Reviewerly/exHarmony huggingface dataset:
Improved Particle Swarm Optimization for Optimizing The Deep Convolutional Neural Network

In recent years, Deep Neural Networks (DNN) have been employed in different types of fields for recognizing, classifying, detecting and sorting, etc. Thus, optimizing the DNN is very essential to obtain a potential solution with high accuracy. Neural network(NN) can be optimized by optimizing the weight values of the network. Many studies have been done utilizing conventional optimization techniques such as Stochastic Gradient Descent(SGD), Adam, Ada Delta, and so on. Employing traditional optimization approaches in optimizing the deep neural network, on the other hand, results in poor performance due to trapping at local extremes and premature convergence. As a result, researchers looked into Swarm Intelligence(SI) optimization algorithms, which are fast and robust global optimization methods that have gained a lot of attention due to their capability to deal with complicated optimization problems. Among different types of SI algorithms, Particle Swarm Optimization (PSO) is mostly used in NN optimization as it has a few parameters to be tuned, and no derivative for simplification. However, recent studies have shown that the standard PSO is not the best tool for tackling all engineering problems since it is slow in some contexts, such as biomedical engineering and building construction, and converges to local optima. Therefore, improving the PSO algorithm is critical for obtaining a feasible solution to NN optimization problems. Hence, the main goal of this study is to make advanced enhancements to the PSO algorithm to optimize DNN while addressing several concerns, such as minimizing the computational cost or Graphical Processing Unit (GPU) dependency and having large input data in Deep Convolutional Neural Network (DCNN) training.
InPRA – An Intelligent System for Writing while Doing Research

Research students have difficulty following at least some steps in research methodology. Nowadays, computing technology has made it easy for researchers to execute the steps with the support of software tools. Although many research students gradually manage to conduct the research, some find it challenging to write the thesis in time. This issue stems from separating the conduct of research from writing the thesis. This paper presents the extension to our Intelligent Personal Research Assistant, InPRA, to enable a researcher to write the thesis incrementally while doing the research. Here, we have introduced an intelligent writing template, which guides the writing process differently from the order of chapters of the thesis. The intelligent template is also integrated with tools such as Zotero, MS Word, and Grammarly software. The incremental writing process enables the generation of research proposals, progress reports, conference papers, theses, and journal papers. The writing extension to InPRA has been evaluated with research students at the postgraduate level.
Accelerated Adversarial Attack Generation and Enhanced Decision Insight

Adversarial Attack is a rapidly growing field that studies how intentionally crafted inputs can fool machine learning models. This can have severe implications for the security of machine learning systems, as it can allow attackers to bypass security measures and cause the system to malfunction. Finding solutions for these attacks involves creating specific attack scenarios using a particular dataset and training a model based on that dataset. Adversarial attacks on a trained model can significantly reduce accuracy by manipulating the decision boundary, causing instances initially classified correctly to be misclassified. This alteration results in a notable decline in the model’s ability to classify instances after an attack accurately. The above process helps us develop strategies to defend against these attacks. However, a significant challenge arises because generating these attack scenarios for a specific dataset is time-consuming. Moreover, the disparity between the model’s prediction outcomes before and after the attack tends to lack clear interpretability. In both above limitations, the common limiting factor is time. The time it takes to devise a solution is crucial because the longer it takes, the more opportunity an attacker has to cause harm in real-world situations. In this paper, we propose two approaches to address the above gaps: minimizing the time required for attack generation using data augmentation and understanding the effects of an attack on the model’s decision-making process by generating more interpretable descriptions. We show that description can be used to gain insights into how an attack affects the model’s decision-making process by identifying the most critical features for the model’s prediction before and after the attack. Our work can potentially improve the security of machine learning systems by making it more difficult for attackers to generate effective attacks.
Fully-Fusible Convolutional Neural Networks for End-to-End Fused Architecture with FPGA Implementation

The inference of artificial intelligence algorithms, and especially Convolutional Neural Networks (CNNs) in edge devices has gained attention in recent years. However, low on-chip memory and restricted off-chip memory bandwidth have hindered its advancement. The conventional bottom-up and top-down approaches in design, which start from either a network structure or a hardware architecture and go to the other one, have not been adequate to rectify memory barriers. In this paper, with a network-hardware co-design approach, we revisit the architecture and network several times, looking for opportunities to shrink the hardware of CNN while achieving acceptable accuracy. In this line, we introduce Fully-Fusible CNNs (FFCNNs) that can be implemented with the layer fusion technique to build fully-fused architectures that omit memory components between layers. We showcase fully-fused architectures by implementing an end-to-end (including the fully-connected layer) fused CNN on FPGA. We also design an FFCNN for CIFAR-10 as a proof of concept for the suitability of our approach for edge devices. The FFCNN achieved 78.79% with only 95522 trainable parameters and 97660 FLOPs. The results show a promising opportunity for efficient edge implementation of CNNs using the techniques in this paper.
Improving Stochastic Quantum-Like Annealing Based on Rerandomization

