Reviewerly/exHarmony huggingface dataset:
An Ordered Sample Consensus (ORSAC) Method for Data Cleaning Inspired by RANSAC: Identifying Probable Mislabeled Data

&lt;p&gt;In classification problems, mislabeled data can have a dramatic effect on the capability of a trained model. The traditional method of dealing with mislabeled data is through expert review of the data. However, this is not always ideal, due to both the large volume of data in many classification datasets, as with image datasets supporting deep learning models, and the limited availability of human experts for review of the data. Herein we propose an Ordered Sample Consensus (ORSAC) method to support data cleaning by flagging mislabeled data. This method is inspired by the Random Sample Consensus (RANSAC) method for outlier detection. In short, the method involves iteratively training and testing a model on different splits of the dataset, recording misclassifications, and flagging data which is frequently misclassified as probable mislabels. We evaluate the method by purposefully mislabeling subsets of the data and assessing the method’s capabilities to find such data. We demonstrate with three datasets, a mosquito image dataset, CIFAR-10, and CIFAR-100, that this method is reliable in finding mislabeled data with a high degree of accuracy. Our experimental results indicate a high proficiency of our methodology in identifying mislabeled data across these diverse datasets, with performance assessed using different mislabeling frequencies. &lt;/p&gt;
An intelligent mission-oriented and situation aware quadruped robot: a novel embodied explainable AI approach

Quadruped locomotion and gait patterns as trotting, galloping, trot running has been investigated and applied to a variety of existing quadruped robots such as Big Dog from Boston Dynamics, A1 Robot dog from Unitree. Most of these studies are based either on biology inspired gaits or the best possible locomotion that can be performed by the individual robot with its pre-set mechanics and its availability of the degree of freedoms. While these are already available as their basic modes, a wide number of researchers are investigating locomotion via deep neural nets. These are making headlines in the research community for efficiency of use, and yet the explainability is lacking in most cases. Just like a Large Language Model might give spurious results here and there for basic common sense questions, these deep neural nets also make errors with unknown interpretability to the inputs. Regarding training, they require careful tuning of hyperparameters and training with a number of parameters unknown to user predictions. For example, on the field we might have a terrain which is flat for a certain length, in addition to a rocky climb, followed by a slippery slope. The combinations are as many as possible and the existing state of the art is heavily depending on human intervention and training predictions to handle the change of modes of the gait patterns that can fit into the terrain underneath. In this paper, we develop a novel embodied explainable machine learning algorithm which can help minimize the training as well as human intervention when autonomous operations are required. Specifically, we utilize the Markov Decision Process (MDP) along with rules set forth by DARPA in the Explainable AI (XAI) research. The XAI research enables us to generate textual explanations of the behavior by utilizing the MDP and reinforcement learning to generate mission oriented and situation aware cost functions along with the ones which are already pre-programmed. We validate our hypothesis in real hardware across different conditions.
Design of basic educational foundation for entrepreneurship: from passive to active action

This paper proposes a foundation for entrepreneurship education that changes passive behavior into active behavior and encourages the creation of entrepreneurs. In this method, a mock entrepreneurship project was introduced as part of an integrated PBL/entrepreneurship education that elicits aspirations (dreams) and includes career education. As a result, students' awareness of their future and entrepreneurial spirit seems to have changed, shifting from passive to active behavior toward their future dreams.
Outside the Sandbox: A Study of Input/Output Methods in Java

Programming languages often demarcate the internal sandbox, consisting of entities such as objects and variables, from the outside world, e.g., files or network. Although communication with the external world poses fundamental challenges for live programming, reversible debugging, testing, and program analysis in general, studies about this phenomenon are rare. In this paper, we present a preliminary empirical study about the prevalence of input/output (I/O) method usage in Java. We manually categorized 1435 native methods in a Java Standard Edition distribution into non-I/O and I/O-related methods, which were further classified into areas such as desktop or file-related ones. According to the static analysis of a call graph for 798 projects, about 57% of methods potentially call I/O natives. The results of dynamic analysis on 16 benchmarks showed that 21% of the executed methods directly or indirectly called an I/O native. We conclude that neglecting I/O is not a viable option for tool designers and suggest the integration of I/O-related metadata with source code to facilitate their querying.
Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices

