Reviewerly/exHarmony huggingface dataset:
Implementation of LightGBM and Random Forest in Potential Customer Classification

Classification is one of the data mining techniques that can be used to determine potential custumers. Previous research show that the boosting method, especially LGBM, produces the highest accuracy value of all models, namely 100%. Meanwhile, for the two bagging methods, Random Forest produced the highest accuracy compared to Extra Trees, namely 99.03%. The research uses the LGBM and Random Forest methods to classify potential customers. The results of this study indicate that in imbalance data the LightGBM method has better accuracy than the Random Forest, which is 85.49%, when the Random Forest is unable to produce a model. The SMOTE method used in this study affects the accuracy of the random forest but does not affect the accuracy of LightGBM. Over all the Accuracy, Recall, Specificity, and Precision values, Random Forest produces a good value compared to LightGBM on balanced data. Meanwhile, LightGBM is able to handle unbalanced data.
Multi-treatment casual analysis using improved meta learner and uplift tree

Causality is an appealing but challenging domain for researchers in generations. Recently, researchers have shifted their focus to combining traditional causal inference methods and machine learning models to get both advantages. Meta learner is an algorithm for causal inference, including T-learner, S-learner, and X-learner. Another popular way in causal inference is based on decision tree learning, one of the predictive modeling approaches. Many existing works focus on estimating the causal effect of binary treatment. However, there are also many cases in the real world when the treatment has more than two values. These methods cannot be used directly in multivalued treatment cases. According to the mathematization of causality, we improved the binary meta-learner process to be applicable in multi-treatment situations. At the same time, we also preliminarily explored the technique of uplifting trees. Finally, we applied the two methods to analyze parents' and children's learning situations in hundreds of families to test the effect of improvement.
Learning to Resolve Conflicts in Multi-Task Learning

Multi-task learning, a promising direction for sharing structure and knowledge between different tasks, has been widely studied for improving the data efficiency of deep learning. Unfortunately, training the MTL model is non-trivial since multi-tasks might negatively influence each other due to gradient conflicts. Toward this end, many algorithms have been proposed to handle the gradient conflicts by designing sophisticated neural architectures or gradient modification methods in task granularity. However, in this work, we argue that it is necessary to resolve the conflicts from the sample granularity. Specifically, we propose a framework named learning to resolve conflicts (LTRC), which adaptively reweights the sample gradient for diminishing the gradient conflicts over multi-tasks. Finally, we conduct extensive experiments on two benchmark datasets. The superior experimental results of the proposed method demonstrate its effectiveness.
A Depth-Guided Attention Strategy for Crowd Counting

Crowd counting, an essential technology with numerous applications, often encounters challenges due to non-uniform crowd distributions and noisy backgrounds in congested scenes. To address these issues, this paper proposes the utilization of depth information as an independent indicator. Specifically, we introduce a depth-guided attention strategy (DAS) to fuse depth and crowd density information, effectively modeling the relationship between crowd density and depth of field. Additionally, we propose a depth-guided method to generate the target density map by leveraging the negative correlation between the depth of field and head size in crowd scenes, enabling better supervised learning. To achieve fast inference speed, we design two lightweight crowd counting networks within a knowledge distillation framework that require only a small number of parameters. Furthermore, we propose a two-step network inference algorithm to reduce counting errors. Extensive experiments conducted on four challenging datasets demonstrate that our proposed methods significantly improve counting accuracy over baseline networks.
Heterogeneous Federated Learning Based on Graph Hypernetwork

Federated learning is a distributed machine learning framework over a large number of possible clients without data leakage. However, most federated learning methods are limited to clients with isomorphic network architectures. This restricts heterogeneous clients equipped with different computation and communication resources. To address this issue, this paper introduces a new heterogeneous federated learning method called heterogeneous federated graph hyperNetwork (hFedGHN), which takes the clients as a graph to integrate both structure and content information for deep latent representation learning and generates heterogeneous model for each client. HFedGHN can share effective parameters across clients, and maintain the capacity to generate unique and diverse personal models. Moreover, hFedGHN can be regarded as a process to learn a meta-model for federated learning, therefore it has better generalization ability to novel clients. As a group of novel clients are added dynamically, they can get acceptable performance after just one round of communication. With extensive experiments on MNIST, CIFAR-10, and CIFAR-100, the results demonstrate the superiority of hFedGHN.
Boundary Attentive Spatial Multi-scale Network for Cardiac MRI Image Segmentation

