Reviewerly/exHarmony huggingface dataset:
Ensemble MagNet:A fast training network against adversarial examples

In recent years, deep learning has excellent applications in many fields, especially in computer vision. However, it has been found that deep learning systems are vulnerable to subtle interference, which is almost imperceptible to humans. This leads to model decision errors and potentially disastrous consequences. Therefore, AI scientists have carried out much research on this, and MagNet, Feature Squeezing and Defense-GAN are excellent research achievements. In this paper, we propose to improve MagNet. By proposing a new network structure and integrating ensemble learning technology, experiments show that this MagNet combined with ensemble learning technology has many advantages over the original MagNet. From the perspective of training time, the training time required by MagNet based on ensemble learning is reduced by half compared with the original MagNet. From the perspective of defense capability, Under the condition that the training time is sufficient and long, there is little difference between their defense capabilities. Under the condition that the training time is limited, MagNet based on ensemble learning is much better than the original MagNet. Similarly, according to this experimental idea, this paper conducts comparative experiments with Feature Squeezing algorithm and Defense-GAN algorithm from the perspective of training time and defense capability and finds that MagNet based on ensemble learning also has advantages.
A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic

The COVID-19 pandemic has highlighted the importance of effective knowledge management in mitigating the impact of public health crises. Big data analytics can play a critical role in providing insights and informing decision-making during a pandemic. However, the challenges associated with collecting, analyzing, and managing the data, especially with privacy and security concerns, make it a complex task. This paper proposes a knowledge management framework for COVID-19 mitigation using a big data analytics approach. The framework includes a systematic process for data collection, analysis, and dissemination, as well as a set of best practices for knowledge management. Additionally, the framework complies with data protection and privacy regulations. The proposed framework aims to support public health officials and other stakeholders in effectively managing the COVID-19 pandemic by providing timely and accurate information. It can also be adapted and applied to other public health crises and be a useful tool for addressing the challenges associated with big data analytics in the context of public health. The paper presents the proposed framework in detail and provides components of how the framework can be applied to COVID-19 in Indonesia. Doi: 10.28991/ESJ-2023-SPER-015 Full Text: PDF
ATTITUDE OF SECONDARY SCHOOL TEACHERS TOWARDS THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGY

The aim of this study was to examine the attitude of secondary school teachers towards the use of information and communication technology in education.For this, the investigator has used descriptive survey method.One hundred sample (57 female and 43 male) were selected by using stratified purposive sampling.For the collection of data, the researcher has prepared a scale.To derive the results from the data the investigator has used simple percentage, mean, standard deviation, and t-test.Finally, the researcher has found that 3% teachers have highly favorable, 21% favorable, 39% neutral, 17% unfavorable, and 20% highly unfavorable attitude towards the use of ICT in education.This study also shown that there is significant difference between male and female teachers' attitude towards the use of ICT in education.On the other hand, there is no significant difference between rural and urban teachers' attitude towards the use of ICT in education.
Optimization of Machine Learning Model by Applying a Random Projection Algorithm for Breast Lesion Classification

Malignant and benign lesion classification is a challenging process that requires the best possible fusion of several imaging parameters in relation to tissue density heterogeneity, prediction of lesion boundaries, and change of surrounding tissues.Recent research has shown that important picture properties such as intensity, energy, homogeneity, entropy, and statistical moments, among others, may be modelled using statistics and texture features.As a result, this method was created to make early predictions about the identification of breast lesions in digital image processing.Preprocessing, features extraction, and classification are among the phases that make up the project.The main goal of the suggested technique is to categorise lesions into benign and malignant categories.Additionally to enhance feature categorization.
Data Summarization Web Application

Abstract: As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis are required to solve problems. It is available in a structured or unstructured format. Therefore, to get corresponding data as per the application purpose, easily and quickly from different sources of data on the internet, an online content summarizer is desired. Summarizers make it easier for users to understand the content without reading it completely. Abstractive Text Summarizer helps in defining the content by considering the important words and helps in creating summaries that are in a human-readable format. The main aim is to make summaries in such a way that they should not lose their context. Various Neural Network models are employed along with other machine translation models to bring about a concise summary generation. This paper aims to highlight and study the existing contemporary models for abstractive text summarization and also to explore areas for further research
Secure Data Encryption Scheme for Cloud Computing

