ASSESSING THE RELIABILITY AND VALIDITY OF KNOWLEDGE, ATTITUDE, AND PRACTICE (KAP) ASSESSMENTS ON COVID-19 TRANSMISSION KNOWLEDGE AND PREVENTIVE MEASURES AMONG ECOTOURISM OPERATORS

Abstract: This cross-sectional study conducted in rural Pahang state, Malaysia, aimed to validate a questionnaire examining ecotourism operators’ Knowledge, Attitude, and Practice (KAP) regarding COVID-19 transmission and preventive measures. Data collection utilised the snowball technique. The questionnaire, comprising 34 items covering knowledge, attitude, and practice constructs, underwent rigorous validation and piloting before the actual fieldwork. All factor loading scores (>0.65) and Cronbach’s alpha (α≥0.69) were greater than the reference value, relaying indicators of reliability and internal consistency of the measured latent variables. The findings revealed that the KAP model met the goodness-of-fit criteria (HTMT<.90, SRMR<0.08, NFI>0.90) and convergent validity was achieved (AVF≤0.50). The study confirms the meticulous instrument validation, ensuring the survey tool’s effectiveness in gauging KAP among ecotourism operators. This study’s novelty lies in its focus on the KAP spectrum vis-à-vis COVID-19 among operators engaged in these ecotourism domains. By bridging this gap, the research aspires to inform tailored interventions, ultimately fortifying resilience against future health crises in ecotourism communities.


Brave New World: Navigating New Paradigms in the Educational Landscape for the Evolving Learner in a Rapidly Changing Era

Abstract: This literature review explores the significant transformations in the educational landscape prompted by technological advancements and changing learner profiles in the digital era. The review examines the evolution of learner profiles, highlighting the shift towards multisensory learning, digital fluency, and a preference for interactive, personalized learning experiences. It delves into integrating innovative technologies such as virtual reality (VR), augmented reality (AR), and blockchain in education and discusses their implications for teaching and learning. The review also considers the impact of neuroscience on educational practices, emphasizing the importance of understanding brain functioning and neuroplasticity in developing effective teaching strategies. The unprecedented shift to online and blended learning models, accelerated by the COVID-19 pandemic, is analyzed for its long-term effects on educational delivery and pedagogy. Additionally, the review addresses the growing emphasis on interdisciplinary learning, sustainability, mental health, and well-being within educational curricula. The implications of these transformative trends for educators, policymakers, and learners are discussed, highlighting the need for adaptive and responsive approaches to education in the face of rapid technological and societal changes. The review concludes with reflections on the future trajectory of education, anticipating continued technical integration, evolving pedagogical theories, and an increased focus on preparing learners for global challenges and lifelong learning.


Changing the Game: Liner Hanger Running and Setting Using an Integrated Acoustic Telemetry Network

Abstract: Abstract Thousands of liner hangers are deployed on a global basis per year. They are deployed to reduce the time and risk, with the inherent associated cost, of running longer single casing strings. Originally these liner hanger systems required a mechanical actuation, such as rotating and or reciprocating the drill pipe in order to deploy the liner system. In the last several decades there has been an ever-increasing trend in the complexity of wells, particularly in relation to the complexity of the well profile, with thousands of highly deviated or horizontal wells. This increase in deviation and complex trajectories or increased tortuosity limits the ability to activate the liner system through rotation and reciprocation. To combat this the industry then also developed hydraulically actuated systems, and these are now found in both conventional and expandable liner hanger systems. The traditional method for setting a hydraulic liner hanger is through dropping an activation ball. This is either a single activation or a multiple activation device. Hydraulic activation allows for reciprocation and rotation, within the limits of the well deviation. Dropping a ball and landing it on a ball seat creates a pressure increase to the work string which then activates the liner hanger and running tool. There are, however, still multiple failures of liner hanger deployments and setting. These can include, in these highly complex wells, the inability to get the liner to the correct setting depth. In complex well trajectories, in high pressure high temperature wells and in wells with very narrow mud weight windows, or being operated under managed pressure conditions, failure to set the liner hanger systems correctly or in the correct place can lead to significant Non-Productive Time (NPT). It could also lead to the well being unable to make the desired depth or be unable to run an appropriate completion. The main failure points in deploying these systems have been hindered by not having downhole measurements to verify what is happening downhole, for example what is the actual downhole torque and weight at the running tool? What is the pressure at the critical points of the well. Has the ball correctly set on the seat? We will demonstrate a fully integrated system that allows both real-time data to be sent both from just above the liner hanger, but also along the string whilst running the system. This acoustic telemetry system, which transmits data through the wall of steel drill pipe without the need for surface or downhole modification of existing systems, includes measurements of downhole weights, downhole torques and internal and external pressures and temperatures. In addition, the system transmits all the time and can be run independent of flow or fluid and operates whilst tripping. The system is also fully through bore allowing the passage of balls, bars or even wireline tools through the bore. This acoustic telemetry system is also intrinsically bidirectional, meaning that not only can data be sent up from downhole and along the string, but also acoustic commands can be sent down to activate downhole devices, in this case the liner hanger and running tool. The development of such a system providing both real-time downhole measurements that neither relies on mechanical manipulation or dropping an activation ball from surface has the potential to dramatically decrease high impact NPT and increase the overall operational capabilities of liner hanger systems.


Feasibility Study of Autonomous Wave-Photovoltaic (PV) Buoy to Power Subsea Equipment

Abstract: Abstract Autonomous wave-photovoltaic (PV) buoy is a cost-effective and efficient solution for powering subsea equipment in offshore oil and gas operations. The buoy can be deployed in remote locations where fixed structures may not be feasible. This study assesses the feasibility of the buoy to electrify a subsea Christmas tree in the Johan Sverdrup field in the North Sea. A Python mathematical model is developed to evaluate the power output of the buoy. The finding shows that wave energy harnessed by using a wave energy converter (WEC) at the selected site is sufficient to electrify a subsea Christmas tree. Further investigation is required to identify the suitable subsea equipment to be electrified by the buoy.


Unsupervised framework for evaluating and explaining structural node embeddings of graphs

Abstract: Abstract An embedding is a mapping from a set of nodes of a network into a real vector space. Embeddings can have various aims like capturing the underlying graph topology and structure, node-to-node relationship, or other relevant information about the graph, its subgraphs or nodes themselves. A practical challenge with using embeddings is that there are many available variants to choose from. Selecting a small set of most promising embeddings from the long list of possible options for a given task is challenging and often requires domain expertise. Embeddings can be categorized into two main types: classical embeddings and structural embeddings. Classical embeddings focus on learning both local and global proximity of nodes, while structural embeddings learn information specifically about the local structure of nodes’ neighbourhood. For classical node embeddings, there exists a framework which helps data scientists to identify (in an unsupervised way) a few embeddings that are worth further investigation. Unfortunately, no such framework exists for structural embeddings. In this article, we propose a framework for unsupervised ranking of structural graph embeddings. The proposed framework, apart from assigning an aggregate quality score for a structural embedding, additionally gives a data scientist insights into properties of this embedding. It produces information which predefined node features the embedding learns, how well it learns them, and which dimensions in the embedded space represent the predefined node features. Using this information, the user gets a level of explainability to an otherwise complex black-box embedding algorithm.


Does corruption sand the wheels of sustainable development? Evidence through green innovation

Abstract: Abstract This study investigates the impact of corruption on green innovation, as corruption may impede or foster green innovation in developing economies due to their weak governance systems. We develop a dataset of Chinese non‐financial firms listed between 2007 and 2020 and apply static and dynamic regression techniques. The results indicate a highly significant negative association between corruption and green innovation. This supports the notion that corruption culture reduces corporate legitimacy concerns ( institutional theory ), increases managerial rent‐seeking ( agency theory ), and hinders green innovation, thus impeding sustainable development and supporting the “ sand the wheels ” hypothesis. Our analysis also reveals that corruption's negative impact on green innovation is particularly significant for firms with lower media and analyst coverage, non‐state‐owned firms, and firms in heavy‐polluting industries. These results are robust to alternate proxies of green innovation and corruption as well as econometric specifications that account for endogeneity issues and industry, region, and time‐fixed effects.


A quantum secure multi-party summation protocol based on one-direction quantum walks on a circle

Abstract: <p indent="0mm">This paper presents a novel quantum secure multi-party summation protocol based on one-direction quantum walks on a circle (ODQWC), which allows the summation computation of different integer sequences from "<italic>n</italic>" secret holders. Crucially, it ensures that this process does not infringe on the privacy of these secret holders by delivering quantum walk states in a circular pattern. Our proposed protocol employs two-particle product states rather than quantum entangled states as the initial quantum resource and eliminates the need for different secret holders to perform quantum entanglement swapping operations. Instead, it emphasizes imposing one-direction evolution operators on quantum walk states. Additionally, our protocol requires single-particle measurements. Security analysis validates that our proposed protocol can effectively counter external attacks and those initiated by participants. Under specific conditions, our proposed protocol can be adapted into a quantum secure multi-party bit summation protocol based on ODQWC.


