Reviewerly/exHarmony huggingface dataset:
Feature Selection Mechanism for Attention Classification using Gaze Tracking Data

The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time.
Control of Quantum Systems: Comparison of Different Techniques by the Example of Charge and Spin Semiconductor Qubits

The semiconducting technologies appear to be an ideal platform to construct quantum systems, inheriting the classical silicon techniques and controllable circuits, and exploiting the quantum nature of confined particles. Meanwhile, quantum optimal control algorithms have been proven to be an efficient tool to assist quantum system manipulation, quantum computing and quantum applications. In this paper, we review some of the main quantum optimal control algorithms in closed quantum systems, and we explore their implementation to charge and spin-based semiconductor qubits. In addition, we propose a bang-bang control algorithm and we examine its performance in selected case studies.
Interactive English Teaching and Learning Based on Mobile Application

English is a universal language used worldwide as the primary language. Learning English can open more opportunities for someone to get involved with the international world. In Indonesia, English is not the primary language, but English still occupies an important position in daily life. For some students, English is not something easy to learn, and they constantly face difficulties such as lack of self-confidence, hard to understand grammar, and hard memorize English vocabulary. The author developed an Android application to help high school students to learn English. This application contains theory for grammar, English vocabulary to memorize with a track record, and a chatbot to increase self-confidence to speak in English. Interactive English Learning Application has user’s status to track how far users have already learned English and helped with quizzes. This application was developed using flutter as its front-end, MySQL as its database, and PHP as the backend.
Design of Hexagonal Oscillator for True Random Number Generation

This paper presents the design of a low power, high speed true random number generator (TRNG) based on hexagonal ring oscillator-based topology targeted for cryptographic application. The proposed design takes the advantage of jitter provided by the two hexagonal oscillators to produce highly random sequence. The hexagonal oscillator improves the level of entropy due to more number of oscillating stages and a high phase noise. The proposed design is simulated in CADENCE tool on UMC 180nm CMOS process. The design provides a delay of 2.15ns with a data rate of 465Mbps while dissipating 1.27mW of total power with a FoM of2.73pJ/bit. The TRNG is tested with diehard test suite for the verification of random sequence.
A Comparative Study on Machine Learning based Prediction Models for Public Participation Rate in an Election Voting

Prediction of public participation in elections is one measure of election success. Voter participation is at the polling station level and involves four data sources: voters, polling stations, recapitulation, and village profiles. The preprocessing stage is carried out on each dataset, including maintenance, transformation, and integration. Two types of datasets are defined, involving all attributes and removing the result from attribute correlation. Classification method with five machine learning algorithms (ML) with participation prediction classes labelled High and Low. The highest result is 85.90% for the type 1 dataset and Artificial Neural Network (ANN) algorithm with 60% training and 40% testing split dataset. Furthermore, for detachment type 2, by eliminating several attributes, 100% results are obtained for the K-Nearest Neighbor (kNN) algorithm with a split dataset of 70% training and 30% testing. Of the five ML algorithms, only the Naïve Bayes (NB) algorithm did not experience an increase in prediction results. Furthermore, the significant influence of attributes on the prediction class is shown in each attribute of the dataset, including the Permanent Voter List at the Polling Place (DPT TPS), Local, Health Access and Total Recapitulation.
Toward a Behavioral-Level End-to-End Framework for Silicon Photonics Accelerators

Convolutional Neural Networks (CNNs) are widely used due to their effectiveness in various AI applications such as object recognition, speech processing, etc., where the multiply-and-accumulate (MAC) operation contributes to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 95\%$</tex> of the computation time. From the hardware implementation perspective, the performance of current CMOS-based MAC accelerators is limited mainly due to their von-Neumann architecture and corresponding limited memory bandwidth. In this way, silicon photonics has been recently explored as a promising solution for accelerator design to improve the speed and power-efficiency of the designs as opposed to electronic memristive crossbars. In this work, we briefly study recent silicon photonics accelerators and take initial steps to develop an open-source and adaptive crossbar architecture simulator for that. Keeping the original functionality of the MNSIM tool [1], we add a new photonic mode that utilizes the pre-existing algorithm to work with a photonic Phase Change Memory (pPCM) based crossbar structure. With inputs from the CNN's topology, the accelerator configuration, and experimentally-benchmarked data, the presented simulator can report the optimal crossbar size, the number of crossbars needed, and the estimation of total area, power, and latency.
ViT-LR: Pushing the Envelope for Transformer-Based on-Device Embedded Continual Learning

