Reviewerly/exHarmony huggingface dataset:
A Breast Lesion Segmentation Method Based on Radio Frequency Ultrasound Signals

Accurate breast detection and segmentation methods can improve the effectiveness of detection and diagnosis of breast disease, while simultaneously alleviating the workload of medical practitioners. In recent years, numerous methods have emerged for segmenting breast lesions. However, most of them rely on B-mode ultrasound images and exhibit limited understanding of the primary data. To improve the accuracy of segmentation, a segmentation algorithm based on the original ultrasound RF signal is proposed in this paper. The algorithm first uses the MimickNet technique for noise reduction and compression of the original radio frequency (RF) signal. Then, the boundary prediction is accomplished using the Visual Geometry Group 16 (VGG16) neural network as a boundary probability detector. To mitigate the error introduced by the binarization of the boundary probability matrix, a negative feedback-based optimizer is utilized. In the experiments, medical ultrasound images from the publicly available dataset OASBUD are segmented using the algorithm in this paper. The results are compared with those by the U-net method, threshold method, watershed algorithm and texture-based algorithm. It turns out that the algorithm in this paper has great accuracy and stability in noise reduction, compression processing, boundary prediction and accuracy maintenance.
A STUDY OF MECHANICAL PROPERTIES OF REINFORCED CONCRETE BEAM USING ARDUINO MICROCONTROLLER

Thispaper presentsthe use of an open source-based technology, Arduino microcontroller, which is used for free vibration test by using accelerometer ADXL345 comparing with one of common accelerometer and data-logger. The test was carried out in 3 variations: pre-loading, elastic damage (L/240), and inelastic damage (L/120). This research used reinforced concrete beam as specimens with 3000mm length, 150mm width, and 250mm depth. Specimen was installed by 2 monitoring system, Arduino equipped with accelerometer ADXL345 andcommercial data-logger NI equipped with commercial accelerometer KISTLER. The results show that relative error between ADXL345 to KISTLER is less than 5% for mode shape 1, 2, and 3 except in case inelastic damage for mode shape 3 is almost 10%.
WORD GROUPING ACTIVITY TO IMPROVE STUDENTS' VOCABULARY MASTERY: A CLASSROOM ACTION RESEARCH

This research aimed to improve students' vocabulary mastery through Word Grouping Activity. The subject of this research was the 11th IPA 6 At SMA Negeri 16 Bandar Lampung. It consisted of 30 students', this research used Classroom Action Research (CAR) which was consisted of 2 cycle in collecting the data, the writer used test and non-test. The test used in this research were formative and summative test. Meanwhile in non-test, the writer used observation sheet and field note. In analyzing the data, the writer used quantitative and qualitative method. Refer to the result of the test, it showed that there were 13 students who got score &gt; 75 with the presentage 70.67 % in first cycle. Meanwhile, there were 22 students who got score &gt; 75 with the presentage 78.67 % in the second cycle. In the other hand, the mean score of the students' learning activity in the first cycle was 6.4 and in the second cycle was 7.29.It showed that were was an Increased in the students' learning activity. Based on the result of the research, the Word Grouping Activity can improve students' vocabulary mastery through at the eleventh grade at SMA Negeri 16 Bandar Lampung.
Federated Learning Model Training Mechanism with Edge Cloud Collaboration for Services in Smart Cities

With the development of big data and artificial intelligence, problems related to data privacy have emerged in smart cities. In the context of large-scale data, federated learning can effectively utilize data resources and ensure user data privacy. This paper designs a training mechanism of edge cloud collaborative federated learning model for smart city applications, so that the model training is carried out on the edge side, without the need to gather the original data set to the cloud computing center, to ensure the privacy and security of data. Finally, it is verified and tested in the vehicle recognition scene in the traffic field. The results show that this mechanism has certain advantages in detecting delay and protecting privacy.
ICARE: An Intuitive Context-Aware Recommender with Explanations

The chapter presents a framework, called Intuitive Context-Aware Recommender with Explanations (ICARE), that can provide contextual recommendations, together with their explanations, useful to achieve a specific and predefined goal. We apply ICARE in the healthcare scenario to infer personalized recommendations related to the activities (fitness and rest periods) a specific user should follow or avoid in order to obtain a high value for the sleep quality score, also on the base of their current context and the physical activities performed during the past days. We leverage data mining techniques to extract frequent and context-aware sequential rules that can be used both to provide positive and negative recommendations and to explain them.
On Testing Ethical Autonomous Decision-Making

