Quantitative Skills Training for SNBT Preparation at SMA Negeri 2 Parepare South Sulawesi

Abstract: This service activity aims to improve students' quantitative abilities, especially in the SNBT test. The method of implementing this service used training, test simulations and discussions, as well as tips and strategies for solving quantitative ability questions. Assessment tools used during the activity included a pretest, posttest, and questionnaire. The analytical technique to assess the activity's success involved comparing the outcomes of the pre-test and post-test administered. The Results showed that there was an average increase in the scores of the participants' Quantitative Skills test. Overall, the average percentage increase in the participants' Quantitative Skills test scores was 13% from the pre-test scores. It can be concluded that the quantitative skills training provided positively enhanced the student's knowledge and skills, particularly in solving scholastic problems related to the Quantitative Skills test for the upcoming SNBT. Based on the survey, 53.1% of participants were highly satisfied with the conducted training activities and agree that such activities should be continued.


Edukasi Literasi Digital : Pendampingan Transformasi Digital bagi Generasi Muda di Desa Nepo Kabupaten Polewali Mandar

Abstract: This service activity aims to increase the understanding of young people in Nepo village regarding digital literacy and digital transformation assistance. The method of implementing this service uses socialization and training with a participatory approach. The evaluation instrument for this activity uses a questionnaire and is analyzed descriptively. The results of this service show that there has been a significant increase in participants' understanding of the concepts of digital literacy, digital transformation and social media. Digital literacy understanding increased from 50% to 79%, while benefits of digital literacy understanding rose from 50% to 87%. Participants also showed an increase in understanding the era of digital transformation (from 38% to 79%) and the impact of social media (from 43% to 93%). They also understand ethics and cyber security better, with significant improvements. The digital literacy education program in Nepo village has successfully achieved its goals, empowering the younger generation in digital literacy, digital transformation and social media ethics.


Hebrew nonverbal sentences wear reconstruction on their sleeve

Abstract: In this paper, I discuss a particle in Hebrew which has been termed Pron by Doron (1983). While its surface form is that of a pronoun, its distribution resembles that of a copula – it appears between the subject and the predicate in nonverbal present-tensed sentences. However, its distribution is limited in unexpected ways for a copula, which gained it some attention in the literature. Contra the standard line of analysis, I argue that Pron is in fact a resumptive pronoun left by the subject as it raises to a higher position. I show that this analysis ties together many of Pron’s prima facie-surprising distribution patterns.


Contextual Complexities and Second Language Acquisition in Cameroon

Abstract: The complexity of the linguistic environment in Cameroon raises the question of context and its role in the acquisition of another language. While examining progress in English language learning in Cameroon, this chapter draws a dichotomy between learners in such contexts considered rural and those regarded as urban or cosmopolitan with its inherent complexities. Using the irregular verb as a yardstick, an evaluation of the acquisition of irregular verb patterns by 80 final year primary school learners from two contexts in the Northwest Region of Cameroon serves as a guide. Oral and written tests are used to check learners’ acquisition of verb inflectional categories, verb tenses and general written and oral productions within the mixed method design. The findings reveal similar trends in the acquisition of inflectional categories and verb tenses by learners in both contexts and divergent trends in general oral and written productions. For instance, learners in both contexts exhibit similar challenges using the Vs, Ved and Ven inflections with a very low average frequency of 26% and with a high frequency of 67.2% for the Ving and Vo inflections. Though learners in the urban centres have higher degrees of efficiency in oral productions, their counterparts in the rural areas exhibit more challenges in verbal as against written productions. The chapter concludes that second language acquisition is not a consequence of a unilateral context but a result of a plethora of other factors both within and without the learning environment with evident pedagogic implications for stakeholders in the second language acquisition industry. While context is noted to have impending implications in language learning as highlighted in the chapter, the finality of SLA is also dependent on other linguistic factors like innate capabilities inherent in all learners.


Perancangan Sistem Informasi Akuntansi Penggajian Pegawai Instansi Pemerintah di BKPSDM Kab. Cianjur

Abstract: The process of employee payroll system at BKPSDM Kab. Cianjur Agency already uses a computer but it is not optimal because it still uses Microsoft Excel in data collection and calculation salaries. The purpose of this research is to create a computerized payroll information system that make it easier for agency in the payroll process for employees and minimize the problems that often occur. This information system designed using PHP Myadmin programming language and MySQL as a database. The approach to developing this system uses the waterfall method and the tools involved include Flowmap, Context Diagram, Data Flow Diagrams (DFD), and Entity Relationship Diagrams (ERD). The results of this employee payroll application can make it easier to collect employee data and calculation of employee salaries accurately and can streamline time in making reports so as to reduce errors in the employee payroll system.


Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex

Abstract: Abstract Discrete neural states are associated with reaching movements across the frontoparietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuo-motor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatio-temporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example in brain-machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.


Libraries in the Age of Intelligent Information: AI-Driven Solutions

Abstract: This study explores the paradigm change that artificial intelligence (AI) has brought about in the context of libraries in the age of intelligent information. A quantitative survey was carried out, and a methodical description of the study's procedure is provided. The research sampling strategy, data gathering, data collection tool, and statistical analysis are all included in this quantitative approach. Additionally, a random sampling procedure was used to choose the study sample. Similar to this, questionnaires were employed as response instruments to collect data in both physical and digital formats. Findings from the investigation showed that 45% of institutions are private nature and 50% institutions shows the status public. 1-10 years’ experience were 62% respondents, 11-20 years’ experience were 31% and only 8% of respondents were 21 years above experience. The study emphasizes how important it is to create a welcoming and open library ecosystem where artificial intelligence (AI) enhances operational effectiveness while protecting user privacy and pleasure. All things considered, this thorough analysis highlights the many ways that artificial intelligence (AI) might transform libraries and meet the many demands of their patrons. Looking forward, the article discusses the promising future of AI in libraries, envisioning AI-powered chatbots, predictive analytics, and virtual helpers as integral components of an AI-driven library landscape.


Deep Learning Methods for Diagnosing Thyroid Cancer

Abstract: Abstract One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.


Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network

Abstract: Abstract Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using Transfer Entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.


Maximization of rf-signal recovery in quantum microwave photonics systems

Abstract: Quantum microwave photonics (QMWP) technology, which harnesses energy-time-entangled biphotons as optical carriers and employs time-correlated single-photon detection for high-speed rf-signal recovery, has demonstrated remarkable potential in achieving nonlocal rf-signal coding and efficient rf-signal recovery from significant dispersion interference. In this paper, we expound on the principle of maximizing the recovered rf signals in QMWP systems. Through theoretical analysis and experimental validation, we establish a precise relationship between the recovered rf signal's magnitude, the input rf signal's frequency, involved dispersions, and the width of the coincidence selection window. This enables us to determine the optimal width for the coincidence selection window and the corresponding magnitude of the maximized rf signal given the dispersion. These findings offer valuable guidance for improving QMWP systems, thus expanding their potential applications.


FPGA-based multipliers for elliptic curve cryptography

Abstract: This paper presents an approach to build large-integer multipliers for Elliptic Curve Cryptography (ECC). Compared to the traditional divide and conquer method, our method introduces the 𝑛-term Karatsuba-like algorithm and adopts a cascaded structure for integer multiplications, which simplifies the complexity of the whole structure and contributes to a fine-grained multiplier design. Target at the required width of random elliptic curves over prime fields, we implement the design on virtex-7 FPGA and utilize internal DSPs as the fundamental multiplier resources. The synthesis results are compared quantitatively in terms of designs with different parameters and demonstrate the flexibility of our method.


Requirements modeling for weather modification services based on problem frames

Abstract: This paper conducts an in-depth requirement analysis of artificial rainmaking operations using the ontology-guided problem framework method, constructing a clear and organized structure to systematically reveal the complexity and multi-level nature of artificial rainmaking operations. In the process of requirement modeling, we precisely define key aspects such as business goals, technical constraints, and environmental impacts, contributing to a more comprehensive understanding of the nature of the business. This ontology-guided problem framework method, based on ontology guidance, provides a unique and practical approach for artificial weather modification operations, offering systematic and actionable guidance for relevant decision-making and implementation. The aim of this paper is to provide more precise guidance for the planning and implementation of artificial rainmaking projects, thereby promoting the further development and application of artificial weather modification technology.


