Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Techniques for Eclipsing Binary Light Curves Classiﬁcation

In a binary system, two stars rotate together around a common center of mass. Long-term photometric observations have revealed to astronomers very rich and diverse types of periodic variability associated with different parameters of binary systems. The classiﬁcation of this zoo of variable stars across the sky is a challenging, useful, and fascinating task that may lead to new discoveries such as compact objects in binary systems. In this work, we use the photometric time series of binary systems as input to classify the systems into several typical classes without any pre-assumption of period. We show that the classiﬁer trained on the ASAS-SN survey data effectively characterizes and classiﬁes the stellar parameters.
Composable and Reusable Neural Surrogates to Predict System Response of Causal Model Components

Surrogate models, or machine learning based emulators of simulators, have been shown to be a powerful tool for accelerating simulations. However, capturing the system response of general nonlinear systems is still an open area of investigation. In this paper we propose a new surrogate architecture which is capable of capturing the input/output response of causal models to automatically replace large aspects of block model diagrams with neural-accelerated forms. We denote this technique the Nonlinear Response ContinuousTime Echo State Network (NR-CTESN) and describe a training mechanism for it to accurately predict the simulation response to exogenous inputs. We then describe a scienceguided or physics-informed surrogate architecture based on Cellular Neural Networks to enable the NR-CTESN to accurately reproduce discontinuous output signals. We demonstrate this architecture on an inverter circuit and a Sky130 Digital to Analog Converter (DAC), showcasing a 9x and 300x acceleration of the respective simulations. These results showcase that the NR-CTESN can learn emulate the behavior of components within composable modeling frameworks and thus be reused in new applications without requiring retraining. Together this showcases a machine learning technique that can be used to generate nonlinear model order reductions of model components in SPICE simulators, Functional Markup Interface (FMI) representations of causal model components, and beyond.
Policy Search for Automatic Polyculture Farming using Reinforcement Learning

Today monoculture farming is the standard for industrialized agriculture. It allows producing food on a large scale. Nevertheless, a significant drawback is the loss of diversity. Polyculture farming mitigates this issue while benefiting from reducing the demand for resources, such as water. However, polyculture farming is a complex task as it has to consider multiple growth characteristics, and the environment is often only partially observable. This work addresses the challenge of automating polyculture farming using a first-order simulator to identify efficient treatment policies. We utilize randomized environment dynamics combined with a reinforcement-learning-based approach to developing a policy that increases garden coverage and diversity in simulation. We add a network architecture with a memory layer to the utilized Proximal Policy Optimization algorithm to overcome garden configurations that are only partially observable due to occlusion. In simulation experiments, the policy outperforms the previous state-of-the-art heuristic policy. Our simulation experiments serve as a groundwork for real-world application.
Preparing for urban floods in Mozambique: A field experiment on risk communication

Accurate forecasts and risk information do not always bring about the desired preparedness actions from communities at risk of natural disasters. This paper provides evidence on the effectiveness of two video interventions aimed at increasing flood risk awareness and preparedness of households in a Mozambican coastal city. Awareness and the intention to prepare increase after watching the video in which public officials disseminate the information. The video featuring flood victims is less effective, but recent flooding experience matters for the results. Finally, I demonstrate that the information disseminates among neighbors. JEL Classification: C93, D83, D91, O12, Q54
Artificial artificial intelligence over the web: The Mechanical Turk API

This paper explores Mechanical Turk utility on the web, mainly focused around Amazon‟s Mechanical Turk API. It includes a short history, operating principles, discussion on strengths and weaknesses, the intended and unintended applications that came forth of this technique and an explanation on how to get started using this API. It draws the conclusion that Mechanical Turk API‟s will be used as long as there are computations in which humans outperform computers
Job Satisfaction Of Civil Servants In Local Governments In Indonesia Influenced By Perceived Organizational Support And Affective Commitment

The purpose of this study was to determine the impact of perceived organizational support and affective commitment on civil servants' job satisfaction at the Penukal Abab Lematang Ilir Regency Government South Sumatra Province Indonesia partially and simultaneously. The method used is a descriptive method with multiple regression analysis techniques. This study uses Proportional Random Sampling as a sampling technique. The population of this study was civil servants at the regional government offices and agencies in the Penukal Abab Lematang Ilir Regency Government, with a total of 331 people and a sample of 176 people.
Intelligent Rooms Based on IoT and Component Technology

