Reviewerly/exHarmony huggingface dataset:
Program Code Simulation by Using Ozobot

Visualization of the problem solving process greatly supports the deep learning and problemsolving skills. In this paper, we focus on using of modern technologies in the teaching of informatics, whichis highly motivational for programmers. The presented programmable robotic tool Ozobot contributes toexperiential teaching of informatics. The robot is easy to control and is capable of making visualization ofprogram code with immediate feedback regarding the current situation in the game. The learners can easilycreate programs without the need to write commands or to understand programming concepts. The nontextual code representation (using symbolic notation or block-based programming) supports the preventionof syntax errors in high level. The beginners in programming can store the program code by the selectionof valid commands and style it by moving blocks or tiles. Young programmers can think creatively whileplaying and solving a new algorithmical problems. We will introduce the possibilities of Ozobot codingand detail the possibilities of building programming skills and techniques at the lowest as well as at mediumlevel, based on the programming activities. Based on the complexity and variety of the commandsOzoBlockly programming environment provides the programming in five categories of difficulty. Ourrecommended programming activities are based on the interactivity and experimentation.
Adiabatic Quantum Support Vector Machines

Machine learning's ability to analyze and predict complex data enables many scientific discoveries and industrial advances. However, state-of-the-art machine learning requires extensive computational resources for training. In machine learning, the training phase is crucial because it enables the model to acquire knowledge from the provided data. The duration of the training process varies significantly, ranging from a few hours to several months. As a result, the computational cost and time constraints hinder researchers from using machine learning. To address this issue, we investigated the possibility of using quantum computers for machine-learning tasks. We compared Date's [1] quantum support vector machine (SVM) approach on a D-Wave Advantage to a classical implementation on AMD Ryzen and Intel Xeon processors. We evaluated both the time-to-solution and the machine-learning performance of the quantum and classical systems. The results of our study demonstrated that SVMs scaled <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(N^{2})$</tex> on the quantum computer and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(N^{2}d)$</tex> on the classical computer. Here, N represents the number of samples, and d represents the number of features. So, when trained with a data set with eight million features, we found that the quantum annealer outperformed the classical computer by 3.5-4.5x. The quantum approach maintained a comparable accuracy to the classical approach as well. In conclusion, quantum computers offer a computational advantage over classical computers for machine learning.
DistXplore: Distribution-Guided Testing for Evaluating and Enhancing Deep Learning Systems

Deep learning (DL) models are trained on sampled data, where the distribution of training data differs from that of real-world data (i.e., the distribution shift), which reduces the model's robustness. Various testing techniques have been proposed, including distribution-unaware and distribution-aware methods. However, distribution-unaware testing lacks effectiveness by not explicitly considering the distribution of test cases and may generate redundant errors (within same distribution). Distribution-aware testing techniques primarily focus on generating test cases that follow the training distribution, missing out-of-distribution data that may also be valid and should be considered in the testing process. In this paper, we propose a novel distribution-guided approach for generating valid test cases with diverse distributions, which can better evaluate the model's robustness (i.e., generating hard-to-detect errors) and enhance the model's robustness (i.e., enriching training data). Unlike existing testing techniques that optimize individual test cases, DistXplore optimizes test suites that represent specific distributions. To evaluate and enhance the model's robustness, we design two metrics: distribution difference, which maximizes the similarity in distribution between two different classes of data to generate hard-to-detect errors, and distribution diversity, which increase the distribution diversity of generated test cases for enhancing the model's robustness. To evaluate the effectiveness of DistXplore in model evaluation and enhancement, we compare DistXplore with 14 state-of-the-art baselines on 10 models across 4 datasets. The evaluation results show that DisXplore not only detects a larger number of errors (e.g., 2×+ on average). Furthermore, DistXplore achieves a higher improvement in empirical robustness (e.g., 5.2% more accuracy improvement than the baselines on average).
Using Hyperlocal Delivery System to Accelerate Localised Business

Hyperlocal organizations provide purchasers with merchandise delivery and utility administrations. Merchandise delivery includes staple goods, food, prescriptions, and personal needs, while utility administrations include plumbing, home cleaning, yard care, electrical, and drainage. Each of these services and products is provided by an organization of individuals from businesses or neighborhoods. Hyperlocal administration stages enable eateries, lodging, discount food outlets, organic products or vegetables, fish, and meat, and other retailers to attract and manage customers effectively without worrying about shipment. Simple web connectivity and the proliferation of cell phones have significantly increased interest in hyperlocal administrations. Occupied way of life accelerates the growth of the online food and staples ordering market, which in turn accelerates the growth of hyperlocal services in the emerging online market.
D2S2: Drag ’n’ Drop Mobile App Screen Search

