Reviewerly/exHarmony huggingface dataset:
Attribute-Based Data Sharing Scheme With Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations.
Web service adaptation: A decade’s overview

With the exponential growth of communication and information technologies, adaptation has gained a significant attention as it becomes a key feature of service-based systems, allowing them to operate and evolve in highly dynamic and uncertain environments. Although several Web service standards and frameworks have been proposed and extended, existing solutions do not provide a suitable architecture, in which all aspects of monitoring and adaptation (e.g., proactive, cross-layer, and autonomic adaptation) can be expressed. In addition, the emergence of new computing environments to host and execute various types of services (Web/cloud services, big data-intensive services, mobile services, microservices, etc.) raises the need for more efficient monitoring and adaptation systems. This survey aims to bring a synthesis and a road-map to the adaptation of service-based systems. We also discuss adaptation solutions in emerging service models, such as cloud services and big services. Based on an adaptation taxonomy which we extracted from the surveyed approaches, and by identifying the main requirements and goals of service adaptation in Web, cloud and big data environments, detailed analysis and discussions, as well as the open issues, are provided.
Machine learning approaches to sentiment analysis in online social networks

The online social network presents the quantitative measure of the psychological behaviour of individuals and helps to analyse the generic standpoint of social or political issues. As the field of research in text mining, it follows a computational approach to determine the opinions, sentiments, and subjectivity of text and other expressions. Moreover, the majority of approaches try to model the syntactic information of words without considering sentiment. The present study gives a brief narration of different machine learning (ML) models used for sentiment analysis and also proposes an efficient modular approach to give precise accuracy in validating and testing the Twitter data. The objective is to solve the problems through evaluation and comparison of different methods based on accuracy and training time. The proposed model achieves an accuracy of 88.37% with minimum possible training time. Simulation study states an effective way in which dataset may be thoroughly analysed and implemented with a focus on further validation of sentiment dataset to make tweet sentiment analysis more accurate.
Understanding Continued Use Intention of AI Assistants

In recent years, smart home assistants have been used by a large number of people due to their simple, hands-free, voice-based operation. To ensure the long-term success and widespread dissemination of a product, it is important to evaluate its continued use. This study is mainly based on uses and gratifications theory to explore the relationship between the initial use of, gratification provided by, and continued use intention of smart home assistants, and to analyze differences in use by different age groups. The results confirm that different types of SHAs use lead to different levels of gratification in different categories. And gratification of different categories of users has a significant positive impact on the continued use intention. In addition, significant differences exist in the impact path of using smart home assistants to alleviate loneliness, among different age groups.
Augmented reality navigation application to promote tourism to local state attraction “Lawang Sewu”

The writing of this paper and the creation of this application is a means to improve and add on facilities on the local state-run attraction. This subject matter was picked in conjunction with the decline of tourism in the said historical site due to a pandemic that rankles Indonesia's tourism industry and drives it to the ground. Augmented Reality as a method of deliverance was picked due to its popularity and freshness in the tourism market. The research method was conducted starting with analysis, development, implementation, then evaluation. Analysis was done using sourcing available to the public and a questioner. Implementation was done using Unity, Vuforia, and Maya. And with the positive responses of 66.6 percent of the sample market finding enjoyment in the use of this application in their exploration during User Acceptance testing with the existing prototype. This shows that the resulting use of this application improves user enjoyment in experiencing the state attraction.
An IoT Enable Anomaly Detection System for Smart City Surveillance

Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods.
HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval

Image-text retrieval (ITR) is a challenging task in the field of multimodal information processing due to the semantic gap between different modalities.In recent years, researchers have made great progress in exploring the accurate alignment between image and text.However, existing works mainly focus on the fine-grained alignment between image regions and sentence fragments, which ignores the guiding significance of context background information.Actually, integrating the local finegrained information and global context background information can provide more semantic clues for retrieval.In this paper, we propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval.First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively.Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities.Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment.To justify the proposed model, we perform extensive experiments on MS-COCO and Flickr30K datasets.Experimental results show that the proposed HGAN outperforms the state-of-the-art methods on both datasets, which demonstrates the effectiveness and superiority of our model.
RDGCN: Reasonably dense graph convolution network for pedestrian trajectory prediction

The pedestrian trajectory prediction remains challenging due to its uncertainty and interference from surrounding pedestrians. There are two deficiencies in previous pedestrian trajectory prediction methods: 1. The temporal correlation of social interaction and the movement factors of groups are ignored; 2. Unreasonable interaction weight allocation. In order to eliminate these two deficiencies, a reasonably dense graph convolution network (RDGCN) was developed in this study. Spatial and temporal graphs were first constructed to model social interactions and movement factors. Then, asymmetric three-dimensional (3D) convolution was employed for the fusion of spatial-temporal information to capture the temporal correlation of social interactions and the movement factors of groups. The RSigmoid function was designed to assign interaction weights and to make the setting of interaction weight more reasonable. Finally, a U-TCN module was designed to estimate two-dimensional Gaussian distribution parameters of the future trajectories. On the ETH and UCY datasets, the proposed method outperformed versus other models in terms of average displacement error and final displacement error, and it was capable of predicting complex social behaviors and movement factors.
Human Activity Recognition Based on Two-Channel Residual–GRU–ECA Module with Two Types of Sensors

