Reviewerly/exHarmony huggingface dataset:
Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels

The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss.However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization.To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.The merits of the proposed MORF come from the following two components: a tree-wise weighting net (TWW-Net) and a grouped feature selection (GFS) module.First, the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree.Hence, all the trees possess varying weights, which is helpful for alleviating the tree-wise prediction variance.Second, the GFS module enables a dynamic forest rather than a fixed one that was previously used, allowing for random feature perturbation.During training, we alternatively optimize the parameters of the CNN backbone and TWW-Net in the metalearning framework through calculating the Hessian matrix.Experimental results on two medical image classification datasets with ordinal labels, i.e., LIDC-IDRI and Breast Ultrasound Dataset, demonstrate the superior performances of our MORF method over existing state-of-the-art methods.
Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.
Diet code is healthy: simplifying programs for pre-trained models of code

Pre-trained code representation models such as CodeBERT have demonstrated superior performance in a variety of software engineering tasks, yet they are often heavy in complexity, quadratically with the length of the input sequence. Our empirical analysis of CodeBERT's attention reveals that CodeBERT pays more attention to certain types of tokens and statements such as keywords and data-relevant statements. Based on these findings, we propose DietCode, which aims at lightweight leverage of large pre-trained models for source code. DietCode simplifies the input program of CodeBERT with three strategies, namely, word dropout, frequency filtering, and an attention-based strategy that selects statements and tokens that receive the most attention weights during pre-training. Hence, it gives a substantial reduction in the computational cost without hampering the model performance. Experimental results on two downstream tasks show that DietCode provides comparable results to CodeBERT with 40% less computational cost in fine-tuning and testing.
Coordinating Momenta for Cross-Silo Federated Learning

Communication efficiency is crucial for federated learning (FL). Conducting local training steps in clients to reduce the communication frequency between clients and the server is a common method to address this issue. However, it leads to the client drift problem due to non-i.i.d. data distributions in different clients which severely deteriorates the performance. In this work, we propose a new method to improve the training performance in cross-silo FL via maintaining double momentum buffers. One momentum buffer tracks the server model updating direction, and the other tracks the local model updating direction. Moreover, we introduce a novel momentum fusion technique to coordinate the server and local momentum buffers. We also provide the first theoretical convergence analysis involving both the server and local standard momentum SGD. Extensive deep FL experimental results show a better training performance than FedAvg and existing standard momentum SGD variants.
The Role of Human Knowledge in Explainable AI

As the performance and complexity of machine learning models have grown significantly over the last years, there has been an increasing need to develop methodologies to describe their behaviour. Such a need has mainly arisen due to the widespread use of black-box models, i.e., high-performing models whose internal logic is challenging to describe and understand. Therefore, the machine learning and AI field is facing a new challenge: making models more explainable through appropriate techniques. The final goal of an explainability method is to faithfully describe the behaviour of a (black-box) model to users who can get a better understanding of its logic, thus increasing the trust and acceptance of the system. Unfortunately, state-of-the-art explainability approaches may not be enough to guarantee the full understandability of explanations from a human perspective. For this reason, human-in-the-loop methods have been widely employed to enhance and/or evaluate explanations of machine learning models. These approaches focus on collecting human knowledge that AI systems can then employ or involving humans to achieve their objectives (e.g., evaluating or improving the system). This article aims to present a literature overview on collecting and employing human knowledge to improve and evaluate the understandability of machine learning models through human-in-the-loop approaches. Furthermore, a discussion on the challenges, state-of-the-art, and future trends in explainability is also provided.
An Ensemble Deep Learning Approach for Emotion Detection in Arabic Tweets

