Reviewerly/exHarmony huggingface dataset:
Prescribed time convergence and robust zeroing neural network for solving time-varying linear matrix equation

Zeroing neural network offers a new solution method to solve the time-varying linear matrix equation. As an important component, the activation function directly affects the performance of zeroing neural network in solving time-varying linear matrix equation. Focusing on the unification of prescribed time convergence and strong robustness without changing the basic structure of zeroing neural network, a novel activation function is proposed for the first time in this paper. Compared with commonly used activation functions in previous work, the novel activation function has superior performance for zeroing neural network to solve the time-varying linear matrix equation. The first item is global asymptotic convergence, capable of converging from random initial states to the theoretical solution. The second item is the prescribed time convergence, i.e. the upper bound of convergence time is only related to the parameters of the novel activation function and zeroing neural network, which facilitates the prediction of the convergence process. The third item is strong robustness, which ensures that the solution converges in various noisy environments. Theoretical analysis and comparative simulation experiments verify that zeroing neural network with the novel activation function has these performances for both low- or high-dimensional time-varying linear matrix equation.
The evaluation of machine learning algorithms for detecting islamophobic tweets

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Alice Takoh, Daphne T. C. Lai, Rosyzie Apong; The evaluation of machine learning algorithms for detecting islamophobic tweets. AIP Conference Proceedings 10 January 2023; 2643 (1): 040017. https://doi.org/10.1063/5.0110891 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Designing and Evaluating ML Systems

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Dimensionality Reduction

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Lithology recognition of core image based on ResNet

In the era of artificial intelligence information, image recognition technology as a core technology has penetrated various industries and has brought about changes to human production and lifestyles. During the geotechnical engineering investigation, core photographing and description need to be completed manually, which is inefficient. This paper aims to realize image-based lithology intelligent auxiliary identification and automatic cataloging of the survey field, with the application of an improved ResNet model. 8,000 typical core samples from Guangzhou were selected and divided into 8 categories. The predicted accuracy achieves 94% after 50 epochs of training, which verify the advanced model. In addition, the effectiveness of our proposed framework was proved in real-world application scenarios as well.
MiDA: Membership inference attacks against domain adaptation

Domain adaption has become an effective solution to train neural networks with insufficient training data. In this paper, we investigate the vulnerability of domain adaption that potentially breaches sensitive information about the training dataset. We propose a new membership inference attack against domain adaption models, to infer the membership information of samples from the target domain. By leveraging the background knowledge about an additional source-domain in domain adaptation tasks, our attack can exploit the similar distributions between the target and source domain data to determine if a specific data sample belongs in the training set with high efficiency and accuracy. In particular, the proposed attack can be deployed in a practical scenario where the attacker cannot obtain any details of the model. We conduct extensive evaluations for object and digit recognition tasks. Experimental results show that our method can achieve the attack against domain adaptation models with a high success rate.
How to understand modern digital technologies so as not to fall into the traps of cybercriminals and misinformation online

The digital world with all its gadgets, sites and useful services has become too comprehensive and comprehensive to fundamentally ignore it. The digital world offers significant opportunities for both individual users and institutions. Reforming the health care sector in Ukraine involves a significant emphasis on not using an increasing number of digital tools in the daily work of both medical institutions and doctors. Every day patients try to communicate with doctors through messengers, sending the latter their questions, test results, and other medical data. And although such online consultations are mostly not institutionalized, patients are primarily guided by the logic of convenience, accessibility and time saving, pressing the «send» button in the messenger to send a message to the doctor. At the same time, the use of digital technologies in health care is gaining more and more expressive forms due to the introduction of telemedicine, electronic prescriptions, or the availability of publicly available dashboards from the National Health Service. The dashboards contain arrays of data on the provision of medical services at the level of hospitals and the corresponding payments under contracts with the National Health Service. Access to all these benefits of the digital space requires at least a basic understanding of digital technologies to use them effectively. The article explains principals of digital literacy with emphasize on health care providers and explains how to understand modern digital technologies and not to fall into the traps of cybercriminals and misinformation online.
Eardrum symphony

