Reviewerly/exHarmony huggingface dataset:
Desempeño de biomarcadores de las imágenes con tensor de difusión en regiones tumorales del glioblastoma. Análisis exploratorio de datos mediante gráficos de zombie y pruebas de diagnóstico

Introduction: Using diffusion tensor imaging (DTI), 11 biomarkers have been reported in different glioblastoma (GB) regions.Objective: To compare the efficacy of GB biomarkers using "zombie plots".Methods: Retrospective cohort of 29 subjects with GB who underwent 3-Tesla brain magnetic resonance imaging.DTI major, intermediate and minor eigenvalues were used to calculate biomarkers at five tumor regions: normal-appearing white matter (NAWM), proximal and distal edema, tumor tissue and necrosis.Contingency tables with true and false positive and negative results allowed the calculation of zombie plots based on the Bayes factor and previously unreported diagnostic tests.Results: The MD, FA, q, L, Cl, Cp and RA biomarkers had a good performance at the optimal zone for NAWM diagnosis.The proximal and distal edema, enhancing rim and necrosis regions do not have biomarkers that identify them with an optimal performance level.Conclusions: Zombie plots allow simultaneous comparison of biomarkers based on likelihood ratios.MD, FA, q, L, Cl, Cp, RA discriminated NAWM normal brain tissue at the optimal zone, but performance for other regions was at the mediocre, diagnostic inclusion and diagnostic exclusion zones.
Demo-Net: A Low Complexity Convolutional Neural Network for Demosaicking Images

This paper presents a novel Convolutional Neural Network (CNN) and an associated effective training approach that can be used for demosaicking images generated by different Color Filter Array (CFA) patterns, used in imaging sensors. The proposed CNN, Demo-Net, is a low complexity, auto-encoder based generalized CNN architecture, that can specifically take a CFA pattern as an additional input during training, thus creating a trained model for demosaicking images created by the specific CFA. The proposed Demo-Net allows one to create low complexity demosaicking systems that can be effectively deployed in consumer electronic devices with known sensor specifications.
Selective Multimedia Data Encryption Based on Discrete Hyperchaotic S-Box Generation with Motion-Based Fragmentation

Abstract Cloud computing has become the significant technology with rapid popularization offering many services with highly effective computation and large scale storage solutions. As the multimedia files are growing explosively, securely storing it for authorized access is the major task. Concentrating on video file storage in public cloud environment, security and privacy of those outsourced videos are major concerns. The semantic elements of the HEVC videos are extracted on the horizontal and vertical video fragments and Selective Encryption on those semantic elements of videos are done when the data owner uploads video for cloud storage. Modified AES is the proposed selective encryption scheme to protect the video by encrypting each fragments in cipher feedback (CFB) process mode. A dynamic, nonlinear 2D discrete hyperchaotic mapping based S-box generation is proposed to enhance the security level by providing good confusion, diffusion and scrambling. The performance analysis of generated S-box and experimental results shows that the proposed selective encryption highly resists attacks and encrypts with reduced time complexity.
Manifold learning for novelty detection and its application in gesture recognition

Abstract As a state-of-the-art novelty detection method, Kernel Null Foley–Sammon Transform (KNFST) could identify multiple known classes and detect novelties from an unknown class via a single model. However, KNFST only captures the global information of the training set. The local geometrical structure is neglected. In this paper, a manifold is incorporated into KNFST to solve this issue. First, we use manifold graphs to depict the local structure for within-class scatter and total scatter. Second, the training samples from the same class are mapped into a single point in null space via null projected directions (NPDs). The proposed method can overcome the weakness of KNFST caused by ignoring local geometrical structure in the class. The experimental results on several toy and benchmark datasets show that manifold learning novelty detection (MLND) is superior to KNFST.
Multi-window Local Variance Feature Detection Based Ultrasound Image Sharpening

&lt;div&gt;Ultrasound images suffer speckle noises and several artifacts that degrade image contrast and cause difficulties in observing borders and features. In this paper, we propose an ultrasound image sharpening algorithm that actively enhances edges and borders in ultrasound images. The sharpening is done by amplifying the high-frequency features in the image. However, ultrasound images contain speckle noises which are also high frequency components. To avoid the undesired sharpening of speckle noises, a feature mask is needed to adaptively adjust the enhancing strength and bypass homogeneous speckle regions. The feature mask is provided via a novel multi-window local variance (MLV) feature detection technique that utilizes the constant variance characteristic of Fisher-Tippett distribution in log-compressed speckle regions and could detect features robustly on ultrasound images. The proposed image sharpening algorithm is tested on simulated and clinical ultrasound B-mode images and the results show improvements in objective metrics and subjective evaluations. In addition, we demonstrate that the proposed image sharpening technique can be cascaded with other enhancement algorithms and compensate for the blur caused by speckle reduction algorithms.&lt;/div&gt;
Generating Arabic TAG for syntax-semantics analysis