The lack of diverse UI element representations in publicly available datasets hinders the scalability of sketch-based interactive mobile search. This paper introduces D2S2, a novel approach that addresses this limitation via drag-and-drop mobile screen search, accommodating visual and text-based queries. D2S2 searches 58k Rico screens for relevant UI examples based on UI element attributes, including type, position, shape, and text. In an evaluation with 10 novice software developers D2S2 successfully retrieves target screens within the top-20 search results in 15/19 attempts within a minute. The tool offers interactive and iterative search, updating its search results each time the user modifies the search query. Interested users can freely access D2S2 (http://pixeltoapp.com/D2S2), build on D2S2 or replicate results via D2S2’s open-source implementation (https://github.com/toni-tang/D2S2), or watch D2S2’s video demonstration (https://youtu.be/fdoYiw8lAn0).
Employee Surveillance System Using Face Recognition

Advancement in technology has made face recognition system more prevalent and convenient to identify a person without a manual system which contributes to time consumption. In this system, facial recognition is by the means by which the employees are monitored. Our project addresses the problems present with manual surveillance by automating it in an efficient manner. Machine learning and deep learning have benefited people from all walks of life, and we plan to use machine learning in our surveillance system to build this specific project with the aid of Python and its comprehensive modules. The project involves a real-time detection of faces which are then matched with the corresponding face in the database. An excel sheet stores the time at which the login has taken place when the detection occurs. To achieve the goals, we used a combination of machine learning techniques and various logic-based algorithms.
Owning the Police: Crime Data, Copyright, and Public Information

Online crime maps, which plot law enforcement crime data, promise to promote civic engagement and increase government transparency. This study reviewed the websites of the 250 largest U.S. cities and found 65% host or link to a crime map, and that 116 police departments entered into agreements with crime mapping companies that provide the companies preferred or exclusive access to local crime data, curtailing public access and use. This study examines the legal tactics used by commercial mapping companies to maintain information control, explores recent examples of legal battles over the data, and proposes solutions, including targeted public record law amendments, parallel access, and a generalized right of access.
GPS/IMU/Hydrodynamic Model Fusion State Estimation for Unmanned Surface Vehicles Based on Moving Horizon Estimation Method

With the growing application of unmanned surface vehicles (USVs) in marine explorations, high precision state estimation of USVs becomes increasingly important. However, sensors for consumer use are often with noise, and the real states cannot be inferred from a single sensor. Thus, GPS, IMU, and the Hydrodynamic Model of USV are combined here to achieve multi-sensor fusion and reduce the noise interference. This paper proposes a GPS/IMU/Hydrodynamic Model fusion state estimation strategy based on the moving horizon estimation (MHE) method. The performance of MHE is compared against the Extended Kalman Filter (EKF) in simulations of a traditional circular trajectory and a complex trajectory. In addition, the correlation between estimation horizon and MHE performance is studied. The results demonstrate that the designed estimator could substantially improve pose and velocity estimation accuracy compared to EKF when the real-time performance is required.
An Anti-occlusion Object Tracking Algorithm Using KCF and ORB Feature Detector

To address the problem that Kernelized correlation filter (KCF) has poor tracking effect when the target is occluded, this paper proposes an anti-occlusion tracking algorithm based on Oriented FAST and Rotated BRIEF (ORB) and KCF. In the initial stage of tracking, the target features are extracted and feature templates are generated by ORB, and then the target is tracked by KCF. If the target is occluded, the training and updating of the filter are stopped, the target features in the current tracking frame are extracted by ORB, and then the transformation relationship between them is obtained by matching the features with the template features. The exact position of the target in the current frame is then obtained. The experimental results show that the precision and success rate of our proposed algorithm improve by 18.85% and 19.23%, respectively, compared with the KCF algorithm, which effectively improves the tracking effect of KCF in the case of target occlusion.
Noise Modeling of the IBM Quantum Platform

The influence of noise in quantum dynamics is one of the main factors preventing current quantum processors from performing quantum computations. Errors must be addressed, which requires a thorough understanding of the nature and interplay of different sources of noise. In this work, we propose an effective error model of single-qubit operations on the IBM Quantum Platform (IBMQP), that possesses considerable predictive power. We showcase how Quantum Noise Spectroscopy (QNS) can be used alongside other error characterization techniques to obtain a more complete error model of the system. The model allows for both Markovian and spatio-temporally correlated non-Markovian environmental and control effects. Model parameters are obtained from a small set of characterization experiments. We show that simulations using this error model are capable of accurately predicting new experimental results.
FPGA-based Garbling Accelerator with Parallel Pipeline Processing