Expandable residual attention-based high-performance embedded gesture recognition

Abstract: In embedded human-computer interaction systems, the development of high-performance gesture recognition technology is crucial due to its demand for low power consumption and efficient processing. Addressing the challenge of highprecision gesture recognition in complex backgrounds, a high-performance embedded gesture recognition method based on the Expandable Residual Attention mechanism is proposed. This method enhances the capability of extracting differentscale gesture features by introducing the Expandable Residual Attention mechanism into YOLOv7. Additionally, to address the characteristics of high degrees of freedom and self-occlusion in hand gestures, SoftNMS is introduced with a penalty term to effectively reduce the probability of target omissions. Finally, the gesture recognition model is compressed and accelerated with TensorRT. Experimental results on the Jochen Triesch Static Hand Posture Database demonstrate that the proposed method significantly improves gesture recognition accuracy while maintaining high inference efficiency.


Rules as code and the rule of law: ensuring effective judicial review of administration by software

Abstract: This paper considers the possible benefits and substantial risks of ‘Rules as Code’, the parallel drafting of legislation and codification in software, which has been the subject of attention from policy-makers and pilot studies in some jurisdictions. It highlights the long history of these approaches, and the challenges of ossification, mis-translation of rules, and separation of powers problems. It also examines in the detail the Australian Pintarich case, which demonstrates the inadequacy of conventional judicial review of automated decision-making. It outlines some possible solutions to these issues — two ‘internal’ to development processes (greater transparency, and literate pair programming) and two ‘external’ (expanding the capacity of judicial review to look beyond a specific citizen/state interaction and consider the design and development of the controlling software system, and greater cross-disciplinary awareness by lawyers).


Temporal analysis of hospitalizations for Diabetes Mellitus in Brazil and its relationship with COVID-19: ecological study

Abstract: Diabetes mellitus (DM) is a condition resulting from changes in cartilage or the effect of insulin, resulting in chronic hyperglycemia. In diabetic patients, the presence of COVID-19 increases the rates of serious complications and there is a greater risk of mortality. To analyze the phenotypic characteristics of hospitalizations and mortality rates due to DM in Brazil through epidemiological data and their relationship with COVID-19. This is an observational, descriptive, cross-sectional, quantitative and retrospective study; carried out with analysis of secondary data on hospitalizations for DM, through the Information Technology Department of the Unified Health System (DataSUS) in the period from 2018 to 2022 in Brazil. Variables of year of service, region, race/color, age group and sex were considered. In the present analysis, there is a greater MT in the Southeast and Northeast regions of Brazil. In terms of gender distribution, the highest prevalence is in males (51.05%), but with lower MT than females (2.69 x 2.84), with the population aged between 50 and over. 80 years old with higher percentages of international and TM. Finally, it is clear that the Brown race required more hospitalizations (41.68%), while the Black race was associated with the highest mortality rates. In the present study, it is possible to analyze that the phenotypic characteristics of diabetic patients influence the mortality prognosis, providing the basis for further research with the aim of analyzing the mechanisms that lead to the epidemiological aspects with greater prevalence of TM.


Hierarchical Perceptual Graph Attention Network for Knowledge Graph Completion

Abstract: Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networks (GNNs) perform well in knowledge graph completion, but most existing graph neural network-based knowledge graph completion methods tend to aggregate neighborhood information directly and individually, ignoring the rich hierarchical semantic structure of KGs. As a result, how to effectively deal with multi-level complex relations is still not well resolved. In this study, we present a hierarchical knowledge graph completion technique that combines both relation-level and entity-level attention and incorporates a weight matrix to enhance the significance of the embedded information under different semantic conditions. Furthermore, it updates neighborhood information to the central entity using a hierarchical aggregation approach. The proposed model enhances the capacity to capture hierarchical semantic feature information and is adaptable to various scoring functions as decoders, thus yielding robust results. We conducted experiments on a public benchmark dataset and compared it with several state-of-the-art models, and the experimental results indicate that our proposed model outperforms existing models in several aspects, proving its superior performance and validating the effectiveness of the model.


Time-based sampling: Theory and an application to video reconstruction from events

Abstract: Traditional signal processing is based on the idea that an analogue waveform should be converted in digital form by recording its amplitude information at specific time instants. Nearly all data acquisition, processing and communication methods have progressed by relying on this fundamental sampling paradigm. Interestingly, we know that the brain operates differently and represents signals using networks of spiking neurons where the timing of the spikes encodes the signal's information. This form of processing by spikes is more efficient and is inspiring a new generation of event-based audio-visual sensing and processing architectures. In this talk, we investigate time encoding as an alternative method to classical sampling, and address the problem of reconstructing classes of sparse non-bandlimited signals from time-based samples. We consider a sampling mechanism based on first filtering the input, before obtaining the timing information using a time encoding machine. Leveraging specific properties of these filters, we derive sufficient conditions and propose novel algorithms for perfect reconstruction of classes of sparse signals. We then extend our analysis to multi-dimensional signals and introduce a deep neural networks for the reconstruction of intensity videos from events generated by event-driven cameras. The network is obtained by embedding prior-models in its architecture. We show that this approach leads to simple and yet effective neural networks.


Enhancing sentence semantic matching with isotropic batch normalization and generalized pooling operator

Abstract: Sentence semantic matching plays a crucial role in various natural language processing (NLP) tasks such as question answering, information retrieval, and text classification. Prior research has made significant contributions to sentence semantic matching, but there is still room for improvement, especially in handling subtle semantic representations. This paper introduces a new approach to sentence semantic matching that integrates Isotropic Batch Normalization and Generalized Pooling Operator, two advanced neural network architectures. By combining these techniques, we aim to enhance the accuracy and efficiency of semantic matching and address the challenges of capturing subtle semantic representations. We compare our approach to existing state-of-the-art models and demonstrate its effectiveness through comprehensive experiments on benchmark datasets.


Media Criminology – The role of the media and its influence on contemporary society

Abstract: This study explores the role of media criminology and its influence on contemporary society, emphasizing the portrayal of crime and penal justice by the media. It aims to clarify the significant consequences of media representations on public perception of crime and justice. Our research methodology is grounded on an extensive review of significant literature, avoiding direct quotes but emphasizing the importance of understanding media's role in shaping societal perspectives. The study reveals that media, through its depictions of crime, significantly influences public perception and understanding of criminal justice. Moreover, we found that the evolution of technology, specifically social media, plays a substantial role in media criminology. We conclude by asserting the crucial necessity for a critical understanding and questioning of media representations of crime in our contemporary society.


Weighted median autoregressive graph filters

Abstract: The graph filter can extract the desired features from the graph signal and filter out the noise signal. Most of the graph filters proposed in the literature are linear. Autoregressive moving average (ARMA) filter is a polynomial filter. Compared to finite-impulse response (FIR) graph filters, ARMA graph filters are robust to changes in the signal and/or graph, but are still linear. In this work, we propose a weighted median autoregressive graph filter (WMAF) based on a first-order ARMA graph filter. The proposed filter is a combination of weighted median filter in the traditional signal processing field and median autoregressive filters (MAF), and can be implemented in a distributed way. Compared with linear filter and MAF, the proposed WMAF filter has better filtering effect on pulse noise. In the denoising application of real sensor network data set, the filtered signal has a better signal-to-noise ratio.


Job resource prediction and completion status prediction on large-scale computing platforms

Abstract: Predicting the resource consumption and completion status of jobs is beneficial to improve the scheduling performance of the system. Many studies have shown that job name can effectively improve the accuracy of prediction. Therefore, by mining the structural semantic information of job name, this paper introduces new features of job name habit, including job name length, number of job name elements, editing distance, and analyzes each substructure of job name, adding classification features after clustering. The introduced new features can better characterize the similarity between jobs and provide strong support for model prediction. Based on the model trained by the new feature data set, the prediction accuracy is significantly improved compared with the model that only introduces the job name.


Weighted and Mumford-Shah losses for weakly supervised semantic segmentation

Abstract: In weakly supervised semantic segmentation (WSSS) tasks, the noise in pseudo labels poses a significant challenge to the training of segmentation networks. However, the widely used cross-entropy loss, which only considers individual pixel information in the images, is insufficient to address this issue. In order to effectively train segmentation networks using noisy labels, we propose a Weakly Supervised Semantic Segmentation method (WMS) based on weighted loss and Mumford-Shah loss. Firstly, we introduce a weighted loss with confidence weights for pseudo labels. This loss assigns weights to each pseudo label by incorporating a confidence indicator, enhancing the network's ability to resist noise interference. Additionally, we propose a Mumford-Shah (MS) loss based on variational segmentation. By leveraging the similarity between pixels in the original image, this loss introduces additional noise-free self-supervised information to assist in the training of the segmentation network, further suppressing the interference caused by noisy labels. Extensive experiments on the PASCAL VOC 2012 dataset demonstrate that the proposed method significantly improves WSSS performance.


Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks

Abstract: Abstract Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1)-d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1)-d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent \;$z$\; and spatial correlation length correlation exponent \;$\nu_{\perp}$\ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1)-d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.


Using high-precision travel time extraction to detect time deviation of seismic instruments

Abstract: Abstract The method of seismic ambient noise cross-correlations (NCFs) has been demonstrated to be applicable for time offset measurement, especially in evaluating instrument clocks. Continuous recording of seismic ambient noise data makes it possible to analyze the performance of seismic instruments online. However, long-term cross-correlation and stacking calculations are required to obtain accurate travel time from ambient noise, which greatly reduces the time resolution of instrument performance detection. Therefore, we propose a travel time extraction method based on time–frequency analysis, which could obtain the travel time accurately even though the NCF has a low signal-to-noise ratio (SNR), and it is applied to the measurement of clock offsets in seismic instruments. The method combines S Transform and dictionary learning to improve the SNR of NCFs, and uses a peak extraction algorithm based on short-time Fourier transform to obtain accurate travel times. Additionally, the travel time drift of the station pair can be obtained by calculating the travel time difference between causal and acausal parts of NCFs. Using the data from ambient noise observations and the real data with time errors to verify that the proposed method can obtain accurate travel time for NCF with SNR lower than 6 and it can pick up a time offset as low as one sampling point, the detectable change is 0.046% of travel time, which is crucial for detecting weak changes in the performance of seismic instruments.


Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results

Abstract: We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graph-structured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analytics tasks and graph learning tasks with GNNs. We conclude our work with a discussion of open questions and potential directions for further research in this area.


Compensation Method for Correcting the Topography Convolution of the 3D AFM Profile Image of a Diffraction Grating

Abstract: Any 3D AFM image is a convolution of the geometry of the AFM tip and the profile of the scanned sample, especially when the dimensions of the scanned sample are comparable to those of the AFM tip shape. The precise profile of the scanned sample can be extracted from the 3D AFM image if the geometry of the AFM tip is known. Therefore, in order to separate the geometry of the AFM probe tip from the 3D AFM image of a diffraction grating with a rectangular profile and to correct for the topographic convolutions induced by the AFM probe tip, a method is used to quantitatively evaluate the geometry of the AFM probe tip, including the tip radius and the included angle. A model for reconstructing the measured AFM image is proposed to correct topography convolutions caused by the AFM tip shape when scanning a diffraction grating with rectangular profiles. A series of experiments were performed to verify the effectiveness of the proposed AFM tip geometry evaluation method, and comparison experiments were conducted to demonstrate the feasibility and reliability of the proposed reconstruction model.


HUMAN ACTIVITY RECOGNITION IMPROVEMENT ON SMARTPHONE ACCELEROMETERS USING CIMA

Abstract: Human activity recognition (HAR) is a research field that focuses on detecting user activities and has wide applications. However, the problems that need to be solved are real-time constraints and imbalanced datasets due to different activity frequencies. Our research aims to apply classification integrated moving averages (CIMA) to HAR by evaluating its performance regarding real-time constraints and imbalanced datasets. We achieved the smartphone accelerometer dataset from Kaggle, which consists of several activities: walking, jogging, climbing, and descending stairs. We develop a general CIMA windowing algorithm with hyperparameters J and W. We benchmark CIMA with two state-of-the-art HAR methods: distributed online activity recognition system (DOLARS) and convolutional neural network (CNN). We conducted some imbalance and model size analysis. The test results show that, with J = 10 and W = 240, CIMA performs better than DOLARS and CIMA with recall, precision, and f1-score of 0.996, 0.993, and 0.994. We also prove that CIMA, assisted by quantization, has the smallest model size compared to the CNN and DOLARS model sizes. Finally, we demonstrate that CIMA performs well for imbalanced datasets, where CIMA’s recall on upstairs and downstairs activities is better than DOLARS and CNN, with values of 1.00 and 0.98, respectively. Key Words: classification integrated moving average, human activity recognition, smartphone, accelerometer, imbalanced dataset


Breast Cancer Classification Based on KNNI Imputation of Missing Data

Abstract: Some disease datasets have different degrees of missing, which will lead to the problem of low classification accuracy. To improve the effectiveness of breast cancer disease detection and diagnosis, a classification prediction method combining KNNI and XGBoost was proposed and applied to the classification and analysis of breast cancer data. First, the KNNI method is used to impute the missing data in the breast cancer patient dataset; Then, the original dataset is equalized by the SMOTE oversampling method; Finally, XGBoost is used to extract features that are strongly related to breast cancer malignancies as the input of the model, optimize the XGBoost model by grid search algorithm, find the optimal model parameters, and classify and diagnose the breast cancer dataset. The experimental results show that KNNI can effectively recover the lost data, improve the data quality, and improve the subsequent classification accuracy. Applying imputation methods to flexibly apply missing data to machine learning methods holds great promise.


Machine Learning System for the Effective Diagnosis and Survival Prediction of Breast Cancer Patients

Abstract: Breast cancer is one of the most significant global health challenges. Effective diagnosis and prognosis prediction are crucial for improving patient outcomes in the case of this disease. As machine learning (ML) has significantly improved prediction models in many disciplines, the goal of this study is to develop a ML system for medical specialists that can accurately predict tumor diagnosis and patient survival for breast cancer patients. For the training of diagnosis and survival prediction, five algorithmic models—decision tree (DT), random forest (RF), naive bayes (NB), support vector machines (SVMs), and gradient boosting—were trained with 569 records from the Breast Cancer Wisconsin dataset and 1,980 records from the Breast Cancer Gene Expression Profiles dataset. The results showed that the NB model exhibited better performance for tumor diagnosis, achieving an accuracy of 95.0%, while RF presented the best results for patient survival, with an accuracy of 76.0%. A survey of medical experts’ experience with the resulting system showed high scores in reliability, performance, satisfaction, usability, and efficiency, confirming that ML systems have the potential to improve breast cancer patient outcomes.


Abstract: Citation: Frontiers Editorial Office (2024) Retraction: Increasing interpersonal trust through divergent thinking. Front. Psychol. 15:1384407. doi: 10.3389/fpsyg.2024.1384407


Sentiment Analysis using Support Vector Machine and Random Forest

Abstract: Sentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain. Based on the results obtained, two machine learning algorithms named as Random Forest and SVM were evaluated based on their accuracy in a classification task. The Random Forest algorithm achieved an accuracy of 0.78564, while SVM outperformed it slightly with an accuracy of 0.80394. Both Random Forest and SVM have demonstrated their strengths in achieving respectable accuracies in the given classification task. These results suggest that SVM, with its slightly higher accuracy of 0.80394, may be a more suitable choice when accuracy is the primary concern. However, the basic configuration need and characteristics of the problem at hand should be considered when choosing the better algorithm with better results.


Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance

Abstract: Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise variance without compromising the image details. The AGWF combines the Gaussian filter for initial noise reduction, followed by the implementation of Wiener filter, which can adaptively estimate noise variance and signal power in localized regions. This approach not only outperforms other existing techniques but also showcases a remarkable balance between noise reduction and image detail preservation. The experiment evaluates 300 images from the dataset and each image is corrupted with Gaussian noise variance to ensure a comprehensive evaluation of the AGWF’s performance. The evaluation indicated that AGWF can improve the Signal-to-Noise Ratio (SNR) value and reduce the Root Mean Square Error (RMSE) and Mean Square Error (MSE) value, showing a qualitative improvement in CT scan imagery. The proposed method holds promising potential for advancing medical imaging technology with the implementation of deep learning.


Changes in patterns of peer relationships in primary education classroom networks through cooperative learning.