: This paper provides the layout of an intelligent room primarily based on Internet of Things (IoT) and provider factor technology. The present day scenario of IoT has been analyzed in element. An approach primarily based on Service Oriented Architecture (SOA) and aspect technology has been proposed and carried out, which could assist to recognize every converting a dynamic semantic integration of the internet offerings. Furthermore, the software program structure and essential modules are explained as nicely. Lastly, the algorithms and methods use for implementation
The Predictive Power of Intraday-Data Volatility Forecasting Models: A Case Study of Taiwan Stock Indices

The purpose of this study was to compare the predictive power of various volatility forecasting models. Using intraday high-frequency data, this study investigated the influence of time frequency on the predictive power of a volatility forecasting model. The empirical results revealed that the realized volatility increased when the time frequency of forecasts reduced. The overall results showed that when the forecast range was 1 day, among various volatility forecasting models, the autoregressive moving average-generalized autoregressive conditional heteroskedasticity(1, 1) model presented the optimal forecasting performance and the implied volatility model presented the worst forecasting performance for all time frequencies. JEL classification:C14; C32; C53
Perlustrate of Detection Methodology against Clone Attacks in Wireless Sensor Networks

-Wireless Sensor Networks (WSNs) is a capable technology and have immense potential to be employed in decisive situation like battle field and commercial application such as construction, traffic observation, environment monitoring and numerous other scenarios. One of the major challenges WSNs faces today is security breaches. A network security is very much necessary to face these security breaches. The WSN deployed in aggressive environments are susceptible to clone attacks. Clone attack would be probably be the most vigorous adversary in WSN especially in battlefield. And is waking up, belatedly, to the threat of an clone in wsn. It should be better organized for the research community to develop new architectures, systems and applications, and to assess alternatives and tradeoffs in developing technologies for its successful deployment. This paper is well emerge naturally in response to survey on various detection methodology and its evaluation metrics based on security primitives against clone attack in WSN. This paper promises many benefits for the research field in advance.
License Plate Detection and Recognition using YOLO v4

: -Object identification, one of the most fundamental and difficult tasks in computer vision, aims to find object instances, particularly small objects, in natural images from a variety of specified categories. Detection frameworks, object feature representation, and object proposal are all aspects of generic object detection. The proposed work performs small object detection like locating and recognizing the number plate, color of the number plate and character on the number plate by using Yolov4 and feature fusion approach. Proposed method can overcome different challenges in object detection and shows competitive results for small object detection with 86% detection accuracy at 45fps.
Fused descriptor based framework for recognition of fruits and veggies using color, texture and shape feature

This paper present a fused descriptor based framework for the recognition of fruits and veggies. A different category of image has been captured under real field conditions suffering from uneven illumination and complicated background. The framework of proposed system is divided in three steps:1) Background Subtraction, 2) Feature extraction 3) Training and classification. Initially, K-means clustering techniques were implemented for background subtraction. In second step colour, texture and shape based feature extraction method is applied, finally A "merging" fusion feature is analysed with a C4.5, Support vector machine (SVM), and K-nearest neighbors (KNN). Overall, the recognition system produces an adequate performance accuracy with the value 97.89, 94.60, 90.25 percentage by utilizing C4.5, SVM, and KNN respectively. The experimentation points out that the proposed fusion scheme can significantly support accurate recognition of the variety of fruits and vegetables.
How Much Do Platform Workers Value Reviews? An Experimental Method

Previous qualitative work has documented that platform workers place an immense importance on their reputation due to the use of algorithmic management by online labor platforms. We provide a general experimental method, which can be used across platforms and time, for numerically quantifying the intensity with which platform workers experience reputation system-based algorithmic management. Our method works via an experiment where workers choose between a monetary bonus or a positive review. We demonstrate this method by measuring the value that freelancers assigned to positive feedback on Upwork in June 2020. The median freelancer in our sample valued a single positive review at ∼ $49 USD. We also find that less experienced freelancers valued a positive review more highly than those with more experience. Qualitative data collected during the experiment indicates that many freelancers considered issues related to reputation system-based algorithmic management while choosing between the monetary reward and the positive review.
Developing Mobile Augmented Reality Location-based Service in Academic Library (LibMAR-1)

