Computational Ability Research Articles

PENGARUH PEMBELAJARAN BERDIFERENSIASI TERHADAP KEMAMPUAN COMPUTATIONAL THINKING SISWA SEKOLAH DASAR

Education Transformation in Indonesia is now demanded to be faster and able to produce students who are not only proficient in reading and writing but also students who have skills in matters directly related to problem solving in real life. The ability to think critically, apply creativity, work together, solve problems and be communicative are some of the abilities that are the main capital in 21th Century Learning besides other abilities such as reading, writing science and mathematics. The purpose of this study is in accordance with the existing problem formulation, namely to determine the effect of differentiated learning on the computational thinking abilities of elementary school students. In this study the authors used a quantitative approach with quasi-experimental research methods. The research design used was Time Series Design Intervention Analysis. The selection of the method and design of this study took into account the implementation of repeated treatments. The results of testing the hypothesis using the paired sample t-test from the pre-test and post-test data in series 1, 2, and 3 show the results t-count> t-table. In the series one t-count obtained is 7.319. In series 2 the t-count got a value of 9.729 and in series 3 the t-count was 11.660. The results obtained were that the Computational Thinking ability of elementary school students had increased after being treated with Differentiated Learning.

Chatbots, Generative AI, and Scholarly Manuscripts: WAME Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publications Revised May 31, 2023

Introduction
 This statement revises our earlier “WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications” (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These Recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work, and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop.
 A chatbot is a tool “[d]riven by [artificial intelligence], automated rules, natural-language processing (NLP), and machine learning (ML)…[to] process data to deliver responses to requests of all kinds.”1 Artificial intelligence (AI) is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”2
 “Generative modeling is an artificial intelligence technique that generates synthetic artifacts by analyzing training examples; learning their patterns and distribution; and then creating realistic facsimiles. Generative AI (GAI) uses generative modeling and advances in deep learning (DL) to produce diverse content at scale by utilizing existing media such as text, graphics, audio, and video.”3, 4
 Chatbots are activated by a plain-language instruction, or “prompt,” provided by the user. They generate responses using statistical and probability-based language models.5 This output has some characteristic properties. It is usually linguistically accurate and fluent but, to date, it is often compromised in various ways. For example, chatbot output currently carries the risk of including biases, distortions, irrelevancies, misrepresentations, and plagiarism many of which are caused by the algorithms governing its generation and heavily dependent on the contents of the materials used in its training. Consequently, there are concerns about the effects of chatbots on knowledge creation and dissemination – including their potential to spread and amplify mis- and disinformation6 – and their broader impact on jobs and the economy, as well as the health of individuals and populations. New legal issues have also arisen in connection with chatbots and generative AI.7
 Chatbots retain the information supplied to them, including content and prompts, and may use this information in future responses. Therefore, scholarly content that is generated or edited using AI would be retained and as a result, could potentially appear in future responses, further increasing the risk of inadvertent plagiarism on the part of the user and any future users of the technology. Anyone who needs to maintain confidentiality of a document, including authors, editors, and reviewers, should be aware of this issue before considering using chatbots to edit or generate work.9
 Chatbots and their applications illustrate the powerful possibilities of generative AI, as well as the risks. These Recommendations seek to suggest a workable approach to valid concerns about the use of chatbots in scholarly publishing.

