Reviewerly/exHarmony huggingface dataset:
Forecasting Rice Production in Indonesia using Regression Techniques: A Comparative Analysis of Support Vector Machine, Linear Regression, and XGBoost Regression

Population growth is straining the agricultural sector, posing a significant threat to global food security. In Indonesia, rice is a staple diet, with annual demand growth of 1.16 percent outpacing production growth of 0.81 percent. Given Indonesia's projected population growth, meeting this demand is crucial. Indonesia is the fourth most populous nation in the world. In order to evaluate government policies on food sustainability, such as rice imports and agricultural programs, it is necessary to forecast rice production levels. Using Support Vector Machine, Linear Regression, and XGBoost Regression, the objective of this study was to forecast the paddy production in Indonesia. The study employed a dataset spanning the years 2018 to 2022, and the efficacy of each model was evaluated using standard metrics including MAPE, MAE, RMSE, and R2. With an RMSE of 0.015, an R2 of 0.997, and an MAE of 0.008, XGBoost Regression outperformed all other models across all metrics. Support Vector Regression had the highest MAPE, the lowest RMSE, the highest R2, and the lowest MAE, whereas Linear Regression had the highest MAPE, the lowest RMSE, the highest R2, and the lowest MAE.
A Novel Approach for Human Activity Recognition Using Vision Based Method

Human Activity Recognition (HAR) is a fascinating process that involves identifying and categorizing human activities based on observations of subject behavior and environmental factors. Out of the major phases of HAR, preprocessing and feature extraction, in particular, require a lot of attention because they constitute the foundation for the training phase. The current evaluation effectively examines the trends in vision-based research approaches. Many models based on the artificial intelligence are suggested for the activity recognition; however, they couldn't perform satisfactorily on real world long-term HAR due to their lacuna in the extraction of spatial and temporal data. In light of these drawbacks, we propose a hybrid methodology for the human activity recognition which integrates Convolutional Neural Network (CNN) with the Long Short-Term Memory (LSTM), where CNN works for extracting the spatial characteristics and LSTM is used for the learning of the temporal information. A complete evaluation of the substantial work done in HAR domain and its vision-based techniques would help the readers.
PALOMA: Binary Separable Goppa-Based KEM

In this paper, we propose PALOMA, a new code-based key encapsulation mechanism, which is designed by combining an NP-hard SDP(Syndrome Decoding Problem)-based trapdoor with a binary separable Goppa code and FO(Fujisaki-Okamoto) transformation. Cryptographic schemes based on an SDP defined with a binary Goppa code have not been found to be vulnerable to critical attacks, and the FO transformation ensures IND-CCA2 security in the ROM(Random Oracle Model). The combination is highly regarded in cryptographic communities for its strong security guarantees. PALOMA has a public key size of approximately 300KB or more due to its SDP-based trapdoor nature. Furthermore, the key generation process, which involves generating the parity-check matrix of the scrambled Goppa code, is relatively slow compared to other post-quantum ciphers. However a primary role of post-quantum cryptography is to serve as an alternative to current cryptosystems that are vulnerable to quantum computing attacks. Therefore, in post-quantum cryptography, ensuring strong security guarantees is more important than efficiency. Consequently, we have designed PALOMA with a focus on conservative security guarantees, while ensuring that there is no significant degradation in application quality.
FuLeeca: A Lee-Based Signature Scheme

In this work, we introduce a new code-based signature scheme, called FuLeeca, based on the NP-hard problem of finding codewords of given Lee-weight. The scheme follows the Hash-and-Sign approach applied to quasi-cyclic codes. Similar approaches in the Hamming metric have suffered statistical attacks, which revealed the small support of the secret basis. Using the Lee metric, we are able to thwart such attacks. We use existing hardness results on the underlying problem and study adapted statistical attacks. We propose parameters for FuLeeca and compare them to an extensive list of proposed post-quantum secure signature schemes including the ones already standardized by NIST. This comparison reveals that FuLeeca is competitive. For example, for NIST category I, i.e., 160 bit of classical security, we obtain an average signature size of 1100 bytes and public key sizes of 1318 bytes. Comparing the total communication cost, i.e., the sum of the signature and public key size, we see that FuLeeca is only outperformed by Falcon while the other standardized schemes Dilithium and SPHINCS+ show higher communication costs than FuLeeca.
Financial Fraud Detection Based on Deep Learning: Towards Large-Scale Pre-training Transformer Models

