Reviewerly/exHarmony huggingface dataset:
Attention-Aware Based Differentiable Architecture Search Network for Waste Recognition

Manually designed deep neural networks have successfully forwarded waste recognition tasks in the resource recycling field. However, due to the diversity of waste samples, the feature extraction ability of inherently designed models fails to fully satisfy the requirements of real world applications. In this article, an attention-aware based differentiable architecture search (ADAS) network for waste recognition is proposed, which self-organizes to generate an optimal network structure according to diverse waste data. First, a structured search space with attention-aware modules is designed to enhance the diverse feature representations of waste data. Secondly, an efficient and differentiable structure search is achieved by continuously relaxing the representation of the network architecture and search space. Finally, the optimal architecture search process is evaluated by a bi-level optimization algorithm. Experimental results show that the proposed method achieves more satisfactory classification performances than the manually designed ResNet, DenseNet networks in the TrashNet dataset and the self-built household waste dataset.
A Continuous Illumination Estimator for Shadow Removal

Shadow removal methods often encounter difficulties in completely removing shadows or avoiding excessively bright removal results in shadow areas. To address this challenge, we propose a continuous-intensity shadow synthesis model and an illumination estimator for shadow removal. Our shadow synthesis model generates visually realistic images with continuously varying shadow intensity to augment the current shadow removal dataset. By utilizing the synthesized shadow images, we train a network to accurately estimate the illumination intensity in shadow areas. The estimated illumination intensity is positive for a shadow input image and negative for an over-lit input image, thus our method can illuminate shadow areas or suppress over-lit areas. Experimental results demonstrate that our method outperforms current state-of-the-art shadow removal methods, particularly when handling shadows with a wide range of intensity variations.
ECGAIN: An Enhanced Conditional Missing Data Imputation Method For Process Industries

Missing data is a quite pervasive phenomenon in process industries due to the failure of signal transmission and data storage, which brings a great challenge to the downstream data-driven modeling. Existing missing data imputation (MDI) methods do not consider the sparsity of observed data which would affect the imputation performance and impair the modeling accuracy of downstream applications further. Therefore, a novel Enhanced Conditional Generative Adversarial Imputation Network (ECGAIN) is proposed in this paper to improve the MDI performance when the observed data is sparse. Specifically, the generator observes some observed data and outputs a vector. The discriminator takes the vector as the input to judge which one is actually observed and which one is imputed by generator. During the above process, two data enhancement mechanisms termed adaptive update mechanism (AUM) and adaptive attention mechanism (AAM) are designed to improve the MDI performance. Meanwhile, label-relevant information is taken as the condition of both the generator and discriminator to guide the imputation. Case studies on two University of California Irvine (UCI) public datasets and a real-word thermal power process dataset have proved the reliability and effectiveness of the proposed ECGAIN by comparing with other state-of-the-art MDI methods.
A Hybrid Intelligent Method for Path-Constrained Switched Systems with Free Switching Sequences

In this article, a hybrid intelligent optimization method is proposed, which can simultaneously find the optimal input, switching instants, and switching sequences within a finite number of iterations. Firstly, the infinite-dimensional program is transformed into a finite-dimensional program through parameterization methods, and then, since the switching sequences are discrete variables, the discrete-continuous finite-dimensional program is converted into a continuous finite-dimensional program by introducing linear and quadratic constraints. Secondly, the path constraint is discretized into finite point constraints, and the right side of the constraint is replaced with a negative value. Thirdly, an improved differential evolution (IDE) is used for random search to find the global optimal region that satisfies the point constraints, and then a deterministic optimization method is used to find an approximate KKT optimal solution that rigorously satisfies path constraints in the global optimal region. Fourthly, the convergence of the hybrid intelligent method has been proven. Finally, a numerical example shows the effectiveness of the proposed method.
Pattern Classification with Holographic Neural Networks: A New Tool for Feature Selection

This chapter aims to develop classification algorithms based on holographic neural networks (HNN) that can explain the inferences made in the classification of driver states during conditional driving automation. Holographic neural networks (HNN) are a type of associative memory developed more than thirty years ago. Among its main advantages is the speed of learning, as it develops as a linear model in the complex domain. HNNs are a type of associative memory developed more than thirty years ago that, being a simple linear model in the complex domain, can be updated at high speed during online learning. After a chronological review of the technology to solve the pattern classification problem, a new tool for feature selection is proposed. The new tool maps the complex numbers in the holographic memory to the interval [0, 1] using Pythagorean membership degrees. The performance of holographic classifiers is compared with the Iris dataset, and the explainability of the models is analyzed by the predicting power and stability of the selected features. The holographic classifiers with fuzzy quantization show an accuracy result higher than 90% with the Iris dataset, being the most explainable according to the extracted rules and the ones with the highest stability of the features selected by the proposed tool.
Text Classifier of Sensationalist Headlines in Spanish Using BERT-Based Models

