Reviewerly/exHarmony huggingface dataset:
Coding for Forensic 3D Fingerprinting

3D printing brings about a revolution in consumption and distribution of goods, but poses a significant risk to public safety. Any individual with internet access and a commodity printer can now produce untraceable firearms (ghost guns), keys, and dangerous counterfeit products. To combat these threats, it is important to embed identifying information inside the object, so that the perpetrator could be identified if the (potentially damaged) object is confiscated or left at a crime scene. The talk will begin by surveying some existing 3D printing technologies, and presenting new and existing techniques for information embedding. Related adversarial noise patterns will be analyzed, and an abstract coding problem will be formulated. Then, our progress in solving this problem, as well as experimental proof-of-concept, will be presented.
Pelatihan Akuntansi Keuangan Daerah bagi Guru Akuntansi SMK Se-Kabupaten Mempawah

This Community These Community Service Activities were initiated by the UPPM Team Accounting Department after coordinating with the Principal of SMKN 1 Mempawah Hilir in April 2022. The results of an initial survey via questionnaire of 8 accounting teachers in the Financial and Institutional Accounting Expertise Program showed that teachers still lack understanding of financial accounting area, as important material in learning Accounting Practicum for Government Agencies/institutions which focuses on recording transactions and completing the accounting cycle of Regional Work Units (SKPD). This is indicated by the fact that most of the accounting teachers in the Institutional Accounting and Finance Expertise Program do not understand the types of source documents for recording transactions and techniques for recording transactions, as well as completing the SKPD accounting cycle. The solution offered by the UPPM Team of the Accounting Department to solve partner problems is to organize Regional Financial Accounting Training for Accounting Teachers at Vocational Schools throughout Mempawah Regency. The target of this activity is that 12 teachers who teach in the Institutional Accounting and Financial Expertise Program in Mempawah Regency can record transactions and complete SKPD accounting cycles, making it easier for teachers to teach every material in the Accounting Practicum subject for Government Agencies/Institutions to students in their schools. each. This training will be held on Monday, October 3, 2022 in the Lab Room. Computer SMK Negeri 1 Mempawah Hilir. This training was attended by 12 accounting teachers from SMK Negeri 1 Mempawah Hilir and SMK Negeri Sungai Kunyit. Activities run smoothly from 08.30 – 16.00 WIB. After attending the training, there was a significant difference in the level of understanding of the participants compared to before attending the training. Participants' knowledge of regional financial accounting increased, as well as skills in carrying out the practice of preparing SKPD financial reports. This shows that the training was carried out effectively. Hopefully the results of this training activity will be useful for teachers in transferring knowledge to their students in the Accounting Practicum subject for Government Agencies/Institutions in their respective schools. Before the event was closed, the PPM Team of the Polnep Accounting Department handed over 12 textbooks to the participants. The session of presenting souvenirs to the Principal of SMK Negeri 1 Mempawah Hilir and taking a group photo ended this activity.
Category of Discrete Dynamical System

A dynamical system is a method that can describe the process, behavior, and complexity of a system. In general, a dynamical system consists of a discrete dynamical system and a continuous dynamical system. This dynamical system is very interesting if seen from the algebraic side. One of them is about category theory. Category theory is a very universal theory in mathematical concepts. In this research, the dynamical system used is a discrete dynamical system represented as a directed graph with nodes in the graph called states. This discrete dynamical system has a height which is shown on the dynamical map in which the number of states at each height is called a profile. In this research, it will be proved whether the discrete dynamical system with the same profile is a category. Also, why category theory is needed in discrete dynamical systems will be investigated. The result of this study shows that the discrete dynamical system with the same profile is a category with its morphism is an evolution from one state to another state in different dynamical systems. Furthermore, category theory is needed for discrete dynamical systems to know about the properties and structure of discrete dynamical system.
Image-to-Text Conversion and Aspect-Oriented Filtration for Multimodal Aspect-Based Sentiment Analysis