Emojis as heuristic cues: The multifaceted role of emojis in online service interactions

Abstract: Abstract Recent technological advances have allowed businesses to adopt emojis when interacting with consumers. To gain in‐depth theoretical and managerial insight into this trend, five pre‐registered studies (1 field observation and four controlled experiments) indicate that emojis in digital communication work as heuristic cues that might have a differential effect depending on elaboration likelihood and outcome valence. Drawing on the Heuristic Information Processing and elaboration likelihood model, this research reveals that emojis can systematically influence consumers' elaboration. Findings indicate that low elaboration in positive encounters results in a positive heuristic cue boost (emojis improve customer evaluation). In turn, high elaboration on negative service outcomes makes the heuristic content relevant, such that positive (vs. negative) emojis will bring attention to and reinforce the interaction's positive (vs. negative) aspects. This research contributes to emerging studies on the role of emojis in digital communication.


Recognition of Fiber Optic Vibration Signals Based on Laplace Wavelet Transform and Deep Learning

Abstract: Abstract Convolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.


Ambiguity Tests, Polysemy, and Copredication

Abstract: A family of familiar linguistic tests purport to help identify when a term is ambiguous. These tests are philosophically important: a familiar philosophical strategy is to claim that some phenomenon is disunified and its accompanying term is ambiguous. The tests have been used to evaluate disunification proposals about causation, pain, and knowledge, among others. These ambiguity tests, however, have come under fire. It has been alleged that the tests fail for polysemy: a common type of ambiguity, and one that is at issue in philosophically interesting cases. Furthermore, the objection that the tests fail for polysemy is often taken to be an undeniable bit of linguistic data. We argue that this is mistaken. The objection implicitly relies on controversial assumptions about how to account for copredicational sentences, in which a single argument is ascribed prima facie incompatible properties. Furthermore, on several viable theories of copredication, the objection fails. However, our discussion also reveals that even if ambiguity tests are preserved, they may be significantly harder to execute than previously thought.


Formulation of Energy Bars by using Sunflower Seeds

Abstract: The study discusses the popularity and nutritional benefits of snack foods, with a focus on granola bars as a versatile and nutrient-dense option. The importance of energy bars for athletes and individuals with busy lifestyles is highlighted. The study aims to develop a high-protein, high-energy nutritional bar using plant sources. Oats are emphasized for their rich nutrient profile, including carbohydrates, fiber, proteins and various bioactive compounds. Dates and almonds contribute additional nutrients and health benefits. Sunflower seeds are recognized for their therapeutic benefits, while honey is for its antibacterial and nutritional properties. The energy bars, including a basic version and two variations, were analyzed for various nutritional parameters. The moisture content exhibited slight increments, measuring from 6.83±0.01, to 7.03±0.01, while the ash content showed minimal differences at 0.05±0.01. Energy content increased from 380±0.032 to 410±0.026, and protein content ranged from 9.17±0.01 to 9.19±0.01. Fat composition showed marginal variations while the fiber content was measured at 6.67±0.01, 6.77±0.01 and 6.78±0.01 grams. Ascorbic acid concentrations increased from 0.7±0.015 to 0.81±0.0026, while thiamine and riboflavin concentrations showed slight fluctuations. Sensory evaluation revealed that T2 (30% honey) scored highest in various categories, including color (8.5±0.527), taste (8.6±0.516), texture (8.2±0.788), flavor (8.5±0.707), appearance (8.5±0.527) and overall acceptability (8.6±0.516). T2 emerged as the most preferred, followed by T1 (25% honey) and T3 (35% honey).


A Study on the Visual Recognition Patterns of Japanese Beginner Learners Regarding Hangul Characters: Focusing on Consonant Letters

Abstract:
Objectives The purpose of this study is to examine the visual recognition of Hangul consonants among Japanese beginner learners using data from a visual recognition survey. It aims to identify the consonants that learners find visually challenging to distinguish and to provide educational implications for the instruction of Hangul characters at the introductory level.
 Methods An online survey using Google Forms was conducted among 129 Japanese beginner learners studying Korean as an elective liberal arts course at universities in Japan. This survey involved two rounds of visual recog-nition tests on 19 consonant characters. The tests were employing a form constancy testing method where partic-ipants were required to identify Hangul characters that matched the form of the given examples.
 Results In comparison to the first survey, the average score of the second survey increased by 0.39 points, but the difference between the average scores of the first and second surveys was not statistically significant. It was also confirmed that the order of learning Hangul characters and their frequency of appearance did not have a sig-nificant effect on the visual recognition of character forms. In the error analysis of Japanese beginner learners, er-rors due to native language, target language, and curriculum were found, and some errors due to native language and target language were interpreted as errors due to the influence of font styles.
 Conclusions Japanese beginner learners encounter difficulties in the visual recognition of Hangul characters dur-ing the Korean language learning process. Exposure to these characters in textbooks is insufficient for accurate recognition of their forms, necessitating continuous character form education. It is particularly noted that the de-tailed features of Hangul characters and variations in font styles are significant factors for Japanese learners. This approach is anticipated to serve as foundational data for enhancing understanding of Hangul character learning and for developing effective teaching methods.


A bibliometric analysis of metaverse technologies in healthcare services

Abstract: Abstract A bibliometric analysis was conducted to examine healthcare metaverse research using 3721 Web of Science publications covering the period 1994–2022. Using bibliometric and bibliographic link network analyses, this study aimed to uncover emerging trends, country and university contributions, author collaborations, and common metaverse themes. Contrary to expectations, inconsistent correlations between publication volume and citation impact emerged. This pioneering research evaluated empirical bibliometric laws, identified influential entities, and provided a structured thematic categorization for metaverse studies. Serving as the first comprehensive investigation of the healthcare metaverse, this study lays a solid foundation for subsequent research efforts and guides further scholarly research.


Enhancement of Seismic Image Based on Novel Approach with Custom U-Net Architecture

Abstract: Abstract This paper introduces an innovative approach for enhancing the vertical resolution of seismic images, augmenting conventional signal processing with deep learning algorithms and modeling techniques. The algorithm outlined in this study is specifically designed to be applicable across diverse datasets, operating independently of well data. It effectively addresses noise reduction, distinguishing geological features from disturbances and by improving the signal-to-noise ratio, and implements enhancements to the frequency spectrum through a nonlinear approach. The research endeavors to create an unconventional methodology, focusing on vertical resolution improvement. Notably, the effectiveness of this approach is demonstrated using real seismic data collected from the South Caspian Basin. Detailed results are presented, underscoring the potential to significantly enhance the accuracy and reliability of seismic exploration procedures in the realm of geophysics.


ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM)

Abstract: The Coronavirus, also known as the Covid-19 pandemic, has reached every country worldwide, including Indonesia. Covid-19 is still prevalent and has killed many people in Indonesia. This makes it impossible to stop Covid-19 from spreading. The government's attempt to stop the Covid-19 pandemic is acquiring the vaccine. The administration of the Covid-19 vaccine has generated much discussion on social media, particularly Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Covid-19 vaccine. 20,000 tweets were collected by Twitter crawling between January 10 and January 15, 2022. 3.290 tweets were left after pre-processing and meaningless tweets were eliminated. The data were processed using the Recurrent Neural Network method with the Long Short-Term Memory algorithm to determine its accuracy and identify topics often discussed by the public on Twitter. The LSTM method is capable of storing old information/data. A model with 70% training data, a learning rate of 0.01, 100 LSTM units, 32 batch sizes, 100 epochs, a cross-entropy loss function, and Adam optimizers was used to build the classification in this study. The accuracy value obtained from the performance evaluation of the Long Short-Term Memory model research was 80.34%.


Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Abstract: Abstract. Seismic phase picking and magnitude estimation are fundamental aspects of earthquake monitoring and seismic event analysis. Accurate phase picking allows for precise characterization of seismic wave arrivals, contributing to a better understanding of earthquake events. Likewise, accurate magnitude estimation provides crucial information about an earthquake's size and potential impact. Together, these components enhance our ability to monitor seismic activity effectively. In this study, we explore the application of deep-learning techniques for earthquake detection and magnitude estimation using continuous seismic recordings. Our approach introduces DynaPicker, which leverages dynamic convolutional neural networks to detect seismic body-wave phases in continuous seismic data. We demonstrate the effectiveness of DynaPicker using various open-source seismic datasets, including both window-format and continuous recordings. We evaluate its performance in seismic phase identification and arrival-time picking, as well as its robustness in classifying seismic phases using low-magnitude seismic data in the presence of noise. Furthermore, we integrate the phase arrival-time information into a previously published deep-learning model for magnitude estimation. We apply this workflow to continuous recordings of aftershock sequences following the Turkey earthquake. The results of this case study showcase the reliability of our approach in earthquake detection, phase picking, and magnitude estimation, contributing valuable insights to seismic event analysis.


