Reviewerly/exHarmony huggingface dataset:
Naïve Bayes and SMART Metods to Determine Toddler Development and Status

Integrated Service Post (Posyandu) is a forum used for health services carried out from and for the community under the guidance of health workers. Currently, toddlers need to be given more attention to monitor their growth and development. The number of children under five who are monitored by posyandu cadres and puskesmas officers will certainly take a lot of time and require maximum accuracy in classifying the status of each toddler. The purpose of the design of this decision support system is to assist posyandu cadres in determining the developmental decisions of active and inactive toddlers. This research is very important to assist posyandu cadres in providing proper analysis of the developmental status of each toddler. The results of this study are an application that can be used to determine the criteria for growth and development of toddlers using the Naive Bayes and SMART methods so that they can help posyandu cadres in determining the development of toddler status.
Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance.
Fool a Hashing-Based Video Retrieval System by Perturbing the Last 8 Frames of a Video

Studies on adversarial attack have brought people’s attention to the safety of deep neural networks (DNNs). Sparse adversarial attack, which is more dangerous than dense adversarial attack, can fool a threat model with a low amount of pixels and perceptibility. However, sparse adversarial attack has not been done extensively on video hashing retrieval. We propose a method to craft sparse adversarial videos on deep hashing retrieval by adding temporal masks on video frames. Adversarial perturbation produces propagation during the video adversarial attack. To study the propagation of sparse adversarial perturbation in video hashing in depth, we develop a cosine similarity curve to show the difference between adversarial video frames and clean video frames. The results show that the perturbation can only propagate from front to back. In addition, to exclude the propagation of perturbations, we conduct experiments to only perturb the last few frames in order to analyze the influence of sparsity on the results. The experimental results show that even when there is no propagation, perturbing the last eight frames can significantly show the ability of adversarial attack to video hash retrieval model. We propose the first targeted white-box sparse adversarial attack on hashing-based video retrieval.
Design and Implementation of Smart Teaching System Based on Microservice Architecture

Under the promotion of new technology, it has become an important trend of educational reform to strengthen the integration and innovation of smart technology and teaching, and promotes the reform of talent training mode and teaching methods. Aiming at the problems existing in the construction of smart teaching system, this paper analyzes the connotation of smart teaching environment and the requirements of smart teaching system, and puts forward the design objectives, physical structure, application architecture and technical scheme of smart teaching system. The micro service architecture improves the performance and scalability of the system, ensures the accurate and timely transmission of teaching information, and effectively supports smart teaching.
Information loss

It is a rule of physics that information can't disappear. So what happens to the information in my brain when I die?
Proposed Image Encryption Algorithm based on Paillier and Elliptic Curve Algebra

with the increasing use of media in communications, the content security of digital images attracts attention from both academia and industry. In This paper proposes an asymmetric image encryption algorithm based on a Homomorphic cryptosystem (Paillier) with an elliptic curve. The algorithm is used for securing images that are transmitted over a public unsecured channel. The Homomorphic property with an elliptic curve is used in this paper, the proposed approach consists of three stages: key generation, encryption, and decryption. To realize such an approach, the encryption cryptosystem must support additional operations over encrypted data. This cryptosystem can be effective in protecting images and supporting the construction of programs that can process encrypted input and produce encrypted output while preserving the original execution time of the proposed algorithm.
Perceptrons

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An overview of heavy-tail extensions of multivariate Gaussian distribution and their relations

Many extensions of the multivariate normal distribution to heavy-tailed distributions are proposed in the literature, which includes scale Gaussian mixture distribution, elliptical distribution, generalized elliptical distribution and transelliptical distribution. The inferences for each family of distributions are well studied. However, extensions are overlapped or similar to each other, and it is hard to differentiate one extension from the other. For this reason, in practice, researchers simply pick one of many extensions and apply it to the analysis. In this paper, to enlighten practitioners who should conduct statistical procedures not based on their preferences but based on how data look like, we comparatively review various extensions and their estimators. Also, we fully investigate the inclusion and exclusion relations of different extensions by Venn diagrams and examples. Moreover, in the numerical study, we illustrate visual differences of the extensions by bivariate plots and analyze different scatter matrix estimators based on the microarray data.
Supervised Recurrent Networks

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Anomaly Detection in Region Mobility Utilization Using Daily Taxi Trajectory Dataset

Anomaly detection in urban big datasets is getting wide attention with the presence of different and various urban big data sources. Urban anomaly detection is an important application area because discovered anomalies in urban areas would provide essential information about urban areas and their utilization, especially human mobility analytics and traffic condition monitoring. In the literature, there are several studies performed for urban anomaly detection using taxi trajectory datasets, such as events detection, regional urban anomaly detection and traffic incident detection. In this study, anomaly detection in regional mobility utilization of daily taxi trajectory datasets is performed based on DBSCAN clustering algorithm. A new algorithm and a threshold value are proposed to detect taxi regions as normal and anomalous for both incoming and outgoing taxi trip records. Experiments are performed on New York taxi trajectory big dataset and the experimental results show that proposed algorithm is efficient on detecting regions as normal or anomalous based on daily taxi trip record counts.
An Encryption Scheme for Internet of Things Monitoring System

