Miracle-dz/UniFAR-FTUs huggingface dataset:
RELATIONS OF SECONDARY STUDENTS’ INTRINSIC MOTIVATION AND ASPIRATIONS WITH THEIR SELF-CONCEPT IN SCIENCE

Science is a critical area as it focuses on meeting basic human needs by laying the necessary foundation in everybody in diverse fields. Thus, science impacts everybody in the world and science education demonstrate utmost important. If students have intrinsic motivation, they are well engaged, enquiring, attentive and willing to participate in learning process and the intrinsically motivated behaviors help them acquire knowledge and experience regarding science. Educational aspirations, career aspirations and self-concept promote for better science education. Thus, the present study aimed to address the relations of secondary students’ intrinsic motivation and aspiration with their self-concept in science. This is a mixed methods study and sample comprised 2384 students in Grade 8 and Grade 9. In this study, an intervention was implemented to explore students’ intrinsic motivation, aspirations and self-concept using appropriate teaching methodologies and strategies; results reveal that, there were strong positive relationships of students’ intrinsic motivation and aspirations with their self-concept (r2= .934;.926 respectively) in the experimental group. However, there were very low positive relationships of students’ intrinsic motivation and aspirations with their self-concept (r2 = .093; .058 respectively) in the control group. Further, there were significant differences of students’ intrinsic motivation, aspirations and self-concept before and after the intervention in the experimental group; t=51.926(1232) p=. 000; t= 48.513(1232) p=. 000; t= 53.689(1232) p=. 000 respectively. There was no such significant difference of the above, before and after the intervention in the control group; t=-. 5(1150) p=. 323; t=-. 989(1150) p=. 116; t= 1.572 (1150), p=. 617 respectively. The findings of the study provided greater understanding of the above relations, develop suitable teaching and learning methodologies and inform the practice of science education.
Removal of Dyes Using Various Organic Peel-based Materials: A Systematic Review

This review recapitulates the recent adsorption study literature regarding the activated biochar and nanocomposites of agricultural peel-based bio-adsorbents for the removal of dyes from wastewater. The use of agricultural peel in decoloration technology is promising for efficiency, cost-effectiveness, and eco-friendliness. Various types of bio-adsorbents have been studied in the literature, such as orange peel, potato peel, banana peel, cucumber peel, Pomelo peel, and rambutan peel, respectively. The equilibrium data and kinetics were analyzed, and they fit known models are also summarized. This review article will help to know the applicability and potential of various peel-based bio-adsorbents in wastewater treatment. The conclusion has been drawn from the literature review, and few suggestions for further future studies are also proposed.
Sub-6 GHz Quad-Band Reconfigurable Antenna for 5G Cognitive Radio Applications

─ This article describes a quad-band frequency tunable antenna for 5G applications that operates in the sub-6 GHz frequency range. On the top side, a stub-loaded square patch is printed on an inexpensive glass epoxy substrate, and on the opposite side, a C-shaped slot embedded into the partial ground plane. To achieve frequency reconfigurability with a consistent radiation pattern, the C-shaped slot and matching stub are used. The antenna is electronically frequency tunable by placing two positive intrinsic negative diodes (PIN) in the C-shaped slot and one PIN diode between the stub and feed line. The frequency is tunable between one ultra-wideband (UWB) and three communication bands based on the switching conditions of the PIN diodes. The proposed configuration is small, with a substrate dimension of 25×25×1 mm 3 . The antenna was fabricated, tested and found measurements result back up the simulation; it can switch between UWB (3.31.0-6.03 GHz) and three communication modes (3.31-4.32, 3.78-4.98, 4.98-5.96 GHz). The antenna has peak gains of 1.91, 1.86, 2.0 and 2.0 dB, and radiation efficiencies of 80, 78, 83 and 86%, respectively, in the four frequency bands. The developed antenna is ideally suited for multi-functional wireless systems and cognitive radio applications since it covers the frequency bands below 6 GHz and is tunable between wide and narrow bands.
Centralized Information Technology Decision Making and Cybersecurity Breaches: Evidence from U.S. Higher Education Institutions

Despite the consensus that information security should become an important consideration in information technology (IT) governance rather than the sole responsibility of the IT department, important IT governance decisions are often made on the basis of fulfilling business needs with a minimal amount of attention paid to their implications for information security. We study how an important IT governance mechanism—the degree of centralized decision making—affects the likelihood of cyberse-curity breaches. Examining a sample of 504 U.S. higher-education institutions over a four-year period, we find that a university with centralized IT governance is associated with fewer breaches. Interestingly, the effect of centralized IT governance is contingent on the heterogeneity of a university’s computing environment: Universities with more heterogeneous IT infrastructure benefit more from centralized IT decision making. In addition, we find the relationship between centralized governance and cybersecurity breaches is most pronounced in public universities and those with more intensive research activities. Collectively, these findings highlight the tradeoff between granting autonomy and flexibility in the use of information systems and enforcing standardized, organization-wide security protocols.
FastTran: Improving Performance of Fine-tuning in Transformer Models

