Reviewerly/exHarmony huggingface dataset:
Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning

Despite considerable progress, image captioning still suffers from the huge difference in quality between easy and hard examples, which is left unexploited in existing methods. To address this issue, we explore the hard example mining in image captioning, and propose a simple yet effective mechanism to instruct the model to pay more attention to hard examples, thereby improving the performance in both general and complex scenarios. We first propose a novel learning strategy, termed Metric-oriented Focal Mechanism (MFM), for hard example mining in image captioning. Differing from the existing strategies for classification tasks, MFM can adopt the generative metrics of image captioning to measure the difficulties of examples, and then up-weight the rewards of hard examples during training. To make MFM applicable to different datasets without tedious parameter tuning, we further introduce an adaptive reward metric called Effective CIDEr (ECIDEr), which considers the data distribution of easy and hard examples during reward estimation. Extensive experiments are conducted on the MS COCO benchmark, and the results show that while maintaining the performance on simple examples, MFM can significantly improve the quality of captions for hard examples. The ECIDEr-based MFM is equipped on the current SOTA method, e.g., DLCT [1], which outperforms all existing methods and achieves new state-of-the-art performance on both the off-line and on-line testing, i.e., 134.3 CIDEr for the off-line testing and 136.1 for the on-line testing of MSCOCO. To validate the generalization ability of ECIDEr-based MFM, we also apply it to another dataset, namely Flickr30k, and superior performance gains can also be obtained.
MVFStain: Multiple virtual functional stain histopathology images generation based on specific domain mapping

To the best of our knowledge, artificial intelligence stain generation is an urgent requirement for histopathology images. Pathological examinations usually only utilize hematoxylin and eosin (H&E) regular staining to show histomorphological characteristics, but to accurately diagnose the disease, functional staining (such as oil red O and Ki67) are also required to provide important auxiliary information. However, the same tissue section is usually stained with one stain, and additional functional staining is not only time-consuming but also causes inevitable morphological misalignment due to manual manipulation. This brings difficulties to the development of artificial intelligence pathological image analysis tools. In this work, we propose a histopathology staining transfer framework to generate virtual functional staining images from H&E regular staining images. Compared with the framework that emphasizes generation diversity in the natural image field, we use KL loss and histo loss to align and separate style feature spaces in different domains to obtain domain-variant style features. The proposed multiple virtual functional stain (MVFStain) abstracts staining conversion to domain mapping and comprehensively utilizes multiple staining information. We evaluated the proposed method on four datasets (lung lesion, lung lobes, breast, and atherosclerotic lesion). The experiment involves the translation of H&E to nine other functional stains: CC10, Ki67, proSPC, HER2, PR, ER, oil red O, α-SMA, and macrophages. The major quantitative results are divided into image quality and positive signal prediction. MVFStain is close to or even surpasses one-to-one image translation on psnr and HTI image quality metrics. The best psnr reaches 26.1919, and HIT reaches 0.9430. We used mIOD to evaluate the optical density of positive signals, and CNR and gCNR to evaluate the lesion detectability. The results show that the mIOD of positive signals of virtual staining was slightly lower than the ground truth and close the lesion detectability of artificial staining. These results prove that the potential exists to develop a successful clinical alternative to artificial functional stains.
A Novel Long-Term Iterative Mining Scheme for Video Salient Object Detection

The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one critical limitation, which conflicts with the real mechanism of our visual system — a typical long-term methodology. As a result, failure cases keep showing up in the results of the current SOTA models, and the short-term methodology becomes the major technical bottleneck. To solve this problem, this paper proposes a novel VSOD approach, which performs VSOD in a complete long-term way. Our approach converts the sequential VSOD, a sequential task, to a data mining problem, i.e., decomposing the input video sequence to object proposals in advance and then mining salient object proposals as much as possible in an easy-to-hard way. Since all object proposals are simultaneously available, the proposed approach is a complete long-term approach, which can alleviate some difficulties rooted in conventional short-term approaches. In addition, we devised an online updating scheme that can grasp the most representative and trustworthy pattern profile of the salient objects, outputting framewise saliency maps with rich details and smoothing both spatially and temporally. The proposed approach outperforms almost all SOTA models on five widely used benchmark datasets.
Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.
XIV International Conference on Mathematics, Science and Technology Education

