STRATEGY IN SAMPRADAYA DISCOURSE ON FACEBOOK

Abstract: Background: The term sampradaya is used to denote the Hindu spiritual community in Bali with new traditions and ways originating in India starting in 1980. The debate about sampradaya also took place on social media, resulting in contesting discourses. Research on sampradaya discourse on social media is still very poorly conducted. Based on this, the study aims to determine the strategy of sampradaya discourse in the battle of discourse about sampradaya on Facebook. Methods: This research uses a combination of critical di methods Teun Van Dijk model and netnographic methods. Research data in the form of discourse was collected by copying communication between participants in the Facebook community environment. Result: Based on research, the strategies used by discourse in combat are symbolic capital strategies, reproduction strategies, distinctive strategies, rejection and exclusion strategies, and transformation strategies. Conclussion: The strategies employed in the discourse battles of the Sampradaya on Facebook encompass symbolic capital, reproduction, distinction, rejection and exclusion, and transformation strategies. The use of these strategies in discourse battles aims to maintain and, in some cases, alter the distribution of symbolic resources in relation to power hierarchies, thereby influencing the online community.


Lightweight Machine Learning Method for Real-Time Espresso Analysis

Abstract: Coffee crema plays a crucial role in assessing the quality of espresso. In recent years, in response to the rising labor costs, aging population, remote security/authentication needs, civic awareness, and the growing preference for non-face-to-face interactions, robot cafes have emerged. While some people seek sentiment and premium coffee, there are also many who desire quick and affordable options. To align with the trends of this era, there is a need for lightweight artificial intelligence algorithms for easy and quick decision making, as well as monitoring the extraction process in these automated cafes. However, the application of these technologies to actual coffee machines has been limited. In this study, we propose an innovative real-time coffee crema control system that integrates lightweight machine learning algorithms. We employ the GrabCut algorithm to segment the crema region from the rest of the image and use a clustering algorithm to determine the optimal brewing conditions for each cup of espresso based on the characteristics of the crema extracted. Our results demonstrate that our approach can accurately analyze coffee crema in real time. This research proposes a promising direction by leveraging computer vision and machine learning technologies to enhance the efficiency and consistency of coffee brewing. Such an approach enables the prediction of component replacement timing in coffee machines, such as the replacement of water filters, and provides administrators with Before Service. This could lead to the development of fully automated artificial intelligence coffee making systems in the future.


An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function

Abstract: Image decolorization is an image pre-processing step which is widely used in image analysis, computer vision, and printing applications. The most commonly used methods give each color channel (e.g., the R component in RGB format, or the Y component of an image in CIE-XYZ format) a constant weight without considering image content. This approach is simple and fast, but it may cause significant information loss when images contain too many isoluminant colors. In this paper, we propose a new method which is not only efficient, but also can preserve a higher level of image contrast and detail than the traditional methods. It uses the information from the cumulative distribution function (CDF) of the information in each color channel to compute a weight for each pixel in each color channel. Then, these weights are used to combine the three color channels (red, green, and blue) to obtain the final grayscale value. The algorithm works in RGB color space directly without any color conversion. In order to evaluate the proposed algorithm objectively, two new metrics are also developed. Experimental results show that the proposed algorithm can run as efficiently as the traditional methods and obtain the best overall performance across four different metrics.


Effects of Non-Compliance with the Protocol on InBody 770 in Students of Different Training

Abstract: The research aimed to investigate whether alterations in the measurement protocol, accounting for dehydration, resulted in variations in body composition parameters among participants with different weekly training frequencies. The sample consited of ninety healthy participants categorized into three subgroups; the first group (n=28, body height 172.13±9.12 cm, body weight 67.05±11.38 kg) included students with low levels of weekly PA, the second group (n=35, body height 172.93±7.76 cm, body weight 67.53±9.06 kg) consisted of students whith medium level of PA, and the third group (n=27, body height 175.87±9.27 cm, body weight 71.14±11.58 kg) comprised students with high weekly training frequencies. Using the body composition analyzer, InBody770, various morphological characteristics were measured, including Body Height (BH), Body Weight (BW), Body Mass Index (BMI), Percent Body Fat (PBF%), Body Fat Mass (BFMkg), Fat Free Mass (FFM), Total Body Water (TBW), Intracellular Water (ICW), Extracellular Water (ECW), Proteins (PROT), Minerals (MNRL), Soft Lean Mass (SLM), Skeletal Muscle Mass (SMM), Waist-Hip Ratio (WHR), Visceral Fat Level (VFL), Visceral Fat Area (VFA), and Obesity Degree (OD). It can be detected that there is generally no significant difference between the initial and final measurements within the variables describing body composition. Additionally, when analyzing the effect size on the overall sample, it was found to be insignificant in almost all variables, except for the following parameters: Body Fat Mass (ES=.28); Body Mass Index (ES=.21); Percent Body Fat (ES=.21); Visceral Fat Level (ES=.24); Visceral Fat Area (ES=.26); Obesity Degree (ES=.22). The results of this study, following the water intake treatment, revealed a notable overall difference in body composition parameters. However, upon closer examination by group, it becomes evident that a statistically significant difference is particularly pronounced in individuals with a high exercise frequency (Group III), indicating their body’s efficient capacity for rapid water absorption into various body composition parameters. These findings underscore the critical importance of adhering to the prescribed protocol when diagnosing body composition using the InBody 770 device, particularly among highly trained individuals.


Service robot acceptance in museums: an empirical study using the service robot acceptance model (sRAM)

Abstract: Purpose This study aims to apply the service robot acceptance model (sRAM) to examine how attitude toward human–robot interaction (HRI) and engagement influence consumer acceptance of service robots in a frontline setting. Design/methodology/approach Data was collected from 255 visitors who interacted with a robotic tour guide at a city museum. The data was analyzed using smart PLS 4.0. Findings The findings show the positive effects of subjective norms, appearance, perceived trust and positive emotion on both attitude toward HRI and engagement. In addition, social capability impacted attitude toward HRI, whereas perceived usefulness affected engagement. Practical implications To deliver engaging museum experiences that bring about positive word-of-mouth and intention to visit, managers need to incorporate the sRAM dimensions in the design and deployment of service robots. Originality/value This research uses field data to empirically validate the sRAM in the context of service robot acceptance. It introduces engagement as a novel mediating variable, enriching current understanding of human-like qualities in HRIs.


Teaching Software Quality Assurance (SQA) During COVID-19 Using the HyFlex Approach -- Course Design, Results, and Experiences

Abstract: Abstract Teaching Software Quality Assurance (SQA) During COVID-19 using the HyFlex Approach -- Course Design, Results, and Experiences Abstract This full paper discusses the HyFlex pedagogical approach to teaching a highly interactive face-to-face Software Quality Assurance (SQA) course during the COVID-19 pandemic. HyFlex, short for hybrid-flexibility, is a teaching model where instructors and students have the option to attend scheduled courses face-to-face (F2F) or remotely. In this teaching model the learning outcomes for the course remain the same for all who enroll irrespective of the mode of attendance. This poses significant challenges for the instructor, as the learning outcomes have to be delivered in different modalities, but with the same quality. This is particularly difficult in SQA courses, as these require instruction in formal methods as well as systematic justification of engineering choices, both of which are best facilitated in a face-to-face fashion. Our HyFlex SQA approach consists of lectures (in class, with video recordings), face-to-face activities, group assignments (blackboard), group projects (blackboard), and exams (blackboard). During the lecture period, the instructor delivers content in the form of lecture slides and writing on a whiteboard. Think-Pair-Share (TPS) is an active learning technique in which students are allocated adequate time to think individually on a task posed by the instructor, followed by pair discussion and then as a class discussion. The task given by the instructor is of similar complexity to that which is covered as an example during lecture. Face-to-face activities involve students participating in TPS by working collaboratively as a group on tasks that correspond with concepts covered in the PowerPoint lecture. This way students learn from each other by thinking individually then sharing ideas in the classroom thus contributing to better understanding of course content. For assignments and projects students are allocated a portion of the class time to meet with group members and discuss their activities. Groups also have the option to ask the instructor questions aloud that will help other groups to complete their assignments and projects successfully. Delivering these in a hybrid format was required during the fall of 2020 due to COVID-19 restrictions. The results show an average, but not significant, decrease in students’ performance during fall 2020. We also noted a lower enrollment, higher participatory effort on both instructors and students, and a subjectively decreased feeling of collaboration. In this paper we adopted a HyFlex teaching model that incorporates reduced F2F seating, educational tools such as blackboard, blackboard collaboration, panopto, google docs, google meet, and zoom. We conclude by discussing some challenges experienced with HyFlex teaching model and recommendations for adopting the teaching model by other instructors who teach CS courses that involve a considerable amount of group activities.


Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

Abstract: Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection.


A novel estimator for the two-way partial AUC

Abstract: Abstract Background The two-way partial AUC has been recently proposed as a way to directly quantify partial area under the ROC curve with simultaneous restrictions on the sensitivity and specificity ranges of diagnostic tests or classifiers. The metric, as originally implemented in the R package, is estimated using a nonparametric estimator based on a trimmed Mann-Whitney U-statistic, which becomes computationally expensive in large sample sizes. (Its computational complexity is of order $$O(n_x n_y)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>x</mml:mi> </mml:msub> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>y</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , where $$n_x$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>x</mml:mi> </mml:msub> </mml:math> and $$n_y$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>y</mml:mi> </mml:msub> </mml:math> represent the number of positive and negative cases, respectively). This is problematic since the statistical methodology for comparing estimates generated from alternative diagnostic tests/classifiers relies on bootstrapping resampling and requires repeated computations of the estimator on a large number of bootstrap samples. Methods By leveraging the graphical and probabilistic representations of the AUC, partial AUCs, and two-way partial AUC, we derive a novel estimator for the two-way partial AUC, which can be directly computed from the output of any software able to compute AUC and partial AUCs. We implemented our estimator using the computationally efficient R package, which leverages a nonparametric approach using the trapezoidal rule for the computation of AUC and partial AUC scores. (Its computational complexity is of order $$O(n \log n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , where $$n = n_x + n_y$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>x</mml:mi> </mml:msub> <mml:mo>+</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>y</mml:mi> </mml:msub> </mml:mrow> </mml:math> .). We compare the empirical bias and computation time of the proposed estimator against the original estimator provided in the package in a series of simulation studies and on two real datasets. Results Our estimator tended to be less biased than the original estimator based on the trimmed Mann-Whitney U-statistic across all experiments (and showed considerably less bias in the experiments based on small sample sizes). But, most importantly, because the computational complexity of the proposed estimator is of order $$O(n \log n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , rather than $$O(n_x n_y)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>x</mml:mi> </mml:msub> <mml:msub> <mml:mi>n</mml:mi> <mml:mi>y</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , it is much faster to compute when sample sizes are large. Conclusions The proposed estimator provides an improvement for the computation of two-way partial AUC, and allows the comparison of diagnostic tests/machine learning classifiers in large datasets where repeated computations of the original estimator on bootstrap samples become too expensive to compute.


Collaborative Encoding Method for Scene Text Recognition in Low Linguistic Resources: The Uyghur Language Case Study

Abstract: Current research on scene text recognition primarily focuses on languages with abundant linguistic resources, such as English and Chinese. In contrast, there is relatively limited research dedicated to low-resource languages. Advanced methods for scene text recognition often employ Transformer-based architectures. However, the performance of Transformer architectures is suboptimal when dealing with low-resource datasets. This paper proposes a Collaborative Encoding Method for Scene Text Recognition in the low-resource Uyghur language. The encoding framework comprises three main modules: the Filter module, the Dual-Branch Feature Extraction module, and the Dynamic Fusion module. The Filter module, consisting of a series of upsampling and downsampling operations, performs coarse-grained filtering on input images to reduce the impact of scene noise on the model, thereby obtaining more accurate feature information. The Dual-Branch Feature Extraction module adopts a parallel structure combining Transformer encoding and Convolutional Neural Network (CNN) encoding to capture local and global information. The Dynamic Fusion module employs an attention mechanism to dynamically merge the feature information obtained from the Transformer and CNN branches. To address the scarcity of real data for natural scene Uyghur text recognition, this paper conducted two rounds of data augmentation on a dataset of 7267 real images, resulting in 254,345 and 3,052,140 scene images, respectively. This process partially mitigated the issue of insufficient Uyghur language data, making low-resource scene text recognition research feasible. Experimental results demonstrate that the proposed collaborative encoding approach achieves outstanding performance. Compared to baseline methods, our collaborative encoding approach improves accuracy by 14.1%.


Stability assessment of forestry plant power system based on improved long short-term memory network

Abstract: Abstract The stability assessment of forestry power system plays a vital role in many aspects. First of all, the stability of the power system is directly related to the production efficiency, employee safety and environmental sustainability of the factory. If the power system is unstable, it may cause power outage or power fluctuations, which will interrupt the production and turn the production line, which will lead to a reduction in production capacity and the increase in production costs. In addition, the stability of the power system is directly related to the security of employees. Sudden power failure or voltage fluctuations may cause improper equipment operation, and may even cause accidents and damage. Therefore, by evaluating the stability of the power system, potential security risks can be reduced. In addition, unstable power systems may cause waste and increase environmental burden. In order to improve the stability of the power system, this article proposes an improved LSTM (long and short memory) model of improved stability prediction. This model uses the power system to operate data, including current, voltage, frequency, and other parameters, and external factors related to weather conditions and load changes to predict the stability of the power system. Compared with traditional methods, this method performs well in predicting the failure and abnormalities of the power system, and has the ability to evaluate high accuracy and stability. Therefore, the improved LSTM model method can be regarded as a powerful tool for power system management and maintenance.


HIGH-PRECISION CYCLIC CORRELATION AS A BASIS FOR DETAILED PALEOCLIMATIC RECONSTRUCTIONS FOR THE CRIMEAN-CAUCASIAN REGION IN JURASSIC TIME

Abstract: An astrochronological (cyclostratigraphic) binding of the lithological and geochemical characteristics of the Jurassic deposits of the Mountainous Crimea and the Caucasus to the cycles of eccentricity of the Earth’s orbit is carried out, an analysis of the relationship between the established variations of climate and paleotemperature with the moments of coincidence of different-order cycles of eccentricity and their paleoclimatic characteristics is given. New data on paleotemperatures of the Southern Demerdzhi mountain section are presented.


Automatic MTF Conversion between Different Characteristics Caused by Imaging Devices

Abstract: Depending on various design conditions, including optics and circuit design, the image-forming characteristics of the modulated transfer function (MTF), which affect the spatial resolution of a digital image, may vary among image channels within or between imaging devices. In this study, we propose a method for automatically converting the MTF to the target MTF, focusing on adjusting the MTF characteristics that affect the signals of different image channels within and between different image devices. The experimental results of MTF conversion using the proposed method for multiple image channels with different MTF characteristics indicated that the proposed method could produce sharper images by moving the source MTF of each channel closer to a target MTF with a higher MTF value. This study is expected to contribute to technological advancements in various imaging devices as follows: (1) Even if the imaging characteristics of the hardware are unknown, the MTF can be converted to the target MTF using the image after it is captured. (2) As any MTF can be converted into a target, image simulation for conversion to a different MTF is possible. (3) It is possible to generate high-definition images, thereby meeting the requirements of various industrial and research fields in which high-definition images are required.