Fraud detection is a critical issue in the field of finance, as it can help to prevent fraud and minimize losses caused by fraud. Deep learning techniques learn the intrinsic knowledge of huge data, build explainable transaction knowledge graphs, and effectively predict potential fraudulent transactions, making it an essential technique in financial fraud detection. In this paper, we systematically review the existing financial fraud detection technologies, focusing on deep learning-based financial fraud detection methods. To the best of our knowledge, our work is the first to systematically introduce financial fraud detection methods based on transformer models, including the most recent pre-training transformer models, which can be thought of as parametric knowledge. Finally, we also analyze and summarize the challenges of financial fraud detection research, to promote its future development of research.
DCA-DAFFNet: An End-to-End Network With Deformable Fusion Attention and Deep Adaptive Feature Fusion for Laryngeal Tumor Grading From Histopathology Images

Laryngeal Tumor Grading is a challenge task for computer-aided clinical diagnosis (CACD), mainly because the nuclei in histopathological images have large differences in shape and distribution, and the complex spatial relationship between nuclei. However, the existing CNN-based tumor grading models cannot adaptively represent the nuclei with variable morphology, and lack of effective modeling for the semantic information of nuclear spatial location. Therefore, we propose an end-to-end network (DCA-DAFFNet) with deformable convolution guided attention block (DCAB) and deep adaptive feature fusion (DAFF) to improve the grading ability. Specifically, a novel DCAB is designed to represent variable nuclei adaptively with more flexible receptive fields of deformable convolution. Besides, to solve the problem of poor semantic information modeling ability of nuclear spatial location, the vision transformer (ViT) block is incorporated into the attention-based CNN models to construct DCA-DAFFNet with powerful global information representation capability. Furthermore, a DAFF method containing multi-kernel max mean discrepancy (MK-MMD) and adaptive fusion block is proposed to solve the problem of negative fusion in DCA-DAFFNet. Extensive experiments results demonstrate the effectiveness of DCA-DAFFNet, and its average grading accuracy is increased to 90.78% ( ↑ 11.80%). By comparing visualization results, our proposed DCA-DAFFNet can pay more attention to the heterogeneous nuclei (polymorphic-nucleus, mega-nucleus, etc.) concerned by pathologists, which shows that our method more interpretable and human-computer interactive.
Regional Contextual Information Modeling for Small Object Detection on Highways

Small object detection based on highways is still a highly challenging research topic due to the limitation of the number of pixels belonging to an object. Traditional small object detection methods lack the exploration of contextual information since the features of small objects do not have sufficient discriminative information, leading to poor performance. In our research, we present a straightforward yet powerful approach that can be seamlessly integrated as a plug-and-play component, to use contextual information as clues to enrich small object features to improve detection performance. In particular, we design a novel region context module that utilizes the self-attention mechanism to capture the context information in a regional manner. In order to encourage cross-regional interaction of context information, we address a shifted-region partitioning method to offset the features circularly. Additionally, we introduce a channel selection module, which enables the network to adaptively select the suitable channel to enhance the ability for classification. Comprehensive experiments demonstrate that our method is effective, e.g., our method achieves mean Average Precision (mAP), AP0.5, AP0.75, and APs of 56.8%, 89.6%, 61.9%, and 49.6% on the Small Object Detection on Highways (HSOD) dataset, respectively. Besides, extensive ablation studies and visualizations are provided for a better understanding of our method.
Drone Detection Method Based on the Time-Frequency Complementary Enhancement Model

Drone audio detection methods have become a key component of anti-drone systems. Traditional audio feature extraction methods have problems such as large fluctuations in feature vectors, fixed extraction resolution, and redundant feature information extraction. Moreover, the dataset is ideal and not representative of real application scenarios. In this study, a dual-domain audio feature extraction method in the time and frequency domains is proposed that improves the accuracy of drone detection by combining the more richly detailed information in the time domain and the relatively stable property of the signal in the frequency domain. A real-world sound dataset that contains low signal-to-noise ratio audio was collected for experimental validation. The results showed that, compared with existing methods, the proposed method took full advantage of the “zoom” feature of the wavelet packet transform, the local feature extraction capability of a one-dimensional convolutional neural network, and the global modeling capability of a self-attention mechanism, thereby effectively improving the success rate of drone detection in common scenarios. The proposed method also outperformed other methods with respect to several evaluation metrics.
Robustification of Finite Impulse Response Filter for Nonlinear Systems With Model Uncertainties