This paper introduces the Rerandomization technique of simulated stochastic quantum annealing (SSQA), which enables the solution of large-scale combinatorial optimization problems. Rerandomization is a technique that explores a wider solution space by resetting the spin states, and it shows excellent performance for large-scale problems when executed at the optimal timing. The optimal timing can be predicted using mathematical equations. The adoption of the predicted rerandomization timing is demonstrated to significantly improve the accuracy from 42% (without rerandomization) to 100% in the graph isomorphism problem, a common large-scale combinatorial optimization problem with a size of 2,500 bits. Furthermore, the proposed method shows to solve problems of four times the size at the same speed as traditional SA.
A Deep Learning Approach for Unifying Object Re-Identification and Cross-view Geo-localization on Autonomous UAVs

In recent years, with the rapid development of Unmanned Autonomous Vehicles (UAVs) technologies, the applications of autonomous UAVs in aerial surveillance-related missions have been increasing. These missions often involve image processing-related tasks, such as object (human, vehicle) detection, re-identification, image-based localization, and navigation. However, these tasks often require much computation to achieve the best performance, which is quite a challenge for autonomous UAVs due to the limitation of hardware and computing resources on these devices. Unfortunately, no architecture has been established to perform these tasks adequately. In this study, for the first time, we combine three tasks that can significantly contribute to the operation of autonomous UAVs in real-life missions: Person Re-Identification, Vehicle Re-Identification, and Cross-view Geo-Localization. We exploit the generalization ability of currently available deep metric learning methods and propose a framework to learn to re-identify objects (humans, vehicles) and perform geo-localization simultaneously. The proposed method was evaluated on benchmark UAV datasets and received promising results.
Proposal of a Fault-tolerant controller for wheeled mobile robots with faulty actuators

This article introduces a fault-tolerant control scheme for a two-wheeled mobile robot. The focus of this model is to address the impact of faults within the actuators, a critical aspect that significantly impacts the robot’s performance and operational efficiency. An observer is designed to monitor the dynamic state of the robot’s system, allowing it to promptly identify and assess the actuator’s faults that may arise during its operation. Based on this data, the extent of the fault’s influence on the overall system is estimated, providing essential information for subsequent control decisions. Analyzing and synthesizing control laws are built on mathematics and Lyapunov stability theory. The simulation is done by MATLAB-Simulink to validate the efficiency of the proposed control law, contributing valuable insights to the domain of robotics and control engineering.
Fusion of Hand Landmarks Detection and Body Region via Semantic Segmentation into Visual Cue for Hands Tracking over Steering Wheel

Aiming at hands tracking over steering wheel, fusion of hand landmarks detection and body region segmentation into visual cue has been proposed in order to make likelihood evaluation simpler and efficient. Labelled multi-Bernoulli filter in Track-Before-Detect manner utilizes the visual cue by separable likelihood to evaluate pixels categorized into hands and body region. Experimental result has been shown to demonstrate performance of the proposed method.
Advanced Algorithm for Detecting Frequency Hopping Signal in High Noise Environment

This work investigates a new method for detecting frequency hopping signals under a high noise environment, which the amplitude method can not detect. In the first part of the work, the performance of the proposed method is tested with simple signals, such as radar pulse and binary shift keying signal in MATLAB environment to confirm if the method is able to detect them successfully. Next, Costas frequency codes, one kind of frequency hopping signal, which is our primary interest, will be examined to determine the lowest value of SNR, at which the proposed method can still detect all signals efficiently. The simulation results confirm that the proposed method can detect Costas frequency codes in Gaussian noise and Rayleigh fading channel and under the jamming condition by linear frequency modulation (LFM) signal with signal-to-noise ratio $\mathrm{S}\mathrm{N}\mathrm{R}\geq-12\mathrm{d}\mathrm{B}$ and $\mathrm{S}\mathrm{N}\mathrm{R}\geq-$6dB, respectively.
A Motion Status Discrimination Method Based on Velocity Estimation Embedded in Multi-object Tracking

