Reviewerly/exHarmony huggingface dataset:
FastSpeechStyle: Fast, Emotion Controllable, and High-Quality Speech Synthesis

Non-autoregressive text-to-speech models such as Fastspeech2 can fast synthesize high-quality speech. This model also allows explicit control of the speech signal’s pitch, energy, and speed. However, controlling emotion while maintaining natural human-like speech is still a problem. In this work, we propose an expressive speech synthesis model that can synthesize high-quality speech with desired emotion. The proposed model includes two main components (1) Mel Emotion Encoder extracts emotion embedding from the Mel-spectrogram of audio, (2) the FastSpeechStyle, a non-autoregressive model, which is modified from vanilla Fastspeech2. The FastSpeechStyle used an Improved Conformer block, which replaces normal LayerNorm with Style Adaptive LayerNorm to “shift” and “scale” hidden features according to emotion embedding, instead of vanilla FFTBlock [Y. Ren, Y. Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao and T.-Y. Liu, Fastspeech: Fast, robust and controllable text to speech, Adv. Neural Inf. Process. Syst. 32 (2019)] to better model the local and global dependency in the acoustic model. We also propose a specific inference strategy to control the desired emotion of speech. The experimental results show that (1) The proposed model with improved Conformer achieved higher scores than the baseline model in all naturalness and emotion similarity scores; (2) The proposed model still maintains fast inference speed like the baseline model.
Generating SAS+ Planning Tasks of Specified Causal Structure

Recent advances in data-driven approaches in AI planning demand more and more planning tasks. The supply, however, is somewhat limited. Past International Planning Competitions (IPCs) have introduced the de-facto standard benchmarks with the domains written by domain experts. The few existing methods for sampling random planning tasks severely limit the resulting problem structure. In this work we show a method for generating planning tasks of any requested causal graph structure, alleviating the shortage in existing planning benchmarks. We present an algorithm for constructing random SAS+ planning tasks given an arbitrary causal graph and offer random task generators for the well-explored causal graph structures in the planning literature. We further allow to generate a planning task equivalent in causal structure to an input SAS+ planning task. We generate two benchmark sets: 26 collections for select well-explored causal graph structures and 42 collections for existing IPC domains. We evaluate both benchmark sets with the state-of-the-art optimal planners, showing the adequacy for adopting them as benchmarks in cost-optimal classical planning. The benchmark sets and the task generator code are publicly available at https://github.com/IBM/fdr-generator.
Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning

The UK financial sector increasingly employs machine learning techniques to enhance revenue and understand customer behaviour. In this study, we develop a machine learning workflow for high classification accuracy and improved prediction confidence using a binary classification approach on a publicly available dataset from a Portuguese financial institution as a proof of concept. Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. We use stratified k-folding (k=5) cross-validation and assemble the top-performing classifiers into a decision-making committee, resulting in over 92% accuracy with two-thirds voting confidence. The workflow is simple, adaptable, and suitable for UK banks, demonstrating the potential for practical implementation and data privacy. Future work will extend our approach to UK banks, reformulate the problem as a multi-class classification, and introduce pre-training automated steps for data analysis and transformation.
Pengaruh Penggunaan Model Pembelajaran Course Review Horay Berbantuan Multimedia Terhadap Prestasi Belajar Siswa Pada Pembelajaran IPA

