Miracle-dz/UniFAR-FTUs huggingface dataset:
Image captioning model for mobile app

The latest developments in Deep Learning based Machine Translation and Computer Vision based Object Detection have led to high accuracy Image Captioning models. Although these models are very accurate, they tend to rely on the use of expensive computational power making it difficult to use these models in real-time applications like processing the video streams in real time and extracting the information. In this paper, we carefully follow some of the heuristic strategies and core ideas of Image Captioning and its common methods and present our simple sequence to a sequence based implementation with a remarkable transformation and efficiency such as using beam search instead of greedy search that allows us to implement these on low-end hardware. The proposed system compares the results calculated using a variety of metrics with high-quality models and analyses the reasons behind the model trained on the MS-COCO dataset that are lacking due to trade-off between computation speed and quality. In this proposed system, Restful API endpoint will be created to be used on any device with an internet connection such as a mobile phone, IoT devices, clock, etc, this endpoint used to sent an image to the model running on remote server which in response will generate and sent an caption describing the objects and their relationship with each other in image in a natural language.
sgRNA design for DLT gene editing using CRISPR-Cas9 and in-silico mutation prediction in Rice cv. Hawara Bunar

The DWARF AND LOW TILLERRING (DLT) gene is a transcription factor for a gene involved in Brassinosteroid (BR) biosynthesis. Manipulating BR biosynthesis will affect the height and tiller number of rice. CRISPR-Cas9 is an accurate tool to edit a gene sequence. The accuracy of site editing of the CRISPR-Cas9-mediated target gene editing is determined by the 20 nucleotide sequences in the sgRNA and the binding site known as the Protospacer Adjacent Motif (PAM). The study aimed to design sgRNA and predict the DLT gene mutation sites in rice cv. Hawara Bunar. The exon 1 of the DLT gene was amplified using a primer pair designed from the reference gene. The PCR product was then sequenced, and the sequence was used to design sgRNA. The study has designed sgRNA located on the targeted sequence that corresponds to the Gras family protein domain of the exon1 DLT gene. The mutation sites were predicted to be at the domain site through the alignment of the nucleotide and amino acid sequences of the DLT gene and the reference gene. It is predicted that mutations in the target site that corresponds to the protein domain will change the protein structure and its function.
Search Engine Optimization Method of Online Course Management Platform

This study proposes a method of keyword selection in search engine optimization to improve the accuracy of search engine and website rankings. To promote the development of scientific and technological innovation, this paper selects the innovation and entrepreneurship curriculum platform as the experimental object. By comparing different search engine optimization strategies, the keyword search volume data is analyzed based on “comprehensive index evaluation method”, which analyzes and calculates the change of keyword search comparison and the number of related keywords. Therefore, this paper will use the comprehensive index evaluation method for keyword selection, and establish a set of practical keyword selection method combined with the actual situation of innovation and enterprise curriculum platform. The results show that this program can improve search accuracy and website ranking.
Metaphors and Website Design: A Cross-Cultural Case Study of the Tide.com Stain Detective

Summary: This study investigated the generalization of a home metaphor used in the Tide.com Stain Detective (Nelson & Hibner, 2003) to middleclass Indian females. The stain detective was developed with American women based on a card sorting activity. A similar card sorting activity was conducted with six Indian females. Results showed that the Indian participants grouped the stains by the amount of work that was required to remove it, rather than by the location where it occurred.
Prediction of the COVID-19 Spread in China Based on Long Short-Term Memory Network

The sudden outbreak of COVID-19 has caused great losses to the economy and the life of the masses. Long short-term memory (LSTM) network is a time recursive neural network, which is suitable for processing and predicting important events with relatively long interval and delay in time series. Using LSTM network to predict and analyze the development trend of epidemic situation, it is imperative to prevent epidemic situation from causing secondary harm to China’s development. In this paper, we first obtained the COVID-19 data published by China Health Net using crawler technology, which is the accurate value of infection trend after the outbreak of COVID-19 in China. Then, based on these data, the LSTM model is used to predict the development trend of the epidemic in one year, and the mean square error is used to calculate the error between the prediction and the real data. The experimental model is used to predict and analyze the development trend of COVID-19. The results show that the error between predicted data and real data is small and the effect is very good, which provides a reasonable basis and forecast for scientific prevention and control of epidemic situation.
Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

Kinetic energy (KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density.
LDA-CBOW-Based Mining Model for Risky Driving Behavior in Traffic Accidents

