Reviewerly/exHarmony huggingface dataset:
Software/Code for "Federated Optimization Under Intermittent Client Availability"

Federated learning is a new distributed machine learning framework, where numerous heterogeneous clients collaboratively train a model without sharing training data. In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process. Such intermittent client availability would seriously dete-riorate the performance of the classical federated averaging algorithm (FedAvg). Thus, we propose a simple distributed nonconvex optimization algorithm, called federated latest aver-aging (FedLaAvg), which leverages the latest gradients of all clients, even when the clients are not available, to jointly update the global model in each iteration. Our theoretical analysis shows that FedLaAvg achieves guaranteed convergence and a sublinear speedup with respect to the total number of clients. We implement FedLaAvg along with several baselines and evaluate them over the benchmarking MNIST and Sentiment140 data sets. The evalua-tion results demonstrate that FedLaAvg achieves more stable training than FedAvg in both convex and nonconvex settings and reaches a sublinear speedup.
Hybrid unstructured text features for meta-heuristic assisted deep CNN-based hierarchical clustering

The text clustering model becomes an essential process to sort the unstructured text data in an appropriate format. But, it does not give the pave for extracting the information to facilitate the document representation. In today’s date, it becomes crucial to retrieve the relevant text data. Mostly, the data comprises an unstructured text format that it is difficult to categorize the data. The major intention of this work is to implement a new text clustering model of unstructured data using classifier approaches. At first, the unstructured data is taken from standard benchmark datasets focusing on both English and Telugu languages. The collected text data is then given to the pre-processing stage. The pre-processed data is fed into the model of the feature extraction stage 1, in which the GloVe embedding technique is used for extracting text features. Similarly, in the feature extraction stage 2, the pre-processed data is used to extract the deep text features using Text Convolutional Neural Network (Text CNN). Then, the text features from Stage 1 and deep features from Stage 2 are all together and employed for optimal feature selection using the Hybrid Sea Lion Grasshopper Optimization (HSLnGO), where the traditional SLnO is superimposed with GOA. Finally, the text clustering is processed with the help of Deep CNN-assisted hierarchical clustering, where the parameter optimization is done to improve the clustering performance using HSLnGO. Thus, the simulation findings illustrate that the framework yields impressive performance of text classification in contrast with other techniques while implementing the unstructured text data using different quantitative measures.
Collateral Coupling between Superconducting Resonators: Fast High-Fidelity Generation of Qudit-Qudit Entanglement

Superconducting circuits are highly controllable platforms to manipulate quantum states, which make them particularly promising for quantum information processing. We show here how the existence of a distance-independent interaction between microwave resonators coupled capacitively through a qubit offers an alternative control parameter toward this goal. This interaction is able to induce an idling point between resonant resonators, and its state-dependent nature allows one to control the flow of information between the resonators. The advantage of this scheme over the previous one is demonstrated through the generation of high-fidelity NOON states between the resonators, with a lower number of operations than previous schemes. Beyond superconducting circuits, our proposal could also apply to atomic lattices with clock transitions in optical cavities, for example.
YOLO-CORE: Contour Regression for Efficient Instance Segmentation

Instance segmentation has drawn mounting attention due to its significant utility. However, high computational costs have been widely acknowledged in this domain, as the instance mask is generally achieved by pixel-level labeling. In this paper, we present a conceptually efficient contour regression network based on the you only look once (YOLO) architecture named YOLO-CORE for instance segmentation. The mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multi-order constraint consisting of a polar distance loss and a sector loss. Our proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and speed. It achieves 57.9% AP@0.5 with 47 FPS (frames per second) on the semantic boundaries dataset (SBD) and 51.1% AP@0.5 with 46 FPS on the COCO dataset. The superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding field. Moreover, our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost (65.86 BFLOPs (billion float operations per second) to 66.15 BFLOPs with the YOLOv3 detector).
Exploring Breast Cancer Texture Analysis through Multilayer Neural Networks

