Reviewerly/exHarmony huggingface dataset:
Crypto Pay: Design of Public Blockchain Platform

Blockchain is a decentralized distributed platform that allows multiple peers to exchange digital assets securely all over the globe without third party. In blockchain, blocks are connected by using hash that maintains the order of transaction. In this paper, a crypto currency transfer in Web 3.0 platform is built based on Ethereum Blockchain to implement decentralized application. This paper is also focused on the latest cutting-edge technology to transfer funds easily and very efficiently and quickly. The working model can solve issues regarding many spheres of the multiverse and can be connected to transfer real cryptocurrency anywhere across the globe. The main uniqueness of the model that it offers to its users with full trustiness and curb the wrong uses of third parties to control any transactions of Web 2.0, especially tracking or hacking sites or data servers and block any transactions and transact money into a wrong account. Here, the transaction is verified, decentralized, and secured where no third party is involved in the process actively and no personal logged data is required. Each transaction can be seen and matched by the address stored on the transaction site of Ethereum. This research paper concludes with the implemented methodology for creation, deployment on Ethereum, blockchain, and interaction of smart contract using ReactJS, EthersJS, alchemy API and hardhat. Just people need to understand the appropriate usage of Cryptocurrency transactions using virtual wallets.
Surface bilateral profile measurement of curved transparent components based on chromatic confocal sensor

The system performance of many industrial products is dominated by the surface profile accuracy of their primary component, thus it is of great essence to achieve high precision profile characterization. The chromatic confocal technology provides an effective means for precise measurement of surface profile, yet there exists non-negligible errors when measuring transparent curved component. The aim of this paper is to investigate the effects of the curvature of component on the confocal rays and obtain its bilateral surface profile by chromatic confocal technology simultaneously. The assumption of tilted plate approximation for curved specimen was made. By adopting the optical ray tracing analysis, a modified model was then proposed to correct the measurement error existed in the traditional one. The validity of the proposed model is conducted by 2D bilateral profile measurements of the hemispherical shells after the calibration with tilted fused silica plates. Experimental results show that the proposed model significantly improves the measurement accuracy of bilateral profile of curved transparent components at large equivalent tilt angle. This method overcomes the drawbacks of other spatial coordinate based profile measurement methods, and achieves high accuracy simultaneous measurement of bilateral profile of transparent components.
On Distance Learning, Open Educational Resources, and the Role of University Libraries in These Processes

Objective. The article is aimed to improve the understanding of university communities (teachers, students, librarians) concerning the development of distance learning, open educational resources, and the new role of university libraries in these processes. Methods. The main data on the development of distance learning, OER, and increasing the role of university libraries in these processes were obtained due to the analysis of professional publications in periodicals and on the university websites of around the world, the application of methods of method of participant and non-participant observation, a description of the authors’ own experience (the director of the scientific library, the associate professor of IT disciplines and the heads of the scientific library departments) who work at the Ukrainian State University of Science and Technology (Dnipro, Ukraine). Results. It has been established that the implementation of distance learning systems, the use and availability of open (preferably free) information sources on open access platforms or open educational resources (OER), exchange of scientific research in the digital scientific space, etc. – these components are closely interconnected and used among teachers, scientists, students. Distance education requires inclusion in the processes of both teachers – authors of open educational resources, and employees of certain services providing information and IT support for both teachers and the resources created by them. Such structures are university libraries, which were among the first to support the philosophy of openness and to join the processes of supporting open access, open science, and open education based on intellectual information systems and technologies. The experience of the scientific library of USUST is considered – a structure that enables online access to text and non-text OER as a necessary element while ensuring limited personal interaction between a teacher and a student. Conclusions. Today, during the crisis caused by the Covid-19 pandemic and the war in Ukraine, the foundations are being actively laid to make the provision of educational services at universities, in particular distance education, dynamic and multidimensional. It is necessary to integrate electronic library resources with distance learning platforms to ensure unhindered access for students and teachers.
Pendampingan Penguatan Literasi Digital di SD Muhammadiyah Program Khusus Baturan dan SD Muhammadiyah Plus Malangjiwan