Abstract: We studied the impact of cooperative learning on positive peer relationships, that is, liking to work together, in classroom networks. Cooperative learning was implemented as part of the "Success for All" program. Longitudinal social network analysis was used to investigate the development of structures and patterns of relationships in 16 intervention and 16 control classrooms, including a total of 791 students. Results showed significantly less reciprocation and transitivity in the dynamics of the intervention networks, while the number of nominations in the intervention classes was at least not smaller than in the control classes, indicating less tendency toward the formation of small clusters or cliques. We did not find that peer relationships became more diverse with regard to gender, socioeconomic status, or academic performance through the implementation of cooperative learning. In intervention classrooms, children tended to nominate their teammates with whom they work together in cooperative learning activities; however, also in control classrooms, children tended to nominate children with whom they are seated in the classroom. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Research on the dehazing effect of orthogonal polarization method based on atmospheric scattering model and considering extinction ratio parameter

Abstract: The hazy weather is currently one of the most common atmospheric environments. It is an urgent problem to realize clear imaging in hazy environment. Orthogonal polarization dehazing is one of the classical dehazing techniques attracting a widespread attention from research workers. However, while using this method, the extinction ratio (ER) parameter of the polarizer was neglected leading to poor dehazing. Therefore, we have introduced the ER of polarizers into the orthogonal polarization dehazing technique in this paper. In the process of sky region division, this study proposes to use HSV, RGB and Lab colour channels to create a comprehensive mask to select the sky region. And the morphological corrosion operation is utilized to increase the region connectivity for better sky region division. Based on subjective visualization, it can be found that the dehazing effect is significantly enhanced with the introduction of ER. The dehazing effect was evaluated based on four parameters of contrast (C0), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and information entropy (En). It is further demonstrated the necessity of introducing ER. The experimental results show that the ER can make the dehazing effect of the orthogonal polarization dehazing technique improved, which is valuable for the engineering application of image processing in hazy environment. The experimental results proved to be encouraging.


Underwater Image Enhancement Based on Multiscale Fusion

Abstract: To improve the visibility and clarity of images in underwater environment, this paper proposes an underwater image enhancement method based on multi-scale fusion. Firstly, the white balance method is used to correct the color of the underwater image, and the original color of the underwater elements is restored as much as possible. Secondly, the dark channel prior algorithm is used to solve the underwater image blur problem. Then, the CLAHE algorithm is used to enhance the contrast of the image. Finally, using weight allocation or fusion rules, multi-scale information and multi-stage fusion of the image are used to generate the final enhanced underwater image. Quantitative image quality evaluation indexes PSNR, SSIM and UIQE are used to evaluate underwater image quality. The results show that the proposed method can effectively solve the problem of underwater image color deviation, make the color of the image more accurate and natural, improve the contrast and brightness of the underwater image, and have better visual effect and richer detail information.


Strong quantum nonlocality with genuine entanglement in an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -qutrit system

Abstract: In this paper, we construct orthogonal genuinely multipartite entangled bases in ${({\mathbb{C}}^{3})}^{\ensuremath{\bigotimes}N}$ for $N\ensuremath{\ge}3$, where every state is a one-uniform state. By modifying this construction, we successfully obtain strongly nonlocal orthogonal genuinely entangled sets and strongly nonlocal orthogonal genuinely entangled bases, which provide an answer to the problem raised by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]. The strongly nonlocal orthogonal genuinely entangled set we constructed in ${({\mathbb{C}}^{3})}^{\ensuremath{\bigotimes}N}$ contains much fewer quantum states than all known ones. Meanwhile, our results also answer the question given by Wang et al. [Phys. Rev. A 104, 012424 (2021)].


Variations of nursing care in patients with COVID-19 in the northern region of Brazil

Abstract: Although nurses are directly involved in the treatment of patients with COVID-19, their possibilities and limits in dealing with the disease are not well defined. This is at least partly due to the fact that nursing procedures are still being modified. This study aimed to analyze the variations in nursing care for patients with COVID-19 in the public health network in a municipality in the northern region of Brazil. This is a retrospective observational study in which data from the medical records of 499 patients hospitalized in the public health network of the city of Marabá, Brazil, between March and October 2020 were cataloged. The sample was divided into two groups: the first group consisted of the medical records of patients hospitalized between March and June (1st semester, n = 327) and the second group consisted of the medical records of patients hospitalized between July and October 2020 (2nd semester, n = 172). Regarding the 1st semester of 2020, it was observed that 16 nursing care (59.3%) were statistically lower than those of the 2nd semester of 2020 and eight nursing care (29.6%) were statistically higher than those of the 2nd semester of 2020. Furthermore, three nursing care (11.1%) did not show significant differences between the 2 semesters. Overall, the percentage of nursing care increased from the 1st to the 2nd semester (52.8 ± 17.6% vs. 62.7 ± 5.1%, P < 0.001); the greatest changes occurred in relation to the musculoskeletal system (37.3 ± 22.9% vs. 53.3 ± 7.7%, P < 0.001). In conclusion, in the initial months of the pandemic, nursing care was less intense, which may have been a consequence of the lack of knowledge about COVID-19. This may have had a negative impact on epidemiological data from the beginning of the pandemic.


Technologies applied to the curriculum for the recomposition of learning in the post-pandemic period

Abstract: In light of the need for curricular adaptations as a strategy to mitigate the impacts caused by the COVID-19 pandemic, this theoretical study aimed to investigate how digital technologies, when applied to the educational curriculum, can contribute to the reconstitution of learning in the post-pandemic period. To support the theories explored in this article, a Systematic Literature Review (SLR) was chosen, using the Google Scholar database as the main source of consultation. The reconstitution of learning is understood as a set of actions that involve prioritizing competencies and skills of the National Common Curricular Base in line with the new curricula that align with them. Technologies emerge as valuable tools for educators, facilitating the implementation of innovative teaching methodologies and corroborating with learning processes and the necessary curricular prioritization. The insertion of education technologies in teaching methodologies assists the learning process by promoting greater interactivity and involvement of students. In addition, it makes learning more dynamic, playful, meaningful, and enriching. Therefore, this study highlights the importance of integrating education technologies into the school curriculum and teaching methodologies, aiming not only to improve the quality of education but also to prepare students for a world increasingly immersed and interconnected in the digital realm.


Application Research of Fish-Plant Symbiosis Technology in a Urban Lake: A Case Study of the Environment Remediation Project with Machine Learning Application

Abstract: This research examines the Jiefang Park Water Environment Remediation Project, a pioneering initiative in urban lake rehabilitation. The project is anchored in a fish-plant symbiosis system, which has significantly improved water quality, upgrading it from Category V to Category III, and in certain zones, achieving the commendable Category II. This enhancement is quantitatively marked by a notable reduction in nitrogen and phosphorus levels, and a marked increase in water transparency, with submerged plants covering over 70% of the lakebed.A key innovation is the utilization of a machine learning model to predict chlorophyll-a concentration, a vital water quality metric. The model’s accuracy is underscored by its R² values, ranging from 0.23 to 0.99, and RMSE values between 6.921 and 0.237, with the best performance at point 1. Additionally, comprehensive measurement data highlight the project’s effectiveness. Water transparency significantly improved from baseline levels, as evidenced by increased dissolved oxygen levels at various sampling sites, indicative of restored ecological equilibrium in the lake. This study represents an exemplary fusion of ecological restoration and advanced data analytics, signifying a substantial advancement in urban lake restoration. It underscores the potential of combining synergistic ecological approaches with technological innovation to further sustainable urban environmental management.


Autonomous navigation and obstacle avoidance in smart robotic wheelchairs

Abstract: This review research paper provides a comprehensive analysis of the advancements, challenges, and methodologies in autonomous navigation and obstacle avoidance for smart robotic wheelchairs. The integration of robotics and assistive technology has revolutionized mobility solutions for individuals with impairments, enabling them to navigate complex environments independently. The paper examines the various sensor modalities, machine learning algorithms, and computer vision techniques employed for environment perception and obstacle recognition. It discusses path planning algorithms, motion control strategies, and decision-making processes for autonomous navigation. The review also addresses limitations, such as localization accuracy and dynamic environment modelling, while highlighting recent research advancements and suggesting future directions. Overall, this paper serves as a valuable resource for researchers and practitioners in the field of smart robotic wheelchairs, aiming to enhance mobility and quality of life for individuals with mobility impairments.