Now-a-days people use social media websites for different activities such as business, entertainment, following the news, expressing their thoughts, feelings, and much more.This initiated a great interest in analyzing and mining such usergenerated content.In this paper, the problem of emotion detection (ED) in Arabic text is investigated by proposing an ensemble deep learning approach to analyze user-generated text from Twitter, in terms of the emotional insights that reflect different feelings.The proposed model is based on three state-ofthe-art deep learning models.Two models are special types of Recurrent Neural Networks RNNs (Bi-LSTM and Bi-GRU), and the third model is a pre-trained language model (PLM) based on BERT and it is called MARBERT transformer.The experiments were evaluated using the SemEval-2018-Task1-Ar-Ec dataset that was published in a multilabel classification task: Emotion Classification (EC) inside the SemEval-2018 competition.MARBERT PLM is compared to one of the most famous PLM for dealing with the Arabic language (AraBERT).Experiments proved that MARBERT achieved better results with an improvement of 4%, 2.7%, 4.2%, and 3.5% regarding Jaccard accuracy, recall, F1 macro, and F1 micro scores respectively.Moreover, the proposed ensemble model showed outperformance over the individual models (Bi-LSTM, Bi-GRU, and MARBERT).It also outperforms the most recent related work with an improvement ranging from 0.2% to 4.2% in accuracy, and from 5.3% to 23.3% in macro F1 score.
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation

Interactive neural machine translation (INMT) is able to guarantee high-quality translations by taking human interactions into account.Existing IMT systems relying on lexical constrained decoding (LCD) enable humans to translate in a flexible translation manner beyond left-to-right.However, they typically suffer from limitations in translation efficiency and quality due to the reliance on LCD.In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation.The key idea to BiTI-IMT is the Bilingual Text-infilling (BiTI) task which aims to fill missing segments in a manually revised translation for a given source sentence.We propose a simple yet effective solution by casting this task as a sequence-tosequence task.The benefits of our solution are that it performs efficient decoding with the same complexity as the standard decoding in NMT and makes full use of revised words for better translation prediction.Experiment results show that BiTiIMT performs significantly better and faster than state-of-the-art LCD-based IMT on three translation tasks.
Stochastic gradient line Bayesian optimization for efficient noise-robust optimization of parameterized quantum circuits

Abstract Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices. However, the existing algorithms for such optimization require an excessive number of quantum-measurement shots for estimating expectation values of observables and repeating many iterations, whose cost has been a critical obstacle for practical use. We develop an efficient alternative optimization algorithm, stochastic gradient line Bayesian optimization (SGLBO), to address this problem. SGLBO reduces the measurement-shot cost by estimating an appropriate direction of updating circuit parameters based on stochastic gradient descent (SGD) and further utilizing Bayesian optimization (BO) to estimate the optimal step size for each iteration in SGD. In addition, we formulate an adaptive measurement-shot strategy and introduce a technique of suffix averaging to reduce the effect of statistical and hardware noise. Our numerical simulation demonstrates that the SGLBO augmented with these techniques can drastically reduce the measurement-shot cost, improve the accuracy, and make the optimization noise-robust.
Retinex-Based Variational Framework for Low-Light Image Enhancement and Denoising

Low-light image enhancement is an important task in the domain of computer vision. Images taken under insufficient lighting conditions manifest low visibility and unknown noises which disrupt image contents and pose considerable challenges for low-light image enhancement. Most of Retinex-based methods usually attempt to design different priors on the gradient of both illumination and reflectance. However, noises can be involved in the Retinex-based models. To address the problem, we explore the problem of low-light image restoration through joint contrast enhancement and denoising. We propose a Retinex-based variational model for low-light image enhancement that effectively generates a noise-free image, yet proves to generalize well to diverse light-conditions. First, we present a simple constraint on the fidelity term between the fractional derivative of an observed image and the fractional derivative of the recomposed one which is the product of the reflectance and illumination. This strategy aims to model spatial consistency to preserve natural variation. Second, we introduce a weighted regularization term for the reflectance that can remove noise with a adaptive texture map. We evaluate our proposed approach using three challenging datasets: NPE, LOL and GladNet. Extensive experiments demonstrate that our proposed method outperforms other competing methods in terms of visual quality and quantitative comparisons.
A Perceptual Encryption-Based Image Communication System for Deep Learning-Based Tuberculosis Diagnosis Using Healthcare Cloud Services

