Reviewerly/exHarmony huggingface dataset:
Effect of Microscopy Magnification Towards Grading of Breast Invasive Carcinoma: An Experimental Analysis on Deep Learning and Traditional Machine Learning Methods

Image-based features of breast cancers have an important role in clinical prognostics, such as grading breast invasive carcinoma (BIC). Magnification is useful for investigating poorly defined abnormal tissue present in mammogram images. But, the disadvantage of viewing at a higher magnification is that less of the slide can be viewed. So, the question raised on which scale of magnification is better for grading BIC. In this study, the four scales of magnification such as 4x, 10x, 20x, and 40x are considered to evaluate their contribution towards BIC grading in deep learning, transfer learning, and the traditional machine learning approach. Here, 13 CNN models are considered for the transfer learning approach. In the deep learning approach, the deep feature of 13 CNN models with three classifiers is considered. Further, the handcrafted feature such as LBP, HOG, and GLCM with three classifiers like SVM, KNN, and Naïve Bayes are evaluated for grading of BIC. Finally, the 40x scale of magnification performed better in all classification models.
Speech Coding Using Discrete Cosine Transform and Chaotic Map

Recently, data of multimedia performs an exponentially blowing tendency, saturating daily life of humans. Various modalities of data, includes images, texts and video, plays important role in different aspects and has wide. However, the key problem of utilizing data of large scale is cost of processing and massive storage. Therefore, for efficient communications and for economical storage requires effective techniques of data compression to reduce the volume of data. Speech coding is a main problem in the area of digital speech processing. The process of converting the voice signals into a more compressed form is speech coding. In this work, we demonstrate that a DCT with a chaotic system combined with run-length coding can be utilized to implement speech coding of very low bit-rate with high reconstruction quality. Experimental result show that compression ratio is about 13% when implemented on Librispeech dataset.
Kernel-based learning of birth process from evolving spatiotemporal RFS data stream in SMC CPHD filter for multi-target tracking

This paper presents a kernel-based framework to estimate the birth process in the sequential Monte Carlo cardinalized probability hypothesis density (SMCCPHD) filter from the evolving spatiotemporal birth random finite set data stream that can enhance the performance of the SMCCPHD based trackers when tracking targets with no a priori information on where and when targets can appear. The time-varying spatial distribution of birth events is modelled as a map with adaptive grid points and their corresponding density values. While the spatial distribution of birth density is estimated from the elements of the birth random finite sets (RFSs), the time-varying birth cardinality distribution is estimated from the cardinality of the birth RFSs. The corresponding estimations are eventually used by a SMCCPHD based tracker to adjust its filtering strength spatially and temporally for target tracking. Experimental results on both the synthetic and real video surveillance datasets show that the proposed algorithm can improve the tracking accuracy by more precisely eliminating clutters and more effectively capturing newborn targets.
L-DETR: A Light-Weight Detector for End-to-End Object Detection With Transformers

Now most high-performance models are deployed to the cloud, which will not only affect the real-time performance of the model, but also restrict the wide use of the model.How to designing a lightweight detector that can be deployed off-line on non-cloud devices is a promising way to get highperformance for artificial intelligence applications.Hence, this paper proposes a lightweight detector based on PP-LCNet and improved transformer named L-DETR.We redesign the structure of PP-LCNet and use it as the backbone in L-DETR for feature extraction.More-over, we adopt the group normalization in the encoder-and-decoder module and H-sigmoid activation function in the multi-layer perceptron to improve the accuracy of the transformer in L-DETR.The quantity of parameters of our proposed model is 26 percent and 46 percent of the original DETR with backbones of resnet50 and resnet18.The experimental results on several datasets show that this method is superior to the DETR model in object recognition, and the convergence speed is fast.Experi-mental results on multiple data sets have shown that our proposal has a higher performance than DETR models on object recognition and bounding box detection.The code is available at https://github.com/wangjian123799/L-DETR.git.
Toward Driving Scene Understanding: A Paradigm and Benchmark Dataset for Ego-Centric Traffic Scene Graph Representation