Accurate automatic segmentation of cardiac MRI images can be used for clinical parameter calculation and provide visual guidance for surgery, which is important for both diagnosis and treatment of cardiac diseases. Existing automatic segmentation methods for cardiac MRI are based on U-shaped network structure introducing global pooling, attention and etc. operations to extract more effective features. These approaches, however, suffer from a mismatch between sensing receptive field and resolution and neglect to pay attention to object boundaries. In this paper, we propose a new boundary attentive multi-scale network based on U-shaped network for automatic segmentation of cardiac MRI images. Effective features are extracted based on channel attention for shallow features. With the goal of increasing segmentation accuracy, multi-scale features are extracted using densely coupled multi-scale dilated convolutions. In order to improve the ability to learn the precise boundary of the objects, a gated boundary-aware branch is introduced and utilized to concentrate on the object border region. The effectiveness and robustness of the network are confirmed by evaluating this method on the ACDC cardiac MRI dataset to produce segmentation predictions for the left ventricle, right ventricle, and myocardial. Comparative studies demonstrate that our suggested method produces superior segmentation outcomes when compared to other cardiac MRI segmentation methods.
Extraction Method of Rotated Objects from High-Resolution Remote Sensing Images

In recent years, the rapid development of remote sensing technology has made intelligent interpretation possible. However, remote sensing images have arbitrary object orientation, small object size and complex background compared with natural images, and these problems cause difficulty in accomplishing accurate object detection. In this study, several classical traditional object detection methods are first used to compare the detection effects based on the self-made high-resolution remote sensing image rotation detection dataset. Then, a dual-mode rotation regression network, namely, DRRN, is designed to solve the problem of arbitrary object orientation in remote sensing images. DRRN mainly consists of two parts: dual-mode region proposal network and rotation regression network. Meanwhile, a combined regression loss with intersection over union is proposed to improve the traditional smooth L1 loss. Next, a bi-directional cross-layer connected feature pyramid network, namely, bc-FPN, is proposed to solve the problem of small objects. And a supervised hybrid attention mechanism, namely, SHAM, is proposed to solve the problem of complex background, and the two modules are portable and plug-and-play. Experiments show that the proposed methods can effectively improve the detection effects of object in remote sensing images.
Investigation of Information Processing Mechanisms in the Human Brain During Reading Tanka Poetry

Recent advances in non-invasive brain function measurement technologies, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), and the development of machine learning techniques, including deep learning, have led to increased research on the elucidation and quantitative understanding of information processing processes in the human brain. Since the emergence of word2vec, which represents the meaning of natural language words as vectors, features of language stimuli given to the human brain have been represented using large language models in natural language processing and used to estimate brain states. In this study, we used GPT-2, which is known to perform well as a feature for predicting brain states, and investigated the information processing processes in the human brain when reading Japanese short poems, i.e., tanka poetry. In particular, we investigated the hubness of the regions of interest in the brain by applying the PageRank algorithm. As a result, we have found that the cingulate cortex and the insula, which are said to be related to emotion, have hubness in brain regions, while occipital lobe, which are not said to be related to emotion, have also hubness.
Incremental Recommendation Algorithm Based on the Influence Propagation Model

Recently, Graph based recommendation algorithms have gained more and more attention due to their flexible and unified embedding representation of both users and items as well as the effective modeling of context information for efficient recommendation. However, most of the existing graph recommendation methods are designed upon the static interaction graph, while neglecting the dynamic evolution of the graph. This paper proposes a lightweight Influence Propagation Model, namely IPM, for efficient recommendation on the dynamic evolving graphs. Specifically, IPM models the accumulating and propagating procedure of influence on the user-item interaction graph to obtain the characteristics of users and items. For dynamic changed edges of the graph, the amount of information about the relevant users and items can be quickly updated by propagating the impact associated with the added interactions. Our model exhibits very efficient performance and comparable recommendation results with the same experimental setup compared to advanced graph recommendation algorithms and dynamic graph embedding algorithms.
Getting your toes wet in R: Hydrology, meteorology, and more