This paper investigates an iterative finite impulse response filter for a nonlinear system with model uncertainties. We begin by designing a simple initialization method for the iterative FIR structure and use the unscented transformation to realize iterative state estimation in each finite horizon. Additionally, a compensation variable is introduced to compensate for the inaccuracies in the nonlinear dynamic model, thereby reducing uncertainties and improving the estimation accuracy. To estimate the compensation variable, we employ the variational Bayesian method. Compared with other filters, our design can not only avoid calculating the Jacobian matrix and batch processing to reduce computational load, but also enhance robustness. Simulation results demonstrate the validity of the proposed filter.
MaskFaceGAN: High-Resolution Face Editing With Masked GAN Latent Code Optimization

Face editing represents a popular research topic within the computer vision and image processing communities. While significant progress has been made recently in this area, existing solutions: (i) are still largely focused on low-resolution images, (ii) often generate editing results with visual artefacts, or (iii) lack fine-grained control over the editing procedure and alter multiple (entangled) attributes simultaneously, when trying to generate the desired facial semantics. In this paper, we aim to address these issues through a novel editing approach, called MaskFaceGAN that focuses on local attribute editing. The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i.e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially-selective treatment of local image regions. The constraints are enforced with the help of an (differentiable) attribute classifier and face parser that provide the necessary reference information for the optimization procedure. MaskFaceGAN is evaluated in extensive experiments on the FRGC, SiblingsDB-HQf, and XM2VTS datasets and in comparison with several state-of-the-art techniques from the literature. Our experimental results show that the proposed approach is able to edit face images with respect to several local facial attributes with unprecedented image quality and at high-resolutions ( 1024×1024 ), while exhibiting considerably less problems with attribute entanglement than competing solutions. The source code is publicly available from: https://github.com/MartinPernus/MaskFaceGAN.
Melody Slot Machine on iPhone: Dial-type Interface for Morphed Melody

We present Melody Slot Machine on iPhone, an iPhone application using the melodic morphing method on the basis of the Generative Theory of Tonal Music (GTTM). We previously developed a demonstration system called Melody Slot Machine to introduce the melodic morphing method and presented it at international conferences and exhibitions. Since in-person demonstrations were reduced due to the Covid-19 Pandemic, we implemented an application with the same functionality and made it available for downloading to experience it. Our Melody Slot Machine on iPhone currently has two contents available for download, and we plan to add more contents in the future.
LearnThatDance: Augmenting TikTok Dance Challenge Videos with an Interactive Practice Support System Powered by Automatically Generated Lesson Plans

In this demo we showcase an interactive application to support the learning of “TikTok dance challenge” short dance choreographies. Our system utilizes dance challenge videos as the information source, performing music analysis and pose estimation to segment the dance into learnable chunks and generate a practice plan that implements motor learning techniques such as incremental part-learning and fading guidance. These plans are presented in a web app that implements video demonstration, augmented webcam mirroring, practice recording/review functionality, and both concurrent and terminal feedback. By operating on a ubiquitous information source, generating the lessons automatically, and requiring only a web browser and webcam in the user interface, our system is a step towards significantly expanding the reach of dance choreography learning and a platform for further research into dance HCI.
Touch'n'Draw: Rapid 3D Sketching with Fluent Bimanual Coordination

In perspective drawing, designers express 3D shapes by drawing auxiliary lines that construct surfaces and drawing design curves on them. However, drawing auxiliary lines can be challenging, and too many of them can make the drawing difficult to understand. To address these issues, we present a novel 3D sketching system that allows the user to quickly and easily create instant auxiliary lines and instant sketch surfaces for drawing desired 3D curves with fluent bimanual touch and pen interactions. We produced a concept sketch using our system to showcase its potential usefulness.
Multi-AUV cooperative search method based on dynamic optimal coverage

This paper introduces a multi-AUV target search method founded on dynamic optimization coverage, aiming to plan paths that achieve complete coverage without backtracking and with reduced travel costs for participating AUVs in cooperative search missions. The study comprises three key components: 1) Environment Modeling:We present a mixed-type grid approach, capable of accurately modeling concave–convex polygons. 2) Area Allocation:A dynamic cooperative partition strategy based on K-means+ is employed to facilitate optimal area allocation for AUVs. 3) Path Planning: The paper introduces a search path planning algorithm called the Optimal Allocation Minimum Spanning Tree (OA-MST). OA-MST generates globally smooth and non-backtracking scanning paths within irregular task areas using an adaptive spanning tree. Moreover, the study includes multiple simulation experiments to validate the method's efficacy. OA-MST is compared with the lawnmower strategy and four advanced Minimum Spanning Tree (MST) generation methods under identical area allocation conditions. The results demonstrate that the proposed collaborative search method significantly enhances path smoothness and reduces path costs across diverse scenarios. In addition, sea trials are conducted to verify the feasibility and practicality of implementing the proposed methods in engineering applications.
Experimental Quantum Channel Discrimination Using Metastable States of a Trapped Ion