In recent years, Transformer models have been widely used for various Natural Language Processing tasks and have achieved state-of-the-art accuracy. However, these models are computationally expensive and require a lot of time even for ﬁne-tuning. In this work, we use various proﬁling techniques to understand the workload patterns of Transformer models. We introduce FastTran, a system that uses a caching mechanism to reduce the ﬁne-tuning time for Transformer models. We also propose some future research directions that could further increase performance and reduce times for this workload.
Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region

Regarding different aspects of management of drainage basins and droughts, prediction of evaporation is very important. Evaporation is an essential part of the water cycle and plays an important role in the evaluation of climatic characteristics of any region. The purpose of this research is to predict daily pan evaporation rate of Damghan city using an artificial neural network model. The data applied in this research are daily minimum and maximum temperatures (Tmin and Tmax), average relative humidity (RHmean), wind speed (WS), sunny hours (n), air pressure (PA) and evaporation during the statistical time period of 16 years (2002-2018). Also, the multilayer perceptron was used as a non-linear method to simulate evaporation. Since the units of the inputs and outputs of the prediction model were different, all the data were normalized. In the multilayer perceptron model, 7 different scenarios were considered. About 70 and 30 percentage of the data were used for training and testing, respectively. The model was analyzed using appropriate statistics such as mean square error (RMSE), coefficient of determination (R2),mean absolute error (MAE) and mean square error (MSE). Results showed that the seventh scenario including Tmin, Tmax, RHmean, WS, ‎ n ‎, and PA proved to be the superior scenario among others. The values of RMSE, R2, MAE and MSE for the superior scenario were 2.75 mm/day, 0.8030, 1.88 mm/day and (mm/day)2, respectively.
Molecular screening of rice (Oryza sativa L.) genotypes for submergence tolerance

Rice (Oryza sativa L.) is one of the most consumed staple food crops, providing food to about half of the world population. Submergence stress is an important constraint in rice cultivation faced by rice growers globally. In Kerala condition, flood prone areas like Kuttanad, Pokkali, as well as low lying paddy fields are mainly affected by submergence. Sub1 is the major quantitative trait locus (QTL) that confers submergence tolerance to rice genotypes. Earlier researchers have already identified the presence of Sub1 gene in rice varieties showing submergence tolerance. Such rice genotypes are characterized by a higher survival rates and better yield even after 2 weeks of submergence stress. The advancement of marker assisted selection (MAS) have a striking impact on developing improved Sub1 varieties which can meet the increasing demand for submergence tolerant varieties. The present study was planned to ascertain the rice genotypes having submergence tolerance by in vitro phenotyping followed by marker assisted selection. Genotypes KAUM 7 and KAUM 19 showed a better survival rates than other test genotypes during in vitro phenotyping. Among the ten genotypes evaluated for submergence tolerance with the help of SSR markers, KAUM 7 and KAUM 19 were identified with Sub1 QTL. Besides, this finding lays the foundation for further researches on breeding for submergence tolerance in rice.
Sentiment and Emotion Analysis of Text: A Survey on Approaches and Resources

The evolution of internet has given the ability to the users to give their reviews, ratings, and opinions on social media or commercial websites. Sentiment and emotion analysis is an ongoing field of research in text processing, which aims to classify these reviews automatically. This paper presents the survey regarding approaches and resources used for sentiment and emotion analysis of text. We summarize the techniques, datasets, and resources available for text analysis. Additionally, we focus on summarizing literature and resources available for Urdu, a low resource language, along with some open problems for Urdu text analysis. The presented survey can be used effectively to understand challenges and to take future direction for research in sentiment and emotion analysis field, especially for Urdu.
On Performance of Ultra Wideband Signals in Gaussian Noise and Dense Multipath

|An ultra wideband (UWB) signal is characterized by a radiated spectrum with a very wide bandwidth around a relatively low center frequency. In this work we study the reduced fading margin property of UWB signals. To evaluate the fading margin we compare the performance of UWB signals in an environment with only additive white Gaussian noise (AWGN) versus the performance of UWB signals in a dense multipath environment with AWGN. The assumption here is that that the presence of multipath causes a small increase in the signal-to-noise ratio required to achieve reasonable levels of bit error rate. A numerical example con rms this assumption. More speci cally, the example shows that that to achieve a bit error rate equal to 10 5 we require about 13:5 dB in the AWGN case, and about 15 dB in the multipath case, resulting in a fading margin of just 1:5 dB. The reason for this small fading margin can be understood by the ability of the UWB signal to resolve the dense multipath. Keywords|Communication, multipath channels
One-Sided Matching Markets (DRAFT: Not for distribution)

