Miracle-dz/UniFAR-FTUs huggingface dataset:
Bonseyes AI Pipeline—Bringing AI to You

Next generation of embedded Information and Communication Technology (ICT) systems are interconnected and collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the keys for the next technological revolution: the seamless integration of AI in our daily life. However, training and deployment of custom AI solutions on embedded devices require a fine-grained integration of data, algorithms, and tools to achieve high accuracy and overcome functional and non-functional requirements. Such integration requires a high level of expertise that becomes a real bottleneck for small and medium enterprises wanting to deploy AI solutions on the Edge, which, ultimately, slows down the adoption of AI on applications in our daily life. In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together. By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of particular tools and provide a modular end-to-end development of AI products for embedded devices. Our AI pipeline consists of four modular main steps: (i) data ingestion, (ii) model training, (iii) deployment optimization, and (iv) the IoT hub integration. To show the effectiveness of our pipeline, we provide examples of different AI applications during each of the steps. Besides, we integrate our deployment framework, Low-Power Deep Neural Network (LPDNN), into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices. Finally, we demonstrate the results of the AI pipeline by showing the deployment of several AI applications such as keyword spotting, image classification, and object detection on a set of well-known embedded platforms, where LPDNN consistently outperforms all other popular deployment frameworks.
On the breeding ecology of House Martins Delichon urbica (Linnaeus 1758) in Northeast Algeria

AbstractThe study of the reproductive biology of House Martins Delichon urbica was carried out in the cities of Guelma and Hammam Debagh, Northeast Algeria during two years 2013 and 2014. Birds of this species produce two broods per year and per breeding pair. At both study sites, the egg-laying period lasted for almost four months. Egg weight and volume differed between broods and years. The mean clutch size was 3.87 ± 1.29, 4.19 ± 1.02 in Guelma and 3.93 ± 1.13, 3.64 ± 1.30 in Hammam in 2013 and 2014, respectively, with a seasonal decline. Hatching success reached 69 and 74.25% in Guelma, and 74.20 and 63.39% in Hammam in 2013 and 2014, respectively. Fledging success was 64.57 and 72.62% in Guelma, whereas, in Hammam it was 72.22 and 58.16% in 2013 and 2014, respectively. Hatching failure was the main cause of mortality. The breeding parameters and morphometrics of the House Martin’s eggs in Algeria determined during our study differed from those reported in previous studies carried out in different par.
Secrets of the South: A Location-based Game for the Development of 21st Century Social Skills and Promotion of Social Interaction

Location-based games (LBGs) successfully promote playful experiences engaging millions of players throughout the world. The potential of embedding such location-based experiences in educational practice has been recognised but not yet fully embraced. LBGs and educational location-based applications have been used to enhance critical thinking, but not for the acquisition and development of 21 st century skills: key competences required to understand, live and thrive in the local communities of today. This paper introduces the LBG ‘Se-crets of the South’, designed to orchestrate social interaction in public space, and foster communication, collaboration, IT literacy, and social/cultural skills through 1) interaction-based social encounters with both friends and unknown members of the community, and 2) in-situ learning about the history and social context of the neighbourhood. A 4-step general procedure is proposed for the creation of LBGs designed to foster 21 st century skills.
Soil Salinity and Food Security in India