A Dynamic Image Encryption Scheme Based on Quantum Walk and Chaos-Induced DNA

The development of quantum information technology and increasing attention of people to the secure transmission of image information in the Internet have put forward higher requirements for traditional image encryption algorithms that not only take advantage of the exponential acceleration ability of quantum computing compared with classical computing but also reduce the risk of encryption algorithms being cracked. Therefore, in order to seek the combination of the advantages of quantum computing and classical image encryption algorithms, this paper proposes a new dynamic encryption image scheme of quantum walk and chaos-induced DNA. Firstly, the RGB three-channel pixels of the color image are extracted and combined into a one-dimensional array, and a random sequence is generated by quantum walk to reorder it to obtain a preliminary scrambled image; secondly, the color image is processed by the SHA-256 algorithm and divided into the generated message digest as the initial condition of the chaotic model. The random sequence was generated by the high-dimensional chaotic model which encodes each pixel independently and disorderly as DNA bases. The difference of the chaotic sequence ensures the dynamic selection of random DNA encoding and decoding rules during encryption. At the same time, the number of times of DNA encryption of the encoded pixel value is also controlled by the dynamic induction of the chaotic sequence, and ultimately, the DNA coding sequence is replaced with the decimal pixel value to obtain the encrypted image. The simulation results show that the information entropy of the encrypted image is above 7.99, and the correlation of each channel is close to 0, which can effectively resist brute force attacks, plaintext attacks, statistical analysis attacks, noise attacks, etc. In addition, in this paper, extracting the watermark embedded in the encrypted image to judge whether image information is tampered or forged further improves the security of the image information.

Delay-Minimization Offloading Scheme in Multi-Server MEC Networks

In this letter, we consider a multi-server mobile edge computing (MEC) network where a user with limited computation ability aims to complete an intensive computation task in a short time under the help of multiple edge servers. We study the delay-minimization offloading schemes with and without user’s energy constraint, which involve the joint optimization of task allocation ratio for local computation, the wireless offloading manner (i.e., non-orthogonal multiple access (NOMA) offloading or single-server offloading mode), and the server pair selection. Specifically, the joint optimal solutions are derived in semi-closed forms and the critical conditions of switching offloading manners are derived in closed form. Numerical results corroborate the superiority of our proposed offloading scheme in terms of overall latency under various settings.

The potential role of machine learning in modelling advanced chronic liver disease.

The use of artificial intelligence is rapidly increasing in medicine to support clinical decision making mostly through diagnostic and prediction models. Such models derive from huge databases (big data) including a large variety of health-related individual patient data (input) and the corresponding diagnosis and/or outcome (labels). Various types of algorithms (e.g. neural networks) based on powerful computational ability (machine), allow to detect the relationship between input and labels (learning). More complex algorithms, like recurrent neural network can learn from previous as well as actual input (deep learning) and are used for more complex tasks like imaging analysis and personalized (bespoke) medicine. The prompt availability of big data makes that artificial intelligence can provide rapid answers to questions that would require years of traditional clinical research. It may therefore be a key tool to overcome several major gaps in the model of advanced chronic liver disease, mostly transition from mild to clinically significant portal hypertension, the impact of acute decompensation and the role of further decompensation and treatment efficiency. However, several limitations of artificial intelligence should be overcome before its application in clinical practice. Assessment of the risk of bias, understandability of the black boxes developing the models and models' validation are the most important areas deserving clarification for artificial intelligence to be widely accepted from physicians and patients.

Differentiated Effects of Media Exposure During Preschool and Early School Ages on Later Development, Based on Media Use

Objectives: This study aimed to differentiate between the effects of media exposure in preschool and early school ages on later development, based on media use intentions. The study specifically explored the impact of media use for education and entertainment at ages 5-6 and 8 on subsequent developmental outcomes at the age of 10, in areas of school- and media- related outcomes.Methods: The study included 1,766 children who participated in the Panel Study of Korean Children. Path analyses were conducted to examine the effect of early media use on later development. All independent variables regarding children’s media exposure were simultaneously included in the model. The developmental outcomes were then analyzed separately in the model.Results: Media exposure in preschool for entertainment negatively influenced children’s academic achievement and computer ability, increasing their addictive media use and behavioral problems. However, early school-age media exposure for education positively affected children’s academic achievement and computer ability. The adverse effects of media exposure for entertainment during preschool ages were apparent at age 10. Conversely, the positive impact of media exposure for education was only observed at the age of 10, when the exposure occurred at the age of 8.Conclusion: The influence of early media exposure varied significantly based on the media content and the age of exposure. Notably, media exposure for educational purposes after school entrance predicted the most desirable developmental outcomes.