Use of metacognitive strategies in the learning of mixture separation processes in elementary school (final years)

Abstract: In the learning of concepts related to the processes of separating mixtures in elementary school (final years) some difficulties may arise related to the lack of use of some metacognitive strategies that help in the approximation of a meaningful learning. In this context, it becomes relevant to elaborate didactic intervention proposals that show students metacognitive strategies that facilitate learning and provide autonomy. This article aims to raise an environment for reflection and dialogue in the face of an intervention proposal that establishes, in a structured way, a sequence of metacognitive questions linked to the learning of the aforementioned concepts. The results show some potential that can be generated with the implementation of the suggested didactic intervention, except for the necessary adaptations to the school context in which the proposal is intended to be applied.


Substation anomaly detection method based on attention mechanism and feature fusion

Abstract: To ensure the safe, stable, and reliable operation of substations and improve the intelligent anomaly monitoring ability, this paper proposes an algorithm for detecting abnormal behaviors and equipment defects of substations based on an attention mechanism and feature fusion network. This algorithm designs the feature fusion structure of BiMFPN. It constructs multiple green channels of shallow input to deep output feature fusion, thus fusing a more extensive range of practical features without increasing the cost. At the same time, lightweight CBAM is added to the fusion network to effectively combine the extracted spatial location features with the rich semantic information in the image. In addition, the algorithm also adds a new detection head, which effectively improves the detection performance of multi-scale targets and is more sensitive to small targets. After experimental tests, the mAP and F1 scores of the algorithm are 69.3%and 71.7%, respectively, which are 9% and 8% higher than that of YOLOv5m. The experimental results show that this method can effectively detect abnormal behavior and faulty equipment around the substation and improve the intelligent detection level of the substation.


Fast algorithm for intra prediction mode of 360 degree panoramic video based on VVC

Abstract: The Joint Video Exploration Team published the Versatile Video Coding standard in 2020. The standard improves the efficiency of coding for 360-degree panoramic video. However, it also causes an increase in coding complexity, especially in the selection of the intra prediction mode. Therefore, in order to reduce the time required for intra prediction mode decision in the VVC standard, this paper proposes a fast decision algorithm for intra prediction modes from the perspective of reducing the number of candidate modes in the intra prediction modes by exploiting the relationship between the gradient direction and the angular prediction modes of the Sobel operator. Experimental results show that this algorithm can save an average of 6.27% of coding time compared with VTM11.0, while causing an average increment of BDBR of only 0.198% and an average reduction of BDPSNR of only 0.009dB.


Performance Analysis of Quantum Federated Learning in Data Classification

Abstract: Federated learning is a method with the advantage of allowing various institutions to create a global model by sharing model parameters without sharing the data they possess. Also, with the advent of the era of quantum computing, efforts to combine traditional machine learning algorithms and quantum computing are gaining momentum. In this paper, we intend to discuss quantum federated learning (QFL) methods. We examine the fundamentals of quantum computing and the structure of quantum neural networks, and introduce the methods of quantum federated learning. QFL demonstrated a potential for practical application in the real world, achieving up to 92% performance in the MNIST data classification task.


ADAPTATION OF REY AUDITORY VERBAL LEARNING TESTFOR INDONESIA: ITS VALIDITY AND RELIABILITY

Abstract: Rey’s Auditory Verbal Learning Test (RAVLT) assesses different as- pects of verbal memory and learning. This test is often used in clinical, educational, and selection contexts. We adapted the Geffen et al’ ver- sion for Indonesia and here we present sychometric properties. We tested the validity via its seven recall trials (A1 to A7), based on theore- tical constructs (e.g. the presence and recency effect) via analyses of variance and factor analyses and the test’s reliability via e.g. correlation analyses and Cronbach’s alpha. The objective of the research was to study the psychometric properties of Rey Auditory Verbal Learning Test which were adapted from Geffen et al’s to Indonesian version.Two groups of healthy participants with varying ages (16-80) and edu- cation (from Elementary school to postgraduate alumni) included 492 persons for the validation, and 50 for the test-retest reliability part of the study with a 6–14-day interval. We tested the validity via its seven recall trials, based on theoretical constructs, and factor analyses. The test’s reliability via Cronbach’s alpha test, and test-retest.The recall scores increased over trials A1 to A5. The expected position effect, primacy, and recency were found for A1 to A5. Factor analysis that the Rey Auditory Verbal Learning Test revealed three memory con- structs. The word “ibu (mother)” caused an anomaly in the position effect of the 15 words. Both Cronbach’s Alpha and the test-retest corre lations increased from low A1 to satisfying A7. Carry-over results were found in the retest.The Indonesian version of the Rey Auditory Verbal Learning Test is valid based on the recall related to the position of the words, the three clusters describing the memory of all seven trials. The score of relia- bility is only moderate for the recall score, it might be due to the test- retest procedure.


Steady-state charging of quantum batteries via dissipative ancillas

Abstract: We investigate the steady-state charging process of a single-cell quantum battery embedded in an $N$-cell star network of qubits, each interacting with a fermion reservoir, collectively and individually in equilibrium and nonequilibrium scenarios, respectively. We find an optimal steady-state charging in both scenarios, which grows monotonically with the reservoirs' chemical potential and chemical potential difference, where the high base temperature of the reservoirs has a destructive role in all parameter regimes. We indicate that regardless of the strength of the nonequilibrium condition, the high base chemical potential of the battery's corresponding reservoir can significantly enhance the charging process. On the other hand, a weak-coupling strength can strongly suppress the charging. Consequently, our results could counteract the detrimental effects of self-discharging and provide valuable guidelines for enhancing the stable charging of open quantum batteries in the absence of an external charging field.


The role of perceptual similarities in determining the asymmetric mixed-category advantage.

Abstract: Considering working memory capacity limitations, representing all relevant data simultaneously is unlikely. What remains unclear is why some items are better remembered than others when all data are equally relevant. While trying to answer this question, the literature has identified a pattern named the mixed-category benefit in which performance is enhanced when presenting stimuli from different categories as compared to presenting a similar number of items that all belong to just one category. Moreover, previous studies revealed an asymmetry in performance while mixing certain categories, suggesting that not all categories benefit equally from being mixed. In a series of three change-detection experiments, the present study investigated the role of low-level perceptual similarities between categories in determining the mixed-category asymmetric advantages. Our primary conclusion is that items' similarity at the perceptual level has a significant role in the asymmetric performance in the mixed-category phenomenon. We measured sensitivity (d') to detect a change between sample and test displays and found that the mixed-category advantage dropped when the mixed categories shared basic features. Furthermore, we found that sensitivity to novel items was impaired when presented with another category sharing its basic features. Finally, increasing the encoding interval improved performance for the novel items, but novel items' performance was still impaired when these items were mixed with another category that shared their basic features. Our findings highlight the significant role low-level similarities play in the asymmetric mixed-category performances, for both novel and familiar categories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


BUSTLE: a Versatile Tool for the Evolutionary Learning of STL Specifications from Data

Abstract: Abstract Describing the properties of complex systems that evolve over time is a crucial requirement for monitoring and understanding them. Signal Temporal Logic (STL) is a framework that proved to be effective for this aim because it is expressive and allows state properties as human-readable formulae. Crafting STL formulae that fit a particular system is, however, a difficult task. For this reason, a few approaches have been proposed recently for the automatic learning of STL formulae starting from observations of the system. In this paper, we propose BUSTLE (Bi-level Universal STL Evolver), an approach based on evolutionary computation for learning STL formulae from data. BUSTLE advances the state-of-the-art because it (i) applies to a broader class of problems, in terms of what is known about the state of the system during its observation, and (ii) generates both the structure and the values of the parameters of the formulae employing a bi-level search mechanism (global for the structure, local for the parameters). We consider two cases where (a) observations of the system in both anomalous and regular state are available, or (b) only observations of regular state are available. We experimentally evaluate BUSTLE on problem instances corresponding to the two cases and compare it against previous approaches. We show that the evolved STL formulae are effective and human-readable: the versatility of BUSTLE does not come at the cost of lower effectiveness.