State-of-the-Art Edge Artificial Intelligence (AI) is currently mostly targeted at a train-then-deploy paradigm: edge devices are exclusively responsible for inference, whereas training is delegated to data centers, leading to high energy and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> impact. On-Device Continual Learning could help in making Edge AI more sustainable by specializing AI models directly on-field. We deploy a continual image recognition model on a Jetson Xavier NX embedded system, and experimentally investigate how Attention influences performance and its viability as a Continual Learning backbone, analyzing the redundancy of its components to prune and further improve our solution efficiency. We achieve up to 83.81% accuracy on the Core50's new instances and classes scenario, starting from a pre-trained tiny Vision Transformer, surpassing AR1 *free with Latent Replay, and reach performance comparable and superior to the SoA without relying on growing Replay Examples.
The impact of Under-Sampling Techniques on Classification Accuracy in multi-class Imbalance Data

Class imbalance can make the performance of a classification technique problematic. The main problem of class imbalance can be solved by using an Under-Sampling technique, namely adjusting the amount of data in the majority class to the minority class (removing some data in the majority class). However, many studies do not explain the impact of the Under-Sampling technique on the performance of classification techniques. Our study uses LVQ3 and K-fold Cross-Validation to prove this issue. LVQ3 is used to classify and k-fold cross-validation to perform classification performance tests. The research parameters used were learning rate (0.00001, 0.0001, 0.001, 0.01 and 0.1), window (0.001 and 0.2), n-prototype (10, 13, 26, 41 and 46), epoch 2000 and epsilon 0.2. The results showed a significant difference in accuracy when using old and new data. This research suggests that the balanced distribution of the data, the experimental setting, and differences in data sampling affect the accuracy. As a result, data not used in the technique becomes useless. However, the data cannot be said to be useless, especially regarding accuracy.
Data Augmentation Based on Contrastive Learning in Audio Anomaly Detection

Abstract Based on the contrastive supervised learning algorithm, this paper studies the impact of the linear combination of data augmentation in audio anomaly detection. Previous studies have shown that the linear combination of data enhancement performs well in image detection. However, there are few studies on similar methods in sound anomaly detection, and this study makes up for this gap. We apply transformations to one branch of the algorithm with the audio augmentation module of the AugLy library, and the other branch is directly transformed into the Mel spectrogram. We find that more data augmentation patterns can help neural networks learn more high-quality representations, and a suitable linear data combination augmentation can improve the performance of model generalization at a low cost. The combination effect of Add Background Noise + Pitch Shift is the best. The AUC index of the fan dataset is increased by 10.06% on average, and other datasets are also improved to varying degrees.
Programmable logic controller memory management vulnerability analysis

Abstract Programmable logic controller (PLC) is omnipresent in industrial control system (ICS), which plays an imperative part in critical infrastructures. However, once a PLC is compromised by an attacker, it will create destructive effects on critical infrastructures. The memory systems in PLCs are considered significant targets for attackers to disrupt real-world physical processes. In this paper, we proposed five types of attacks on the PLC memory management mechanism. Furthermore, we evaluated the feasibility of our memory attack generation approach on six PLCs from three leading controller vendors. A total of 22 memory attack payloads are implemented with several vulnerabilities on the target PLCs.
The Research and Analysis of Different Face Recognition Algorithms

Abstract With the background of fast developing artificial intelligence and Internet technology, face recognition, one of the product technologies under the theory of machine learning, has become widely applied on safety tests, identity confirmation, public security photos, and access control system. Based on the trend of its development speed, this technology will have well developed circumstances and the field of application will expand. One of the most essential reasons why it has become so popular is that the algorithms used for face recognition are advanced. In this paper, the author mainly researches five algorithms to realize face recognition based on relevant literature. They are separately CNN, PCA, SVM, MTCNN, and Facenet. After comparison and analysis, it can be found that CNN is one of the most suitable algorithms to recognize human faces, and PCA and SVM have problems of calculating speed. The use of complex neural networks can intensify the accuracy of face recognition just like MTCNN and Facenet. If combining different algorithms together and using their advantages in face recognition in different steps, the prediction of the algorithms will be better.
Experiments of Mutual Information Maximization on Deep Infomax