Digital literacy must be instilled as early as possible, even in elementary schools (SD). However, the facts on the ground show that digital literacy in elementary schools has not been implemented effectively. Some schools still encounter obstacles, one of which is caused by the limitations of digital literacy tools or media in schools. This obstacle was also experienced by the Baturan Special Program Muhammadiyah Elementary School (PK) and the Muhammadiyah Plus Elementary School Malangjiwan. Basic digital literacy skills, such as typing in Microsoft Word, have not been fully mastered by students. In fact, these skills are very important in this digital era. This community service is carried out by providing direct assistance to students to practice typing in Microsoft Word. The stages in this activity are starting from observation, pre-test, to post-test in the form of typing practice. The hands-on mentoring method allows students to learn to type and recognize the features and uses of Microsoft Word. The results of this dedication show that there has been an increase in students’ competence in understanding Microsoft Word, providing real experience for students to practice typing, and can grow good digital literacy. This activity is carried out with the hope that in the future, students who already have the initial ability to type from the results of this service, can continue to practice and the school can support it by continuing this kind of activity program through a digital literacy program on an ongoing basis.
Differential Evolution Algorithm with Dual Information Guidance

As an effective tool to solve continuous optimization problems, differential evolution (DE) algorithm has been widely used in numerous fields. To enhance the performance, in recent years, many DE variants have been developed based on the idea of multiple strategies. However, there still exists an issue for them that the strategy selection method relies on the historical search experience. The experience may be suitable for the problems with smooth fitness landscapes, but not for the problems with rugged fitness landscapes. To alleviate this issue, in this work, a new multiple strategies-based DE variant with dual information guidance is proposed, called DIGDE. In the DIGDE, to avoid the unreliable historical search experience, the fitness information and spatial information are utilized simultaneously as a guidance to estimate the evolutionary states for each individual, and then the most appropriate strategy can be chosen correspondingly. To verify the effectiveness of the DIGDE, 52 test functions are included in the experiments, and three well-established DE variants and four other evolutionary algorithms are involved in the performance comparison. The results show that the DIGDE achieves competitive performance on not only the result accuracy but also the convergence rate.
Robust Federated Learning for Ubiquitous Computing through Mitigation of Edge-Case Backdoor Attacks

Federated Learning (FL) allows several data owners to train a joint model without sharing their training data. Such a paradigm is useful for better privacy in many ubiquitous computing systems. However, FL is vulnerable to poisoning attacks, where malicious participants attempt to inject a backdoor task in the model at training time, along with the main task that the model was initially trained for. Recent works show that FL is particularly vulnerable to edge-case backdoors introduced by data points with unusual out-of-distribution features. Such attacks are among the most difficult to counter, and today's FL defense mechanisms usually fail to tackle them. In this paper, we present ARMOR, a defense mechanism that leverages adversarial learning to uncover edge-case backdoors. In contrast to most of existing FL defenses, ARMOR does not require real data samples and is compatible with secure aggregation, thus, providing better FL privacy protection. ARMOR relies on GANs (Generative Adversarial Networks) to extract data features from model updates, and uses the generated samples to test the activation of potential edge-case backdoors in the model. Our experimental evaluations with three widely used datasets and neural networks show that ARMOR can tackle edge-case backdoors with 95% resilience against attacks, and without hurting model quality.
Realization of a magnonic analog adder with frequency-division multiplexing

Being able to accurately control the interaction of spin waves is a crucial challenge for magnonics in order to offer an alternative wave-based computing scheme for certain technological applications. Especially in neural networks and neuromorphic computing, wave-based approaches can offer significant advantages over traditional CMOS-based binary computing schemes with regard to performance and power consumption. In this work, we demonstrate precise modulation of phase- and amplitude-sensitive interference of coherent spin waves in a yttrium–iron–garnet based magnonic analog adder device, while also showing the feasibility of frequency-division multiplexing. Using time-resolved scanning transmission x-ray microscopy, the interference was directly observed, giving an important proof of concept for this kind of analog computing device and its underlying working principle. This constitutes a step toward wave-based analog computing using magnons as an information carrier.
A survey of elementary school students’ digital literacy skills in science learning

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 Arnelia Dwi Yasa, Sri Rahayu; A survey of elementary school students’ digital literacy skills in science learning. AIP Conf. Proc. 10 January 2023; 2569 (1): 060015. https://doi.org/10.1063/5.0113483 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
Estimating geothermal and background radiation hotspots from primordial radionuclide concentrations in geology of South Africa

Naturally occurring radionuclides are the main generator of geothermal energy in the Earth's crust and mantle. The generated energy is consequently directly proportional to the concentrations of the three main naturally occurring radionuclides (uranium, thorium and potassium), which are primordial in origin. Concentrations of these naturally occurring radionuclides were extracted for all the different geological rock units in South Africa. The radionuclide concentrations were then mapped and integrated by using QGIS. The results were used to estimate and map the geothermal energy production rates for the rock units. The radionuclide concentrations in the rock units were also used to identify regions with high radiation background. These radiation hotspots were plotted and investigated. The estimated geothermal energy and background radiation hotspots were compared to measurements and projections of other studies and good corelations were found.
CNN Based Transfer Learning for Malaria Parasite Detection Using Thin-Blood Smear Images

