Reviewerly/exHarmony huggingface dataset:
Dilated convolution based RCNN using feature fusion for Low-Altitude aerial objects

The low-altitude aerial objects are hard to detect by existing deep learning-based object detectors because of the scale variance, small size, and occlusion-related problems. Deep learning-based detectors do not consider contextual information about the scale information of small-sized objects in low-altitude aerial images. This paper proposes a new system using the concept of receptive fields and fusion of feature maps to improve the efficiency of deep object detectors in low-altitude aerial images. A Dilated ResNet Module (DRM) is proposed, motivated from the trident networks, which works on dilated convolutions to study the contextual data for specifically small-sized objects. Applicability of this component builds the model strong towards scale variations in low-altitude aerial objects. Then, Feature Fusion Module (FFM) is created to offer semantic intelligence for better detection of low-altitude aerial objects. We have chosen vastly deployed faster RCNN as the base detector for the proposal of our technique. The dilated convolution-based RCNN using feature fusion (DCRFF) system is implemented on a benchmark low-altitude UAV based-object detection dataset, VisDrone, which contains multiple object categories of pedestrians, vehicles in crowded scenes. The experiments exhibit the enactment of the given detector on chosen low-altitude aerial object dataset. The proposed system of DCRFF achieves 35.04% mAP on the challenging VisDrone dataset, indicating an average improvement of 2% when compared.
Determinants of blockchain adoption as decentralized business model by Spanish firms – an innovation theory perspective

Purpose Large attention surrounds identifying the meaningful blockchain business model on financial services, while a little focus about non-financial organizations and solutions in terms of how the blockchain business model can affect the organization and bring more value. To address the complex structure of businesses that have public goods, it is important to develop sustainable blockchain-based business models. Design/methodology/approach This study offers the first qualitative research that uses an integrated technological, environmental and organizational (TOE) framework with technology acceptance theory (TAM) to study the adoption of blockchain technology by Spanish firms. Findings The results of the paper discuss how that competitive pressure, competence, top management support and relative advantage have a positive impact on intention to adopt blockchain technology while complexity affects the intention to adopt the technology negatively. Contrary to many adoption studies, the findings show that intention to adopt negatively impacts adoption and outline the effect of blockchain on business model elements on the macroeconomic level. Originality/value The key contribution of this study lies in providing a comprehensive understanding of the environmental, technological and organizational factors that impact the intention to adopt blockchain that eventually affects adoption.
Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study

A regular task by developers and users of synthetic data generation (SDG) methods is to evaluate and compare the utility of these methods. Multiple utility metrics have been proposed and used to evaluate synthetic data. However, they have not been validated in general or for comparing SDG methods.This study evaluates the ability of common utility metrics to rank SDG methods according to performance on a specific analytic workload. The workload of interest is the use of synthetic data for logistic regression prediction models, which is a very frequent workload in health research.We evaluated 6 utility metrics on 30 different health data sets and 3 different SDG methods (a Bayesian network, a Generative Adversarial Network, and sequential tree synthesis). These metrics were computed by averaging across 20 synthetic data sets from the same generative model. The metrics were then tested on their ability to rank the SDG methods based on prediction performance. Prediction performance was defined as the difference between each of the area under the receiver operating characteristic curve and area under the precision-recall curve values on synthetic data logistic regression prediction models versus real data models.The utility metric best able to rank SDG methods was the multivariate Hellinger distance based on a Gaussian copula representation of real and synthetic joint distributions.This study has validated a generative model utility metric, the multivariate Hellinger distance, which can be used to reliably rank competing SDG methods on the same data set. The Hellinger distance metric can be used to evaluate and compare alternate SDG methods.
Cooperative multi-function approach: A new strategy for autonomous ground robotics

The paper presents multi-objective optimization technique for the exploration of unknown space. Exploration refers to the building of an estimated finite map of the environment using sensor data. Conventionally, in robotics, the optimization is performed utilizing a single optimization technique with a particular objective function. This although simplifies the process but greatly compromise the map accuracy and exploration depth. Realizing this aspect, we present a new exploration technique with augmented objective functions, so as maximize the search area and map accuracy of the exploration space. The proposed framework termed as Multi-Objective Whale Optimization Algorithm (MO-WOA), is based on bio-inspired Whale Optimizer. It starts with the initialization of the whale's population, which are referred to as way-points. These way-points are assumed to be constant once they are set in the initial stage/iterations. The next step involves the position update from the non-dominated way-points catered by the robots. The algorithm utilizing this optimization approach formulates the optimal way-points. The performance metrics are presented through extensive simulations. The results efficacy is then demonstrated by comparing the results of the proposed algorithm with those achieved from contemporary techniques namely Coordinated Multi-Robot Exploration (CME), conventional Whale Optimizer (WO) integrated with CME and Arithmetic Optimization (AO). Results demonstrate that the proposed algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time.
Experimental Demonstration of Remotely Creating Wigner Negativity via Quantum Steering

