Reviewerly/exHarmony huggingface dataset:
Developing Students’ Worksheet for English Language Teaching to Cope with the 4C’s Skill Through Project Based Learning

This research aims to implement and evaluate design concepts and develop a PjBL-based students worksheet adapted to teaching materials to improve the 4C’s skill of Senior High School students in English subjects. Development research was used in this research. The PjBL-based students' worksheet was developed by matching the criteria for the components of the students' worksheet model: model syntax, social systems, management reaction principles, support systems, instructional impacts, and accompaniments, as well as other necessary instruments. The instruments developed were students' worksheet model validation sheets and learning tools validated by experts and practitioners. The data were analyzed by using descriptive statistical analysis. The results of this research indicated that the Project-based Learning (PjBL)-based students' worksheet model met the level of validity, practicality, and effectiveness to improve the 4C's skills of Senior High School students which contained: (a) syntax, (b) social system built on the principle of collaboration, (c) the principle of management reaction in which teachers act as facilitators, motivators, moderators, and consultants, (d) support systems in the form of Learning Implementation Plans, Teacher Books, Student Books, and Participant Worksheets Educate, (e) instructional impact in the form of students' 4C's skills and accompaniment impact in the form of critical thinking, creative thinking, responsibility, openness, and understanding of concepts.
Analysis of the Effect of Powtoon Application Interactive Learning Media on Mathematics Learning Ability of Elementary School Students

This study aims to see the positive and significant effect of interactive learning media on the application of powtoon in developing the learning abilities of fourth-grade students at SDN Pekayon 12 in learning mathematics. This study used the Tru-Experiment research method, using a post-test-only design. The samples came from Class IV A and B, with 27 students in Class A and 24 in Class B. Research instruments are measuring tools to do something and research tools to collect data as processed materials. One instrument makes it easy to measure, collect, compare and process. Whether or not a research instrument is determined by its validity and reliability. Instrument validity is the accuracy with which you measure what you want to measure, and reliability reflects variable data correctly and does not deviate from the actual situation. The instrument is valid if it can provide reliable data consistent with the actual situation. Instruments are said to be reliable when they can reveal reliable data. The results of this study indicated an effect of interactive learning media on the application of powtoon on students' learning abilities in learning mathematics in class IV SDN Pekayon 12. This was evidenced by the data of the Validity Test, Reliability Test, Normality Test (Lillifors Test), Homogeneity Test (Fisher's Test) and T-Test.
Detection of Synthesized Videos using CNN

Deepfake refers to changing a person's face or their facial emotions. Many fake videos are being overused and going viral in a matter of seconds. Deep learning is used to generate fake videos and images. Deepfakes are synthetic media in which the people who appear there are not real. Increasingly sophisticated technology makes it difficult to distinguish between real and fake videos and images. This will make both men and women more concerned about their safety. It is essential to create a model that can distinguish between real and fake videos in order to maintain and respect everyone's right to privacy. In this research, a Deep Learning Approach is used to offer a framework to identify these Deepfake's, a convolutional neural network architecture will be trained using a database of faces that were taken out of a mixed dataset.
TV News Database Indexing System with Video Structure Analysis, Representative Images Extractions and OCR for News Titles

In this paper, we present the semiautomatic system for analysis of the temporal structure of a news show, where the indexing of TV news program material is done following a classification resulting from the segmentation of video sequences, the definition of areas of interest and the recognition of news titles through automatic optical text recognition. Several experiments are presented regarding video segmentation methods, text extraction methods, and models of temporal and spatial structures that define a TV news program.
Improved Fully Convolutional Neural Networks for Automated Handwritten Character Recognition

Automated handwritten character recognition (AHCR) is one of the fascinating areas of artificial intelligence and pattern recognition, gaining popularity due to its broad array of applications in medical, finance, biometric authentication, invoicing imaging, computerized number identification, and handwriting recognition. The documents are digitized and converted into editable formats as a result of this recognition procedure. Although the AHCR has a long history, established methods, and configurations, it also presents several advantages and disadvantages. The diverse backdrops, differences in handwriting, font sizes, angles, low contrast, and multi-language text make character recognition in natural images a time-consuming and challenging. Deep-learning algorithms, specifically fully convolutional neural networks (FCNs), have emerged as a viable option for HCR. This paper employs deep learning methods based on fully convolutional networks (FCNs) to develop a novel solution for AHCR. The model was validated using independent dataset testing and 10-fold cross-validation, which yielded accuracy rates of 96.6 percent accuracy, 96.1 percent precession, 97.6 percent recall, and 97 percent F1-Measure, respectively.
A Novel Method for Sample Selection in Audio Stenographic Systems

