Reviewerly/exHarmony huggingface dataset:
Logic-Based Explainability in Machine Learning

The last decade witnessed an ever-increasing stream of successes in Machine Learning (ML). These successes offer clear evidence that ML is bound to become pervasive in a wide range of practical uses, including many that directly affect humans. Unfortunately, the operation of the most successful ML models is incomprehensible for human decision makers. As a result, the use of ML models, especially in high-risk and safety-critical settings is not without concern. In recent years, there have been efforts on devising approaches for explaining ML models. Most of these efforts have focused on so-called model-agnostic approaches. However, all model-agnostic and related approaches offer no guarantees of rigor, hence being referred to as non-formal. For example, such non-formal explanations can be consistent with different predictions, which renders them useless in practice. This paper overviews the ongoing research efforts on computing rigorous model-based explanations of ML models; these being referred to as formal explanations. These efforts encompass a variety of topics, that include the actual definitions of explanations, the characterization of the complexity of computing explanations, the currently best logical encodings for reasoning about different ML models, and also how to make explanations interpretable for human decision makers, among others.
Anomaly Detection Using LSTM-Based Variational Autoencoder in Unsupervised Data in Power Grid

This article proposes a deep generative model for anomaly detection in unsupervised power grid data. One-class classifier-based methods suffer from performance degradation when training data contain anomalous samples. Due to the temporal characteristics in most of the power grid datasets, we explore a long short-term memory-variational autoencoder-based deep generative model that can tolerate the moderate presence of anomalous data during training instead of standard data. This work demonstrates the advantage of reconstruction-based methods over clustering-based methods. As part of the reparameterization of the latent layer, a method is proposed by employing wavelet decomposition of the wavelet coefficients found from the high and medium frequency representations of the input time-series data. For further improvement, we have incorporated a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log \text{cos}h$</tex-math></inline-formula> -based cost function instead of the traditional consideration of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> norm-based cost function. The numerical results demonstrate an improvement of performance metrics, such as AUC by 0.1–0.2 of our method over other benchmark methods. The transient stability threshold ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> ) is an important system parameter in the performance assessment of power grid systems. Through time domain simulations, it has been shown that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta =0.3$</tex-math></inline-formula> obtains optimal accuracy for transient stability assessment in the IEEE NewEngland-39 bus.
Deep SE-BiLSTM with IFPOA Fine-Tuning for Human Activity Recognition Using Mobile and Wearable Sensors

Pervasive computing, human-computer interaction, human behavior analysis, and human activity recognition (HAR) fields have grown significantly. Deep learning (DL)-based techniques have recently been effectively used to predict various human actions using time series data from wearable sensors and mobile devices. The management of time series data remains difficult for DL-based techniques, despite their excellent performance in activity detection. Time series data still has several problems, such as difficulties in heavily biased data and feature extraction. For HAR, an ensemble of Deep SqueezeNet (SE) and bidirectional long short-term memory (BiLSTM) with improved flower pollination optimization algorithm (IFPOA) is designed to construct a reliable classification model utilizing wearable sensor data in this research. The significant features are extracted automatically from the raw sensor data by multi-branch SE-BiLSTM. The model can learn both short-term dependencies and long-term features in sequential data due to SqueezeNet and BiLSTM. The different temporal local dependencies are captured effectively by the proposed model, enhancing the feature extraction process. The hyperparameters of the BiLSTM network are optimized by the IFPOA. The model performance is analyzed using three benchmark datasets: MHEALTH, KU-HAR, and PAMPA2. The proposed model has achieved 99.98%, 99.76%, and 99.54% accuracies on MHEALTH, KU-HAR, and PAMPA2 datasets, respectively. The proposed model performs better than other approaches from the obtained experimental results. The suggested model delivers competitive results compared to state-of-the-art techniques, according to experimental results on four publicly accessible datasets.
SANet-SI: A new Self-Attention-Network for Script Identification in scene images