A case study in underspecification of UG: External Pair Merge of <i>v</i> and T

Abstract: Abstract Previous works have explored the options of Pair Merging R and v as well as T and C, respectively, yielding R‐ v and T‐C with various consequences. This paper proposes that v and T can yield the complex head v ‐T, an amalgam formed by external Pair Merge. Some empirical ramifications for the verb cluster and the middle field in German are explored. If tenable, the current approach supports the idea that heads can Merge freely, either by internal or external Set Merge, as well as by internal or by external Pair Merge.


Outdoor activity classification using smartphone based inertial sensor measurements

Abstract: Abstract Human Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model’s performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model’s capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports.


TV shows popularity prediction of genre-independent TV series through machine learning-based approaches

Abstract: Abstract The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of “ never seen ” shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages.


Optimization of multi-vehicle obstacle avoidance based on improved artificial potential field method with PID control

Abstract: In the context of multi-vehicle formation, obstacle avoidance in unknown environments presents a number of challenges, including obstacles near the target, susceptibility to local minima, and dynamic obstacle avoidance. To address these issues in multi-vehicle formation control and obstacle avoidance within unknown environments, this paper uses PID control to optimize the potential field function of the artificial potential field method and conducts simulation experiments. The results demonstrate that the proposed algorithm achieves reductions of 39.7%, 41.9%, 24.8% and 32.0% in four efficiency functions (total iteration times, formation efficiency function value, energy consumption and standard deviation of iteration times) compared to other algorithms. The improved algorithm more effectively addresses the challenge of slow obstacle avoidance when vehicles approach the target and can handle unexpected situations such as local minima and dynamic obstacles. It achieves energy-efficient optimization for multi-vehicle obstacle avoidance in complex environments.


Analysing the quality of real analysis lectures using a structured observation tool

Abstract: This paper reports a study investigating the quality of advanced mathematics lectures following three goals. First, we present our conceptualisation and operationalisation concerning the quality of mathematics lectures. Second, we test the reliability of a structured observation tool that is based on the aforementioned conceptualisation. Third, we describe the quality of mathematics lectures based on data collected with the observation tool and illustrate its use. The results show that our observation tool is suitable for collecting data about characteristics of mathematics lectures observed on-site and from video recordings. The analysis of the data shows that our findings are in line with previous research: formal aspects of mathematical statements are presented in verbal and written forms mostly correctly; informal aspects of mathematical statements (such as examples, visual or mental representations) are, if they are presented, usually presented only verbally. Furthermore, students have the opportunity to participate during lectures, but there are seldom presentations of alternative solutions, examples or proofs and only a few opportunities for formative assessment. Finally, we discuss implications for future research.


The predictive robustness of organizational and technological enablers towards blockchain technology adoption and financial performance

Abstract: Purpose Blockchain technology has brought about significant transformation among organizations worldwide. This study aimed to explore the effects of organizational and technological factors on blockchain technology adoption (BTA) and financial performance (FP) in Pakistan. Design/methodology/approach This is a co-relational study which used the cross-sectional data. We gathered the data from the managers of Pakistan’s small and medium-sized enterprises (SMEs), which functioned their industries with blockchain technology. We applied convenience sampling to identify the respondents. Finally, we based this study’s findings on 274 valid cases. Findings We used structural equation modeling (SEM) in this study, to exert a positive and significant impact on organizational factors such as organizational innovativeness (OI), organizational learning capability (OLC), top management support (TMS) and organizational work climate (OWC) on BTA. In addition, the technological factors, such as complexity (CTY), technology readiness (TR), compatibility (CBTY) and technology capability (TC), have a positive and significant effect on BTA. Finally, this study’s findings show that BTA positively and significantly impacts FP. Practical implications This study’s findings will help policymakers and planners to design policies to adopt other blockchain technologies to improve SMEs’ operations. Moreover, this study’s findings will inspire policymakers and planners to actively seek new ideas, knowledge and skills through acquiring new knowledge to assist with their IT-related decisions. Originality/value This study empirically confirms the role of organizational and technology factors toward BTA and FP among Pakistan's SME managers.


What if ChatGPT generates quantitative research data? A case study in tourism

Abstract: Purpose This study aims to explore whether Chat Generative Pre-training Transformer (ChatGPT) can produce quantitative data sets for researchers who could behave unethically through data fabrication. Design/methodology/approach A two-stage case study related to the field of tourism was conducted, and ChatGPT (v.3.5.) was asked to respond to the first questionnaire on behalf of 400 participants and the second on behalf of 800 participants. The artificial intelligence (AI)-generated data sets’ quality was statistically tested via descriptive statistics, correlation analysis, exploratory factor analysis, confirmatory factor analysis and Harman's single-factor test. Findings The results revealed that ChatGPT could respond to the questionnaires as the number of participants at the desired sample size level and could present the generated data sets in a table format ready for analysis. It was also observed that ChatGPT's responses were systematical, and it created a statistically ideal data set. However, it was noted that the data produced high correlations among the observed variables, the measurement model did not achieve sufficient goodness of fit and the issue of common method bias emerged. The conclusion reached is that ChatGPT does not or cannot yet generate data of suitable quality for advanced-level statistical analyses. Originality/value This study shows that ChatGPT can provide quantitative data to researchers attempting to fabricate data sets unethically. Therefore, it offers a new and significant argument to the ongoing debates about the unethical use of ChatGPT. Besides, a quantitative data set generated by AI was statistically examined for the first time in this study. The results proved that the data produced by ChatGPT is problematic in certain aspects, shedding light on several points that journal editors should consider during the editorial processes.


Blockchain technology adoption for collaborative emission reduction considering carbon information asymmetry in supply chains

Abstract: Purpose Collaborative emission reduction among supply chain members has emerged as a new trend to achieve climate neutrality goals and meet consumers’ low-carbon preferences. However, carbon information asymmetry and consumer mistrust represent significant obstacles. This paper investigates the value of blockchain technology (BCT) in solving the above issues. Design/methodology/approach A low-carbon supply chain consisting of one supplier and one manufacturer is examined. This study discusses three scenarios: non-adoption BCT, adoption BCT without sharing the supplier’s carbon emission reduction (CER) information and adoption BCT with sharing the supplier’s CER information. We analyze the optimal decisions of the supplier and the manufacturer through the Stackelberg game, identify the conditions in which the supplier and manufacturer adopt BCT and share information from the perspectives of economic and environmental performance. Findings The results show that adopting BCT benefits supply chain members, even if they do not share CER information through BCT. Furthermore, when the supplier’s CER efficiency is low, the manufacturer prefers that the supplier share this information. Counterintuitively, the supplier will only share CER information through BCT when the CER efficiencies of both the supplier and manufacturer are comparable. This diverges from the findings of existing studies, as the CER investments of the supplier and the manufacturer in this study are interdependent. In addition, despite the high energy consumption associated with BCT, the supplier and manufacturer embrace its adoption and share CER information for the sake of environmental benefits. Practical implications The firms in low-carbon supply chains can adopt BCT to improve consumers’ trust. Furthermore, if the CER efficiencies of the firms are low, they should share CER information through BCT. Nonetheless, a lower unit usage cost of BCT is the precondition. Originality/value This paper makes the first move to discuss BCT adoption and BCT-supported information sharing for collaborative emission reduction in supply chains while considering the transparency and high consumption of BCT.


AI‐ALOE: AI for reskilling, upskilling, and workforce development

Abstract: Abstract The National AI Institute for Adult Learning and Online Education (AI‐ALOE) develops AI learning and teaching assistants to enhance the proficiency of adult reskilling and upskilling, and thereby transform workforce development. The AI assistants both address known problems in online education for reskilling/upskilling and help personalize adult learning for workforce development. AI‐ALOE develops new AI models and techniques for self‐explanation, machine teaching, and mutual theory of mind to make the AI assistants usable, learnable, teachable, and scalable. AI‐ALOE is also developing a data architecture for deploying and evaluating the AI assistants, collecting and analyzing data, and personalizing learning at scale.


