Reviewerly/exHarmony huggingface dataset:
Pengembangan Tracking Pole Sebagai Tongkat dan Alat Penjepit Sampah bagi Pendaki Gunung

The increase in the number of mountain climbers has an impact on increasing the volume of rubbish along the climbing route which results in damage to the natural scenery on the climbing route, pollution of water sources, and changes in eating behavior patterns in animals. Trash handling on climbing routes has been carried out by providing sanctions for climbers who do not bring rubbish down to several base camps. The process of picking up climbers' rubbish experienced several obstacles, including: picking up dirty rubbish with unhygienic hands, limited water to wash their hands after picking up rubbish, difficulty for climbers picking up rubbish while wearing large bags, and rubbish on the edge of cliffs which endangered climbers. The aim of this research is to design the development of a tracking pole as a trash clamp tool for mountain climbers. The product development design for this research uses product development theory according to Ulrich which is divided into several phases, namely: planning phase, concept development phase, system level design phase, detailed design phase, testing and improvement phase, and product launch phase. This research is limited to the testing phase. The designed tracking pole is a tool that is simple and easy to use, as a stick and also a tool for collecting rubbish when climbers are climbing, a comfortable tool. The designed tracking pole was well received by climbers. This is shown in the panelists' responses after using the tracking pole during technical testing of the tool.
One-Stage Wireframe Parsing in Fish-Eye Images

This paper presents a simple yet efficient algorithm for detecting wireframes in a fish-eye image. Given a fish-eye image, our objective is to extract the semantically and geometrically salient lines and their corresponding junctions directly and without any undistortion processing. To this end, we introduce the circular-arc-based representation, converting the distorted line segments into a part of a circle by drawing inspiration from the projection characteristics of fish-eye lenses. Then a compact and end-to-end trainable model that tailors the redundant multi-module is realized and is used to directly output the vectorized wireframe in a one-stage fashion. Compared with previously considered the state-of-art two-stage method for line segment detection in the fish-eye image, our method achieves an average improvement of 2.8 in structural average precision, and boasts a remarkable increase in inference speed. Moreover, our method demonstrates universality when handling images captured by standard cameras, outperforming existing straight wireframe parsing methods.
Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.
Sampling Nambu-Goto theory using Normalizing Flows

Effective String Theory (EST) is a non-perturbative framework used to describe confinement in Yang-Mills theory through the modeling of the interquark potential in terms of vibrating strings. An efficient numerical method to simulate such theories where analytical studies are challenging is still lacking. However, in recent years a new class of deep generative models called Normalizing Flows (NFs) has been proposed to sample lattice field theories more efficiently than traditional Monte Carlo methods. In this contribution, we show a proof of concept of the application of NFs to EST regularized on the lattice. Namely, we introduce Physics-Informed Stochastic Normalizing Flows and we use them to sample the Nambu-Goto string action with two goals: use the known analytical results of this theory as a benchmark and demonstrate the efficiency of our method in obtaining new results of physical interest and in particular in providing a numerical proof for a conjecture regarding the width of the string.
Electronic Payment Systems and Money Deposit Banks’ Deposits Mobilisations in Nigeria

This study examined the effect of electronic payment systems on money deposits banks’ deposits mobilizations in Nigeria. The study modelled deposits mobilizations of banks in Nigeria as a function of two key variables; money supply (M2) and Remita payment transaction value, with payment transaction values of NIBSS (Nigeria Inter-Bank Settlement System) instant payments (NIP) and National Electronic Fund Transfer (NEFT) as control variables. Secondary data were sourced from Central Bank of Nigeria (CBN) Statistical Bulletin. The study employed ordinary least square (OLS) regression method to analyze the data. Findings from the study revealed that Remita payment transaction value is not good predictor of deposits of banks Nigeria with p-values = 0.3732 &gt;0.05 critical value as it had a negative linear relationship with money deposit banks’ deposits mobilizations. NEFT payment transaction value is not good predictor of deposits of banks Nigeria with p-values = 0.4434 &gt;0.05 critical value as it had a negative linear relationship with money deposits banks’ deposits mobilizations. However both money supply and National Instant Payment (NIP) recorded positive linear relationship with deposits of banks in Nigeria. Furthermore, the findings from the causality test indicated no directional causality between money supply, NEFT, NIP and deposits of banks in Nigeria but causality runs from Remita payment transaction value to deposit of banks in Nigeria. Consequently, the study concluded that, there is no significant linear relationship between electronic payment systems and deposits mobilizations of deposits money banks in Nigeria. Thus, the study recommended that Federal Government of Nigeria as part of her current modifications in monetary and fiscal policies should mandate all states and local governments to adopt Treasury Single Account (TSA) policy and subsequently patronize Remita payment channel as done by the Federal Government of Nigeria.
Fast Data Generation for Training Deep-Learning 3D Reconstruction Approaches for Camera Arrays