A data-driven model for predicting the mixed-mode stress intensity factors of a crack in composites

A data-driven model is trained to predict mixed-mode stress intensity factors (SIFs) of composites through an artificial neural network (ANN) method. The model is based on a database generated by combining the interaction integral and the extended finite element method (XFEM). To reduce the input dimensionality and improve predictive performance, feature engineering is performed on the input data, and principal component analysis is conducted. Hyperparameters of the model are adjusted by using K-fold cross-validation and Bayesian optimization algorithm (BOA) to enhance the adaptability and generalization of the model. To overcome the data scarcity issue, an active learning knowledge extraction framework is constructed, which allows for accurate knowledge extraction even with limited data. By utilizing data-driven models to solve mixed-mode SIFs, the computational cost and complexity are greatly reduced compared to numerical simulations, while computational stability and ability to deal with high-dimensional nonlinear problems are significantly improved.

Soundproofing Autonomous Vehicle Computers for Passenger Comfort

The computing superstructure necessary for self-driving cars to function presents a unique acoustics problem - the fans produce significant amounts of noise within the cabin that is uncomfortable for passengers. To ensure consumer comfort and promote more widespread adoption of the technology, soundproofing is required. Any soundproofing solution must not compromise the ability of the self-driving computer to function, meaning that air flow must be preserved. To this end, the UTA Senior Design team creates testing procedures to identify the sound profile of May Mobility's Toyota Sienna. After identifying the frequency range and intensity of the fan noise within the cabin, the team can narrow down potential soundproofing solutions. Given the noise frequency range and air flow requirements, three materials that show promise are identified. The team's testing of varying configurations of cork, open-cell charcoal foam, and closed-cell neoprene foam to determine the most effective solution resulted in the identification of charcoal foam as the optimal material. Soundproofing structures were strategically placed around the computing superstructure and achieved a noticeable reduction of noise within the cabin.

A REVIEW PAPER ON INDUSTRIES REVOLUTION 4.0 POWERING THE FUTURE OF HEALTH CARE SECTOR

In the age of globalisation and digitization, the ability of computers to perform tasks normally associated with intelligent beings is known as artificial intelligence (AI), which can play a critical role in assisting in dealing with every type of business and function of human resource management in a proficient and timely manner. The unparalleled data processing capabilities of AI and ML in the pharmaceutical industry serve as a harness in the enhancement and improvement of efficiency in diagnostic assistance and clinical trials, pharmaceutical marketing, foster innovation, quality control, reduce materials waste, improve production reuse, perform predictive maintenance, and improve decision-making skills. The Fourth Industrial Revolution (industries 4.0) in Health Sector implement artificial technologies to performed functions by machines and small robotic systems programmed to do human-like tasks result great potential to transform drug discovery by accelerating the research and development timeline. This technology is being adopted by many pharmaceutical companies to help with the drug discovery and development process, which will enable them to work on a variety of research-oriented projects to improve health facilities and easily cure the diseases in the future. With the development of artificial intelligence and machine learning, the pharmaceutical industry is undergoing significant advancement like decrease the time interval gap from development of a drug product to its commercialization. We will discuss the various roles of AI and ML in health care sector and challenges in this article. As a result, this clearly demonstrates that artificial intelligence has a positive impact on the pharmaceutical industries.