Abstract: The cloud computing is the architecture in which no central controller is present due to which various breaches occurred in the network. To secure data transmission from source to destination two type of encryption schemes i.e: fully homomorphism and fully disk encryption are introduced. The fully homomorhic encryption scheme is more security and light as compared to fully disk encryption. In the paper, improvement in the fully homomorphic encryption is proposed using elliptic curve cryptography and OTP generation.
Salient Co-Speech Gesture Synthesizing with Discrete Motion Representation

Synthesizing co-speech gestures is challenging because the mapping from speech to gesticulation is inherently non-deterministic. When giving talks, people conduct not only gentle and rhythmic motions but also abrupt and salient gesticulations. Most previous research efforts, however, ignore this nature of co-speech gestures and synthesize deterministic results, producing over-smoothed movements with limited expressiveness. To address this issue, we propose a new co-speech gesture generation approach that produces high-quality salient gesticulations. Specifically, we build a discrete motion representation (DMR) space to bridge the speech-gesture mapping and the gesture generation stages. The incorporation of DMR enables random sampling in motion space and avoids the over-smooth problem in speech-gesture mapping. Based on DMR, we devise a novel multi-modal co-speech gesture synthesis model with temporal attention (MCGT). MCGT explicitly models DMR’s categorical distribution conditioned on the speech context, which captures complex context patterns and produces more salient gesticulations in sync with the context. In addition, we construct a new benchmark for evaluating salient motion quality in co-speech gestures, containing a large-scale co-speech gesture dataset with salient gesticulations. We also introduce a new metric, referred to as salient motion similarity, to evaluate the salient motion quality. Experiments demonstrate superior results from our approach over several competing baselines.
Measuring the Consistency and Diversity of 3D Face Generation

3D-aware GANs have shown their impressive power on 3D controlling for synthesized portraits. While the plausible facial reality is achieved, the inherent 3D properties of the generated results have actually not been well analyzed. One of the reasons is that the wildly-used metrics, such as Inception Score (IS) or Fréchet Inception Distance (FID), focus more on the perceptual features rather than explicit 3D clues. In this paper, we propose two novel 3D metrics, which measure the face consistency and diversity on a 3D level, to compensate for IS or FID on GAN evaluation. With such metrics, the generated faces are systematically analyzed on a wide range of GANs, where we obtain reasonable but different conclusions from the wildly-used approaches. Inspired by the performance on the proposed metrics, we then discuss what is crucial for the GANs to get robust 3D properties, and what may bring uncertainty or inconsistency to the generating procedure. Finally, we propose two simple but efficient solutions to contribute to superior 3D synthesizing accuracy across different architectures, which further demonstrate the significance of the proposed metrics.
Contextual Variability of Responsive Utterances in Blog Interaction Compared to Face-to-Face Communication

In comparison with face-to-face communication, the respondent's contribution to the generating of the blog text is realized on a deferred basis.The blogger and his/her interlocutors' interaction models reveal similar features with the face-to-face dialogic patterns.Compared with face-to-face communication, the dialogue in blog interaction has a more multidimensional structure.The respondent reacts with a message that contains more than one illocution, and the question-and-answer unities are characterized by a three-step scheme, since the final message is absent in the majority of contexts.On joint unfolding of the blog, the interlocutors implement many communicative strategies that are characteristic of face-to-face dialogic interaction.The system architectonics of the massage producing and transmitting plays the essential role in the implementing the effective communication models.The technical blogosphere support and the relevant communicative context largely determine the compensatory possibilities of "docking" stimuli and reactions against the general background of the whole variety of face-to-face communications.The repertoire of communicative strategies that acquire the essential importance in the process of overcoming the problems of establishing and maintaining reactions in blog communication is determined.The current aspects of designing the interfaces of blogging platforms that influence the pragmatic contexts of reactive utterances are discussed.
IoT-Based Banknotes Saving Automation System

Many problems occur with traditional savings, such as users not knowing the nominal amount that has been saved later, and the authenticity of the money saved. To overcome this problem, the "Savings Storage Automation System with IoT-Based Banknotes" tool was created, this tool aims to help users save money easily and safely. The system design is based on Arduino Mega. The inputs on the Arduino Mega consist of a GY-33 TCS34725 as a color sensor and a keypad to perform functions. The output produced is in the form of nominal data of banknotes that can be seen by the user through an LCD and sent to a database, checking the authenticity of the money is done manually by using ultraviolet. The method used to identify the nominal banknotes is based on the reading of each RGB value and color temperature on each nominal banknote. The average accuracy of reading currency values on real banknotes reaches 88%, while in testing counterfeit banknotes it is 14%. Testing execution time when entering money into savings is 3.57 seconds and data to the database is 6.57 seconds.
COMPARISON OF POISSON, ZIP, ZINB, HURDLE AND ZIGP REGRESSION ANALYSIS METHODS IN SCHOOL-AGED SMOKING CASE MODELING IN KUDUS DISTRICT, CENTRAL JAVA