India would require around 311 million tons of food grains (cereals and pulses) during 2030 to feed around 1.43 billion people, and the requirement expectedly would further increase to 350 million tons by 2050 when India's population would be around 1.8 billion. To achieve food security in the country, the attempts need to focus on both area expansion under agriculture as well as rise in crop productivity. Massive urbanization is putting pressure on agricultural lands, resulting in shrinking of land holdings. The possibility of area expansion under agriculture, therefore, exists in restoring the degraded lands. Nearly 147 million ha of land is subjected to soil degradation, including 94 million ha from water erosion, 23 million ha from salinity/alkalinity/acidification, 14 million ha from water-logging/flooding, 9 million ha from wind erosion and 7 million ha from a combination of factors due to different forces. Government of India has fixed a target of restoring 26 million ha of degraded lands, including salt-affected soils, by the year 2030 to ensure food security for the people. Around 6.74 million ha area in the country is salt-affected. Estimates suggest that every year nearly 10% additional area is getting salinized, and by 2050, around 50% of the arable land would be salt-affected. Saline soils occupy 44% area covering 12 states and one Union Territory, while sodic soils occupy 47% area in 11 states. The ICAR-Central Soil Salinity Research Institute and many State Agricultural Universities are engaged in studying salt-affected soils and developing reclamation technologies and strategies. Several innovative technologies have been developed and on-farm tested. Gypsum-based sodic soil reclamation, sub-surface drainage of water-logged saline lands, salt tolerant crop varieties and improved agroforestry techniques are some of the well-adapted technologies in the country. Reclamation of 2.18 million ha of salt-affected soils (2.07 million ha barren sodic soils and 0.11 million ha saline soils) has contributed more than 17 million tons of food grains per annum to the country's food basket, with additional annual income of Rs. 15.5 billion, and employment generation of 2.8 million man-days. Other technologies of management of salt-affected soils, viz. alternate land-use systems, saline aquaculture, cultivation of salt tolerant crop varieties, agro-forestry, phytoremediation, bioremediation etc. have positively impacted food and nutritional security, women empowerment, involvement of landless laborers and minimizing rural migration etc. The ongoing consistent research efforts for the management and reclamation of such soils would hopefully continue ensuring food security in the country. The Government needs to make policies favorable for implementation of reclamation technologies in the country.
Editorial: Evolution and Functional Mechanisms of Plant Disease Resistance

1 Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, China, College of Horticulture, Nanjing Agricultural University, Nanjing, China, Molecular Plant Pathology, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands, Department of Biology and Microbiology, South Dakota State University, Brookings, SD, United States, Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany, 6 School of Life Sciences, Nanjing University, Nanjing, China
Decreased Risk in the Pancreatic Cancer With History of Hay Fever: A Meta-Analysis

Background: An increasing incidence of pancreatic cancer has been observed worldwide over the last few decades. Previous reports suggested that hay fever, a common allergic disease, may function in pancreatic cancer. Data on hay fever as a risk or protective factor for pancreatic cancer was controversial in several case–control reports. So, we here did a meta-analysis on published studies to evaluate the association of hay fever and the risk of pancreatic cancer. Methods: A comprehensive literature search was performed through public databases. The association between hay fever and pancreatic cancer was evaluated by odds ratios (ORs) and 95% confidence intervals (CIs). The Cochran's Q test and I2 index were used to evaluate heterogeneity. Results: We included 8 population-based case–control studies involving 10,454 participants from 1986 to 2014. A history of hay fever was associated with a decreased risk of pancreatic cancer (OR, 0.57; 95% CI, 0.50–0.64, P < 0.00001) through fixed effect model. Conclusion: The result of our study suggested that hay fever may significantly decrease the risk of pancreatic cancer.
Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers' ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. “Full models” were built using all four features types, while “nested models” were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and “black-box” (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree- and rule-based learners) for both outcomes. However, nested eaeA-stx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently out-performed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).
An Efficient Retrieval System for Biomedical Images Based on Radial Associated Laguerre Moments

The ability of any retrieval system to extract features by using its feature descriptor is the primary criterion to measure its efficiency. In this paper a novel technique for feature extraction of biomedical images is presented. The mooted system uses the Radial Associated Laguerre Moments (RALMs) as a feature descriptor to obtain features from two types of medical images: computer tomography (CT) and magnetic resonance images (MRI). RALMs represent one sort of discrete orthogonal moments. RALMs extract the features from images using orthogonal moments to retrieve images from a database. Our approach is extensively assessed with noise-free and noisy images from three different benchmark databases: Emphysema-CT, NEMA CT, and NEMA MRI. The first two databases are used for CT image retrieval, while the third is for MR image retrieval. The proposed approach was tested against the state-of-the art local feature descriptors: Local Binary Pattern (LBP), and local diagonal extrema pattern (LDEP). It was also evaluated against orthogonal Fourier-Mellin moments (OFMMs) as a global descriptor. The comparison shows a significant improvement in favor of the proposed approach in terms of three different performance metrics: ARP, ARR, and F_score. The proposed approach was also compared against the convolutional neural network (CNN) as a deep learning based method over the NEMA-MRI dataset. The RALMs based approach showed a significant improvement when compared against two state-of-the-art medical image retrieval approaches: Histogram of Compressed Scattering Coefficients (HCSCs) and a local bit-plane decoding-based AlexNet descriptor (LBpDAD), the study has done over the TCIA-CT dataset. The proposed approach was also tested with big well-known dataset from the international skin imaging collaboration (ISIC) 2018.
List of Most Usability Evaluation in Mobile Application: A Systematic Literature Review