Block-based perceptual encryption (PE) algorithms are becoming popular for multimedia data protection because of their low computational demands and format-compliancy with the JPEG standard. In conventional methods, a colored image as an input is a prerequisite to enable smaller block size for better security. However, in domains such as medical image processing, unavailability of color images makes PE methods inadequate for their secure transmission and storage. Therefore, this study proposes a PE method that is applicable for both color and grayscale images. In the proposed method, efficiency is achieved by considering smaller block size in encryption steps that have negligible effect on the compressibility of an image. The analyses have shown that the proposed system offers better security with only 12% more bitrate requirement as opposed to 113% in conventional methods. As an application of the proposed method, we have considered a smart hospital that avails healthcare cloud services to outsource their deep learning (DL) computations and storage needs. The EfficientNetV2-based model is implemented for automatic tuberculosis (TB) diagnosis in chest X-ray images. In addition, we have proposed noise-based data augmentation method to address data deficiency in medical image analysis. As a result, the model accuracy was improved by 10%.
EiMOL: A Secure Medical Image Encryption Algorithm based on Optimization and the Lorenz System

Nowadays, the demand for digital images from different intelligent devices and sensors has dramatically increased in smart healthcare. Due to advanced low-cost and easily available tools and software, manipulation of these images is an easy task. Thus, the security of digital images is a serious challenge for the content owners, healthcare communities, and researchers against illegal access and fraudulent usage. In this article, a secure medical image encryption algorithm, EiMOL , based on optimization and the Lorenz system, is proposed for smart healthcare applications. In the first stage, an optimized random sequence (ORS) is generated through directed weighted complex network particle swarm optimization using the genetic algorithm (GDWCN-PSO). This random number matrix and the Lorenz system are adopted to encrypt plain medical images, obtaining the cipher messages with a relationship to the plain images. According to our obtained results, the proposed EiMOL encryption algorithm is effective and resistant to the many attacks on benchmark Kaggle and Open-i datasets. Further, extensive experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches.
Quantum Fredkin and Toffoli gates on a versatile programmable silicon photonic chip

Abstract Quantum logic gates are backbones of quantum information processing (QIP), wherein the typical three-qubit Fredkin and Toffoli gates are essential in quantum computation and communication. So far, the quantum Fredkin gate has only been demonstrated with pre-entangled input states in free-space optics, which limits its usage for independent input photons. Here, we put forward an exquisite scheme and experimentally perform a proof-of-principle demonstration of three-qubit Fredkin and Toffoli gates on a programmable quantum photonic chip. Our scheme can also be used to realize a series of other two-qubit quantum gates. Our work sheds light on the merits of quantum photonic chip in implementing quantum logic gates, and paves the way for advanced quantum chip processors.
Sensor-based fall detection using a combination model of a temporal convolutional network and a gated recurrent unit

Due to the serious problem of the world's aging population and the harm caused by unintentional falls to elderly individuals, the question of how to precisely identify falls has steadily attracted the public's interest. In this article, a novel deep learning (DL) model, a combination model of a temporal convolutional network and gated recurrent unit (TCN-GRU) architecture, is proposed to obtain high-level features for classification. We evaluate its relative performance against two widely used machine learning (ML) based classifiers and six DL architectures using two popular open-source datasets collected using inertial sensors. Our algorithm results show that the proposed method outperformed other algorithms in nearly all four performance metrics we examined, for the datasets we tested. For the MobiAct dataset and Mosi-F dataset (which is a mixture of the MobiAct dataset with the Sisfall dataset), the prediction accuracy reached 99.5% and 97.6%, and the F1_Score reached 98.9% and 97.6%, respectively, demonstrating satisfactory performance. Moreover, the proposed algorithm had higher detection accuracy despite a small data volume, and it correctly detected all types of fall events from ten primary daily activity groups.
Salient object detection in low-light images via functional optimization-inspired feature polishing

Salient object detection under low-light conditions remains a challenge in many practical applications. Most recent works focus on feature integration without considering filtering out clutter from the darkness, thus limiting the detection performance. To tackle this issue, we propose a low-light image salient object detection network (LLISOD), which generates highly accurate saliency maps by functional optimization-inspired feature polishing strategy. The LLISOD includes: (1) An unfolded implicit nonlinear mapping (UINM) module uniquely designed for polishing feature maps; and (2) the hierarchical feature polishing (HFP) streams proposed for fusing the outputs of the UINM module on the top-down pathway to refine the saliency predictions. Furthermore, we provide a new dataset for benchmarking the investigation of salient object detection in low-light images. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. Code will be available at https://github.com/yuehuihui000/LLISOD.
A survey of urban visual analytics: Advances and future directions