Parameterized complexity of weighted team definability

Abstract: Abstract In this article, we study the complexity of weighted team definability for logics with team semantics. This problem is a natural analog of one of the most studied problems in parameterized complexity, the notion of weighted Fagin-definability, which is formulated in terms of satisfaction of first-order formulas with free relation variables. We focus on the parameterized complexity of weighted team definability for a fixed formula $\varphi$ of central team-based logics. Given a first-order structure $\mathcal{A}$ and the parameter value $k\in \mathbb N$ as input, the question is to determine whether $\mathcal{A},T\models \varphi$ for some team T of size k . We show several results on the complexity of this problem for dependence, independence, and inclusion logic formulas. Moreover, we also relate the complexity of weighted team definability to the complexity classes in the well-known W-hierarchy as well as paraNP.


A modified shuffled frog leaping algorithm with inertia weight

Abstract: Abstract The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.


Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Abstract: Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.


Design optimization of Solar Power Inverter using the GRA Method

Abstract: Among the most crucial components of a solar-powered system is an inverter. It is an apparatus that transforms the direct current (DC) produced by solar panels into the alternating current (AC) required by the electric system. Flashing hybrid solar inverter is the best solar inverter for home which offers the following features: - Its operating voltage is between 100-290V. - It has 700 VA of power. 5kW solar inverter price budget starts at $2,000 for excellent single-phase devices and $1,000 for existing datasets (like Sungrow) (eg Fronius or SMA). The most popular size, 5kW, can accommodate arrays up to 6.6kW in power. A solar charge controller can only function effectively during the day, because when the ultraviolet irradiance is strong and, the system's Voltage rating must reach the inverter at a moderate DC voltage level in order for it to function. Your photovoltaic systems and inverter operate at night power will be off. The inverter doesn't run overnight because it doesn't want to draw electricity. Instead, it rises again in the morning when the sun shines. Your home and solar system are connected to the utility grid. By switching to solar PV-based power generation from the airport's current conventional energy source, the carbon footprint of the facility can be decreased. Power distribution solar PV power facilities may be built in regions that are required to be broad and free of obstructions around runways. Based on first year operating data, the current study seeks to evaluate the operational efficiency of a 12 MWp solar-powered airport at Cochin Airport Limited (CIAL), India. With the aid of the most well-liked PV simulation programmes, such as Deterioration in quality and Solar Gis, the plant effectiveness is precisely rendered. The software's performance metrics were discovered to perfectly resemble the observed values. Economic and ecological studies of Kochi airports powered by solar energy attest to its efficiency in lowering carbon footprint, resulting in an airport with almost no emissions that is clean, green, and sustainable.Gray correlation analysis is widely used to measure the degree of relationship between sequences through the gray correlation coefficient. Gray relational analysis has been used by many researchers to optimize control parameters with multiple responses through gray relational grading. The fundamental tenet of the GRA approach is that the chosen option must have the "greatest degree of Green relation" to the positive-ideal solution and the "minimum of grey relation" to the negative-best answer. A technique for determining whether or not variables are connected and how much they are correlated is called grey correlation coefficient analysis. The primary method of determining these curves' geometric similarity is by the construction of characteristic series curves. Type of Solar Module, No of modules per string, No of string, No of inverters and No of transformers Short circuit current (ISC), Open circuit voltage (VOC), Operating Temperature and Dimension. Stress Management in Healthcare Institutions in Work overload is got the first rank whereas is the Overtime is having the lowest rank.


The construction of smart city community environment under the application of animation immersion technology integrated into southern Fujian Folk Culture

Abstract: With the rapid development of global information technology, data resources play an increasingly prominent role in society. Countries have issued ‘smart city’ strategic plans one after another, taking the sustainable construction of smart cities as an important means to enhance national competitiveness and seize the commanding heights of the new round of the information technology revolution. This paper selects southern Fujian for experiments to develop modern communication methods in some areas of China that have not undergone smart city upgrades. First, the animation immersion method under smart city technology is taken as the starting point. The folk culture of southern Fujian is integrated to analyze the importance of developing the smart city community under big data. Second, a combined system dynamics model and structural equation modeling are established. It is applied to intelligent, sustainable urban construction to explore the possibility of new development paths. The cities that apply the model proposed here for intelligent, sustainable construction have increased their output value in all aspects within five years. The maximum annual growth rate has reached 17.11%. Planners and designers base the smart city community environment on animation immersion technology. From the perspective of animation immersion planning, integrating the animation immersion technology of southern Fujian folk culture into the sustainable construction and development of the smart city community environment will help enhance the comprehensive competitiveness of the region.


Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices

Abstract: Abstract In the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.


Improving Semi-Supervised Text Classification with Dual Meta-Learning

Abstract: The goal of semi-supervised text classification (SSTC) is to train a model by exploring both a small number of labeled data and a large number of unlabeled data, such that the learned semi-supervised classifier performs better than the supervised classifier trained on solely the labeled samples. Pseudo-labeling is one of the most widely used SSTC techniques, which trains a teacher classifier with a small number of labeled examples to predict pseudo labels for the unlabeled data. The generated pseudo-labeled examples are then utilized to train a student classifier, such that the learned student classifier can outperform the teacher classifier. Nevertheless, the predicted pseudo labels may be inaccurate, making the performance of the student classifier degraded. The student classifier may perform even worse than the teacher classifier. To alleviate this issue, in this paper, we introduce a dual meta-learning ( DML ) technique for semi-supervised text classification, which improves the teacher and student classifiers simultaneously in an iterative manner. Specifically, we propose a meta-noise correction method to improve the student classifier by proposing a Noise Transition Matrix (NTM) with meta-learning to rectify the noisy pseudo labels. In addition, we devise a meta pseudo supervision method to improve the teacher classifier. Concretely, we exploit the feedback performance from the student classifier to further guide the teacher classifier to produce more accurate pseudo labels for the unlabeled data. In this way, both teacher and student classifiers can co-evolve in the iterative training process. Extensive experiments on four benchmark datasets highlight the effectiveness of our DML method against existing state-of-the-art methods for semi-supervised text classification. We release our code and data of this paper publicly at https://github.com/GRIT621/DML.


Monitoring Temperature and Humidity in Biomass Gasification Using Arduino Technology

Abstract: Gasification of biomass can be done with pure oxygen or with ambient air. We have chosen gasification with ambient air in our project for costs considerations. Relative humidity (RH) of the air we used with a downdraft co-current gasifier is monitored in this paper. RH sensor used in this paper presents a capacitor which depends on the amount of water contained in the gaseous mixture. That RH, behaving as a variable capacitor is inserted in serial with the RC circuit of an astable multivibrator. Frequency of the oscillator is dependent of the resistance and the capacity which depends on the RH. We have inserted the RH sensor as a variable capacitor in the RC circuit of a NE555 oscillator. From the RH sensor datasheet, a mathematical model has been developed and the equation giving RH found to be $$Rh=496,23 * ( {1.44*T \over (R_4+2R_2 )* C_55}) -443,26.$$ The frequency of the oscillator is calculated with Arduino and the RH is deduced. Temperature can be measured with a K type thermocouple from 25 to 1024C with accuracy of +/-1.5C.The effect of temperature on the RH is also taken into account. Using Arduino Uno3 as a computer, we have calculated corrected RH respective to temperature and displayed both ambient and real RH on 20 columns with 4 lines LCD display. Arduino has also enabled us to save measured data in an Excel sheet, allowing us to analyses measured temperature and RH and plot different curves from it. The same circuit is also used to monitor RH of produced synthesis gas. Results of the Arduino temperature and monitoring system shows that visual observation of air and human feeling can be very misleading in respect to the air RH.