In the last decade, many neural network algorithms have been proposed to solve depth reconstruction. Our focus is on reconstruction from images captured by multi-camera arrays which are a grid of vertically and horizontally aligned cameras that are uniformly spaced. Training these networks using supervised learning requires data with ground truth. Existing datasets are simulating specific configurations. For example, they represent a fixed-size camera array or a fixed space between cameras. When the distance between cameras is small, the array is said to be with a short baseline. Light-field cameras, with a baseline of less than a centimeter, are for instance in this category. On the contrary, an array with large space between cameras is said to be of a wide baseline. In this paper, we present a purely virtual data generator to create large training datasets: this generator can adapt to any camera array configuration. Parameters are for instance the size (number of cameras) and the distance between two cameras. The generator creates virtual scenes by randomly selecting objects and textures and following user-defined parameters like the disparity range or image parameters (resolution, color space). Generated data are used only for the learning phase. They are unrealistic but can present concrete challenges for disparity reconstruction such as thin elements and the random assignment of textures to objects to avoid color bias. Our experiments focus on wide-baseline configuration which requires more datasets. We validate the generator by testing the generated datasets with known deep-learning approaches as well as depth reconstruction algorithms in order to validate them. The validation experiments have proven successful.
Gene Selection Using Multi-Objective Optimization for Disease Classification

The expression levels of thousands of genes in two sample cells are compared using micro array technology. It is possible to make key medical diagnoses, treat a specific disease, track the progression of the disease, and predict how the disease will respond to therapy using those sample cells, depending on their diverse sources. We must choose the most valuable genes from among the thousands of genes present in the microarray because not all of them are helpful in categorizing a particular condition. Our method of gene selection involves two stages. Thousands of genes are reduced to a few hundred (N=100) by utilizing a parametric technique in stage 1, and in stage 2, the collected genes chosen in stage 1 are going through a process of multi-objective genetic algorithm (MOGA), employing Pareto GA (Genetic Algorithm). A smaller fraction of the top-ranked genes chosen in stage 2 is taken into consideration classifying the disease to find with the highest accuracy.
A Novel Implementation of a Cohesive Regional Language Tweet Translator

Twitter has evolved into a forum for people to connect with others, exchange news and events, and voice their ideas in the age of social media. Despite the availability of Twitter's translation feature for several regional Indian languages, there have been concerns about the accuracy and consistency of translations. One area of language translation that remains unexplored is the interpretation of text written in a different language script based on phonetic pronunciation. This paper introduces a translator application that reads tweets written in regional languages scripts that otherwise are a different language and translates them into a common language using the Indic- Transliteration library and deep learning models deployed on Hugging Face. To accurately transcribe the tweets and make them intelligible and available to a larger audience, the system uses both rule-based and deep learning models. According to the results, the technology is capable of accurately transcribing tweets written in several regional languages, making them available to a wider audience. Potential applications of this research include overcoming linguistic barriers and fostering communication and understanding among diverse cultures. The proposed application enables the translation of tweets from regional Indian languages into a language selected by the user, thus facilitating greater intercultural communication and expanding the reach of regional content on social media platforms.
SEFA: A Shared Encoder and Feature Adaptation Framework for Breast MRI Segmentation

Significant progress has been made in developing accurate automatic segmentation systems for various biomedical applications using convolutional networks (CNNs). However, these systems often lose effectiveness when they encounter a domain shift caused by variations in imaging protocols. Traditional supervised transfer learning approaches are not ideal because manually annotating new data for every testing domain is impractical, while unsupervised domain adaptation is a challenging subject in the field of biomedical image analysis. In this paper, we introduce a new framework for unsupervised domain adaptation called Shared Encoder and Feature Adaptation (SEFA). This framework focuses on cross-sequences adaptation and is more resilient to disparities in input data and does not require any annotations on the test domain. Specifically, our segmenter model is based on a compact fully convolutional network designed for breast mask prediction. First, the shared encoder is designed to minimize the distribution disparity between the source and target domains. Then, a domain discriminator is set up to discriminate the feature space of both domains. Our model is optimized by using unpaired TSE/DCE sequences in an unsupervised manner. This eliminates the need for labeling additional medical datasets. Using our unsupervised approach, we achieve segmentation accuracies that are comparable to those obtained through supervised training. These accuracies are measured by metrics such as Case/Global Dice Score and average symmetric surface distance (ASD), which are close to the best possible performance.
Robust Lesion Segmentation on Emission Computed Tomography with nmODE

Nuclear medicine imaging plays a pivotal role in the diagnosis of systemic bone diseases. However, conventional discrete models struggle to handle the inherent fuzziness and randomness of such images. Recently proposed, the neural memory ordinary differential equation (nmODE) model exhibits intriguing characteristics and has shown significant improvements in classification tasks. In this study, we leverage the powerful nonlinear and dynamic modeling capabilities of nmODE and extend its application to the field of nuclear medicine image segmentation. The experimental results affirm that the inherent stability of nmODE is highly suitable for addressing the task of lesion segmentation in nuclear medicine images characterized by indistinctness and stochasticity.
Learning Dual-Domain Calibration and Distance-Driven Correlation Filter: A Probabilistic Perspective for UAV Tracking