How can a vibrating eardrum convey the rich symphony that we hear? Why don't the various frequencies interfere with each other? Derek Johnson, Moreton-in-Marsh, Gloucestershire, UK
Design and Experimental Verification of a Mobile Robot for Picking Up Objects on the Ground

This study aims to automate the work of picking up objects on the ground for agricultural industries. We design an arm-type mobile robot to effectively pick up objects while considering its compactness, low cost, and usability. Furthermore, we show appropriate force conditions for the end-effector against the ground experimentally. The effectiveness of the proposed system was confirmed by both indoor and outdoor experiment.
Mobile Applications in Modern Social and Cultural Educational Practices

The paper is devoted to the study of the development of educational mobile applications. By comparing the specifics of their distribution over the past five years, the authors found out the main trends in their distribution. Firstly, the formation of complex, multi-thematic and multi-format platforms for providing access to training courses has been defined as such. Secondly, the tendency to formalize self-education (the possibility of obtaining official certificates confirming the successful completion of the course). Thirdly, the growing role of various types of mobile applications as tools in the classical learning process. The principle of collaboration of different institutions in the creation of complex and high-tech mobile applications has also been noted as a trend. The connection between changes in the audience of educational services and the specifics of mobile applications as their tools has been substantiated. Emphasis has been placed on such characteristics of mobile applications as interactivity, virtuality, multimodality, personalization, and gamification.
Restoring the values of geo-fields using a combination of kernel smoothing methods and artificial neural networks models

The purpose of the study, the results of which are described in the article, was to improve solving the matter of the geo-fields’ values restoring based on processing high-intensity spatial-temporal data streams received from a highly mobile geo-sensors network. Previously, the authors proposed an original approach to solving this task, which means applying the kernel smoothing methods, the nuclear function for which is determined automatically, using discrete stochastic optimization, in particular, the annealing simulation method. The idea of a new approach proposed by the authors is as follows
Indexical Images: Trace, Resemblance, and Code

The chapter departs from discussing the applicability of Peirce's semiotics (with the distinction of icon, index, and symbol) to images, which forms the background for the concept of the ‘indexical image’. It approaches the position of the latter within image-theory through the question whether the images designated as indexical images refer to traces and/ or resemblance. Taking this question as the focus, the chapter develops a distinction between ‘resemblance through contact’ (left behind traces that produce resemblance), ‘shadow resemblance’ (ephemeral traces captured by means of drawing, photography, or film), ‘non-resemblant traces’ that have to be deciphered, and ‘diagrams and coded indicators’ (not referring to traces but rather to a code or calculation).
Self-supervised Obstacle Detection During Autonomous UAS Taxi Operations

This research explores the application of self-supervised learning techniques for obstacle detection and collision avoidance during UAS auto-taxi. Autoencoders were used to detect obstacles as anomalies by comparison of reconstruction errors. RGB cameras and millimetre wave radars covering conflict free zones (CFZs) around the own-ship were chosen to provide inputs to autoencoders. Results demonstrated that autoencoders were able to detect obstacles as anomalies within the CFZs but with certain limitations at lay the foundations of further work and investigation within the research area.
Augmented reality and gamification in pharmacy education: A call for implementation in African countries and other low resource settings

Background: Augmented reality (AR) and gamification, which involve the use of mobile devices, tablets, and laptops to enhance learning experiences, are relatively new in tertiary education. This article calls for the implementation of AR and gamification in pharmacy education in African countries and other low-resource settings. Method: A search strategy was conducted using the following keywords: ‘gamification’, ‘augmented reality’, ‘game-based learning’, ‘pharmacy education’, ‘African Countries’ and ‘low resource settings’ on PubMed, PubMed Central and Google Scholar databases. Results: Pharmacy students in African countries and other low resources settings face challenges which include: a limited number of lecturers, underdeveloped infrastructure, paucity of knowledge, and restricted educational resources coupled with a lack of pedagogy related to teaching courses. Incorporation of AR and gamification systems into their learning process will enhance student motivation and understanding. Conclusion: Pharmacy schools and concerned stakeholders in African countries and other low-resource settings should consider the rapidly evolving technology by developing appropriate and productive AR and game-based learning concepts that would enhance learning experiences, given its numerous benefits.
Memorable Messages for More Productive Family Conversations with LGBTQ Youth

