are synthesized on the surface of hematite. Synthesized powders were characterized and examined by FTIR, XRF, XRD, FESEM, BET and EDS analysis and the dye degradation was investigated by UV-vis and AAS methods. Specific surface area increased especially in the sample containing cobalt, which indicates the precise of synthesis and the creation of high surface nanoparticles at hematite surface. The size of particles was in the nano scale and a good uniformity observed in the structure. The results indicated a significant increase in the catalytic ability of hematite nanocomposite. Their catalytic capability investigated by the Fenton reaction with complete removal of methylene blue from the solution via UV-vis irradiation. The samples stability discovered to be excellent by the AAS method.
TOURISM O(cid:9)TOLOGY A(cid:9)D SEMA(cid:9)TIC MA(cid:9)AGEME(cid:9)T SYSTEM: STATE-OF-THE-ARTS A(cid:9)ALYSIS

The global importance of tourism is steadily rising,creating new job opportunities in many countries. Today’s information management solutions for the complex tasks of tourism intermediaries are still at an early stage from a semantic point of view. This paper presents some preliminary results of OnTourism Project. OnTourism is aimed at (1) applying, concretizing and evaluating Semantic Web technologies such as ontologies, semantic annotation of content, and semantic search to the information-rich and economically important tourism domain, (2) identifying, developing and integrating reference ontologies for the tourism industry, and (3) showing the proof-of-concept in a real-world scenario of the Austrian tourism industry. First results presented in this paper identify publicly available tourism ontologies and existing freely available ontology management tools for the tourism domain. We identify seven tourism ontologies which are suitable as a basis for creating problem-specific ontologies. Furthermore we review and evaluate five freely available ontology management tools that are suited for application in the tourism domain.
Why Do People Migrate from Egypt? Determinants of International Migration- A Review and call for Research

Examining the migration decision is a complex task given that migration is changing over time and multicausal complicated processes. This paper provides an overview of international migration globally and focusing mainly on Egypt as the most populated country in the Arab world. Moreover, it explores the determinants of international migration in Egypt based on a systematic review of empirical studies. To identify potential studies, we conducted searches of JSTOR, Scopus, PubMed, Google Scholar and Web of Science databases published between January 1, 1961 and January 31, 2020 using combinations of “determinants of migration” and “international migration in Egypt” search terms. Furthermore, an investigation of estimated migrant stock flows, and its determinants can provide fertile ground for potential migration forecasts through furnish some required indications for the demographic and economic analyses and assist policymakers in preparing legislative frameworks and adequate policy and for their own country‟s development.
ADTOF: A large dataset of non-synthetic music for automatic drum transcription

The state-of-the-art methods for drum transcription in the presence of melodic instruments (DTM) are machine learning models trained in a supervised manner, which means that they rely on labeled datasets. The problem is that the available public datasets are limited either in size or in realism, and are thus suboptimal for training purposes. Indeed, the best results are currently obtained via a rather convoluted multi-step training process that involves both real and synthetic datasets. To address this issue, starting from the observation that the communities of rhythm games players provide a large amount of annotated data, we curated a new dataset of crowdsourced drum transcriptions. This dataset contains real-world music, is manually annotated, and is about two orders of magnitude larger than any other non-synthetic dataset, making it a prime candidate for training purposes. However, due to crowdsourcing, the initial annotations contain mistakes. We discuss how the quality of the dataset can be improved by automatically correcting different types of mistakes. When used to train a popular DTM model, the dataset yields a performance that matches that of the state-of-the-art for DTM, thus demonstrating the quality of the annotations.
Detecting Phishing Attacks Using Natural Language Processing and Deep Learning models