Abstract This paper represents a preface to the Proceedings of the XIV International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2022) held at the Kryvyi Rih State Pedagogical University, Ukraine, 18-20 May 2022. Background information and the organizational structure of the meeting, proceedings structure, and acknowledgments of the contributions of the many people who made the conference a success are presented.
Value creation in an algorithmic world: Towards an ethics of dynamic pricing

Choice of pricing strategy plays a central role in value creation and the effective functioning of markets. Shifts in technology and the growing availability of data are facilitating ever more innovative forms of pricing strategy. Within the emerging literature on pricing ethics, there is a gap in our understanding of the specific challenges of algorithmically generated dynamic pricing. Increasing pricing automation shifts the managerial focus from the selection of prices to the choice of algorithms. This paper expands the literature on pricing ethics by conceptualizing the ethical challenges raised by the contemporary use of dynamic pricing. We propose a governance model for algorithmically generated dynamic pricing, taking into account the role of the customer as a stakeholder in value generation.
Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras

We propose a novel and pragmatic framework for traffic scene perception with roadside cameras. The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, including object detection, object localization, object tracking, and multi-camera information fusion. Unlike previous vision-based perception frameworks rely upon depth offset or 3D annotation at training, we adopt a modular decoupling design and introduce a landmark-based 3D localization method, where the detection and localization can be well decoupled so that the model can be easily trained based on only 2D annotations. The proposed framework applies to either optical or thermal cameras with pinhole or fish-eye lenses. Our framework is deployed at a two-lane roundabout located at Ellsworth Rd. and State St., Ann Arbor, MI, USA, providing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7\times 24$</tex> real-time traffic flow monitoring and high-precision vehicle trajectory extraction. The whole system runs efficiently on a low-power edge computing device with all-component end-to-end delay of less than 20ms.
A Multi-Objective Reinforcement Learning Based Controller for Autonomous Navigation in Challenging Environments

In this paper, we introduce a self-trained controller for autonomous navigation in static and dynamic (with moving walls and nets) challenging environments (including trees, nets, windows, and pipe) using deep reinforcement learning, simultaneously trained using multiple rewards. We train our RL algorithm in a multi-objective way. Our algorithm learns to generate continuous action for controlling the UAV. Our algorithm aims to generate waypoints for the UAV in such a way as to reach a goal area (shown by an RGB image) while avoiding static and dynamic obstacles. In this text, we use the RGB-D image as the input for the algorithm, and it learns to control the UAV in 3-DoF (x, y, and z). We train our robot in environments simulated by Gazebo sim. For communication between our algorithm and the simulated environments, we use the robot operating system. Finally, we visualize the trajectories generated by our trained algorithms using several methods and illustrate our results that clearly show our algorithm’s capability in learning to maximize the defined multi-objective reward.
Generating the captions for remote sensing images: A spatial-channel attention based memory-guided transformer approach