The van der Pol physical reservoir computer

The van der Pol oscillator has historical and practical significance to spiking neural networks. It was proposed as one of the first models for heart oscillations, and it has been used as the building block for spiking neural networks. Furthermore, the van der Pol oscillator is also readily implemented as an electronic circuit. For these reasons, we chose to implement the van der Pol oscillator as a physical reservoir computer (PRC) to highlight its computational ability, even when it is not in an array. The van der Pol PRC is explored using various logical tasks with numerical simulations, and a field-programmable analog array circuit for the van der Pol system is constructed to verify its use as a reservoir computer. As the van der Pol oscillator can be easily constructed with commercial-off-the-shelf circuit components, this PRC could be a viable option for computing on edge devices. We believe this is the first time that the van der Pol oscillator has been demonstrated as a PRC.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN RUMPUT TERBAIK UNTUK TAMAN MENGGUNAKAN METODE AHP BERBASIS ANDROID

Grass is one of the most commonlyused types of ground cover plants, especially for gardens. Because grass is the besttype of plant and has several funcitions.Besides being able to absorb water as well to reduce dustfrom the ground which is blown by the wind, besides that covering the gronud which is blown by the wind, besides that coverinbg the ground with grass will also make the garden look more exotic. Decision support systems(DSS)are part of a computer based information system icluding knowledge –based systemor knowledge management that are used to support decision making withun an organization or company.it can also be said as a computerb data into information to make decision on specific semi –structured problems. The abilility of the selection of the best grass for gardering. Decision support system assistance can be felt more quickly and accurately if the decision support system is in the form of a computer. The ability of computer to process data to produce information is beyond doubt. The AHP method haas the advantage that there are paiwise comparisons for each criterion, so that each criterion . Based on these advantages , the AHP method is suintable and appropriate to use. Computer as a tool can process data based on software that is entered into the computer, to produce alternative decisionsi in choosing the best grass for gardening.Keywords : SPK, Selection of Grass, AHP Method. Park. Androids

A Self-Sensing Shape Memory Alloy Actuator Physical Reservoir Computer

A self-sensing shape memory alloy actuator is harnessed as a computational resource by utilizing it as a physical reservoir computer. Physical reservoir computing is a machine learning technique that takes advantage of the dynamics of a physical system for computation. Compared to recurrent neural networks, this architecture can be both fast and efficient with a cheaper training procedure. A shape memory alloy actuator is designed, fabricated, and tested for processing information. Voltage variation along the shape memory alloy wire is used as the reservoir's nodes. The physical reservoir is then used to predict the future trajectory of the actuator's end effector under various driving signals. This self-prediction method is also reconfigurable, as demonstrated by training the reservoir for one waveform but testing it for a different one. A nonlinear autoregressive moving average prediction task was also used to highlight the physical reservoir computer's abilities. Following this methodology, the soft actuator can be used for actuation and computation at the same time without altering its design.

A fast method for ammonia emission reduction scenario studies: Combining EMEP4NL with the SHERPA tool

We present a fast method for calculating the effects of ammonia emission reduction scenarios on reduced nitrogen components over the Netherlands. The method combines the EMEP4NL model, which is a full atmospheric chemical transport model (ACTM), with the SHERPA tool, which is an approximate representation of a full ACTM. The SHERPA tool uses a geographically weighted regression method to derive source-receptor relations, based on a limited number of “training” runs of the ACTM (here, EMEP4NL). The training runs consist of a base run of the year of interest plus uniform emission reduction runs for the primary pollutants of interest. Once the source-receptor relations are derived, scenario studies can be performed at a much lower computational cost with SHERPA (i.e. order 104–105 more efficient) as compared to a full run with the EMEP4NL model. Here, we study five specific ammonia emission reduction scenarios, in which a 30% emission reduction is enforced for specific regions in The Netherlands, Germany and Belgium. Our analysis shows that the EMEP4NL model and the SHERPA tool yield similar results in terms of the reduction of the yearly average concentration and deposition of reduced nitrogen components. Because of its computational efficiency and ability to adequately reproduce the results of the EMEP4NL model, the SHERPA tool is relevant for policy making. It can provide predictions of the effects of many different local emission reduction measures in a computationally efficient manner.