All-optical image denoising using a diffractive visual processor

Abstract: Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.


A circuit area optimization of MK-3 S-box

Abstract: Abstract In MILCOM 2015, Kelly et al. proposed the authentication encryption algorithm MK-3, which applied the 16-bit S-box. This paper aims to implement the 16-bit S-box with less circuit area. First, we classified the irreducible polynomials over $$\mathbb {F}_{2^n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> </mml:msub> </mml:math> into three kinds. Then we compared the logic gates required for multiplication over the finite field constructed by the three types of irreducible polynomials. According to the comparison result, we constructed the composite fields, $$\mathbb {F}_{(2^4)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>4</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> and $$\mathbb {F}_{(2^8)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> . Based on the isomorphism of finite fields, the operations over $$\mathbb {F}_{2^{16}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>16</mml:mn> </mml:msup> </mml:msub> </mml:math> can be conducted over $$\mathbb {F}_{(2^8)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> . Similarly, elements over $$\mathbb {F}_{2^8}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> </mml:msub> </mml:math> can be mapped to the corresponding elements over $$\mathbb {F}_{(2^4)^2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>4</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:msub> </mml:math> . Next, the SAT solver was used to optimize the operations over smaller field $$\mathbb {F}_{2^4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>4</mml:mn> </mml:msup> </mml:msub> </mml:math> . At last, the architecture of the optimized MK-3 S-box was worked out. Compared with the implementation proposed by the original designer, the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.


A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching

Abstract: Abstract With the exponential growth of various data interactions on network systems, network intrusions are also increasing. The emergence of edge computing technology brings a new solution to network security. However, due to the difficulty of processing massive and unbalanced data at the edge, higher accuracy requirements are necessary for deployed detection models. This paper proposes a multi-classification model for network intrusion detection based on reconstruction and feature matching. This model can be deployed on small-scale edge nodes, effectively identifying various attack behaviors through the utilization of reconstruction errors and adaptive scaling. Furthermore, we proposed a model transfer method based on feature matching to enhance the training and detection efficiency of multi-classification models under different data distribution conditions. The proposed model has been evaluated on the CICIDS2017 dataset in terms of accuracy, recall, precision and F1 score. The model demonstrates high accuracy for normal flows in the network, majority class attacks, and minority class attacks, achieving an overall multi-class accuracy of 99.81%, outperforming similar models. Furthermore, this model demonstrates faster convergence and training speed after feature matching, exhibiting better robustness and outstanding performance at the edge.


Recommendation systems-based software requirements elicitation process—a systematic literature review

Abstract: Abstract Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions.


A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and <scp>BiLSTM</scp>

Abstract: In this paper, a hybrid photovoltaic power forecasting model is proposed based on bidirectional long‐short‐term memory network. Firstly, the photovoltaic power and meteorological data are decomposed by ensemble empirical mode decomposition. Secondly, key features in the meteorological subsequences are extracted by kernel principal component analysis method to eliminate the correlation and redundancy in the original meteorological sequence and reduce the input dimension of the model, then the coupling time characteristics between meteorological subsequences and photovoltaic power subsequence are further explored. Thirdly, a bidirectional long‐short‐term memory network optimized by improved particle swarm optimization based on inertia weight of anti‐sine function is proposed, and then the eventual photovoltaic power forecasting results are obtained by superimposing the predicted values of each prediction component. The experiment is carried out with the operation data of the Inner Mongolia power grid, and the results show that the proposed forecasting model exhibits higher prediction accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.


Detection of activities in bathrooms through deep learning and environmental data graphics images

Abstract: Automatic detection activities in indoor spaces has been and is a matter of great interest. Thus, in the field of health surveillance, one of the spaces frequently studied is the bathroom of homes and specifically the behaviour of users in the said space, since certain pathologies can sometimes be deduced from it. That is why, the objective of this study is to know if it is possible to automatically classify the main activities that occur within the bathroom, using an innovative methodology with respect to the methods used to date, based on environmental parameters and the application of machine learning algorithms, thus allowing privacy to be preserved, which is a notable improvement in relation to other methods. For this, the methodology followed is based on the novel application of a pre-trained convolutional network for classifying graphs resulting from the monitoring of the environmental parameters of a bathroom. The results obtained allow us to conclude that, in addition to being able to check whether environmental data are adequate for health, it is possible to detect a high rate of true positives (around 80%) in some of the most frequent and important activities, thus facilitating its automation in a very simple and economical way.


Effects of Multimodal Exercise With Augmented Reality on Cognition in Community-Dwelling Older Adults

Abstract: This study aims to investigate the effects of an exercise intervention using multimodal exercise with augmented reality and multimodal exercise-only on cognitive function in older adults living in a community dwelling.Quasi-experimental research study.In this control study, 78 participants were divided into 2 experimental groups (with sessions 3 times a week for 12 weeks) and a control group (CG).EG1 participated in a multimodal exercise-only intervention program, EG2 participated in a multimodal exercise program with augmented reality exergames, and CG continued its usual activities. Participants were assessed at baseline and postintervention after 12 weeks.Comparison between baseline and postintervention at 12 weeks showed significant improvements in executive functions, verbal fluency, choice reaction time, and dual task in EG1, whereas there were improvements in general cognition, executive functions, verbal fluency, discrimination reaction time, and depression in EG2 (P ≤ .05). The clinical effect sizes of the interventions were large for overall cognition, executive functions, and reaction time on single- and dual-task reaction time in EG1 and for overall cognition, executive functions, and verbal fluency in EG2.The intervention programs showed significant improvements in several cognitive domains. The multimodal exercise-only showed improvements in more variables than the multimodal exercise with augmented reality, but the augmented reality group showed greater changes between baseline and postintervention.


Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction

Abstract: A Temporal Knowledge Graph (TKG) is a sequence of Knowledge Graphs (KGs) attached with time information, in which each KG contains the facts that co-occur at the same timestamp. Temporal knowledge prediction (TKP) aims to predict future events given observed historical KGs in TKGs, which is essential for many applications to provide intelligent analysis services. However, most existing TKP methods focus on entity and relation prediction tasks but ignore the importance of time prediction tasks. Furthermore, there is uncertainty in time prediction, and it is difficult for prediction models to model it completely. In this work, we propose a collaboration framework with Bayesian Hypernetwork and Time-Difference Evolutional Network (BH-TDEN) to address these problems. First, we begin with the time prediction task, and we present a Bayesian hypernetwork to model the uncertainty of events time. For the input of Bayesian hypernetwork, we design a novel time-difference evolutional network to obtain the entities and relations embedding. Specifically, we propose an auto-regressive time gate parameterized by the time difference of adjacent KGs in entity and relation encoder to learn the time-sensitive TKG embedding, which not only learns the relationship between the given time information and TKG embedding but also provides more expressive TKG embedding for Bayesian hypernetwork to accurately predict the time of future events. Furthermore, we also present a novel relation updating mechanism that employs the neighbor relations of the subject corresponding to the current relation to learn more adaptive relation embedding. Extensive experiments demonstrate that the proposed method obtains considerable time prediction and link prediction performance on four TKG benchmark datasets.


Magneto-optic interferometric system: Exploring building blocks and subcircuits

Abstract: Recent developments in the standardization of magnetic field generators have been considered for optoelectronic industrial-scale applications. In this work, a strategy to incorporate all optical components that can be used to implement a broad range of functions for communications applications is presented. We propose a circuit design for a magneto-optic interferometric system based on building blocks and subcircuits consisting of an interferometer with magneto-optic phase shifters and a magnetic field generator. The paper shows the building blocks based on elementary 2 × 2 photonics coupling components. We demonstrate that such a design can be configured as a tunable system based on the phase shift experienced by the incoming signals from two input waveguides and their transitions.


Enhancing <scp>scene‐text</scp> visual question answering with relational reasoning, attention and dynamic vocabulary integration

Abstract: Abstract Visual question answering (VQA) is a challenging task in computer vision. Recently, there has been a growing interest in text‐based VQA tasks, emphasizing the important role of textual information for better understanding of images. Effectively utilizing text information within the image is crucial for achieving success in this task. However, existing approaches often overlook the contextual information and neglect to utilize the relationships between scene‐text tokens and image objects. They simply incorporate the scene‐text tokens mined from the image into the VQA model without considering these important factors. In this paper, the proposed model initially analyzes the image to extract text and identify scene objects. It then comprehends the question and mines relationships among the question, OCRed text, and scene objects, ultimately generating an answer through relational reasoning by conducting semantic and positional attention. Our decoder with attention map loss enables prediction of complex answers and handles dynamic vocabularies, reducing decoding space. It outperforms softmax‐based cross entropy loss in accuracy and efficiency by accommodating varying vocabulary sizes. We evaluated our model's performance on the TextVQA dataset and achieved an accuracy of 53.91% on the validation set and 53.98% on the test set. Moreover, on the ST‐VQA dataset, our model obtained ANLS scores of 0.699 on the validation set and 0.692 on the test set.