: Phishing attacks are one of the most prevalent and least defended security threats today. An approach is proposed which uses Natural Language Processing (NLP) techniques to analyze text and detect inappropriate statements that are indicative of phishing attacks. NLP offers a wholesome solution for this problem as it is capable of analyzing the textual content to perform intelligent recognition and performing semantic analysis of text to detect malicious intent. The approach also makes use of Deep Learning frameworks with Neural Network. The Phishector (Phishing Detector) takes input as an email and gives the output as the accuracy with which it is predicting whether an email is phished or legitimate. We have worked upon 2 datasets: SpamAssassin and Ham-Spam datasets. Also build our model using various Machine Learning techniques such as Bagged decision tree, Random Forest, Extra Trees, Adaboost, Stochastic Gradient Descent, Naive Bayes, SVM, and Voting Ensemble. As per our observance, Random Forest and Extra Trees outperform for SpamAssassin and HSD dataset respectively. Neural Network also outperforms when tested with a variety of layers, epoch, and batch size using Adam optimizer.
A Deep Learning Based Approach for Banana Plant Leaf Diseases Classification and Analysis

India is a country where agriculture is very important sector. The production and profit gained from agriculture mainly depends on the quality and quantity of the crop being grown. For this it is very important that the plants should be protected from any of disease that may adversely affect the quality and quantity of the crop. Thus, it is very important to detect the disease affected crop if any and diagnosis of the disease at the early stage itself. This can reduce the loss of the crop. In our project, a deep-learning approach is being proposed to detect and classify banana leaf diseases. In particular, we make use of the LeNet architecture as a convolutional neural network to classify image data sets. Our system can efficiently predict the banana leaf disease and will suggest remedies for the disease being predicted.
COVID-19, Home Advantage in Professional Soccer, and Betting Market Efficiency

Fan support has often been considered to be a main driver of the home advantage in sports. Using the natural experiment of ghost games during the COVID-19 pandemic in German professional soccer, we test this claim. Indeed, we identify a reduction in the home performance though with much heterogeneity across leagues. We moreover observe the home advantage to recover over time. In analysing whether betting markets anticipated this drop in the home advantage and its recovery, we test the efficient market hypothesis. We find that betting odds do not properly reflect the effect of ghost games regarding changes in the home advantage. Furthermore, we present evidence for a slow to non-existent adaptation process with respect to new match results, indicating a lack of semi-strong efficiency. Based on these findings, we show how simple betting strategies could have exploited the discussed phenomenon.
Driver En Route Guidance Compliance and Driver Learning with Advanced Traveler Information Systems: Analysis with Travel Simulation Experiment

If real-time driver en route guidance advice does not meet driver preferences (e.g., preference for taking the freeway) or the advice is not correct, drivers are very likely to ignore the information, and the guidance system becomes ineffective in their route choice no matter how advanced the system. There is a need to investigate the factors affecting driver compliance with en route guidance advice. A travel simulation experiment was used to investigate significant factors affecting driver route choice behavior. A linear mixed model was developed for describing the factors affecting driver compliance with guidance advice using the compliance rate over several simulated trips as a dependent variable. The issue of repeated observations is addressed. The system accuracy and subjects’ learning experience in their spatial experience at the same intersection and temporal experience in the same day are also taken into account. The model results show that significant factors are involved: freeway advice, turning advice, congestion occurrence, incident occurrence, subjects’ spatial experience, subjects’ temporal experience, and subjects’ education level; there are several important interactions as well.
Some novel antileishmanial compounds inhibit normal cell cycle progression of Leishmania donovani promastigotes and exhibits pro-oxidative potential

In the midst of numerous setbacks that beclouds the fight against leishmaniasis; a neglected tropical disease, the search for new chemotherapeutics against this disease is of utmost importance. Leishmaniasis is a disease closely associated with poverty and endemic in Africa, Asia, southern Europe and the Americas. It is caused by parasites of the genus Leishmania and transmitted by a sandfly vector. In this study, we evaluated the antileishmanial potency of eighteen pathogen box compounds and elucidated their biosafety and possible mechanisms of action against Leishmania donovani promastigotes and amastigotes in vitro. IC50s range of 0.12±0.15 to >6.25 μg/ml and 0.13±0.004 to >6.25μg/ml were observed for the promastigotes and amastigotes, respectively. We demonstrated the ability of some of the compounds to cause cytocidal effect on the parasites, induce increased production of reactive oxygen species (ROS), disrupt the normal parasite morphology and cause the accumulation of parasites at the DNA synthesis phase of the cell cycle. We recommend a further in vivo study on these compounds to validate the findings.
Fabrication Scheduling of Electrical House of FPSO using Dependency Structure Matrix

