Reviewerly/exHarmony huggingface dataset:
Utilizing of ASTER multispectral data in alteration zones and granitic rock types discrimination at Gabal Abu El-Tiyur area, central Eastern Desert, Egypt

The present work aims at examining the capability of ASTER VNIR-SWIR bands to discriminate the granitic rocks and detection of alteration types within them, which could be a possible hosting for Umineralization in the arid climate.The applied image processing techniques comprising relative band depth, band ratios, minimum noise fraction and color band composites supervised classification as well as two spectral mapping methods (matched filtering and mixture tuned matched filtering) were efficient in the granitic rocks discrimination and alteration end-members (hematite, muscovite, kaolinite and chlorite) identification.These results easily differentiate the mélange matrix into a) metasediments rich in blocks and b) metasediments poor in blocks.Also, the granitic rocks are classified into granodiorite, monzogranite, syenogranite, alkali-feldspar granite and alkaline (riebeckite) granite.The associated abnormal level of radioactivity exhibits 18 ppm and 31 ppm for eU, eTh respectively and mainly restricted to the alkali-feldspar granites, which significantly have higher hematite and muscovite content rather than the other exposures.A detailed geologic map of scale 1:100,000 is constructed from the interpretation of the processed ASTER images, fieldwork and petrographic studies.The results confirmed the ability of ASTER data and recommended it as a rapid and cost-effective tool in geological and mineralogical mapping in arid climate especially when the processing based on a detailed knowledge of the rock mineral assemblages.
The Correlation between Training Set Perturbations and Robustness for CNN Models

Convolutional Neural Network (CNN) models perform well in image processing and are increasingly used in face recognition, self-driving cars, etc. However, CNN models are susceptible to perturbation and thus fail. Adding perturbation samples to the training sets is a method to improve the perturbation resistance of CNN models. In this paper, we give the methods including dataset construction, model retraining, and robustness metrics. The empirical study on the correlation between Project Gradient Descent (PGD) adversarial training and CNN model robustness is carried out in perturbation degree, proportion, and sample feature in training sets. The results show that the trend of CNN model robustness is related to the network architecture and it changes with the perturbation degree and proportion, and that perturbing images with one feature in the training set can improve the ability of CNN models to recognize images with that feature, and training for the scenario to which the CNN model is applied can improve the robustness of the model.
SPEECH DEVELOPMENT OF STUDENTS AS A PEDAGOGICAL PROBLEM (ON THE EXAMPLE OF STUDYING EPITHETS IN A SECONDARY SCHOOL)

The article deals with the formation of knowledge about the epithet as one of the tropes at the lessons of the Russian language and literature. The novelty lies in the fact that the poetry of the Tatar poet Akhmed Yerikeev was used to confirm the theoretical provisions on the epithet. The didactic material above will be useful for a teachers, prepare students for the exams.
The Empirical Study of Factors Affecting Foreign Language Attrition Based on the Structural Equation Model

The research samples are junior college students who have finished compulsory English study.According to their foreign language learning situation, the thesis is to make an systematically empirical research on the factors affecting college students' foreign language attrition, based on the questionnaire survey and data of language tests and interviews, using the method of data mining and SPSS AMOS structural equation model.Through constructing the model of affecting factors of foreign language attrition and analyzing the paths and intensity between different factors, the thesis is to provide a new perspective for language attrition study and put forward effective strategies for reducing learners' foreign language attrition, in order to promote the theoretical study of foreign language lifelong education.
MATNet: Exploiting Multi-Modal Features for Radiology Report Generation

Medical imaging is widely used in hospital clinical workflows. Assisting physicians in diagnosis by automatically generating reports from radiological images is an unmet clinical demand and requires urgent attention. However, this task suffers from two significant problems: 1) visual and textual data biases, and 2) the Transformer decoder makes no distinction between visual and non-visual words. We propose a novel multi-task approach combining natural language processing with machine learning techniques to meet this clinical need, i.e., creating fluent and accurate radiology reports. We name our system as Multi-modal Adaptive Transformer (MATNet), which consists of three key modules. First, Multi-Modal Encoder (MME) explores the relationship between radiology images and clinical notes. Second, Disease Classifier (DC) classifies the states of each disease topic and provides state-aware disease embeddings to alleviate visual data bias. Last, Adaptive Decoder (AD) dynamically measures the contribution of source signals and target signals when generating the next word. Based on our evaluations using benchmark IU-XRay and MIMIC-CXR datasets, the proposed MATNet outperformed previous state-of-the-art models on language fluency and clinical accuracy metrics such as BLEU scores.
Distilling the Undistillable: Learning from a Nasty Teacher