Multi-object tracking(MOT) has been a research hotspot in the field of computer vision. Currently, the most popular tracking paradigm involves first using object detection models to process the images, and then combining methods such as Kalman filtering for tracking. This paradigm has achieved good results on various datasets, but it lacks the ability to accurately determine whether an object is in motion. In certain scenarios, researchers may be interested only in moving objects and desire information specifically related to their motion, without the need for information about stationary objects. However, there has been limited research on determining the motion state of targets in the field of multi-object tracking. To address this issue, this paper proposes a velocity-based motion state discrimination method for object tracking. This method can be integrated with different multi-object tracking methods to provide more information about the tracking results. Additionally, we evaluate the effectiveness of the method on a custom dataset and test it on selected scenes of the re-annotated CDnet-2014 dataset. The experimental results demonstrate significant achievements of our proposed method. When combined with the YOLOv8+StrongSORT tracking method, the motion state discrimination accuracy (F1 score) of our method reaches 0.6975 on the custom dataset and 0.7831 on the CDnet-2014, showcasing the practicality of our method.
Mitigation in online interactions on controversial topics: A methodological proposal

&#x0D; This article puts forward a methodological proposal for the pragmatic analysis of mitigation in online written interactions, specifically in discursive genres such as the digital press forums and Twitter. Mitigation is a rhetorical and pragmatic strategy linked to the face needs and communicative goals of the participants in the communication (Albelda Marco &amp; Briz Gómez 2020). For this reason, drawing on the existing literature concerning the selected discursive genres and on an analysis of some contributions about a controversial Spanish tradition, namely the Moors and Christians festivals, this article highlights both situational and linguistic features to be taken into account when analysing mitigation in these genres, and proposes adaptations to Albelda Marco et al.’s (2014) methodology for analysing mitigation in spoken interactions. In doing so, this study presents a methodological proposal directly suitable for analysing mitigation in online interactions in discursive genres such as digital press forums and Twitter. &#x0D;
A survey image classification using convolutional neural network in deep learning

The processing of image data for transmission and machine perception, in addition to providing enhanced visual data for human simplification, makes image processing an always fascinating field. Digital images can be made better through the use of image processing. Grayscale conversion, image segmentation, edge detection, feature extraction, and classification are all methods of image processing. The current deep learning convolutional neural network-based image classification system is discussed in detail in this chapter. The fundamental motivation behind the work introduced in this chapter is to think about and examine the various models involving convolutional brain network in profound learning for picture grouping. DenseNet, MobileNet, VGGnet, GoogleNet, ResNet, ImageNet, NasNet, and AlexNet were among the standard models that the algorithm was applied to. The most famous convolutional brain network is utilized for object discovery and item classification groupings from pictures are AlexNet, GoogleNet, and ResNet. Generally, the chapter gives definite information on picture handling and order strategies.
Comparison Analysis of Clustering Methods for Clustering of Indonesian’s Gender Empowerment Conditions in 2022

Gender empowerment is one of the components of gender development achievement measures that is an important agenda at the global level in realizing the Sustainable Development Goals. The Gender Empowerment Index (GEI) of Indonesia has been continuously improving since 2010, indicating an increasing involvement of women in various areas of life. However, behind this upward trend in GEI, there is still inequality at the provincial level. Therefore, there is a need to formulate development strategies, one of which is gender-based. One possible step is to categorize regions in Indonesia based on their gender empowerment characteristics, so that government interventions can be targeted effectively. This research utilizes two clustering approaches, namely Hierarchical Methods and Partitioning Methods, with data consisting of three variables representing the components of GEI for 34 provinces in Indonesia in 2022. The selection of the best method and number of clusters is based on internal validity and stability validity, followed by the determination of the smallest within and between standard deviation ratios. From the cluster analysis results, the best method is found to be K-means with a total of 5 clusters.
Blockchain paradigm in the metaverse

Blockchain technology is currently a technology that is being widely used in various sectors, one of the fields where it is being widely used is the metaverse, which is another booming technology. These two technologies are closely related and play an essential role in technological progress in various areas, such as education, medicine and teleworking. For this reason, it was decided to carry out an investigation which would explain how blockchain technology is being applied within the metaverse. To carry out this research, the Prisma method was used, which facilitated the completion of an appropriate investigation following the established criteria. Three databases were consulted: Scopus, IEEE Xplore and ACM, in which the specific search strings for each database were applied and the articles used to carry out this systematic review were obtained. In this study, it was possible to expand the general concepts of blockchain and metaverse. The structure of the blockchain was detailed and other structures proposed by several authors were presented, such as HCNCT, MIS, FileInsurer and BlockNet. The use of blockchain in the metaverse was also addressed, describing its purpose and how it is being used to improve various aspects, such as security, data storage, transactions, and economics. Likewise, the current use of Blockchain in technologies such as NFTs and cryptocurrencies was explored. Finally, we delved into how the blockchain is an essential component of the metaverse, especially when it comes to the security of virtual worlds
Developing and Validating E-Learning Module for Islamic Higher Education Digital Literacy in Preventing Hoax (EMODILPH)