As more and more programs handle personal information, the demand for secure handling of data is increasing. The protocol that satisfies this demand is called Secure function evaluation (SFE) and has attracted much attention from a privacy protection perspective. In two-party SFE, two mutually untrustworthy parties compute an arbitrary function on their respective secret inputs without disclosing any information other than the output of the function. For example, it is possible to execute a program while protecting private information, such as genomic information. The garbled circuit (GC) — a method of program obfuscation in which the program is divided into gates and the output is calculated using a symmetric key cipher for each gate — is an efficient method for this purpose. However, GC is computationally expensive and has a significant overhead even with an accelerator. We focus on hardware acceleration because of the nature of GC, which is limited to certain types of calculations, such as encryption and XOR. In this paper, we propose an architecture that accelerates garbling by running multiple garbling engines simultaneously based on the latest FPGA-based GC accelerator. In this architecture, managers are introduced to perform multiple rows of pipeline processing simultaneously. We also propose an optimized implementation of RAM for this FPGA accelerator. As a result, it achieves an average performance improvement of 26% in garbling the same set of programs, compared to the state-of-the-art (SOTA) garbling accelerator.
Using Image Data for Machine Learning-Based Defect Detection

The inspection process, which was previously based primarily on human sampling, has now been fully automated by the introduction of inspection machines that use images. However, depending on the inspection conditions of the system, there is a risk of the over- or non-detection of defective products. This problem may result in a loss for companies that have implemented automated inspection machines. Therefore, with the introduction of such machines, the establishment of discrimination techniques for defect detection has become a critical issue. This study aimed to develop a method for automatically detecting defects in image data with high accuracy. This study used images of metal products. We attempted to detect defects inside metal products. This study proposed a method using machine learning. Additionally, this research made recommendations for data preprocessing that may be required before machine learning. Then, we chose the best combination of the proposed machine learning approach and data preprocessing, that is the most capable of detecting defects, using the L16 orthogonal table or ANOVA. The classification accuracy exceeded 90% using the proposed method, and we partly solved the problem thrown up by previous studies.
Single-Line Text Detection in Multi-Line Text with Narrow Spacing for Line-Based Character Recognition

Text detection is a crucial pre-processing step in optical character recognition (OCR) for the accurate recognition of text, including both fonts and handwritten characters, in documents. While current deep learning-based text detection tools can detect text regions with high accuracy, they often treat multiple lines of text as a single region. To perform line-based character recognition, it is necessary to divide the text into individual lines, which requires a line detection technique. This paper focuses on the development of a new approach to single-line detection in OCR that is based on the existing Character Region Awareness For Text detection (CRAFT) model and incorporates a deep neural network specialized in line segmentation. However, this new method may still detect multiple lines as a single text region when multi-line text with narrow spacing is present. To address this, we also introduce a post-processing algorithm to detect single text regions using the output of the single-line segmentation. Our proposed method successfully detects single lines, even in multi-line text with narrow line spacing, and hence improves the accuracy of OCR.
A Semi-Supervised Learning Model for Predicting User Attributes Based on Ladder Network

In recent years, marketing has often relied on using attribute information associated with the accounts registered in online services. However, most people use such services without a personal account, and it is impossible to obtain attribute information for these unregistered users. To deal with this situation, semi-supervised learning is an effective way to increase the number of users with attribute information. Attributes can be predicted from the historical data of unregistered users, having the historical data of registered users who have attribute information. One such semi-supervised learning method is the ladder network, which is a neural-network-based model that adds and removes noise. This model provided highly accurate predictions for image data and is also considered to be useful for predicting user attributes from historical data, where the feature vector is high-dimensional. However, this method does not account for cases where the label takes an ordered value, such as the user’s age category. In this study, we propose an extended model based on a ladder network that incorporates a mechanism that can appropriately predict a user’s attribute information, including ordinal-scale variables. We also conducted an evaluation experiment using browsing history data to demonstrate the effectiveness of the proposed method.
Education System-As A Factor in Ensuring the Quality of the Educational Process