This research work proposes a new method for performing sample selection in audio steganographic systems, which can also be applied to strictly frequency-based and spatial steganographic systems. This method exploits the predictability of pseudo-random number generators to generate a sequence of pseudo-random indices to develop a steganographic system that offers security without compromising the payload capacity of the carrier signal. A comparative study has also been performed among existing sample selection algorithms, namely, sequential selection and pseudo-random selection with indices embedded within the stego object. Our analysis also looks at the effect of applying RSA encryption on the source message having the stego object created. For this study, the source messages are both grayscale and RGB image files. The carrier file used is a 32-bit stereo WAV file. The metrics chosen to perform the comparative study include PSNR, Mean Square Error, Normalized Absolute Error, and Structural Content. To judge the resilience of the steganographic systems from a security point of view, the histogram analysis method is utilized to perform steganalysis. Further, the ineffectiveness of encryption in the steganographic process is also analyzed. The proposed method overcomes the vulnerabilities inherent to sequential and random sample selection methods using the unique sample selection method devised in this paper.
A Secured Communication in Watermarking using Fast Walsh Hadamard Transformation in Image Processing

E-commerce, or electronic commerce, refers to the purchasing and selling of goods and services conducted over electronic systems such as the internet and other computer networks. Consequently, there is a pressing need to bolster the security of data transmission over the internet to accommodate the expansion of e-commerce applications on the World Wide Web. Data encryption and information masking techniques were introduced and developed to secure these applications' data. The creation of digital picture watermarking schemes is the subject of this thesis. Our main emphasis in this thesis is on the advancements made possible by the Fast Walsh Hadamard Transformation (FWHT). The primary result of this study is a proposed method for implementing FWHT in digital image watermarking. If a picture is described at multiple resolutions, its decoding can proceed in stages, starting at a lower resolution and working its way up to a higher one. In order to better analyse an image, the FWHT separates it into high- and low-frequency components. The edge components are described in the high frequency portion, while the low frequency portion is further divided into high and low frequency segments. Since the human visual system is more insensitive to abrupt changes in edge contrast, low frequency components are typically employed for watermarking. Several metrics, including Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Metric, were used to compare the efficacy of various watermarking methods (SSIM). The aforesaid analysis forms the basis for applying Level-2 and FWHT to enhance the image quality characteristics. It is determined which of these FWHT produced the best outcomes by comparing the others. The outcome demonstrates that the extracted watermark is just as easy to recognize as the original.
Quantum Image Watermarking Based on Least Significant Bit for Color Images

The development of future technology allows the transfer of information transmission in quantum form to be more widely used. However, research related to data security is still in progress and is limited so that it can provide greater potential for data theft with quantum technology. One of them is the theft of visual information data (image). Thus, we need a data security system that can protect image information in quantum computing. Quantum image watermarking is a data security technique by inserting a watermark that aims to protect property rights for multimedia products, such as images. This study proposes a quantum image watermarking scheme based on (Least Significant Bits) LSB for copyright protection for color images. In the embedding process, the watermark image is inserted into the cover image using the LSB method, then transformed into a quantum state. In the receiver, the quantum value detection is carried out on the watermarked image to extract back the watermark that has been inserted. From simulation result, the quantum watermarking system obtains high imperceptibility, the watermark robustness to Pauli-X and Pauli-Controlled-NOT (CNOT) noise and high watermark payload.
A Comprehensive Analysis of Text Analysis Algorithms for E-mail Itemization

Electronic mail or E-mail is the message in a formatted manner that is used for personal or official communication. Communication through email is preferable because it is quick, easy, and requires an only internet connection which makes it accessible from anywhere in the world. As email is a quicker way of communication so the proportion of usage increases gradually which leads to an increase in the demand for unmanned governance. Automatic management is required for spam categorization, phishing of email, multiple folder arrangement, etc. The spam message quantity has increased by 30 percent in the past year across different services. An average person receives 40–50 emails per day out of which 55 percent are identified as spam. These days spammers use numerous techniques to fulfill their ill intentions through fraudulent activities. So there is a requirement for email text summarization and analysis. The backbone of this paper is a comprehensive study of existing techniques for spam mail categorization using various machine learning algorithms. Further, the aim of this paper is to build a robust method to categorize these spam and ham mail.
Digital Signatures and El Gamal Scheme Integration for Secure Data Transmission in Digital Transaction Survey