ANALISIS KEMAMPUAN COMPUTATIONAL THINKING SISWA PADA MATERI ALJABAR DITINJAU DARI PEMECAHAN MASALAH MATEMATIS

Many students still have difficulty solving problems related to mathematics, one of which is algebraic material. Computational thinking is an ability needed to assist students in solving problems faced by students through abstraction, pattern recognition, decomposition, and algorithm skills. This study aims to describe the computational thinking abilities of IX grade junior high school students in solving mathematical problem solving. This type of research is a qualitative descriptive research. The time for conducting research is in the odd semester of the 2022/2023 academic year. The subjects taken were IX A grade students at SMP Negeri 2 Karangrayung. Using the problem- solving ability test, 2 students with high-level problem-solving abilities, 2 students with medium-level problem-solving abilities, and 2 students with low-level problem-solving abilities were selected using the problem-solving ability test. The data collection techniques used were problem solving ability tests, computational thinking ability tests, and interviews. Data analysis techniques were carried out in 3 stages, namely data reduction, data presentation, and drawing conclusion. Data validity checking technique uses source triangulation. The results of this study show that students with high-level problem-solving skills are able to meet all indicators of computational thinking, students with medium- level problem-solving abilities are able to meet fulfill 3 of 4 computational thinking indicators, while students with low-level problem-solving skills are able to fulfill 2 of 4 computational thinking indicators.

Privacy-Preserving Indoor Trajectory Matching with IoT Devices.

With the rapid development of the Internet of Things (IoT) technology, Wi-Fi signals have been widely used for trajectory signal acquisition. Indoor trajectory matching aims to achieve the monitoring of the encounters between people and trajectory analysis in indoor environments. Due to constraints ofn the computation abilities IoT devices, the computation of indoor trajectory matching requires the assistance of a cloud platform, which brings up privacy concerns. Therefore, this paper proposes a trajectory-matching calculation method that supports ciphertext operations. Hash algorithms and homomorphic encryption are selected to ensure the security of different private data, and the actual trajectory similarity is determined based on correlation coefficients. However, due to obstacles and other interferences in indoor environments, the original data collected may be missing in certain stages. Therefore, this paper also complements the missing values on ciphertexts through mean, linear regression, and KNN algorithms. These algorithms can predict the missing parts of the ciphertext dataset, and the accuracy of the complemented dataset can reach over 97%. This paper provides original and complemented datasets for matching calculations, and demonstrates their high feasibility and effectiveness in practical applications from the perspective of calculation time and accuracy loss.

A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design

PurposeExtreme support vector regression (ESVR) has been widely used in the design, analysis and optimization of engineering systems of its fast training speed and good computational ability. However, the ESVR model is only able to utilize one-fidelity information of engineering system. To solve this issue, this paper extends extreme support vector regression (ESVR) to a multi-fidelity surrogate (MFS) model which can make use of a few expensive but higher-fidelity (HF) samples and a lot of inaccurate but cheap low-fidelity (LF) samples, named ESVR-MFS.Design/methodology/approachIn the ESVR-MFS model, a kernel matrix is designed to evaluate the relationship between the HF and LF samples. The root mean square error of HF samples is used as the training error metric, and the optimal hyper-parameters of the kernel matrix are obtained through a heuristic algorithm.FindingsA number of numerical problems and three engineering problems are used to compare the ESVR-MFS model with the single-fidelity ESVR model and two benchmark MFS models. The results show that the ESVR-MFS model exhibits competitive performance in both numerical cases and practical cases tested in this work.Practical implicationsThe proposed approach exhibits great capability for practical multi-fidelity engineering design problems.Originality/valueA MFS model is proposed based on ESVR, which can make full use of the advantages of both HF data and LF data to achieve optimal results at same or lower cost.