Perceptual Image Quality Prediction: Are Contrastive Language–Image Pretraining (CLIP) Visual Features Effective?

Abstract: In recent studies, the Contrastive Language–Image Pretraining (CLIP) model has showcased remarkable versatility in downstream tasks, ranging from image captioning and question-answering reasoning to image–text similarity rating, etc. In this paper, we investigate the effectiveness of CLIP visual features in predicting perceptual image quality. CLIP is also compared with competitive large multimodal models (LMMs) for this task. In contrast to previous studies, the results show that CLIP and other LMMs do not always provide the best performance. Interestingly, our evaluation experiment reveals that combining visual features from CLIP or other LMMs with some simple distortion features can significantly enhance their performance. In some cases, the improvements are even more than 10%, while the prediction accuracy surpasses 90%.


Real-Time Object Detection and Tracking Based on Embedded Edge Devices for Local Dynamic Map Generation

Abstract: This paper proposes a camera system designed for local dynamic map (LDM) generation, capable of simultaneously performing object detection, tracking, and 3D position estimation. This paper focuses on improving existing approaches to better suit our application, rather than proposing novel methods. We modified the detection head of YOLOv4 to enhance the detection performance for small objects and to predict fiducial points for 3D position estimation. The modified detector, compared to YOLOv4, shows an improvement of approximately 5% mAP on the Visdrone2019 dataset and around 3% mAP on our database. We also proposed a tracker based on DeepSORT. Unlike DeepSORT, which applies a feature extraction network for each detected object, the proposed tracker applies a feature extraction network once for the entire image. To increase the resolution of feature maps, the tracker integrates the feature aggregation network (FAN) structure into the DeepSORT network. The difference in multiple objects tracking accuracy (MOTA) between the proposed tracker and DeepSORT is minimal at 0.3%. However, the proposed tracker has a consistent computational load, regardless of the number of detected objects, because it extracts a feature map once for the entire image. This characteristic makes it suitable for embedded edge devices. The proposed methods have been implemented on a system on chip (SoC), Qualcomm QCS605, using network pruning and quantization. This enables the entire process to be executed at 10 Hz on this edge device.


A Convolutional Neural Network-Based Auto-Segmentation Pipeline for Breast Cancer Imaging

Abstract: Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline.


Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms

Abstract: For text big data analysis, we preprocessed text data and constructed a document–keyword matrix. The elements of this matrix represent the frequencies of keywords occurring in a document. The matrix has a zero-inflation problem because many elements are zero values. Also, in the process of preprocessing, the data size of the document–keyword matrix is reduced. However, various machine learning algorithms require a large amount of data, so to solve the problems of data shortage and zero inflation, we propose the use of generative models based on statistics and machine learning. In our experimental tests, we compared the performance of the models using simulation and practical data sets. Thus, we verified the validity and contribution of our research for keyword data analysis.


UViT: Efficient and lightweight U-shaped hybrid vision transformer for human pose estimation

Abstract: Pose estimation plays a crucial role in human-centered vision applications and has advanced significantly in recent years. However, prevailing approaches use extremely complex structural designs for obtaining high scores on the benchmark dataset, hampering edge device applications. In this study, an efficient and lightweight human pose estimation problem is investigated. Enhancements are made to the context enhancement module of the U-shaped structure to improve the multi-scale local modeling capability. With a transformer structure, a lightweight transformer block was designed to enhance the local feature extraction and global modeling ability. Finally, a lightweight pose estimation network— U-shaped Hybrid Vision Transformer, UViT— was developed. The minimal network UViT-T achieved a 3.9% improvement in AP scores on the COCO validation set with fewer model parameters and computational complexity compared with the best-performing V2 version of the MobileNet series. Specifically, with an input size of 384×288, UViT-T achieves an impressive AP score of 70.2 on the COCO test-dev set, with only 1.52 M parameters and 2.32 GFLOPs. The inference speed is approximately twice that of general-purpose networks. This study provides an efficient and lightweight design idea and method for the human pose estimation task and provides theoretical support for its deployment on edge devices.


Practical security of continuous-variable quantum key distribution with an optical amplifier

Abstract: In a practical continuous-variable quantum key distribution (CVQKD) system, the optical amplifier can be used to improve the performance of the system by compensating for the imperfections of the detectors. However, the eavesdropper Eve can actively utilize a reverse external magnetic field to deteriorate the performance of the optical amplifier, which may affect the practical security of the system. In this paper, we investigate the practical security of a CVQKD system with an optical amplifier under the effect of the reverse external magnetic field. Based on the investigation of the parameter estimation under the above influence, we find that the evaluation of the secret key rate may be overestimated, which will open a security loophole for Eve to successfully launch an intercept-resend attack in the practical CVQKD system with an optical amplifier. In order to close this security loophole, we can monitor the reverse external magnetic field or change the type of the optical isolator to make the secret key rate of the system evaluated exactly.


Variational approach to unique determinedness in pure-state tomography

Abstract: In pure-state tomography, the concept of unique determinedness (UD), the ability to uniquely determine pure states from measurement results, is crucial. This study presents a variational approach to examining UD, offering a robust solution to the challenges associated with the construction and certification of UD measurement schemes. We put forward an effective algorithm that minimizes a specially defined loss function, enabling the differentiation between UD and non-UD measurement schemes. This leads to the discovery of numerous optimal pure-state Pauli measurement schemes across a variety of dimensions. Additionally, we discern an alignment between uniquely determined among pure states and uniquely determined among all states in qubit systems when utilizing Pauli measurements, underscoring its intrinsic robustness under pure-state recovery. We further interpret the physical meaning of our loss function, bolstered by a theoretical framework. Our study not only propels the understanding of UD in quantum state tomography forward, but also delivers valuable practical insights for experimental applications, highlighting the need for a balanced approach between mathematical optimality and experimental pragmatism.


Classification of high-dimensional imbalanced biomedical data based on spectral clustering SMOTE and marine predators algorithm

Abstract: The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm’s optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods.


Issues Of Development Of Professional Competence Of Future Builders-Engineers On The Basis Of Foreign Languages

Abstract: Today, students in higher education institutions of developed countries are taught using the latest techniques and technologies. Also, in order to develop the professional competence of future builders-engineers in the field of construction in our republic, it is becoming important to expand the opportunities for students to work with multimedia, graphic imagination, architectural graphic tasks-projects, design drawings of buildings and structures based on foreign languages. Education in foreign languages is an important factor in providing future builders-engineers with competitive staff, knowledge, skills and literacy in their specialty, and the justification of the demands placed on them is of great relevance.


Preparation of cavity-Fock-state superpositions by reinforcement learning exploiting measurement backaction

Abstract: Preparation of bosonic and general cavity quantum states usually relies on using open-loop control to reach a desired target state. In this work, a measurement-based feedback approach is used instead, exploiting the nonlinearity of weak measurements alongside a coherent drive to prepare these states. The extension of previous work on Lyapunov-based control is shown to fail for this task. This prompts for a different approach, and reinforcement learning (RL) is resorted to here for this purpose. With such an approach, cavity-eigenstate superpositions can be prepared with fidelities over $98%$ using only the measurements backaction as the nonlinearity, while naturally incorporating detection of cavity photon jumps. Two different RL frameworks are analyzed: an off-policy approach recently introduced called truncated quantile critic (TQC) and the on-policy method commonly used in quantum control, namely proximal policy optimization. It is shown that TQC performs better at reaching higher target state fidelity preparation.