We present experimental demonstrations of accurate and unambiguous single-shot discrimination between three quantum channels using a single trapped $^{40}{\mathrm{Ca}}^{+}$ ion. The three channels cannot be distinguished unambiguously using repeated single channel queries, the natural classical analogue. We develop techniques for using the six-dimensional ${D}_{5/2}$ state space for quantum information processing, and we implement protocols to discriminate quantum channel analogues of phase shift keying and amplitude shift keying data encodings used in classical radio communication. The demonstrations achieve discrimination accuracy exceeding 99% in each case, limited entirely by known experimental imperfections.
Exploring the Benefits of Representing Multiplayer Game Data in a Coordinate System

In the realm of game theory, a range of mathematical approaches exists for the representation of game data, with the extensive form (depicted as a game tree) and the normal form (illustrated as a payoff matrix) standing out as the most prevalent. However, a significant drawback associated with these approaches is their limited scalability. As the number of players or their strategic options increases, these techniques progressively lose their feasibility and become less practical for meaningful analysis. The present work proposes an alternative approach that significantly enhances the representation of data in two- or three-player games. Within this framework, the conventional payoff matrix is substituted with a payoff coordinate system, employing a coordinate plane for two-player games and a coordinate space for three-player games. This approach offers numerous advantages when compared to other methods. For instance, the Nash equilibrium can be readily identified within a game without requiring an extensive duration to exhaustively examine all strategies for its determination. By employing this approach, the representation of game data becomes more convenient and efficient, making it easier to analyze and comprehend the underlying strategies employed by players.
GAN-Based Image Compression with Improved RDO Process

GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates. However, there are two main issues, including 1) the reconstructed image perceptual degeneration in color, texture, and structure as well as 2) the inaccurate entropy model. In this paper, we present a novel GAN-based image compression approach with improved rate-distortion optimization (RDO) process. To achieve this, we utilize the DISTS and MS-SSIM metrics to measure perceptual degeneration in color, texture, and structure. Besides, we absorb the discretized gaussian-laplacian-logistic mixture model (GLLMM) for entropy modeling to improve the accuracy in estimating the probability distributions of the latent representation. During the evaluation process, instead of evaluating the perceptual quality of the reconstructed image via IQA metrics, we directly conduct the Mean Opinion Score (MOS) experiment among different codecs, which fully reflects the actual perceptual results of humans. Experimental results demonstrate that the proposed method outperforms the existing GAN-based methods and the state-of-the-art hybrid codec (i.e., VVC).
Unbacked Crypto-Assets, Stablecoins and CBDC

This chapter reviews three innovations in payments: (1) unbacked crypto-assets (i.e. Bitcoin); (2) stablecoins; and (3) central bank digital currencies (CBDC). To begin with, the chapter provides definitions of numerous terms which are often misconstrued. This is followed by a discussion of Bitcoin, DeFi and blockchain/DLT. The chapter then turns its attention to stablecoins, and describes some of the policy and regulatory issues associated with stablecoins. It then introduces CBDC as a to-be-expected modernization of central bank money available to all, and provides a summary of various viewpoints with regard to whether or not CBDC should be issued. The flow of funds mechanics of CBDC creation and transfers are developed, and in addition, the many design challenges of issuing a CBDC are outlined.
A multi-task network for speaker and command recognition in industrial environments

In industrial environments, it is crucial to establish a strong collaboration between humans and robots to enhance productivity. However, the nature of the work demands that workers have the authority to provide specific instructions to the robots. The scientific community has extensively investigated these dual requirements, aiming to develop advanced systems capable of recognizing voice commands and implementing speaker authentication. Nevertheless, in the industrial context, these tasks should be executed simultaneously on low-cost and low-power embedded devices that can be mounted on board the robotic platform. To overcome this challenge, we propose a multi-task network for Speech-Command Recognition and Speaker Identification. Additionally, we employ the GradNorm adaptive algorithm to address the issue of task imbalance. To evaluate the proposed system, we introduce a new dataset, MIVIA-ISC, consisting of 20,857 samples uttered by 562 speakers for 31 distinct commands. Our approach significantly reduces the network size by 47% and its execution time by 48% compared to the commonly used methodology, which employs one network for each task. Furthermore, our approach demonstrates a significant improvement in the accuracy of the Speaker Identification task, achieving an 11% increase compared to the corresponding single-task network. Importantly, this enhancement is achieved without compromising the accuracy of the Speech-Command Recognition task, which experiences only a minimal 3% decrease in performance.