Miracle-dz/UniFAR-FTUs huggingface dataset:
Painting Style Classification Using Deep Neural Networks

In this paper we describe the problem of painting style classification into five classes: impressionism, realism, expressionism, post-impressionism and romanticism. While most previous approaches relied on image processing and manual feature extraction from painting images, our model based on the ResNet architecture and pre-trained on the ImageNet dataset operates on the raw pixel level. The training has been performed on a large dataset (about 43k images for five class style classification problem). To increase the quality of final model a large number of various augmentations were used: random Affine transform, crop, flip, color jitter (i.e. contrast, hue, saturation), normalization, a scheduler for the optimizer. Finally model weights were pruned which allowed increasing accuracy up to 51.5% and decreasing computation time as well.
Robust Immersion and Invariance Adaptive Control with Disturbance Observer for a Quadrotor UAV*

In this paper, a robust immersion and invariance (I&I) adaptive control with disturbance observer (DO) is proposed for an uncertain quadrotor unmanned aerial vehicle (UAV) subjected to disturbance. The parametric uncertainties in both position loop and attitude loop of a quadrotor are compensated by I&I adaptation laws, while the disturbances in the attitude loop are attenuated by a constructed disturbance observer. The ultimate boundedness of the attitude tracking errors and the satisfying trajectory tracking performance of the quadrotor are guaranteed by the presented control law with stringent proof. Physical experiment results are carried out to illustrate the effectiveness of the proposed control law.
Fault-tolerant control for a Multi-propeller Aerostat based on sliding mode control allocation method

This article develops a fault-tolerant control strategy for a multi-propeller aerostat based on sliding mode control allocation approach. And the loss of effectiveness of propeller faults and the wind disturbance is considered in the system. The proposed control approach consists of two modules: the upper-level virtual control part, which is developed to enable the closed loop system asymptotically stable; the lower-level control allocation part, which can accommodate the propeller faults, and redistribute the virtual control vector to the available propellers. Stability analysis indicates that the aerostat plant is globally asymptotically stable. The validity of the developed fault tolerant control strategy is proved through simulation results based on a simplified multi-propeller aerostat with propeller faults.
Deep Convolution Neural Networks for the Classification of Robot Execution Failures

Deep convolution neural networks (DCNNs) are popular deep neural networks and are widely used in object recognition, handwriting recognition, image processing, and so on. In this paper, manipulator fault classifier based on DCNNs is proposed, and the sensor data from force and torque sensors are preprocessed and reconstructed into a new form that is suitable for the input of DCNNs. The experimental results show that the designed classifier can effectively distinguish time-series sensor data from the manipulator's normal state and various fault states. The proposed method aids in measurement, allowing the manipulator to recover from the fault state to normal working state, and is useful for enhancing the executive capability of manipulators.
IMPACT OF VERMICOMPOST, FARM YARD MANURE, FLY ASH AND INORGANIC FERTILIZERS ON GROWTH AND YIELD ATTRIBUTING CHARACTERS OF MAIZE (ZEA MAYS L.)

A field experiment was conducted to determine the effect of different soil amendments constituting various combinations of inorganic fertilizers, vermicompost, farm yard manure and fly ash on the growth and yield of maize crop. All treatments showed significant positive influence over control in respect of leaf chlorophyll content, plant height, stem girth, number of leaves, number of cobs per plant, number of grains per cob, grain yield and harvest index. While growth and yield attributing characters were studied, it was found that treatment constituting recommended fertilizer dose (T1, 100% RDF + 100% vermicompost) exhibited maximum positive influence over control, producing a total grain yield of 8.4 t ha-1. However, when harvest index was considered, T1 and T4 treatments (20 % FA + 80 % RDF + 80 % VC) were found as the best performer in producing economic yield (49.7%). Therefore, from the findings of the present investigation, combination of 100 % RDF + 100% vermicompost and 20% fly ash + 80% RDF + 80% vermicompost are recommended as the best application input as soil amendment in maize cultivation.
Identification of Incorrect Data Labels Using Conditional Outlier Detection