Exploring variables explaining elementary school student’s cognitive, social and emotional competencies using random forest

Abstract: This study was designed to explore major predictors related to cognitive, social and emotional competencies of elementary school students to discuss the implication for competencies development. To do this, random forest was applied to 1st wave data(4th graders) of the Seoul Educational Longitudinal Study of Students(SELS) 2020, and Shapley addictive explanations (SHAP) analysis was conducted to explore the importance of major predictors and the relationship between competencies and the predictors. The main result are as follows. First, the predictors commonly derived from three competencies were 'class attitude' and 'ICT competency'. The more positive class attitude and higher ICT competency, the higher cognitive and social-emotional competencies. Second, 'Anxiety' was found to major predictor of three competencies, but it was found to have positive relationship with cognitive competency and negative relationship with social-emotional competency. Third, there were the most common predictors explaining social and emotional competencies. Based on these findings, implications for developing competencies and suggestions for future research are discussed.


Potensi Konsep Matematika dalam Penggunaan Aperture di Fotografi

Abstract: This study aims to help understand how the use of aperture works with mathematical concepts so that the use of this method is applied to photography activities and as a medium for learning mathematics in schools. The method used in this study is descriptive with the form of action research, because this study describes the mathematical concepts contained in the use of aperture in photography. From the interview, researchers collected information on mathematical concepts contained in the sequence located behind the F-stop number and explored the relationship between the F-stop number and the diameter of the circular aperture opening area in the lens, there are several mathematical concepts such as fractional numbers, geometric series, scales, comparisons, magnitudes & units, space builds, to the area of a circle. In this case, it proves that in the use of Aperture in photography, there are mathematical concepts related to school mathematics learning


Analysis of the Readability Characteristics of Educational Text: based on Texts in Korean Language, Social Studies, and Science

Abstract: In this study, an educational text database of Korean language, social studies, and science departments was established, and a qualitative checklist for measuring readability was developed. After setting the level of readability through the teacher


Análisis de accesibilidad y visibilidad para los portales académicos de las universidades de Cartagena

Abstract: Introduction: With the growing demand for applications in the market, companies have increasingly focused on meeting the technological needs of the average user, often overlooking those with functional diversity. Consequently, accessibility has emerged as a critical challenge across various contexts, particularly within the educational sphere, where the diverse range of individuals accessing web content is particularly pronounced. Objective: This paper presents a study centered on the accessibility inspection of six university websites located in the city of Cartagena. The primary aim is to ascertain the relationship between the integration of accessibility guidelines and web positioning factors as determined by Webometrics. Methodology: The methodology comprises five distinct phases: website selection, tool selection for inspection, execution of accessibility inspection, analysis of obtained results, and formulation of conclusions and recommendations. Results: The study revealed a correlation exceeding 0.8 between the local ranking of universities according to Webometrics and the ranking of accessibility issues identified through automated inspection tools. Conclusions: It is recommended that educational institutions adopt automatic web evaluation tools to enhance the accessibility of their institutional websites. This regular review and rectification of issues represent best practices for improving both local and international visibility and usability.


Factors influencing gender and youth integration in agricultural research and innovation in Africa

Abstract: Abstract Integrating gender and youth in Agricultural Research and Innovation (R & I) is important for strengthening the impact and inclusiveness of the research initiatives. The integration is more critical in Africa due to the prevalence of exclusive gender and age norms that undermine the place and role of women and youth in the food systems. The consideration of women and youth as ‘helpers’ instead of farmers aggravated inequalities in accessing and controlling over land and other resources, opportunities for income and employment, and decision-making processes. Gender and youth integration are also central for achieving equal economic and social outcomes in the agricultural sector. There is increased recognition among the research and development community that effective gender and youth integration in R & I would help increase the adoption of products, services, and processes of R & I. In addition, gender and youth integration helps in the promotion and dissemination of R & I as well as in increasing accessibility of innovation and technology by end users. This study examined current discussions on gender and youth integration across projects with particular emphasis on the Food and Nutrition Security and Sustainable Agriculture (FNSSA) project database. The desk review was supplemented by 30 interviews with key informants in FNSSA projects across Africa including Benin, Democratic Republic of the Congo, Cameroon, Ghana, Madagascar, Nigeria, Kenya, Senegal, Sudan, Togo, and Uganda, and an interactive workshop with key stakeholders working on gender and youth integration issues across Africa. The study used the gender integration continuum conceptual framework, which was spearheaded by the Interagency Gender Working Group (IGWG). This framework was proposed to critically examine the different approaches across the continuum to increase the understanding of how gender integration, or the lack of it, determines the impact of the R & I projects on women and youth. The findings suggest that gender and youth inclusion was primarily project-based and was strongly tied to donor requirements. Meaning, projects that strongly included women and youth were mainly designed to target them as a primary or the only target. Some R & I projects made promising efforts in developing youth and gender-specific work packages as well as action plans outlining the end-to-end engagement of men, women, and youth in R & I. However, there was an imbalance in gender integration vis-à-vis youth integration as youth got little consideration in the R & I policies and practices, thereby reinforcing intergenerational inequality. Factors limiting youth and gender integration in R & I included inadequate gender expertise and competence and women and youth’s insufficient access to leadership roles, therefore, limiting their participation at decision-making levels. In conclusion, gender and youth inclusion and participation in R&I requires a holistic approach, one that considers the critical role they can play in innovation scaling. Consequently, there is a need for increased investment in translating the products and processes of R&I interventions into opportunities for entrepreneurship and employment. Intersectional lens is also needed for meaningful involvement of different community groups in the R&I interventions, and for reducing the different causes of inequalities and marginalization that hamper transformation of Africa’s food system. This could be an entry point for increasing the positive impact of R & I projects on livelihoods, gender equality, women’s empowerment, and entrepreneurship.


CT-GCN+: a high-performance cryptocurrency transaction graph convolutional model for phishing node classification

Abstract: Abstract Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This poses a threat to the property security of users and hinders the healthy development of the entire blockchain community. While numerous studies have been conducted on identifying cryptocurrency phishing users, there is a lack of research that integrates class imbalance and transaction time characteristics. This paper introduces a novel graph neural network-based account identification model called CT-GCN+, which utilizes blockchain cryptocurrency phishing data. It incorporates an imbalanced data processing module for graphs to consider cryptocurrency transaction time. The model initially extracts time characteristics from the transaction graph using LSTM and Attention mechanisms. These time characteristics are then fused with underlying features, which are subsequently inputted into a combined SMOTE and GCN model for phishing user classification. Experimental results demonstrate that the CT-GCN+ model achieves a phishing user identification accuracy of 97.22% and a phishing user identification area under the curve of 96.67%. This paper presents a valuable approach to phishing detection research within the blockchain and cryptocurrency ecosystems.


Easy Ontology and Undecidable Sentences

Abstract: Abstract According to Thomasson’s Easy Ontology, all existential questions have straightforward answers and are solvable by conceptual and empirical work. So there is no need for traditional metaphysics to solve them. First, I give some counterexamples to this thesis from incomplete and undecidable theories. Then I discuss some possible responses, I consider a wider sense of conceptual analysis and argue that even in this sense Easy ontology is not able to resolve the problem and must sacrifice either easiness or answerability. Finally, I will argue that although traditional metaphysics does not make the undecidable sentences answerable, it can still make sense of them and helps us to understand why there are unanswerable questions at all.


Persona/scenario (P/S) toolkit enhancing inclusive fashion design education

Abstract: Abstract This study aimed to address the diversity issue in fashion design education by developing two prototypes for Inclusive Fashion Design (IFD) education. The prototypes were constructed based on the 3C3R model of Project-Based Learning (PBL) and incorporated the Persona/Scenario (P/S) toolkit for IFD. Additionally, the study examined the effectiveness of the FEA (Function, Expressive, and Aesthetics) self-checklist and the P/S toolkit in the IFD process. The experiments involved two design instructors, two teaching assistants, and 12 senior-level students divided into four teams. Thematic analysis was conducted on the interview data collected from the participants. The results revealed that FEA factors were highly considered in both education prototypes, regardless of the presence of the P/S toolkit. Maintaining consistency of the IFD concept throughout each stage of the process was found to be crucial, and the use of the P/S toolkit played a significant role in achieving this consistency. Team communication emerged as an essential factor in IFD education, as team collaboration using the P/S toolkit triggered diverse perspectives on targets, facilitated design expansion, and extended individual competences. Overall, this study contributes to the understanding of the diversity issue in fashion design education and promotes the adoption of the IFD education methodology, emphasizing the significance of consistent concept development, effective toolkits, and team collaboration in achieving inclusive design practices.


Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

Abstract: Alzheimer's disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph creation, and region-based convolutional neural network approach for image-to-knowledge graph generation. Additionally, we integrated various explainable AI (XAI) techniques to interpret and elucidate the prediction outcomes derived from multimodal data. Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images. We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided. Genetic expression values play a crucial role in AD analysis. We used a graphical gene tree to identify genes associated with the disease. Moreover, a dashboard was designed to display XAI outcomes, enabling experts and medical professionals to easily comprehend the prediction results.


Evaluation of Governance in Information Systems Security to Minimize Information Technology Risks

Abstract: Information system security within XYZ University constitutes a vital component of its IT framework, exerting significant influence over security levels across all facets of the information systems. Among the numerous implemented information system services at the university, a considerable portion lacks active security measures within operational systems. In pursuit of achieving uniform governance, this study adopts the most recent COBIT 2019 framework. The primary objective of this research is to evaluate the degree to which current information system security management aligns with the process achievement values stipulated in the COBIT 2019 standard. This evaluation entails the calculation of maturity level values that gauge performance levels in managing information system security. Findings from the COBIT 2019 Design assessment conducted at XYZ University's LTIK reveal that individuals scoring above 80 or those requiring Capability Level 4 include APO12 and BAI10. Moreover, the calculation outcomes for each subdomain reveal the presence of 2 subdomains at Level 4, 4 subdomains at Level 3, 15 subdomains at Level 2, and 19 subdomains at Level 1. The identification outcomes underscore the existence of gaps within each domain. Particularly, the APO12 and BAI10 domains exhibit a gap spanning 2 levels.


The Empirical Study On Algorithm Optimization In Expert Systems For Diagnosing Rice Plant Diseases

Abstract: Rice is one of the most important cultivated plants for human survival. The activity of cultivating rice plants becomes a livelihood for most of these residents, so the success rate of the amount of rice harvested becomes very important because they depend on how much rice can be harvested, disease diagnosis is very important for farmers, this is very important to reduce economic losses due to diseases that cause crop failure. Therefore, when dealing with rice diseases, an expert is needed to make diagnoses or solutions to rice diseases. However, an expert does not know when to come to the village, and farmers also do not understand all rice diseases. Therefore, a web-based expert system application using the forward chaining method is proposed to represent an expert to help farmers diagnose and solve diseases of rice plants with existing symptoms.


A multimodal dialogue system for customer service based on user personality adaptation and dialogue strategies

Abstract: This paper presents a multimodal dialogue system with user personality adaptation and multiple nonverbal behavior generation for a conversational android robot that plays a customer service role in recommending travel plans. It is useful to estimate the user's personality to realize a dialogue system that adapts the dialogue strategy to individual users. To improve the user's impression of the conversation robot as a customer service agent, we must control the nonverbal behavior of the robot, such as its facial expressions and motions. Moreover, we need to design the dialogue strategy to provide information and make users enjoy the conversation by adding an ice-breaking step. Against this background, we develop a dialogue system prototype that adjusts the dialogue strategy based on the user's personality, as estimated by a pre-trained multimodal sensing model. Furthermore, we implement appropriate nonverbal behavior patterns for each situation, including the voice, motion, and facial expressions of the android robot, to provide it appropriate and polite service agent behaviors. A personality assessment game is introduced into the dialogue to prevent the user from becoming bored. The game plays an ice-breaking role in the dialogue. To let the user engage with the robot, we implement a complement to the user as a dialogue act. We evaluate the proposed multimodal dialogue system for customer service by means of participation in a dialogue robot competition. The fair evaluation results from 26 dialogue users who conversed with multiple systems show that the proposed dialogue system achieved the best score among 13 teams participating in the preliminary round of the competition, indicating that the user personality adaptation and various elements implemented in our dialogue system improve the dialogue experience of general users. The results of a third-party evaluation and multiple regression analysis show that natural response is the most important factor contributing to user impression.


Setting up classroom libraries in rural areas: The case of Mogodumo circuit in Limpopo

Abstract: There is an urgent need to establish classroom libraries, especially in rural areas where learners hardly come across any reading material. The purpose of this study was to investigate how teachers set up classroom libraries in rural areas of the Mogodumo circuit in the Limpopo province of South Africa. The study was qualitative in nature. Fifteen English First Additional Language (EFAL) teachers from 12 schools participated in the study. Observation and semi-structured interviews were employed to collect data. It was found that while all participants recognised the importance of reading for their learners, only 7 of the 15 teachers managed to establish classroom libraries. The study revealed that teacher self-efficacy (TSE) played a crucial role in setting up classroom libraries, given the serious challenges of funding, resources and support experienced by rural schoolteachers. Teachers with high TSE managed to establish classroom libraries by using their own time, money, and resources, while those with low TSE failed to establish classroom libraries. We recommend that school managers should make time and resources available to rural schoolteachers to enable them to establish classroom libraries.


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Usage of Multiplane Image Information SEI Message for Distribution of Lightweight 3DoF+ Immersive Content with Conventional 2D Decoder

Abstract: In this presentation, we describe a new SEI message under consideration for VSEI/H.274 - the multiplane image information SEI message (MPII SEI) [1]. The motivation to design this MPI is to establish open non-proprietary standards to enable distribution of lightweight 3DoF+ immersive consumer experiences encoded in a single bitstream compatible with already deployed smartphones, laptops, etc. equipped with a conventional 2D decoder.


Overview of Visual Signal Compression towards Machine Vision

Abstract: Machine analysis of visual signals has been providing fundamental infrastructure support for artificial intelligence (AI) applications. As such, the compact representation of visual signal towards machine analysis is critical in current AI systems. A straightforward and general pipeline of visual signal compression for machines is to compress the visual signal, then perform the machine analysis on the reconstructed visual signal for ultimate results. This pipeline naturally supports multiple machine vision tasks and human perception, and is denoted as visual signal compression towards machines (VSCM). This paper provides an overview to summarize the VSCM related technologies in terms of various modules, and envision the future development of VSCM.


No-Reference Quality of Experience Model for Dynamic Point Clouds in Augmented Reality

Abstract: Point cloud streaming is becoming increasingly popular due to its ability to provide six degrees of freedom (6DOF) for immersive media. Measuring the quality of experience (QoE) is essential to evaluate the performance of point cloud applications. However, most existing QoE models for point cloud streaming are complicated and/or not open source. Therefore, it is desirable to provide an open-source QoE model for point cloud streaming.


Accelerated and Adaptive Non-local Mean Temporal Filtering for Improved HEVC Coding Efficiency

Abstract: As modern video codecs exploit at their basis temporal and spatial correlations in video signal for compression, and such correlations are corrupted by noise that is present in the video signal, temporal filtering has been shown to be effective for improving coding efficiency in various encoder implementations [1], [2], [3]. In particular, the non-local mean approach in [1] has been applied in AV1, an open video coding format competing with HEVC (aka H.265), which includes built-in mechanisms that allow effective temporal filtering using non-displayable reference frames (aka ALTREF frames).


E-WISH

Abstract: HTTP Adaptive Streaming (HAS) is the de-facto solution for delivering video content over the Internet. The climate crisis has highlighted the environmental impact of information and communication technologies (ICT) solutions and the need for green solutions to reduce ICT's carbon footprint. As video streaming dominates Internet traffic, research in this direction is vital now more than ever. HAS relies on Adaptive BitRate (ABR) algorithms, which dynamically choose suitable video representations to accommodate device characteristics and network conditions. ABR algorithms typically prioritize video quality, ignoring the energy impact of their decisions. Consequently, they often select the video representation with the highest bitrate under good network conditions, thereby increasing energy consumption. This is problematic, especially for energy-limited devices, because it affects the device's battery life and the user experience. To address the aforementioned issues, we propose E-WISH, a novel energy-aware ABR algorithm, which extends the already-existing WISH algorithm to consider energy consumption while selecting the quality for the next video segment. According to the experimental findings, E-WISH shows the ability to improve Quality of Experience (QoE) by up to 52% according to the ITU-T P.1203 model (mode 0) while simultaneously reducing energy consumption by up to 12% with respect to state-of-the-art approaches.