Developing an automatic method for identifying scripts in natural scene text images is of great importance for improving performance of multilingual OCR. This paper presents a new Self-Attention Network (SANet-SI) for script identification in natural scene text images. The rationale behind proposing SANet-SI is that each script exhibits its own pattern because of different characteristics of scripts. To extract such observations, we explore self-attention-based CNN with a multi-scale feature extraction approach. The proposed multi-scale feature extraction involves local, global features extraction and fusion of both the features. Furthermore, to extract dominant features from the pool of features that contribute more for script identification, we explore Style-based Recalibration Module (SRM) in a new way. In addition, to improve the performance of the identification and reduce the model size, the proposed model uses the Global Average Pooling (GAP) layer, instead of Fully Connected(FC) layers in this work. The proposed model is evaluated on standard datasets, namely, RRC-MLT2017, SIW-13, and CVSI2015 to show effectiveness over state-of-the-art methods in terms of confusion matrix and classification rate. In addition, we also conducted experiments for Cross Dataset Validation to show that the proposed model is independent of the number of scripts and different datasets.
Financial Time Series Forecasting: A Data Stream Mining-Based System

Data stream mining (DSM) represents a promising process to forecast financial time series exchange rate. Financial historical data generate several types of cyclical patterns that evolve, grow, decrease, and end up dying. Within historical data, we can notice long-term, seasonal, and irregular trends. All these changes make traditional static machine learning models not relevant to those study cases. The statistically unstable evolution of financial market behavior yields a progressive deterioration in any trained static model. Those models do not provide the required characteristics to evolve continuously and sustain good forecasting performance as the data distribution changes. Online learning without DSM mechanisms can also miss sudden or quick changes. In this paper, we propose a possible DSM methodology, trying to cope with that instability by implementing an incremental and adaptive strategy. The proposed algorithm includes the online Stochastic Gradient Descent algorithm (SGD), whose weights are optimized using the Particle Swarm Optimization Metaheuristic (PSO) to identify repetitive chart patterns in the FOREX historical data by forecasting the EUR/USD pair’s future values. The data trend change is detected using a statistical technique that studies if the received time series instances are stationary or not. Therefore, the sliding window size is minimized as changes are detected and maximized as the distribution becomes more stable. Results, though preliminary, show that the model prediction is better using flexible sliding windows that adapt according to the detected distribution changes using stationarity compared to learning using a fixed window size that does not incorporate any techniques for detecting and responding to pattern shifts.
Multi-MCCR: Multiple models regularization for semi-supervised text classification with few labels

Semi-supervised learning has achieved impressive results and is commonly applied in text classifications. However, in situations where labeled texts are exceedingly limited, neural networks are prone to over-fitting due to the non-negligible inconsistency between model training and inference caused by dropout mechanisms that randomly mask some neurons. To alleviate this inconsistency problem, we propose a simple Multiple Models Contrast learning based on Consistent Regularization, named Multi-MCCR, which consists of multiple models with the same structure and a C-BiKL loss strategy. Specifically, one sample first goes through multiple identical models to obtain multiple different output distributions, which enriches the sample output distributions and provides conditions for subsequent consistency approximation. Then, the C-BiKL loss strategy is proposed to minimize the combination of the bidirectional Kullback−−Leibler (BiKL) divergence between the above multiple output distributions and the Cross-Entropy loss on labeled data, which provides consistency constraints (BiKL) for the model and simultaneously ensures correct classification (Cross-Entropy). Through the above setting of multi-model contrast learning, the inconsistency caused by the randomness of dropout between model training and inference is alleviated, thereby avoiding over-fitting and improving the classification ability in scenarios with limited labeled samples. We conducted experiments on six widely-used text classification datasets, including sentiment analysis, topic categorization, and reviews classification, and the experimental results show that our method is universally effective in semi-supervised text classification with limited labeled texts.
Interpretability for reliable, efficient, and self-cognitive DNNs: From theories to applications

In recent years, remarkable achievements have been made in artificial intelligence tasks and applications based on deep neural networks (DNNs), especially in the fields of vision, speech, text, and multimodal analysis. The learning of DNNs is not only the process of abstracting essential laws from data but also the result of nonlinear fitting from massive high-dimensional data. However, the architecture, operation mode, and learning ability of DNNs are still far from those of human brain neurons, and the calculation and reasoning are extremely complex, making the model's analysis and interpretation crucial. To free DNNs from their dependence on complex structures and massive data, a lot of related works toward the interpretability of DNNs have been proposed. In this review, we elaborate on the definition of model interpretability from the three perspectives of model reliability, feature efficiency, and self-cognition. The interpretability theory of DNNs is summarized from four aspects: model adversarial attack and defense, feature representations, information and geometry, and causal counterfactual. In addition, we categorize the interpretable methods involved according to typical application scenarios. Finally, we discuss the research goals that have not yet been achieved. We sincerely hope that our work will benefit the field and attract more researchers to devote their energy to the interpretability of DNNs, thereby pushing forward the long-term development of artificial neural networks and artificial intelligence.
Multi-modal multi-hop interaction network for dialogue response generation