Multimodal aspect-based sentiment analysis (MABSA) aims to determine the sentiment polarity of each aspect mentioned in the text based on multimodal content. Various approaches have been proposed to model multimodal sentiment features for each aspect via modal interactions. However, most existing approaches have two shortcomings: (1) The representation gap between textual and visual modalities may increase the risk of misalignment in modal interactions; (2) In some examples where the image is not related to the text, the visual information may not enrich the textual modality when learning aspect-based sentiment features. In such cases, blindly leveraging information from visual modal may introduce noises in reasoning the aspect-based sentiment expressions. To tackle the shortcomings mentioned above, we propose an end-to-end MABSA framework with image conversion and noise filtration. Specifically, to bridge the representation gap in different modalities, we attempt to translate images into the input space of a pre-trained language model (PLM). To this end, we develop an image-to-text conversion module that can convert an image to an implicit sequence of token embedding. Moreover, an aspect-oriented filtration module is devised to alleviate the noise in the implicit token embeddings, which consists of two attention operations. The former aims to create an enhanced aspect embedding as a query, and the latter seeks to use this query to retrieve relevant auxiliary information from the implicit token embeddings to supplement the textual content. After filtering the noise, we leverage a PLM to encode the text, aspect, and image prompt derived from filtered implicit token embeddings as sentiment features to perform aspect-based sentiment prediction. Experimental results on two MABSA datasets show that our framework achieves state-of-the-art performance. Furthermore, extensive experimental analysis demonstrates the proposed framework has superior robustness and efficiency.
<i>QuantumEyes</i>: Towards Better Interpretability of Quantum Circuits

Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QuantumEyes</i> , an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dandelion chart</i> to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QuantumEyes</i> as well as the novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dandelion chart</i> integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.
Prediction intervals for time series forecasting using Transformers

In recent years, there has been a growing interest in time series forecasting, particularly on quantifying the uncertainty in neural model predictions using prediction intervals. This study utilizes the Joint Supervision (JS) method to construct prediction intervals, a technique that has consistently outperformed similar approaches. The JS method employs a neural network with two outputs representing the interval’s boundaries and another the specific prediction. Each output is optimized with a unique loss function, incorporating tunable parameters. This work introduces a modified version of the JS (JSM), which enhances in an average 8% improvement in coverage probability while maintaining a similar or slightly greater average width. Furthermore, this research compares the JSM method implemented with both Long Short-Term Memory (LSTM) and Transformer architectures. Experiments conducted on three different databases reveal that JSM with the Transformer outperforms the LSTM version, with an average 1.77% increase in coverage probability and 12% narrower intervals.
Overview Of Modern Augmented Reality Capabilities For Creating A Navigation Aid For The Blind

This paper discusses the hardware requirements, prospects, and possibilities of using the ARCore, library to work with augmented reality in order to create a portable navigation assistant for the blind. Examples of the obtained depth map from street frames are evaluated.
Fast and Efficient Brain Extraction with Recursive MLP Based 3D UNet

Extracting brain from other non-brain tissues is an essential step in neuroimage analyses such as brain volume estimation. The transformers and 3D UNet based methods achieve strong performance using attention and 3D convolutions. They normally have complex architecture and are thus computationally slow. Consequently, they can hardly be deployed in computational resource-constrained environments. To achieve rapid segmentation, the most recent work UNeXt reduces convolution filters and also presents the Multilayer Perception (MLP) blocks that exploit simpler and linear MLP operations. To further boost performance, it shifts the feature channels in MLP block so as to focus on learning local dependencies. However, it performs segmentation on 2D medical images rather than 3D volumes. In this paper, we propose a recursive MLP based 3D UNet to efficiently extract brain from 3D head volume. Our network involves 3D convolution blocks and MLP blocks to capture both long range information and local dependencies. Meanwhile, we also leverage the simplicity of MLPs to enhance computational efficiency. Unlike UNeXt extracting one locality, we apply several shifts to capture multiple localities representing different local dependencies and then introduce a recursive design to aggregate them. To save computational cost, the shifts do not introduce any parameters and the parameters are also shared across recursions. Extensive experiments on two public datasets demonstrate the superiority of our approach against other state-of-the-art methods with respect to both accuracy and CPU inference time.
Near-optimal quantum circuit construction via Cartan decomposition

We show the applicability of the Cartan decomposition of Lie algebras to quantum circuits. This approach can be used to synthesize circuits that can efficiently implement any desired unitary operation. Our method finds explicit quantum circuit representations of the algebraic generators of the relevant Lie algebras allowing the direct implementation of a Cartan decomposition on a quantum computer. The construction is recursive and allows us to expand any circuit down to generators and rotation matrices on individual qubits, where through our recursive algorithm we find that the generators themselves can be expressed with controlled-not (cnot) and swap gates explicitly. Our approach is independent of the standard cnot implementation and can be easily adapted to other cross-qubit circuit elements. In addition to its versatility, we also achieve near-optimal counts when working with cnot gates, achieving an asymptotic cnot cost of $\frac{21}{16}{4}^{n}$ for $n$ qubits.
Content and opinion-enhanced neural model for opinion sentence classification of Chinese microblog comments