Information technologies play a crucial role in keeping society informed during global events like pandemics. However, sensational headlines can negatively impact public perception and trust in institutions. In this chapter, several BERT-based text classifiers were developed to classify sensational and non-sensational health-related headlines in Spanish. The models were fine-tuned on almost 2000 headlines from major Mexican newspapers, achieving up to 94% F1-Score and accuracy. This demonstrates the effectiveness of machine learning techniques in detecting sensationalism in news headlines.
An Approach for Tamper-Proof QR Code Using Deep Learning Based-Data Hiding

An application that significantly improves the traceability of the manufacturing sector and agriculture is the two-dimensional barcode (QR code). The QR code, however, is simple to copy and fake. Therefore, we suggest a novel strategy to prevent tampering with this code. The method entails two stages: concealing a security element in the QR code, and assessing how closely the QR code on the goods resembles genuine ones. For the first problem, error-correcting coding is used to encode and decode the secret feature in order to manage faults in noisy communication channels. A deep neural network is used to both conceal and decode the encoded data in the QR code. The suggested network has the ability to be resilient to actual distortions brought on by the printing and photographing processes. For the latter problem, we measure the similarity of QR codes using the Siamese network design. To assess if a QR code is real or false, the extracted secret feature and the outcome of the QR code similarity estimation are merged. With an average accuracy of 98%, the suggested method performs well and may be used to validate QR codes in practical applications.
When are Latent Topics Useful for Text Mining?

The Bag-of-Words (BOW) model is simple but one of the successful representations of text documents. This model, however, suffers from the sparse matrix, in which most of the elements are zero. Topic modeling is an unsupervised learning method that can represent text documents in a low-dimensional space. Latent Dirichlet Allocation (LDA) is a topic modeling technique used for topic extraction and data exploration, with interpretable output. This paper presents a thorough study of potential benefits of applying LDA, as a feature extraction, to topic discovery and document classification in Thai news articles, comparing with TF–IDF and Word2Vec. We also studied how much of the top Thai terms extracted from LDA with the different numbers of topics can be interpretable and meaningful, and can be a representative of the corpus. Besides, a set of Topic Coherence measures were included in our study to estimate the degree of semantic similarity of extracted topics. To compare the performance and optimization time of classification of features from the different feature extraction methods, various types of classifiers, e.g., Logistic Regression, Random Forest, XGBoosting, etc., were experimented.
The Statistical Analysis of Style: Aims and Methods

This chapter outlines the basic premises of stylometry to arts and humanities scholars. It then presents an overview of stylometric methods that have previously been successful in discriminating between authors and the range of data they used.
Fast Camera Motion Compensation Based Kalman Filter and Cascade Association for Multi-object Tracking

Numerous ways to improve the power of tracking algorithms have emerged in modern multi-object tracking problems. Tracking-by-detection, On the other hand, is one of the most precise approaches in the field, balancing the trade-off between precision and run-time. This method divides the tracking process into two steps: the detection process to localize objects in the image and the tracking process to assign identity for each response from the object detector. In this study, we optimize the tracking process by generalizing the BYTE technique (Cascade Association) and integrating camera-motion compensation to the Association stage. Our new tracker KCM-Track, sets a new state-of-the-art accuracy on MOT17 dataset in terms of the primary MOT metrics: MOTA, IDF1, and HOTA. On MOT17 test sets: $$\mathbf{80.6}\%$$ MOTA, $$\mathbf{79.7}\%$$ IDF1, and $$\mathbf{64.6}\%$$ HOTA are achieved at $$\textbf{314}$$ FPS for the tracking process.
Mitigating Catastrophic Forgetting in Neural Machine Translation Through Teacher-Student Distillation with Attention Mechanism

The catastrophic forgetting is a critical problem for deep learning models, where the models learning a sequence of tasks forgets the previously learned knowledge during being trained on new data of the new task. The main reason is that a new task may likely override the weights that have been learned in the past. In this research, we propose a novel approach to address this issue for the neural machine translation model based on improving the COKD model proposed by S. Shao and Y. Feng (2022). The main idea is to divide the training data into $$n+1$$ parts, train n teacher models into the first n parts, and let the student model learn from the remaining part. We propose ModifiedCOKD, a method to initialize the effective teacher model parameters and use an attention mechanism to distil knowledge from the teacher models to the student models. Experimental results on the task of English-to-Vietnamese translation demonstrate that ModifiedCOKD outperforms the baseline method in mitigating catastrophic forgetting.
Crime Scene Detection in Surveillance Videos Using Variational AutoEncoder-Based Support Vector Data Description