Mobile application is one of the most growth technology that can help people do better. Mobile applications nowadays have created tremendously compared with the website. Usability issues will be questioning as a measurement of mobile application accepted in the market. This paper reports on the systematic literature review (SLR) from 2010 up to 2020 to investigate how usability evaluation of mobile applications will change along the time through the evaluation of mobile devices. From 168 paper retrieved, 32 papers are selected to find the relevance of the research question. The result shows several famous indicators of usability evaluation, which are effectiveness, efficiency, and user satisfaction. There are new inclusions that come as new indicators such as error, learnability, memorability, simplicity, and cognitive load. This result indicates the need for future research especially for new rapid applications created
Approach to the Analysis of the Multidimensional Time Series Based on the UMAP Algorithm in the Problems of the Complex Systems Proactive Maintenance

The approach to the preliminary analysis of the multidimensional time series, applied to develop the intellectual classifiers in the problems of proactive maintenance of the complex systems, has been studied. It is proposed to execute the data analysis with application of the UMAP algorithm, which implements non-linear dimension reduction of data. It is made for the purpose of the data visualization and early identifying of the border states on the boundary of the classes, which separates normal and abnormal states of the complex systems, confirmed by the facts. The examples of the preliminary analysis of the theme dataset are given. This dataset is represented by the multidimensional time series, which characterize normal and abnormal operation of the aero engines in various time moments. These time series were obtained with application of the appropriate sensors. The possibility of the early prediction of the abnormal states (damages) of the aero engines, identified after their emergence, was confirmed.
Terrorism, Social Media and Text Mining Technique: Review of Six Years Past Studies

The aim of this paper is to examine the Islamic State of Iraq and Syria (ISIS) recruitment actual behavior through social media. The ISIS has targeted Indonesia as the main source of participants due to the large of Muslim community with active internet users. Despite many ways to connect with social media like Twitter, Facebook, Instagram, and YouTube using videos and images, this paper is selected text as the main of source of materials. This paper is deployed a qualitative technique to analyze related literature and then it was combined with media tracking systems from texts posted on Twitter, Facebook, Website, and Telegram. The text mining was captured words that frequently appears that mentioned their support to the ISIS accounts from 2011 to 2017. This paper found that Indonesian tends to pay more attention to the existence of ISIS in Syria. This paper suggests that the Indonesian related agency to closely monitor the accounts which positively associated with ISIS terrorism and propaganda and take necessary action to clean up the massive haywire issues.
Robust Airborne Ultrasonic Positioning of Moving Targets in Weak Signal Coverage Areas

Current ultrasonic Local Positioning Systems (LPS) based on an infrastructure of beacons can provide centimeter-level accuracy employing the spread spectrum technique, which also adds robustness against noise. However, the strong attenuation of the acoustic waves at high frequencies, the high directionality of ultrasound transducers, and the Doppler effect caused by moving targets still affect the correct performance of LPS. These phenomena reduce the availability of these systems in weak signal coverage areas, as they are no longer able to distinguish weak arrivals from spurious peaks, failing to calculate the position of the target. In this work, the aforementioned problems are dealt with by transmitting Doppler resilient waveforms together with a validation code based on Complementary Set of Sequences. This validation code is leveraged at the receiver after Doppler compensation to reduce the number of spurious arrival candidates and therefore increase the system availability. Compared to a system with no validation, experimental tests with a moving robot have shown that the proposed system increased the availability in weak coverage areas between 20 and 25%. The robot’s average 2D positioning error at rest and in motion was 4.6 cm and 6 cm, respectively.
Competitive Advantage Strategy Model in The Banking Industry through The Implementation of The National Payment Gateway