Non-Gaussian states with Wigner negativity are of particular interest in quantum technology due to their potential applications in quantum computing and quantum metrology. However, how to create such states at a remote location remains a challenge, which is important for efficiently distributing quantum resource between distant nodes in a network. Here, we experimentally prepare an optical non-Gaussian state with negative Wigner function at a remote node via local non-Gaussian operation and shared Gaussian entangled state existing quantum steering. By performing photon subtraction on one mode, Wigner negativity is created in the remote target mode. We show that the Wigner negativity is sensitive to loss on the target mode, but robust to loss on the mode performing photon subtraction. This experiment confirms the connection between the remotely created Wigner negativity and quantum steering. As an application, we present that the generated non-Gaussian state exhibits metrological power in quantum phase estimation.
How Experienced Designers of Enterprise Applications Engage AI as a Design Material

HCI research has explored AI as a design material, suggesting that designers can envision AI's design opportunities to improve UX. Recent research claimed that enterprise applications offer an opportunity for AI innovation at the user experience level. We conducted design workshops to explore the practices of experienced designers who work on cross-functional AI teams in the enterprise. We discussed how designers successfully work with and struggle with AI. Our findings revealed that designers can innovate at the system and service levels. We also discovered that making a case for an AI feature's return on investment is a barrier for designers when they propose AI concepts and ideas. Our discussions produced novel insights on designers' role on AI teams, and the boundary objects they used for collaborating with data scientists. We discuss the implications of these findings as opportunities for future research aiming to empower designers in working with data and AI.
Negotiating sustainable futures in communities through participatory speculative design and experiments in living

This paper responds to sustainable HCI's call to design long-term participatory projects with grassroots communities to counter the local effects of climate change and support more viable practices. We contribute a methodological approach to participatory speculative design as a series of interrelated experiments in living, working in symbiosis with a food-growing community moving towards collective resilience and food sovereignty. As an example of sustainability research within HCI, community food-growing has predominantly focused on collaborative acts of growing rather than disagreements, divergences and frictions. Limited attention has been paid to the challenges of effectively negotiating collaborative, sustainable speculative futures in this context. This paper reports on a workshop series on sustainable community food-growing using situated participatory speculation to address potential tensions when working collaboratively towards socio-technical alternatives.
DeepGaze III: Modeling free-viewing human scanpaths with deep learning

Humans typically move their eyes in "scanpaths" of fixations linked by saccades. Here we present DeepGaze III, a new model that predicts the spatial location of consecutive fixations in a free-viewing scanpath over static images. DeepGaze III is a deep learning-based model that combines image information with information about the previous fixation history to predict where a participant might fixate next. As a high-capacity and flexible model, DeepGaze III captures many relevant patterns in the human scanpath data, setting a new state of the art in the MIT300 dataset and thereby providing insight into how much information in scanpaths across observers exists in the first place. We use this insight to assess the importance of mechanisms implemented in simpler, interpretable models for fixation selection. Due to its architecture, DeepGaze III allows us to disentangle several factors that play an important role in fixation selection, such as the interplay of scene content and scanpath history. The modular nature of DeepGaze III allows us to conduct ablation studies, which show that scene content has a stronger effect on fixation selection than previous scanpath history in our main dataset. In addition, we can use the model to identify scenes for which the relative importance of these sources of information differs most. These data-driven insights would be difficult to accomplish with simpler models that do not have the computational capacity to capture such patterns, demonstrating an example of how deep learning advances can be used to contribute to scientific understanding.
The Last Decade of HCI Research on Children and Voice-based Conversational Agents

Voice-based Conversational Agents (CAs) are increasingly being used by children. Through a review of 38 research papers, this work maps trends, themes, and methods of empirical research on children and CAs in HCI research over the last decade. A thematic analysis of the research found that work in this domain focuses on seven key topics: ascribing human-like qualities to CAs, CAs? support of children?s learning, the use and role of CAs in the home and family context, CAs? support of children?s play, children?s storytelling with CA, issues concerning the collection of information revealed by CAs, and CAs designed for children with differing abilities. Based on our findings, we identify the needs to account for children’s intersectional identities and linguistic and cultural diversity and theories from multiple disciples in the design of CAs, develop heuristics for child-centric interaction with CAs, to investigate implications of CAs on social cognition and interpersonal relationships, and to examine and design for multi-party interactions with CAs for different domains and contexts.
The Effects of Logistics Websites’ Technical Factors on the Optimization of Digital Marketing Strategies and Corporate Brand Name