Breast cancer is a significant health problem for women globally; however, timely detection can reduce female morbidity and mortality. Early breast screening has become imperative for all women, though, adequate screening facilities are necessarily required in developing countries like Pakistan, where breast cancer is a leading cause of death. To encounter this chronic disease, various image processing techniques have been introduced to automatically diagnose breast cancer from digital mammograms. The current study deployed data from a population of 35 participants. The mammograms used for screening were 5 normal, 15 benign, and 15 malignant patients. The breast images were marked by the radiologist and the system was trained with normal, benign, and malignant classes. Moreover, Multilayer Neural Networks (MNN) based texture analysis methodology was adopted to distinguish normal, benign, and malignant breast images. Reportedly, an automated approach was used to detect breast conditions after conducting the analysis of digital mammograms. Statistical parameters, namely sum, mean, variance, standard deviation, kurtosis, skewness, energy, and entropy were calculated, analyzed, and compared for the normal, malignant, and benign breast images. The results indicated a 100% accuracy after the analysis. The results of the extracted statistical parameters were promising and reliable in distinguishing between normal, malignant, and benign breast mammograms, again indicating the need for early detection of the disease to minimize the risk of breast cancer among women.
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving

Traditional online advertising systems for sponsored search follow a cascade paradigm with retrieval, pre-ranking, ranking, respectively. Constrained by strict requirements on online inference efficiency, it tend to be difficult to deploy useful but computationally intensive modules in the ranking stage. Moreover, ranking models currently used in the industry assume the user click only relies on the advertisements itself, which results in the ranking stage overlooking the impact of organic search results on the predicted advertisements (ads). In this work, we propose a novel framework PCDF (Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items. Our PCDF effectively reduces the overall inference latency compared with the classic framework. The whole module is end-to-end offline training and adapt for the online learning paradigm. To our knowledge, we are the first to propose an end-to-end solution for online training and deployment on complex CTR models from the system framework side.
Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.
Face Recognition-Based Automatic Attendance System in a Smart Classroom

The smart classroom is a fully automated classroom where repetitive tasks, including attendance registration, are automatically performed. Due to recent advances in artificial intelligence, traditional attendance registration methods have become challenging. These methods require significant time and effort to complete the process. Therefore, researchers have sought alternative ways to accomplish attendance registration. These methods include identification cards, radio frequency, or biometric systems. However, all of these methods have faced challenges in safety, accuracy, effort, time, and cost. The development of digital image processing techniques, specifically face recognition technology, has enabled automated attendance registration. Face recognition technology is considered the most suitable for this process due to its ability to recognize multiple faces simultaneously. This study developed an integrated attendance registration system based on the YOLOv7 algorithm, which extracts features and recognizes students’ faces using a specially collected database of 31 students from Mustansiriyah University. A comparative study was conducted by applying the YOLOv7 algorithm, a machine learning algorithm, and a combined machine learning and deep learning algorithm. The proposed method achieved an accuracy of up to 100%. A comparison with previous studies demonstrated that the proposed method is promising and reliable for automating attendance registration.
Generating Robust Counterfactual Explanations

Counterfactual explanations have become a mainstay of the XAI field. This particularly intuitive statement allows the user to understand what small but necessary changes would have to be made to a given situation in order to change a model prediction. The quality of a counterfactual depends on several criteria: realism, actionability, validity, robustness, etc. In this paper, we are interested in the notion of robustness of a counterfactual. More precisely, we focus on robustness to counterfactual input changes. This form of robustness is particularly challenging as it involves a trade-off between the robustness of the counterfactual and the proximity with the example to explain. We propose a new framework, CROCO, that generates robust counterfactuals while managing effectively this trade-off, and guarantees the user a minimal robustness. An empirical evaluation on tabular datasets confirms the relevance and effectiveness of our approach.
The Sentiment of Latin Poetry. Annotation and Automatic Analysis of the Odes of Horace