The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense efforts considering its critical nature. Recent work Nasty Teacher proposed to develop teachers which can not be distilled or imitated by models attacking it. However, the promise of confidentiality offered by a nasty teacher is not well studied, and as a further step to strengthen against such loopholes, we attempt to bypass its defense and steal (or extract) information in its presence successfully. Specifically, we analyze Nasty Teacher from two different directions and subsequently leverage them carefully to develop simple yet efficient methodologies, named as HTC and SCM, which increase the learning from Nasty Teacher by upto 68.63% on standard datasets. Additionally, we also explore an improvised defense method based on our insights of stealing. Our detailed set of experiments and ablations on diverse models/settings demonstrate the efficacy of our approach.
IMPLEMENTATION OF IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM ON VEHICLES IMAGES

The use of surveillance cameras for most agencies only relies on video recordings and storing them for a certain time. The use of this surveillance camera can be applied to determine the type of vehicle even if the camera is not in the right position. Regarding the background of the problem, this research will use the Convolutional Neural Network (CNN) algorithm, which is part of Deep Learning with the help of Keras Library and TensorFlow, to carry out the learning process on videos captured by surveillance cameras so that it can detect images from 3 types of vehicles. The dataset used is 100 images of motorcycles, 100 images of cars, and 100 images of buses. The method used is the Image Classification Method, and the model used is the best model selected from several experiments. Researchers used training and test data distribution, namely 80% and 20%. The best results were obtained with an accuracy rate of 96.49% using epoch 100, learning rate 0.001, and batch size 32. Meanwhile, vehicle images produced image accuracy for motorcycle images when using test data from outside the dataset is 78.92%, car image is 81.71%, and bus image is 82.26%.
Internet of Things Experimentation: Linked-Data, Sensing-as-a-Service, Ecosystems and IoT Data Stores

The vision of integrating IoT platforms, testbeds and their associated silo applications is related with several scientific challenges, such as the need to aggregate and ensure the interoperability of data streams stemming from different IoT platforms or testbeds, as well as the need to provide tools and techniques for building applications that horizontally integrate diverse IoT Solutions. The convergence of IoT with cloud computing is a key enabler for this integration and interoperability, since it allows the aggregation of multiple IoT data streams towards the development and deployment of scalable, elastic and reliable applications that are delivered on-demand according to a pay-as-you-go model. During the last years we have witnessed several efforts towards IoT/cloud integration (e.g., [1, 2]), including open source implementations of middleware frameworks for IoT/cloud integration [3, 4] and a wide range of commercial systems (e.g., Xively (https://xively.com), ThingsWorx (www.thingsworx.com), ThingsSpeak (www.thingspeak.com), Sensor-Cloud (www.sensor-cloud.com)). While these cloud infrastructures provide the means for aggregating data streams and services from multiple IoT systems, they are not fully sufficient for alleviating the fragmentation of IoT platforms, facilities and testbeds, this is because they emphasize on the syntactic interoperability (i.e. homogenizing data sources and formats) rather on the semantic interoperability of diverse IoT platforms, provided services and data streams.
Application of region of interest extraction method based on deep learning in UAV high performance image compression

UAV has been widely used in detecting targets, but the image transmission of UAV still faces the problems of distortion and frame loss.Most image compression methods based on deep learning are lossy compression, and lossy compression reduces image quality in exchange for higher compression ratio.In order to improve the quality of the region of interest (ROI) in the reconstructed image with a certain bit rate, an importance map extraction module is embedded in the encoder, and the importance map is generated by extracting the output features of the last layer of the encoder.Finally, the mask is generated to guide the efficient allocation of bit rate in the process of drop coding.At the same time, a decoder enhancement module is embedded in the decoder output to predict the high frequency components in the reconstructed image and improve the quality of the reconstructed image by enhancing the details in the reconstructed image.The experimental results show that the proposed method is superior to the comparison method when multi-scale structural similarity (MS-SSIM) is used as the evaluation index, and the proposed method achieves better visual perception quality.
Learning Normal Maps for Robust 3D Face Recognition from Kinect Data

Face recognition using 3D scans can be achieved by many approaches, but most of these approaches are based on high quality depth sensors. In this paper, the authors use the normal maps obtained from the Kinect sensor to investigate the usefulness of data augmentation and signal-level fusion derived from depth data captured by a low quality sensor. In this face recognition process, the authors first preprocess the captured 3D scan of each person by cropping the face and reducing the noise; normals are computed and separated into three maps: Nx, Ny, and Nz. the authors combine the three normal maps to form an RGB image; these images are used to train a convolutional neural network. The authors investigate the order of components that yields to the best accuracy and compare it with previous results obtained on CurtinFaces and KinectFaceDB databases, achieving rank one identification rate of 94.04% and 91.35%, respectively.
Factors Affecting the Demand for Customized Bus: Empirical Evidence Using Machine Learning