Outlier detection methods help one to identify unusual instances in data that may correspond to erroneous, exceptional, or surprising events or behaviors. This work studies conditional outlier detection, a special instance of the outlier detection problem, in the context of incorrect data label identification. Unlike conventional (unconditional) outlier detection methods that seek abnormalities across all data attributes, conditional outlier detection assumes data are given in pairs of input (condition) and output (response or label). Accordingly, the goal of conditional outlier detection is to identify incorrect or unusual output assignments considering their input as condition. As a solution to conditional outlier detection, this paper proposes the ratio-based outlier scoring (ROS) approach and its variant. The propose solutions work by adopting conventional outlier scores and are able to apply them to identify conditional outliers in data. Experiments on synthetic and real-world image datasets are conducted to demonstrate the benefits and advantages of the proposed approaches
Remote Sensing Mapping of Land Use and Land Cover Changes in Upper Casamance, Senegal, 1987-2018

In order to study the evolution of land use and the changes in land cover in Upper Casamance, from Landsat images acquired in 1987 and 2018, we used a method of multistage unsupervised classification. Recently developed to mapping and quantification of vegetation land cover changes in West-African coastal, this method is based on the following stages: first classification (K-means) over 15 classes, interpretation of the spectral signatures of the resulting classes followed by reclassification according to the chosen nomenclature, creation of thematic masks, classifications on masks, and finally reclassification. With the only difference, our study aims also to contribute, methodologically, to the knowledge of the spectral profiles of plant formation types. On a thematic level, the results show that between 1987 and 2018, forest areas decreased by 377,118.7 ha (or 27.4%), while wooded areas and agricultural and soil surfaces increased by 263,172.4 ha (or 19.1%) and 1560 75.5 ha (11.3%). In terms of change, deforestation by increasing agricultural and soil surfaces is noted along the border with the Gambia, savanization and anthropization is noted along the central part, and stability of forest surfaces followed by a slight increase in savannas and agricultural and soil surfaces is noted in the south.
DIRECTION OF ARRIVAL ESTIMATION WITH SENSOR ARRAYS

With recent increase of hardware capabilities, the application of sensor arrays in the Direction-Of-Arrival estimation is gaining more and more popularity. The behaviour of the array is the complex function of the geometry, sensor characteristic, signal and noise properties and other different factors. Before building the array it is essential to simulate its performance in different conditions with different algorithms. Most of the time the simulations are performed using custom ad-hoc created code. This code is error prone and difficult to maintain. Our goal was to create a reusable and extensible library that would provide the components used in the simulation process. In this paper we present the initial version of the library along with several illustrative examples.
Efficient Image Encryption Scheme Based on 4-Dimensional Chaotic Maps

This paper proposes a new family of 4-dimensional chaotic cat maps. This family is then used in the design of a novel block-based image encryption scheme. This scheme is composed of two independent phases, a robust light shuffling phase and a masking phase which operate on imageblocks. It utilizes measures of central tendency to mix blocks of the image at hand to enhance security against a number of cryptanalytic attacks. The mixing is designed so that while encryption is highly sensitive to the secret key and the input image, decryption is robust against noise and cropping of the cipher-image. Empirical results show high performance of the suggested scheme and its robustness against well-known cryptanalytic attacks. Furthermore, comparisons with existing image encryption methods are presented which demonstrate the superiority of the proposed scheme.
A systematic review of text classification research based on deep learning models in Arabic language

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.
Soil-Management History and Host Preference by Ostrinia nubilalis: Evidence for Plant Mineral Balance Mediating Insect–Plant Interactions

Organic-farming practitioners have long suggested that maximizing soil biotic activity results in crops of reduced susceptibility to pests. In the current study, we examined the ovipositional preference of Ostrinia nubilalis (Hubner), the European corn borer, on sweet corn grown in the greenhouse using soils from neighboring organic and conventional farms and fertilized with NH 4 NO 3 , cow-manure compost, or left unamended. In addition to ovi-positional preference, we compared photosynthetic potential, leaf-mineral profiles, and biochemical profiles measured by near-infrared (NIR) spectroscopy. Significant variation in O. nubilalis oviposition among fertilizer treatments was measured for plants in conventional soil, but not for those in organic soil. Photosynthetic parameters, notably net photosynthesis and stomatal conductance, correlated strongly with plant biomass accumulation, but no correlations with O. nubilalis preference were detected. In contrast, a quadratic model of 3 leaf-mineral levels (Zn, Al, and N) showed a strong relationship with O. nubilalis oviposition (adjusted 7.2 r 2 = 0.71); plant growth was best described by a quadratic model of N alone (adjusted r 2 = 0.69). The greatest differences in NIR spectra were caused by protein, and soil-fertilizer combinations producing the lowest protein levels were those whose plants received the greatest number of O. nubilalis eggs. We suggest that differences in corn acceptibility to O. nubilalis is at least in part mediated by plant mineral balance, which incorporates both absolute levels and ratios of minerals, and that an optimal balance of these minerals is more likely to occur in organically managed soil because of an inherent property of reducing variation in mineral availability in those soils.
Graph-Based Analysis of Team Collaboration in an Agile Medical Engineering Project