Abstract Image search and photo search at the current level of science and technology have become increasingly mature, and the underlying logic of these is the clustering of pictures. Mutual information(MI) is widely used in machine learning and deep learning, its value cannot be computed directly. Therefore, there are several methods to estimate MI, such as Deep VIB, MINE, and MoCo. Deep Infomax(DIM) technique is applied to maximize the MI based on Jensen-Shannon divergence. The paper derives the mathematical formula of Deep Infomax first. If two images have the maximum MI, it could be concluded that they are similar images. In the experiment, this technique is applied in clustering of datasets that are cifar10, celeba. In the experiment, the results of cifar10 and celeba are close to expectations and can be considered to have achieved good results. In cifar10, it clusters images of similar colors. In celeba, it clusters together faces of the same skin tone and even expressions. This paper and experiment could conclude that Deep Infomax is an effective way to cluster the images and maximize the mutual information.
Literature Review on Image Restoration

Abstract Image restoration is an essential part in the field of computer vision, which aims at predicting and filling the pixels of the missing images to achieve satisfactory visual effects, it has extensive application value in the fields of film and television special effects production, image editing, digital cultural heritage protection and virtual reality. With the introduction and application of the concept of deep learning in recent years, it has been widely studied in the academic and industrial fields, the performance of image restoration has been significantly improved, so that this long-standing research topic has once again aroused widespread concern and heated discussion on the social level. In order to enable more researchers to explore the theory of image restoration and its development, this paper reviews the related technologies in this field: firstly, the traditional image restoration methods are described, secondly, the background of deep learning is introduced, then the image restoration methods based on deep learning are described, subsequently, the several deep-learning based methods are compared and analyzed, finally, the future research direction and emphasis of image restoration are analyzed and prospected.
Antimatter and Its Application-Collecting Antimatter and Storage It as Energy Source

Abstract As antimatter and its potential to use as an energy source is discovered by modern scientists, there are few reviews to introduce the history and applications of antimatter which is the aim of the paper. This paper provides a brief introduction to how antimatter was discovered and some possible applications of antimatter in the future. In this paper, the physics of antimatter and the creation, storage and using ways of antimatter will be described. After that, I will summarize the feasibility of utilizing the energy from the annihilation of antimatter-matter. And some possibilities of applications based on this energy, like antimatter ships and antimatter factories, will be discussed.
Basic GAN Models and the Application In Medical Image Field

Abstract Generative adversarial networks (GANs) have shown its prominent performance to the world and be exploited in many areas. For example, generating human face image, translating image between different domain, generating supplementary data for tasks which lacks training and labeled data. Specially, GANs also shows its potential in medical image analysis. It can tackle the problems in medical image analysis such as medical image translation, segmentation, reconstruction, detection and image classification. Being capable of generating images at amazing level of realisim, GANs gives the opportunity to resolve the problem that labeled data for those rare diseases cannot meet the need for medical field. In this paper, we give an overview of GANs and its application in medical image analysis. Defects and advantages of those GANs methods are also thoroughly reviewed. We also discuss its potential improvement in future. We review those most frequently methods published until now. And essential details about papers we discussed in this paper and access to that paper is attached at the endpage.
The characteristics of teapots made of plastic and clay: what are their designs, functions, and impacts on the environment?

Abstract Currently, people are starting to reduce the use of traditional products and switch to ready-to-use products made from plastic, such as teapots. Nevertheless, clay crafts still maintain their existence in the widespread use of plastic products. Bayat is one of the subdistricts in Klaten Regency, Central Java, which is famous for its ceramics and pottery craft centres. Most artisans in the Bayat make crafts with clay as raw materials. This research focused on the advantages of Bayat pottery over other plastic products. This study aimed to increase public awareness to reduce plastic products’ use in daily life. This study used the descriptive qualitative method. The research was conducted in Bayat, Klaten, from March – June 2022. The results showed that clay crafts were considered more eco-friendly than plastic products. The development of Bayat pottery from clay has implications for design, function, usability, and environmental aspects. In the design aspect, the Bayat teapot can increase the aesthetic value of the craft. In terms of function and usability, the clay teapot functions as a water storage container as well as interior decoration. Meanwhile, in the environmental aspect, raw clay materials are easier to decompose, while plastic products are difficult to decompose.
Utilization of plastic bottle waste in eco-friendly wayang design for childrens