Recently, beyond object detection and segmentation, high-level understanding of autonomous driving scenarios is attracting increasing attention. And traffic scene graph has become a potential way to model and represent the cognitive semantics of driving scenarios. However, it remains a challenging problem to construct traffic scene graph due to the complexity of driving scenes and the lack of benchmark datasets with various traffic entities and their relationships. In this paper, we propose a novel driving scene understanding paradigm, which can explicitly model traffic entities and the relationships between them from the view of ego-vehicle. Meanwhile, based on parallel vision and our paradigm, we propose an ego-centric Traffic Scene Graph dataset (TSG-451), which contains 451 images, 2,266 entities, and 4,272 relationships in real and artificial traffic scenarios. Furthermore, through qualitative and quantitative analysis, we validate the advancement of our proposed paradigm and dataset. Further investigation shows that our work can more comprehensively represent traffic scenarios and provide more fine-grained detailed semantics benefit to ego-vehicle’s decision-making.
A novel secured Euclidean space points algorithm for blind spatial image watermarking

Abstract Digital raw images obtained from the data set of various organizations require authentication, copyright protection, and security with simple processing. New Euclidean space point’s algorithm is proposed to authenticate the images by embedding binary logos in the digital images in the spatial domain. Diffie–Hellman key exchange protocol is implemented along with the Euclidean space axioms to maintain security for the proposed work. The proposed watermarking methodology is tested on the standard set of raw grayscale and RGB color images. The watermarked images are sent in the email, WhatsApp, and Facebook and analyzed. Standard watermarking attacks are also applied to the watermarked images and analyzed. The finding shows that there are no image distortions in the communication medium of email and WhatsApp. But in the Facebook platform, raw images experience compression and observed exponential noise on the digital images. The authentication and copyright protection are tested from the processed Facebook images. It is found that the embedded logo could be recovered and seen with added noise distortions. So the proposed method offers authentication and security with compression attacks. Similarly, it is found that the proposed methodology is robust to JPEG compression, image tampering attacks like collage attack, image cropping, rotation, salt-and-pepper noise, sharpening filter, semi-robust to Gaussian filtering, and image resizing, and fragile to other geometrical attacks. The receiver operating characteristics (ROC) curve is drawn and found that the area under the curve is approximately equal to unity and restoration accuracy of [67 to 100]% for various attacks.
A Lightweight Blockchain-Based Framework for Medical Cyber Physical System

Abstract A medical cyber physical system(MCPS) combines intelligent medical devices with a network. Nowadays, MCPS is extensively used in the healthcare system. While sharing the patient data into MCPS, the data privacy of the MCPS is a significant challenge in healthcare as most of the devices are suffered from data leaking and data manipulation attack. In this current work, blockchain implementation ensures patient data security and privacy with no delay. The blockchain module controls unauthorized data access from illegitimate users. The result and performance analysis of the proposed framework shows the low latency in the data sharing, high transaction rate, and high throughput with an increasing number of users in the network.
Empowering Global Citizens with Digital Literacy: Modeling the Factor Structure

Digital literacy is an important skill for global citizens to possess in the 21st century in the creation of information media using appropriate digital tools.The purpose of this research is to investigate the components of digital literacy of global citizens using a second-order confirmatory factor analysis.The key finding of this research was that the digital literacy of global citizens consisted of four components: (1) Self-initiation, (2) Trendiness, (3) Usefulness, and (4) Good attitude.The study found that the factor loading of all latent variables was positive, ranging from 0.82 to 1.06.The trendiness component had the highest factor loading (β = 1.06), followed by the self-initiation component (β = 0.87), the usefulness component (β = 0.86), and the good attitude component (β = 0.82), respectively.Participants in this study consisted of 633 higher education students selected from a nationwide survey representing populations in six regions of the country using a multistage sampling method.The results of the study found that the trendiness component had the highest factor loading, followed by self-initiation, usefulness, and good attitude.Therefore, to promote digital literacy, when designing learning or learning tools, self-learning should be emphasized together with the use of smart wearable devices to provide flexible learning.Students will thus be able to learn anywhere anytime according to their individual needs, and this also maximizes their good attitudes toward the digital literacy.This study contributes to the understanding of the components of the digital literacy of global citizens.It can provide policymakers and educators insight into the critical skills needed in the 21st century toward the creation of information media using appropriate digital tools in Thai higher education.
Factors of Learning towards Creating Blended Learning Curriculum Using Learning Management System in Higher Education during Covid-19