Abstract Arabic presents many challenges for automatic processing. Although several research studies have addressed some issues, electronic resources for processing Arabic remain relatively rare or not widely available. In this paper, we propose a Tree-adjoining grammar with a syntax-semantic interface. It is applied to the modern standard Arabic, but it can be easily adapted to other languages. This grammar named “ArabTAG V2.0” (Arabic Tree Adjoining Grammar) is semi-automatically generated by means of an abstract representation called meta-grammar. To ensure its development, ArabTAG V2.0 benefits from a grammar testing environment that uses a corpus of phenomena. Further experiments were performed to check the coverage of this grammar as well as the syntax-semantic analysis. The results showed that ArabTAG V2.0 can cover the majority of syntactical structures and different linguistic phenomena with a precision rate of 88.76%. Moreover, we were able to semantically analyze sentences and build their semantic representations with a precision rate of about 95.63%.
The Effect of Augmented Reality on Achievement and Spatial Visualization Skills in Technical Drawing Course

This study investigated the effect of using the technical visual arts lessons’ material on visualization skills and attitudes. For this purpose, an augmented reality application called BTRS was designed. The application was created using Unity3D, 3Ds Max, and Vuforia programs. The study was conducted for eight weeks, with a total of 33 students (26 boys and seven girls) attending a vocational and technical high school. The study was designed based on Creswell’s sequential explanatory design. While a semi-experimental method with a pretest-posttest control group was conducted for the quantitative part of the study, semi-structured interviews were used for the qualitative component. “Lappan Spatial Visualization Skill Scale," “Purdue Spatial Skill Scale," “AR Attitude Scale,” and semi-structured interview form were used at the beginning and the end of the application. The Mann-Whitney U, paired-samples t-test, and the Wilcoxon signed-rank test were used for quantitative data. Qualitative data were analyzed by the descriptive analysis method. The study results indicated that the Augmented Reality Application did not significantly affect the achievement of the students and their spatial visualization skills. However, it has been observed that students’ excitement and curiosity increased. The students reported that the material was enjoyable, fun, attractive, remarkable, helpful, easy to use, understandable, and persistent. Rotating, zooming, and moving interactive digital models and buttons in the real world have attracted the students. The experimental group described abstract concepts and required spatial thinking skills to be more explicit and more practical.
CODE-SWITCHING BETWEEN ARABIC AND ENGLISH IN SELECTED DISCOURSES

Code-switching is a linguistic phenomenon that has been studied on both written and spoken discourses in the recent years. This study is an attempt to analyze code-switching in selected discourses. The data were collected from Arabic Interviews on YouTube. The first interview 'Studying in the best university ever' is a broadcast by Abdullah Al-Alawy. The second interview 'Karak session' is with the broadcaster Mohammed Al-Hinai. The methodology of this study was a mixed (quantitative as well as qualitative) method. The research tries to answer three questions: 1.How many codeswitching Instincts from Arabic language to English language occurred in the selected discourses? 2. Is there any translation for the code-switching? If yes, then where the translation takes its place whether before or after the codeswitching? 3. How many intra-word switching occur by using Arabic grammatical morphemes within these English code-switching? The research's primary goal is to improve our understanding of the instinctive uses of code-switching from Arabic to English. In this study I found that there are 101 occurrences of code-switching within about forty minutes of Arabic discourses. The most frequent type of code-switching in these two discourses was intra-sentential code-witching. While the least used one was inter-sentential code-switching. Most of the code-switching Instincts occurred without any translation to Arabic.
OVERCOMING THE CHALLENGES OF DISTANCE LEARNING DURING THE COVID-19 PANDEMIC IN MACEDONIA: WHAT CAN WE LEARN FROM THE NEIGHBORING COUNTRIES?