Traffic accident data of traffic management department is recorded in unstructured text form, which contains a large number of characteristic descriptions related to risky driving behavior. However, such data has short text length and abundant professional vocabulary. Many text mining techniques cannot effectively analyze such text data. This paper proposes an improved LDA algorithm based on CBOW—LDA-CBOW model for the study of traffic accident text data containing illegal behaviors. This model can better extract the topics of traffic accident data and filter the keywords under the corresponding topics, which provides a better way to study the dependence relationship between traffic data and illegal behaviors. Experiments show that compared to other models, this model can better extract related topics of traffic accident data with higher model efficiency and better robustness.
Different approaches to measuring specific deterrence: some examples from speeding offender management

Traffic law enforcement sanctions can impact on road user behaviour through both general and specific deterrence mechanisms. General deterrence is typically measured in terms of community-wide changes in offending behaviour, while specific deterrence is measured by re-offence or recidivism rates. However, recidivism can be conceptualised in a number of different ways. This paper compares and contrasts different measures of recidivism within the context of managing speeding offenders with regard to absolute and marginal specific deterrent effects. As part of a larger study evaluating the effects of penalty increases in 2003 on speeding offenders in Queensland, the research team considered four definitions of recidivism across two cohorts of drivers detected for speeding prior to (2001) and after (2003) the speeding penalty changes: 1) the proportion of offenders who re-offended in the follow up period; 2) the overall frequency of re-offending in the follow up period; 3) the length of delay to re-offence among those who re-offended; and 4) the average number of re-offences during the follow up period among those who re-offended. Any reduction in speeding will have road safety benefits, however, the ways in which a ‘reduction’ is determined deserves greater methodological attention and has implications for countermeasure evaluation more generally. Overall, results suggested evidence of an absolute deterrent effect of penalty changes in that there were significant reductions in the proportion of drivers who re-offended and the overall frequency of re-offending, consistent with prediction. However, there was no evidence of a marginal specific deterrent effect among those who re-offended. Contrary to prediction, there was a significant reduction in the length of time to re-offence and no significant change in average number of offences committed. Speed camera and radar data were analysed to explore whether the changes in re-offence patterns may have been influenced by changes in speed enforcement activity. This analysis indicated that the speed enforcement operational hours increased by 43% from the pre to post-penalty change period, suggesting that the reduction in offences observed in the follow-up period was not due to an enforcement effect. Limitations regarding the use of routinely-collected data, as well as issues of exposure, enforcement levels and public education accompanying penalty changes are discussed and areas for future research are outlined.
The disease knowledge and self-care abilities of elderly chronic obstructive pulmonary disease patients following a rehabilitation program

Objective: To investigate the impact of a rehabilitation program on the disease knowledge and self-care abilities of elderly patients with chronic obstructive pulmonary disease (COPD). Method: A total of 45 elderly patients with COPD, ranging in age from 70-80 years old and admitted to our hospital were enrolled as the study cohort and randomly divided into a control group (n=22) and an experimental group (n=23). The control group was treated with routine nursing care, and the experimental group was given additional rehabilitation education. The two groups of patients were compared in terms of their knowledge of the disease and self-care abilities before the intervention and at 1 and 3 months after the intervention. Results: After the intervention, the patients’ disease knowledge was assessed using questionnaires compiled by the hospital, and the scores in the experimental group were higher than the scores in the control group at the same time points (P < 0.05). The patients’ self-care abilities were evaluated using the ESCA scale. The experimental group scored higher than the control group in four items, namely self-responsibility, health knowledge, self-care awareness, and self-care skills, and at one month after the intervention, the difference between the two groups was not significant. At 3 months after the intervention, the two groups showed statistically significant differences in these scores (P < 0.05). Conclusion: A rehabilitation program can effectively improve the disease knowledge and self-care abilities of elderly patients with COPD.
Understanding the Dynamics of the Fiscal Deficit and Economic Performance in Zimbabwe

The Government of Zimbabwe's desire to meet the country's development expenditure needs, following the attainment of independence in 1980, resulted in high fiscal expenditures which were not supported by adequate fiscal revenue inflows, and this resulted in high and persistent fiscal deficits, with a negative impact on the growth of the economy. This paper uses a descriptive approach to analyse developments in the Zimbabwean economy over the period 1980‒2018, with emphasis on the relationship between fiscal deficits and economic growth. The paper also provides a descriptive analysis of the POLICY BRIEF Understanding the Dynamics of the Fiscal Deficit and Economic Performance in Zimbabwe
Shallow water sonar conditions and radar backscatter

Sonar and radar signals are scattered by small-scale roughness elements of the sea surface. The characteristics of the roughness depend on wind, sea state, and other parameters. The dynamic behavior of this roughness is related to radar backscatter. This provides a basis for oceanographic radar remote sensing and links radar backscatter to sea surface sonar conditions. Radar, sonar, and roughness experiments have been conducted at the research platform NORDSEE. The results are combined and provide a method to estimate shallow water sonar conditions from the radar backscatter cross section. >
On-Demand Routing Algorithm Base on Energy Balancing in MANET

