Reviewerly/exHarmony huggingface dataset:
HousePrice_ML: An Efficient Framework for House Price Prediction Using Soft Computing

Predicting housing prices is important to many people, such as home buyers, real estate agents, and investors. By harnessing the power of machine learning models, this paper aims to develop a highly efficient system to calculate reliable housing price forecasts. The results of this research can facilitate decision-making processes, enable more informed investments, and improve the overall buying and selling experience in the real estate market. The relationship between house prices and the economy is an important motivating factor for predicting house prices. This paper focuses on how to predict housing prices using machine learning techniques. This paper proposes an efficient framework For prediction houses using six machine learning algorithms ( SVM, Tree, Neural Network, KNN, Linear Regression, Gradient Boosting). In best model 1, the number of fields equals Gradient Boosting; in best model 2, the number of fields equals Linear Regression; in Best model 3 number of fields equals Gradient Boosting. The best all model equal model 2 equal Linear Regression.
How engagement in internet fraud impact academic activities of undergraduate students in Nigeria: A socio-legal study

This study examined how engagement in internet fraud by undergraduate students impact their academic activities. Specifically, we examined the factors that drive the engagement of undergraduate students in internet fraud, how engagement in internet fraud activities affect their academic activities, and how they navigate their academic challenges to achieve academic objectives. We conducted this study between 2020 and 2022 and relied on both quantitative and qualitative data. 400 questionnaire instruments were randomly administered to students of four purposively selected tertiary institutions to gauge their perception of engagement in internet fraud activities among undergraduate students. We then relied on snowball sampling and semi-structured interview to elicit responses from sampled respondents. The respondents cut across five tertiary institutions in Nigeria. Analysis was done using Microsoft excel spread sheet for the quantitative data and inductive analysis for the qualitative data. We found that pressure from peers, failure of traditional morality/ethics gatekeepers, among others, are major drivers of engagement in internet fraud among undergraduate students, and that to navigate academic challenges and achieve academic objectives, they unethically induce some fellow students and staff.
Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

Abstract Today's ever‐increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non‐static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance‐specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average‐accuracy of 0.69 on the real‐world data streams, while the chunk‐based algorithms AWE, AUE, and KUE achieve average‐accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average‐accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO .
Comparative Analysis of Transformer-Based Method In A Question Answering System for Campus Orientation Guides

The campus introduction process is a stage where new students acquire information about the campus through a series of activities and interactions with existing students. However, the delivery of campus introduction information is still limited to conventional methods, such as using guidebooks. This limitation can result in students having a limited understanding of the information needed during their academic period. The one of solution for this case is to implement a deep learning system with knowledge-based foundations. This research aims to develop a Question Answering System (QAS) as a campus introduction guide by comparing two transformer methods, namely the RoBERTa and IndoBERT architectures. The dataset used is processed in the SQuAD format in the Indonesian language. The collected SQuAD dataset in the Indonesian language consists of 5046 annotated data. The result shows that IndoBERT outperforms RoBERTa with EM and F1-Score values of 81.17 and 91.32, respectively, surpassing RoBERTa with EM and F1-Score values of 79.53 and 90.18.
THE "YOUNG PROFESSIONALS" CHAMPIONSHIP AS A TOOL FOR DEVELOPING PROFESSIONAL SKILLS OF PARTICIPANTS IN THE COMPETENCE OF THE "INTERNET OF THINGS"

The article discusses issues regarding the organization of the national championship “Young Professionals”, which takes place within the framework of the federal project “Young Professionals (increasing the competitiveness of vocational education)” of the national project “Education”. Currently, there are many competitions aimed at improving the level of vocational education. The Young Professionals Championship is no exception. Its main goal is to increase the prestige of working professions and the development of vocational education by combining the best practices and professional standards with the involvement of qualified specialists as judges and teachers who are able to transfer the accumulated experience to a new generation of young professionals. The "Young Professionals" championships in our country are held according to the international rules of WorldSkills International (WSI), which reflect the requirements for a specialist of the highest level in the conditions of modern realities. Of course, the Young Professionals Championship movement plays an important role in the training of qualified personnel. The championship makes an invaluable contribution to the education and upbringing of young professionals, as it encourages students to accumulate experience, search for non-standard solutions to problems. Subsequently, students show great independence and interest in learning.
Vulnerability issues in Automatic Speaker Verification (ASV) systems