On Circular q-Rung Orthopair Fuzzy Sets with Dombi Aggregation Operators and Application to Symmetry Analysis in Artificial Intelligence

Abstract: Circular q-rung orthopair fuzzy sets (FSs) were recently considered as an extension of q-rung orthopair FSs (q-ROFSs), circular intuitionistic FSs (Cir-IFSs), and circular Pythagorean FSs (Cir-PFSs). However, they are only considered for some simple algebraic properties. In this paper, we advance the work on circular q-ROFSs (Cirq-ROFSs) in Dombi aggregation operators (AOs) with more mathematical properties of algebraic laws. These include the circular q-rung orthopair fuzzy (Cirq-ROF) Dombi weighted averaging (Cirq-ROFDWA), Cirq-ROF Dombi ordered weighted averaging (Cirq-ROFDOWA), Cirq-ROF Dombi weighted geometric (Cirq-ROFDWG), and Cirq-ROF Dombi ordered weighted geometric (Cirq-ROFDOWG) operators. Additionally, we present the properties of idempotency, monotonicity, and boundedness for the proposed operators. In the context of artificial intelligence, symmetry analysis plays a significant and efficient role that can refer to several aspects. Thus, to compute the major aspect, we identify the multi-attribute decision-making (MADM) technique based on the proposed operators for Cirq-ROF numbers (Cirq-ROFNs) to enhance the worth of the evaluated operators. Finally, we use some existing techniques for comparison to our results to show the validity and supremacy of the proposed method.


PENERAPAN MODEL WATERFALL DALAM PERANCANGAN APLIKASI DIGITAL CUSTOMER RELATIONSHIP MANAGEMENT PRODUK FASHION

Abstract: To win business competition and maintain market share, companies are required to be able to adapt to market conditions and use appropriate marketing strategies. Customer Relationship Management (CRM) is a marketing strategy to create and maintain good relationships with customers thereby reducing the possibility of customers moving to competitors. The aim of the research is to analyze and develop web-based CRM in the Marketing Information System at Toko Agung, East Jakarta. The research method used in this research is the Software Development Method using the waterfall model. The result is a web-based CRM system application that can manage customer data, product orders, payments and delivery of goods to customers. The implementation of this web-based digital CRM application is expected to make it easier for customers to select the desired product, place an order, make payments and receive goods without having to come to Toko Agung. For busy customers, this system is more efficient and effective. It is hoped that this convenience will have an impact on repeat purchases and will increase sales volume, which can then increase company profits. The Customer Relationship Management (CRM) application can simplify, speed up and optimize the quality of customer service, especially at Toko Agung, East Jakarta.


Digital, Self-Regulated Vocabulary Learning and Device Control In Out-Of-Class, Higher Education Settings

Abstract: Self-regulation of learning behaviour is particularly important when it comes to vocabulary learning for academic purposes in a second language because it often needs to be done on a regular and consistent basis and mostly in out-of-class, self-directed settings to be successful. Self-regulation is also vital when this learning takes place using digital activities on smartphones because these are now ubiquitous devices and deeply embedded in both daily life and higher education settings. Features such as notifications from social media applications can end up distracting students from their academic tasks unless they have the capacity to manage and control their behaviour. This naturalistic, mixed methods study conducted with students on an academic English foundation course in a higher education context aimed to measure their capacity for self-regulated vocabulary learning through technology before and after 10 weeks of intentional digital vocabulary learning in out-of-class settings and to see if there was any difference between learning on a laptop and a smartphone. The purpose of this study was to find out if device control was a relevant dimension of self-regulation, which is an under-researched area. The study collected quantitative data through a recently developed self-report survey tool, and differences in scores were measured using Wilcoxon signed-rank tests. Qualitative data was also collected from students through paired-depth interviews, and this was analysed using typological analysis. The results revealed that the students’ self-reported capacity for self-regulated vocabulary learning through laptops was significantly higher than their capacity for self-regulated vocabulary learning through smartphones. In addition, commitment regulation when using a smartphone decreased significantly over the 10-week period primarily due to distractions from social media notifications. At the same time, students were aware of when to use each device for different types of learning activities and under different temporal and spatial conditions. Overall, this study showed that device control should be considered an additional dimension of a model of digital, self-regulated vocabulary learning and should also be incorporated into future research in the field of e-learning. In addition, students in higher education need to be given more guidance about the benefits and drawbacks of different devices and how to develop their capacity and strategies for greater device self-regulation.


The Role of Customer Satisfaction and Awareness as A Measure of Customer Loyalty- A Study on Select Banks

Abstract:
Purpose: The study wants to examine customer loyalty through customer satisfaction and awareness using developed constructs for select Nifty Banks.

 Theoretical framework: The theoretical framework for the study is to analyze customer loyalty through customer satisfaction and awareness using constructs of General Services (GS), Product Cognizance (PC), Technology Services (TS), and Social Responsibility (SR).

 Design/methodology/approach: The cluster sampling method was used to collect data from bank customers (n=360) using the self-administered questionnaire. The research used Partial Least Squares – Structural Equation Modelling (PLS-SEM) to test the hypothesis using SmartPLS 4.

 Findings: The result showed, that there is low customer satisfaction with Technology Services (TS) and that hypothesis was rejected.

 Research, Practical & Social implications: The study has provided recommendations for banks were discussed to pay more attention to customer fulfilment and awareness which convert satisfaction to customer delight.

 Originality/value: Due to market sustainability and dynamics, the Indian banking industry has undergone many major changes such as digital banking units (DBU), digital currencies, and social green initiatives. Bank has incorporated many advancements to overcome its rivals. So, the study wants to know the customers are aware of these changes among their banks and whether they are satisfied with products and services.

 Contribution/Originality: The study wants to rank the select banks of India based on the constructs built, which measure customer loyalty through customer satisfaction and awareness.


Multiple image encryption acting at the RNA level

Abstract: The purpose of this research is to develop a new method for encrypting multiple superimposed or side-by-side images. The process begins by extracting the red, green, and blue channels from each image and converting them into vectors that combine to produce a single image that undergoes an advanced pixel-level Vigenere transform. In the next step, a pseudorandom transition occurs at the nucleotide, followed by a passage to codons for genetic crossover implementation specifically designed for image scrambling. The latter process is controlled by many random tables developed from selected chaotic maps, which ensures a high degree of flexibility and security in the encryption method. To evaluate the effectiveness and security of this innovative multi-image encryption algorithm, extensive simulations were performed using a large number of images randomly selected from the database. The simulation results prove the reliability and robustness of the method.


Experiences in the education of mathematics during the digital curriculum from the perspective of high school students

Abstract: Due to the COVID-19 epidemic, Hungarian schools had to switch to a digital curriculum for an extended period between 2019 and 2021. In this article, we report on the experiences regarding the education of mathematics during the digital curriculum in the light of the reinstated on-site education, all through the eyes of high school students. Distance education brought pedagogical renewal to the lives of many groups. Students were asked about the positives and negatives of this situation. Subject Classification: 97C90


Pengaruh Penggunaan Internet Terhadap Keterampilan Literasi Informasi Masyarakat Di Kelurahan Balai-Balai Kota Padang Panjang

Abstract: The purpose of this research is to describe (1) the diversity of people's internet use in the Balai-Balai Kota Padang Panjang subdistrict, (2) the information literacy skills of the community in the Balai-Balai Kota Padang Panjang subdistrict, (3) the influence of internet use on the information literacy skills of the community in Padang Panjang City Hall sub-district. This research uses quantitative research with descriptive methods. The population in this study was the community in the Balai-Balai City District of Padang Panjang with sampling using simple random sampling. The main instrument used was a research questionnaire which had been tested for validity and reliability, the sample size was 98 people. The population in the study was the community in the Balai-Balai City District of Padang Panjang. Data collection techniques are questionnaire techniques and questionnaire data processing through editing and tabulation stages. The results of the research are: (1) The community's internet use has an average answer score of 3.21. This score is on an interval scale of 2.50 – 3.24, which means that the internet use of the Padang Panjang City Hall community is classified as good. This can be seen from the intensity, usefulness and effectiveness of internet use carried out well. (2) Public information literacy has an average answer score of 3.11. This score is on an interval scale of 2.50 – 3.24, which means that the information literacy of the Padang Panjang City Hall community is classified as good. This can be seen from information literacy skills in determining information needs, finding information, evaluating information and using information for certain purposes by understanding aspects that are considered good. (3) The influence of internet use on the information literacy skills of people in the Balai-Balai City District of Padang Panjang is strong. From the results of the SPSS regression test, a correlation coefficient value of R = 0.787 was obtained, meaning that internet users have a positive influence on information literacy skills. The higher the use of the internet, the higher the information literacy skills in the Balai-Balai City District of Padang Panjang.


Towards trustworthy AI: An analysis of the relationship between explainability and trust in AI systems

Abstract: As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.