A MANET is an autonomous infrastructureless network system of mobile hosts which are connected with each other via wireless links. The traditional routing protocol for wireless MANET is unable to achieve balanced energy consumption and could not adapt to the dynamic topology changes well. A novel on-demand algorithm is proposed based on energy balancing and LMST algorithm. The proposed algorithm excludes the unstable links in routing discovery, and allows the node with more energy forward the routing request packet preferentially. The simulation experiments demonstrate, comparing with the LMST algorithm, the algorithm increases the average life time, decreases the average transmission power, and achieves energy balancing in the network and prolong the life-span of the network, which shows the proposed algorithm is highly practical.
Synthesis of single-resistance-controlled oscillators using CFOAs: simple state-variable approach

A simple state-variable synthesis approach has been presented which makes it possible to evolve current feedback op-amp (CFOA)-based single-resistance-controlled oscillators (SRCO) systematically. Synthesised SRCOs possess several novel features and their workability has been confirmed experimentally as well as through SPICE simulations using commercially available AD844 CFOAs.
Mobbing unter Klinikärztinnen und -ärzten

Zusammenfassung Ziel der Studie Mobbing am Arbeitsplatz gilt als interpersoneller Stressfaktor. Beruflicher Stress und mentale Gesundheit bei Ärztinnen und Ärzten rücken zunehmend in den Fokus der Öffentlichkeit. Inwiefern Mobbing dabei eine Rolle spielt ist noch kaum untersucht. Die Studie soll daher Daten zu Prävalenz von Mobbing unter KlinikärztInnen in Deutschland und möglichen Zusammenhängen mit beruflichem Stress und mentaler Gesundheit liefern. Methoden Im Rahmen zweier Querschnittstudien wurden 692 KlinikärztInnen des Fachgebietes Psychiatrie/Psychotherapie (P/PT) und 667 KlinikärztInnen der Intensivmedizin (IM) auf Kongressen befragt. Zum Einsatz kamen standardisierte Fragebögen zu Mobbingerfahrung, Berufsstress und mentaler Gesundheit (Einzelitem aus dem COPSOQ, BDI-II, ERI, MBI). Ergebnisse Mobbing erlebten 4,6% (N=61) der Befragten. In der Tendenz sind IM und Frauen häufiger betroffen (nicht signifikant) und es zeigten sich Korrelationen mit Berufsstress (ERI), Overcommitment (OC), Emotionaler Erschöpfung (MBI) und Depressivität (BDI-II). Schlussfolgerung Unsere Daten an einer großen Kohorte von Ärztinnen und Ärzten in Fachgebieten mit unterschiedlichem Belastungsprofil zeigen, dass ein nicht unerheblicher Anteil von Mobbing betroffen ist und Mobbing in Zusammenhang mit dem Erleben von Berufsstress sowie Beeinträchtigungen der mentalen Gesundheit stehen. Daraus lassen sich Implikationen zu institutionellen und individuellen Präventions- und Unterstützungsangeboten ableiten.
Zero-voltage- and zero-current-switched PWM DC-DC converters using active snubber

A novel active snubber soft-switched method is proposed to achieve zero-voltage and zero-current switching (ZV-ZCS) for pulsewidth modulation (PWM) DC-DC power converters. The unique location of the snubber capacitor and inductor ensures low current stress and commutation losses in the auxiliary circuit. A boost converter adopting this technique is presented as an example, to illuminate its operation principles and derive the design procedures. Simulation and hardware implementation of a 350-W 150-kHz boost converter have been made to validate the operation and the efficiency improvement. Some other basic PWM DC-DC topologies with the proposed snubber are also been given.
HAND GESTURE RECOGNITION BASED ON EMG SIGNALS USING ANN

The electromyography is the measure of electrical activity produced by the muscles which is usually represented as a function of time. This electromyography can be used in various applications including identifying neuromuscular diseases ,control signal for prosthetic devices ,controlling machines, robots etc. EMG based hand gesture can help to develop good computer interface that increases the quality of life of the disabled or aged people. The main purpose of this work is to identify the hand gestures that is predefined using artificial neural network which is particularly useful for classification purpose. The EMG patterns are extracted from the signals for each movement and the features extracted from the signals are given to the neural network for training and classification since it is the good technique for classifying the bio signals. The features like mean absolute value, root mean square, variance, standard deviation, Mean frequency, zero crossing and slope sign change are chosen to train the neural network
Rancang Bangun Game Android I Siap Selem Bergenre Puzzle Games