Abstract Claimed identities of speakers can be verified by means of automatic speaker verification (ASV) systems, also known as voice biometric systems. Focusing on security and robustness against spoofing attacks on ASV systems, and observing that the investigation of attacker’s perspectives is capable of leading the way to prevent known and unknown threats to ASV systems, several countermeasures (CMs) have been proposed during ASVspoof 2015, 2017, 2019, and 2021 challenge campaigns that were organized during INTERSPEECH conferences. Furthermore, there is a recent initiative to organize the ASVSpoof 5 challenge with the objective of collecting the massive spoofing/deepfake attack data (i.e., phase 1), and the design of a spoofing-aware ASV system using a single classifier for both ASV and CM, to design integrated CM-ASV solutions (phase 2). To that effect, this paper presents a survey on a diversity of possible strategies and vulnerabilities explored to successfully attack an ASV system, such as target selection, unavailability of global countermeasures to reduce the attacker’s chance to explore the weaknesses, state-of-the-art adversarial attacks based on machine learning, and deepfake generation. This paper also covers the possibility of attacks, such as hardware attacks on ASV systems. Finally, we also discuss the several technological challenges from the attacker’s perspective, which can be exploited to come up with better defence mechanisms for the security of ASV systems.
Silent and oral reading methods on improving English reading comprehension among generation alpha pupils

Reading comprehension enables each child to make meaning of the world. Therefore, it is important to develop this during the child’s primary years in school. Using ex post facto design, this study investigated the effects of reading methods on English reading comprehension of randomly selected 75 Grade 2 pupils in a private school in the Philippines. The scores of pupils who read two comparable narrative passages in oral and silent were compared. Paired t-test results revealed a significant difference between oral reading and silent reading. Silent reading had a greater positive effect on the comprehension of the pupils. Likewise, it was the most preferred reading method among the respondents. Focus group discussion with English teachers' results suggested that silent reading was preferred specifically because it helps in understanding the story better, remembering words, and concentrating given its quiet and peaceful nature. It is recommended that teachers employ sustained silent reading classroom practices such as Silent Reading Activity (SRA) Reading Laboratory, Genuine Love for Reading activities through the Four-pronged approach, and Drop Everything and Read time. These methods will help to support the reading needs and preference of the generation alpha pupils and to further strengthen the positive effects of silent reading on improving their reading comprehension.
Human activity recognition with fine-tuned CNN-LSTM

Abstract Human activity recognition (HAR) by deep learning is a challenging and interesting topic. Although there are robust models, there is also a bunch of parameters and variables, which affect the performance such as the number of layers, pooling type. This study presents a new deep learning architecture that is obtained by fine-tuning of the conventional CNN-LSTM model, namely, CNN (+3)-LSTM. Three changes are made to the conventional model to increase the accuracy. Firstly, kernel size is set to 1×1 to extract more information. Secondly, three convolutional layers are added to the model. Lastly, average pooling is used instead of max-pooling. Performance analysis of the proposed model is conducted on the KTH dataset and implemented on Keras. In addition to the overall accuracy of the proposed model, the contribution of each change is observed individually. Results show that adding layers made the highest contribution followed by kernel size and pooling, respectively. The proposed model is compared with state-of-art and outperformed some of the recent studies with a 94.1% recognition rate.
An improved column-generation-based matheuristic for learning classification trees

Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision trees are fast but generate suboptimal trees in terms of accuracy. Other discrete optimization models in the literature address the optimality problem but only work well on relatively small datasets. Firat et al. (2020) proposed a column-generation-based heuristic approach for learning decision trees. This approach improves scalability and can work with large datasets. In this paper, we describe improvements to this column generation approach. First, we modify the subproblem model to significantly reduce the number of subproblems in multiclass classification instances. Next, we show that the data-dependent constraints in the master problem are implied, and use them as cutting planes. Furthermore, we describe a separation model to generate data points for which the linear programming relaxation solution violates their corresponding constraints. We conclude by presenting computational results that show that these modifications result in better scalability.
Hierarchical Controller for Obstacle Avoidance Task in WMRs Considering Actuators and Power Electronics Subsystems: When Artificial Potential Fields Approach is Used

Through the development of a three-level hierarchical controller, this study proposes a solution to the obstacle avoidance task in wheeled mobile robots (WMRs). The proposed controller takes into consideration the dynamics of the three subsystems that typically compose a WMR (mechanical structure, actuators, and power electronics). At the high level, a control based on input-output linearization that uses the artificial potential fields technique is used and a Lyapunov-alike function is proposed for stability analysis purposes. At the medium level, two controls based on differential flatness are employed and a Hurwitz-stable-type polynomial is defined with the aim of locate the poles on the left-hand-side of the complex plane. Finally, at the low level, two cascaded controls are implemented using sliding modes and PI control. The performance of the three-level hierarchical controller is evaluated through a co-simulation using Matlab-Simulink and PSpice software. The obtained results are assessed with a two-level controller that does not take into account the dynamics of the power stage. Such results demonstrate that both controllers accomplish the obstacle avoidance task when no kind of perturbations are considered. However, and unlike the two-level controller, the three-level hierarchical controller successfully achieves the obstacle avoidance task even when disturbances in some parameters of the system are introduced.
Generative adversarial network based on frequency domain data enhancement: Dual-way discriminator structure Copes with limited data

The excellent image-generation ability of generative adversarial networks (GANs) has been widely used. However, training a GAN requires large-scale data support, which hinders in-depth development. Therefore, the research on stable training of GANs under limited data conditions is helpful to further expand the application scenarios. To solve this problem, a new network based on a dual-ways discriminator structure is designed, used to eliminate the problem that a single discriminator model is prone to overfitting under the condition of limited data. Then, the problem that the traditional data augmentation strategy is limited to pixel space and lacks attention to the overall structure and contour of the image is analyzed. An adaptive dynamic data augmentation strategy based on the Laplace convolution kernel is proposed from the frequency domain space, which realizes the purpose of implicitly increasing the training data in the training process. This new designed module improves the performance of the generative adversarial network. Through extensive experiments, it was confirmed that the new network, named FD-GAN, achieved prefer image generation ability, and its Fid score reached 4.58, 12.007, and 10.382 in the AFHQ-Cat, AFHQ-Dog, and TankDataSet datasets, respectively.
Identity fraud victimization: a critical review of the literature of the past two decades

Abstract This study aims to provide an understanding of the nature, extent, and quality of the research evidence on identity fraud victimization in the US. Specifically, this article reviews, summarizes, and comments on the state of empirical research of identity fraud victimization in the US based on a narrative review of 52 published empirical studies. Studies included in this review suggest that the prevalence of identity fraud in the US has increased over the years and existing account frauds is the most prevalent type of identity fraud. There is a pressing need for more research on the prevalence of identity fraud victimization among minors, institutionalized individuals, and individuals from minority groups; long-term prevalence of identity fraud victimization; and emerging forms of identity fraud such as synthetic identity fraud victimization. Studies included in this review further suggest that identity fraud risk factors vary based on the fraud type considered. Identity fraud victims can experience a variety of harms. Longitudinal studies following identity fraud victims are essential for reliably estimating the risk factors for identity fraud victimization and the impact of identity fraud victimization on individual victims. The research on services for identity fraud victims is limited and suggests the positive impact of trauma-informed services for serious identity fraud victims. The overwhelming lack of research on the impact of programs and services for identity fraud victims necessitates more attention from scholars to study the impact of programs, interventions, and services for identity fraud victims on reporting of victimization, prevention of victimization, experiences of victims, and victim-centered cost benefit analysis of services. Policy and practice implications of these findings are discussed.
Confounder balancing in adversarial domain adaptation for pre-trained large models fine-tuning