Smart homes present a serious challenge for the aged and those with mobility issues due to the environment's inherent danger. Unwary people have the propensity to fall over when bending over in these settings. Here, they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for (CM-I and CM-II). The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization criteria. They compared actual and simulated data involving missteps, mishaps, and crashes to gauge the programmers’ performance. Results suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future study. The CM-II model had the highest prediction accuracy of any model identified in the literature, at 0.98 when compared to the test parameter. Since the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers, the number of hacks aimed at the IoT has grown dramatically. There is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the attacks. Pursuing a deeper understanding of the technologies, protocols, and architecture of IoT systems, as well as the potential consequences of using infected IoT devices, is the overarching goal of this study. There are many Internet of Things (IoT) systems vulnerable to cybercriminal manipulation, so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.
LATTE: Lattice ATTentive Encoding for Character-based Word Segmentation

A character sequence comprises at least one or more segmentation alternatives. This can be considered segmentation ambiguity and may weaken segmentation performance in word segmentation. Proper handling of such ambiguity lessens ambiguous decisions on word boundaries. Previous works have achieved remarkable segmentation performance and alleviated the ambiguity problem by incorporating the lattice, owing to its ability to capture segmentation alternatives, along with graph-based and pre-trained models. However, multiple granularity information, including character and word, in a lattice that encodes with such models may not be attentively exploited. To strengthen multi-granularity representations in a lattice, we propose the Lattice ATTentive Encoding (LATTE) method for character-based word segmentation. Our model employs the lattice structure to handle segmentation alternatives and utilizes graph neural networks along with an attention mechanism to attentively extract multi-granularity representation from the lattice for complementing character representations. Our experimental results demonstrated improvements in segmentation performance on the BCCWJ, CTB6, and BEST2010 datasets in three languages, particularly Japanese, Chinese, and Thai.
Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

This article focuses on developing a continuous authentication system using behavioral biometrics to recognize users accessing computing devices. The user’s distinct behavioral biometric is captured through keystroke dynamics, and reward-based reinforcement learning (RL) ideas are applied to recognize them throughout the session. The suggested system adds an extra layer of security to traditional authentication methods, forming a robust continuous authentication system that can be added to static authentication systems. The methodology involves training a RL model to detect unusual user typing patterns and flag suspicious activity. Each user has an agent trained on their historical data, which is preprocessed and used to create episodes for the agent to learn from. The environment involves fetching observations and randomly corrupting them to learn out-of-order behavior. The observation vector includes both running features and summary features. The re-ward function is binary and minimalistic. The Principal Component Analysis (PCA) model is used to encode the running features, and the Double Deep Q-Network (DDQN) algorithm with a fully connected neural network is used as the policy net. The evaluation achieved an average training accuracy and EER (equal error rate) of 94.7% and 0.0126 and test accuracy and ERR of 81.06% and 0.0323 for all users when the number of encoder features was increased. Therefore, it is concluded that by continuously learning and adapting to changing behavior patterns, this approach can provide more secure and personalized authentication, lowering the possibility of unauthorized access and cyberattacks. Overall, the use of reinforcement learning and behavioral biometrics for continuous authentication has the potential to significantly enhance security in the digital age and are effective in identifying each user.
Performance Analysis of Personalized Federated Learning Algorithms for Image Classification

Abstract Federated learning (FL) has emerged as a promising technique for training machine learning models collaboratively, without the need to share raw data. By enabling decentralized model updates while retaining data locally on individual devices, FL addresses privacy concerns and reduces communication costs. In this study, we assess the efficacy of personalized FL algorithms—FedAvg, APPLE, FedBABU, and FedProto—in a decentralized setting, explicitly focus-ing on the Fashion MNIST dataset with a non-iid data distribution to observe which one performs better for the image classification task. Our experimental results demonstrate that both FedProto and APPLE exhibit nearly equivalent and superior performance compared to FedBABU and FedAvg. Interestingly, we observe that increasing the number of uploads in FedBABU leads to outcomes similar to APPLE and FedProto. However, when the number of uploads is limited, FedBABU performs similarly to FedAvg. These findings highlight the varying performance of personalized FL algorithms under non-iid data distributions , shedding light on their behavior in a distributed environment for image classification tasks. Such insights are invaluable for guiding the development and deployment of FL systems, particularly in scenarios like medical, military, and other confidential image analysis tasks, where data privacy and communication efficiency are paramount considerations.
Optimization Model for Determining Global Solar Radiation in the Northeastern States of Nigeria Using Both Meteorological and Satellite Imagery Data