This study aims to determine the effect of the Multimedia-assisted Course Review Horay model on student achievement in science learning. This research was conducted in the fourth grade of SDN Krincing, Secang District, Magelang Regency, 2016/2017 Academic Year. The type of research used in this research is Quasi Experiment with Matching Pretest-Posttest Control Group Design. Learning in the experimental group by conducting three meetings, which was given treatment using the Horay Course Review Model. Pretest was given before treatment and posttest was given after treatment was given. The subjects in this study were 20 students in the experimental group and 20 students in the control group. The sampling technique in this research is saturated sampling. The sampling technique in this research is total sampling. There are two variables used in this study, namely the independent variable in the form of the Course Review Horay method assisted by Multimedia, and the dependent variable in the form of student achievement in science learning. Data collection techniques in the form of tests and observations. Analysis of the data obtained by using the Mann Whitney U Test. The results showed that the Course Review Horay method assisted by Multimedia had a positive effect on students' science learning. Based on the results of the posttest Mann Whitney U Test of the two groups conducted by the researcher, it was obtained that tcount &lt; ttable, namely 0.013 &lt;0.05. These results indicate that the Course Review Horay learning model assisted by Multimdeia has a significant effect on students' science learning achievement.
European Union’s Approach to Crypto-Assets and Distributed Ledger Technologies

Goal – to analyse the different and comprehensive aspects of the European Union’s approach to distributed ledger technologies and crypto-assets with a teleological interpretation then finally foresee what kind of policy is going to be adopted by the European Union regarding distributed ledgers. Research metodology – the article includes an analysis of the current and upcoming legal acts and regulations of the European Union regarding distributed ledger technologies and crypto-assets. A multi-disciplinary approach is adapted to understand the financial and technological reasons of European Union’s policy. Score/results – many regulations and directives have been legislated by the European Union and more regulations are in the final phase of the legislation process. Since the global hubs of the distributed ledgers (especially the United States and the Republic of China) have put negative pressures on the crypto-assets and their underlying technologies, the European Union is a candidate to be a new global hub with its ongoing technology-agnostic approach. Originality/value – the effort for the harmonization between a range of different regulatory areas such as anti-money laundering regulations, public financial regulations and the legal infrastructure for pilot regimes regarding distributed ledger technologies and crypto-assets will bring a new impulse to the promoting innovation in a politically and financially safe zone of the European Union.
Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model

Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the search system since it helps improve performance. When online serving a relevance model, the model is required to perform fast and accurate inference. Currently, the widely used models such as Bi-encoder and Cross-encoder have their limitations in accuracy or inference speed respectively. In this work, we propose a novel model called the Entity-Based Relevance Model (EBRM). We identify the entities contained in an item and decompose the QI (query-item) relevance problem into multiple QE (query-entity) relevance problems; we then aggregate their results to form the QI prediction using a soft logic formulation. The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy as well as cache QE predictions for fast online inference. Utilizing soft logic makes the prediction procedure interpretable and intervenable. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance. The proposed method is evaluated on labeled data from e-commerce websites. Empirical results show that it achieves promising improvements with computation efficiency.
WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution

Image super-resolution research recently been dominated by transformer models which need higher computational resources than CNNs due to the quadratic complexity of self-attention. We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture which uses a 2D-discrete wavelet transform for spatial token-mixing. Unlike transformer-based models, WaveMixSR does not unroll the image as a sequence of pixels/patches. It uses the inductive bias of convolutions along with the lossless token-mixing property of wavelet transform to achieve higher performance while requiring fewer resources and training data. We compare the performance of our network with other state-of-the-art methods for image super-resolution. Our experiments show that WaveMixSR achieves competitive performance in all datasets and reaches state-of-the-art performance in the BSD100 dataset on multiple super-resolution tasks. Our model is able to achieve this performance using less training data and computational resources while maintaining high parameter efficiency compared to current state-of-the-art models.
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed ``unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956).
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding

Deep learning advancements have revolutionized scalable classification in many domains including computer vision. However, when it comes to wearable-based classification and domain adaptation, existing computer vision-based deep learning architectures and pretrained models trained on thousands of labeled images for months fall short. This is primarily because wearable sensor data necessitates sensor-specific preprocessing, architectural modification, and extensive data collection. To overcome these challenges, researchers have proposed encoding of wearable temporal sensor data in images using recurrent plots. In this paper, we present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. Our approach incorporates an efficient Fourier transform-based frequency domain angular difference estimation scheme in conjunction with the existing temporal recurrent plot image. Furthermore, we employ mixup image augmentation to enhance the representation. We evaluate the proposed method using accelerometer-based activity recognition data and a pretrained ResNet model, and demonstrate its superior performance compared to existing approaches.
Tuning Support Vector Machines and Boosted Trees Using Optimization Algorithms