With the widespread deployment of surveillance equipment for social security purposes, video data analysis has become increasingly challenging. To address this problem, we study video anomaly detection for automatically identifying crime scenes. Our proposed method adopts an one-class semi-supervised strategy, assuming that the anomalous behaviors that need to be detected make up only a small fraction of the entire video frames. To facilitate effective detection, we adopt a two-step approach that utilizes a 3-dimensional convolutional neural network (3D CNN) and a Variational AutoEncoder-based Support Vector Data Description (VAE-SVDD). To further enhance the performance, we propose to add the Kullback-Leibler divergence loss to the anomaly score. Our method is evaluated on the Abnormal Behavior CCTV Video Dataset obtained from AI-Hub and compared to a state-of-the-art supervised approach. The experimental results demonstrate the effectiveness of our proposed method in detecting crime scenes.
Introduction

Every 10 years, Sight & Sound magazine carries out a survey to compile a list of critically acclaimed films. For the 2012 poll, the magazine’s editor explained that “we approached more than 1,000 critics, programmers, academics, distributors, writers and other cinephiles, and received (in time for the deadline) precisely 846 top-ten lists that between them mention a total of 2,045 different films” (James 2021). From 1962 to 2002, Citizen Kane (1941) topped Sight & Sound’s list, although in 2012 it came second to Hitchcock’s Vertigo (1958) and fell to third place in 2022. Despite the apparent frivolity in compiling a top ten, a list presents a straightforward way to arrange data (such as film titles) in a descending order of rank (determined by the number of mentions), and 846 responses constitute a sufficiently large data set to represent meaningful results. These polls confirm Citizen Kane’s status as one of the most critically acclaimed films in cinema history. The making of Citizen Kane has even been the subject of two feature films: RKO 281 (Benjamin Ross, 1999) and Mank (David Fincher, 2020), while Orson Welles—the producer, director, and cowriter of Citizen Kane (as well as its star)—was and still is a celebrated public figure.
Federated Deep Reinforcement Learning - Based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk’s bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, etc., the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.
A Distilled 2D CNN-LSTM Framework with Temporal Attention Mechanism for Action Recognition

Action Recognition has been studied for many years. In recent years, there are some methods using 3D-CNN (C3D, I3D, R2 + 1D), which have high accuracy, but it is hard to train and quite time-consuming due to the network architecture of extracting spatial–temporal features and the huge action dataset. Since 2D-CNN has a pre-trained model with high accuracy and speed in object recognition, there is also a method of fine-tune it on Recurrent neural network (RNN), Long Short-Term Memory (LSTM) network and other network that can extract temporal features, but due to the poor performance of fine-tune, although the speed is increased, the accuracy has dropped significantly. Therefore, this research wants to use the high accuracy of 3D-CNN to distill 2D-CNN produce a great pre-trained model for action recognition and combine it with Attention Mechanism LSTM to make model on fine-tune on other action dataset can accelerate and achieve the accuracy of approximating 3D-CNN.
Comparing Mankiewicz and Welles to the Citizen Kane Screenplay (1): Relative Frequencies, Distinctiveness Ratios, and Confidence Intervals

This chapter employs Mankiewicz’s and Welles’s statistical profiles to assign authorship in a precise way to the Citizen Kane screenplay. The screenplay is analyzed three times: firstly, as a single document; secondly, by segmenting it into 4000-word samples; and thirdly, by dividing it into its 13 major scenes.
Images Retrieval and Classification for Acute Myeloid Leukemia Blood Cell Using Deep Metric Learning