During the recent years, an always growing number of linguistic resources and automatic systems for sentiment analysis have been developed covering a wide range of languages. However, research in this field is still not much explored for texts written in Classical languages. Working on such languages means dealing with peculiar textual genres such as philosophical, historical or religious treatises, epic narratives, plays and poems. Poems are particularly suitable for sentiment analysis because they tell us about emotions and passions. In this paper, we describe the creation of the first small gold standard of Latin made of poems written by Horace and manually annotated with emotion polarity, but we also report about the results of a set of automatic classification experiments. In particular, we test both a lexicon-based approach, which uses a Latin polarity lexicon called LatinAffectus, and a zero-shot transfer method. We provide details about the methodology adopted for the annotation of the gold standard, the creation of LatinAffectus, the development of our experiments and we give details about the results and the limitations of the proposed approaches.
Interactive Visualization of Counterfactual Explanations for Tabular Data

In this paper we present an interactive visualization tool that exhibits counterfactual explanations to explain model decisions. Each individual sample is assessed to identify the set of changes needed to flip the output of the model. These explanations aim to provide end-users with personalized actionable insights with which to understand automated decisions. An interactive method is also provided so that users can explore various solutions. The functionality of the tool is demonstrated by its application to a customer retention dataset. The tool is compatible with any counterfactual explanation generator and decision model for tabular data.
Counterfactual Explanations for Remote Sensing Time Series Data: An Application to Land Cover Classification

Enhancing the interpretability of AI techniques is paramount for increasing their acceptability, especially in highly interdisciplinary fields such as remote sensing, in which scientists and practitioners with diverse backgrounds work together to monitor the Earth’s surface. In this context, counterfactual explanations are an emerging tool to characterize the behaviour of machine learning systems, by providing a post-hoc analysis of a given classification model. Focusing on the important task of land cover classification from remote sensing data, we propose a counterfactual explanation approach called CFE4SITS (CounterFactual Explanation for Satellite Image Time Series). One of its distinctive features over existing strategies is the lack of prior assumption on the targeted class for a given counterfactual explanation. This inherent flexibility allows for the automatic discovery of relationship between classes. To assess the quality of the proposed approach, we consider a real-world case study in which we aim to characterize the behavior of a ready-to-use land cover classifier. To this end, we compare CFE4SITS to recent time series counterfactual-based strategies and, subsequently, perform an in-depth analysis of its behaviour.
Filtered Observations for Model-Based Multi-agent Reinforcement Learning

Reinforcement learning (RL) pursues high sample efficiency in practical environments to avoid costly interactions. Learning to plan with a world model in a compact latent space for policy optimization significantly improves sample efficiency in single-agent RL. Although world model construction methods for single-agent can be naturally extended, existing multi-agent schemes fail to acquire world models effectively as redundant information increases rapidly with the number of agents. To address this issue, we in this paper leverage guided diffusion to filter this noisy information, which harms teamwork. Obtained purified global states are then used to build a unified world model. Based on the learned world model, we denoise each agent observation and plan for multi-agent policy optimization, facilitating efficient cooperation. We name our method UTOPIA, a model-based method for cooperative multi-agent reinforcement learning (MARL). Compared to strong model-free and model-based baselines, our method shows enhanced sample efficiency in various testbeds, including the challenging StarCraft Multi-Agent Challenge tasks.
Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning

To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input images. Following Masked Auto-Encoders, SPIRL is based on Vision Transformer models pre-trained in a self-supervised fashion to reconstruct images from randomly-sampled patches. These pre-trained models can then be exploited to detect and select salient patches, defined as hard to reconstruct from neighboring patches. In RL, the SPIRL agent processes selected salient patches via an attention module. We empirically validate SPIRL on Atari games to test its data-efficiency against relevant state-of-the-art methods, including some traditional model-based methods and keypoint-based models. In addition, we analyze our model’s interpretability capabilities.
CO2 Emission Mitigation in Container-Based Cloud Computing by the Power of Resource Management