Statistical learning methods have been growing in popularity in recent years. Many of these procedures have parameters that must be tuned for models to perform well. Research has been extensive in neural networks, but not for many other learning methods. We looked at the behavior of tuning parameters for support vector machines, gradient boosting machines, and adaboost in both a classification and regression setting. We used grid search to identify ranges of tuning parameters where good models can be found across many different datasets. We then explored different optimization algorithms to select a model across the tuning parameter space. Models selected by the optimization algorithm were compared to the best models obtained through grid search to select well performing algorithms. This information was used to create an R package, EZtune, that automatically tunes support vector machines and boosted trees.
Impact of Local Search in the Memetic Particle Swarm Optimization

Memetic algorithms have become popular partly due to their high performance in multidimensional and non-differentiable problems and their high exploitation capability due to the implementation of local search algorithms helping the population-based algorithms. They were used to solve several theoretical and practical optimization problems. Despite this, there is still a need to study further these algorithms’ behavior and what makes an algorithm successful in different problems. In this paper, we modeled the interaction networks of four memetic swarm-based algorithms. The interaction networks can capture all the communication between the individuals of the algorithm throughout its execution. These networks are analyzed using several metrics. We successfully demonstrated that the applied methodology could not only show the difference between standard swarm algorithms and memetic algorithms, as already demonstrated in the literature. We can also identify unique characteristics of each memetic variety of the same algorithm. It allows a better understanding of the behavior of convergence, stagnation, and the balance between exploitation and exploration in their various stages of execution.
Detecting Hoax Content on Social Media Using Bi-LSTM and RNN

Online media, such as websites and applications, have become a communication tool available on the internet. Social media is a part of online media that can be used to spread news, opinions, or even hoaxes, such as through Twitter. Although hoaxes are difficult to eliminate, several systems have been built using deep learning approaches that can process text and images to detect the truthfulness of news. In this study, four systems were built using four deep learning methods, namely Bi-directional Long Short-Term Memory (Bi-LSTM), Recurrent Neural Network (RNN), hybrid RNN-Bi-LSTM, and hybrid Bi-LSTM-RNN. Feature extraction was performed using Term Frequency - Inverse Document Frequency (TF-IDF) and feature expansion was performed using Global Vectors (GloVe). The data used has been adjusted according to the keyword of fake news on mainstream news portals. This study attempted several scenarios to compare the various methods that have been built, with the aim of finding the best method that provides the highest accuracy. The results showed that the Bi-LSTM method had the highest accuracy of 96.48%, while the hybrid Bi-LSTM-RNN method ranked second with an accuracy of 96.36%, followed by the RNN method with an accuracy of 95.49%, and the hybrid RNN-Bi-LSTM method with an accuracy of 95.34%.
On the equivalence of different adaptive batch size selection strategies for stochastic gradient descent methods

In this study, we demonstrate that the norm test and inner product/orthogonality test presented in \cite{Bol18} are equivalent in terms of the convergence rates associated with Stochastic Gradient Descent (SGD) methods if $\epsilon^2=\theta^2+\nu^2$ with specific choices of $\theta$ and $\nu$. Here, $\epsilon$ controls the relative statistical error of the norm of the gradient while $\theta$ and $\nu$ control the relative statistical error of the gradient in the direction of the gradient and in the direction orthogonal to the gradient, respectively. Furthermore, we demonstrate that the inner product/orthogonality test can be as inexpensive as the norm test in the best case scenario if $\theta$ and $\nu$ are optimally selected, but the inner product/orthogonality test will never be more computationally affordable than the norm test if $\epsilon^2=\theta^2+\nu^2$. Finally, we present two stochastic optimization problems to illustrate our results.
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review