A one-sided matching allocation problem consists of a set A of n agents, a set G of n indivisible goods and preferences of agents over goods1. The problem asks for a matching of each agent to a distinct good so as to ensure desirable normative properties, such as Pareto optimality, individual rationality and envy-freeness. A market mechanism is often used to address this problem; such a mechanism should have game-theoretic properties such as strategyproofness and core stability. Additionally, in the interest of usability of this mechanism in real world applications, the mechanism needs to run in polynomial time.
Single-cut Far-Field Antenna Radiation Pattern Reconstruction Accuracy Analysis in Compact Anechoic Chamber Setup

─ In production testing, it is of importance to measure the key radiation parameters of an antenna under test (AUT), e.g., main beam peak and direction, sidelobes, and null depth and direction in a cost-effective setup with a short measurement time. As a result, practical measurement setups are often compact and equipped with only a few probes (or probe locations). However, these system limitations would introduce errors for antenna testing. This problem has become even more pronounced for 5G radios due to utilization of large-scale antenna configurations and high frequency bands. Spherical near-field measurements are nowadays an accurate and mature technique for characterizing AUTs, which however, necessitates a full spherical acquisition, leading to a long measurement time. Singlecut near-to-far-field transformation is a promising strategy since most of the key AUT parameters are available in the single-cut pattern and it requires much reduced measurement time. In this work, a simple and flexible scheme is proposed to evaluate errors introduced by limitations in practical setups for single-cut far-field (FF) antenna radiation pattern reconstruction, where the near-field data can be easily generated and modified according to the limitations introduced in practical multi-probe anechoic chamber setups, e.g., measurement distance, truncation range, and sampling interval. The reconstructed FF pattern is obtained using a commercial near-field to far-field transformation tool, SNIFT. The proposed scheme is numerically validated via comparing the reference FF pattern of a 4 × 8 uniform planar array composed of ideal Hertzian dipoles and reconstructed FF pattern. With the proposed scheme, the impact of practical system limitations on single-cut reconstruction accuracy can be easily analyzed. Index Terms ─ Antenna pattern measurement, nearfield far-field transformation, near field measurement, over-the-air testing, and single-cut antenna pattern.
q-Rung Orthopair Fuzzy Rough Einstein Aggregation Information-Based EDAS Method: Applications in Robotic Agrifarming

The main purpose of this manuscript is to present a novel idea on the q-rung orthopair fuzzy rough set (q-ROFRS) by the hybridized notion of q-ROFRSs and rough sets (RSs) and discuss its basic operations. Furthermore, by utilizing the developed concept, a list of q-ROFR Einstein weighted averaging and geometric aggregation operators are presented which are based on algebraic and Einstein norms. Similarly, some interesting characteristics of these operators are initiated. Moreover, the concept of the entropy and distance measures is presented to utilize the decision makers' unknown weights as well as attributes' weight information. The EDAS (evaluation based on distance from average solution) methodology plays a crucial role in decision-making challenges, especially when the problems of multicriteria group decision-making (MCGDM) include more competing criteria. The core of this study is to develop a decision-making algorithm based on the entropy measure, aggregation information, and EDAS methodology to handle the uncertainty in real-word decision-making problems (DMPs) under q-rung orthopair fuzzy rough information. To show the superiority and applicability of the developed technique, a numerical case study of a real-life DMP in agriculture farming is considered. Findings indicate that the suggested decision-making model is much more efficient and reliable to tackle uncertain information based on q-ROFR information.
A FACIAL EXPRESSION GENERATION SYSTEM

Face, as one of the most important ﬁrst-glance impressions of human nature, has always been the interest of Computer Vision. Hundreds of thousands of face related databases that specialists in Computer Vision are constructing and utilizing. In this project, a system for facial expression generation is proposed with the help of Haar-Cascades Face Detection and Generative Adverbial Networks trained on both Cohn-Kanade Dataset and Japanese Female Facial Expression (JAFFE) datasets. Tests on both modules are showing good performance and the system can achieve fair generation results from input. The potential of the sys-tem can be real-time facial expression generation, and has been tested on several real world scenarios. This system shows its promising value in generating facial expression in an interactive and efﬁcient way, and a good prospect to be utilized on large scale for different purposes.
Automated temporal tracking of coherently evolving density fronts in numerical models