H/W Accelerated Unsupervised Codec Modernization

Abstract: Following a decade where AVC/H.264 was the dominant codec in many video applications, the last years have finally seen more modern video codecs gain traction. This raises the need for cost effective, fast, fully automatic transcoding into modern codec formats which will preserve the input perceptual quality, at the lowest possible bitrate. The availability of such a solution makes the switchover to newer encoders a smooth, easy, and low-cost process accelerating adoption of HEVC, AVI or VVC.


Combining Deep Learning and Feature Engineering for No-Reference Video Quality Assessment

Abstract: With the ever-growing demand for high-quality video content, methods for evaluating the aesthetic quality of digital videos have become invaluable tools to ensure a satisfying viewing experience for the end-user. Over the years, many accurate and reliable Full-Reference (FR) video quality assessment (VQA) metrics [4, 5, 10, 16, 17] have been developed that assess quality degradation by comparing the processed video to its original source. However, in scenarios where the reference video is unavailable or impractical to obtain, No-Reference (NR) metrics have to be used, which assess quality solely on the distorted video. Despite intensive research, no NR method so far has been found accurate enough to be widely adopted by industry [9].


Inferring Video Streaming Quality of Experience at Scale using Incremental Statistics from CDN Logs

Abstract: With video streaming applications, end-user Quality of Experience (QoE) is generally estimated using metrics provided by the video players. While QoE monitoring is critical for Content Delivery Networks (CDNs), player metrics are not always provided due to technical, contractual or regulatory limitations. We propose to leverage CDN access logs to infer five common QoE player metrics, with the help of machine learning models. Our approach is based on constant-memory statistic accumulators, allowing large-scale analysis of video streams. We evaluated our implementation with more than 100 000 concurrent streaming sessions on a single CPU core, showing good correlation with QoE ground truth (ρ > 0.7, R2 > 0.5).


A New Blockchain-Based Medical Model for Smart Hospital Integrity Management of the Drug Supply Chain

Abstract: Counterfeit medications are currently one of the biggest issues in pharmacology. According to a research from the Health Research Funding organization, between 10–30% of pharmaceuticals in underdeveloped nations are counterfeit. The primary problem is not in counterfeiting per such, but rather in the fact that these pharmaceuticals have distinct adverse consequences on human health when compared to regular drugs. The WHO estimates that over 30% of all medications sold in Latin America, Asia, and Africa are fake. This is the main issue facing the entire world, and things become worse in underdeveloped nations where one in ten medications are either counterfeit or do not adhere to drug standards. It is becoming increasingly challenging to standardize drug safety due to the growth of online pharmacies. It is challenging to identify fakes. The safety of the pharmaceutical supply chain is becoming a serious public health concern, a process that affects all of us. In this work, we present a new approach to medication supply chain management that handles safe drug supply chain data using Hyperledger Fabric, a blockchain-based platform. By implementing drug record transactions on a blockchain, a drug supply chain and smart healthcare ecosystem may be constructed. The suggested method addresses this issue. To allow for time-limited access to patient health information and electronic prescription records, a smart contract has been introduced.


Content Steering

Abstract: DASH-IF Content Steering [4] (to be soon published as ETSI TS 103 998) is a new standard developed by the DASH Industry Forum (DASH-IF), defining means for managing DASH [5] media delivery using multiple content delivery networks (CDNs). At the server-client interaction level, this standard is compatible with the CDN steering features of HTTP Live Streaming (HLS) [1, 6], effectively enabling the same content steering servers to control delivery for both HLS and DASH-based media distribution.


AWS Compute Video Super-Resolution powered by the Intel® Library for Video Super Resolution

Abstract: The rise of free ad-supported streaming TV (FAST) services has boosted personalized content, including classic movies and shows. Most of this content is available in lower-resolution formats (SD) and needs an enhanced viewing experience. Traditionally, low-complexity upscaling methods like Lanczos and bicubic have been used. However, they often introduce image artifacts such as blurring and pixelation. While deep learning (DL) techniques such as SRCNN [1], and EDSR [2] have shown remarkable results in terms of quality metrics (VMAF, SSIM, PSNR). However, they are computationally expensive, and can require GPUs to implement, making them unsuitable for low-cost channels,the most typical case in FAST services. Therefore, we (Intel®1 and AWS) propose a cost-effective architecture for video super-resolution that leverages the benefits of Amazon Spot instance to process video assets using the Intel® Library for Video Super Resolution (Intel® Library for VSR) on Intel® Processors balancing quality and performance for real world use cases.


Toward WebTransport Support in dash.js

Abstract: Over the recent years, video streaming has gained more and more popularity [14]. This not only puts much strain on the Internet infrastructure [4, 10] but also creates a drive to continue improving the viewer experience. One avenue to tackle both points is the new transport protocol--- QUIC [8], which has attracted enough attention to have led to the founding of the Media over QUIC (MoQ) working group in the Internet Engineering Task Force (IETF) [6]. This group is avidly working on creating a standard for transmitting delay-sensitive media using QUIC. QUIC can reduce initial latency thanks to the combination of its handshake and TLS handshake [8].


Ultra-low Latency Video Delivery Over WebRTC Data Channels

Abstract: Over the past years, the video streaming industry has focused great interest in Low-Latency streaming protocols, targeting a sub-5-seconds end-to-end delay comparable to live broadcast TV. Such low delays are considered critical for streaming live sports, gaming, online learning, and interactive video applications, but are hard to achieve using traditional HTTP-based live streaming technologies. Media Over QUIC (MoQ) has been proposed as a promising alternative for ultra-low delay streaming, but limited network infrastructure compatibility and the lack of a mature and optimized custom application-layer protocol have been major hurdles for practical deployments. In this paper, we discuss the alternative use of WebRTC data channels to achieve latencies lower than two seconds on live streaming applications.


SSIM Revisited

Abstract: We review the design of the SSIM quality metric and offer an alternative model of SSIM computation, utilizing subband decomposition and identical distance measures in each subband. Based on this model, we also propose an extension of this metric, using a finer-grain sub-band structure resembling the spatial channel model of contrast sensitivity function (CSF). We use a weighted combination of subband-specific scores into a single error visibility score for each spatial location within an image. The averaging is calibrated based on the characteristics of the viewing setup (translation from pixels to spatial frequencies). We test this proposed model using several subjective score datasets and show that it can outperform the original SSIM and MS-SSIM in the accuracy of fit to MOS scores. We discuss applications of this proposed model for the design and improvement of video encoding applications. Specifically, we show how to design pre-filters or inloop noise shaping filters improving SSIM scores of encoded video sequences.


The TDC profile of JPEG XS 3rd edition

Abstract: The JPEG XS [1,2,3] activity started around 2015 to address the problem of the ever- increasing bandwidth requirements for transmitting video content in professional production environments. In such environments, ultra-low latency, low complexity, and visually lossless image quality are key features, ruling out the existing state of the art video compression technology. A paradigm shift was needed to find a solution where these three key features were prioritized over the pure compression performance.


Standards-Based Neural-Network Post-Filters for Improved Video Quality

Abstract: In this paper we provide an overview of Versatile Supplemental Enhancement Information Messages for coded video bitstreams (VSEI) standard-based neural-network post-filter (NNPF) design. The paper provides details about the key aspects of neural-network post-filter characteristics (NNPFC) message which defines neural-network post-filter (NNPF) and its activation and deactivation via neural-network post-filter activation (NNPFA) messages. We also describe using NNPF SEI design to implement a picture rate upsampling NNPF based on an off-the-shelf neural network. Additionally, technologies under consideration for enhancements to the version 3 NNPF VSEI design are described.


Building a Digit Classifier with MNIST

Abstract: Students use feature engineering to build a classifier that can accurately recognize digits from images.


Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

Abstract: Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification. Although this technique is widely used in multi-label classification problems, batch learning deals with most issues, which consumes a lot of time and space resources. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets. However, existing online learning research has done little to consider correlations between labels. On the basis of existing research, this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples. We evaluate the performance of the proposed algorithm on several public datasets. Experiments show the effectiveness of our algorithm.


The Implementation Of Daily Journal To Improve Writing Skills For University Students

Abstract: This study focused on implementing Daily Journal as a student’s writing activity. The study's objective is to impact the implementation of a daily journal on students' writing skills. The research method used in this study is quantitave. The subject of the study was 32 students majoring in English Education in one of Islamic University in Indonesia. Participants will take a pre and post-test and from there the researcher can compare their vocabulary and grammar skills in written form. The results of both tests will be subjected to a two-sample paired t-test analysis conducted using the Statistical Package for the Social Sciences (SPSS) software program. The test resulted in a significance value of 5%, providing sufficient evidence for a statistical change in post-treatment testing. The results of this study indicate that Daily Journal is an effective tool in enhancing English writing skills among the students in university level.