Pengembangan Sistem Cerdas Berbasis Data Mining untuk Meningkatkan Akurasi Prediksi Kebutuhan Obat di Puskesmas Parit Rantang

Abstract: Effective inventory management is crucial in providing quality healthcare services. Predicting drug needs in the pharmacy warehouse is vital to ensuring adequate availability for patients. This study developed and implemented a prediction system using Artificial Neural Network (ANN) method to forecast drug requirements. Training data comprised drug usage from January 2020 to July 2023, while testing data covered drug usage from August to December 2023. Through several experiments, the best model identified was 12-6-1, with a Mean Absolute Percentage Error (MAPE) of 6.817 and an accuracy of 93.18%. Predictions for Paracetamol drug usage in August were 4603, whereas the actual usage was 4785. This system is expected to enhance drug inventory management efficiency, reduce costs, and improve drug availability for patients.


Enhancing Students’ Participation Through Question and Answer on SMAN 2 Sungai Kakap Kubu Raya

Abstract: The aim of this research is to enhance students’ participation by using question and answer technique. Therefore, the question of the research is how does Question and Answer enhance students’ participation on the 11th grade students of SMAN 2 Sungai KakapKubu Raya in academic year 2021/2022?”. The research was conducted in classroom action research design. The research was conducted in two cycles. On the first cycle, there were only some students who actively participated in the learning activity. On the second cycle, most of the students were very active. The problem about lack of participation was solved. Therefore, it is concluded that question and answer technique is useful in improving students’ participation.


Tree HR Model of Human Resource Development in Higher Education

Abstract: Research related to human resource development models in the world of education is very important, because the world of education is one of the determinants of the quality of HR assets. This research aims to find the HR model that best suits the development of Human Resources (HR) in higher education. The research uses qualitative methods. The type of research used is R&amp;D (Research &amp; Development). In the first year using the Analysis and Design approach from the ADDIE model (Analysis, Design, Development, Implementation and Evaluation). Data collection was carried out through a literature review study of 90 articles indexed by Scopus, Sinta 1, Sinta 2, and interviews with 15 experts from the academic and professional world. This research was analyzed using the SWOT Analysis and FGD approach. The results of this research found a prototype tree-based HR model and important variables that were mapped to the tree structure, namely root variables, stem variables, and fruit/leaf variables. This research is used to improve the quality of higher education human resources. This has implications for improving the quality of lecturers both in self-development and teaching in higher education, which has an impact on the quality of students and Indonesia's human resource assets.


The Threshold level of Institutional Quality in the Nexus between Financial Development and Environmental Sustainability in Nigeria

Abstract: Empirics on the relationship between financial development and environmental sustainability remain ambiguous in the literature. The threshold level at which institutional quality facilitates the relationship between financial development and environmental sustainability in respect to the Nigerian economy is still an open question. This study investigates the threshold level of institutional quality in the link between financial development and environmental sustainability in Nigeria from 1986 to 2020. Times series threshold autoregression technique was applied to determine the threshold level of institutional quality. The result of the threshold revealed that 4.32 is the threshold level of institutional quality in Nigeria. Below the threshold level financial development is not stimulated to improve environmental sustainability but above the threshold level institutional quality stimulates financial development to improve environmental sustainability. This study suggests that institutional credibility and transparency should be enhanced beyond the threshold level to effect the needed change in increasing environmental preservation in Nigeria.


Multi-sensor Integration for Land Vehicular Navigation

Abstract: In this dissertation, a multi-sensor integrated system is developed to provide an accurate positioning solution under open-sky, challenging environments such as downtown areas and GNSS-denied environments such as indoor parking lots. A PPP system is first developed by utilizing a geodetic-grade GNSS receiver. An Improved Robust adaptive Kalman Filter (IRKF) is adopted and used as the estimation filter to compensate for the GNSS measurement outliers and the dynamic error modeling. Centimeter-level horizontal positioning accuracy is achieved under an open sky environment, while decimeter-level horizontal positioning accuracy is achieved under a challenging environment. An IRKF-based PPP/INS integration algorithm is then developed by utilizing a geodetic-grade GNSS receiver and a tactical-grade IMU. The integrated system is assessed through two ground vehicular field trials. The developed integrated system achieves centimeter-level positioning accuracy under open-sky environments and decimeter-level positioning accuracy under simulated GNSS outages. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052º, 0.048º, and 0.165º for pitch, roll, and azimuth angles, respectively. Thereafter, the performance of the dual-frequency (DF) Xiaomi mi 8 smartphone is tested in static and kinematic PPP modes. The smartphone-based PPP solution achieves decimeter-level positioning accuracy in the static mode and meter-level positioning accuracy in kinematic mode. A DF u-blox GNSS receiver and xsens industrial-grade MEMS IMU are further used to develop an ultra-low-cost PPP/INS integrated system. The integrated system achieves sub-meter-level positioning accuracy in both the north and up directions, and meter-level positioning accuracy in the east direction. Additionally, the integrated GNSS PPP/INS system achieves attitude accuracy of about 0.878°, 0.804°, and 2.905° for the pitch, roll, and azimuth angles, respectively. To provide an accurate positioning solution for GNSS-denied environments, a LiDAR odometry (LO)/INS/simulated ultra-wide band (UWB) integrated system is developed. The simulated UWB solution is used as an external frequent update to augment the accuracy of the LO/INS solution. Meter-level horizontal positioning accuracy is achieved through the LO/INS integration with frequent simulated UWB-based updates.


Efficient Traffic Classification Using Hybrid Deep Learning

Abstract: Network traffic classification is important for QoS, security, and billing. Using a single machine learning classification model suffers from low prediction and classification accuracy, especially for high dimensional datasets with a high sparsity level. Recently, hybrid-deep learning algorithms have shown high efficiency for traffic forecasting and classification. In this research, we suggested two hybrid models – the Convolutional Neural Network (CNN) combined with the Recurrent Neural Network (RNN) models and the Gated recurrent unit (GRU) combined with Long ShortTerm Memory (LSTM) – to improve traffic classification accuracy in terms of the ratio of correct predictions to total input samples. The hybrid CNN-LSTM and CNN-GRU have achieved an accuracy of up to 99.23% and 93.92%, respectively, for binary classification and 67.16% for multiclass classification. We employed Markov Blanket Algorithm to make the reduced feature set selection, which significantly improved classification accuracy and fscore for multi-class scenarios.


Optimal path for automated pedestrian detection: image deblurring algorithm based on generative adversarial network

Abstract: The pedestrian detection technology of automated driving is also facing some challenges. Aiming at the problem of specific target deblurring in the image, this research built a pedestrian detection deblurring model in view of Generative adversarial network and multi-scale convolution. First, it designs an image deblurring algorithm in view of Generative adversarial network. Then, on the basis of image deblurring, a pedestrian deblurring algorithm in view of multi-scale convolution is designed to focus on deblurring the pedestrians in the image. The outcomes showcase that the peak signal to noise ratio and structural similarity index of the image deblurring algorithm in view of the Generative adversarial network are the highest, which are 29.7 dB and 0.943 dB respectively, and the operation time is the shortest, which is 0.50 s. The pedestrian deblurring algorithm in view of multi-scale convolution has the highest peak signal-to-noise ratio (PSNR) and structural similarity indicators in the HIDE test set and GoPro dataset, with 29.4 dB and 0.925 dB, 40.45 dB and 0.992 dB, respectively. The resulting restored image is the clearest and possesses the best visual effect. The enlarged part of the face can reveal more detailed information, and it is the closest to a real clear image. The deblurring effect is not limited to the size of the pedestrians in the image. In summary, the model constructed in this study has good application effects in image deblurring and pedestrian detection, and has a certain promoting effect on the development of autonomous driving technology.


Never-Ending Lonely Distancing Dance Graphs

Abstract: Lonely distancing is the idea that people can crave company at the same time they fear too much of it. Taking inspiration from this, we construct a scenario in which k players on an n × n grid jockey to occupy space next to a single companion while maintaining at least a minimum distance from everyone else. The tension behind these conflicting needs—companionship versus isolation—induces a dynamical system on the grid we model with directed graphs. Depending on n and k, players are likely to redistribute themselves into one or more equilibrium states. In very special cases, no such resolution is possible, and the players enter a cycle of perpetual redistribution in what we call a never-ending lonely distancing dance.