The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA) methods to transfer knowledge learned from the source domain to target domains. However, existing ADA methods fail to account for the confounder properly, which is the root cause of the source data distribution that differs from the target domains. This study proposes a confounder balancing method in adversarial domain adaptation for PLMs fine-tuning (CadaFT), which includes a PLM as the foundation model for a feature extractor, a domain classifier and a confounder classifier, and they are jointly trained with an adversarial loss. This loss is designed to improve the domain-invariant representation learning by diluting the discrimination in the domain classifier. At the same time, the adversarial loss also balances the confounder distribution among source and unmeasured domains in training. Compared to newest ADA methods, CadaFT can correctly identify confounders in domain-invariant features, thereby eliminating the confounder biases in the extracted features from PLMs. The confounder classifier in CadaFT is designed as a plug-and-play and can be applied in the confounder measurable, unmeasurable, or partially measurable environments. Empirical results on natural language processing and computer vision downstream tasks show that CadaFT outperforms the newest GPT-4, LLaMA2, ViT and ADA methods.
Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier

Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the Hybrid LXGB (Long short-term memory Extreme Gradient Boosting) Model, a novel approach for multimodal sentiment analysis that merges the strengths of long short-term memory (LSTM) and XGBoost classifiers. The primary objective is to address the intricate task of understanding emotions across diverse data sources, such as textual data, images, and audio cues. By leveraging the capabilities of deep learning and gradient boosting, the Hybrid LXGB Model achieves an exceptional accuracy of 97.18% on the CMU-MOSEI dataset, surpassing alternative classifiers, including LSTM, CNN, DNN, and XGBoost. This study not only introduces an innovative model but also contributes to the field by showcasing its effectiveness and balance in capturing the nuanced spectrum of sentiments within multimodal datasets. The comparison with equivalent studies highlights the model's remarkable success, emphasizing its potential for practical applications in real-world scenarios. The Hybrid LXGB Model offers a unique and promising perspective in the realm of multimodal sentiment analysis, demonstrating the significance of integrating LSTM and XGBoost for enhanced performance.
Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

Abstract Offline Handwritten Text Recognition (HTR) systems concern the automatic recognition and transcription of handwritten text from scanned images to digital media. Recently, HTR research field has become increasingly important due to the growing need for digitizing documents and automating data entry across various industries. However, achieving satisfactory results depend on the amount of available samples to train an optical model. Creating and labeling large enough datasets for this purpose often require significant time and effort, that in some situations may be impractical. To address this problem, data augmentation approaches are commonly used as an essential component of HTR systems. In this way, the present work aims to identify, explore, and analyze the scope of data augmentation approaches for offline HTR systems. Furthermore, we detailed our research protocol and answered four pertinent research questions, which enabled us to discuss trends and possible gaps. A search was conducted across five scientific databases, focusing on papers published between 2012 and 2023. The search yielded 976 primary papers, with 32 meeting the criteria for inclusion in this review. Our results indicate that handwriting synthesis is an emerging research field, and we observed that Digital Image Processing (DIP) is still widely used as an image generator. Nevertheless, the application of Generative Adversarial Networks (GAN) has gained traction in recent years owing to its impressive ability to synthesize images of handwritten text with arbitrary style and content. In addition, we explored and analyzed the most commonly used datasets and text recognition levels in the selected works.
Steady state engineering of a two-level system by the mixed-state inverse engineering scheme

The mixed-state inverse engineering scheme is a control scheme used for engineering the quantum state of a driven open quantum system from an initial steady state to a final steady state. In this paper, we present an analytical study of this scheme applied to the driven two-level model coupled to a heat reservoir. Typically, when the purity of the quantum state varies, incoherent control techniques are required for mixed-state engineering. However, we show that for both Markovian and non-Markovian dynamics, coherent control protocols can transfer the quantum state into the target state. This simplification comes at a cost, as the evolution of the quantum state must be limited to restricted conditions, resulting in special trajectories in its Hilbert space that connect the initial and target states.