Implementation of TikTok as Medium for Learning of English Vocabulary: The Perspective of Department English Education Students

Abstract: This research aims to examine students' perspectives on implementing the TikTok application as a medium for learning English in the modern era. Especially with the latest technological advances, in the post-pandemic COVID-19 social media can be used as a learning media such as YouTube, Twitter, Instagram, Facebook, and TikTok applications. TikTok can help students improve their English language proficiency, as well as what problems and difficulties arise when using the application as a vocabulary teaching tool and what difficulties students may encounter when using TikTok to learn new vocabulary. This research uses a qualitative design with a survey method where students fill out an online questionnaire using Google Forms and interviews. Questionnaires were given to students, and then students were interviewed. The participants were 30 students who took part in the English language learning program. The research results show that the students had a positive response to the use of TikTok in learning English vocabulary. TikTok ease of use and various functions, the TikTok application can be applied to learning activities to increase English vocabulary. This interactive learning media can provide much educational content about English vocabulary and make the learning process more efficient and effective for students. The implementation of This research makes it possible for educators. Content creator educators can easily create a learning process that attracts students' attention. It has been known that the TikTok application is efficient in increasing English vocabulary for students. Through the TikTok application, students can gain more vocabulary and continue to have fun learning by presenting information through interesting content, audio and visuals. Teachers can also process and develop various sources of interactive, interesting and unique TikTok content. So that it makes students interested in learning English and considers TikTok to be an effective medium for learning.


Optimal Asymmetric Quantum Codes from the Euclidean Sums of Linear Codes

Abstract: In this paper, we first give the definition of the Euclidean sums of linear codes, and prove that the Euclidean sums of linear codes are Euclidean dual-containing. Then we construct two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of the Reed-Solomon codes, and two new classes of optimal asymmetric quantum error-correcting codes based on Euclidean sums of linear codes generated by Vandermonde matrices over finite fields. Moreover, these optimal asymmetric quantum error-correcting codes constructed in this paper are different from the ones in the literature.


Three New Classes of Subsystem Codes

Abstract: In this paper, we construct three classes of Clifford subsystem maximum distance separable (MDS) codes based on Reed-Solomon codes and extended generalized Reed-Solomon codes over finite fields [see formula in PDF] for specific code lengths. Moreover, our Clifford subsystem MDS codes are new because their parameters differ from the previously known ones.


NAVIGATING THE COMPLEXITY: PSEUDO-CHAOTIC SYSTEMS AND MACHINE LEARNING

Abstract: This discourse elucidates the intricate interplay between pseudo-chaotic systems and machine learning, highlighting a frontier where complexity meets computational prowess. Pseudo-chaotic systems, characterized by their deterministic yet intricately unpredictable behavior, present a unique challenge and opportunity for scientific exploration. Machine learning, with its robust pattern recognition and predictive capabilities, offers a promising toolkit for deciphering the nuanced dynamics of these systems. The text delves into the essence of pseudo-chaotic systems, explores the transformative potential of machine learning, and examines the synergistic fusion of these domains. Through this exploration, we uncover the profound implications of this synergy across various disciplines, from environmental science to healthcare and engineering. The discourse also addresses the challenges and ethical considerations inherent in this interdisciplinary pursuit, advocating for a future where the complexity of pseudo-chaotic systems is not merely understood but harnessed for advancement and innovation.


BRIEF APPROACH ON TEACHING HYDROGRAPHY IN ELEMENTARY SCHOOL II

Abstract: This work is a possibility for us to think about rivers.The objective of the work is to show that rivers have a history of formation.Rivers are part of the entire systemic dynamics of terrestrial evolution, making up life in the system; as well as the lives of many traditional peoples.In this sense, the work data is constituted through scenes from the Landsat 5 and 8 satellites, and the National Common Curricular Base.As an example, there is a stretch of the Amambaí-MS river, close to the mouth, aiming to analyze the dynamics of evolution aimed at elementary school II.This way, the work demonstrates a way of learning about rivers that is more attractive to students than just memorizing nomenclatures.In this context, the work addressed the dynamics of the channel in the years 2006, 2013 and 2020, identifying the morphological change in the Amambaí river channel, mainly between the years 2013 and 2020, with the abandonment of the meander.From this perspective, the student understands that the dynamics of nature are constant, and, in the case of river dynamics, 14 years is a period in which there is the possibility of changing the channel of a meandering river.


Comprehensive Analysis of Attacks and Defenses in IoT Sensory Big Data Analysis

Abstract: This chapter takes a deep dive into the security issues surrounding IoT sensory big data analysis, which is becoming increasingly important in today's world of ubiquitous connectivity and big data analysis. Starting with an introduction to this crucial juncture, the chapter proceeds to guide the reader through the complex terrain of IoT systems, with a focus on the far-reaching consequences of security precautions. From simple denial of service (DoS) attacks to complex man-in-the-middle (MitM) incursions, data manipulation, injection exploits, and unauthorised access are all under the microscope. Readers will obtain a sophisticated understanding of the many methods used by bad actors to exploit vulnerabilities in IoT systems as they are systematically explored. In addition, the chapter details a wide variety of defence measures, providing a versatile toolkit for shoring up IoT ecosystems. Intrusion detection systems (IDS), secure communication protocols, secure boot procedures, and strong data encryption are all part of these multi-layered plans of action.


Elevating IoT Sensor Data Management and Security Through Blockchain Solutions

Abstract: The internet of things, security, and blockchain all come together to deal with the influx of sensor data. The autonomous vehicle industry, for example, relies heavily on effective data management. IoT applications in supply chains, healthcare, and smart cities can all benefit from the immutability, decentralization, and transparency offered by blockchain technology. The research gives recommendations for optimizing blockchain integration and managing scalability concerns. The methodology is supported by real-world case studies, which highlight the potential of blockchain technology to improve efficiency and security in dynamic environments.


Unveiling the Depths of Explainable AI

Abstract: Explainable AI (XAI) has become increasingly important in the fast-evolving field of AI and ML. The complexity and obscurity of AI, especially in the context of deep learning, provide unique issues that are explored in this chapter. While deep learning has shown impressive performance, it has been criticised for its opaque reasoning. The fundamental motivation behind this research was to compile a comprehensive and cutting-edge survey of XAI methods applicable to a wide variety of fields. This review is achieved through a meticulous examination and analysis of the various methodologies and techniques employed in XAI, along with their ramifications within specific application contexts. In addition to highlighting the existing state of XAI, the authors recognize the imperative for continuous advancement by delving into a meticulous examination of the limitations inherent in current methods. Furthermore, they offer a succinct glimpse into the future trajectory of XAI research, emphasizing emerging avenues and promising directions poised for significant progress.


SISTEM INFORMASI PENERIMAAN PESERTA DIDIK BARU DI SMA NUR EL-FALAH KUBANG MENGGUNAKAN FRAMEWORK CODEIGNITER

Abstract: Nur-Elfalah High School currently registers new prospective students manually, namely by coming directly to the school and filling out the registration form. In this way, sometimes queues often occur during registration, and when finding out initial information about parents or prospective students. having to spend time coming in person, this is considered less effective. Nur-Elfalah's New Student Admission (PPDB) is designed to be web-based using an internet network, prospective new students can view information related to the school online, can register online, making it easier to register and view information. Apart from that, by implementing the PPDB system, all registration data will be stored neatly in a database so that if necessary it is easy to search and display in report format. In designing the Information System for accepting new students (PPDB), the waterfall development method is used, where the system is developed in stages and continues to be refined on an ongoing basis. With the new student admission information system (PPDB), it is hoped that it will make it easier for prospective new students to view information and register at the Nur-Elfalah school. It is hoped that prospective new students can register easily anywhere and at any time, as long as the prospective students Just able to access the internet. By implementing this system, it is hoped that it will be able to help Nur-Elfalah school improve its services which will indirectly have an impact on increasing the number of prospective students


Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory

Abstract: The COVID-19 pandemic has permeated the global sphere and influenced nearly all nations and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of sense of smell. The impact of COVID-19 on public health and the economy has made it a significant global concern. It has caused economic contraction in Indonesia, particularly in face-to-face interaction and mobility sectors, such as transportation, warehousing, construction, and food and beverages. Since the pandemic began, Twitter users have shared symptoms in their tweets. However, they couldn't confirm their concerns due to testing limitations, reporting delays, and pre-registration requirements in healthcare. The classification of text from Twitter data about COVID-19 topics has predominantly focused on sentiment analysis regarding the pandemic or vaccination. Research on identifying COVID-19 symptoms through social media messages is limited in the literature. The main objective of this study is to identify symptoms using word embedding techniques and the LSTM algorithm. Various techniques such as Word2Vec, GloVe, FastText, and a composite approach are used. LSTM is used for classification, improving upon the RNN technique. Evaluation criteria include accuracy, precision, and recall. The model with an input dimension of 147x100 achieves the highest accuracy at 89%. This study aims to find the best LSTM model for detecting COVID-19 symptoms in social media tweets. It evaluates LSTM models with different word embedding techniques and input dimensions, providing insights into the optimal text-based method for COVID-19 detection through social media texts.