Opinion sentence classification of Chinese microblog comments aims to recognise those comments with opinions about the specific microblog content, which is the basis of internet public opinion analysis and opinion mining. However, existing opinion sentence classification methods do not consider whether the opinion sentences point at concerned objects or not. To address these issues, we propose a novel neural model, which combines the microblog content relevance-enhanced module and the opinion representation-enhanced module. In the first module, we propose a mutual attention operation that enables the model to extract better features representing microblog content. In the second module, we employ sentiment word embedding and self-attention operations to enhance the ability of the model to extract the opinion features. We evaluate our model using a Chinese microblog comment dataset. Experimental results show that the accuracy of the proposed model is 2%-5% higher than that of the baseline models, which shows that the proposed content relevance-enhancement and attention mechanism are beneficial to this task.
Reversible data hiding algorithm in encrypted images using adaptive total variation and cross-cyclic shift

To reduce prediction error and improve the embedding rate of secret messages, a new reversible data hiding algorithm for encrypted images (RDHEI) is designed using adaptive total variation and cross-cyclic shift called RDHEIAC. The adaptive total variation is first used to generate the prediction error image. Then, the bit-plane rearrangement based on Hilbert scanning is performed to make room for data hiding. Thirdly, an improved run-length encoding is applied to compress the error image. Subsequently, a cross-cyclic shift operation and a diffusion operation based on chaotic maps are used to produce encrypted images. Finally, secret messages are inserted into the encrypted image using bit substitution. Experimental results indicate that the proposed RDHEI scheme is privacy secure and has high embedding capacity and image fidelity. Moreover, information extraction and image restoration are separable. Compared with previous RDHEI algorithms, the proposed RDHEI algorithm has a 47.07% higher embedding rate.
Study on recommendation of personalised learning resources based on deep reinforcement learning

In order to overcome the problems of the traditional network personalised learning resource recommendation methods, such as low recommendation accuracy, poor recommendation quality and poor F1 comprehensive evaluation index, a network personalised learning resource recommendation method based on deep reinforcement learning was proposed. This method uses web crawler technology to capture learning resource data. Based on this, a deep reinforcement learning strategy model is built, and the recommended trajectory of network personalised learning resources is divided into independent states. The correlation degree between different network personalised learning resource variables is measured, and the objective function of personalised learning resource recommendation is constructed by combining resource keywords and track segmentation results to complete the resource recommendation. The experiment proves that the accuracy rate of the personalised resources recommended in this paper is above 70%, and the comprehensive evaluation index obviously reaches the highest 99%, which improves the resource recommendation effect.
Call Me Iggy by Jorge Aguirre (review)

Reviewed by: Call Me Iggy by Jorge Aguirre Kate Quealy-Gainer Aguirre, Jorge Call Me Iggy; illus. by Rafael Rosado. First Second, 2024 [256p] Trade ed. ISBN 9781250204158 $25.99 Paper ed. ISBN 9781250204134 $18.99 Reviewed from digital galleys Ad Gr. 6-9 Iggy wanted to be placed in French class his freshman year, but instead he’s stuck in Spanish, where his classmates assume he’ll get an A since his family is from Colombia. In truth, he doesn’t know any Spanish nor does he want to learn it—until he sees a cute girl in his class. Unfortunately, he fails to impress her when he fails his first assignment rather spectacularly. However, a smashed urn becomes an unlikely source of aid when Iggy frees his estranged grandfather’s ghost, who offers to help Iggy with both Spanish and his crush if Iggy promises to find a beautiful orchid garden in which to spread Abuelito’s ashes. Iggy’s crush eventually shifts to Marisol (another cute girl), but her status as an undocumented immigrant leads to trouble that has Iggy’s parents convinced she’s a bad influence, all while Abuelito is telling Iggy to forget her. Set against the backdrop of the 2016 election, this graphic novel tackles a variety of both evergreen and timely themes: young love blooming [End Page 126] despite the prejudice and limitations of adults is a familiar story, and even echoed in Iggy’s involvement in the West Side Story production at his school. The specifics here, too, will be familiar to teens who have lived the last few years within a tumultuous political climate that has made immigration and the U.S. southern border a central discussion in politics. Unfortunately, the narrative stumbles with both an uneven pace and a disjointed focalization that has characters coming in and out of the story as the plot needs it, giving no real depth to any of them. The art is similarly imbalanced, with dialogue bubbles crowding the pages and repeating what is already visually obvious. Nonetheless, the romance between Iggy and Marisol is tender and chaste, and Iggy’s growth is clear as he negotiates the needs and advice of others with his own hopes for the future. Copyright © 2023 The Board of Trustees of the University of Illinois
A Colossal Catalog Adventure: Representing Indie Video Games and Game Creators in Library Catalogs