Blended learning is a mix of online and face-to-face instruction.It refers to a variety of possibilities made available by integrating the internet and streaming media with traditional educational formats that necessitate the physical co-presence of teachers and students.While the Covid-19 pandemic is being referred to as the greatest challenge facing education systems around the world, it has prompted authorities to issue a drastic order requiring institutions to switch from face-to-face teaching to online teaching and virtual education for students.This present study aims to determine the model of integration between learning style and active learning towards enhancing blended learning curriculums among higher education students.A total of 208 students at a selected Malaysian higher education institution were selected randomly in this study.For data collection, this study used the quantitative approach and random sampling technique.The instrument was developed based on the literature review, and expert validation was obtained from various universities.The survey forms were distributed using an online medium (Google forms).Pearson Coefficient correlation (r) was employed to examine the relationship between variables, while Structural Equation Modelling (SEM) was utilized to examine the effect of the mediator variable.By dopting a two-stage method, the measurement model was first tested, followed by the structural model.Confirmatory Factor Analysis (CFA) is to test whether the data is compatible with the hypothesized measurement model as well as for the purpose of validation and construct reliability.The results show that the measurement model was a good fit for the data, and that the constructs were reliable and valid for testing the hypotheses based on the results.The assessment of the structural model, on the other hand, involved testing the hypothesized relationships about the direct effects.Four hypothesized direct paths were found to be statistically significant.
Mega: Moving Average Equipped Gated Attention

The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences. In this paper, we introduce Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) moving average to incorporate inductive bias of position-aware local dependencies into the position-agnostic attention mechanism. We further propose a variant of Mega that offers linear time and space complexity yet yields only minimal quality loss, by efficiently splitting the whole sequence into multiple chunks with fixed length. Extensive experiments on a wide range of sequence modeling benchmarks, including the Long Range Arena, neural machine translation, auto-regressive language modeling, and image and speech classification, show that Mega achieves significant improvements over other sequence models, including variants of Transformers and recent state space models.
Systems and Requirements Engineering: An Approach and a Software Tool for the Interactive and Consistent Functional Requirement Specification

Managing the complexity of modern systems is a current challenge involving all the phases of a system development process. Complex systems also mean a great number of stakeholders involved and just as many needs to satisfy. In the Systems Engineering approach for complex systems design, Requirements Engineering provides methods to collect stakeholders needs and to translate them into system requirements. Furthermore, several tools currently allow creating requirements repository and manage their properties and changes. Traceability is also implemented to verify design against requirements. Although there are several standards and guidelines which provide criteria and rules for writing requirement statements, it is still missed a process that drives the user in the correct requirement construction. The present paper faces the requirement specification process in order to provide tools that can support and drive designers in the requirements writing. In particular, the paper (i) provides a framework for writing syntactically correct requirements that can be early verified against pre-defined criteria such as clarity, singularity, conformity, and descriptiveness; (ii) presents a software tool for creating well-defined requirements statements offering the user the possibility to formulate “correct requirements by construction”. The driven definition of system requirements leads to a well-defined specification, thus allowing time and efforts reduction during the following steps of system development process.
PENGEMBANGAN MEDIA BIG BOOK PADA TEMA PERTUMBUHAN DAN PERKEMBANGAN MAKHLUK HIDUP SUBTEMA 1 UNTUK SISWA KELAS III SEKOLAH DASAR

This study aims to develop a big book learning media on the theme of growth and development of living things sub-theme 1 which is valid and practical for third grade elementary school students. This research is research and development, and the researcher applies the ADDIE model which consists of 5 stages: analysis, design, development, implementation, and evaluation. The learning media developed was validated by 2 media experts. To test the practicality, the researcher distributed questionnaires to teachers and students. The data in this study were obtained through interviews, questionnaires, and student learning outcomes in the form of LKPD. The data obtained were then analyzed quantitatively and descriptively. The learning media developed was very valid with an average score of 87.03% and included in the very practical category with an average score of 93.05% from teachers and 90.55% from students. Student learning outcomes in the form of LKPD with an average score of 83.33%. Therefore, the big book learning media with the theme of growth and development of living things in sub-theme 1 can be used by elementary school students and teachers.
A Study of Language Use Impact in Radio Broadcasting: A Linguistic and Big Data Integration Approach