Data Theft

Abstract: The Elgar Encyclopedia of Crime and Criminal Justice stands apart as the most comprehensive global reference title in its field. New entries will be added every month and PDF downloads will be available once the Encyclopedia is complete.


Fine-Grained Image Generation Network With Radar Range Profiles Using Cross-Modal Visual Supervision

Abstract: Electromagnetic imaging methods mainly utilize converted sampling, dimensional transformation, and coherent processing to obtain spatial images of targets, which often suffer from accuracy and efficiency problems. Deep neural network (DNN)-based high-resolution imaging methods have achieved impressive results in improving resolution and reducing computational costs. However, previous works exploit single modality information from electromagnetic data; thus, the performances are limited. In this article, we propose an electromagnetic image generation network (EMIG-Net), which translates electromagnetic data of multiview 1-D range profiles (1DRPs), directly into bird-view 2-D high-resolution images under cross-modal supervision. We construct an adversarial generative framework with visual images as supervision to significantly improve the imaging accuracy. Moreover, the network structure is carefully designed to optimize computational efficiency. Experiments on self-built synthetic data and experimental data in the anechoic chamber show that our network has the ability to generate high-resolution images, whose visual quality is superior to that of traditional imaging methods and DNN-based methods, while consuming less computational cost. Compared with the backprojection (BP) algorithm, the EMIG-Net gains a significant improvement in entropy (72%), peak signal-to-noise ratio (PSNR; 150%), and structural similarity (SSIM; 153%). Our work shows the broad prospects of deep learning in radar data representation and high-resolution imaging and provides a path for researching electromagnetic imaging based on learning theory.


Exploring Multiple Hypergraphs for Heterogeneous Graph Neural Networks

Abstract: Graph neural networks have demonstrated significant power in learning graph representations for homogeneous networks. However, real-world network data can often be denoted by heterogeneous networks with different types of nodes and edges, such as social, traffic, and molecular networks. Network heterogeneity presents significant challenges for network analysis and mining. Motif-based hypergraphs preserve high-order proximity and capture composite semantic interactions. Because not all nodes and edges in the original network always exist in a specific hypergraph, it is essential that multiple motif-based hypergraphs are considered to enhance the network representation. Therefore, we propose a novel framework for exploring Multiple Motif-based Hypergraphs for Heterogeneous Graph Neural Networks to learn network representations, named MoH-HGNN, which leverages hypergraph convolution and attention operations to capture complex connectivity patterns. Specifically, we conducted two levels of attention networks with hierarchical structures, namely hyperedge-level attention to learn the importance among different types of nodes and comprehensive semantic-level attention to capture the importance of different types of motif structures. We extensively experimented on four real-world datasets to verify the effectiveness of our proposed framework.


Variational Bayesian and generalized maximum-likelihood based adaptive robust nonlinear filtering framework

Abstract: An adaptive robust nonlinear filtering framework is proposed based on the variational Bayesian (VB) method and generalized maximum likelihood estimation (GM estimation) to simultaneously handle the outliers and uncertain measurement noise covariance matrix (MNCM) for state estimation. This framework utilizes the cubature criterion to solve nonlinear integration problems. By embedding GM estimation in the VB method, the uncertain MNCM is modified while robust estimation is performed. The modified MNCM also provides more accurate model parameters for GM estimation, achieving the unity of adaptability and robustness. The VBCEECKF algorithm was obtained by applying the strong robust centered error entropy criterion to the adaptive robust nonlinear filtering framework. The simulation results of target tracking under different noise conditions verify the superiority of the adaptive robust filter compared to existing methods.


Large-scale data classification based on the integrated fusion of fuzzy learning and graph neural network

Abstract: Deep learning and fuzzy models provide powerful and practical techniques for solving large-scale deep-learning tasks. The fusion technique on deep learning and fuzzy system are generally classified into ensemble and integrated modes and materializes in information fusion, model fusion, and feature fusion. In an ensemble-based fusion, the fuzzy model either acts as an activation function or is operated as a separate process aggregating/preprocessing the information. Some early attempts in the field have successfully fused deep neural networks and fuzzy modeling concepts in ensemble mode. However, no effective attempts were made to fuse fuzzy models as an integrated feature-level fusion learning with graph neural networks (GNNs). This is mainly due to two challenges related to this fusion: (1) the number of fuzzy rules grows exponentially with the number of features that causes computational inefficiency, and (2) the solution space created by this fusion of fuzzy rules becomes complex due to multiple regression relations between inputs and outputs. Additionally, a simple linear regression at the output space would not be sufficient to model deep learning tasks. Therefore, this paper addresses these challenges by proposing a feature-level fusion method to fuse deep learning and fuzzy modeling where the latter technique is for integrated feature learning, called fuzzy forest graph neural network (FuzzyGNN), which creates a fuzzy learning forest fusing the linear graph transformers for deep learning tasks. We conducted experiments on fourteen machine learning datasets to test and validate the efficiency of the proposed FuzzyGNN model. Compared to state-of-the-art methods, our algorithm achieves the best results on four out of five machine learning datasets. The source code will be available at https://github.com/lingping-fuzzy/ and https://github.com/P-N-Suganthan.


Coordinate Descent Optimized Trace Difference Model for Joint Clustering and Feature Extraction

Abstract: Joint clustering and dimensionality reduction methods are a promising solution to clustering due to its scalability to high-dimensional data. Some methods leverage trace ratio criterion and attain clusters by borrowing the K-means algorithm. However, trace ratio criterion has no close-formed solution for the discriminative projection matrix and the K-means algorithm has a limited capacity to handle the many-cluster problem. In this paper, Coordinate Descent Optimized Trace Difference model (CDOTD) is proposed for joint clustering and feature extraction. Formulating the objective function as a direct trace difference criterion containing a balance parameter, CDOTD harmonizes between-cluster scatter maximization and within-cluster scatter minimization by the balance parameter. Using the direct trace difference criterion, CDOTD can straightforward solve for the discriminative projection matrix and avoid obtaining a poor discriminative projection matrix in the iterative manner when a bad cluster start is given. CDOTD uses the coordinate descent method for clustering optimization, improving the ability to address the many-cluster problem. Extensive experiments show that CDOTD has achieved significant performance improvements compared to previous trace ratio criterion related joint clustering and feature extraction methods, and also outperformed other clustering methods in most cases.


Real-Time Local Greedy Search for Multiaxis Globally Time-Optimal Trajectory

Abstract: Time-optimal trajectory planning aims to minimize the traversal time of arbitrary geometric paths. The demand for real-time planning widely exists in robotics, numerical control manufacturing, and autonomous vehicle applications. Existing trajectory planning methods either compromise on time optimality to improve computational efficiency or suffer from at least linear time complexity, preventing the planning of long trajectories in real time. Motivated by these challenges, this article proposes a novel set invariant trajectory planning (SITP) method to address the problem of real-time time-optimal planning for continuous multiaxis trajectories under complete second-order kinodynamic constraints. First, a backup control strategy is synthesized to construct an implicit control invariant set (CIS). This set is designed so that a feasible control input always exists to keep the system within the defined kinodynamic bounds. Then, utilizing the principles of bang–bang control theory, the optimal control is sought within the CIS. A local greedy linear programming method is proposed to calculate the time-optimal trajectory at each control cycle. The proposed method is computationally efficient for even 1-kHz real-time applications and the planned results maintain strict global time optimality, which makes it promising in real-time planning scenarios of various automatic applications.


MSHGN: Multi-scenario adaptive hierarchical spatial graph convolution network for GPU utilization prediction in heterogeneous GPU clusters

Abstract: Accurately predicting GPU utilization is crucial for effectively managing heterogeneous GPU clusters, yet existing prediction methods are tailored to homogeneous clusters or ignore the unique characteristics of heterogeneous ones. To address this problem, we propose the Multi-Scenarios Adaptive Hierarchical Spatial Graph Convolution Network (MSHGN) model. This model leverages the hierarchical relationships among users, tasks, and machines to construct multiple scenarios' undirected graphs and uses Graph Convolution Neural (GCN) to capture the spatial dependency relationships between the computational resources consumed during execution. The MSHGN model accurately predicts the future GPU utilization rates of each machine in a heterogeneous GPU cluster, achieving an RMSE of only 0.0101, an MAE of 0.0072, and an MAPE of 0.0641. We evaluate the MSHGN model on a real-world Alibaba dataset collected from a heterogeneous GPU cluster and find that it achieves superior accuracy and robustness in predicting resource utilization, outperforming other baseline models.