The digital literacy curriculum in universities, especially Islamic universities, needs to be developed intensively to respond to the rapid exposure of hoax news among students. Therefore, this study aims to develop an e-module for digital learning and to investigate how this product can be implemented in a digital literacy curriculum model at Islamic universities. The Rasch measurement was used to assess fit validity and reliability criteria based on validator assessment and student responses. Many Facet Rasch Measurement was used to confirm validity and reliability from 4 different validators based on e-module development in phase 1 and phase 2 using 10-item criteria. A rating scale analysis was used to check validity and reliability according to 233 student responses and item category interactions. The e-module achieved fit validity and reliability. The e-module implementation showed students gave higher responses, 55% (agree) and 39% (strongly agree), if the e-module can help them with digital literacy for understanding hoax information. The bias interaction and the differential item functioning have been discussed in this study. The findings will provide initial knowledge in the development and implementation of e-modules for preventing hoaxes and will improve students' understanding of filtering information in the digital literacy curriculum.
Text recognition using CRNN models based on temporal classification and interpolation methods

Analysis and recognition of text by hand is a very challenging task and is almost needed in every field either through technical or non-technical process. These days there is a huge demand and need for both digitalization of handwritten text and minimizing the use of hardware such as keyboard for text entry. The digitalization of handwritten text has various purposes like storage, searching, modification and sharing of data while the need to recognize and read the data by writing in the air is needed to support the growing technical advancements such as Augmented Reality (AR), Virtual Reality (VR), etc. One simple way to cater to these needs is to do it manually but it becomes really difficult to get it done when there is either a lot of data or not a normalized structure. Due to these challenges, there is a need to present a fully independent model. While there have been various attempts at to get to a solution some of them are not efficient and the others are not generalized. In this chapter, a structure is presented to recognize text from handwritten data and air-writing using a combination of recurrent and convolutional neural networks (CRNN) along with connectionist temporal classification (CTC) and interpolation methods.
Video Blog (Vlog) English Learning Media Innovation explores creative English learning

Students frequently require assistance acquiring knowledge about English language learning delivered in class within a restricted timeframe, without the chance for repeated conversations or thorough study. Furthermore, the task of retaining previously taught content may require help. Thus, educators must implement efficient instruction that stems from innovative teaching methods. An accomplishment that has been made is the utilization of educational media. This study investigates the current body of literature on using video blogs (vlogs) to facilitate education. This study employs the methodology of systematic literature review (SLR). The data collection process involved systematically gathering and analyzing papers about vlogs as an instructional medium, published from 2013 to 2023. This study utilized a selection of eight scholarly articles, theses, and dissertations sourced from esteemed national journals or conferences. Data is gathered using the Publish or Perish tool. Research indicates that incorporating vlog media into the educational process might enhance instructional efficacy and positively affect student learning achievements. Hence, vlogs might serve as an instructional instrument for imparting English learning knowledge. This study offers a thorough examination of the feasibility of this invention for enhancing English language teaching.
The Teacher Professional Education Students' Perception on Learning Management System-based Online Learning

This research aimed to investigate the perceptions and impacts of online learning utilizing the SPACE (Electronic religious learning system application) among students enrolled in Teacher Professional Education (TPE) programs at State Islamic Religious Colleges. Employing a quantitative approach, the study utilized linear regression analysis and structural equation modeling through AMOS 23. Effectiveness was gauged using indicators proposed by Robert E. Slavin, encompassing quality of learning, appropriateness of learning level, intensity, and time. The study participants comprised students across three Teacher Professional Education Study Programs. The findings revealed that nearly all participants expressed ease in learning and perceived the modules within SPACE as more effective. They also indicated that the materials/modules in SPACE aligned well with the needs of TPE participants and found the learning procedures within SPACE easy to follow. Notably, incentive variables within implementation indicators had a direct influence on perceived ease of use (user convenience), subsequently impacting the perceived usefulness variable. For future research, integrating direct interview data from students and conducting comparative analyses between offline and online implementations in professional education would provide valuable insights.