The increasing amount of data processing through cloud systems leads to high energy costs and CO2 emissions challenges. Advanced monitoring tools are used to track resource utilization and identify possible optimization areas to mitigate CO2 emissions and reduce energy consumption. Thus, resource and power management in cloud environments is essential to ensure efficient performance and effective cost. Recently, container-based clouds become more widely used than hypervisor-based clouds. Scheduling the huge number of user-submitted tasks and optimizing both network and cloud resources are important aspects of cloud computing to decrease network overhead, reduce both Data Center (DC) energy consumption and Service-Level Agreement (SLA) violations and in turn, mitigate CO2 emissions. However, the state-of-the-art resource schedulers that consider power management and network transmissions are unfortunately not container-aware schedulers. The main objective of this paper is to introduce a solid analysis of the most significant techniques and tools in resource management to minimize power consumption and CO2 emissions with the advantage of being a container-aware scheduler; Applying the first stage of the proposed solution leads to mitigating the CO2 by an average of 2.12% against the Non-Clustered approach and 18.04% against the Clustered approach.
Image-Pair Correlation Learning for Vehicle Re-Identification

Vehicle re-identification plays an important role in intelligent transportation systems. It aims to identify vehicles with the same identity between images captured by different cameras. How to reasonably estimate the similarity between features plays an important role in vehicle re-identification. Traditional vehicle re-identification methods suffer from high intra-class difference and low inter-class difference due to view difference, which poses a significant challenge for accurate vehicle re-identification. Many Siamese network-based methods for vehicle re-identification can learn intra- and inter-class distances, but they tend to overlook similarity metrics between classifiers and similarity learning of element-level features, which could further enhance similarity learning between images. To address this issue, we propose an image-pair correlation learning network for vehicle re-identification. Imposing constraints on the distances between features in different ways to reduce the intra-class distance and increase the inter-class distance. We design a classifier similarity estimation module and a similarity metric module of features at element-level to learn the similarity of images from different views. Extensive experiments on AI City Challenge 2020 Track2 dataset and VeRi-776 dataset demonstrate the effectiveness of our methods.
Ensuring Port Safety: Improved YOLOX and DeepSORT for Accurate Detection and Tracking of Trucks and Truck Drivers

An optimized YOLOX+DeepSORT method is proposed to accurately detect and track container trucks and truck drivers at the working position of automated rubber tire gantries in ports, while ensuring their safety during the whole working process. In the proposed method, the improved YOLOX performs object detection and its output is used as the input for multi -object tracking using DeepSORT. The improved YOLOX model is developed through replacing standard convolution with depthwise separable convolution, adding the convolutional block attention module to enhance feature extraction, and using Focal Loss in the loss function to address sample imbalances. Comparative experiments were carried out on a self-built dataset, showing a 4.32% increase in mAP and improved reasoning speed for improved YOLOX compared to the original YOLOX. Furthermore, the optimized method shows a 3.57% increase in Multi-Object Tracking Accuracy and a 1.73% increase in Multi-Object Tracking Precision compared to the benchmark YOLOX+DeepSORT.
Weakly-Supervised Temporal Action Localization Based on Graph Convolution and Attention

Weakly-supervised temporal action localization aims to locate the start and end times of action instances in videos and identify action classes under coarse-grained video-level labels supervision training. However, the segment-level features extracted by weakly supervised temporal action localization have incomplete and inconsistent actions and will ignore ambiguous fragments resulting in the model's inability to predict complete action examples accurately we propose a weakly supervised temporal action localization network based on graph convolution and attention(GCANet). Our network first employs graph convolution to design feature-complementary graphs to enhance segment-level feature information to enable spatiotemporal complementation between different segments, the enhanced features are then fed into a three-branch attention mechanism to simultaneously measure the likelihood of action frames, blurred frames and background frames at each time point to improve localization accuracy GCANet is experimented on two public benchmark datasets, THUMOS14 and ActivityNet1.3, and achieves 43.4% and 26.1% average detection performance, respectively, demonstrating the effectiveness of our proposed method.