Smoking behavior among adolescents and children, especially boys, is increasing from time to time. This is very unfortunate considering the many harmful substances in cigarettes that can interfere with health. Modeling related to smoking cases for adolescents and school-age children is needed as a step to anticipate and deal with this problem. In this study we use the Poisson, ZIP, ZINB, Hurdle, and ZIGP regression methods to modeling the number of cigarettes consumed by adolescents and boys. The best model selection is done by the Vuong test. The results showed that the most suitable model was ZIGP with variables that had a significant effect on the amount of cigarette consumption in adolescents and children are age and education level
Real time Earthquake detection using Deep Learning

&amp;lt;p&amp;gt;Seismic event detection and its magnitude estimation are the two crucial steps in real-time earthquake monitoring and early warning systems. Traditional EEW systems can be limited in their ability to accurately detect the arrival phases (P and S waves) and locate earthquakes, particularly for events with high levels of background noise. Deep learning has emerged as a promising alternative to traditional EEW algorithms, since these algorithms can automatically learn complex patterns and features in seismic data, allowing them to more accurately detect the seismic phase arrival times in the signals. In this study, we first propose a deep learning based architecture DynaPicker which uses a dynamic convolutional neural network to detect seismic body wave phases. Then, the pre-trained model is used to pick the seismic phases on the continuous seismic recording. This model is further combined with another deep-learning model CREIME to perform magnitude estimation. The experimental results on several open-source seismic datasets demonstrate that DynaPicker achieved a higher testing accuracy in seismic phase identification compared to other benchmark models. Additionally, DynaPicker&amp;amp;#8217;s robustness in classifying seismic phases was tested on the low-magnitude seismic data polluted by noise. DynaPicker can be adapted to handle input data of varying lengths, making it well-suited for P/S phase picking. When applied to continuous seismic data, DynaPicker can identify more seismic events accurately and produce lower arrival time picking errors than baseline methods. We also found that using the estimated P-phase arrival time of DynaPicker, the CREIME model shows reliable results in estimating the magnitude of the aftershocks of the Turkey earthquake.&amp;lt;/p&amp;gt;
Advancements in Yoga Pose Estimation using Artificial Intelligence: A Survey

Abstract: Human pose estimation has been a prevalent field of computer vision and sensing study. In recent years, it has made many advances that have helped humanity in the fields of sports, surveillance, healthcare, etc. Yoga is an ancient science intended to improve physical, mental and spiritual well-being. It involves many kinds of asanas or postures that a practitioner can perform. Thus, the benefits of pose estimation can also be used for Yoga to help users assume Yoga postures with better accuracy. The Yoga practitioner can detect their own current posture in real-time, and the pose estimation method can provide them with corrective feedback if they commit mistakes. Yoga pose estimation can also help with remote Yoga instruction by the expert teacher, which can be a boon during a pandemic. This paper reviews various Machine Learning, Artificial Intelligence-enabled techniques available for real-time pose estimation and recent research pursued recently. We classify them based on the input they use for estimating the individual's pose. We also discuss multiple Yoga posture estimation systems in detail. We discuss the most commonly used keypoint estimation techniques in the existing literature. In addition to this, we discuss the real-time performance of the presented works. The paper further discusses the datasets and evaluation metrics available for pose estimation.
An Energy Efficient and High Performance Modified Trng based Two Phase Multi Bit Per Cycle Ring Oscillator for IoT Applications