Aiming at the security problem of the Internet of Things monitoring system, based on two algorithms of blind decryption and threshold RSA signature, this paper proposes an encryption scheme for the Internet of Things system for sensors such as cameras. This solution double-encrypts the recorded video information before the camera sends it to the cloud service provider for storage, and shares the private key for decryption at the key escrow center in a secret sharing manner. This solution can not only effectively protect the confidentiality and integrity of video information, but can also be used to ensure information security for other IoT systems. This article also analyzes the security of the encryption scheme from different perspectives. This scheme is a relatively novel encryption scheme for the Internet of Things system, which can meet the needs of users to store and access information through the Internet of Things system in the current era.
Anthropocene, Education, and the Politics of Extinction

Climate change from the burning of fossil fuels, the toxification of oceans and waterways from plastics and chemicals, habitat loss from urbanization, extractive mining, and deforestation for industrial agriculture, all play a role in a sixth mass extinction within a new geologic era variously referred to as the Anthropocene, or Capitalocene, Eurocene, Technocene, and Plantationocene. This chapter argues that educational systems and researchers are in denial and that we need new postcapitalist imaginaries and structures of feeling to reorient educational thought and practice. It argues that, given our catastrophic horizon, all education should be evaluated on very simple terms: Does it expand thought? Does it expand capacities? Does it expand love? Does it expand joy? This is another way of saying that all forms of education and all the academic disciplines—history, philosophy, sociology, science, computing, engineering, design, literature, and art should now be directed toward enacting and expanding the common against cultures of exploitation, atomization, cynicism, misery, and death. Anything that doesn't contribute to this basic imperative needs to be abandoned.
Pupils’ and Parents’ Perspectives Towards Using Smartphone Strategies to Improve Reading Skills

In this 21st century, reading skill is one of the most essential skills to achieve language literacy. On this basis, an early introduction to proper reading strategies shall develop good attitudes towards reading. Based on a study conducted by the Malaysia National Library in 2015, the rate of reading is lower among Malaysians, with only 15 to 20 books on average per year. Due to this, the researchers conducted a reading workshop, “Using Smartphone to Improve English Skills” in three districts in three different states. This qualitative study was embarked to explore the perception of workshop participants (pupils and parents) towards the reading workshop. Despite the pupils’ and parents’ perceptions towards the workshop which generally are positive, however, some suggestions are given to further improve the reading workshop. The participants in this study consisted of 79 people of different ages, races and socioeconomic statuses. The pupils are from primary schools with the ages of 10 to 12 years old (Year 4 to Year 6) and the parents are from the ages of 25 to 50 years old. They are residents of the Southern region of Malaysia which are Negeri Sembilan, Melaka and Johor. The research instruments employed were field notes, open-ended interview responses from Google Form and semi-structured interview responses. The findings suggested that the majority of the students and parents agreed that the various reading strategies introduced using smartphones are both interesting and exciting and may serve as the focus of future studies. The study implies that the smartphone reading strategy workshops should be carried out at different locations to enhance the pupils’ reading skills in reading English materials. Both parents and their children should participate in this workshop to ensure the strategies could be practiced effectively at home.
Expert System for Diagnosing Newborn Babies Disease Using the Sorgenfrei Similarity Method

Newborn neonates who are 0-28 days old. At that time the baby has a physical condition that is very weak and helpless to the surrounding environment. Newborns need special handling different from babies at the age of 1 month also above. Diagnosis also treatment is quickly required by the midwife in an emergency. Without there are still many midwives who have not been able to handle it properly, causing the baby's condition to become worse. To get fast and accurate handling information, we need a system in the form of an expert system. Expert systems can diagnose newborn diseases using the Sorgenfrei similarity algorithm. The system can display information about the type of disease, symptoms, solutions, and the percentage of similarity from the results of consulting the symptoms input. The results of testing the system with the consultation of the symptoms included got the highest percentage of similarity results 53.33%. The percentage of similarity results below 20% will be entered into the revised table which will later be corrected by experts. This expert system is built based on a website that can be accessed by all midwives who need handling information
A SURVEY ON ADVANCED ATTENDANCE API

In today's rapidly changing world, automatic face recognition (AFR) technologies have made significant advances. Keeping up with the attendance register day by day is a troublesome and tedious process. There are many robotized strategies for a similar application like biometrics, radio-frequency identification (RFID), iris detection, voice detection, and more. The objective of this paper is to review some of the state-of-the-art techniques used in the implementation of marking attendance. The existing methodologies show that there is a scope for improvement in the implementation of smart attendance. In our future work, we want to present a framework by utilizing deep learning and thereby tap into the potential that it guarantees in the field of face-based biometrics.
A Deep Convolutional Neural Networks for the Detection of Cervical Cancer Using MRIs