A simple probabilistic neural network for machine understanding

Abstract: Abstract We discuss the concept of probabilistic neural networks with a fixed internal representation being models for machine understanding. Here, ‘understanding’ is interpretted as the ability to map data to an already existing representation which encodes an a priori organisation of the feature space. We derive the internal representation by requiring that it satisfies the principles of maximal relevance and of maximal ignorance about how different features are combined. We show that, when hidden units are binary variables, these two principles identify a unique model—the hierarchical feature model—which is fully solvable and provides a natural interpretation in terms of features. We argue that learning machines with this architecture possess a number of interesting properties, such as the continuity of the representation with respect to changes in parameters and data, the possibility of controlling the level of compression and the ability to support functions that go beyond generalisation. We explore the behaviour of the model with extensive numerical experiments and argue that models in which the internal representation is fixed reproduce a learning modality which is qualitatively different from that of traditional models, such as restricted Boltzmann machines.


A variable clustering approach for overdispersed high-dimensional count data using a copula-based mixture model

Abstract: In this paper, we propose a latent variable model for the analysis and clustering of high-dimensional correlated and overdispersed count data. We use a set of random effects to capture within-group correlations and a copula to address between-group correlations. Since maximum likelihood (ML) estimation is computationally intensive for high-dimensional data, we propose a fast moment-based estimation procedure. Additionally, we implement the proposed estimation procedure into a clustering algorithm, borrowing ideas from the K-means algorithm. Based on a simulation study, we demonstrate that the estimation procedure shows good stability and precision. Furthermore, it is computationally fast, unlike the ML approach which shows serious convergence issues. In addition, the clustering algorithm with our moment-based estimation procedure can identify the simulated grouped data structure. The approach is further illustrated with a high-dimensional RNA-Seq dataset of commercial potatoes.


Pose Measurement Method Based on Machine Vision and Novel Directional Target

Abstract: Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02°, and the repeatability can reach 0.0004° after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03°, and the repeatability can go to 0.002° after removing the systematic error. The measurement range of the pitch angle is [−30°, +30°]; the measurement range of the roll angle is [−179°, +179°]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement.


Liger: Interleaving Intra- and Inter-Operator Parallelism for Distributed Large Model Inference

Abstract: Distributed large model inference is still in a dilemma where balancing cost and effect. The online scenarios demand intraoperator parallelism to achieve low latency and intensive communications makes it costly. Conversely, the inter-operator parallelism can achieve high throughput with much fewer communications, but it fails to enhance the effectiveness.


Image Steganography and Style Transformation Based on Generative Adversarial Network

Abstract: Traditional image steganography conceals secret messages in unprocessed natural images by modifying the pixel value, causing the obtained stego to be different from the original image in terms of the statistical distribution; thereby, it can be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial-Intelligence-Generated Content (AIGC) becoming popular on social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image-style-transformation neural network with a steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder–decoder model, and finally, generates a styled image containing the secret messages at the same time. An adversarial training technique was applied to enhance the imperceptibility of the generated art-style stego image from plain-style-transferred images. The lack of the original cover image makes it difficult for the opponent learning steganalyzer to identify the stego. The proposed approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of three bits per pixel for a color image, according to the experimental results.


<scp>GraphZeppelin</scp> : How to Find Connected Components (Even When Graphs Are Dense, Dynamic, and Massive)

Abstract: Finding the connected components of a graph is a fundamental problem with uses throughout computer science and engineering. The task of computing connected components becomes more difficult when graphs are very large, or when they are dynamic, meaning the edge set changes over time subject to a stream of edge insertions and deletions. A natural approach to computing the connected components problem on a large, dynamic graph stream is to buy enough RAM to store the entire graph. However, the requirement that the graph fit in RAM is an inherent limitation of this approach and is prohibitive for very large graphs. Thus, there is an unmet need for systems that can process dense dynamic graphs, especially when those graphs are larger than available RAM. We present a new high-performance streaming graph-processing system for computing the connected components of a graph. This system, which we call GraphZeppelin , uses new linear sketching data structures ( CubeSketch ) to solve the streaming connected components problem and as a result requires space asymptotically smaller than the space required for an lossless representation of the graph. GraphZeppelin is optimized for massive dense graphs: GraphZeppelin can process millions of edge updates (both insertions and deletions) per second, even when the underlying graph is far too large to fit in available RAM. As a result GraphZeppelin vastly increases the scale of graphs that can be processed.


Abstract: Strategic Content Generation and Monetization in Financial Social Media Abstract Financial social media, which relies on social media analysts (SMAs) to contribute content to investors, is a crucial channel for investors to gain financial information and for SMAs to monetize their content. The interactive nature of financial social media has given SMAs the opportunity to gain access to the investor preferences of their own audience base for financial content. Our study documents that SMAs would exploit this opportunity to strategically generate and monetize content by catering to investor preferences. Specifically, SMAs would increase the (negative) sentiment of the content if paid subscribers’ preferences for (negative) sentiment grow. Additionally, an SMA is more likely to produce paid content when the expected free readership increases and is less likely to do so when the expected paid subscriptions increase. Our findings suggest that the sentiment of financial social media content is not a mere reflection or prediction of stock market movements but also a result of SMAs’ reaction to investor preferences. We thus illustrate an approach to identify the SMAs who may amplify the investors’ confirmation biases because of such catering behaviors so that platform managers and regulators alike can utilize this method to improve the content quality of financial social media.


An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks

Abstract: The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL’s effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.


Online Learning State Evaluation Method Based on Face Detection and Head Pose Estimation

Abstract: In this paper, we propose a learning state evaluation method based on face detection and head pose estimation. This method is suitable for mobile devices with weak computing power, so it is necessary to control the parameter quantity of the face detection and head pose estimation network. Firstly, we propose a ghost and attention module (GA) base face detection network (GA-Face). GA-Face reduces the number of parameters and computation in the feature extraction network through the ghost module, and focuses the network on important features through a parameter-free attention mechanism. We also propose a lightweight dual-branch (DB) head pose estimation network: DB-Net. Finally, we propose a student learning state evaluation algorithm. This algorithm can evaluate the learning status of students based on the distance between their faces and the screen, as well as their head posture. We validate the effectiveness of the proposed GA-Face and DB-Net on several standard face detection datasets and standard head pose estimation datasets. Finally, we validate, through practical cases, that the proposed online learning state assessment method can effectively assess the level of student attention and concentration, and, due to its low computational complexity, will not interfere with the student’s learning process.


An Iteratively Extended Target Tracking by Using Decorrelated Unbiased Conversion of Nonlinear Measurements

Abstract: Extended target tracking (ETT) based on random matrices typically assumes that the measurement model is linear. However, nonlinear measurements (such as range and azimuth) depending on locations of a series of unknown scattering centers always exist in many practical tracking applications. To address this issue, this paper proposes an iteratively extended target tracking based on random matrices by using decorrelated unbiased conversion of nonlinear measurements (ETT-IDUCM). First, we utilize a decorrelated unbiased converted measurement (DUCM) method to convert nonlinear measurements depending on unknown scatters of target extent in polar coordinates into the ones in Cartesian coordinates with equivalent measurement noise covariances. Subsequently, a novel method, combining iteratively extended Kalman filter (IEKF) updates with variational Bayesian (VB) cycles is developed for precise estimation of the target’s kinematic state and extension. This method leverages the synergy between external IEKF iterations, which use the estimated state as a new prediction and input for DUCM, and internal VB iterations, which realize a closed-form approximation of the joint posterior probability. This approach progressively enhances estimation accuracy. Simulation results demonstrate the ETT-IDUCM algorithm’s superior precision in estimating the target’s kinematic state and extension compared to existing methods.


