Reviewerly/exHarmony huggingface dataset:
The School Literacy Movement in Elementary School: From Pre-Pandemic to Post-Pandemic Covid-19

The results of the international study on reading literacy show that Indonesian students' reading ability lags far behind other countries. Even nationally, the government measures students' reading literacy levels inadequately. The government initiated the School Literacy Movement (Gerakan Literasi Sekolah/GLS) to increase interest in reading in 2016. The purpose of this study is to describe the implementation of GLS in four elementary schools in Cisauk District, South Tangerang, during the pre-pandemic, pandemic, and post-pandemic covid-19 period. The participants of this study were 17 people, consisting of school principals, teachers, and librarians. This research is descriptive qualitative with data collection techniques in the form of FGDs, interviews, and observation. Data analysis techniques include reducing data, presenting data, and drawing conclusions. This study found that the GLS applied in the four elementary schools is still at the habituation stage of 15 minutes of reading before studying, providing a reading corner, reading in the library, and displaying students' work in wall magazines and classrooms. During the pandemic, students have more opportunities to access readings on the internet to do assignments compared to the pre-pandemic period. In the post-pandemic era, literacy has not been fully revived. Due to a lack of understanding, school principals and teachers have not varied the practice of GLS and still need mentoring.
KOMPARASI ALGORITMA KLASIFIKASI NAIVE BAYES DAN K-NEAREST NEIGHBORS DALAM ANALISIS SENTIMEN TERHADAP OPINI FILM PADA TWITTER

The fact that social media is so unreliable does not prevent Twitter users from using the service. Twitter is one of a few social media platforms that allows users to engage in conversation, share information, or even reveal their true identities, such as when discussing a movie's plot. A tweet or comment about a movie that is posted on Twitter may be viewed as a tool to improve the quality of movie production. To understand this, one can use sentimen analysis to categorize as either negative or positive by comparing the Naive Bayes and k-Nearest Neighbors algorithms to determine which one is the most accurate. The results of the two algorithms' comparative testing reveal that the Nave Bayes algorithm has a higher rata-rata accuracy of 99.63% with an AUC of around 1.000, while the K-NN algorithm has a higher rata-rata accuracy of 99.25% with an AUC of 1.000.
SISTEM PENDUKUNG KEPUTUSAN PEMBELIAN LAPTOP MENGGUNAKAN METODE FUZZY DAN METODE TOPSIS

Before buying a laptop, you need to pay attention to the specifications and standards that are tailored to your needs and requirements. Some of the standards and requirements include looking at several aspects of the criteria including RAM (random access memory), laptop prices, hard disk capacity, processor, screen size, VGA (video graphic adapter), battery power, availability of spare parts, laptop weight, distance to service centers, long warranty. The fuzzy method is an algorithm used to find a value that is still vague and also linguistic, using the TOPSIS algorithm (Technique for Order by. Similarity to Ideal Solution) to support calculations in decision-making. Determination in using the TOPSIS algorithm results from careful calculations. From the results of calculations using the TOPSIS algorithm, the total ideal positive and negative ideal values ​​are generated with the following ratings: laptop 5 with a value of 4.637 in rank 1, laptop 3 with a value of 4.174 in rank 2, laptop 4 with a value of 4.077 in rank 3, for laptop 1 with a value 3.880 is ranked 4th, and laptop 2 with a value of 3.472 is ranked 5th.&#x0D; &#x0D; Keywords: fuzzy, TOPSIS, laptop.
MITIGATING PARTIAL CONTENT UPDATE ISSUES ON WEB PAGES THROUGH SPA PARAMETER OPTIMIZATION

The experience of recent years has shown that the number of active web applications developed according to the fundamental principles of single-page applications continues to grow steadily despite a decrease in the number of newly created ones. This indicates a shift in focus from the importance of developing anew to the necessity of maintaining, servicing, and optimizing the existing end product. Furthermore, with the advancement and improvement of web development technologies, the potential expectations of users increase, necessitating the provision of a worthy user experience and the enhancement of application competitiveness. A significant role in this process is played by the partial content update process on web pages, widely used in the development of single-page applications. Most modern JavaScript frameworks designed for client-side web development employ the "out-of-box" technique, thereby providing the developer with complete discretion in deciding on approaches to its application. However, the automatic use of the web page content update mechanism does not guarantee desired results and often leads to average or sometimes unsatisfactory values that could have been avoided. This article provides an analysis of the characteristics of the performance parameters of a single-page web application, explores alternative methods to solve the stated problem, investigates potential "problematic" areas of the client-side of applications, compares the functioning of content update mechanisms of the most popular JavaScript frameworks Angular and React.js, and formulates recommendations for optimizing the overall application performance. The article concludes with tables of Core Web Vitals metrics for evaluating the effectiveness of the conducted optimization. Finally, potential vectors for further research are proposed.
Introduction to Inversion Theory