Remote sensing image captioning (RSIC) is cross-modal interaction task in an artificial intelligence that leads to automatic description of Earth’s geological properties captured from an aerial view. It is noted that, convolutional neural network (CNN) and recurrent neural network (RNN) based encoder–decoder methods are widely adopted for RSIC, but has two main constrains: first, insufficient to capture inherent geographical characteristics due to single level static convolutional features; second, difficult to train regressive time-step sequences. To address these challenges, a novel fully-attentive framework entitled Spatial-Channel Attention based MEmory-guided Transformer (SCAMET) is proposed, which calibrates multilevel visual attentive features and aligns with linguistic information through persistent memory. Here, CNN is integrated with Transformer to generate captions for remote sensing image. To comprehend deeper semantic knowledge of multi-scale, multi-shape, multi-object in remote sensing image, multi-attentive visual features are extracted by employing spatial and channel attention separately. To decode multi-attentive feature into caption, this work proposes memory-guided Transformer as linguistic decoder. Specifically, learnable memory elements are incorporated in multi-head attention block, which perceives intrinsic association within visual multi-attentive features and reconciles with linguistic information. The ablation studies are conducted on three public RSIC datasets, Sydney-captions, UCM-captions and RSICD to evaluate performance of proposed method. The quantitative and qualitative analyses reveal that proposed method performs satisfactory compared to state-of-the-art approaches. This work also proposes a “Weighted Mean Score” index to evaluate conclusive performance of model across all datasets by leveraging global contribution of each test set. The implementation of proposed work is available at: https://github.com/GauravGajbhiye/SCAMET_RSIC.
Blockchain as a resource for building trust in pre-owned goods’ marketing: a case of automobile industry in an emerging economy

Blockchain technology (BCT) has caught tremendous attention from businesses. In the present study, the authors propose that if BCT is used to track a product’s life cycle, that is, when it was manufactured, purchased, and used by the first and subsequent owners, the information asymmetry present in the pre-owned automobile market can be significantly reduced. The study uses a qualitative procedure to explore the challenges which buyers, sellers and intermediaries face while buying and selling pre-owned automobiles, specifically in emerging economies. Theoretical constructs of the resource-based view and the trust-based marketing theory are used to explore the extent to which BCT can influence customer purchase intention and the marketing performance of firms dealing in pre-owned automobile products in emerging economies. We collected data by conducting eight focus group discussions with generation Y and Z individuals who had experience of buying or selling pre-owned automobiles. We also conducted five personal interviews with managers of pre-owned automobile online/omnichannel retailers to understand the industry’s point of view. Three themes emerged during the study: ‘current challenges in pre-owned automobile transactions,’ ‘scope of blockchain in pre-owned automobile transactions,’ and ‘concerns regarding blockchain adoption in pre-owned automobile transactions.’ We present our findings based on these three themes. Further, we propose a conceptual model and provide theoretical and managerial implications for the pre-owned automobile industry.
IDeAuth: A novel behavioral biometric-based implicit deauthentication scheme for smartphones

Many studies have shown that single entry-point authentication schemes for smartphones can easily be circumvented. IDeAuth is an implicit deauthentication scheme that aims to minimize unauthorized access to security-sensitive applications and services running on users’ smartphones when unauthorized access or intrusions are detected. IDeAuth verifies legitimate owners of their smartphones by exploiting their micro hand-movements and decides to sign off the default user account revoking security-sensitive applications and services linked with it. We design and develop an Android-based prototype application as a proof-of-concept and collect a new dataset consisting of 21263 observations from 41 users in a real scenario. The user verification process employs four different one-class classifiers (OCCs), which is evaluated on the collected dataset using the holdout test method. IDeAuth achieves a Half Total Error Rate (HTER) of ≈4% after applying a decision-level-fusion enhancing the best individual classifier’s performance by ≈1%.
Towards a resource efficient and privacy-preserving framework for campus-wide video analytics-based applications

Abstract Video surveillance and analytics solutions based on Artificial Intelligence (AI) are increasingly being deployed across industries, including academia. There are a number of use-cases for campus-wide video analytics applications. Detecting events of interest in real-time and generating alerts is a core requirement for such applications, making them both network and compute intensive. Thus, the underlying framework needs to be resource optimized in terms of latency, compute and storage requirements for a multitude of video applications. Increasingly privacy concerns have been voiced against the pervasive deployment of video analytics-based applications. Thus, protecting the privacy of students and staff in a campus setting shall be a major design consideration for such systems going forward. This paper presents a resource optimized and privacy preserving framework for campus-wide video analytics applications. Several use-cases are presented and early results from the deployment of the proposed framework establish its feasibility and effectiveness.
Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare

The lack of interpretability in artificial intelligence models (i.e., deep learning, machine learning, and rules-based) is an obstacle to their widespread adoption in the healthcare domain. The absence of understandability and transparency frequently leads to (i) inadequate accountability and (ii) a consequent reduction in the quality of the predictive results of the models. On the other hand, the existence of interpretability in the predictions of AI models will facilitate the understanding and trust of the clinicians in these complex models. The data protection regulations worldwide emphasize the relevance of the plausibility and verifiability of AI models’ predictions. In response and to take a role in tackling this challenge, we designed the interpretability-based model with algorithms that achieve human-like reasoning abilities through statistical analysis of the datasets by calculating the relative weights of the variables of the features from the medical images and the patient symptoms. The relative weights represented the importance of the variables in predictive decision-making. In addition, the relative weights were used to find the positive and negative probabilities of having the disease, which indicated high fidelity explanations. Hence, the primary goal of our model is to shed light and give insights into the prediction process of the models, as well as to explain how the model predictions have resulted. Consequently, our model contributes by demonstrating accuracy. Furthermore, two experiments on COVID-19 datasets demonstrated the effectiveness and interpretability of the new model.
Conditional generative adversarial network with dual-branch progressive generator for underwater image enhancement

Underwater images are of great significance for exploring and utilizing the marine environment. However, the raw underwater images usually suffer from distorted color and low contrast due to the attenuation of light. To solve this problem, we present a conditional generative adversarial network with dual-branch progressive generator, which can asymptotically enhance underwater images. In particular, the generator consists of two independent branches and a progressive enhancement algorithm. The dual-branch structure is designed to generate a base image and numerous parameter maps required by progressive enhancement respectively. The progressive enhancement algorithm is proposed to iteratively improve the quality of underwater images. Meanwhile, an iterative function is constructed to guide image enhancement in the progressive enhancement algorithm. Finally, a simple discriminator and multiple effective loss functions are adopted to optimize the progressive process of underwater image enhancement. The qualitative and quantitative experiments on synthetic and real-world underwater datasets demonstrate that the proposed method can achieve superior performance against several representative underwater image processing methods. Furthermore, a series of ablation studies are presented to show the contribution of each branch in our model.
Local Feature Methods Based Facial Recognition

In both business and academics, 3D face recognition is a hot topic. This technology's wide range of uses and the natural recognition procedure make traditional 2D face recognition a superior choice. Additionally, 3D face recognition systems are able to successfully identify human faces even in low light and with varying facial postures and expressions, while 2D face recognition systems would have a tough time operating in these settings. Attempting to recognize faces based on their statistical distribution is pointless. Local traits may be used to identify these faces, though. It is hypothesized here that local feature-based face recognition may be achieved by acquiring local features and their dimensions.
Bayesian Imaging with Data-Driven Priors Encoded by Neural Networks

This paper proposes a new methodology for performing Bayesian inference in imaging inverse problems where the prior knowledge is available in the form of training data. Following the manifold hypothesis, we adopt a data-driven prior that is supported on a submanifold of the ambient space, which we can learn from the training data using a generative model, such as a variational autoencoder or generative adversarial network. We establish the existence and well-posedness of the associated posterior distribution and posterior moments under easily verifiable conditions, providing a rigorous underpinning for Bayesian estimators and uncertainty quantification analyses. Bayesian computation is performed using a parallel tempered version of the pCN algorithm on the manifold, which is shown to be ergodic and robust to the nonconvex nature of these data-driven models. In addition to point estimators and uncertainty quantification analyses, we derive a model misspecification test to automatically detect situations where the data-driven prior is unreliable, and we explain how to identify the dimension of the latent space directly from the training data. The proposed approach is illustrated with a range of experiments with the MNIST dataset and is compared with some variational and message passing image reconstruction approaches from the state of the art that also use data-driven regularization. A model accuracy analysis suggests that the Bayesian probabilities reported by the proposed data-driven models are also accurate under a frequentist definition of probability, suggesting that the learnt prior is close to the true marginal distribution of the unknown image.