We present an initial proposal for a testing framework for ethical decisions in autonomous agents, based on the well-known perception-action model. We identify three main components in our proposed framework for test-case generation, conformance analysis, and learning and adaptation of ethical models based on examples from stakeholders. We define a number of templates formalising the main ethical theories in the literature that can be further instantiated for testing concrete systems according to such theories.
THE ROLE OF SECURITY SCIENCE IN THE CONTEMPORARY SOCIETY

Security is without a doubt at the top of the list of disciplines that are struggling to achieve acceptance as a specialty. Despite the important part that private security plays in society, it is unquestionably regarded as a job, unlike other professions. Security science is currently being treated as an emerging profession founded on logic and an applied set of ideas, which have been tested and modified to strengthen security services in the contemporary society. The aim of this crucial research is to assess the importance of security science in the contemporary society, with an emphasis on the ever-changing security issues. To assess the historical context, evolution, and realistic application of security science in a technologically advanced society, a content analysis research design is used. The compilation of opinion pieces, journals, essays, and speeches by reputable private security bodies and staff serve as the study's foundation. Preliminary literature reviewed reveals that security science directs, informs, and quantifies the security mitigation strategies as it integrates security principles, the built environment, and security management in the contemporary society. Security principles constitute detecting, deterring, and responding to crises and risks while the built environment includes the physical and technical environment in which individuals play, work, and live. Security management, on the other hand, entails policy and procedures, awareness, and administrative roles. As a result, security practitioners and service providers should consider the function of security science in contemporary society and regard it as an evolving discipline and a profession of significance moving forward. Keywords: Security principles, Built environment, and security management.
TOLERANCE OF HATE SPEECH AS A VERBAL CRIME - A SAFETY FACTOR IN THE SOCIETY

The increase of hate speech and aggressive speech that jeopardizes the safety of individuals and groups has been evident in the Republic of North Macedonia in the recent years. Such examples of verbal crimes have mostly been seen on social networks but also in traditional media. This negative trend has a high level on impunity, given the poor court practice on crimes related to hate speech or other serious verbal delicts. Individual journalists and/or media outlets are often targeted as a subject of verbal assaults on social media and other internet platforms, but also in person. This makes their working environment unsafe. Journalists and media workers who are threatened and subjected to attacks are facing selfcensorship and by this, the private safety of individuals affects the right on information which is a constitutional right to all citizens. Within this research, collected data from 26 basic courts throughout the country resulting in five court cases related to hate speech is a subject of elaboration together with several qualitative interviews with media and legal professionals who have an extensive practice in freedom of expression. A broader context is given to a specific part of the national legislation in relation to hate speech, with reference to some of the local reports by relevant civil society organisations. The expected result is to prove the hypothesis that individual threats related to abusing of the freedom of speech is infringing the freedom of information consequently. Furthermore, if there is tolerance to verbal crimes, primarily hate speech, towards individuals and groups, and especially towards media workers, the freedom of press will suffer in general, and journalists will be prevented from performing/doing their duties professionally.
EFEKTIFITAS PEMBELAJARAN SKILL LAB DENGAN MEDIA VIDEO PEMBELAJARAN DARING TERHADAP MODUL PRAKTIKUM SKILL LAB

Background, The Covid-19 (Coronavirus Disease-2019) pandemic has spread to almost all parts of the world since the end of 2019. The Indonesian government has provided various strategies to prevent the spread of the Coronavirus, which comprehensively affects all levels of Indonesian society. One of the Indonesian government policies that has a wide impact is government regulation in the field of education, both in the professional area and in the field of administration and the environment. With the pandemic, the implementation of educational activities has changed in various forms of learning activities, both theoretical and practical learning, all of which must run online. Purpose, This study aims to determine the effectiveness of learning lab skills using Online Learning Video Media on the Skill Lab Practicum Module for fourth-semester students of the Diploma Three Nursing Study Program, Insan Husada Polytechnic Surakarta. Methods, Lab skill practicum is called a control group. This study is a quasi-experimental study with a post-test only control design, the samples were selected and grouped into the experimental group, and the control group, the group that was treated with the provision of online learning videos, was called the experimental group, while the group that was not given online learning videos and only received the module. The experimental group was 47 students, and the control group was 46 students. Result, (1) There is a difference in the test scores of respondents who learn using the video skill lab media with those who use the skill lab practicum module media. (2) The average value of respondents who received learning using video media was 86.02, higher than respondents who received learning using the practicum module, which was 81.38 Conclusion, The skill lab learning model using video is more effective than using the practicum module for practicum learning on nursing actions for physiological reflex examinations
Cloud Computing: Programming Model and Information Exchange Mechanism