Security can be considerably a major concern when it comes to high-end data transmissions like satellite parameter communication, Defence security codes, communication, etc. Sensitive information of any type can lead to various attacks from intruders who have malicious thoughts. These attacks can be significantly harmful to the loss of user data (sensitive or non-sensitiw data). This problem is look thoughtfully for a long time and resolved by integrating Digital Signature and ElGamal scheme (Elliptic Curve Cryptography). This Integration provides a Cognitive Feature for Authorization. The encryption is done using the standard ElGamal scheme with a well-built reputation for itself in secure data transmission. It provides a secure communication channel between the two ends by authenticating the sender with Digital Signature. One of the thrust areas of this project is digital transactions.
Performance Evaluation of Solar Power Plant Using Exploratory Data Analysis

This study enhances the current monitoring and maintenance system of a solar power plant in a rural area in Sabah, Malaysia by performing data analytics and data visualizations on the collected data. The specific objectives of this project are to investigate the parameters of the solar power plant monitoring system, to conduct exploratory data analysis on the collected data by developing data visualizations, and to integrate the developed data visualizations into dashboards. The methodology used in the development of this project is Agile, and the final outcome are dashboards developed using Power BI. Based on the exploratory data analysis, faulty or underperforming inverters were detected, and factors affecting the daily yield were identified.
IoT Based Fall Detection System

The assistance services in medical scenarios keep expanding, stated falls prove to be the second greatest cause of involuntary injury deaths globally, according to the World Health Organization. As per statistics, adults over 65-70 years tend most likely to be the prey for fatal falls. Hence for old people and certain patients, developing a fall detection system with the assistance of technology and daily life simplicity to improvise the quality of living is necessary. Keeping up with the existing systems and in order to elevate prevention of falls, this fall detection system provides results in less time and in a more specified manner. We use a host chair with an attached device based on the Internet of Things (IoT) for sensing and monitoring required important factors. The novelty of this project goes hand in hand with automating facets of the fall detection process, notifying the fall, displaying the required update on the app, and apprising the user.
A Methods of Medical Image Security Using Visual-Cryptography Techniques

Today in this technology era, most of the information is exchanged across open channels, and the security of such information has been a major concern. Medical Image Security has emerged as a new modern study subject as a result of the development of digital health-care data systems. To address medical picture security, the majority of researchers have employed image watermarking and image encryption. Though there are various other methods are also used for securing information in digital images such as steganography, digital signing, the Visual cryptography techniques is still popular among researcher for information security. The Visual Cryptography Scheme has the unique virtue of being able to decode visually the secure image by superimposing shares without the need for computation. The public key encryption approach is also used to make the visual cryptography image sharing more safe. In this article we carried the review of all these information hiding methods, types and technologies available in the literature along with their need and challenges for implementation.
RGBD-based Real-time Volumetric Reconstruction System: Architecture Design and Implementation

With the increasing popularity of commercial depth cameras, 3D reconstruction of dynamic scenes has aroused widespread interest. Although many novel 3D applications have been unlocked, real-time performance is still a big problem. In this paper, a low-cost, real-time system: LiveRecon3D, is presented, with multiple RGB-D cameras connected to one single computer. The goal of the system is to provide an interactive frame rate for 3D content capture and rendering at a reduced cost. In the proposed system, we adopt a scalable volume structure and employ ray casting technique to extract the surface of 3D content. Based on a pipeline design, all the modules in the system run in parallel and are designed to minimize the latency to achieve an interactive frame rate of 30 FPS. At last, experimental results corresponding to implementation with three Kinect v2 cameras are presented to verify the system's effectiveness in terms of visual quality and real-time performance.
A Transfer Learning Approach for Electromagnetic Side-channel Attack and Evaluation

Side channel analysis has posed a huge threat to cryptographic function security. And some mitigation techniques including masking and trace data desynchronization are used to prevent the adversary from attacking. In this article, we propose a transfer learning framework to attack this countermeasure and recover the secret key effectively and efficiently, which can not only reduce the trace number required for side channel attacks but also reduce the time to train deep learning models. We first deserve a well trained deep learning model by taking the simplest source trace synchronization dataset as the source domain. Then we migrate and adjust this well trained model to the target trace desynchronization dataset by pre-training, freezing, fine-tuning, etc. In addition, we implement four different deep learning models including AutoEncoder, CNN, MLP and ResNet into the transfer learning framework for fixed key scenario and variable key scenario to demonstrate the framework scalability and generality. Finally, we evaluate the transfer learning framework using the public ASCAD datasets. The experimental results show that our method can successfully recover the key with 75 traces on the ASCAD synchronous dataset, and the transfer learning framework can solve the situation that the signal is biased, it only needs 60 traces to recover the keys on the random offset 50 dataset, and only 75 to recover the keys on the random offset 100 dataset.
Practical Exploration of Educational Metaverse——A Teacher Training System Based on Virtual Reality