As an innovative, app-based, and demand-responsive mode of public transport, customized bus (CB) services are rapidly growing in popularity across the world, but little is known about how it performs and what factors affect its performance. This study utilizes a practical subscription dataset for more than two years to assess the spatiotemporal patterns of CB and the key factors associated with the demand for CB over time. The results suggest: (1) CB services still exhibit regular (peak/off-peak) and recurrent travel patterns over time; (2) CB services are not only popularly used for commuting but for transport station (e.g., airport/railway station) transfer; (3) passenger loyalty, trip fare, and trip distance are the top three factors affecting the demand for CB; (4) the nonlinearities indicate that trip distance can be set at 20 km for maximizing the demand. To inform practice, long-distance commuting and airport/train station transfer services from suburbs should be the two targeted niche markets for CB. Retaining existing customers rather than attracting new customers may be a more efficient strategy to increase the profitability of CB service. Locating stops near the neighborhoods with high-activity centres helps increase service demand.
Metaphorical synergetics in pedagogical modeling of educational transformation processes: validity of application

The process of transformation of education is currently at the stage of formation of teaching methods and practices, including those based on the use of predictive models. The problem of formation is the change in the functional place of education in the conditions of the fourth industrial revolution. The purpose of the study is to substantiate the validity of the use of metaphorical synergetics in predictive modeling of pedagogical processes within the educational ecosystem. The scientific novelty lies in the justification of the use of the ecosystem approach in predictive pedagogical modeling with the inclusion of an interdisciplinary metaphorical language of synergetics and the definition of the content of its basic concepts within the subject area. As a result, the subject content of the main metaphors of synergetics in relation to the modeling of predictive models of education transformation is revealed.
Closed Form HJB Solution for Path Planning of a Robot Manipulator with Warehousing Applications

Real-time optimal path planning for robotic manipulations in task space is a very fundamental and important problem. In this paper, the problem of generating robot trajectories in an obstacle-ridden environment is formulated under an optimal control framework using Hamilton-Jacobi-Bellman (HJB) equation. The novel contribution of this paper is that a closed form HJB control solution (a necessary and sufficient condition for global optimality of a control solution with respect to a cost function) has been achieved for generating real-time optimal trajectories for a robot manipulator. In contrast with the decoupled end-effector path planning and subsequent trajectory generation, the proposed scheme can exploit sensory input for real-time trajectory generation where the end-effector path as well as the joint trajectory is recomputed online while satisfying the real-time constraints. The stability and the performance of the proposed control framework is shown theoretically via Lyapunov approach and also verified experimentally using a 6 degrees of freedom (DOF) Universal Robot (UR) 10 robot manipulator. It is shown that a significant saving in cost metrics can be obtained over similar trajectory generation approaches from the state-of-the-art with obstacle-ridden environment and also has better performance in high speed tracking applications. Warehouse applications of the proposed scheme in case of static and dynamic targets with respect to the robot manipulator is also included.
Detecting Points of Interest on 3D Model via Alternating Optimization Strategy

A supervised 3D points of interest (POI) detection algorithm is proposed based on alternating optimization. Firstly, the geometric features of a 3D shape are calculated from several hand-crafted feature descriptors and used as the input of the neural network. Secondly, the biharmonic distance field is utilized to assign a label to each vertex, which is regarded as the neural network’s output. Thirdly, the complex mapping relationships between the feature vectors and the labels are learned through the neural network. Fourthly, predictions are made on the training set using the trained neural network. The differences between the predicted points of interest and ground truth are compared to further optimize the vertices’ labels, which are then used as the output to train the neural network. The third and fourth steps for alternating optimization are repeated for several times, and a neural network is finally obtained. The experimental results on the SHREC 2011 dataset show that, due to the alternate optimization strategy, our algorithm is better than the traditional methods in the key evaluation indicators FNE and FPE, and the accuracy of proposed algorithm has achieved an average improvement of more than 11%.
EFEKTIFITAS LKPD BERBASIS PENDIDIKAN KARAKTER MENGGUNAKAN MODEL GUIDED DISCOVERY PADA MATERI GARIS DAN SUDUT DI KELAS VII SMP NEGERI 2 KOTA BENGKULU