: Agile development approaches are gaining popularity in hardware product development and medical engineering. Consequently, techniques of structural complexity management can be applied on agile projects. This paper analyses the collaboration data of a twelve-month project to design a 3D-printed microtiter plate. Exchanged e-mails, created artifacts, and conducted tasks were analyzed to identify typical barriers of inter-disciplinary collaboration. Appropriate improvement measures to overcome these barriers were suggested and evaluated in a four-hour workshop with members of the core team of the project. As a result, out of the collaboration network with 851 nodes and 9001 edges, six main barriers were identified. The most hindering barriers according to the experts’ opinion were matched with appropriate improvement measures. After an assessment of the cost/benefit ratio, two measures were chosen for implementation.
Assessing impact of weather variability and changing climate on oil-palm yield in major growing regions of southern Thailand RAWEE CHIARAWIPA, KANJANA THONGNA and SAYAN SDOODEE

Oil palm yield is very responsive to weather fluctuations in the growing season. The purpose of this study was to investigate the relationship between yield variation and climate trends in the major oil palm-growing regions, especially in Southern Thailand (Chumphon; CP, Ranong; RN, Krabi; KB, Trang; TR, Satun; ST, Phang-Nga; PN, SuratThani; SR and Nakhon Si Thammarat; NS) where oil palm has been grown in a large plantation. Monthly weather variables from 16 agricultural meteorological stations were analyzed by linear and non-linear regressions over 28 years in each major oil palm-producing region. To evaluate the trends of changes in weather parameters and yield, a statistical model was developed fo r estimating oil palm yield based on climatic trends during 1994-2017. The results showed that warming trends were observed at all major oil palm-growing regions. There were pieces of evidence of significant correlation in temperature trends which had the strongest values in KB (Tmax, R 2=0.534**) and PN (Tmin, R2=0.670**). The highest trends of ET and RH were also markedly increased in SR (R2=0.618**). Whereas precipitation trend had slightly increasing changes in CP (R2=0.220**) and PN (R2=0.233**). In addition, the annual trends in the values of Heliothermal Index, Dryness Index and Cool Night Index were markedly increased in NS, RN and KB, respectively. Comparing climate variables and yield variations over 19 years, the study indicated that the relationships between observed yield and estimated yield had highly significant differences in CP (R2=0.468**), SR (R2=0.735***) and NS (R2=0.579***), but there was lower value in KB (R2=0.098*) than those of the other regions. Therefore, this study indicates that recent climate trends have had an implicit effect on oil palm yield in the major producing regions in Southern Thailand. This study could be a guideline to further planning for oil palm management.
The Graphic Organizers (GOs) in the Development of Communicative Skills in Class Presentation

This study aims at uncovering on how a language skill could properly be embedded to import another skill’s impact. The effectiveness of GOs with patterned structures and their functions can facilitate English spoken proficiency in class presentation. In order to obtain the formulated research purpose, non-participation observation had been carried out in support with video-recording. The positive effectiveness of GOs could be varying towards certain English proficiency levels; however, the negative impacts produced by the elementary learners indicate the psychological problems and the technical problems. GOs could worthily be as recommended ELL media, since they can effectively stimulate EFL intermediate learners’ intrinsic elements of learning achievement. Those therefore could effectively scaffold the intermediate learners through questioning technique in form of guided-Graphic Organizers (GOs). GOs could dynamically be incorporated into multimodal ways; by re-writing, chronological overview, and note-taking which would be employed by the upper intermediate learners. There are three situations which must be required for advanced learners; experiences-associated learning, topic-based selection, and the comprehensive understanding of GOs concepts and the topics of reading passages. Thereby, GOs can be regarded as marvelous strategies that can positively contribute to induce EFL learners’ psychological achievements and effectively promote English speaking performance.
Students’ reflections in teaching practicum: A case study of EFL pre-service teachers