With the thriving development of sensor technology and pervasive computing, sensor-based human activity recognition (HAR) has become more and more widely used in healthcare, sports, health monitoring, and human interaction with smart devices. Inertial sensors were one of the most commonly used sensors in HAR. In recent years, the demand for comfort and flexibility in wearable devices has gradually increased, and with the continuous development and advancement of flexible electronics technology, attempts to incorporate stretch sensors into HAR have begun. In this paper, we propose a two-channel network model based on residual blocks, an efficient channel attention module (ECA), and a gated recurrent unit (GRU) that is capable of the long-term sequence modeling of data, efficiently extracting spatial–temporal features, and performing activity classification. A dataset named IS-Data was designed and collected from six subjects wearing stretch sensors and inertial sensors while performing six daily activities. We conducted experiments using IS-Data and a public dataset called w-HAR to validate the feasibility of using stretch sensors in human action recognition and to investigate the effectiveness of combining flexible and inertial data in human activity recognition, and our proposed method showed superior performance and good generalization performance when compared with the state-of-the-art methods.
Video Search with CLIP and Interactive Text Query Reformulation

Nowadays, deep learning based models like CLIP allow simple design of cross-modal video search systems that are able to solve many tasks considered as highly challenging several years ago. In this paper, we analyze a CLIP based search approach that focuses on situations, where users cannot find proper text queries to describe searched video segments. The approach relies on suggestions of classes for displayed intermediate result sets and thus allows users to realize missing words and ideas to describe video frames. This approach is supported with a preliminary study showing potential of the method. Based on the results, we extend a respected known-item search system for the Video Browser Showdown, where more challenging visual known-item search tasks are planned.
The Prospects and Challenges of ChatGPT on Marketing Research and Practices

The prospects and impact of AI and technology on marketing are critical and well-recognised. The emergence of ChatGPT is a recent tool developed using AI that has created a boom worldwide since its launch with significant implications on all business practises. While recognising the emerging literature on ChatGPT, this paper aims to contribute to the ongoing discussion and identifies the plethora of opportunities for using ChatGPT as an enabler in marketing research and practice. In addition to that, it also discusses the challenges related to using ChatGPT in marketing research. The paper further highlights prospects for integrating ChatGPT in various fields of marketing, such as consumer behaviour, advertising, branding, and sales, providing relevant practical implications for managers. Finally, the paper recognises the need for further study to understand the scope of ChatGPT better and offers future research agenda. The paper concludes by reemphasising the need for using ChatGPT with human intervention in marketing research and practise.
The effect of neural network architecture on virtual H&amp;E staining: Systematic assessment of histological feasibility

Conventional histopathology has relied on chemical staining for over a century. The staining process makes tissue sections visible to the human eye through a tedious and labor-intensive procedure that alters the tissue irreversibly, preventing repeated use of the sample. Deep learning-based virtual staining can potentially alleviate these shortcomings. Here, we used standard brightfield microscopy on unstained tissue sections and studied the impact of increased network capacity on the resulting virtually stained H&E images. Using the generative adversarial neural network model pix2pix as a baseline, we observed that replacing simple convolutions with dense convolution units increased the structural similarity score, peak signal-to-noise ratio, and nuclei reproduction accuracy. We also demonstrated highly accurate reproduction of histology, especially with increased network capacity, and demonstrated applicability to several tissues. We show that network architecture optimization can improve the image translation accuracy of virtual H&E staining, highlighting the potential of virtual staining in streamlining histopathological analysis.
Towards a Preliminary Ontology of Dark Patterns Knowledge

Deceptive design practices are increasingly used by companies to extract profit, harvest data, and limit consumer choice. Dark patterns represent the most common contemporary amalgamation of these problematic practices, connecting designers, technologists, scholars, regulators, and legal professionals in transdisciplinary dialogue. However, a lack of universally accepted definitions across the academic, legislative and regulatory space has likely limited the impact that scholarship on dark patterns might have in supporting sanctions and evolved design practices. In this late breaking work, we seek to harmonize regulatory and academic taxonomies of dark patterns, proposing a preliminary three-level ontology to create a shared language that supports translational research and regulatory action. We identify potential directions for scholarship and social impact building upon this ontology.
Low-light image enhancement based on sharpening-smoothing image filter