&lt;strong&gt;Importance of Hydrology&lt;/strong&gt; Given that liquid water is essential to life on Earth, water research cuts across numerous disciplines including hydrology, meteorology, geography, climate science, engineering, ecology, and more. Numerous R packages have emerged from this diversity of approaches, and we recently gathered many of them into a new rOpenSci task view which we broadly titled ‘Hydrology’ and published to CRAN.
Anomaly Data Detection Method for Low Carbon Buildings Based on LSTM-R

In the electric power industry, there is a wealth of event information within big data. For electric power companies, effectively mining abnormal power usage data is of utmost importance. However, current methods for detecting abnormal electricity usage lack efficiency, demanding an accurate and efficient solution. To address this, we propose a method for detecting anomalies in low-carbon buildings' data, using Long Short-Term Memory (LSTM) networks to their full potential and a regression residual model. By training on extensive labelled user data, our method autonomously detects whether users' electricity consumption is within the normal range, enabling the identification of abnormal users. This valuable insight guides power companies in their on-site verification efforts, boosting hit rates, enhancing efficiency, and facilitating the timely detection of electricity consumption anomalies.
An AI-based Object Detection Approach for Robotic Competitions

Artificial Intelligence has been introduced in many applications, namely in artificial vision-based systems with object detection tasks. This paper presents an object localization system with a motivation to use it in autonomous mobile robots at robotics competitions. The system aims to allow robots to accomplish their tasks more efficiently. Object detection is performed using a camera and artificial intelligence based on the YOLOv4 Tiny detection model. An algorithm was developed that uses the data from the system to estimate the parameters of location, distance, and orientation based on the pinhole camera model and trigonometric modelling. It can be used in smart identification procedures of objects. Practical tests and results are presented, constantly locating the objects and with errors between 0.16 and 3.8 cm, concluding that the object localization system is adequate for autonomous mobile robots.
Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly.The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.
Legal and technical challenges in the pursuit of cybercriminals: An analysis of the difficulties faced by the authorities

The rapid expansion of information and communication technology has shaped a continually growing digital landscape, wherein the internet and digital networks play a central role. However, this technological ubiquity has brought forth challenges, such as sophisticated and ever-evolving cybercrimes. This article addresses the complexity and diversity of cybercrimes, their legal implications, and the difficulties authorities face in identifying and prosecuting offenders. The research employs a qualitative approach, supported by literature review, to explore the nuances of preventing and combating these crimes. Gaps in specific legislation necessitate constant adaptation and updates within the legal system. Technologies such as digital forensic analysis, IP tracking, and cybersecurity have been utilized by both authorities and criminals. Collaboration and the continuous evolution of counter-strategies are vital to confronting this ever-changing reality in the digital age.
Masked image modeling-based boundary reconstruction for 3D medical image segmentation

Accurate segmentation of 3D medical images is vital for computer-aided diagnosis. However, the complexity of target morphological variations and a scarcity of labeled data make segmentation more challenging. Furthermore, existing models make it difficult to fully and efficiently integrate global and local information, which hinders structured knowledge acquisition. To overcome these challenges, we introduce the TNT Masking Network (TNT-MNet), a groundbreaking transformer-based 3D model that utilizes a transformer-in-transformer (TNT) encoder. For the first time, we present masked image modeling (MIM) in supervised learning, utilizing target boundary regions as masked prediction targets to enhance structured knowledge acquisition. We execute multiscale random masking on inner and outer tokens in online branch to tackle the challenge of segmenting organs and lesion regions with varying structures at multiple scales and to enhance modeling capabilities. In contrast, the target branch utilizes all tokens to guide the online branch to reconstruct the masked tokens. Our experiments suggest that TNT-MNet’s performance is comparable, or even better, than state-of-the-art models in three medical image datasets (BTCV, LiTS2017, and BraTS2020) and effectively reduces the dependence on labeled data. The code and models are publicly available at https://github.com/changliu-work/TNT_MNet.
An Online Game Platform for Intangible Cultural Heritage Tibetan Jiu Chess

Tibetan Jiu Chess is a treasure of Tibetan culture. In order to make this intangible cultural heritage bloom, the Jiu Chess online battle platform has been developed. The platform is developed based on Cocos Creator development engine, and the language used are JavaScript and TypeScript. The SDK of Huawei Technologies Co. Ltd online battle engine is embedded to help the platform realize online battle function. It has successfully completed online game competition task for Tibetan Jiu chess in 2022 Uni versity Computer Games Championship & National Computer Games Tournament in China and can satisfy about 200 people to play online games stably.
K-NEAREST NEIGHBOR (KNN) ANALYSIS FOR CLOTHING SALES CLASSIFICATION BASED ON MATERIALS USED