This study presents an optimization model for determining global solar radiation in the northeastern region of Nigeria using a combination of meteorological data and satellite imagery. Ten recent models were chosen from the literature review and optimized to select the one that best fits the study region. Two models were developed to provide accurate solar radiation predictions, which can be used to improve the planning and implementation of a solar energy project in the region. The model integrates the Angstrom-Prescott model with various climate parameters such as Temperature (∆T), relative humidity (RH), location latitude (Φ), solar declination angle (δ), and the number of days in a year (n) with satellite image data to determine the global solar radiation. The finding of optimization models shows that the model10 performed very well with minimum error as Mean Base Error (0.028), Mean Percentage Error (- 0.001), Root Mean Square Error (0.098), and coefficient of determination R2 (0.994), which suggested as the optimized model for determining of global solar radiation in northeastern Nigeria. The two models were developed, that is, proposed Model1 and proposed Model2. Proposed Model1 slightly overestimated the global solar radiation with Mean Base Error (-0.863), Mean Percentage Error (-0.039), Root Mean Square Error (2.990), and coefficient of determination R2 (0.745), while proposed Model2 performed better with Mean Base Error (-0.005), Mean Percentage Error (0.0003), Root Mean Square Error (0.02) with the coefficient of determination R2 (0.985). The proposed models were validated using the suggested optimized model10 and satellite data model, which show that the proposed model can accurately determine global solar radiation in the northeastern region of Nigeria. This study's findings will benefit the region's solar energy project developers, researchers, and policymakers
Pricing of futures Bitcoin price under fractional volatility

Abstract Volatility of Bitcoin has a long memory, we modeled such a character using FIGARCH processes, afteward we went on for pricing Futures and Options, the price of Futures depends on many factors in the market, we have proposed a model for futures contracts which links their price to spot price and volatility, after calibrating our model the result was consistent with market values, the pandemic of Covid which started earlier in 2020 after hitting the district of Wuhan just before; had not really an effect on derivatives markets until july 2021 when the market started a downward trend. We price Options using a sample of volatilities that we consider determinstic for a matter of calculous. We finally compare our model for Futures to the same model but with a constant volatility. JEL Classification. G13
Penerapan Biopori pada Halaman Rumah Warga RT.01 RW.04 Desa Kuwaron Kecamatan Gubug Kabupaten Grobogan Jawa Tengah

&lt;p align="justify"&gt;&lt;em&gt;The people of Kuwaron Village, especially residents of RT.01 RW.04, have recently had problems of environmental damage, namely floods during the rainy season and drought during the dry season. The impact is the reduction of water catchment areas around the houses of residents of Kuwaron Village RT.01 RW.04. The purpose of this activity is to provide insight and skills regarding applying appropriate techniques for porous infiltration holes. In the hope of increasing groundwater reserves, reducing inundation, and accelerating water infiltration in residents' yards. The bio-pore-making training involved several residents of Kuwaron Village. Bio pores are made with a diameter of 10 cm and a depth of 100 cm. Then, the hole filled with organic kitchen waste. That is expected to reduce organic kitchen waste from each house because porous infiltration holes containing garbage invite various soil fauna and form pores to increase groundwater infiltration. The results of the evaluation of bio pores installation for four weeks showed that the rain puddles in the residents' yards receded faster.&lt;/em&gt;&lt;/p&gt;
MENINGKATKAN KETERAMPILAN SUMBER DAYA MANUSIA DENGAN TANAMAN HIDROPONIK

The emergence of economic problems that occurred as a result of the Covid-19 pandemic, it is necessary to increase the skills of human resources through trainings that can educate the public in order to increase the cost of living for the community. One form of training that can be done is by educating hydroponic plants. The method used in this community service is training and counseling about hydroponic plants. The results of this research are that residents can understand and practice how to grow hydroponic plants
4th Industry Revolution Digital Marketing Adoption Challenges in SMEs and its Effect on Customer Responsiveness

4th industrial revolution of cyber-physical technologies (4IR) intersecting digital technologies and entrepreneurship serves as an external stimulus in fostering a new method of venture creation transforming customers’ purchasing and consuming behavior. Large corporations are leading in leveraging on 4IR digital marketing for their marketing strategy but SMEs are lacking behind. This study explores the conundrum using an exploratory sequential mixed method. Semi-structured interviews were carried out on a sample selected using non-probability purposive sampling and determined through attaining thematic saturation of discursive patterns. Scale development for quantitative instruments performed using SPSS statistical software. A quantitative study was carried out on a sample size of 153 SME respondents. Analysis was undertaken by paired-sample T-test. Kuskal-Wallis H test and Spearman’s rho correlation test due to the nonparametric nature of data distribution. The outcome reveals that although SMEs are increasingly reliant on DM for their marketing strategy, most of these SMEs are only willing to invest in building low-level DM capability citing a lack of financial budget, inadequate technology infrastructure to support such setup, cyber security issues and lack of DM knowledge. Financial budget and technology infrastructure are considered the most critical concerns by SMEs with low and moderate DM adoption. However, these concerns are less pronounced in SMEs with high DM adoption. Finally, the weak but significant correlation between SMEs’ DM adoption and customer responsiveness infers the significant role of 4IR technology as an enabler of digital marketing strategy that also depends on other critical contributing factors such as price and quality
Pemanfaatan Media Online Quizizz dalam Optimalisasi Pelaksanaan Kegiatan Belajar Mengajar (Studi Kasus SMA Negeri 5 Balikpapan)