Information about the paper titled "OVERCOMING THE CHALLENGES OF DISTANCE LEARNING DURING THE COVID-19 PANDEMIC IN MACEDONIA: WHAT CAN WE LEARN FROM THE NEIGHBORING COUNTRIES?" at IATED Digital Library
EFL STUDENTS’ MOTIVATION IN ENGLISH SPEAKING CLASS AT THE TWELFTH GRADE OF SENIOR HIGH SCHOOL DURING COVID-19 PANDEMIC

Learning speaking skill is one of the important components for mastering English skills of EFL students, including Indonesian students. However, this learning process is not easy to do, because English in Indonesia as a foreign language and also difficult to do in the current COVID-19 pandemic situation. This has an effect on students' motivation in learning to speak English. Motivation has an important role in the success of learning speaking foreign languages including English. The purpose of this study is to analyze the motivation of EFL students in English speaking class during COVID-19 pandemic based on Gardner's theory and to analyze the difficulties in speaking English that will affect students' motivation. This study was conducted at a senior high school in Bekasi, Indonesia. The participants consisted of eight students in twelfth grade. The research design in this study used a case study. Data was collected through observation, focus group discussion (FGD), and documentation. Observation and FGD was carried out via Zoom meeting. The data analysis used is thematic analysis. The result of this study is the students have instrumental and integrative motivation. However, students are motivated more instrumentally than integratively. The most students are instrumentally motivated for future interests such as as get a better job, continuing education, and to achieve success in the future. Then, many students experience difficulties in pronunciation and grammar which can influence their motivation in speaking English. This study has limitations, because the participants only consisted of eight students from twelfth grade. Keywords: EFL Students, Motivation, Speaking
Melalui Pendekatan Saintifik Model Pembelajaran Covid-19 untuk Meningkatkan Hasil Belajar Peserta Didik pada Mata Pelajaran Bahasa Indonesia Marteri Teks Pidato Persuasif Metode Luring di Kelas IX. 8 SMP Negeri 1 Payakumbuh Semester Ganjil Tahun Pelajaran

This study aims to see and analyze the improvement of Indonesian language learning outcomes for persuasive speech texts in class IX. 8 SMP Negeri 1 Payakumbuh, West Sumatra. This research was carried out using the Classroom Action Research method which consisted of two cycles, in each cycle consisting of; planning, action, reflection, and evaluation. It is carried out from August to November 2021. The data is processed in each cycle and then analyzed using percentages. The results obtained in the pre-cycle, cycle I and cycle II showed an increase. The value of students' knowledge increased from 74 in the pre-cycle to 81 in the first cycle and to 95 in the second cycle. The skill value also increased from 74 in the pre-cycle to 91 in the first cycle, and to 96 in the second cycle. The percentage of students' acquisition of knowledge scores increased from 35% in the pre-cycle to 79% in the first cycle to 86% in the second cycle. Meanwhile, the percentage of skill scores also increased from 38% in the pre-cycle to 86% in the first cycle to 93% in the second cycle. On the other hand, students who did not complete the percentage decreased. Based on the results of the second cycle of assessment, it can be interpreted that the implementation of the Offline Covid-19 Learning Model is quite effective in learning Indonesian. The implication obtained from this research is that the use of a scientific approach to the Covid-19 Offline Learning model can improve student learning outcomes.
Discriminative Part CNN for Pedestrian Detection

Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to first detect human body parts, and then utilize the parts information to estimate the pedestrians' existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottleneck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by fine-tuning that CNN. In experiments, it shows astonishing effectiveness of optimization and robustness of occlusion handling.
Classic Probability Revisited (I): Mathematical Models of an Extended Probability Theory

Part I of this paper presents a set of extended mathematical models of probability theory in order to explain the nature, properties, and rules of general probability. It is found that probability is a hyperstructure beyond those of the traditional monotonic and one-dimensional discrete structures. The sample space of probability is not invariant in general cases. Types of vents in the sample space may be refined as joint or disjoint and dependent, independent, or mutuallyexclusive. These newly identified properties lead to a three-dimensional dynamic model of probability structures constrained by the type of sample spaces, the relation of events, and the dependency of events. A set of algebraic operators on the mathematical structures of the general probability theory is derived based on the extended mathematical models of probability. It is revealed that the Bayes’ law needs to be extended in order to fit more general contexts on variant sample spaces and complex event properties in fundamental probability theories. The revisited probability theory enables a rigorous treatment of uncertainty events and causations in formal inference, qualification, quantification, and semantic analysis in contemporary fields such as cognitive informatics, computational intelligence, cognitive robots, complex systems, soft computing, and brain informatics.
Transfer Learning Pre-training Dataset Effect Analysis for Breast Cancer Imaging