Most task-oriented dialogue systems generate informative and appropriate responses by leveraging structured knowledge bases which, in practise, are not always available. For instance, in the e-commerce scenario, commercial items often miss key attribute values, while containing abundant unstructured multi-modal information, e.g., text description and images. Previous studies have not fully explored such information for dialogue response generation. In this paper, we propose a Multi-modal multi-hop Interaction Network for Dialogue (MIND) to facilitate 1) the interaction between a query and multi-modal information through the query-aware multi-modal encoder and 2) the interaction between modalities through the multi-hop decoder. We conduct extensive experiments to demonstrate the effectiveness of MIND over strong baselines, which achieves state-of-the-art performance for automatic and human evaluation. We also release two real-world large-scale datasets containing both dialogue history and items' multi-modal information to facilitate future research.
Research on path planning for autonomous vehicle based on Frenet system

Autonomous vehicles drive according to the expected trajectory to achieve horizontal and longitudinal control of the vehicle. Therefore, the research on path planning is of great significance. This paper mainly analyzes the road behavior of the car, studies the five-fold polynomial algorithm model, simulates the path trajectory of different behavior, and analyzes the rate of change of lateral and vertical velocity and lateral and vertical acceleration under different behaviors, to get the car prediction time. Using the optimal control theory, the linear space expression is established to study the variation of transverse velocity, transverse velocity and transverse deflection angle, and the expression of the system to achieve the steady-state response is given. The simulation results are compared and analyzed to find out the optimal path of different scenes and different driving behaviors, which provides theoretical basis for real vehicle debugging.
Federated Learning for Metaverse: A Survey

The metaverse, which is at the stage of innovation and exploration, faces the dilemma of data collection and the problem of private data leakage in the process of development. This can seriously hinder the widespread deployment of the metaverse. Fortunately, federated learning (FL) is a solution to the above problems. FL is a distributed machine learning paradigm with privacy-preserving features designed for a large number of edge devices. Federated learning for metaverse (FL4M) will be a powerful tool. Because FL allows edge devices to participate in training tasks locally using their own data, computational power, and model-building capabilities. Applying FL to the metaverse not only protects the data privacy of participants but also reduces the need for high computing power and high memory on servers. Until now, there have been many studies about FL and the metaverse, respectively. In this paper, we review some of the early advances of FL4M, which will be a research direction with unlimited development potential. We first introduce the concepts of metaverse and FL, respectively. Besides, we discuss the convergence of key metaverse technologies and FL in detail, such as big data, communication technology, the Internet of Things, edge computing, blockchain, and extended reality. Finally, we discuss some key challenges and promising directions of FL4M in detail. In summary, we hope that our up-to-date brief survey can help people better understand FL4M and build a fair, open, and secure metaverse.
A Detection System for Comfortable Locations Based on Facial Expression Analysis While Riding Bicycles

In recent years, the use of bicycle as a healthy and economical means of transportation has been promoted worldwide. In addition, with the increase in bicycle commuting due to the COVID-19, the use of bicycles are attracting attention as a last-mile means of transportation in Mobility as a Service(MaaS). To help ensure a safe and comfortable ride using a smartphone mounted on a bicycle, this study focuses on analyzing facial expressions while riding to determine potential comfort along the route with the surrounding environment and to provide a map that users can explicitly feedback(FB) after riding. Combining the emotions of facial expressions while riding and FB, we annotate comfort to different locations. Afterwards, we verify the relationship between locations with high level of comfort based on the acquired data and the surrounding environment of those locations using Google Street View(GSV).
How Algorithm Awareness Impacts Algospeak Use on TikTok