Abstract Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.
Devising a Mechanism for Analyzing the Barriers of Blockchain Adoption in the Textile Supply Chain: A Sustainable Business Perspective

The adoption of blockchain technology (BCT) in a supply chain holds great potential for textile industries by executing transactions among stakeholders in a most reliable and verifiable way. Textile industries in emerging economies, like Pakistan, confront severe economic pressures and uncertain environment and strive to achieve sustainable supply chain excellence through blockchain implementation. This study is an initiative to analyze the key barriers in adopting BCT-related practices within the textile industry. This study conducts an extensive review of the literature using fuzzy Delphi approach for finalizing the barriers and applied fuzzy analytical hierarchy process (AHP) for prioritizing the barriers under uncertain environment. Based on the extensive review of the literature and panel discussions with experts, a total of five main barriers and 21 sub-barriers were categorized and ranked. The results and findings prioritize technological and system-related barriers (TSB) first, and human resources and R&amp;D (HRB) barriers second among the other barrier dimensions. This paper highlights the need for an inclusive understanding of the various technological, environmental, and socio-economic perspectives to create blockchain applications that work for the textile sector. This study’s key findings and policy guidelines can assist concerned stakeholders in making strategic decisions for adopting BCT within the textile supply chain. The managerial implications are provided for the industrial decision-makers and policymakers aiming to integrate BCT into the supply chain processes. Presently, there exists no research in the context of Pakistan that highlights the challenges faced during the adoption of BCT in the supply chain. For this purpose, an approach in the form of an integrated model based on fuzzy set theory is developed. Finally, the robustness of the proposed model is checked through sensitivity analysis.
Feature Selection for High Dimensional Datasets Based on Quantum-Based Dwarf Mongoose Optimization

Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various domains. Thus, in this paper, based on the recent advances in MH algorithms, we introduce a new FS technique to modify the performance of the Dwarf Mongoose Optimization (DMO) Algorithm using quantum-based optimization (QBO). The main idea is to utilize QBO as a local search of the traditional DMO to avoid its search limitations. So, the developed method, named DMOAQ, benefits from the advantages of the DMO and QBO. It is tested with well-known benchmark and high-dimensional datasets, with comprehensive comparisons to several optimization methods, including the original DMO. The evaluation outcomes verify that the DMOAQ has significantly enhanced the search capability of the traditional DMO and outperformed other compared methods in the evaluation experiments.
Bootstrapped Masked Autoencoders for Vision BERT Pretraining

We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1) momentum encoder that provides online feature as extra BERT prediction targets; 2) target-aware decoder that tries to reduce the pressure on the encoder to memorize target-specific information in BERT pretraining. The first design is motivated by the observation that using a pretrained MAE to extract the features as the BERT prediction target for masked tokens can achieve better pretraining performance. Therefore, we add a momentum encoder in parallel with the original MAE encoder, which bootstraps the pretraining performance by using its own representation as the BERT prediction target. In the second design, we introduce target-specific information (e.g., pixel values of unmasked patches) from the encoder directly to the decoder to reduce the pressure on the encoder of memorizing the target-specific information. Thus, the encoder focuses on semantic modeling, which is the goal of BERT pretraining, and does not need to waste its capacity in memorizing the information of unmasked tokens related to the prediction target. Through extensive experiments, our BootMAE achieves $$84.2\%$$ Top-1 accuracy on ImageNet-1K with ViT-B backbone, outperforming MAE by $$+0.8\%$$ under the same pre-training epochs. BootMAE also gets $$+1.0$$ mIoU improvements on semantic segmentation on ADE20K and $$+1.3$$ box AP, $$+1.4$$ mask AP improvement on object detection and segmentation on COCO dataset. Code is released at https://github.com/LightDXY/BootMAE .