Abstract: As an utterly new Internet application instance, Cloud computing will be the top way to access benefits and announcements momentarily. The hypothesis concentrates on the intention of Cloud computing and its application occasions, putting six technical benefits based on the commencement of its technical characteristics and investigations of the programming and assignment scheduling representative according to the present-used cloud computing strategy. Examples are applied to explain the programming process, its modifying directions, and the circle within which services and resources are exchanged. For the motivation of Cloud computing, how the social web may expand the Qos through transforming the kindness load will be debated. The development of Cloud computing is estimated at the end of the thesis.
Comparative Study of Deep Learning Algorithms for Classification of Lung Cancer Types from Histopathological Images

Abstract: Global cancer statistics shows that Lung cancer is the 2nd most common cancer and the leading cause of cancerrelated mortality worldwide in 2020.Histopathology image analysis act as the prominent technique for cancer diagnosis. The assessment of whole-slide histopathology images remains a limiting factor for timely treatments. The automation of histopathology analysis is a much-needed solution to alleviate the burden of workload and tackle the problem of medical personnel scarcity in underserved regions and populations. To help make the classification of cancer-type techniques less formidable to at least a certain extent, this report presents a comparative analysis of deep learning algorithms for the efficient classification of lung cancer types. We have compared six different pre-trained Convolutional neural networks (CNN) to find the one best suitable for the classification of lung cancer types. The models include ResNet50 , VGG-16 , EfficientNet-B0, InceptionV3 , DenseNet121, and NasNetLarge . The models were trained using histopathology images from dataset LC25000. 4 different evaluation metrics, which are accuracy, precision, recall, and F1-score were used. We could achieve the highest accuracy using EfficientNetB0, which was 99.77%, while ResNet50 yielded an accuracy of 99.66% and VGG-16 gave an accuracy of 93.50%. Overall, we can say that this research can aid in implementing models that can increase the efficiency and accuracy in the lung cancer type classification in biomedical fields.
Increased Interest in Learning Through Smart Apps Creator

Reading is huge because reading is a source of information, but most of the learners open social media more often, play games with laptops or gadgets, compared to reading. Using teaching materials in supporting the teaching learning process can help students more easily understand the learning material, because it is undeniable that the development of teaching materials is very important in learning, so the more interesting the teaching materials used, the more interested students will be in the teaching materials. The purpose of this research is to determine the development of application-based teaching materials and to determine student responses to application-based teaching materials in the Church Administration and Management course in the Christian Leadership Study Program. The results of the study are calculatedbased on the validity calculation formula, then an average score of 3.1875 is obtained with decent criteria. Through this research, it is hoped that the application-based Church Administration and Management teaching program (Smart Apps Creator) is used by students as a practical learning medium and can be used anytime and anywhere
Soil Fertility Prediction

Abstract: Soil fertility plays a important role in figuring out agricultural productiveness and sustainability. Traditional methods of assessing soil fertility involve time-consuming and high-priced laboratory tests, restricting their scalability and real-time applicability. To overcome these challenges, this study proposes a data-driven method utilizing machine learning techniques for accurate and efficient soil fertility prediction. Several machine learning algorithms, inclusive of decision trees, Random Forests, k nearest neighbors, and Gradient Boosting Machines (GBM), are employed to model the complex relationships among soil properties and fertility. Feature selection techniques are carried out to identify the most influential soil parameters for enhanced prediction accuracy and reduced model complexity.The outcomes demonstrate that machine learning models can appropriately predict soil fertility, outperforming traditional approaches in terms of speed and cost-effectiveness. Moreover, the characteristic selection process identifies key soil properties that have the most significant effect on fertility, offering valuable insights for agricultural decision-making and targeted soil management. The proposed approach offers potential applications in precision agriculture, enabling farmers to make knowledgeable choices regarding crop selection, nutrient management, and irrigation strategies based totally on actual-time soil fertility predictions. By optimizing resource allocation and minimizing environmental influences, this data-driven solution contributes to the promotion sustainable agricultural practices and guarantees food safety for a growing global population. These are the essential nutrients that the crop requires for its growth pH nitrogen(N), phosphorus(P), potassium(K), CaCo3, Organic Carbon, Organic matter, CEC (Cation exchange capacity) .
Media Konkret untuk Mengajarkan Bangun Datar