A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems. This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in recommender systems. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in recommender systems. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in recommendation systems, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and recommendation systems. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.
Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling

Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans. We aim to craft adversarial texture for clothes based on 3D modeling, an idea that has been used to craft rigid adversarial objects such as a 3D-printed turtle. Unlike rigid objects, humans and clothes are non-rigid, leading to difficulties in physical realization. In order to craft natural-looking adversarial clothes that can evade person detectors at multiple viewing angles, we propose adversarial camouflage textures (AdvCaT) that resemble one kind of the typical textures of daily clothes, camouflage textures. We leverage the Voronoi diagram and Gumbel-softmax trick to parameterize the camouflage textures and optimize the parameters via 3D modeling. Moreover, we propose an efficient augmentation pipeline on 3D meshes combining topologically plausible projection (TopoProj) and Thin Plate Spline (TPS) to narrow the gap between digital and real-world objects. We printed the developed 3D texture pieces on fabric materials and tailored them into T-shirts and trousers. Experiments show high attack success rates of these clothes against multiple detectors.
MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain

Scientific publications follow conventionalized rhetorical structures. Classifying the Argumentative Zone (AZ), e.g., identifying whether a sentence states a Motivation, a Result or Background information, has been proposed to improve processing of scholarly documents. In this work, we adapt and extend this idea to the domain of materials science research. We present and release a new dataset of 50 manually annotated research articles. The dataset spans seven sub-topics and is annotated with a materials-science focused multi-label annotation scheme for AZ. We detail corpus statistics and demonstrate high inter-annotator agreement. Our computational experiments show that using domain-specific pre-trained transformer-based text encoders is key to high classification performance. We also find that AZ categories from existing datasets in other domains are transferable to varying degrees.
Multi-agent Cooperative Control in Neural MMO Environment Based on MAPPO Algorithm

In recent years, with the increasing number of artificial intelligence and deep learning algorithms and applications, reinforcement learning, as a branch of machine learning, has shown its advantages over other machine learning algorithms in highly complex environments, including famous events such as AlphaGo defeating human chess players. Various branches of reinforcement learning algorithms and experimental environments have been developed for research. Reinforcement learning and multi-agent reinforcement learning are applied in this paper. The multi-agent reinforcement learning algorithm MAPPO with a proposed reward function is validated under a Neural MMO environment. The result verifies that the MAPPO algorithm can provide strategies for agents running in the Neural MMO environment.
A Smart Contract-Based Microgrid Large-Scale Electricity Trading System

With the development of distributed energy resources (DERs) and microgrids, the traditional electricity trading system based on centralized models is no longer suitable for the current energy market. To address this issue, in this paper, we propose a smart contract-based large-scale power trading system for microgrids. To do this, we first model the two-tier large-scale power trading system of the microgrid, then use the Hungarian algorithm to match the power transactions, and package all the power transactions into a blockchain transaction once they are completed via smart contracts. Finally, a simulation experiment is conducted to demonstrate that the proposed algorithm can effectively reduce the time taken for power transactions.
Various Approaches in Deep Learning for Medical Modality Image Segmentation

In computer vision and image processing the surveillance of Video, Virtual reality, robotic perception, and image compression are just a few of the uses for image segmentation. There are a plethora of segmentation algorithms out there right now. These new approaches to picture segmentation have been spurred on by the extensive victory of Deep Learning (DL). Semantic and instance segmentation literature is thoroughly reviewed, with particular attention paid to groundbreaking work in CNN models, architecture of encoder-decoder, recurrent networks, attention models, and GAN settings. This work is a comprehensive review of the most recent research in these fields. Using a wide range of datasets, we assess the performance of different deep learning-based segmentation representation, examine their strengths and drawbacks, and identify promising future avenues