This chapter will discuss how to apply the Theory of Memorable Messages (ToMM) to planning productive family conversations surrounding adolescent and young adult sexual health. We consider the utility of memorable message research and theorizing for designing at-home sexuality education. We will discuss research on parent-child communication about sexual and reproductive health and memorable messages as a way to access agency and empowerment for young adults, focusing primarily on our research about memorable messages and young LGBTQ women.
Zero-Weighted Constraints in Noisy Harmonic Grammar

In Noisy Harmonic Grammar (Boersma and Pater 2016), a stochastic version of Optimality Theory (Prince and Smolensky 1993), the constraints are weighted and the outcomes are probability distributions over GEN, computed by adding a noise factor to the constraint weights at each evaluation. Intuitively, one might expect that constraints bearing zero weights would have zero empirical effect, but this turns out not to be so. First, we show that a constraint with zero weight in NHG continues to affect the probability of candidates that violate it; the effect is either upward or downward, depending on otherfactors. Second, under certain arrangements intended to maintain the principle of harmonic bounding, zero-weighted constraints can force zero probability for candidates that violate them. We suggest what sort of cases linguists should seek in order to test the truth of these predictions, and also point out alternatives we might appeal to if these predictions emerge as false.
Event-Triggered Distributed Consensus Filtering Based on Interval Analysis for Heterogeneous Multienergy Networks With Coupling Output Constraints

This article addresses the distributed consensus filtering problem of heterogeneous multienergy networks, where subnetworks exhibit different dynamics and are coupled by output constraints. The global consensus is reached by propagating constraints between subnetworks in the form of interval vectors. Guaranteed intervals satisfying given constraints are returned, which are then used to correct the local states through the probability density function truncation. Given the dependence and wrapping effects during constraint propagation, a hybrid Newton forward–backward propagation (FBP) contractor is proposed for avoiding overly pessimistic estimates. It extends the FBP contractor by replacing the natural inclusion function with centered forms. In addition, an event-triggered mechanism is designed to enhance the efficiency of constraint propagation, which allows the threshold values to be adaptively adjusted according to interval widths. Finally, the proposal is assessed in a steel industrial gas–electricity network with 25 nodes, and the results verify the performance in estimation accuracy, especially for coupling nodes.
Noise analysis in outdoor dynamic speckle measurement

Dynamic speckle method is an effective tool for estimation of speed of processes. Speed distribution is encoded in a map built by statistical pointwise processing of time-correlated speckle patterns. For industrial inspection,the outdoor noisy measurement is required. The paper analyzes efficiency of the dynamic speckle method in the presence of environmental noise as phase fluctuations due to lack of vibration isolation and shot noise due to ambient light. Usage of normalized estimates for the case of non-uniform laser illumination is studied. Feasibility of the outdoor measurement has been proven by numerical simulationsof noisy image capture and real experiments with test objects. Good agreement has been demonstrated both in simulation and experiment between the ground truth map and the maps extracted from noisy data.
On the limiting rate of generation of random sequences and the principle of particle identity

Abstract Estimates for the limiting rate of generation of random sequences in random number generators dictated by fundamental prohibitions of Nature are obtained. The relationship between the limiting rate of the generation of random sequences and the principle of particle identity is established.
Asynchronous Time-Delay Reservoir Computing Based on Laser Dynamics

We demonstrate a time-delay reservoir computing network based on laser dynamics, where the delay time is asynchronous with the clock period of the input signal. The performance of this asynchronous network is proved to be more competitive than those of common synchronous networks.
Probabilistic Target Encoding of Neural Networks with Softmax Output Nodes

When training neural networks with softmax outputs, one-hot target encoding is commonly used due to its simplicity. This strategy does not incorporate the probability that an input sample belongs to a certain class but adopts “one” or “zero” as the desired output values of neural networks. Instead of the most prevalent one-hot encoding, this paper proposes a probabilistic target encoding to prevent the overfitting of neural networks to training samples. This effect brings about the accuracy improvement of test samples. We demonstrated the effectiveness of the proposed target encoding through simulations of multi-layer perceptrons and convolutional neural networks for various classification problems such as handwritten-digit recognition, isolated-word recognition, image classification, and object recognition tasks. The simulation results show that the proposed probabilistic target encoding is superior to one-hot encoding as it prevents the overfitting of neural networks to training samples.
Recognizing and Recovering Ball Motion Based on Low-Frame-Rate Monocular Camera