Deep metric learning-based image retrieval systems have recently been used in medical applications because they provide clinically relevant information-based similar images based on prior knowledge. Although train examiners and deep learning models successfully analyze leukocyte cells, there are still numerous difficult challenges due to biological variation, time constraints, and a variety of image-related aspects. In this study, we propose a deep metric learning model-based image retrieval and classification system for acute myeloid leukemia blood cells to address these issues and assist physicians. The proposed model utilizes the pre-trained ResNet-34 model as the backbone network, embedding loss with multi similarity miner, and M-Per-Class sampling strategy to learn an embedding function. The five embedding losses were also applied to compare the four performances in order to determine the best loss-based model. Based on the best loss-based model, the class-wise precision and sensitivity using a neighborhood size are also presented. The results show that the contrastive loss-based deep metric learning model achieved the highest precision of 94.90%, sensitivity of 94.85%, specificity of 99.64%, and accuracy of 99.32% in model comparison. Except for a few failures in small classes, the class-wise precision and sensitivity scores looked to be impressive in all classes. Therefore, this proposed system can highly be effective in screening and diagnosing of AML-related white blood cell stages that cause serious cancer.
The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi

Abstract This study provides an in-depth analysis of fluid inclusions (FIs) and stable isotopes from the Manondo-Choma gold prospect in southern Malawi to understand the ore-forming mechanisms, genesis, and evolution of the hydrothermal fluids responsible for gold mineralization at the Manondo-Choma gold prospect. FIs and microthermometry studies were carried out on mineralized quartz veins from the area. The Manondo-Choma gold prospect is located in the southern Malawi within the Kirk range. Gold is mostly found in quartz veins within metamorphic rocks, such as gneiss and schists, and is structurally controlled by the NE–SW ductile shear zones. Gold mineralization is linked to quartz sulfide veins. Mineralization occurs in the following three stages: early, middle, and late, of which the middle stage is more plorific. The following three FI types were recognized in the quartz veins: pure carbonic, aqueous carbonic (CO 2 –H 2 O) and aqueous (H 2 O–NaCl) inclusions. Gold and associated mineralization were likely precipitated due to the lowering of pressure and fluid immiscibility. Oxygen isotope data indicate that the source of ore-forming fluids at the Manondo-Choma gold prospect was largely metamorphic in origin with minor magmatic input.
SHAPE: A Sample-Adaptive Hierarchical Prediction Network for Medication Recommendation

Effectively medication recommendation with complex multimorbidity conditions is a critical yet challenging task in healthcare. Most existing works predicted medications based on longitudinal records, which assumed the encoding format of intra-visit medical events are serialized and information transmitted patterns of learning longitudinal sequence data are stable. However, the following conditions may have been ignored: 1) A more compact encoder for intra-relationship in the intra-visit medical event is urgent; 2) Strategies for learning accurate representations of the variable longitudinal sequences of patients are different. In this article, we proposed a novel Sample-adaptive Hierarchical medicAtion Prediction nEtwork, termed SHAPE, to tackle the above challenges in the medication recommendation task. Specifically, we design a compact intra-visit set encoder to encode the relationship in the medical event for obtaining visit-level representation and then develop an inter-visit longitudinal encoder to learn the patient-level longitudinal representation efficiently. To endow the model with the capability of modeling the variable visit length, we introduce a soft curriculum learning method to assign the difficulty of each sample automatically by the visit length. Extensive experiments on a benchmark dataset verify the superiority of our model compared with several state-of-the-art baselines.
Are Your Dependencies Code Reviewed?: Measuring Code Review Coverage in Dependency Updates

As modern software extensively uses free open source packages as dependencies, developers have to regularly pull in new third-party code through frequent updates. However, without a proper review of every incoming change, vulnerable and malicious code can sneak into the codebase through these dependencies. The goal of this study is to aid developers in securely accepting dependency updates by measuring if the code changes in an update have passed through a code review process. We implement Depdive, an update audit tool for packages in Crates.io, npm, PyPI, and RubyGems registry. Depdive first (i) identifies the files and the code changes in an update that cannot be traced back to the package’s source repository, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">phantom artifacts</i> ; and then (ii) measures what portion of changes in the update, excluding the phantom artifacts, has passed through a code review process, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">code review coverage</i> . <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Using Depdive, we present an empirical study across the latest ten updates of the most downloaded 1000 packages in each of the four registries. We further evaluated our results through a maintainer agreement survey. We find that phantom artifacts are not uncommon in the updates (20.1% of the analyzed updates had at least one phantom file). The phantoms can appear either due to legitimate reasons, such as in the case of programmatically generated files, or from accidental inclusion, such as in the case of files that are ignored in the repository. Regarding code review coverage (<i>CRC</i>), we find the updates are typically only partially code-reviewed (52.5% of the time). Further, only 9.0% of the packages had all their updates in our data set fully code-reviewed, indicating that even the most used packages can introduce non-reviewed code in the software supply chain. We also observe that updates either tend to have high <i>CRC</i> or low <i>CRC</i>, suggesting that packages at the opposite end of the spectrum may require a separate set of treatments.