Abstract Plastic bottle waste can damages the environment in the sea and land. Plastic bottles are non-biodegradable, it takes decades to decompose. The author got a creative idea to utilizing plastic bottle waste into eco friendly wayang design. The research using qualitative methods and prototype designs as a result of research, this program directed at creative process in responding to environmental sustainability and growing childrens awareness used of plastic bottle waste. New concept to answer in the environmental problems and introduces wayang as Indonesian culture which is made from plastic bottle waste.
Fabric scrap interior accessories: A Solution to textile waste in Gulon Asri waste bank community in Solo, Indonesia

Abstract Indonesia is one of the world’s largest contributors to textile waste. Textile waste is difficult to decompose and takes an extremely long time to degrade safely without polluting the environment. This country has also turned into the world’s second-largest waste supplier. In the interior design sector, clothes are typically used as the primary medium to make textile-based products or upholstery. The interior can use fabric waste into a product with beauty, function, and economic value. This community service project report presents a process of turning textile waste into interior accessory products, such as blankets, carpets, and pillows. To this end, we utilized textile waste as the primary material for home accessories to enhance a house’s aesthetic value. This community service consisted of five stages, four of which have been completed and the final stage is still underway.
Fault Diagnosis of Jointless Track Circuit Based on ReliefF-C4.5 Decision Tree

At this stage, when judging the fault type of jointless track circuit, it mainly relies on the staff to analyze the computer monitoring data. This fault identification method has problems such as high dependence on staff and long fault identification cycle. In order to solve the above problems, the combination of the reliefF algorithm and the C4.5 decision tree is introduced into the fault diagnosis of the jointless track circuit. First, the reliefF algorithm is used to give weights to the selected 9 feature parameters according to the fault classification ability of the features. Second, sort the feature parameters according to the weight, and select the first seven feature parameters as the optimal feature subset. Finally, complete the training and testing of the classifier with the optimal feature subset. The experimental results show that the fault diagnosis accuracy of ReliefF-C4.5 Decision Tree is 93.33%, So the fault diagnosis model proposed in this paper is suitable for fault diagnosis of track circuit.
Unsupervised domain adaptation using modified cycle generative adversarial network for aerial image classification

The unsupervised domain adaptation (UDA) of aerial image classification is of great significance in the traffic management and public safety applications. Due to the differences in the distribution of source and target domain datasets from different sensors and cities, and the acquisition difficulty and high annotation cost of target domain datasets, existing domain adaptive methods have difficulty obtaining high accuracy and stability. In this work, a modified cycle generative adversarial network (CycleGAN) (MGAN) based on the multiscale residual block (MSRB) and multipooling coordinate attention (MPCA) modules is proposed to generate fake images, which is the most critical stage of the UDA method. In the MGAN, the MSRB module can extract the multiscale information of the image, and the MPCA module can fuse the position and channel information in the image. These two modules better extract the features of edges, regions and semantic classes, which makes the generated fake images better retain the information of the source domain and possess the style of the target domain. Otherwise, the Deeplabv3+ model is used to obtain a pretrained and final segmentation model in the first and third stages. In the experiment, the proposed method is validated on the aerial images of the Potsdam and Vaihingen datasets in the International Society for Photogrammetry and Remote Sensing (ISPRS) two-dimensional semantic labeling benchmark and improves overall accuracy from 52% to 60% on the segmentation of the target domain, compared with the existing optimal method; it also achieves an accuracy of 77% for buildings. The results demonstrate that the proposed method can achieve a higher accuracy and stability.
The Metaverse, at the crossroads of creating a new world and ambiguous predictions - analysis of trends, features and impacts on consumers and businesses

Massively embraced during Covid-19, the digital environment remains one of the key components of our life for various activities.From working to studying, socializing to shopping in what is called the 'new normal' post pandemic life.The Metaverse is taking this digital world to the next level through a 3D immersive virtual experience.Identified as one of the biggest trends nowadays, The Metaverse concept raises a lot of questions from various perspectives such as business, social or psychological.The favorable forecasts about its evolution are built on quite attractive assumptions.The Metaverse will open a new, boundless world, full of possibilities that is also financially tempting by the anticipation of new revenue streams.As promising as this new world appears, there is still a flip side with a lot of ambiguity.This article looks at the opportunities and challenges of the Metaverse, through the lens of responsibilities that companies and authorities should take in the rise of this new world.