Algospeak refers to social media users intentionally altering or substituting words when creating or sharing online content, for example, using ‘le$bean’ for ‘lesbian’. This study discusses the characteristics of algospeak as a computer-mediated language phenomenon on TikTok with regards to users’ algorithmic literacy and their awareness of how the platform’s algorithms work. We then present results from an interview study with TikTok creators on their motivations to utilize algospeak. Our results indicate that algospeak is used to oppose TikTok’s algorithmic moderation system in order to prevent unjust content violations and shadowbanning when posting about benign yet seemingly unwanted subjects on TikTok. In this, we find that although algospeak helps to prevent consequences, it often impedes the creation of quality content. We provide an adapted definition of algospeak and new insights into user-platform interactions in the context of algorithmic systems and algorithm awareness.
A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

Satellite remote sensing provides a potential technology for detecting fog at dawn and dusk on a large scale. However, the spectral characteristics of fog at dawn and dusk are similar to those of the ground surface, which makes satellite-based fog detection difficult. With the aid of time-series datasets from the Himawari-8 (H8)/AHI, this study proposed a novel algorithm of the self-adaptive threshold of visual background extractor (ST-ViBe) model for satellite fog detection at dawn and dusk. Methodologically, the background model was first built using the difference between MIR and TIR (BTD) and the local binary similarity patterns (LBSP) operator. Second, BTD and scale invariant local ternary pattern (SILTP) texture features were coupled to form scene factors, and the detection threshold of each pixel was determined adaptively to eliminate the influence of the solar zenith angles. The background model was updated rapidly by accelerating the updating rate and increasing the updating quantity. Finally, the residual clouds were removed with the traditional cloud removal method to achieve accurate detection of fog at dawn and dusk over a large area. The validation results demonstrated that the ST-ViBe algorithm could detect fog at dawn and dusk precisely, and on a large scale. The probability of detection, false alarm ratio, and critical success index were 72.5%, 18.5%, 62.4% at dawn (8:00) and 70.6%, 33.6%, 52.3% at dusk (17:00), respectively. Meanwhile, the algorithm mitigated the limitations of the traditional algorithms, such as illumination mutation, missing detection, and residual shadow. The results of this study could guide satellite fog detection at dawn and dusk and improve the detection of similar targets.
Qubit geodesics on the Bloch sphere from optimal-speed Hamiltonian evolutions

In the geometry of quantum evolutions, a geodesic path is viewed as a path of minimal statistical length connecting two pure quantum states along which the maximal number of statistically distinguishable states is minimum. In this paper, we present an explicit geodesic analysis of the dynamical trajectories that emerge from the quantum evolution of a single-qubit quantum state. The evolution is governed by an Hermitian Hamiltonian operator that achieves the fastest possible unitary evolution between given initial and final pure states. Furthermore, in addition to viewing geodesics in ray space as paths of minimal length, we also verify the geodesicity of paths in terms of unit geometric efficiency and vanishing geometric phase. Finally, based on our analysis, we briefly address the main hurdles in moving to the geometry of quantum evolutions for open quantum systems in mixed quantum states.
Improving Efficacy of Tsunami Warnings Along the West Coast of the United States

Abstract Tsunamis cause significant damage and loss of life, particularly for the nearest communities, where the tsunami may arrive in minutes. These local communities often do not receive an informed or timely alert under traditional warning pathways. In response, numerous tsunami early warning (TEW) algorithms have been developed with the goal of providing informed tsunami source characterization for use in rapid, localized warning. An overlooked aspect of TEW is the means that this crucial information is disseminated. Current operations focus heavily on the time an alert is issued from a warning center, however, that alert passes through multiple groups and agencies before it is conveyed to affected communities. This distribution path can create further delays and contributes to inconsistencies in the message timeliness and content. In this study, we provide the framework and advocate for the use of a rapid dissemination tool, that we call WaveAlert, that would leverage preexisting advances in earthquake early warning systems to provide timely, clear, and consistent alerts to the public by use of the MyShake EEW phone app. This proposed tsunami dissemination tool would be able to provide consistent, public facing tsunami alerts over the duration of the hazard with the added benefit of low message latencies and high spatial resolution in who can be targeted for messages. We illustrate the need for rapid alerting strategies through a retrospective look at the alerting process during the 2022 Tonga tsunami and through a modeled potential near-field Cascadia timeline example affecting the west coast of the US.
Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection

Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works.