Reviewerly/exHarmony huggingface dataset:
GEPSO: A new generalized particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic that has been utilized as a powerful optimization tool in a wide range of applications since its inception in 1995. Due to the flexibility of its parameters and concepts, PSO has appeared in many variants, probably more than any other meta-heuristic algorithm. This paper introduces the Generalized Particle Swarm Optimization (GEPSO) algorithm as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation. These terms aim to deepen the interrelations of particles and their knowledge sharing, increase variety in the swarm, and provide a better search in unexplored areas of the search space. Moreover, a novel procedure is utilized for dynamic updating of the particles’ inertia weights, which controls the convergence of the swarm towards a solution. Also, since parameters of heuristic and meta-heuristic algorithms have a significant influence on their performance, a comprehensive guideline for parameter tuning of the GEPSO is developed. The computational results of solving numerous well-known benchmark functions by the GEPSO, original PSO, Repulsive PSO (REPSO), PSO with Passive Congregation (PSOPC), Negative PSO (NPSO), Deterministic PSO (DPSO), and Line Search-Based Derivative-Free PSO (LS-DF-PSO) approaches showed that the GEPSO outperformed the compared methods in terms of mean and standard deviation of fitness function values and runtimes.
Blockchain-based batch authentication protocol for Internet of Vehicles

The vehicles in Internet of Vehicles (IoV) can be used to opportunistically gather and distribute the data in a smart city environment. However, at the same time, various security threats arise due to insecure communication happening among various entities in an IoV-based smart city deployment. To address this issue, we aim to design a novel blockchain-enabled batch authentication scheme in Artificial Intelligence (AI)-envisioned IoV-based smart city deployment. The latest trends and revolutions in technologies incorporate AI/Machine Learning (ML) in blockchaining to produce a secure, efficient and intelligent blockchain based system. The data stored in the blocks in the blockchain are authentic and genuine, which makes the AI/ML algorithms to work at their exceptions in order produce correct predictions on the blockchain data. Through the signing phase of the proposed scheme, each vehicle in a dynamically formed cluster broadcasts a message to its own member and respective road-side unit (RSU). In the proposed scheme, two types of authentication take place: vehicle to vehicle (V2V) authentication allows a vehicle to authenticate its neighbor vehicles in its cluster, while batch authentication permits a group of cluster vehicles to be authenticated by their RSU. At the end, a group key is established among the vehicles and RSU in their cluster. RSU then gathers securely data from its vehicles and form several transactions including the information of vehicles and its own given information to the cluster member vehicles. The transactions are formed later by the nearby fog server associated with RSU and then by the cloud server to form a complete block. The created blocks are mined by the cloud servers in a Peer-to-Peer (P2P) cloud server network through the voting-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The authentic and genuine data of the blockchain are utilized for Big data analytics through AI/ML algorithms. It is shown that the proposed scheme is highly robust against various attacks through formal and informal security analysis, and also through formal security verification tool. A detailed comparative analysis reveals that the proposed scheme achieves superior security and functionality features, and offers comparable storage, communication and computational costs as compared to other existing competing schemes. Finally, the blockchain implementation has been carried out on the proposed scheme to show its effectiveness.
Multi-Modal Repairs of Conversational Breakdowns in Task-Oriented Dialogs

A major problem in task-oriented conversational agents is the lack of support for the repair of conversational breakdowns. Prior studies have shown that current repair strategies for these kinds of errors are often ineffective due to: (1) the lack of transparency about the state of the system's understanding of the user's utterance; and (2) the system's limited capabilities to understand the user's verbal attempts to repair natural language understanding errors. This paper introduces SOVITE, a new multi-modal speech plus direct manipulation interface that helps users discover, identify the causes of, and recover from conversational breakdowns using the resources of existing mobile app GUIs for grounding. SOVITE displays the system's understanding of user intents using GUI screenshots, allows users to refer to third-party apps and their GUI screens in conversations as inputs for intent disambiguation, and enables users to repair breakdowns using direct manipulation on these screenshots. The results from a remote user study with 10 users using SOVITE in 7 scenarios suggested that SOVITE's approach is usable and effective.
Blockchain technology and its applications to combat COVID-19 pandemic