Transfer learning has been used in computer vision research, including in the health sector. In the health sector, the input image is generally an x-ray image or a microscopic image. In this study, transfer learning for models that have been trained using CNN to detect malaria parasites in red blood cell images. The deep CNN pre-trained model uses 3 architectures, namely ResNet50V2, EfficientNetB0, and InceptionV3. For each architecture, experiments will be carried out and compare which architecture is better in detecting malaria parasites. Based on experiments conducted without fine tune, the accuracy ranges from 0.76 – 0.81 for ResNet50v2, 0.76 – 0.80 for EfficientNetB0, and 0.77 – 0.82 for InceptionV3.
Chance constrained conic-segmentation support vector machine with uncertain data

Support vector machines (SVM) is one of the well known supervised machine learning model. The standard SVM models are dealing with the situation where the exact values of the data points are known. This paper studies the SVM model when the data set contains uncertain or mislabelled data points. To ensure the small probability of misclassification for the uncertain data, a chance constrained conic-segmentation SVM model is proposed for multiclass classification. Based on the data set, a mixed integer programming formulation for the chance constrained conic-segmentation SVM is derived. Kernelization of chance constrained conic-segmentation SVM model is also exploited for nonlinear classification. The geometric interpretation is presented to show how the chance constrained conic-segmentation SVM works on uncertain data. Finally, experimental results are presented to demonstrate the effectiveness of the chance constrained conic-segmentation SVM for both artificial and real-world data.
Private Convex Optimization in General Norms

We propose a new framework for differentially private optimization of convex functions which are Lipschitz in an arbitrary norm ||·||x. Our algorithms are based on a regularized exponential mechanism which samples from the density ∞ exp(-k(F + μr)) where F is the empirical loss and τ is a regularizer which is strongly convex with respect to ||·||x, generalizing a recent work of [GLL22] to non-Euclidean settings. We show that this mechanism satisfies Gaussian differential privacy and solves both DP-ERM (empirical risk minimization) and DP-SCO (stochastic convex optimization), by using localization tools from convex geometry. Our framework is the first to apply to private convex optimization in general normed spaces, and directly recovers non-private SCO rates achieved by mirror descent, as the privacy parameter ε → ∞. As applications, for Lipschitz optimization in ℓp norms for all p ∈ (1, 2), we obtain the first optimal privacy-utility tradeoffs; for p = 1, we improve tradeoffs obtained by the recent works [AFKT21, BGN21] by at least a logarithmic factor. Our ℓp norm and Schatten-p norm optimization frameworks are complemented with polynomial-time samplers whose query complexity we explicitly bound.
Streaming algorithms for the missing item finding problem

Many problems on data streams have been studied at two extremes of difficulty: either allowing randomized algorithms, in the static setting (where they should err with bounded probability on the worst case stream); or when only deterministic and infallible algorithms are required. Some recent works have considered the adversarial setting, in which a randomized streaming algorithm must succeed even on data streams provided by an adaptive adversary that can see the intermediate outputs of the algorithm.In order to better understand the differences between these models, we study a streaming task called “Missing Item Finding”. In this problem, for r < n, one is given a data stream a1,…, ar of elements in [n], (possibly with repetitions), and must output some x ∈ [n] which does not equal any of the ai. We prove that, for r = n Θ (1) and δ = 1/poly(n), the space required for randomized algorithms that solve this problem in the static setting with error δ is Θ(polylog(n)); for algorithms in the adversarial setting with error δ, Θ((1 + r2/n)polylog(n)); and for deterministic algorithms, Θ(r/polylog(n)). Because our adversarially robust algorithm relies on free access to a string of O(r log n) random bits, we investigate a “random start” model of streaming algorithms where all random bits used are included in the space cost. Here we find a conditional lower bound on the space usage, which depends on the space that would be needed for a pseudo-deterministic algorithm to solve the problem. We also prove an Ω(r/polylog(n)) lower bound for the space needed by a streaming algorithm with < 1/2polylog(n) error against “white-box” adversaries that can see the internal state of the algorithm, but not predict its future random decisions.
A Nearly Tight Analysis of Greedy k-means++