Bank Indonesia as the Central Bank of the Republic of Indonesia has the main tasks contained in the three main pillars, where the three task areas need to be integrated with the relevant parties so that the goal of maintaining the stability of the rupiah value can be achieved effectively and efficiently. In the corridor of carrying out these tasks and building a strong sovereignty of the Indonesian economic system in the future, Bank Indonesia in 2017 has established a National Payment Gateway policy through the Board of Governors' Member Regulation no. 19/10 / PADG / 2017 dated September 20, 2017 concerning the National Payment Gateway. The implementation of NPG is part of Indonesia's nationalist development strategy, therefore Bank BCA as one of the Anchor Banks in Indonesia has a very strategic role in implementing this policy. This research will analyze the challenges faced by BCA in implementing a competitive advantage strategy in order to implement the National Payment Gateway policy. The results of this study are as follows: (1) Identifying problems in NPG implementation (2) Building NPG models and conditions for companies using the theory of Resource Based View, Value Chain, Factor Analysis and VRIO (3) Planning the company's competitive advantage strategy in implementing NPG.
E-WOM And Social Commerce Purchase Intentions: Applying The Theory of Planned Behavior

The study aims to verify the effect of E-WOM on Instagram as social media on purchase intention among consumers in Jakarta to purchase coffee through online food delivery (OFD). From the total population of 5,307,555 residents with the age range of 20 to 49 years old in DKI Jakarta, this study targetted 398 respondents as samples. This study used convenience sampling to get the subjects followed by face-to-face data collection using questionnaires by the research workers. The study utilized Structural Equation Modeling (SEM) with WarpPLS 6.0 for data examination. The results indicated E-WOM has a notable on attitudes toward purchasing, subjective norms, perceived behavioral control, and intention to purchase coffee through OFD. Attitudes toward purchasing, perceived behavioral control, and subjective norms have a meaningful influence on intention to purchase coffee through OFD.
“Smart Healthcare” a Medical Record System for Effective Health Services

The purpose of this study was to determine the factors faced by developers in building a medical record system in hospitals. Through the application of medical records that have good quality will affect the level of service to patients. Utilization of a good medical record management system, the orderly administration of health services becomes effective and efficient. In this research, the proposed systems are web-based and mobile application. The scope of the design evaluated is architectural design, flow chart design, data flow diagram design, database design and system interface design. The beginning of this research using exploratory method to make confirmation involving 5 doctors and nurses working in hospitals in Jakarta, and 72 road patients who are being treated. This study also involved IT consultants at the Hospital Accreditation Commission as a resource person who understood the problem of integrating the medical record system at the hospital. Data was collected through the distribution of questionnaires during the period January - April 2020. The objective of this research is to propose medical record information system that can improve input and output problems in the patient registration process, speed of service, medical record data processing and ease of tracking medical record information.
Design and Characteristics Investigation of Novel Dual Stator Pseudo-Pole Five-Phase Permanent Magnet Synchronous Generator for Wind Power Application

The main focus of this paper is to design and assess the characteristics investigation of Novel Dual Stator Pseudo-Pole Five Phase Permanent Magnet Synchronous Generator (NDSPPFP-PMSG) for wind power application. The proposed generator has a dual stator and two sets of five phase windings which enhance its power density and fault tolerant capability. The novelty of this generator is based on the fact that, eight magnetic poles are formed using only four poles of actual magnets on both the surfaces of the rotor. For the designing and optimal electromagnetic performance of the proposed generator, a Dynamic Magnetic Circuit Model (DMCM) is reported. To validate the results obtained from DMCM, Finite Element Method (FEM) has been opted owing to its high accuracy. For showing the performance superiority, the proposed generator is compared with two conventional generators namely, Dual Stator Embedded-Pole Five Phase (DSEPFP) and Single Stator Single Rotor Five Phase (SSSRFP) PMSG. To compare their performances, FEM results are considered. The electromagnetic performance namely, generated Electromotive Force(EMF), percentage(%) Total Harmonic Distortion(THD) of generated EMF, generated EMF vs speed, terminal voltage vs load current, electromagnetic torque developed on rotor vs time, %ripple content in the torque, and %efficiency vs load current are investigated for all the three generators. From these investigations, it is found that the power density (power to weight ratio) of the proposed generator is maximum.