The importance of reflection in enhancing teachers’ professional development has been widely acknowledged. Yet, little is known about how EFL pre-service teachers do reflection and how they perceive reflective practice as one of the tools to improve their professional development. The current study attempts to address this gap by investigating how EFL pre-service teachers reflect on their teaching performances and perceive their reflective practices. Employing a qualitative case study, the study involved six teacher candidates who were taking microteaching. The data were gathered from an open-ended questionnaire made by the author, journal writing, participants' analysis on the videos of their teaching performances, and an interview with each participant. To triangulate the data, classroom observations and document analysis were also conducted. The obtained data were analyzed thematically based on emerging themes. The findings show that the participants did reflections in different stages of their teaching performances, reflecting on the surface level. In terms of how they view a reflection, the research participants perceive a reflection as a good practice because it gives them opportunities to look back and scrutinize what they did during their teaching practicum.
Prospective Teachers’ Perceptions of Oral Presentations: An Exploration of Challenges Involved

Presentation skills are considered as one of the most important proficiencies needed for higher education and future careers for teachers. The present study identifies the challenges faced by prospective teachers during classroom oral presentations while studying at a teacher training institute, Institute of Education and Research, (IER). The participants of the study were 400 prospective teachers enrolled in different programs of teacher education at IER. Independent variables were measured, number of semesters already completed at the institute, gender and GPA in the completed semesters. A questionnaire was developed comprising three main sections: personal traits, oral presentation skills and peers and teachers. The statements were developed on a five-point Likert Scale to know the opinion of prospective teachers regarding challenges involved in oral presentations. In addition, a semi-structured interview was conducted from thirty prospective teachers to probe results obtained from quantitative analysis and investigate reasons behind emerging challenges. The findings revealed that prospective teachers experienced medium level of difficulties in oral presentations with mild tendency towards high rank. There was no significant difference found in the results regarding the number of semester students already completed, GPA and gender in general, whereas, statistically significant difference was found on factor peer and teachers with respect to variable gender only. Analysis of qualitative data revealed tangible reasons behind the challenges of oral presentations such as second language, personal traits and use of technology. There is a need to encourage sharing of ideas, discussions and presentations in the classrooms right from the early years of school through higher education in order to enable learners to become confident, vocal and effective teachers.
PROFESSIONAL COUNSELLORS’ PERCEPTIONS AND EXPERIENCES OF MULTICULTURAL COUNSELLING IN MALAYSIA: A QUALITATIVE STUDY

Background and Purpose: The contemporary status of multicultural counselling field in Malaysia is ambiguous as there is limited research in this field. Even though Malaysia is deemed as a multicultural nation, there are still some issues and concerns with regards to the understanding and practice of multicultural counselling. Hence, the present research aims to: (a) explore the definitions of the term ‘multicultural counselling’ as perceived by professional counsellors; (b) identify the dimensions of multicultural competence as perceived by counsellors’ community sample; (c) identify the characteristics of a multiculturally competent counsellor; and (d) explore the most challenging cases or anecdotes experienced by professional counsellors when counselling culturally different clients in the local context. Methodology: Semi-structured in-depth interviews were employed to gather 22 professional counsellors’ reflections on lived counselling experiences and their perceptions of multicultural counselling concepts. Thematic analysis was used to analyse the interview data, but with some adaptation to suit the functions of NVivo 9. Findings: Several themes and categories emerged based on four probing topics (understanding of multicultural counselling, definition of multicultural competence, characteristics of a multiculturally competent counsellor, and challenging multicultural cases). Journal of Nusantara Studies 2020, Vol 5(2) 261-281 ISSN 0127-9386 (Online) http://dx.doi. org/10.24200/jonus.vol5iss2pp261-281 262 Contributions: The results seem to suggest that Malaysian counsellors need to continuously engage in professional development training and to actively seek practice opportunities with culturally diverse clients. The education sector and local professional bodies for counsellors also need to better train and educate preand in-service counsellors in order to become multiculturally competent practitioners.
Topics and Trends in Metadata Research

While the body of research on metadata has grown substantially, there has been a lack of systematic analysis of the field of metadata. In this study, we attempt to fill this gap by examining metadata literature spanning the past 20 years. With the combination of a text mining technique, topic modeling, and network analysis, we analyzed 2,713 scholarly papers on metadata published between 1995 and 2014 and identified main topics and trends in metadata research. As the result of topic modeling, 20 topics were discovered and, among those, the most prominent topics were reviewed in detail. In addition, the changes over time in the topic composition, in terms of both the relative topic proportions and the structure of topic networks, were traced to find past and emerging trends in research. The results show that a number of core themes in metadata research have been established over the past decades and the field has advanced, embracing and responding to the dynamic changes in information environments as well as new developments in the professional field.