UFCC: A Unified Forensic Approach to Locating Tampered Areas in Still Images and Detecting Deepfake Videos by Evaluating Content Consistency

Abstract: Image inpainting and Deepfake techniques have the potential to drastically alter the meaning of visual content, posing a serious threat to the integrity of both images and videos. Addressing this challenge requires the development of effective methods to verify the authenticity of investigated visual data. This research introduces UFCC (Unified Forensic Scheme by Content Consistency), a novel forensic approach based on deep learning. UFCC can identify tampered areas in images and detect Deepfake videos by examining content consistency, assuming that manipulations can create dissimilarity between tampered and intact portions of visual data. The term “Unified” signifies that the same methodology is applicable to both still images and videos. Recognizing the challenge of collecting a diverse dataset for supervised learning due to various tampering methods, we overcome this limitation by incorporating information from original or unaltered content in the training process rather than relying solely on tampered data. A neural network for feature extraction is trained to classify imagery patches, and a Siamese network measures the similarity between pairs of patches. For still images, tampered areas are identified as patches that deviate from the majority of the investigated image. In the case of Deepfake video detection, the proposed scheme involves locating facial regions and determining authenticity by comparing facial region similarity across consecutive frames. Extensive testing is conducted on publicly available image forensic datasets and Deepfake datasets with various manipulation operations. The experimental results highlight the superior accuracy and stability of the UFCC scheme compared to existing methods.


The importance of context in teacher educators’ professional digital competence

Abstract: Digital technologies permeate modern life, and schools are accordingly expected to help students develop related knowledge and skills. As a result, educators’ professional digital competence (PDC) has received substantial attention from school leaders, policymakers, teacher educators, and researchers. Theory and prior research suggest that educators’ PDC does not solely determine their technology use, with contextual factors, such as competing curricular demands and access to technology, influencing technology implementation and ongoing PDC development. While some prior research has addressed how PK-12 teachers’ contexts shape their digital technology use, few studies have explored similar matters with respect to teacher educators. Drawing on an established set of teacher educator technology competencies, we report results from an international survey of 336 teacher educators regarding their self-reported PDC and its development and enactment. Using qualitative coding, exploratory and confirmatory factor analysis, and quantitative analysis, we find that teacher educators develop and enact their competencies in various ways including through informal endeavours as well as formal, institutionally supported, and/or led activities. Three types of assets and barriers (access, leadership, personal characteristics) present differently for participants in different contexts. These findings have implications for how institutions support teacher educators’ PDC development and technology integration in various settings.


A Fast Optimal Coordination Method for Multiagent in Complex Environment

Abstract: Facing the implementation problems such like low growth reward, long training time, and poor stability of the multiagent learning methods when dealing with complex environment and more agents, this paper proposes a fast optimal coordination method for multiagent in complex environment (FOC-MACE). Firstly, the environment exploration strategy is introduced into the policy network based on the MADDPG method for higher growth rewards. Then, the parallel computing technology is adopted in the critic network, in purpose to effectively reduce the training time. These tactics together are beneficial to enhance the stability of multiagent learning. Lastly, the optimal resource allocation is carried out to realize optimal coevolution of the multiagents and further improve the learning ability of the agents’ group. To verify the effectiveness of our proposal, the FOC-MACE is compared with several advanced methods at current stage in the MPE environment. Three different experiments prove that by using our method, the growth reward is increased by up to 37.1%, the training is speed up significantly, and the stability of the method, which represented by standardized variance, is also improved. In addition, this paper validated the fast optimal coordination method for multiagent systems in the context of UAV scenarios, demonstrating the practical performance of the approach. Through comprehensive experiments and scenario validations, the study successfully confirmed the effectiveness of the proposed fast optimal coordination method for multiagent systems in complex environments.


RadiantVisions: Illuminating Low-Light Imagery with a Multi-Scale Branch Network

Abstract: In the realms of the Internet of Things (IoT) and artificial intelligence (AI) security, ensuring the integrity and quality of visual data becomes paramount, especially under low-light conditions, where low-light image enhancement emerges as a crucial technology. However, the current methods for enhancing images under low-light conditions still face some challenging issues, including the inability to effectively handle uneven illumination distribution, suboptimal denoising performance, and insufficient correlation among a branch network. Addressing these issues, the Multi-Scale Branch Network is proposed. It utilizes multi-scale feature extraction to handle uneven illumination distribution, introduces denoising functions to mitigate noise issues arising from image enhancement, and establishes correlations between network branches to enhance information exchange. Additionally, our approach incorporates a vision transformer to enhance feature extraction and context understanding. The process begins with capturing raw RGB data, which are then optimized through sophisticated image signal processor (ISP) techniques, resulting in a refined visual output. This method significantly improves image brightness and reduces noise, achieving remarkable improvements in low-light image enhancement compared to similar methods. Using the LOL-V2-real dataset, we achieved improvements of 0.255 in PSNR and 0.23 in SSIM, with decreases of 0.003 in MAE and 0.009 in LPIPS, compared to the state-of-the-art methods. Rigorous experimentation confirmed the reliability of this approach in enhancing image quality under low-light conditions.


Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

Abstract: With the development of location-based services and data collection equipment, the volume of trajectory data has been growing at a phenomenal rate. Raw trajectory data come in the form of sequences of “coordinate-time-attribute” triplets, which require complicated manual processing before they can be used in data mining algorithms. Current works have started to explore the emerging deep representation learning method, which maps trajectory sequences to vector space and applies them to various downstream applications for boosting accuracy and efficiency. In this work, we propose a universal trajectory representation learning method based on a Siamese geography-aware transformer (TRT for short). Specifically, we first propose a geography-aware encoder to model geographical information of trajectory points. Then, we apply a transformer encoder to embed trajectory sequences and use a Siamese network to facilitate representation learning. Furthermore, a joint training strategy is designed for TRT. One of the training objectives is to predict the masked trajectory point, which makes the trajectory representation robust to low sampling rates and noises. The other is to distinguish the difference between trajectories by means of contrastive learning, which makes the trajectory representation more uniformly distributed over the hypersphere. Last, we design a benchmark containing four typical traffic-related tasks to evaluate the performance of TRT. Comprehensive experiments demonstrate that TRT consistently outperforms the state-of-the-art baselines across all tasks.


Dynamical subset sampling of quantum error-correcting protocols

Abstract: Quantum error-correcting (QEC) stabilizer codes enable protection of quantum information against errors during storage and processing. Simulation of noisy QEC codes is used to identify the noise parameters necessary for advantageous operation of logical qubits in realistic quantum computing architectures. Typical quantum error-correction techniques contain intermediate measurements and classical feedback that determine the actual noisy circuit sequence in an instance of performing the protocol. Dynamical subset sampling enables efficient simulation of such nondeterministic quantum error-correcting protocols for any type of quantum circuit and incoherent noise of low strength. As an importance sampling technique, dynamical subset sampling allows one to effectively make use of computational resources to only sample the most relevant sequences of quantum circuits in order to estimate a protocol's logical failure rate with well-defined error bars. We demonstrate the capabilities of dynamical subset sampling with examples from fault-tolerant (FT) QEC. We show that, in a typical stabilizer simulation with incoherent Pauli noise of strength $p={10}^{\ensuremath{-}3}$, our method can reach a required sampling accuracy on the logical failure rate with two orders of magnitude fewer samples than direct Monte Carlo simulation. Furthermore, dynamical subset sampling naturally allows for efficient simulation of realistic multi-parameter noise models describing faulty quantum processors. It can be applied not only for QEC in the circuit model but any noisy quantum computing framework with incoherent fault operators including measurement-based quantum computation and quantum networks.