Low-light images suffer from poor visibility, severe noise, low contrast, and low brightness. To overcome these issues, many image enhancement methods have been proposed. Few techniques solve these problems simultaneously. This paper presents a low-light image enhancement method. The proposed method first applies the HSV (Hue, Saturation, Value) transform to the input image. Here, a multi-scale decomposition of the Sharpening-Smoothing Image Filter (SSIF) is proposed to obtain approximation and detail sub-images of the V component. After the decomposition process, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is applied to the final approximation image to provide higher contrast. The detail sub-images are amplified and added to the enhanced approximation image to reconstruct the enhanced V component. Finally, inverse HSV transform is applied to the enhanced V component and H, S components to obtain the enhanced image. The experimental results show that the proposed method provides better visual quality and more natural colors than the compared state-of-the-art methods.
IOP-CapsNet with ISEMRA: Fetching part-to-whole topology for improving detection performance of articulated instances

Due to the unique focus on positional understanding, Capsule Network (CapsNet) has received sufficient attention in the field of computer vision. However, since low-level capsules can vote in favor of every high-level capsule irrespective of their interrelationship, such blindly fully-connected communication mode suffers from misassignment. To tackle this issue, we propose to constrain the bottom-up voting scope by quantifying such correlations, hoping the unrelated or weakly related decision paths are cut off. This paper formulas such pipeline as an Inside-Out Perception Capsule Network (IOP-CapsNet) with Interrelationship-Steered Expectation-Maximum Routing-by-Agreement (ISEMRA). The part-whole relationships fetched by IOP-CapsNet is also used to implement accurate object detection of articulated instances. Three constraints, dubbed Intra-Object Cohesiveness Quantification (IOCQ), Part Backtracking (PB), and Vote Screening (VS), are customized and embedded into ISEMRA to restrain the voting scope from the perspectives of intra-object cohesiveness and external association. They stipulate that only these primary capsules (parts) satisfying the criteria of both internal consistency and external association are permitted to update entity capsules (the whole/composites). As a result, firstly, an entire instance can be separated into several representative parts; and secondly, the part-object relationships among split parts are refined during bottom-up "component backtracking" procedure for object detection. ISEMRA enables high-level capsules to optionally aggregate projection from non-spatially-fixed sets of low-level capsules. Quantitative and ablation verifications on VOC2007, VOC2012, OICOD18, ILSVRC17, and COCO18 datasets show the superiority of IOP-CapsNet over the state-of-the-art models.
A similarity matrix low-rank approximation and inconsistency separation Fusion Approach for Multi-view Clustering

In recent years, multi-view clustering algorithms have achieved promising performance by exploiting the com-plementarity and consistency of different views. However, many multi-view spectral clustering methods only focus on the con-sistent information of views, and the time cost of feature decomposition is expensive. Moreover, these methods also require post-processing (e.g., k-means) to obtain the final clustering results. To overcome these limitations simultaneously, we propose a novel multi-view clustering algorithm. Firstly, the method removes the inconsistent information of the views through cross-view measurement to maintain consistent information. These inconsistencies may be caused by noise, corruptions, or view-specific properties and will affect the quality of the similarity matrix. Then, we learn a consensus embedding matrix with non-negative constraints by performing a low-rank decomposition of the consistency information. In this way, we can replace the eigendecomposition of the n × n Laplacian matrix in spectral clustering with the singular value decomposition of a n × c low-rank matrix to reduce the computational burden, where c ≪ n. Furthermore, due to the non-negative constraint, we can directly obtain the clustering results. Also, to consider the diversity of views, adaptive weighting is applied to different view data. Compared to state-of-the-art multi-view clustering methods on five benchmark multi-view datasets, we demonstrate the superiority and effectiveness of our approach. We release the source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hulu88/FAMvC</uri> .
A parallel approach for user-centered QoS-aware services composition in the Internet of Things

The Internet of Things (IoT) refers to an infrastructure of interconnected smart devices that aim to provide various services. The proliferation of IoT objects and devices offering functionally equivalent services but differing in their quality of service (QoS) levels makes the issue of services composition one of the biggest challenges for the service computing community. Various evolutionary-based approaches have been proposed in the literature to find sub-optimal service compositions in a reasonable computation time. However, most of these approaches have high composition time and/or a limited composition quality as they rely on a sequential exploration of the composition search space using a fixed size population. To address these limitations, a parallel differential evolution-based approach with population size reduction for QoS-aware service composition (PDE-QSC) is proposed in this paper. Unlike existing evolutionary-based approaches, the proposed approach is characterized by a parallel exploration of the composition space through a population size reduction strategy. Specifically, in this approach, the composition population is divided into two sub-populations. To reduce the composition time and improve the quality of the composition, the composition sub-populations evolve simultaneously using different evolution processes and are then merged to form a single population, thus increasing the population diversity. To further improve the performance in terms of composition time and composition quality, a linear reduction strategy is proposed to adaptively reduce the size of the composition population by eliminating compositions that do not meet the QoS requirements. Simulations based on real datasets demonstrate the superiority of the PDE-QSC approach over five baseline approaches and its suitability for large-scale IoT environments.