This chapter introduces the definitions of forward and inverse problems of mathematical physics and concepts of the well-posed and ill-posed problems. It also reviews the main principles of ill-posed inverse problem solutions based on the regularization theory developed in the pioneering research by Tikhonov. The notions of sensitivity and resolution, which are important in understanding the regularization principles, are also introduced. Finally, two different main approaches to formulating the inverse problem, one based on the deterministic concept and the other using the framework of the Bayesian probability theory, are considered as well.
Machine Learning in the Context of Inversion Theory

This chapter reviews the basic ideas of Artificial Intelligence (AI) and Machine Learning (ML) methods. The similarities and differences between the machine learning approach and inverse problem solution are discussed in detail. We introduce the concept of multilayer perceptron, which forms an artificial neural network, and discuss the importance of the Universal Approximation Theorem for neural networks. It is shown that network training can be treated as the inverse problem solution. The backpropagation algorithm of deep neural network training is presented as well. We also demonstrate that training the deep neural network (DNN) or finding the optimal weights of the DNN can be reduced to the application of the standard methods of regularized inversion.
Elements of Probability Theory

In this chapter, we review the foundations of the probability theory, which are needed to develop a probabilistic approach to inverse problem solutions. These include basic formulas and notations from probability theory, properties of discrete and continuous random variables, and the concept of Shannon’s entropy. We also study linear correlations between random variables and describe the properties of the correlation coefficient and the covariance matrix of multiple random variables. These properties play a fundamental role in the joint inversion of multiphysics data sets.
AI/ML in the Sonic Arts

Research Article| December 01 2023 AI/ML in the Sonic Arts: Pitfalls and Pathways Stephen Roddy, Stephen Roddy Stephen Roddy is an artist, researcher, and lecturer based at the Radical Humanities Lab and the Department of Digital Humanities at University College Cork. His work spans and integrates both computing and the creative arts. Search for other works by this author on: This Site PubMed Google Scholar Robin Parmar Robin Parmar Robin Parmar is a media artist who creates nonnarrative films, generative music, and environmental compositions exploring psychoacoustics, phenomenology, and epistemologies of nature. Robin has a doctorate in sonic creativity from De Montfort University (Leicester, UK) and currently directs a media program at the University of Limerick (Ireland). Search for other works by this author on: This Site PubMed Google Scholar Resonance (2023) 4 (4): 399–408. https://doi.org/10.1525/res.2023.4.4.399 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Stephen Roddy, Robin Parmar; AI/ML in the Sonic Arts: Pitfalls and Pathways. Resonance 1 December 2023; 4 (4): 399–408. doi: https://doi.org/10.1525/res.2023.4.4.399 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 ContentResonance Search Keywords: AI, artificial intelligence, music, machine learning, ML, Delia Beatriz, Moisés Horta Valenzuela, metaphor This commentary considers the application of artificial intelligence (AI) and machine learning (ML) technologies to music and the sonic arts. It critiques the classical computational theory of mind (CCTM), a doctrine deriving from functionalism, which codifies the “mind” as a mathematical function in which the symbolic representations of the internal mental world are mapped onto states and circumstances in the external physical world. This approach is agnostic insofar as the function can be computed on either biological or electronic “hardware.” This model renders the internal workings of thought and mind irrelevant. Such a function is thought to be computable on either biological or mechanical hardware, thereby rendering the internal workings of thought irrelevant. This technocratic impulse has been used to sell AI and ML products as “magical” solutions, capable of ushering in utopian futures. This viewpoint began with the foundation of computer science itself, as metaphors for computational processes were... You do not currently have access to this content.
Interview

Nao Tokui is an artist, researcher, and DJ who has been finding interesting new ways of integrating artificial intelligence and machine learning (AI/ML) techniques into his eclectic body of work since 2015. Stephen Roddy talked with Nao about his approach to AI in his own work and where he sees the relationship between AI and musical creativity going in the future.
Magnetic Coupling Visualization Using Sensitivity to Magnetic Permeability

This letter presents a method for visualizing magnetic coupling based on its sensitivity to magnetic permeability. Because a magnetic flux causes magnetic coupling that is proportional to permeability, an increase in permeability along the propagation path strengthens the magnetic coupling, whereas an increase in permeability outside the path has little effect on the coupling. The propagation path for magnetic coupling can thus be visualized based on the sensitivity to permeability. Calculating this sensitivity is faster than performing an electromagnetic simulation using the adjoint variable method. Two examples, namely, a loop pair and a simple electromagnetic interference filter, are used to show that the propagation path due to magnetic coupling can be visualized using the proposed method.
Optimizing the Performance of Consistency-Aware Deduplication Using Persistent Memory