The modified TRNG (true random number generator) is mainly focused due to minimize the power wasted by the superfluous oscillations at higher frequency operations. To boost fan-out condition, random bits are collected from both phases of the slow ROs (Ring oscillators), and the fast RO is only engaged during the brief transition time difference between two slow ROs that are symmetrically built. In order to lower the power consumption of the suggested design, the slow jittery ROs are implemented utilising current starved inverters (CSI) biassed in the weak inversion zone. By decreasing the transistors' drain current and oscillation frequency, their jitter amplitudes are made more pronounced. The quickest three-stage RO quantizes the narrow jittery pulse produced by the differential pair of slow ROs. By counting the number of oscillatory cycles of the quick RO, a gigahertz dynamic toggled D flip-flop counter may be used to extract two random bits from each phase of the jittery ROs. The proposed TRNG is fabricated in a standard 45 nm using 1V supply of CMOS process.
Investigating The Hausa Students' Familiarity And Satisfaction With The Applicability And Efficiency Of X-SAMPA For Transcribing African Languages

This article investigates the applicability and efficiency of the X-SAMPA phonetic transcription system for African languages, with a particular focus on the Hausa language. The study aims to assess the familiarity and proficiency of final-year Hausa language students in using X-SAMPA to transcribe Hausa speech sounds accurately. Data for the study was collected through electronic questionnaires distributed to final-year Hausa language students at different universities in Nigeria. The study findings indicate that X-SAMPA is a valuable tool for transcribing the Hausa language, as it provides a standardized and simplified transcription system that can be used across different research works. However, many Hausa language students were unfamiliar with X-SAMPA and required more training to become proficient in its use. Lack of time and practice opportunities were identified as the primary barriers to learning X-SAMPA. The study concludes that X-SAMPA has the potential for wider usage in phonetic transcription of African languages, but more resources such as written or video tutorials and practice exercises are needed to help learners become proficient in its use. Based on the research findings, the article recommends organizing training workshops or courses to enhance the knowledge of Hausa language students interested in learning X-SAMPA.
Back Propagation ELM based Multilayer Feature Extraction for Image and Text

ELM-based hierarchical learning framework for multilayer perceptron in which it has self-taught feature extraction followed by supervised feature classification and they are bridged by random initialized hidden weights is built in a multilayer manner. H-ELM training is divided into two separate phases: 1) unsupervised hierarchical feature representation and 2) supervised feature classification. A new H-ELM-based auto-encoder is developed to extract multilayer sparse features of the input data. The original ELM-based regression is performed for final decision making. H-ELM-based feature extraction and detection algorithms are developed for practical computer vision applications, such as object detection, recognition, and tracking. Since the supervised training is implemented by the original ELM, the unsupervised building blocks of the H-ELM architecture. The hidden layers of the framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. The advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed. To overcome this, in the proposed system, these issues are overcome by implementing with image and text using Back-Propagation–Extreme Learning Machine algorithm (BP-ELM) and then classified both text and image by using Support Vector Machine algorithm. Hence the proposed system detects the image and text in the form accuracy and efficiency of an image.
Conditionals and Knowledge of Choice

We extend our choreographic and process languages with conditionals (if-then-else). The introduction of conditionals has deep ramifications for the theory of EPP since it makes it possible to write choreographies where processes do not have enough information to know what they are supposed to do. This is known as knowledge of choice. We study principles for the transmission of knowledge and extend EPP to address knowledge of choice.
Simple Choreographies

We introduce our first choreography language, Simple Choreographies, which allows for writing sequences of interactions between processes. The key aspect of the language is that interactions are syntactically manifest in choreographies. A semantics of choreographies is obtained in terms of a labelled transition system.
Swarm Exploration Mechanism-Based Distributed Water Wave Optimization

Abstract Using sparrow search hunting mechanism to improve water wave algorithm (WWOSSA), which combines the water wave optimization (WWO) algorithm and the sparrow search algorithm (SSA), has good optimization ability and fast convergence speed. However, it still suffers from insufficient exploration ability and is easy to fall into local optimum. In this study, we propose a new algorithm for distributed population structure, called swarm exploration mechanism-based distributed water wave optimization (DWSA). In DWSA, an information exchange component and an optimal individual evolution component are designed to improve information exchange between individuals. This multi-part information interaction and distributed population structure algorithm can help the population algorithm to establish a balance between exploitation and exploration more effectively. We contrast DWSA with the original algorithms WWOSSA and other meta-heuristics in order to show the effectiveness of DWSA. The test set consists of 22 actual optimization issues from the CEC2011 set and 29 benchmark functions from the CEC2017 benchmark functions. In addition, an experimental comparison of the parameter values introduced in DWSA is included. According to experimental results, the proposed DWSA performs substantially better than its competitors. Assessments of the population diversity and landscape search trajectory also confirmed DWSA’s outstanding convergence.