Rapid calibration method for head-mounted eye-tracker

Abstract: Head-mounted eye-tracking instruments are important devices for sports and human factors research, and their calibration is a more tedious task due to the varied morphology of the human head as well as wear differences. In order to improve the calibration efficiency, we innovatively proposed a Z-shaped moving target instead of a fixed target in our self-research for the calibration of the wearable eye-tracking device, which improved the time needed for calibration from 100 seconds to less than 30 seconds. In addition, in order to reduce the calibration error caused by the user's head movement during the calibration, the world camera image is feature-matched, and the virtual optic target is computed instead of the recognized optic target, thus eliminating the effect of head movement. In the calibration experiments of nine graduate students, the calibration accuracy was less than 20 pixels, and the average calibration time was about 24 seconds.


A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Abstract: Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.


Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net

Abstract: Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved algorithm with horizontal wave transformation (HWT) to augment the dataset for training the Mongolian handwriting recognition. Second, the classical CRNN handwriting recognition model was improved. The structure of the encoder and decoder was adjusted according to the characteristics of the Mongolian script, and the attention mechanism was introduced in the feature extraction and decoding stages of the model. An improved handwriting recognition model, named the EGA model, suitable for the features of Mongolian handwriting was suggested. Finally, the effectiveness of the EGA model was verified by a large number of data tests. Experimental results demonstrated that the proposed EGA model improves the recognition accuracy of Mongolian handwriting, and the structural modification of the encoder and coder effectively balances the recognition accuracy and complexity of the model.


Bridging the Cyber–Physical Divide: A Novel Approach for Quantifying and Visualising the Cyber Risk of Physical Assets

Abstract: Critical infrastructures and their physical assets are under increasing threat of cyber-attacks as technological integration creates cyber–physical systems (CPSs). This has led to an urgent need to better understand which physical assets in these systems are most at risk, but this requires crossing the divide between cyber and physical risk assessments. However, existing cyber-security methods generally focus solely on the vulnerabilities and security of the cyber network and efforts to quantify the impacts of these cyber vulnerabilities on physical assets are generally limited to the consideration of individual attacks, rather than system-wide risk assessments. Similarly, risk assessments of physical infrastructure systems generally ignore potential impacts due to cyber-attacks. To overcome this cyber–physical divide in risk assessment, we introduce a novel approach for assessing risk across this divide. The proposed approach assesses the cyber risk of physical assets as a function of the vulnerabilities of their connected cyber components, and the contribution of cyber components to this risk. The approach is demonstrated with a case study of the C-Town water distribution system. The results indicate that the approach shows a modified prioritisation of risk compared to that obtained using conventional cyber or physical assessments, highlighting the importance of considering the connection between cyber and physical components in risk assessments of critical infrastructure and their physical assets.


Survey on Utilizing Deep Learning Algorithms to Improve Face Detection in Videos with Challenging Conditions

Abstract: Face detection in videos is a fundamental task with significant practical applications in various domains, including security, surveillance, and human-computer interaction. However, it becomes increasingly challenging when dealing with videos that contain various adverse conditions, such as low lighting, occlusions, pose variations, and scale changes. Traditional face detection methods often struggle to provide accurate and reliable results under these challenging conditions. This research explores the application of deep learning algorithms to enhance the performance of face detection in videos under challenging conditions. To validate the effectivenessof our approach, we conduct extensive experiments on benchmark datasets with various challenging conditions. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques in terms of accuracy, robustness, and computational efficiency. Additionally, we provide an in-depth analysis of the model’s performance under different challenging scenarios, highlighting its ability to handle occlusions, pose variations, and low- resolution frames effectively. Keywords: Face Detection , Deep Learning , Video Analysis, Challenging Conditions , Low lighting , Occlusion .


Comprehensive Study on Cloud Computing Services and their Implications for Businesses and Industries

Abstract: A review of the literature on cloud computing is conducted in this work. With its advantages for many kinds of enterprises, cloud computing is one of the computer industry's fastest-growing technologies. Since cloud computing entered the marketplace, service providers have developed a variety of amenities and made them accessible to potential clients. This paper presents the service model for cloud computing. In particular, Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). The study used a mixed research methodology. It entails compiling and assessing both quantitative and qualitative data from the identical literature review and combining the findings of previous, relevant studies. Results introduce readers to the difficulties and issues related to cloud computing while highlighting the trends in cloud computing both now and in the future. The literature study indicated that the use of technology is projected to increase in the future and shows promise. Researchers to solve the issues and difficulties associated with cloud computing, including privacy and security threats, mobile cloud computing, and cloud computing governance. Keywords—Cloud Computing Services, Cloud providers.


Continuing Professional Development for Physical Education Teachers in China: Basis for Teaching Enhancement Program

Abstract: In pursuit of life-long learning, the teaching profession requires continuing education for institutional promotion, salary increase, and administrative position. Supportive academic institutions increase their teacher’s professional learning in the 21st century to increase their students’ further education and work as well. China consistently invested in their teachers’ professional development but empirical evidence on these programs’ effectivity is limited. Specifically, there is a demand for more quality professional development (PD) opportunities for PE teachers focused on the following reasons: (a) the education standards movement, (b) professional organizations, and (c) a call for research on teaching. Apparently, in China, most of them have not recognized their leading role and undervalued their educational career. Thus, this study revisited the impact of the PD programs in China focusing focus its lens on the continuing professional development (CPD) of physical education teachers to address the urgent call to improve the PE-CPD. Guided by the global Continuing Professional Development (CPD) framework, this study investigated the continuing professional development provided to physical education teachers in China and the results of this investigation would be the basis for their teaching enhancement program. The findings revealed that most of the PE teachers in China were male, middle aged with relative bachelor’s degree and permanent position, and developing teachers with 1-10 years teaching experience. They partly considered their involvement in skill development opportunities and interaction and collaboration, but highly considered their involvement in personal growth and reflection in their continuing professional development. Their level of involvement in continuing professional development were significantly different considering their age, sex, educational attainment, and years of teaching. Moreover, the found professional development about classroom management very effective and impactful but teaching practices about ICT skills has minimal effectiveness. However, there is a significant difference in the evaluation of PE teachers on the effectivity of the professional development when they are grouped according to their age, educational attainment, and years of teaching. Nevertheless, the significant relationship found between the their involvement in professional development (PD) and evaluation on the effectivity of the professional development became the basis for the strategic factors to be integrated in the teaching enhancement program for the CPD of PE teachers in the new normal.


Smart Classroom in the Era of Education Informatization 2.0

Abstract: The advent of the era of education informatization 2.0 has brought challenges and opportunities to the educational technology work of colleges and universities. As an important part of education informatization, smart classroom has an important theoretical framework and technical support system. This paper analyzes the characteristics of the era of education informatization 2.0, discusses the concept and importance of smart classrooms, and compares them with traditional classrooms. At the same time, through innovative practices and case studies, the innovative achievements and effect evaluation of educational technology work in colleges and universities are demonstrated. In addition, this paper also introduces the technical support system of the smart classroom, including the functions and architecture of the smart education platform, and the application of big data, artificial intelligence, cloud computing, and mobile learning Xi in the smart classroom. Finally, this paper looks forward to the future-oriented development trend of educational technology in colleges and universities, and puts forward suggestions for the development of smart classrooms and the future prospects of educational technology work in colleges and universities.


How reliable is the scholarly literature?

Abstract: A few years ago, I came across a cartoon that seemed to capture a particular aspect of scholarly journal publishing quite well: The academic journal publishing system sure feels all too often a bit like a sinking boat.


Sentiment Analysis of Code-Mixed Text: A Comprehensive Review

Abstract: Sentiment Analysis is the task of identifying and extracting the opinion expressed in a text to determine the writer&#39;s perception of an entity. Due to globalization, people often mix two or more languages and use phonetic typing and lexical borrowing in web communication. This concept is known as code-mixing. Although extracting the opinion of text written in monolingual languages is simple and straightforward, Sentiment Analysis of code-mixed text is challenging. Classifiers fail within the context of the code-mixed text as text may consist of creative writing, spelling variations, grammatical errors, and different word orders. Hence, SA of code-mixed text is an interesting, challenging, and popular research area. This paper presents the state-of-the-art in Sentiment Analysis of code-mixed text by discussing each concept in detail. The paper also discusses the focused areas, techniques used, limitations, and performances of the studies related to code-mixing.