Block-level data deduplication is a widely-used technology for saving storage space by filtering the data blocks with the same hash value. However, existing block-level data deduplication approaches either ignore the data consistency of deduplication or suffer severe performance degradation for providing consistency guarantees. In this paper, we propose Consistency-Aware Deduplication (CADedup+) to achieve high-performance block-level data deduplication with data consistency. The main idea of CADedup+ is to achieve an efficient journaling mechanism for deduplication by taking advantage of persistent memory (PM), such as byte-addressability and near-DRAM access latency. To balance the trade-offs between performance and consistency requirements in data deduplication, we carefully design three modes of journaling mechanism, i.e., writeback mode, ordered mode, and journal mode, for CADedup+. We properly place the deduplication metadata of CADedup+ onto the DRAM-PM hybrid memory architecture to minimize PM costs according to the features of metadata updates. The deduplication metadata on PM is managed by a set of metadata transactions and updated with the help of the efficient hardware atomic operations provided by CPU. We implement CADedup+ in the generic block layer in Linux kernel 4.9.0. We conduct extensive experiments on Intel Optane PMEM to evaluate CADedup+ with typical benchmarks. Experimental results show that CADedup+ can reduce 63%-70% write volume and 50%-60% I/O latency over Dmdedup, a widely-used open-source block-level data deduplication system, while ensuring deduplication consistency.
SPIRIT: Spectral Awareness Interaction Network with Dynamic Template for Hyperspectral Object Tracking

Hyperspectral (HS) video is able to capture abundant spectral, spatial, and temporal information of objects, which overcomes the limitations of common red-green-blue (RGB) video in complex scenarios such as similar appearances and background clutters. However, most of trackers apply hand-crafted features extracted from manually selected bands instead of deep features for object representations due to limited HS data and the band gap problem. Each HS image consists of many bands, and it is challenging to fully interact with the band information while maintaining tracking speed. To this end, this paper proposes a novel end-to-end spectral awareness interaction network with dynamic template (SPIRIT) for HS video object tracking. Firstly, a spectral awareness module is proposed to learn band contributions with consideration of nonlinear and global interactions between HS bands. It can also cooperate with the feature extraction module pre-trained with RGB data to attenuate the band gap and data-hungry. Secondly, an interaction module is proposed to achieve inter- and intra-band feature interactions to enhance tracking performance while improving efficiency. Furthermore, the proposed method contains a novel update module that evaluates the tracking confidence of the current state to adapt to object changes and attenuate tracking drifts. Extensive experiments demonstrate the superiority of our approach compared to state-of-the-arts while meeting real-time demands.
AiOENet: All-in-One Low-Visibility Enhancement to Improve Visual Perception for Intelligent Marine Vehicles Under Severe Weather Conditions

Benefiting from the higher performance-cost ratio and installation convenience, the visible-light imaging camera has become one of the most widely-used onboard sensor for safe vehicle navigation. However, the captured images inevitably suffer from the color distortion, contrast reduction, or loss of fine details under severe weather conditions (such as haze, low-lightness, rain, and snow). The quality-degraded visual information will lead to the limited perceptual accuracy and range, resulting in the increased navigation risk for intelligent marine vehicles. To suppress the influences of severe imaging conditions on navigation safety, this work proposes an all-in-one low-visibility enhancement network (termed AiOENet) to improve the visual perception for marine surface vehicles under different weather scenarios. In particular, our AiOENet mainly consists of a VGG16-driven scene discriminator, an encoder, parameter-shared Transformer blocks, and a decoder. The scene discriminator is exploited to classify four types of low-visibility images, collected under the hazy, low-light, rainy, or snowy weather conditions. According to the classification results, the low-visibility images are fed into the corresponding encoders for coarse feature extraction. The multiple Transformer blocks are then employed to separate and extract the smaller-scale features. The latent normal-visibility images are finally generated through the corresponding decoders. Therefore, our AiOENet has the capacity of flexibly and adaptively restoring the diverse low-visibility images using a uniform encoder-decoder network architecture under different imaging conditions. Compared with the state-of-the-art imaging methods, AiOENet achieves the comparable or even superior enhancement results in terms of both quantitative and qualitative evaluations. In addition, the AiOENet can contribute to the more accurate and stable object detection with the improved visual perception in maritime low-visibility scenes. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LouisYuxuLu/AiOENet</uri> .
Multilayer Stacked Evolving Fuzzy System Combined with Compressed Representation Learning