Comparing with natural imaging datasets used in transfer learning, the effects of med- ical pre-training datasets are underexplored. In this study, we carry out transfer learning pre-training dataset effect analysis in breast cancer imaging by evaluating three popular deep neural networks and one patch-based convolutional neural network on three target datasets under different fine-tuning configurations. Through a series of comparisons, we conclude that the pre-training dataset, DDSM, is effective on two other mammogram datasets. However, it is ineffective on an ultrasound dataset. What is more, fine-tuning may mask the inefficacy of a pre-training dataset. In addition, the efficacy/inefficacy of DDSM on the target datasets is corroborated by a representational analysis. At last, we show that hybrid transfer learning cannot mitigate the masking effect of fine-tuning.
Metode Analytical Hierarchy Process (AHP) Sebagai Sistem Pendukung Keputusan Penerimaan Karyawan

The rapid development of technology with the use of computers as a means to assist humans in obtaining information and solving problems, requires software makers to seek optimal innovation in an effort to improve performance both in quantity and quality. Especially for business entities, private companies, agencies that are currently able to make appropriate, fast and accurate decisions. Planning and fulfillment of human resource needs when managed professionally will greatly determine the quality and success of the company. With the above reasons, a decision support system was made using the Analytical Hierachial Process (AHP) method to make it easier to select employee recruitment and get results. 0.215. Followed by education criteria with a weight of 0.189 and the last ability to communicate with a weight of 0.106. The results of the weighting with expert choice were obtained by Aisyah Rahmah with the highest weight of 0.367, then Andini with a weight of 0.316, then Irwan Gumilar with a weight of 0.161, and finally Ardian Natapraja with a weight of 0.157.
Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies

The 2008 global financial crisis triggered by subprime mortgage crisis in the United States and the ongoing European debt crisis have urged governments and academics to pay high attention to financial industry risk supervision. The financial industry has actively implemented comprehensive risk management. As an important component of the financial industry, the insurance industry implements comprehensive risk management to control the risks of insurance companies. Propose an integrated learning model based on imbalanced dataset resampling and apply it to UCI dataset (University of California Irvine). First, resampling technology is used to preprocess the unbalanced dataset to obtain a relatively balanced training set. Then, use the classic backpropagation neural network, classic k-nearest neighbor, and classic Naive Bayes three algorithms as the base classifier and use the Bagging strategy to get the ensemble learning model. In order to verify its effectiveness, F-measure and G-mean methods are used to measure the performance of the classifier. The subject mainly focuses on the classification of relevance vector machine (RVM) in two types of large-scale datasets, imbalanced and balanced, and proposes solutions for these two types of data. This explains the effectiveness of the disequilibrium classification algorithm used in the risk analysis of insurance companies.
The Implementation of the Weight Product (WP) Method on the Best Employee Selection

PT. Autogrill Services Indonesia is a private company engaged in the food and beverages selling. There are 8 outlets and 379 employees. To achieve maximum performance within the company environment, PT Autogrill Services Indonesia gives an appreciation to employees in the form of the best rewards every month and year calculated based on certain criteria. PT AutoGrill Services Indonesia needs to have a decision support system to simplify the decision-making process. To meet these needs, a web-based decision support system for selecting the best employees was designed using the weight product (WP) method at PT Autogrill Services Indonesia. The design stage includes needs analysis, context diagrams, data flow diagrams, and designing database tables. This system is web-based, using the programming language PHP and MySQL as database storage. The main features contained in this system include processing user data, outlets, employees, criteria, periods, alternatives, scores, and the calculation of monthly and annual winners. Based on the test results, all system functionality components can run well and by expectations.
Adaptive sparse ternary gradient compression for distributed DNN training in edge computing

Abstract In edge computing, though distributed training of Deep Neural Networks (DNNs) is expected to exchange massive gradients between parameter servers and working nodes, the high communication cost constrains the training speed. To break this limitation, gradient compression algorithms expect the ultimate compression ratio at the expense of the accuracy of the trained model. Therefore, new gradient compression techniques are necessary to ensure both communication efficiency and model accuracy. This paper introduces a novel technique—an Adaptive Sparse Ternary Gradient Compression (ASTC) scheme, which relies on the number of gradients in model layers to compress gradients. ASTC establishes the model compression selection criterion by gradients’ amount, compresses the network layer that meets the model’s standard, evaluates the gradients’ importance based on entropy to adaptively perform sparse compression, and finally conducts ternary quantization compression and a lossless code scheme on sparse gradients. Using public datasets (MNIST, CIFAR-10, Tiny ImageNet) and deep learning models (CNN, LeNet5, ResNet18) for experimental evaluation, we exhibit excellent results that the training efficiency of ASTC is about 1.6 times, 1.37 times, and 1.1 times higher than that of Top-1, AdaComp, and SBC, respectively. Furthermore, ASTC can be improved by an average of about $$1.9\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.9</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in training accuracy compared with the above approaches.