Unmanned Aerial Vehicle (UAV) tracking has been possible because of the growth of intelligent information technology in smart cities, making it simple to gather data at any time by dynamically monitoring events, people, the environment, and other aspects in the city. The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations. But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms. In light of the aforementioned issues, this work suggests a dual-domain Jensen-Shannon divergence correlation filter (DJSCF) model address the probability-based distance measuring issue in the event of filter degradation. The two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and distortion. Two new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response fitting. The model is roughly transformed into a linear equality constraint issue in the iterative solution, which is then solved by the alternate direction multiplier method (ADMM). The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.
A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles

The high-speed movement of Vehicle Users (VUs) in Cognitive Internet of Vehicles (CIoV) causes rapid changes in users location and path loss. In the case of imperfect control channels, the influence of high-speed movement increases the probability of error in sending local spectrum sensing decisions by VUs. On the other hand, Malicious Vehicle Users (MVUs) can launch Spectrum Sensing Data Falsification (SSDF) attacks to deteriorate the spectrum sensing decisions, mislead the final spectrum sensing decisions of Collaborative Spectrum Sensing (CSS), and bring serious security problems to the system. In addition, the high-speed movement can increases the concealment of the MVUs. In this paper, we study the scenario of VUs moving at high speeds, and data transmission in an imperfect control channel, and propose a blockchain-based method to defend against massive SSDF attacks in CIoV networks to prevennt independent and cooperative attacks from MVUs. The proposed method combines blockchain with spectrum sensing and spectrum access, abandons the decision-making mechanism of the Fusion Center (FC) in the traditional CSS, adopts distributed decision-making, and uses Prospect Theory (PT) modeling in the decision-making process, effectively improves the correct rate of final spectrum sensing decision in the case of multiple attacks. The local spectrum sensing decisions of VUs are packaged into blocks and uploaded after the final decision to achieve more accurate and secure spectrum sensing, and then identify MVUs by the reputation value. In addition, a smart contract that changes the mining difficulty of VUs based on their reputation values is proposed. It makes the mining difficulty of MVUs more difficult and effectively limits MVUs' access to the spectrum band. The final simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.
Preface: 2nd International Conference on Environment Engineering, Nanomaterials and Chemical Technology (EENCT 2023)

2023 2nd International Conference on Environment Engineering, Nanomaterials and Chemical Technology (EENCT 2023) was successfully held during September 16-17, 2023 in Miami, USA. The conference invited scholars and experts in the fields of environmental engineering, ecosystems, biotechnology, nanomaterials, and chemical technology from various universities to participate in the review and guidance of this conference. EENCT 2023 aims to provide an international cooperation and exchange platform for experts and scholars to share their research achievements, discuss the key challenges and research directions, and jointly promote the industrialization cooperation and continuous innovation. This collection of proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the special committee.&#x0D; The opening ceremony was held in the main venue and a speech was delivered by the invited speaker. After the main venue, we divided the invited authors into 2 groups. Each author spoke for about 10-15 minutes on detailed material in the slides, with the same amount of time set aside for questions and discussion. This conference has brought together many excellent works and the latest ideas and concepts. We wish to thank all authors and participants for providing their valuable contributions for this proceeding as well as the reviewers for their constructive recommendations and criticism aiding to improve the presented articles.&#x0D; With our best regards,&#x0D; Conference Organizing Chair&#x0D; Miami, USA
A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve the learning capability of deep models and lessen the number of parameters. Besides, this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase. The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm. These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier. The experiment worked on the HyperKvasir dataset, which consists of 23 stomach-infected classes. At last, the proposed method obtained an improved accuracy of 93.90% on this dataset. Comparison is also conducted with some recent techniques and shows that the proposed method’s accuracy is improved.
Enhancing Minerals Prospects Mapping with Machine Learning: Addressing Imbalanced Geophysical Datasets and Data Visualization Approaches

Minerals prospects mapping plays a pivotal role in the sustainable development of mineral resources, offering critical insights into subsurface geology and mineral potential. Traditional geological methods are often labor-intensive and time-consuming. In contrast, machine learning (ML) techniques have emerged as a powerful tool for accelerating and improving the accuracy of mineral prospect mapping. This article explores an innovative approach to enhance the performance of supervised ML models, specifically logistic regression and multilayer perceptron. One of the primary challenges in mineral mapping is dealing with imbalanced geophysical datasets, where positive samples (indicating mineral occurrences) are vastly outnumbered by negative samples (non-mineral areas). This imbalance can lead to biased model predictions, favoring the majority class while neglecting the minority class. To address this issue, we propose a novel oversampling technique that generates synthetic samples for the minority class, effectively rebalancing the dataset. By introducing diversity to the training data, our approach mitigates the bias and enhances the models' ability to identify mineral prospects accurately. The proposed approach empowers ML models to discriminate between mineral-rich and non-mineral areas with unprecedented precision, facilitating more informed decision-making for resource exploration and extraction. Ultimately, the integration of imbalanced dataset handling and data visualization techniques offers a robust framework for harnessing the potential of machine learning in mineral prospect mapping.