Revealing spoofing of classical radar using quantum noise

Abstract: Electromagnetic remote sensing technologies such as radar can be misled by targets that generate spoof pulses. Typically, a would-be spoofer must make measurements to characterize a received pulse in order to design a convincing spoof pulse. The precision of such measurements is ultimately limited by quantum noise. Here we introduce a model of electromagnetic spoofing that includes effects of practical importance that were neglected in prior theoretical studies. In particular, the model includes thermal background noise and digital quantization noise, as well as loss in transmission, propagation, and reception. We derive the optimal probability of detecting a spoofer allowed by quantum physics. We show that heterodyne reception and thresholding closely approaches this optimal performance. Finally, we show that a high degree of certainty in spoof detection can be reached by Bayesian inference from a sequence of received pulses. Together these results suggest that a practically realizable receiver could plausibly detect a radar spoofer by observing errors in the spoof pulses due to quantum noise.


Relating the Glauber-Sudarshan, Wigner, and Husimi quasiprobability distributions operationally through the quantum-limited amplifier and attenuator channels

Abstract: The Glauber-Sudarshan, Wigner and Husimi quasiprobability distributions are indispensable tools in quantum optics. However, although mathematical relations between them are well established, not much is known about their operational connection. In this paper, we prove that a single composition of finite-strength quantum limited amplifier and attenuator channels, known for their noise-adding properties, turns the Glauber-Sudarshan distribution of any input operator into its Wigner distribution, and its Wigner distribution into its Husimi distribution. As we dissect, the considered process, which can be performed in a quantum optical laboratory with relative ease, may be interpreted as realizing a quantum-to-classical transition.


Elastic Deep Sparse Self-Representation Subspace Clustering Network

Abstract: Abstract Subspace clustering model based on self-representation learning often use $$\ell _1, \ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> or kernel norm to constrain self-representation matrix of the dataset. In theory, $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm can constrain the independence of subspaces, but which may lead to under-connection because the sparsity of the self-representation matrix. $$\ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> and nuclear norm regularization can improve the connectivity between clusters, but which may lead to over-connection of the self-representation matrix. Because a single regularization term may cause subspaces to be over or insufficiently divided, this paper proposes an elastic deep sparse self-representation subspace clustering network (EDS-SC), which imposes sparse constraints on deep features, and introduces the elastic network regularization mixed $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and $$\ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> norm to constraint self-representation matrix. The network can extract deep sparse features and provide a balance between subspace independence and connectivity. Experiments on human faces, objects, and medical imaging datasets prove the effectiveness of EDS-SC network.


Formal Software Architecture Rule Learning: A Comparative Investigation between Large Language Models and Inductive Techniques

Abstract: This paper explores the application of inferring software architecture rules from examples using Machine Learning (ML). We investigate different methods from Inductive Rule Learning and utilize Large Language Models (LLMs). Traditional manual rule specification approaches are time-consuming and error-prone, motivating the need for automated rule discovery. Leveraging a dataset of software architecture instances and a meta-model capturing implementation facts, we used inductive learning algorithms and LLMs to extract meaningful rules. The induced rules are evaluated against a predefined hypothesis and their generalizability across different system subsets is investigated. The research highlights the capabilities and limitations of ML-based rule learning in the area of software architecture, aiming to inspire further innovation in data-driven rule discovery for more intelligent software architecture practices.


Enhancing Supply Chain Traceability through Blockchain and IoT Integration: A Comprehensive Review

Abstract: Supply chain traceability is essential for ensuring safety, preventing counterfeit goods, and improving efficiency. The integration of blockchain technology and the Internet of Things (IoT) has emerged as a transformative approach to enhance supply chain traceability by creating a secure, transparent, and efficient way to track the movement of goods and materials. This comprehensive literature review examines how the integration of blockchain and the Internet of Things can enhance supply chain traceability, utilizing a systematic literature search to identify and analyze all relevant studies. Recent and related articles selected from the Scopus database were reviewed. Our analysis underscores the potential for blockchain and IoT integration to provide end-to-end visibility, secure data sharing, and real-time monitoring across the supply chain ecosystem. It also identifies Machine Learning (ML) as another key component that enhances the security challenges of the Internet of Things while simultaneously serving as an analytical tool in Supply Chain Management (SCM). The review concludes that the integration of blockchain, the Internet of Things, and ML has the potential to transform supply chain traceability. By providing a secure, transparent, and efficient way to track the movement of goods and materials, businesses can improve their operations and offer better products and services to their customers. However, these findings do not impact the results of this research work. Additional research and a more extensive examination of the literature could offer a more comprehensive insight into the subject matter.


Life Cycle Assessment and Forecasting for 30kW Solar Power Plant using Machine Learning Algorithms

Abstract: Highly competitiveness of solar power plants in the energy market requires addressing the active research problem of solar energy forecasting. To make precise forecasts, however, historical meteorological, production, or irradiance data is insufficient. As the conservation of these Renewable Energy Sources (RES) is that much essential, the use of Photovoltaic (PV) panels have subsequently increased. The output of these PV panels completely depends on the climate. As the PV panels convert the solar energy to electrical energy, therefore, it produces the most when there is enough sunlight throughout the summer and the least when there is rain. Accurate forecasting of energy generation from solar power plants is crucial in terms of economics due to this uncertainty in the output in different seasons and the change in meteorological conditions. The objective of this paper is to investigate machine learning algorithms can accurately anticipate solar power for the upcoming hour and hourly days in advance. The Naïve Bayes Algorithm, Multilayer Perceptron Theorem (MLP), and Long Short-Term Memory networks (LSTM) are the machine learning algorithms investigated. Both historical weather data and generation data are used in the study. This study also includes the payback period (PB) and life cycle assessment calculation of the roof top solar power plant located in Bhubaneswar India.


Securing IoT Systems in a Post-Quantum Environment: Vulnerabilities, Attacks, and Possible Solutions

Abstract: The Internet of Things (IoT) refers to the distributed systems environment connecting billions of devices to the Internet, and quantum computing is an emerging technology that has a positive impact on IoT security by speeding up data processing while also having a negative impact due to post-quantum security attacks. In this survey, we provide detailed information on the possible post-quantum security attacks that threaten the security of the layers of IoT systems in terms of their vulnerabilities. Also, we provide detailed information on the existing solutions against post-quantum security attacks and show how limitations in these solutions decrease the security performance. Furthermore, we develop classification models to allow the readers to choose the best security approach against post-quantum security attacks in terms of IoT layers. Finally, we show the open challenges of the surveyed quantum security solutions and propose a framework based on quantum machine learning that takes advantage of optical pulses of secure communication as a future solution for detecting post-quantum security attacks.


E-voting System using Cloud-based Hybrid Blockchain Technology

Abstract: With the invention of Internet-enabled devices, cloud and blockchain-based technologies, an online voting system can smoothly carry out election processes. During pandemic situations, citizens tend to develop panic about mass gatherings, which may influence the decrease in the number of votes. This urges a reliable, flexible, transparent, secure, and cost-effective voting system. The proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system, and it is carried out in three phases: the registration phase, vote casting phase and vote counting phase. A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting phases. Using smart contracts, third-party interventions are eliminated, and the transactions are secured in the blockchain network. Finally, to provide accurate voting results, the practical Byzantine fault tolerance (PBFT) consensus mechanism is adopted to ensure that the vote has not been modified or corrupted. Hence, the overall performance of the proposed system is significantly better than that of the existing system. Further performance was analyzed based on authentication delay, vote alteration, response time, and latency.


A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks

Abstract: Recently, Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, a comprehensive evaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets has been conducted. To the best of our knowledge, this is the first work that conducts an extensive evaluation and comparison of various LLMs in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot LLMs even outperform the current state-of-the-art models when they were fine-tuned only on the training set of these datasets. This suggests that pre-training on large text corpora makes LLMs quite specialized even in the biomedical domain. We also find that not a single LLM can outperform other LLMs in all tasks, with the performance of different LLMs may vary depending on the task. While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.