The famous k-means++ algorithm of Arthur and Vassilvitskii [SODA 2007] is the most popular way of solving the k-means problem in practice. The algorithm is very simple: it samples the first center uniformly at random and each of the following k — 1 centers is then always sampled proportional to its squared distance to the closest center so far. Afterward, Lloyd's iterative algorithm is run. The k-means++ algorithm is known to return Θ(log k) approximate solution in expectation.In their seminal work, Arthur and Vassilvitskii [SODA 2007] asked about the guarantees for its following greedy variant: in every step, we sample ℓ candidate centers instead of one and then pick the one that minimizes the new cost. This is also how k-means++ is implemented in e.g. the popular Scikit-learn library [Pedregosa et al.; JMLR 2011].We present nearly matching lower and upper bounds for the greedy k-means++: We prove that it is an O(ℓ3 log3 k)-approximation algorithm. On the other hand, we prove a lower bound of Ω(ℓ3 log3 k/ log2 (ℓ log k)). Previously, only an Ω(ℓ log k) lower bound was known [Bhattacharya, Eube, Röglin, Schmidt; ESA 2020] and there was no known upper bound.
CNN based Recognition of Emotion and Speech from Gestures and Facial Expressions

The major mode of communication between hearing-impaired or mute people and others is sign language. Prior, most of the recognition systems for sign language had been set simply to recognize hand signs and convey them as text. However, the proposed model tries to provide speech to the mute. Firstly, hand gestures for sign language recognition and facial emotions are trained using CNN (Convolutional Neural Network) and then by training the emotion to speech model. Finally combining hand gestures and facial emotions to realize the emotion and speech.
ARENA: Enhancing Abstract Refinement for Neural Network Verification

As neural networks have taken on a critical role in real-world applications, formal verification is earnestly needed to guarantee the safety properties of the networks. However, it remains challenging to balance the trade-off between precision and efficiency in abstract interpretation based verification methods. In this paper, we propose an abstract refinement process that leverages the convex hull techniques to improve the analysis efficiency. Specifically, we introduce the double description method in the convex polytope domain to detect and eliminate multiple spurious adversarial labels simultaneously. We also combine the new activation relaxation technique with the iterative abstract refinement method to compensate for the precision loss during abstract interpretation. We have implemented our proposal into a verification framework named ARENA, and assessed its effectiveness by conducting a series of experiments. These experiments show that ARENA yields significantly better verification precision compared to the existing abstract-refinement-based tool DeepSRGR. It also identifies falsification by detecting adversarial examples, with reasonable execution efficiency. Lastly, it verifies more images than the state-of-the-art verifier PRIMA.
Challenges and constraints when applying few shot learning to a real-world scenario: In-the-wild camera-trap species classification

Few shot learning (FSL) describes the challenge of learning to classify when there are only few labeled examples. The goal is to be able to adapt to a new classification task using a minimum amount of new data. However, when applying FSL to real-world problems, a number of constraints and challenges are encountered: model selection, training data selection, hyper-parameter and cost function, and final decision making. In this paper, we consider a realistic problem that fits perfectly with the narratives of FSL: our goal is to classify animal species that appear in our in-the-wild camera traps located in Senegal, when these species have yet to appear in popular animal datasets. Using the philosophy of FSL, we first train a FSL network to learn to separate animal species, using popular, large animal datasets, and then evaluate the network with our data, for which there are few labeled images. Using this framework, we conduct a comparison between FSL models, parameter selection, training strategy, etc. We also discuss a weakness of the current FSL framework &#x2013; lack of the option &#x201C;does not belong to any of the classes&#x201D;. This is a common problem in real-world systems due to false positives when detecting bounding boxes that contain, in our case, animals. We then propose an additional constraint to overcome this weakness.
Factors Affecting the Security of Information Systems in Africa: A Literature Review

This study determined factors affecting the security of Information Systems in Africa. Also, it established the quality of publications in the area of Information Security. The study is based on peer-reviewed publications conducted in Africa. The study adopted the mixed research approach. The study used a systematic literature review, with part of the analysis using descriptive analysis. In total, 70 papers formed the population of publications extracted in the area of Information Security. In addition, 37 papers had the quality to be included in the analysis of factors affecting Information Security. The study found that information Security factors are in four key categories: human factors, policy-related issues, work environment, and demographic factors. Overall, the work environment is the most reported category affecting the security of Information Systems in Africa. In addition, gender is the highest reported individual factor associated with the insecurity of Information Systems; female is the highly affected gender. Other factors include the lack of Information Security training, the unchecked level of trust, carelessness and poor security policies. The study recommends training programs, policy improvement and promoting behaviours that minimise exposure to attacks.