Abstract Classification is a method to see the behavior and characteristics of certain groups. The K-nearest neighbor method is a learning algorithm for classifying new data based on the K-Nearest Neighbor majority class. The main purpose of this algorithm is to classify new objects based on attributes and training samples. In today's digital era, competition in the business world is getting tougher and growing rapidly, especially when it comes to online marketing systems. Every market driver must always pay attention to the needs and desires of consumer satisfaction when buying products from online stores. However, the problem that consumers often complain about is the use of clothing size charts in online stores that do not match the consumer's body size. This study aims to reduce the frustration of consumers buying clothes online and in such a way that products do not have to be returned via the internet. Based on these problems, these conditions must be improved by selecting clothes to achieve optimal customer satisfaction. This application was built using the K-Nearest Neighbor (KNN) method and Profile Matching to help you determine what clothes are most suitable for your consumer size.
Age-Adaptive Multimodal Biometric Authentication System with Blockchain-based Re-Enrollment

&#x0D; &#x0D; &#x0D; &#x0D; In the long run, a significant time gap between enrollment and probe image challenges the model's prediction ability when it has been trained on variant biometric traits. Since variant biometric traits change over time, it is sensible to construct a multimodal biometric authentication system that must include at least one invariant trait, such as the iris. The emergence of Deep learning has enabled developers to build classifiers on synthesized age-progressive images, particularly face images, to search for individuals who have been missing for many years, to avail a comprehensive portrayal of their appearance. However, in sensitive areas such as the military and banks, where security and confidentiality are of utmost importance, models should be built using real samples, and any variations in biometric traits should trigger an alert for the system and notify the subject about re-enrollment. This paper proposes an algorithm for age adaptation of biometric classifiers using multimodal channels which securely update the biometric traits while logging the transactions on the blockchain. It emphasizes confidence-score-based re-enrolment of individual subjects when the authenticator module becomes less effective with a particular subject's probe image. This reduces the time, cost, and memory involved in periodic re-enrolment of all subjects. The classifier deployed on the blockchain invokes appropriate smart contracts and completes this process securely.&#x0D; &#x0D; &#x0D; &#x0D;
A Research Agenda Towards Culturally Aware Information Systems

Our world is not homogeneous in nature, it has multiple languages, ethnic groups, culture, beliefs, and varying perceptions of things. There is development towards catering experiences based on these varying dimensions by utilizing the available cultural models (such as Trompenaars, Hofstede, and Deal and Kennedy). Though these models can help in the development of globalized information systems that can be adapted for a particular locale (location/language/culture-specific), we don’t have these dimensions in a dereferenceable form. This paper is an attempt to put some light on the possible solution for the problems discussed. Here, we discuss research questions that should be addressed while incorporating cultural representation. We also discuss various cultural models, and available usages and probable usecases.
Monkeypox Detection Through Watershed Segmentation and Appending 2D CNN Based Auto Encoder

Monkeypox, a viral zoonosis, may spread from animals to people. Fever, rashes, and swollen lymph nodes might create medical complications. Its symptoms resemble smallpox. To prevent monkey pox sickness, you must be prepared and treat it immediately. Public health systems should be aware of effective monkeypox mitigation methods because to its global health impacts. Watershed segmentation using CNN-based auto encoder detected monkeypox. Monkeypox may be distinguished from other skin infections. Watershed segmentation, elevation map utilisingsobel, and region-based feature extraction function well on impacted skin photos. Segmenting Monekypox images is tough due to similarities and variations across classes and the difficulties of focusing on skin lesions. Unsupervised learning models like the convolutional autoencoder duplicate the input image in the output layer. Encoders, ConvNets that produce low-dimensional images, process images passed via them.
Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications

People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non-uniform illumination. The four main applications of these image inpainting techniques are scratch removal, text removal, object removal and image restoration. However, all the four image inpainting applications cannot be implemented using a single technique. According to the literature, there has been relatively less work done in the field of image inpainting applications. Investigation has been carried out to find the suitability of these three techniques for the four above mentioned image inpainting applications based on two performance metrics.