The world is currently facing multifaceted problems due to the emergence of the COVID-19 (coronavirus) pandemic. Blockchain technology (BT) plays a vital role in creating a platform for adequately managing the COVID-19 pandemic. The latest information on the blockchain and its application in solving challenging problems due to the COVID-19 pandemic are collected and discussed from the available literature searched through PubMed, Scopus, and Google scholar. In this paper, we propose a blockchain-based platform to combat this pandemic. Furthermore, we have identified and discussed nine significant applications of blockchain in solving the problem arising from the COVID-19 pandemic. The severity of COVID-19 was so tremendous that the World Health Organization (WHO) had to declare it as a pandemic within a month of its full-scale expansion. The greatest challenge most governments are suffering from is the lack of a precise mechanism to detect the newly infected cases and predict coronavirus infection risk. So, we need a technology-empowered solution to fight during this COVID-19 crisis. The various features of blockchain technology, such as decentralization, transparency, and immutability, can help control this pandemic by early detection of outbreaks, fast-tracking drug delivery, and protecting user privacy during treatment.
Medical Visual Question Answering via Conditional Reasoning

Medical visual question answering (Med-VQA) aims to accurately answer a clinical question presented with a medical image. Despite its enormous potential in healthcare industry and services, the technology is still in its infancy and is far from practical use. Med-VQA tasks are highly challenging due to the massive diversity of clinical questions and the disparity of required visual reasoning skills for different types of questions. In this paper, we propose a novel conditional reasoning framework for Med-VQA, aiming to automatically learn effective reasoning skills for various Med-VQA tasks. Particularly, we develop a question-conditioned reasoning module to guide the importance selection over multimodal fusion features. Considering the different nature of closed-ended and open-ended Med-VQA tasks, we further propose a type-conditioned reasoning module to learn a different set of reasoning skills for the two types of tasks separately. Our conditional reasoning framework can be easily applied to existing Med-VQA systems to bring performance gains. In the experiments, we build our system on top of a recent state-of-the-art Med-VQA model and evaluate it on the VQA-RAD benchmark [23]. Remarkably, our system achieves significantly increased accuracy in predicting answers to both closed-ended and open-ended questions, especially for open-ended questions, where a 10.8% increase in absolute accuracy is obtained. The source code can be downloaded from https://github.com/awenbocc/med-vqa.
ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language

Person search by natural language aims at retrieving a specific person in a large-scale image pool that matches given textual descriptions. While most of the current methods treat the task as a holistic visual and textual feature matching one, we approach it from an attribute-aligning perspective that allows grounding specific attribute phrases to the corresponding visual regions. We achieve success as well as a performance boost by a robust feature learning that the referred identity can be accurately bundled by multiple attribute cues. To be concrete, our Visual-Textual Attribute Alignment model (dubbed as ViTAA) learns to disentangle the feature space of a person into sub-spaces corresponding to attributes using a light auxiliary attribute segmentation layer. It then aligns these visual features with the textual attributes parsed from the sentences via a novel contrastive learning loss. We validate our ViTAA framework through extensive experiments on tasks of person search by natural language and by attribute-phrase queries, on which our system achieves state-of-the-art performances. Codes and models are available at https://github.com/Jarr0d/ViTAA.
Feature selection using Binary Crow Search Algorithm with time varying flight length

Crow Search Algorithm (CSA) is a simple yet effective meta-heuristic algorithm that has been applied to solve many engineering problems. In CSA, fl parameter controls the search capability of crows and AP parameter balances the trade-off between exploration and exploitation. The parameter fl is initialized to a constant value in CSA. However, CSA faces the problem of being trapped in local minima. This work proposes the solution to this problem by introducing the new concept of time varying flight length in CSA. The value of fl should be large in initial stages of algorithm in order to support random exploration and it should gradually decrease in later iterations to encourage the exploitation of good solutions found so far. The proposed approach, Binary Crow Search Algorithm with Time Varying Flight Length (BCSA-TVFL) is applied to feature selection problems in wrapper mode. Eight variants of BCSA-TVFL based on eight different transfer functions are tested. The best performing variant is then selected and compared with other state-of-the-art wrapper feature selection techniques and standard filter feature selection techniques. Performance of proposed approach is tested on 20 standard UCI datasets. Experimental result comparison shows that the proposed feature selection technique performs better than other competitors.
Relation Network for Multilabel Aerial Image Classification

Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an LSTM layer can be introduced to modeling such label dependencies in a chain propagation manner, the efficiency might be questioned when certain labels are improperly inferred. To address this, we propose a novel aerial image multi-label classification network, attention-aware label relational reasoning network. Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module, 2) an attentional region extraction module, and 3) a label relational inference module. To be more specific, the label-wise feature parcel learning module is designed for extracting high-level label-specific features. The attentional region extraction module aims at localizing discriminative regions in these features and yielding attentional label-specific features. The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. The proposed network is characterized by its capacities of extracting discriminative label-wise features in a proposal-free way and reasoning about label relations naturally and interpretably. In our experiments, we evaluate the proposed model on the UCM multi-label dataset and a newly produced dataset, AID multi-label dataset. Quantitative and qualitative results on these two datasets demonstrate the effectiveness of our model. To facilitate progress in the multi-label aerial image classification, the AID multi-label dataset will be made publicly available.
Drone-based entanglement distribution towards mobile quantum networks

Satellites have shown free-space quantum-communication ability; however, they are orbit-limited from full-time all-location coverage. Meanwhile, practical quantum networks require satellite constellations, which are complicated and expensive, whereas the airborne mobile quantum communication may be a practical alternative to offering full-time all-location multi-weather coverage in a cost-effective way. Here, we demonstrate the first mobile entanglement distribution based on drones, realizing multi-weather operation including daytime and rainy nights, with a Clauser-Horne-Shimony-Holt S-parameter measured to be 2.41 ± 0.14 and 2.49 ± 0.06, respectively. Such a system shows unparalleled mobility, flexibility and reconfigurability compared to the existing satellite and fiber-based quantum communication, and reveals its potential to establish a multinode quantum network, with a scalable design using symmetrical lens diameter and single-mode-fiber coupling. All key technologies have been developed to pack quantum nodes into lightweight mobile platforms for local-area coverage, and arouse further technical improvements to establish wide-area quantum networks with high-altitude mobile communication.
Appearance-Preserving 3D Convolution for Video-Based Person Re-identification

Due to the imperfect person detection results and posture changes, temporal appearance misalignment is unavoidable in video-based person re-identification (ReID). In this case, 3D convolution may destroy the appearance representation of person video clips, thus it is harmful to ReID. To address this problem, we propose Appearance-Preserving 3D Convolution (AP3D), which is composed of two components: an Appearance-Preserving Module (APM) and a 3D convolution kernel. With APM aligning the adjacent feature maps in pixel level, the following 3D convolution can model temporal information on the premise of maintaining the appearance representation quality. It is easy to combine AP3D with existing 3D ConvNets by simply replacing the original 3D convolution kernels with AP3Ds. Extensive experiments demonstrate the effectiveness of AP3D for video-based ReID and the results on three widely used datasets surpass the state-of-the-arts. Code is available at: https://github.com/guxinqian/AP3D .
Tourists’ Attitudes toward the Use of Artificially Intelligent (AI) Devices in Tourism Service Delivery: Moderating Role of Service Value Seeking

This study examines tourists’ attitudes toward the use of artificially intelligent (AI) devices in either relatively more utilitarian or hedonic tourism services (airline and hospitality services, respectively). Findings suggest that tourists’ acceptance of the use of AI devices in both service contexts is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy, and emotions toward the artificially intelligent devices. Findings further suggest that social influence is a stronger determinant in hospitality services compared to airline services. Tourists have higher performance expectancy from AI devices used to provide airline services compared to hospitality services. Tourists’ willingness to accept the use of AI devices for delivering hospitality services is lower than airline services. These results suggest that while the utilization of AI devices for delivering functional services is acceptable, the use of AI devices in the delivery of hedonic services may backfire.
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency

Recent learning-based approaches, in which models are trained by single-view images have shown promising results for monocular 3D face reconstruction, but they suffer from the ill-posed face pose and depth ambiguity issue. In contrast to previous works that only enforce 2D feature constraints, we propose a self-supervised training architecture by leveraging the multi-view geometry consistency, which provides reliable constraints on face pose and depth estimation. We first propose an occlusion-aware view synthesis method to apply multi-view geometry consistency to self-supervised learning. Then we design three novel loss functions for multi-view consistency, including the pixel consistency loss, the depth consistency loss, and the facial landmark-based epipolar loss. Our method is accurate and robust, especially under large variations of expressions, poses, and illumination conditions. Comprehensive experiments on the face alignment and 3D face reconstruction benchmarks have demonstrated superiority over state-of-the-art methods. Our code and model are released in https://github.com/jiaxiangshang/MGCNet .
Large-Scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline

Prior work in visual dialog has focused on training deep neural models on VisDial in isolation. Instead, we present an approach to leverage pretraining on related vision-language datasets before transferring to visual dialog. We adapt the recently proposed ViLBERT model for multi-turn visually-grounded conversations. Our model is pretrained on the Conceptual Captions and Visual Question Answering datasets, and finetuned on VisDial. Our best single model outperforms prior published work by $$1\%$$ absolute on NDCG and MRR. Next, we find that additional finetuning using "dense" annotations in VisDial leads to even higher NDCG – more than $$10\%$$ over our base model – but hurts MRR – more than $$17\%$$ below our base model! This highlights a trade-off between the two primary metrics – NDCG and MRR – which we find is due to dense annotations not correlating well with the original ground-truth answers to questions.
YOLO-ACN: Focusing on Small Target and Occluded Object Detection

To further improve the speed and accuracy of object detection, especially small targets and occluded objects, a novel and efficient detector named YOLO-ACN is presented. The detector model is inspired by the high detection accuracy and speed of YOLOv3, and it is improved by the addition of an attention mechanism, a CIoU (complete intersection over union) loss function, Soft-NMS (non-maximum suppression), and depthwise separable convolution. First, the attention mechanism is introduced in the channel and spatial dimensions in each residual block to focus on small targets. Second, CIoU loss is adopted to achieve accurate bounding box (BBox) regression. Besides, to filter out a more accurate BBox and avoid deleting occluded objects in dense images, the CIoU is applied in the Soft-NMS, and the Gaussian model in the Soft-NMS is employed to suppress the surrounding BBox. Third, to significantly reduce the parameters and improve the detection speed, standard convolution is replaced by depthwise separable convolution, and hard-swish activation function is utilized in deeper layers. On the MS COCO dataset and infrared pedestrian dataset KAIST, the quantitative experimental results show that compared with other state-of-the-art models, the proposed YOLO-ACN has high accuracy and speed in detecting small targets and occluded objects. YOLO-ACN reaches a mAP50 (mean average precision) of 53.8% and an APs (average precision for small objects) of 18.2% at a real-time speed of 22 ms on the MS COCO dataset, and the mAP for a single class on the KAIST dataset even reaches over 80% on an NVIDIA Tesla K40.
A Novel Blockchain-Based Integrity and Reliable Veterinary Clinic Information Management System Using Predictive Analytics for Provisioning of Quality Health Services

The recent advances in information management systems coupled with machine learning algorithms paved the way for a significant revolution in animal healthcare industries.However, the data in such systems suffer from various challenges such as security, reliability, and convenience, to name a few.Traditional systems are not useful to meet these critical issues because these systems have not a consistent structure for data security and reliability policies.Therefore, a new solution is required to enhance data accessibility and should regulate government security policies to ensure the accountability of the usage of the medical records system.Moreover, it is also required to analyze historical data of veterinary clinic using data mining and machine learning techniques to predict the future appointments scheduling requests, which is essential for veterinary management to drive better future decisions, for instance, future demands of medical supplies and to plan veterinary medical staff, etc.This paper aims to fill the gap by proposing a novel blockchain-based reliable and intelligent veterinary information management system (RIVIMS) using smart contract and machine learning techniques.The proposed RIVIMS consists of two main modules; blockchain-based secured veterinary information management, data and predictive analytics modules.First, a blockchain-based secure and reliable veterinary clinic information management system is developed using Hyperledger Fabric.Second, a smart contract enabled data, and predictive analytics modules are developed using permissioned blockchain framework.The data and predictive modules aim to analyze veterinary clinic patients appointments data in order to discover underlying patterns and build a robust prediction model using machine learning algorithms.The data and predictive helps veterinary management to drive better future business decisions to provide better healthcare services to veterinary patients.Hyperledger Caliper is used as a benchmark tool to evaluate the performance of the developed blockchain-based system in terms of transaction per second, transaction success rate, transaction throughput, and transaction latency.Furthermore, machine learning performance measures have utilized, such as MAE, RMSE, and R2 score to evaluate the overall performance of the prediction model.The experimental results demonstrate the effectiveness and robustness of the proposed RIVIMS.