Metauniverse is a post real world, a multi-user environment combined with physical reality, and a permanent and lasting digital virtual. It is based on the integration of various technologies that can interact with virtual environments, digital objects and people (such as virtual reality (VR) and augmented reality (AR)). Therefore, the meta universe is a network composed of social, networked and immersive environments, which are connected in a lasting multi-user platform. It realizes seamless user communication with real-time and dynamic interaction of digital artifacts. The educational meta universe also provides different possibilities for teacher training, and provides teachers with a virtual training environment that is not limited by time and space. This paper mainly uses the methods of experimental analysis and questionnaire survey to study the design of distance teacher training system based on virtual reality. It aims to tap the advantages of VR, reduce problems in teacher training, and further realize VR based remote training. The design and implementation of the system become possible. The survey results show that when 1100 people access at the same time, the response time is 3.81 seconds, and the server utilization of the system continues to rise with the increase of the number of concurrent users; Most interviewees expressed satisfaction with the design of the teacher remote training system.
How Patients Benefit From Broad Use of Blockchain in Healthcare​

Now and in the near future, blockchain improves the lives of patients. Hear how the patient journey transforms with the ability to control ownership of patient data, and how critical data is shared seamlessly between stakeholders. With blockchain, healthcare is becoming patient-centered, both clinically and administratively.
Going Under the Hood of dApps and DAOs

Physician credentialing is an arduous and lengthy process resulting in delays in physicians being able to work and generate revenue. In this session the audience will follow the ins and outs of authenticity, transparency and explore the building blocks to understanding blockchain technology.
Result Invalidation for Incremental Modular Analyses

To reduce the running time of static analysis tools upon program changes, incremental static analyses reuse and update pre-existing results. Such analyses must efficiently detect and remove outdated results. We introduce three novel, complementary result invalidation strategies for incremental modular analyses. The core idea of our work is to alternate invalidation with computation. We apply our strategies to a recent, state-of-the-art incremental modular analysis that suffers from imprecision, and evaluate them on soundness, precision, and performance. Our strategies lead to precision improvements compared to an incremental analysis without invalidation, though the precision of a full reanalysis is not yet matched. On most benchmarks, our incremental analysis performs well. However, on some benchmarks our analysis performs poorly as the changes drastically change program behaviour, for which the changes are difficult for an incremental analysis to handle.
Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

Abstract The forward kinematics of parallel robots is a challenging issue due to its highly coupled non‐linear relation among branch chains. This paper presents a novel approach to forward kinematics of parallel robots based on kernel extreme learning machine (KELM). To tackle with the forward kinematics solution of fully parallel robots, the forward kinematics solution of parallel robots is equivalently transformed into a machine learning model first. On this basis, a computational model combining sparrow search algorithm and KELM is then established, which can serve as both regression and classification. Based on SSA‐optimised KELM (SSA‐KELM) established in this study, a binary discriminator for judging the existence of the forward kinematics solution and a multi‐label regression model for predicting the forward kinematics solution are built to obtain the forward kinematics general solution of parallel robots with different structural configurations and parameters. To evaluate the proposed model, a numerical case on this dataset collected by the inverse kinematics model of a typical 6‐DOF parallel robot is conducted, followed by the results manifesting that the binary discriminator with the discriminant accuracy of 88.50% is superior over ELM, KELM, support vector machine and logistic regression. The multi‐label regression model, with the root mean squared error of 0.06 mm for the position and 0.15° for the orientation, outperforms the double‐hidden‐layer back propagation (2‐BP), ELM, KELM and genetic algorithm‐optimised KELM. Furthermore, numerical cases of parallel robots with different structural configurations and parameters are compared with state‐of‐the‐art models. Moreover, these results of numerical simulation and experiment on the host computer demonstrate that the proposed model displays its high precision, high robustness and rapid convergence, which provides a candidate for the forward kinematics of parallel robots.
Delivering Security

Silicon Labs’ CSO, Sharon Hagi, talks to Neil Tyler about security and product development
Changing the Game

Sporting stadiums are fast emerging as proving grounds for some of the latest wireless technologies.
Meeting The AI/ML Design Challenge

KEDAR PATANKAR assesses the physical design considerations that need to be taken into account when it comes to AI/ML applications
Strategic Partnership Looks to Drive BNNs

XMOS AND PLUMERAI PARTNER TO ACCELERATE BINARISED NEURAL NETWORKS.