Oceanic density fronts can evolve, be advected, or propagate as gravity currents. Frontal evolution studies require methods to temporally track evolving density fronts. We present an automated method to temporally track these fronts from numerical model solutions. First, at all time steps contiguous density fronts are detected using an edge detection algorithm. A front event, defined as a set of sequential-in-time fronts representing a single time-evolving front, is then identified. At time step i, a front is compared to each front at time step i + 1 to determine if the two fronts are matched. An i front grid point is trackable if the minimum distance to the i + 1 front falls within a range. The i front is forward-matched to the i + 1 front when a sufficient number of grid points are trackable and the front moves onshore. A front event is obtained via forward tracking a front for multiple time steps. Within an event, the times that a grid point can be tracked is its connectivity and a pruning algorithm using a connectivity cutoff is applied to extract only the coherently evolving components. This tracking method is applied to a realistic 3-month San Diego Bight model solution yielding 81 front events with duration ≥ 7 hours, allowing analyses of front event properties including occurrence frequency and propagation velocity. Sensitivity tests for the method’s parameters support that this method can be straightforwardly adapted to track evolving fronts of many types in other regions from both models and observations.
On Customer (Dis)honesty in Priority Queues: The Role of Lying Aversion

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Research Proposal Twitter Sentiment Analysis of COVID-19 related Tweets

Nowadays, people use social media to express their feelings and thoughts regarding different topics. During the COVID-19 pandemic, people have been very active in social media, sharing information, personal experiences, and emotions. Twitter is one of the social media platforms where people are very active. Sentiment Analysis of Tweets can provide really interesting insights about the public’s preferences and sentiments. In this research, I will determine whether the sentiment changes of people’s COVID-19 related Tweets align with significant developments in the COVID-19 time-line.
Optimal study and analysis of the directivity of acoustic antennas multi-sensors

An acoustic antenna is made up of several separately accessible sensors, ie whose individual outputs are physically available. We can then model its directivity diagram by playing on the amplitude and phase distribution of its elements. In this paper we have studied the directivity of an acoustic antenna in order to obtain an optimal and precise result by using a Hamming window to have as few side lobes as possible and a single main lobe. Streszczenie. Antena akustyczna składa się z kilku oddzielnie dostępnych czujników, tj. Których indywidualne wyjścia są fizycznie dostępne. Następnie możemy modelować jego diagram kierunkowości, grając na amplitudzie i rozkładzie fazowym jego elementów. W tym artykule zbadaliśmy kierunkowość anteny akustycznej w celu uzyskania optymalnego i precyzyjnego wyniku przy użyciu okna Hamminga, aby mieć jak najmniej płatów bocznych i jeden płat główny. (Badanie i analiza optymalnej kierunkowości anten akustycznych wieloczujnikowych)
Enhancement of convolutional neural network for urban environment parking space classification

BACKGROUND AND OBJECTIVES: The increase in the number of vehicles has several negative impacts, including traffic congestion, air pollution, noise levels, and the availability of parking spaces. Drivers looking for parking spaces can cause traffic jams and air pollution. The solution offered at this time is the development of a smart parking system to overcome these problems. The smart parking system offers a parking availability information feature in a parking area to break up congestion in the parking space. Deep learning is a successful method to solve parking space classification problems. It is known that this method requires a large computational process. Th aims of this study are to modified the architecture of Convolutional Neural Networks, part of deep learning to classify parking spaces. Modification of the Convolutional Neural Networks architecture is assumed to increase the work efficiency of the smart parking system in processing parking availability information.METHODS: Research is focusing on developing parking space classification techniques using camera sensors due to the rapid advancement of technology and algorithms in computer vision. The input image has 3x3 dimensions. The first convolution layer accepts the input image and converts it into 56x56 dimensions. The second convolution layer is composed in the same way as the first layer with dimensions of 25x25. The third convolution layer employs a 3 x 3 filter matrix with padding of up to 15 and converts it into 10x10 dimensions. The fourth layer is composed in the same way as the third layer, but with the addition of maximum pooling. The software used in the test is Python with a Python framework.FINDINGS: The proposed architecture is the Efficient Parking Network or EfficientParkingNet. It can be shown that this architecture is more efficient in classifying parking spaces compared to some other architectures, such as the mini–Alex Network (mAlexnet) and the Grassmannian Deep Stacking Network with Illumination Correction (GDSN-IC). EfficientParkingNet has not been able to pass the accuracy of Yolo Mobile Network (Yolo+MobileNet). Furthermore, Yolo+MobileNet has so many parameters that it cannot be used on low computing devices. Selection of EfficientParkingNet as a lightweight architecture tailored to the needs of use. EfficientParkingNet's lightweight computing architecture can increase the speed of information on parking availability to users.CONCLUSION: EfficientParkingNet is more efficient in determining the availability of parking spaces compared to mAlexnet, but still cannot match Yolo+MobileNet. Based on the number of parameters, EfficientParkingNet uses half of the number of parameters of mAlexnet and is much smaller than Yolo+MobileNet. EfficientParkingNet has an accuracy rate of 98.44% for the National Research Council parking dataset and higher than other architectures. EfficientParkingNet is suitable for use in parking systems with low computing devices such as the Raspberry Pi because of the small number of parameters.