Current technological developments are closely related to the millennial generation learning system and from elementary schools to tertiary institutions. This phenomenon is closely related to the use of learning software based on information and communication technology. Technology development with the Quizizz application is the right choice because Quizizz makes learning fun, challenging and interactive and adds to the knowledge, education and creativity of participating educators. A paradigm shift where learning develops with the application of technology that continues to grow rapidly, so it is not outdated and boring. This learning requires active learning, interactive communication and participation between students and teachers. Learning activities must also be created to achieve learning objectives.
Automated Detection of Malaria and Sickle Cells in Blood Smears Using Edge Detection and Morphological Operations

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Cloud Infrastructure Security Using a Hybrid AES Encryption Model

The proliferation of smartphones and cloud computing has led to a rise in the volume of digital content, such as photos, videos, and text messages, that is kept in remote servers. There is a lack of complete confidence between CSPs and their customers when it comes to authorising cloud services. Infrastructure, service delivery, and development methodologies are all being rethought in light of cloud computing. Cloud services are becoming increasingly important to businesses for a variety of reasons, including data storage, online banking, data analytics, and predictive modelling. Hybrid clouds, which combine public and private cloud services, have emerged in response to rising demand from businesses. We have created algorithms and strategies to eliminate the aforementioned challenges with cloud data security. In order to help you achieve a greater level of data protection in the clouds, we have developed a variety of models to assure data protection, privacy, and integrity. Encryption allows the user to keep private data private and secure from prying eyes. AES, RSA, and blowfish are the most common types of encryption used today. By utilising the user-supplied input and the Modified ECC algorithm, this study enhances AES encryption.
Small Target Detection Model in Aerial Images Based on YOLOv7X+

Abstract Small target detection has always been a difficult task in UAV target acquisition. The detection accuracy is low due to the high proportion of small targets, severe mutual occlusion, low feature information and the presence of complex background in the target area. To solve this problem, we propose a high-accuracy small target detection model called YOLOv7X+, which aims to improve the accuracy of UAV small target detection and strike a balance between operational efficiency. The model effectively mitigates the problem of severe mutual occlusion of small targets by introducing the Conv2Formers module for finer feature extraction of spatial information. At the same time, we propose a Bi-level routing attention mechanism based on cavity convolution, which controls feature extraction and delivery at different levels while expanding the perceptual field, enhancing the model's ability to understand and detect the connections between dense small targets through pixel-level processing, making the model more robust in detecting dense small targets in complex and variable scenes, and retaining important background information to increase discriminative accuracy. Finally, the model also is taken into account the multi-dimensional input data under the UAV by adaptively adjusting the size of the convolution kernel to make the algorithm more adaptable to the actual scene requirements. Under the validation test IOU of 0.5, the YOLOv7X+ model achieves an average mean accuracy (mAP50) of 60.3%, which is 5.4% improvement in mAP compared to the original YOLOv7X model and outperforms five current advanced detection methods. The experimental data shows that the model can effectively handle small target detection tasks in complex scenarios and achieve a balance between detection accuracy and efficiency.
Hybrid Intelligent Systems for Non-linear Dynamical Systems

This research paper focuses on the use of advanced hybrid intelligent systems for modeling, simulation, and control of complex systems with non-linear behavior. Non-linear dynamical systems, which are prevalent in various industries, present unique challenges that require sophisticated solutions. Hybrid intelligent systems, combining multiple innovative techniques from the field of Soft Computing, have shown great promise in addressing these challenges. In this paper, we provide a comprehensive overview of hybrid intelligent systems and their advantages in dealing with non-linear dynamical systems. We explore the integration of different Soft Computing methodologies, such as Neural Networks, Fuzzy Logic, Genetic Algorithms, and Chaos Theory, to create powerful hybrid systems. We present real-world case studies and experimental results to showcase the effectiveness of these hybrid systems in modeling, simulation, and control tasks. Finally, we discuss future research directions and challenges in this exciting field, emphasizing the importance of continued exploration and development of hybrid intelligent systems for non-linear dynamical systems.