Massive developments of computing application create an opportunity for library administrators to engage with users in an interactive manner. One of the emerging applications is the use of Mobile Augmented Reality (MAR). In essence, various studies reported cost and technical knowledge as a fundamental component in MAR success. However, free and low-cost MAR platforms were made available for navigational purposes but less demonstrated in the literature. Hence, the objective of this study was to demonstrate the use of Mobile Augmented Reality (MAR) and its processes involved in mapping of Tun Abdul Razak Libraries, Universiti Teknologi Mara (UiTM), Shah Alam, Malaysia. For this purpose, location-based MAR application was used and integrated with geographical data on Google Maps through Keyhole Markup Language (KML) file. Relevant data and locational information was also incorporated to aid visualisation of library locations through smartphone. As a result, a new AR world was created and named “PTAR Mobile Augmented Reality (Lib-MAR)”. Library users can use this AR tool with their smartphone as a guide to library locations on university surroundings. Technique shown in this study is applicable for library administrators to engage and aid users in library navigation, improving accessibility and resources discovery.
The Response of AB Mix Utilization on Growth and Yield of Several Melon Varieties (Cucumis melo L.) in Hydroponic Drip Irrigation System

Melon (Cucumis melo L.) is one of popular fruit commodities in Indonesia, because of its sweet and fresh taste, and very high of nutritional content. Appropriate dose of Ab Mix on growth and yield is essential in melon cultivation with a hydroponic system. The study was conducted to determine the best AB Mix dose levels for growth and yield of three melon varieties. A Split Plot Design was used which arranged using a randomized block design with 2 factors. The first factor as the main plot was 2 levels of AB Mix doses (N), and the second factor as the subplot was 3 varieties (V) of melon. The results showed that the effect of AB Mix doses on the growth and yield of three melon varieties were not significantly different between the two treatments (1600 ppm and 1800 ppm). The Golden Aroma variety gave different response at the age of two weeks after planting in term of plant height. Whereas the AB Mix dose level influenced the number of leaves only at the age of two weeks after planting. In conclusion, 1600 ppm of AB Mix dose was sufficiently meet the nutrition level required by the melon in hydroponic drip irrigation system.
Improved measurement placement and topology processing in power system state estimation

Improved Measurement Placement and Topology Processing in Power System State Estimation. (August 2007) Yang Wu, B.S., Xi’an Jiaotong University, China; M.S., Xi’an Jiaotong University, China Chair of Advisory Committee: Dr. Mladen Kezunovic State estimation plays an important role in modern power system energy management systems. The network observability is a pre-requisite for the state estimation solution. Topological error in the network may cause the state estimation results to be seriously biased. This dissertation studies new schemes to improve the conventional state estimation in the above aspects. A new algorithm for cost minimization in the measurement placement design is proposed in this dissertation. The new algorithm reduces the cost of measurement installation and retains the network observability. Two levels of measurement placement designs are obtained: the basic level design guarantees the whole network to be observable using only the voltage magnitude measurement and the branch power flow measurements. The advanced level design keeps the network observable under certain contingencies. To preserve as many substation measurements as possible and maintain the network observability, an advanced network topology processor is introduced. A new method the dynamic utilization of substation measurements (DUSM) is presented. Instead of seeking the installation of new measurements in the system, this method dynamically calculates state estimation measurement values by applying the current law that regulates different measurement values implicitly. Its processing is at the substation level and, therefore, can be implemented independently in substations.
EFISIENSI PENGGUNAAN RADIASI SURYA DAN SEBAGAI DASAR DALAM MODEL JARAK PAGAR (RADIATION USE EFFICIENCY AS BASIS THE CROPS MODELING OF JATROPHA)

Plant growth interpretation in term of accumulated intercepted solar radiation and the radiation use efficiency (RUE) was used to study the growth and analysis of Jatropha (Jatropha curcas L.). A number of crop growth simulation models have been developed using the RUE concept to predict crop growth and yield in various environments. These models generally calculate daily biomass production as the product of the quantity of radiation intercepted and RUE. This research was carried out to quantify the RUE, biomass and leaf area index on Jatropha under rainfall condition, four levels of nitrogen fertilizer (N) and three population densities (P) planted twice. The experiments used a systematic Nelder fan design with 9 spokes and 4 – 5 rings were conducted at SEAMEO-BIOTROP field experiment in 2007. Data from the first experiment were used for parameterization and calibration and the second experiment data for model validation. Values of RUE were determined by nitrogen fertilizer and plant density. Based on parameterization, we found that RUE for prediction above ground biomass accumulation of Jatropha were 0.94 (r=0.83) g MJ-1 to 1.3 (r=0.75) g MJ-1. Validation between model prediction and field experimental data showed that model can simulate crop growth and development of Jatropha.
Shear Performance of Steel Fibre-Reinforced Concrete