Learning will continue to be tiring if educators generally organize learning and teaching exercises. So that in the end Students will experience boredom and lack of enthusiasm enthusiastic to imitate. This arrangement The purpose of this is to show that there is an increase in the learning outcomes of class VI students at SDN Buaran Bambu III Pakuhaji Region, after involving substantial media such as cardboard, wall clocks, three-sided rulers in mathematical illustrations related to the material. Substantial Media for Displaying Level Forms. Basic information acquired in this study expressed that out of 42 understudies, just 25 kids scored over 65 or expressed to have graduated, 17 children had not completed their exams, because the scores obtained were still below 65 in cycle I. The information obtained indicated that the expansion of learning outcomes. student. It was informed that out of 32 students, 27 children had graduated and 15 children had not finished studying. Based on these findings, the second cycle was carried out, Additionally, the outcomes show a huge expansion in understudies' comprehension: 84% of the assignments according to the research findings. Instructors ought to utilize learning media or instructing helps that are genuine on the grounds that they can be utilized as an elective wellspring of advancing by focusing on understudy exercises and custom fitted to their requirements their contents.
The Meaningful Passive Verbal In International Research Articles’ Abstracts

Many authors often receive notifications while typing sentences on their computers and checking grammar using the review and spelling checking menu. These notifications usually appear when sentences are in the passive voice, prompting the authors to consider rewriting them in the active voice. In the abstract of research journal articles, some authors also question whether they should stick to using active sentences. This study aims to investigate the prevalence of passive verbal in abstract research articles (RAS) by the international journal authors, with the sample through purposive technique. It is assumed that all the articles, having been published, underwent blind review, ensuring the publishable scientifically in their status as research articles. The data for analysis were collected from 20 of research articles that met specific criteria, such as being published in international journals. The results indicate that the authors use passive verbal mostly in Move 3 (methodology) and Move 4 (results of the research). Another evidence, the more passive verbal used the more efficient the abstract is. It is advisable to use passive verbal for writing research article abstract for making it efficient
P‐8.1: A New Multitasking Demura Algorithm for Display Defect Compensation

This paper proposes a multitasking Demura algorithm based on backlight modulation and liquid crystal voltage adjustment that significantly increase the luminance uniformity and improves the details of high gray image without saturation issues. The multitasking Demura benefit was experimentally validated in a 55‐inch splicing module display, and the experimental results show that the multitasking Demura can eliminate severe mura, increasing the module's luminance uniformity from 34%, 60% and 88% to 83%, 95% and 98% at L25, L128 and L240 gray levels.
English Language Learning Motivation on Eight Grade Students’ of SMP Satria Dharma Perbaungan

The Purpose of this study is to know the students’ motivation in learning English at the Eigth Grade of Junior High School Satria Dharma Perbaungan. The researcher took 44 students as the sample. This reseach used qualitative method. In collecting the data, the researcher distributed questionnaire to the respondents to measure their motivation in learning English. In analyzing the data, the questionnaire is assessed by rating scale. The result of this research showed that the students’ motivation in learning learning English is 59,31%, and it is categorized into good of motivation. Based on the research finding, it can be concluded that the students have high motivation in learning English.
Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension

Knowledge-based referring expression comprehension (KB-REC) aims to identify visual objects referred to by expressions that incorporate knowledge.Existing methods employ sentencelevel retrieval and fusion methods, which may lead to issues of similarity bias and interference from irrelevant information in unstructured knowledge sentences.To address these limitations, we propose a segment-level and category-oriented network (SLCO).Our approach includes a segment-level and promptbased knowledge retrieval method to mitigate the similarity bias problem and a categorybased grounding method to alleviate interference from irrelevant information in knowledge sentences.Experimental results show that our SLCO can eliminate interference and improve the overall performance of the KB-REC task.
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information

Previous works on the fairness of toxic language classifiers compare the output of models with different identity terms as input features but do not consider the impact of other important concepts present in the context.Here, besides identity terms, we take into account highlevel latent features learned by the classifier and investigate the interaction between these features and identity terms.For a multi-class toxic language classifier, we leverage a conceptbased explanation framework to calculate the sensitivity of the model to the concept of sentiment, which has been used before as a salient feature for toxic language detection.Our results show that although for some classes the classifier has learned the sentiment information as expected, this information is outweighed by the influence of identity terms as input features.This work is a step towards evaluating procedural fairness, where unfair processes lead to unfair outcomes.The produced knowledge can guide debiasing techniques to ensure that important concepts besides identity terms are wellrepresented in training datasets.
Design of Intelligent Elderly Care Platform Based on Intelligent Perception Technology

Pension is a very important social problem. At present, the pension industry is still in the development stage, and there is an urgent need for a comprehensive service platform to help its development. With the rapid development of artificial intelligence, Internet of things and other emerging techn