This study aimed to produce the Development of Student Worksheets Based On Character Education Using Guided Discovery Model on Line and Corner Material In Class VII SMPN 2 Bengkulu that is valid category. This research was a research development (Research and Devolopment) by modifying 4D Thiagarajan procedure into 3 phases namely define, design, and development phase consisting of effectiveness test. Effectiveness LKPD was tested on thirty three students in class VII G SMP Negeri 2 Kota Bengkulu even semester of academic year 2017/2018. This research instruments effectiveness sheet. The results showed that: The Development of Student Worksheets Based On Character Education Using Guided Discovery Model on Line and Corner Material In Class VII SMPN 2 Bengkulu City included in category effective on the basis of: 1) the response of learners consisting of 11 items of statement with average score 47.10 in very good category; 2) the activities of learners consisting of 11 items statement with an average score of 46.25 in very good category; 3) educator activity consisting of 10 items statement with average score 43,13 in very good category; and 4) learning outcomes of learners achieve classical mastery 84.85%.
An Overview of the Evolution of IT-Enabled Education Policy and Practice in the United States

Since the 1990s, with the booming development of computer and other Internet information technology, the impact on the field of education has become more and more profound.Around the world, vigorous development of information technology in education has become a common trend.By systematically reviewing and sorting out the policies and bills of IT-enabled education in the United States at various stages, the article divides the historical process in the field of IT-enabled education in the United States into three stages: exploration and enlightenment (1983-1995), gradual enhancement (1996-2009), and deepening innovation (2010-present), and systematically summarizes the four major characteristics of IT-enabled education.The vigorous development of information technology in education in the United States has, to a certain extent, promoted the development of education and become the pioneer and leader of information technology in education worldwide, and its professional standard ranks among the top in the world.
Determining Political Affinity of Ecuadorian Twitter Users Using Machine Learning Techniques for Authorship Attribution

Social networks are a means of wide dissemination of ideas and expression of opinions in various fields, the political issue is no exception, arousing much interest with passionate comments, proclamations, opinions, advertising of a p a rticula r ca nd id a te or political party. Twitter, as a widely used social network, allows the publication of short messages that can be obtained through some extraction techniques allowing then to be analyzed. Authorship Attribution presents methods that help to determine the author of a certa in text, as well as the stylistic characteristics of writing that allow to identify a feeling, affinity to a certain idea, etc. This article aims to investigate through experimentation, the possibility of classifying Ecuadorian Twitter users according to their political affinity through the analysis of short texts published in this network, using Machine Learning (ML) techniques for Authorship Attribution. For this purpose, the political parties with the highest vote in the first round of the 2021 presidential elections in Ecuador are taken as a reference. Cla ssification methods such as Support Vector Machine (SVM) and, from Naive Bayes, Bernoulli and Multinomial are evaluated, co m paring them with performance measures to establish which is the most suitable for the proposed task.
Database Design for Indonesian Scholarship Recommender Systems

Following the evolution of technology, researchers have conducted several studies to propose reliable scholarship recommender systems. However, few have explored the data storage systems, which are highly beneficial to collect huge volumes of real data in training the recommender systems. In this paper, a database design for collecting such data was proposed, equipped with integer encodings for categorical variables and a method to normalize variables with varying ranges. Using Connoly and Begg's database design method, the final design consisted of a normalized Entity Relationship Diagram (ERD) and data dictionaries. The design was also constructed to support usages of various data for determining scholarship recipients, including education history, achievements, and organizational experiences. The proposed design can be implemented on information systems to allow easier information access for scholarships in Indonesia.
Semi- and Self-supervised Learning for Scene Text Recognition with Fewer Labels

The majority of existing scene recognition methods are trained on synthetic datasets, following which the performance is evaluated on real-world datasets. Real datasets are not used to train scene text recognition models owing to the difficulty and cost of obtaining labels compare to synthetic datasets. With the development of self-supervised learning, many novel methods apply Siamese neural networks and contrastive learning on unlabeled data for pretraining, and subsequently use the trained encoder for downstream tasks. However, a single self-supervised model may not be able to solve all downstream tasks. Therefore, we propose a self-supervised algorithm including data augmentation, loss functions, and an improved semi-supervised learning method to solve the specific downstream field of scene text recognition. We improved the scene text recognition method by using unlabeled data in semi- and self-supervised methods.
Reflections on a Scientific Career and Its Possible Legacy

I give the history of my research career, its evolving scientific topics, my main results, and how the computer science and image processing community reacted to them. I briefly describe my current research on generalized flat morphology based on threshold summation. I finally discuss possible future developments arising from my works, to be pursued by a new generation.