This paper presents results from an experimental study and an analytical assessment on the shear performance of steel fibre-reinforced concrete. Federation Internationale de la Precontrainte (FIP) standard shear tests were conducted on normal- and high-strength steel fibre-reinforced concrete (SFRC) specimens. Test parameters investigated include compressive strength of the concrete and fibre volume fraction. It was found that the steel fibres with a dosage of up to 80 kg/m3 enhance the ultimate shear capacity of the concrete up to 70 precent compared to plain concrete shear capacity. It was also observed that ultimate shear capacity of SFRC, with fibre dosage above 40 kg/m3, was higher than cracking shear stress. An equation, based on regression analysis, is proposed to predict the shear capacity of the tested SFRC.
Attention based Neural Machine Translation of Indian Languages

Machine translation is converting source text in one language to text in another language automatically. In recent years, Neural Machine Translation has emerged as a way of addressing this task and is proved to be more effective than rule-based and statistical techniques. In this paper, the researchers explored different configurations for setting up a Neural Machine Translation (NMT) System for languages Hindi and Telugu. Here, a sequence to sequence translator is built to convert English sentences to Hindi and Telugu sentences using various concepts like applying the encoder-decoder architecture and Bahdanau Attention mechanism. The performance of the developed NMT model is evaluated using the BLEU score metric.
Discordance between fine needle aspiration biopsy cytology and final histopathology of thyroid nodules according to the Bethesda system Bethesda raporlama sistemine göre tiroid nodüllerinde sitopatolojik ve histopatolojik uyumsuzluk

Department of General Surgery, Haydarpasa Numune Education and Research Hospital, Istanbul, Turkey Deparment of Otorhinolaryngology / Head and Neck Surgery, Haydarpasa Numune Education and Research Hospital, Istanbul, Turkey Department of Pathology, Haydarpasa Numune Education and Research Hospital, Istanbul, Turkey Department of Biochemistry, Taksim Education and Research Hospital, Istanbul, Turkey
Purely time-dependent optimal control of quasilinear parabolic PDEs with sparsity enforcing penalization

We prove first- and second-order optimality conditions for sparse, purely time-dependent optimal control problems governed by a quasilinear parabolic PDE. In particular, we analyze sparsity patterns of the optimal controls induced by different sparsity enforcing functionals in the purely timedependent control case and illustrate them by numerical examples. Our findings are based on results obtained by abstraction of well known techniques from the literature.
Pousseur’s Scambi (1957), and the new problematics of the open work

I begin with an irony. Pousseur’s Scambi (1957), cited as an exemplum in Umberto Eco’s famous essay on the ‘open’ work (Eco 1989) has, as a result, been reified as a historical mo(nu)ment. Yet the open work will always be a challenge to any history, since its intention is to live beyond any citation. No matter how many Ecos the open work may sustain, Scambi and works like it will be sound-sources and compendia of possibilities, whether realised or not.
The miR-142 Suppresses U-87 Glioblastoma Cell Growth by Targeting EGFR Oncogenic Signaling Pathway

Glioblastoma is the most lethal malignancy of the brain and is resistant to conventional cancer treatments. Gene-therapy approaches like using tumor suppressor miRNAs are promising in the treatment of glioblastoma. They control the expression of oncogenes and influence tumor features and behaviors. Therefore, in the present study, it was predicted that miR-142 regulates oncogenic epidermal growth factor receptor (EGFR) signaling pathway via TargetScan and miRWalk online tools. Its differential expression level was reduced in glioblastoma according to the previous microarray results, and its predicted target genes were upregulated, as shown by the Expression Atlas. The miR-142 was overexpressed in U-87 glioblastoma cells via lentiviral transduction, and the way it influences proliferation and migration of cells was investigated through MTT assay and wound healing assay. Apoptosis rate was also measured via the Annexin V assay, and cell-cycle analysis was done. Then, real-time polymerase chain reaction (real-time PCR) and western blotting were performed to assess fold changes in mRNA and protein levels of the miR-142 predicted targets. Direct target genes of miR-142 were confirmed through a dual-luciferase reporter assay. The miR-142 significantly suppressed cell proliferation and migration and induced apoptosis and cell-cycle arrest in U-87 glioblastoma cells. This was accompanied by a decrease in expression of SHC adaptor protein 4 (SHC4), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), v-akt murine thymoma viral oncogene homolog 1 (AKT1), Kirsten rat sarcoma viral oncogene homolog (KRAS), and mitogen-activated protein kinase 8 (MAPK8) oncogenes at mRNA and protein levels in glioblastoma cells. Also, AKT1 was demonstrated as a direct target of miR-142. Overall, miR-424 acts as tumor suppressor miRNA in glioblastoma cells.