Electric House is a complete power plant system that supplies the electrical distribution network to site. Installation this power plant system have used in the world wide especially in industrial, utilities, renewable energy, outdoor switchgear and moveable substation. As we can see, in offshore industries application, Electrical House can be installed in Floating Production and Storage Offloading (FPSO). Since, engineering work is procedural and repeatable, complexity process will repeat again and again. Thus, problem happens stemming when complex processes which involves interdependent (feedback and iteration) in designing and fabricating process that affect the schedule of the project. Project management tools such as CPM, PERT and Gantt widely used to scheduling application do not address complexity of the interdependency and feedback iteration. In this research discusses scheduling of FPSO E-House module Fabrication Model using Dependency Structure Matrix (DSM) method in order to address the complexity. Partitioning DSM process which minimize the amount of iteration (iteration) within the process. As a result, reducing probability of iteration will affect to the time reduction to complete the project
SPEECH-INTELLIGIBILITY AND TALKER-RECOGNITION TESTS OF AIR FORCE VOICE COMMUNICATION SYSTEMS

Abstract : Word-intelligibility (psycho-acoustic), Articulation Index (electrical) and voice recognition tests were made on the Thule, Greenland, to Cape Dyer, Baffin Island, link of the DEWDROP tropos pheric-scatter communication system, and on the Gander, Newfoundland, to Harmon Air Force Base, Newfoundland, link of the POLEVAULT tropospheric scatter communication system, and compared against similar tests taken on a laboratory reference system. Harvard phonetically-balanced (PB) word-lists were used in the psycho-acoustic tests; an abbreviated octave-band form of the Articulation Index procedure (after Kryter) was the basis of the electrical tests. These two measures agreed in ranking the three systems in order of intelligibility from highest to lowest: laboratory, POLEVAULT and DEWDROP. It is concluded that the Articulation Index technique suitably modified, is feasibile and useful for voice-system performancuation and quality control testing. It is also concluded, tentatively, that system characteristics affecting intelligibility do not necessarily affect listeners' ability to recognize individual talkers.
Changes in physical activity levels and mental health during COVID-19: Prospective findings among adult twin pairs

Background Physical distancing and other COVID-19 pandemic mitigation strategies have negatively impacted physical activity (PA) levels and mental health in cross-sectional studies. The purpose of this study was to investigate associations between changes in PA and mental health outcomes during the COVID-19 pandemic, following implementation of mitigation strategies, in a sample of adult twins. Methods This was a prospective study of 3,057 adult twins from the Washington State Twin Registry. Study participants completed online surveys in 2020, at baseline (March 26 –April 5), and three follow-up waves (W1: April 20 –May 3; W2: Jul 16 –Aug 2; W3: Sept 16 –Oct 1). Physical activity was operationalized as self-reported moderate-to-vigorous PA (MVPA) and neighborhood walking (minutes/week), and mental health outcomes, operationalized as self-reported anxiety and perceived stress were assessed in the three waves of follow-up. Latent growth curve models (LGCMs) were used to assess changes in PA and mental health outcomes over time. Parallel LGCMs were used to estimate the cross-sectional, parallel, and prospective associations between PA and mental health over time. All models took into within-pair correlations and adjusted for age, sex, and race. Results Individuals’ amount of MVPA and walking decreased over time, whereas levels of anxiety remained stable, and stress increased slightly. Cross-sectional associations observed between both PA predictors and mental health outcomes were weak. After taking into account cross-sectional associations between PA and mental health outcomes, changes in PA over time were not associated with changes in mental health outcomes over time. Conclusions Over a time period aligned with COVID-19 mitigation strategies and social restrictions, changes in physical activity was not associated with changes in anxiety or stress levels in the current sample. Nonetheless, the average decline in PA over time is worrisome. Public health resources should continue to promote PA as a means to improve physical health during the pandemic.
A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries

We consider the problem of generating adversarial malware by a cyber-attacker where the attacker's task is to strategically modify certain bytes within existing binary malware files, so that the modified files are able to evade a malware detector such as machine learning-based malware classifier. We have evaluated three recent adversarial malware generation techniques using binary malware samples drawn from a single, publicly available malware data set and compared their performances for evading a machine-learning based malware classifier called MalConv. Our results show that among the compared techniques, the most effective technique is the one that strategically modifies bytes in a binary's header. We conclude by discussing the lessons learned and future research directions on the topic of adversarial malware generation.
Neural network reconstruction of late-time cosmology and null tests

The prospect of nonparametric reconstructions of cosmological parameters from observational data sets has been a popular topic in the literature for a number of years. This has mainly taken the form of a technique based on Gaussian processes but this approach is exposed to several foundational issues ranging from overfitting to kernel consistency problems. In this work, we explore the possibility of using artificial neural networks (ANN) to reconstruct late-time expansion and large scale structure cosmological parameters. We first show how mock data can be used to design an optimal ANN for both parameters, which we then use with real data to infer their respective redshift profiles. We further consider cosmological null tests with the reconstructed data in order to confirm the validity of the concordance model of cosmology, in which we observe a mild deviation with cosmic growth data.
Effect of Noise Suppression Losses on Speech Distortion and ASR Performance

Deep learning based speech enhancement has made rapid development towards improving quality, while models are becoming more compact and usable for real-time on-the-edge inference. However, the speech quality scales directly with the model size, and small models are often still unable to achieve sufficient quality. Furthermore, the introduced speech distortion and artifacts greatly harm speech quality and intelligibility, and often significantly degrade automatic speech recognition (ASR) rates. In this work, we shed light on the success of the spectral complex compressed mean squared error (MSE) loss, and how its magnitude and phase-aware terms are related to the speech distortion vs. noise reduction trade off. We further investigate integrating pre-trained reference-less predictors for mean opinion score (MOS) and word error rate (WER), and pre-trained embeddings on ASR and sound event detection. Our analyses reveal that none of the pre-trained networks added significant performance over the strong spectral loss.
Answering Fuzzy Queries over Fuzzy DL-Lite Ontologies

A prominent problem in knowledge representation is how to answer queries taking into account also the implicit consequences of an ontology representing domain knowledge. While this problem has been widely studied within the realm of description logic ontologies, it has been surprisingly neglected within the context of vague or imprecise knowledge, particularly from the point of view of mathematical fuzzy logic. In this paper, we study the problem of answering conjunctive queries and threshold queries w.r. t. ontologies in fuzzy DL-Lite. Specifically, we show through a rewriting approach that threshold query answering w.r. t. consistent ontologies remains in ${AC}^{0}$ in data complexity, but that conjunctive query answering is highly dependent on the selected triangular norm, which has an impact on the underlying semantics. For the idempotent Gödel t-norm, we provide an effective method based on a reduction to the classical case.
Overview of Green and Sustainable Remediation for Soil and Groundwater Remediation - 12545

Making remediation efforts more “sustainable” or “green” is a topic of great interest in the remediation community. It has been spurred on by Executive Orders from the White House, as well as Department of Energy (DOE) sustainability plans. In private industry, it is motivated by corporate sustainability goals and corporate social responsibility. It has spawned new organizations, areas of discussion, tools and practices, and guidance documents around sustainable remediation or green remediation. Green remediation can be thought of as a subset of sustainable remediation and is mostly focused on reducing the environmental footprint of cleanup efforts. Sustainable remediation includes both social and economic considerations, in addition to environmental. Application of both green and sustainable remediation (GSR) may involve two primary activities. The first is to develop technologies and alternatives that are greener or more sustainable. This can also include making existing remediation approaches greener or more sustainable. The second is to include GSR criteria in the evaluation of remediation alternatives and strategies. In other words, to include these GSR criteria in the evaluation of alternatives in a feasibility study. In some cases, regulatory frameworks allow the flexibility to include GSR criteria into the evaluation process (e.g., state cleanup programs). In other cases, regulations allow less flexibility to include the evaluation of GSR criteria (e.g., Comprehensive Environmental Response Compensation, and Liability Act (CERCLA)). New regulatory guidance and tools will be required to include these criteria in typical feasibility studies.