Significant changes in how video games are made and distributed require catalogers to critically reflect on existing approaches for representing games in library catalogs. Digital distribution channels are quickly supplanting releases of games on physical media while also facilitating a dramatic increase in independent-made games that incorporate novel subject matter and styles of gameplay. This paper presents an action research project cataloging 18 independently-made digital games from a small publisher, Choice of Games, considering how descriptive cataloging, subject cataloging, and name authority control for these works compares to mainstream video games.
IOT-Based Cultural Movement And Monitoring Systems With Renewable Energy Utilities

The procedure of watering for and keeping track of the temperature and humidity levels in the room has a significant impact on the growth and productivity of oyster mushroom plants. It is often believed to be less effective and efficient because people in the Banyuwangi district perform the irrigation process conventionally with human aid. In order to construct digital businesses, artificial intelligence, cognitive technology, and (IoT) are being developed. People's daily lives are becoming more advanced and connected because to this transformation. This will encourage the creation of fresh innovations. Given this context, the idea to undertake research on the method of automatically and continuously monitoring watering (spraying) mushroom production using the Internet of Things (IoT) developed. In order to improve the effectiveness and productivity of mushroom cultivation, this instrument is designed with a temperature sensor (LM35) to monitor the humidity conditions of oyster mushroom plants. It is also fitted with a solar renewable energy system. In order to improve the effectiveness and productivity of mushroom cultivation, this instrument is designed with a temperature sensor (LM35) to monitor the humidity conditions of oyster mushroom plants. It is also fitted with a solar renewable energy system. The five steps of this study's execution were: a needs analysis; tool design; tool prototype; prototype testing; and conclusion. According to test results, the IoT-based control system for watering mushroom cultivation has an average error of 0.1% when measuring temperature and an average error of 2.66% when measuring humidity. This is almost perfect in terms of sensor readings, indicating that it can be used properly. While the success rate for the spray control system using the IoT system after five trials was 100%, the voltage, current, and power for solar panels were 17.60 V, 4.10 A, and 55.7 Watt at 14.00 during the day.
Depression, anxiety, and stress disorders detection in students during the Covid-19 pandemic using Naïve Bayes algorithm

During the Covid-19 pandemic, students in Indonesia carried out bold learning from home as a social effort during the pandemic. This bold learning process is considered to be still less effective and efficient and has resulted in some students, especially students having homework during the brave learning period. This has an impact on the psychology of students such as the emergence of depression, anxiety, and stress. Sources of psychological disorders not only from academics but from within themselves also affect mental health. The results of a survey on mental health during the pandemic conducted by the Association of Indonesian Mental Medicine Specialists (PDSKJI) showed that 64.8% of respondents experienced psychological problems in the age group of 19-24 years and over 60 years. In this study the author will make a system of Tests for Depression, Anxiety and Stress Disorders in Students. The results of this test are the severity of each psychological disorder and treatment recommendations based on the test results. The psychological scale used in this study is the DASS-42 (Depression, Anxiety, and Stress Scale) which has 42 statements and 3 categories of disorders, namely depression, anxiety, and stress. Each category has 5 levels, namely normal, mild, moderate, severe, and very severe. The Test System for Depression, Anxiety, and Stress Disorders for Students uses the Naïve Bayes method with the accuracy of the dataset obtained by 86.44%, so it can be said that this system is running according to the purpose.
Research and Analysis Based on Cloud Class Data

This article applies the theoretical methods of probability and statistics, using probability and statistics cloud class courses as carriers, to study and analyze a large amount of data generated in cloud class courses, establish a mathematical model of related variables, and predict the final grades of students in cloud class courses. By presenting a learning situation data model, students can timely see their own problems and recognize the importance of process learning. At the same time, it provides a basis for teachers to conduct teaching behavior analysis and provide personalized and precise learning guidance for students.