Medical image classification plays an important role in preventing tumors and other diseases. It helps radiologists to analyze medical images with high efficiency by categorizing the images to the appropriate pathological stage. The Deep Convolutional Neural Networks (DCNNs) are an extension of Machine Learning (ML) methods that has proven its potential and capability for classification tasks. It dominates with best results on different image types. Medical images Dataset are hard to collect because it needs professional radiologists to label them. This paper presents how to apply CNNs based algorithms on Female Pelvis Dataset to classify Cervical Cancer (CC). Two approaches are proposed based on Transfer Learning (TL), the first adopt the Convolutional Neural Networks as Feature Extractors then two classifiers are used to classify the images: SVM (Support Vector Machine) and XGB (Gradient Boosted Tree), the other one process to classification by fine tuning the models with hyperparameters adjustment. The CNNs adopted in this work are: DenseNet121, MobileNetV2 and InceptionResNetV2. Data Augmentation is a necessary preprocessing step to enhance our Female Pelvis Dataset. The experimental results demonstrate that the classification performance achieve an accuracy of 96% for the best two classification models to differentiate Malign cases from Benign ones.
CYBER FORENSIC TOOLS AND ITS APPLICATION IN THE INVESTIGATION OF DIGITAL CRIMES: PREVENTIVE MEASURES WITH CASE STUDIES

Forensics, the science of using physical evidence to crack crime, has taken on a new avatar. The cyber forensic process is also advancing in new ways. Digital forensics is the science of identifying, extracting, analyzing, and presenting the digital evidence that has been stored in digital devices. Various digital tools and techniques are being used to achieve this. Here, the new cyber sleuths step in, armed with cyber forensics tools. According to Section 65 of the Indian IT Act, a person who intentionally conceals or destroys or alters or intentionally or knowingly causes another to conceal, destroy or alter any computer source code used for a computer, computer program, computer system, or network. To primary objective of this study is to nd the role of Cyber Forensic Tools in Cyber investigation. It is better to sue security programmers by the body corporate to control information on sites. Strict statutory laws need to be passed by the legislatures keeping in mind the interest of citizens. Working on this paper helped me understand the primary aim of the cyber lab in solving the cases by analyzing the evidence given to them. Using cyber forensic tools allows us to extract the data from the targeted devices, which plays a signicant role in detecting and collecting information regarding cybercrimes.
EdTech, Market Logics, and Pedagogic Containment

While questions surrounding the relationship between education and technology have captured the attention of critical educational scholars in recent decades, such concerns have taken on new import. Although technology had become a progressively pervasive feature of schooling in the years preceding COVID-19, the swift shift to remote and hybrid forms of instruction rendered necessary by the ongoing pandemic has restructured higher education in ways that are likely to last beyond the current crisis. In light of the corporate interests at stake in the EdTech market, such developments warrant attentive scrutiny. In addition to the ways in which COVID-19 has opened up opportunities for technology companies to financially capitalize on the ongoing crisis, I argue that this moment has, perhaps more importantly, laid the groundwork for ideological capitalization that will likely accelerate market orientations toward education that risk foreclosing possibilities for critical pedagogies. Drawing on the work of Herbert Marcuse and Paulo Freire, I argue that EdTech reifies market logics in higher education and renders teaching increasingly vulnerable to mechanisms of standardization, surveillance, and the logics of neoliberal techno-rationality that foreclose possibilities for pedagogic autonomy. I then explore Freire's theorizing on the roles of freedom and autonomy in a critical educational paradigm to argue that the tradition of critical pedagogy offers important insights for challenging the creeping digital hegemony in higher education.
COMPARATIVE ANALYSIS OF CLASSIFICATION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION

Nowadays, in the global computing environment, online payments are a necessary evil as it makes payment conveniently easier and can be done via an ample of available options like a Credit card, Debit Card, Net Banking, PayPal, Paytm available to make payments easier. The most common mode of payment used in online shopping is Credit Card as it is easier for the customers to directly transfer money from one account to another; without the withdrawal of cash at any point. However, this easy payment mode has opened up paths for multiple frauds which involve theft or illegal tampering of data of the credit card owner. Thus, with the increasing number of fraud cases and losses, it is important to find the best solution to detect credit card fraud as well as minimize the number of frauds in online systems. With the analysis of different sets of research performed on the given problem statement, we have concluded that the issue requires a substantial amount of predictions and application of machine learning to find the accuracy score of those commonly used algorithms to predict which of these three state-of-art-algorithms - Naive Bayes, Logistic Regression and K Neighbours, is best suitable to carry out the research in this area. In order to support our findings, we apply two different approaches i.e. with sampling and without sampling on these algorithms against the same dataset. We claim on the basis of our results that K Neighbours outperformed all in both the approaches and is more suitable to carry forward the fraud detection research using machine learning. The analysis will be useful for those working to derive anti-fraud strategies to predict the fraud patterns and reduce the risk during hefty transactions.
Modeling Inorganic and Organic Objects in Blender

This chapter will cover 3D modeling, the main feature of any three-dimensional computer graphics. Creating virtual objects gives life to an entirely new world. It is the basis for all the expressive possibilities within Blender. We will start by creating objects and spaces, analyzing the main modeling methods. Then we will continue by learning other techniques to create three-dimensional objects, from modifiers to sculpting. Starting in this chapter, we will develop a series of exercises that will demonstrate how to apply the techniques learned in the theoretical part.