General multi-agent reinforcement learning integrating adaptive manoeuvre strategy for real-time multi-aircraft conflict resolution

Reinforcement learning (RL) techniques are under investigation for resolving conflict in air traffic management (ATM), exploiting their computational capabilities and ability to cope with flight uncertainty. However, the limitations of generalisation make it difficult for existing RL-based conflict resolution (CR) methods to be effective in practice. This paper proposes a general multi-agent reinforcement learning (MARL) method that integrates an adaptive manoeuvre strategy to enhance both the solution’s efficiency and the model’s generalisation in multi-aircraft conflict resolution (MACR). A partial observation approach based on the imminent threat detection sectors is used to gather critical environmental information, enabling the model to be applied in arbitrary scenarios. Agents are trained to provide the correct flight intention (such as increasing speed and yawing to the left), while an adaptive manoeuvre strategy generates the specific manoeuvre (speed and heading parameters) based on the flight intention. To address flight uncertainty and performance challenges caused by the intrinsic non-stationarity in MARL, a warning area for each aircraft is introduced. We employ a state-of-the-art Deep Q-learning Network (DQN) method, Rainbow DQN, to improve the efficiency of the RL algorithm. The multi-agent system is trained and deployed in a distributed manner to adapt to real-world scenarios. A sensitivity analysis of uncertainty levels and warning area sizes is conducted to explore their impact on the proposed method. Simulation experiments confirm the effectiveness of the training and generalisation of the proposed method.

Web-Based System for Remote Mammogram Processing

This paper presents a web-based system for remote processing of mammographic images (mammograms) using Matlab Web Service. The Matlab Web Service is a toolbox for accessing Matlab applications from the internet. We created an image processing system that combines the computational and graphical abilities of Matlab with remote access through the internet. Our system for mammogram processing is a Matlab application stored on a local workstation. By using a web browser users can upload their data (mammogram and parameters necessary for image processing) and view the results obtained after processing (notification about the breast cancer existence). The presented computer system for mammogram processing includes three modules: image preprocessing, feature extraction, and classification. The preprocessing phase consists of the image de-noising, region of interest extraction and image contrast enhancement. The second phase is based on feature extraction techniques for detecting abnormalities in digital mammograms. A total of 20 texture features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. To investigate the ability of the feature set in differentiating abnormal from normal tissue, we employed three different classifiers: Support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. The efficiency of classification is provided using a cross-validation technique. The main characteristics of the proposed web-based diagnostic system are that users can use application written in Matlab without installing Matlab software, and they do not need to have any knowledge about programming in Matlab to run application.

A Survey on Adversarial Perturbations and Attacks on CAPTCHAs

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been a topic of interest for several years. The ability of computers to recognize CAPTCHA has significantly increased due to the development of deep learning techniques. To prevent this ability from being utilised, adversarial machine learning has recently been proposed by perturbing CAPTCHA images. As a result of the introduction of various removal methods, this perturbation mechanism can be removed. This paper, thus, presents the first comprehensive survey on adversarial perturbations and attacks on CAPTCHAs. In particular, the art of utilizing deep learning techniques with the aim of breaking CAPTCHAs are reviewed, and the effectiveness of adversarial CAPTCHAs is discussed. Drawing on the reviewed literature, several observations are provided as part of a broader outlook of this research direction. To emphasise adversarial CAPTCHAs as a potential solution for current attacks, a set of perturbation techniques have been suggested for application in adversarial CAPTCHAs.

Photonic reservoir computing using a self-injection locked semiconductor laser under narrowband optical feedback.

Photonic time-delay reservoir computing (TDRC) using a self-injection locked semiconductor laser under optical feedback from a narrowband apodized fiber Bragg grating (AFBG) is proposed and numerically demonstrated. The narrowband AFBG suppresses the laser's relaxation oscillation and provides self-injection locking in both the weak and strong feedback regimes. By contrast, conventional optical feedback provides locking only in the weak feedback regime. The TDRC based on self-injection locking is first evaluated by the computational ability and memory capacity, then benchmarked by the time series prediction and channel equalization. Good computing performances can be achieved using both the weak and strong feedback regimes. Interestingly, the strong feedback regime broadens the usable feedback strength range and improves robustness to feedback phase variations in the benchmark tests.