Reconstructing sphere motion is an essential part of indoor screen-based ball sports. Current sphere recognition techniques require expensive high-precision equipment and complex field deployment, which limits the application of these techniques. This paper proposes a novel method for recognizing and recovering sphere motion based on a low-frame-rate monocular camera. The method captures ball motion streaks in input images, reconstructs trajectories in space, and then estimates ball speed. We evaluated the effectiveness of the streak detection method and obtained an F1-score of 0.97. We also compared the performance of the proposed trajectory reconstruction method with existing methods, and the proposed method outperformed the compared techniques.
Reduce the Privacy and Security Concerns of Current Social Media Platforms Using Blockchain Technology

The escalation of social media has become quite leading in our lives. This enlargement has led to a huge invasion of user data from all these social media websites, and the mining of this user data by corporations owning these platforms has become flourishing. The issue of preserving privacy and security of data will remain as long as these platforms keep monopolizing the social media. Social media is also being used to spread false information and rumors. Large-scale spreading of false information could pose severe social and economic damages. This paper aims to eliminate privacy and security concerns of current social media platforms with the help of a peer-to-peer blockchain network as well as limit the propagation of false information in the network, by reorganizing the entire social media network with the help of blockchain, the aim is to make the network truly owned and monetized by the user.
Relevansi Kebijakan Literasi Dengan Penguatan Pendidikan Karakter Pada Materi Pokok Madrasah Ibtidaiyah

The problem of education is one of the fields which is considered to be very important in the field of government. One of the problems of education is the decline in the quality of reading in students due to lack of literacy that is applied in schools. This article discusses educational policy using literary study methods that use several sources of articles, research and others related to this article that aims to explore the problems and what policies the government is doing to foster literacy at the elementary school level. Ministry of Education and Culture through Minister of Education and Culture Regulation No. 23 of 2015 launched an education policy. This educational policy is in the form of a National Literacy Movement which has 3 domains, one of which is the School Literacy Movement which is used in shaping good moral behavior towards students with language or literacy which is the first step to creating a culture of literacy and the desire to read to students. The School Literacy Movement does not only understand reading and writing skills, but also includes the ability and thoughtfulness of thinking in accordance with the components and stages in literacy. The school literacy movement is expected to be able to bring change both in terms of behavior, attitudes and responsibilities as well as instilling and growing student character values ​​early on, namely the value of independence, nationalism, religious mutual cooperation, and integrity in elementary school students. The character values ​​are also stated in Perpres No. 87 of 2017 concerning Strengthening Character Education (PPK). The National Literacy Movement and the School Literacy Movement are very closely related to the Strengthening of Character Education, because to implement the School Literacy Movement that is in schools and the Strengthening of Character Education can not be said to run optimally but can run when the school institutions and students are active.
GoTo cyber breach underlines risks to corporates

Headline INT: GoTo cyber breach underlines risks to corporates
An Instruction-configurable Post-quantum Cryptographic Processor towards NTRU

Post-quantum cryptography (PQC) is proposed to resist the attack of quantum computer. Among various PQC schemes, lattice-based cryptography depended on learning with errors (LWE) problem has attracted much attention. As one of the lattice-based PQC schemes, number theory research unit (NTRU) algorithm is flexible, simple and fast. In this paper, we propose a high-performance cryptographic processor towards NTRU. In the processor, we optimize instruction set architecture, which also saves memories. Three-level Karatsuba method is utilized to accelerate polynomial multiplication, and the computing time is reduced by 10x. Fixed and custom instructions are used to control the whole data path, with flexibility and high efficiency. Compared to other FPGA implementations, the results show this design performs the highest operating frequency of 200MHz, only consumes 28k look-up tables (LUTs). Besides, it has the shortest time of encryption, decryption and the best area-time product (ATP), which is 1.4x better than state-of-the-art work.