In order to process the high dimensionally complicated problems, the intelligence systems need to go deeper for learning high-level data representation. In this paper, based on the stacked generalization principle, a multilayered stacked learning system is proposed. Like the deep networks, the proposed system is organized in a layer-by-layer way with evolving fuzzy systems (EFSs) as its base-building units. Each EFS is designed as an autoencoder (AE) to learn the simpler data representation, then multiple EFS-based AEs are stacked in a feedforward manner for learning more complex data representation. Since there exists the redundant or irrelevant information in the new data representation which may limit high generalization, a novel feature compressing layer is followed by each EFS-based AE to refine the new data representation and reduce the feature dimension via very sparse random projection (VSRP). The proposed system is featured in the following merits: 1) more expressive and complex data representation can be learned in a stacked multilayer architecture; 2) manually tuning the structure of each AE is avoided in every layer since the EFS can self-adapt both its structure and parameter online; 3) the resulting system can avoid overfitting and obtain higher generalization by removing redundant or irrelevant information via VSRP; 4) the steady-state error analysis of the proposed system is studied based on the separable approximation property, which guarantees the learning convergence of the resulting system. Our experimental results on various benchmark datasets indicate the efficacy of proposed system.
Factorization Vision Transformer: Modeling Long-Range Dependency With Local Window Cost

Transformers have astounding representational power but typically consume considerable computation which is quadratic with image resolution. The prevailing Swin transformer reduces computational costs through a local window strategy. However, this strategy inevitably causes two drawbacks: 1) the local window-based self-attention (WSA) hinders global dependency modeling capability and 2) recent studies point out that local windows impair robustness. To overcome these challenges, we pursue a preferable trade-off between computational cost and performance. Accordingly, we propose a novel factorization self-attention (FaSA) mechanism that enjoys both the advantages of local window cost and long-range dependency modeling capability. By factorizing the conventional attention matrix into sparse subattention matrices, FaSA captures long-range dependencies, while aggregating mixed-grained information at a computational cost equivalent to the local WSA. Leveraging FaSA, we present the factorization vision transformer (FaViT) with a hierarchical structure. FaViT achieves high performance and robustness, with linear computational complexity concerning input image spatial resolution. Extensive experiments have shown FaViT's advanced performance in classification and downstream tasks. Furthermore, it also exhibits strong model robustness to corrupted and biased data and hence demonstrates benefits in favor of practical applications. In comparison to the baseline model Swin-T, our FaViT-B2 significantly improves classification accuracy by 1% and robustness by 7% , while reducing model parameters by 14% . Our code will soon be publicly available: at https://github.com/q2479036243/FaViT.
Near-Memory Computing With Compressed Embedding Table for Personalized Recommendation

Deep learning (DL)-based recommendation models play an important role in many real-world applications. However, an embedding layer, which is a key part of the DL-based recommendation models, requires sparse memory accesses to a very large memory space followed by the pooling operations (i.e., reduction operations). It makes the system overprovision memory capacity for model deployment. Moreover, with conventional CPU-based architecture, it is difficult to exploit the locality, causing a huge burden for data transfer between the CPU and memory. To resolve this problem, we propose an embedding vector element quantization and compression method to reduce the memory footprint (capacity) required by the embedding tables. In addition, to reduce the amount of data transfer and memory access, we propose near-memory acceleration hardware with an SRAM buffer that stores the frequently accessed embedding vectors. Our quantization and compression method results in compression ratios of 3.95–4.14 for embedding tables in widely used datasets while negligibly affecting the inference accuracy. Our acceleration technique with 3D stacked DRAM memories, which facilitates the near-memory processing in the logic die with high DRAM bandwidth, leads to 4.9×–5.4× embedding layer speedup as compared to the 8-core CPU-based execution while reducing the memory energy consumption by 5.9×–12.1×, on average.
SpikeLog: Log-based anomaly detection via Potential-assisted Spiking Neuron Network

The increasing volume and complexity of log data generated by modern systems have made it challenging to analyze and extract useful insights manually. To address this problem, many machine learning methods have been proposed for log-based anomaly detection. However, most of these methods lack interpretability, and their underlying premises do not always reflect real scenarios. In this paper, we consider a more reasonable premise scenario where a large number of logs are unlabeled, while only a small number of anomalous logs are labeled. Moreover, a small proportion of anomaly contamination may be present. To handle this practical scenario, we propose a novel hybrid potential-assisted framework (SpikeLog) using the membrane potential of spiking neurons. SpikeLog adopts a weakly supervised approach to train an anomaly score model, which effectively utilizes a limited number of labeled anomalies alongside abundant unlabeled logs while ensuring computational efficiency without compromising accuracy. Extensive experiments have demonstrated that SpikeLog outperforms baseline methods in terms of performance, robustness, interpretability, and energy consumption.