Cerita rakyat merupakan cerita yang bersumber dari masyarakat serta tumbuh berkembang dalam masyarakat sejak masa lampau. Cerita rakyat di Bali merupakan cerita turun menurun yang memiliki nilai budaya dan pesan moral. Perkembangan teknologi yang cepat menyebabkan Cerita Rakyat Bali yang masih menggunakan media buku mulai ditinggalkan. Media baru dibuat pada penelitian ini untuk menceritakan kembali cerita rakyat dengan memanfaatkan teknologi game. Cerita Rakyat Bali yang diangkat yaitu I Siap Selem. Game I Siap Selem dikembangkan berbasis Android bergenre puzzle games. Hasil pengujian kepada 30 siswa yaitu 100% dari kelompok siswa mengetahui isi cerita dan pesan moral dari Cerita Rakyat Bali I Siap Selem setelah memainkan game, dimana sebelumnya 80% siswa tidak mengetahuinya. Game I Siap Selem dapat digunakan untuk melestarikan Cerita Rakyat Bali melalui game berdasarkan hasil pengujian.Kata Kunci: Android, Cerita Rakyat Bali, I Siap Selem, Puzzle Game
Reliable controller placement in software‐defined networks using reliability covering graph and irregular cellular learning automata

Software‐defined networking is a new paradigm in network management that has received much attention in recent years. One of the important challenges in designing software‐defined networks (SDNs) is the reliable controller placement problem (RCPP) that is the problem of finding the number and locations of controllers to satisfy the reliability requirements. The high complexity of exact reliability measurement led researchers to use estimation techniques that results in overestimating the number of controllers and incorrect placement. In this paper, we introduce a new concept named reliability covering graph (RCG) that enables us to use the exact reliability measurement (ERM) methods in solving the RCPP with desired level of complexity. To construct the RCG, for each candidate node, a subgraph with the adjustable radius r is extracted from the network graph. In the next step, the RCG is developed by calculating the exact reliability between the central node and any other nodes in the resulting subgraphs. In the last step, the RCG is converted to a machine learning tool named irregular cellular learning automata (ICLA) that determines the number and locations of controllers as well as the switches they cover. To study the efficiency of the proposed method, its performance is compared with the optimal results obtained from the ILP solver. The simulation results of 800 case studies on 100 standard topologies show that the average difference in results is 0.00125. Thanks to our innovative structure RCG, the number of controllers required is significantly reduced compared to previous methods.
Improving the Performance of Language Identification System Using Different GMM-Training Approaches

This paper proposed a feasible system for language identification (LID) and designed four different GMM-training approaches to improve the system performance by accuracy recognition rates. In our experiment, we used these model-training approaches to evaluation on the system performance, which utilizes Linear Prediction Cepstrum Coefficients (LPCC) and Gaussian Mixture Model (GMM), rely on a 10-language task. From all the results, we found an optimal approach for training GMM in LID system, which achieves high accuracy of 85.25%, and indicated that different GMM-training approaches have different performances for LID system, but an advisable training method that proposed in our paper can greatly improve the system performance.
A Bootstrap Approach to an Affordable Exploration Program

This paper examines the potential to build an affordable sustainable exploration program by adopting an approach that requires investing in technologies that can be used to build a space infrastructure from very modest initial capabilities. Human exploration has had a history of flight programs that have high development and operational costs. Since Apollo, human exploration has had very constrained budgets and they are expected be constrained in the future. Due to their high operations costs it becomes necessary to consider retiring established space facilities in order to move on to the next exploration challenge. This practice may save cost in the near term but it does so by sacrificing part of the program’s future architecture. Human exploration also has a history of sacrificing fully functional flight hardware to achieve mission objectives. It is difficult to build a convincing case that the exploration program is affordable when it involves billions of dollars of discarded space flight hardware. An affordable program must emphasize preserving its high value space assets and using them to build a suitable permanent infrastructure. Further this infrastructure must reduce operational and logistics cost. The paper examines the importance of achieving a high level of logistics independence by minimizing resource consumption, minimizing the dependency on external logistics, and maximizing the utility of resources available. The approach involves the development and deployment of a core suite of technologies that have minimum initial needs yet are able expand upon initial capability in an incremental “bootstrap” fashion. The “bootstrap approach” incrementally creates an infrastructure that grows and becomes self sustaining and eventually begins producing the energy, products and consumable propellants that support human exploration. The bootstrap technologies involve new methods of delivering and manipulating energy and materials. These technologies will exploit the space environment, minimize dependencies, and minimize the need for imported resources. They will provide the widest range of utility in a resource scarce environment and pave the way to an affordable exploration program.