Broadcasting more culturally educating and language-reviving contents are ways radio stations can help revitalize indigenous languages in Delta North in Nigeria. The challenges faced in communicating through indigenous dialects on radio stations are majorly caused by the lack of indigenous language professionals and linguists in the broadcast stations. The absence of these professionals is a major constraint to the development of the community. The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon. Using these dialects during broadcast gives relevance to all dialects, reduces language dearth, and keeps people connected to their culture. Programmes anchored in indigenous dialects enhance the vocabulary, comprehension, and language vitality of the language. The study examined the impact of local language used in radio broadcasting using a descriptive big data survey research design. The study’s population comprises of all the inhabitants of Delta North from which a sample of 10 broadcast staff and 120 radio listeners in Delta North Senatorial District in Nigeria was drawn using a stratified random sampling technique. The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview. The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data. The results revealed that majority of the respondents in Delta North listened to radio broadcast indicating that the use of indigenous dialects/language can have massive impact on the people. The study also found that majority of the respondents use indigenous languages in their day-to-day activities, with English being used majorly only in schools. The study recommends, among others, that the National Broadcasting Commission review their policy on the allocated time of broadcast in indigenous languages and that more indigenous language experts and linguists should be incorporated into the broadcast system.
Determination of Cyber Security Issues and Awareness Training for University Students

In this study, it is aimed to determine the cyber security problems and awareness training to university students. The research was carried out in the fall semester of 2021–2022. The study, which was carried out with the participation of 410 university education and psychology students, was carried out using the survey model. In the research, 4-week cyber security and cyberattack training was given online to university students. The cyber security measurement tool was developed and used by the researchers in order to collect data in the study. The data collection tool used in the research was delivered and collected by the online method. The analysis of the data was made using the SPSS programme, frequency analysis and t-test. The results were added to the research in the form of tables. As a result of the research, it was concluded that university cyber security and cyberattack training was designed and they were good in this field.&#x0D; &#x0D;
Systematic Literature Review on Infographic Acceptance Factors in Facilitating Teaching and Learning among Students in Higher Education

Infographics are visual representations of information in such a way that information can be easily understood at a glance.Infographics are a form of the most powerful stimulator of visual communication in the digital era, and it is gaining popularity among educators.The creation of teaching and learning materials has become critical for students at all levels of education.However there are still insufficient studies that have systematically reviewed the existing literature on infographic acceptance in higher education.Therefore, this article is intended to conduct a systematic literature review on infographic acceptance in facilitating teaching and learning among students in higher education.The current study has combined multiple research designs, and the review was based on the ROSES publication standard (RepOrting standards for Systematic Evidence Syntheses).This study selected papers using two of the most prominent databases: Scopus and Science Direct, and it used Google Scholar as the secondary database.A thematic analysis was implemented and 4 main themes were produced, namely 1) Presentable and efficient; 2) Understandable, 3) Visual literacy, and 4) Facilitating teaching and learning.The study made several significant contributions to the body of knowledge and for practical purposes.The findings have explained the importance of visual communication specifically infographics as a tool for facilitating teaching and learning process and for providing information on specific research areas and content that should be the focus of future studies.
Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost

Deep learning methods have been shown to be effective for the automatic segmentation of structures and pathologies in medical imaging. However, they require large annotated datasets, whose manual segmentation is a tedious and time-consuming task, especially for large structures. We present a new method of partial annotations that uses a small set of consecutive annotated slices from each scan with an annotation effort that is equal to that of only few annotated cases. The training with partial annotations is performed by using only annotated blocks, incorporating information about slices outside the structure of interest and modifying a batch loss function to consider only the annotated slices. To facilitate training in a low data regime, we use a two-step optimization process. We tested the method with the popular soft Dice loss for the fetal body segmentation task in two MRI sequences, TRUFI and FIESTA, and compared full annotation regime to partial annotations with a similar annotation effort. For TRUFI data, the use of partial annotations yielded slightly better performance on average compared to full annotations with an increase in Dice score from 0.936 to 0.942, and a substantial decrease in Standard Deviations (STD) of Dice score by 22% and Average Symmetric Surface Distance (ASSD) by 15%. For the FIESTA sequence, partial annotations also yielded a decrease in STD of the Dice score and ASSD metrics by 27.5% and 33% respectively for in-distribution data, and a substantial improvement also in average performance on out-of-distribution data, increasing Dice score from 0.84 to 0.9 and decreasing ASSD from 7.46 to 4.01 mm. The two-step optimization process was helpful for partial annotations for both in-distribution and out-of-distribution data. The partial annotations method with the two-step optimizer is therefore recommended to improve segmentation performance under low data regime.