The basis for the ever-accelerating development of world civilization and innovation is today realized by the heuristic capabilities of the modern educational system. After all, the development index of the countries of the world shows that the development of all spheres is associated with the educational process. In particular, the modern educational system is manifested as such a process in which there is a change and stagnation, development or lag, progress or decline. This opens up new opportunities for the dynamics of the formation and development of modern education. At this point, the definition of synergistic laws of the modern educational process as a complex and open system is rising to the current position as one of the requirements of today.
REVITALIZING INDIGENOUS LANGUAGE TEACHING IN EARLY CHILDHOOD EDUCATION: THE CASE OF THE BALINESE LANGUAGE DAY

The present study aims to describe the implementation of Balinese Language Day program at a kindergarten in Denpasar, Bali, Indonesia. The program is a realization of the provincial government effort to maintain the local language. The implementation faced three main problems, namely the lack of students’ motivation and interest, the lack of parental support and the lack of language use in the society due to technological advancement. Despite these problems, teachers considered the program to be beneficial in improving students’ motivation in learning the language as well as in using the language in their communication.
Symmetry Breaking Slows Convergence of the ADAPT Variational Quantum Eigensolver

The accurate treatment of molecular systems exhibiting strong electronic correlations has long been a target of quantum simulation. Strong correlation often results in symmetry-breaking of the mean-field reference, leading to Löwdin's “symmetry dilemma”[1] whereby accuracy in the energy can be increased by breaking intrinsic symmetries of the system. We explore the impact of symmetry breaking on the performance of ADAPT-VQE using two strongly correlated systems where increasing correlation leads to spontaneous symmetry breaking of the mean-field solutions. We analyze the role that symmetry breaking in the reference states and orbital mappings of the fermionic Hamiltonians have on the performance of ADAPT-VQE. We observe that breaking symmetry has a deleterious effect on ADAPT-VQE by increasing the length of the ansatz necessary for energy convergence and exacerbating the problem of “gradient troughs”.
Parents’ System for Developing Bilingual Children in Makassar

This research is a qualitative descriptive study that aims to find out the system used by parents in developing their children to become bilingual in English. There are 2 research questions in this study, namely parents’ system implementation in Makassar in developing their children to be bilingual and the factors that influence the system used by parents in developing their children to be bilingual. The subjects in this study were 5 parents who developed their children to be bilingual and 5 children who were developed to be bilingual by their parents aged 3-5 years. Research data were obtained through in-depth observation and interviews. Data analysis techniques, namely data condensation, data display, and drawing/checking conclusions. Based on data analysis, it was found that there is one system most often used by parents in Makassar, namely Mixed language. It is different with previous research which said that the most parent system used by parents is One Parent One Language (1P1L). It is different because the subjects in previous study are immigrants, while the subject in this research are not immigrants. Then, most of the subject work outside the house, so it is impossible for parents to share two languages. Then, there are 3 factors that influence the system used by parents in developing their children to become bilingual, namely age, media use, and community factors. Based on findings and discussion, it can be concluded that the five parents who were the subjects of this study chose to use the Mixed Language system in the development of bilingual children. Then, the factor that most influences the system used is the media usage factor, especially handphone.
Reduced Gate Count for Quantum State Preparation of 2D Data

This work proposes an efficient quantum state preparation technique for non-independent 2D data. Demonstrated on the DCT, the method performs a change of basis on the data source, compresses it, prepares a quantum state to represent the compressed data, and performs the inverse basis change using an efficient quantum circuit, recreating the original source. We show how the gate count for a quantum circuit to prepare a state representing image data can be reduced by 75% achieving good image quality (PSNR 30), and by 50% retaining very good image quality (PSNR 40). The approach lends itself to achieving similar scale gate count reductions for other transforms, including those with efficient quantum circuit implementations (such as the QFT and QWT).
SNACKs for Proof-of-Space Blockchains

SNACKs are succinct non-interactive arguments of chain knowledge. They allow for efficient and generic solutions to blockchain light-client bootstrapping. Abusalah et al. construct SNACKs in the random oracle model for any single-chain blockchain from any graph-labeling proof of sequential work (PoSW) scheme. Their SNACK construction is a PoSW-like protocol over the augmented blockchain. Unlike single-chain blockchains, such as proof-of-work and proof-of-stake blockchains, proof-of-space (PoSpace) blockchains are composed of two chains: a canonical proof chain and a data chain. These two chains are related using a signature scheme. In this work, we construct PoSW-enabled SNACKs for any PoSpace blockchain. Combined with the results of Abusalah et al., this gives the first solution to light-client bootstrapping in PoSpace blockchains. The space cost of our construction is two hash values in each augmented PoSpace block. Generating SNACK proofs for a PoSpace blockchain is identical to generating SNACK proofs for single-chain blockchains and amounts to looking up a succinct number of augmented blocks.