In a world overwhelmed with unstructured information, logistics companies increasingly depend on their websites to acquire new customers and maintain existing ones. Following this rationale, a series of technical elements may set the ground for differentiating one logistics website from another. Nevertheless, a suitable digital marketing strategy should be adopted in order to build competitive advantage. In this paper, the authors attempt to respond by implementing an innovative methodology building on web analytics and big data. The first phase of the research collects data for 180 days from 7 world-leading logistics companies. The second phase presents the statistical analysis of the gathered data, including regression, correlations, and descriptive statistics. Subsequently, Fuzzy Cognitive Mapping (FCM) was employed to illustrate the cause-and-effect links among the metrics in question. Finally, a predictive simulation model is developed to show the intercorrelation among the metrics studied as well as various optimization strategies. Research findings reveal a significant correlation between the logistics websites’ technical factors and the growth of the corporate brand name.
Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

In recent years, Cloud computing has been developed and become the foundation of a wide range of applications.It allows users to access a catalog of standardized services and respond to their business needs flexibly and adaptively, in the event of unforeseen demands, paying solely for the consumption they have made.Task scheduling problem is considered one of the most critical cloud computing challenges.The problem refers to how to reasonably order and allocate the applications tasks provided by the users to be executed on virtual machines.Furthermore, the quality of scheduling performance has a direct effect on customer satisfaction.The task scheduling problem in cloud computing must be more accurately described in order to improve scheduling performance.In this paper, a multi-objective task scheduling algorithm is proposed based on the decision tree in a heterogenous environment.We introduce a new Task Scheduling-Decision Tree (TS-DT) algorithm for allocating and executing an application's task.To evaluate the performance of the proposed TS-DT algorithm, a comparative study was conducted among the existing algorithms; Heterogeneous Earliest Finish Time (HEFT), Technique for Order of Preference by Similarity to Ideal Solution that incorporates the Entropy Weight Method (TOPSIS-EWM), and combining Q-Learning with the Heterogeneous Earliest Finish Time (QL-HEFT).Our results show that the proposed TS-DT algorithm outperforms the existing HEFT, TOPSIS-EWM, and QL-HEFT algorithms by reducing makespan by 5.21%, 2.54%, and 3.32%, respectively, improving resource utilization by 4.69%, 6.81%, and 8.27%, respectively, and improving load balancing by 33.36%, 19.69%, and 59.06%, respectively in average.
Two-way threshold-based intelligent water drops feature selection algorithm for accurate detection of breast cancer

Breast cancer is one of the common reasons for deaths of women over the globe. It has been found that a Computer-Aided Diagnosis (CAD) system can be designed using X-ray mammograms for early-stage detection of breast cancer, which can decrease the death rate to a large extent. This paper work proposes a novel 2-way threshold-based intelligent water drops IWD “algorithm for feature selection to design an effective and efficient CAD system that can detect breast cancer in early stage. This approach first extracts the local binary patterns in wavelet domain from mammograms and then applies our introduced 2-way threshold-based IWD algorithm to extract most important subset of features from the extracted features set. Two-way thresholding is a technique to find a lower bound and an upper bound on the number of features to be selected in the optimal subset. So, using these threshold values, IWD is capable of producing multiple optimal subsets of features rather than producing a single optimal subset of features. The best subset among the above subsets is then used to train and deploy support vector machine (SVM) to classify new mammograms. The results have shown that the proposed model outperforms many of the existing CAD systems. Further we have compared our introduced feature selection technique with other meta-heuristic features selection techniques such as ant colony optimization, particle swarm optimization, simulated annealing, genetic algorithm, gravitational search algorithm, inclined planes optimization and gray wolf optimization algorithm and found that it outperforms the other feature selection techniques. The accuracy, precision, recall, specificity and F1-score of our proposed framework are measured on MIAS dataset as 99%, 98.7%, 98.123%, 96.2% and 98.4%, respectively, and on DDSM dataset as 97.89%, 96.9%, 96.4%, 94.8% and 96.2%.
Survey of natural image enhancement techniques: Classification, evaluation, challenges, and perspectives

Image enhancement is an essential technique used in many imaging applications. The main motivation of image enhancement is processing an image to be more suitable for specific utilization. Various image enhancement methods have been proposed in the existing literature. On the one hand, a variety of enhancement techniques make the classification more complex. On the other hand, the lack of image quality metrics specifically designed for enhanced images and the number of enhanced image quality databases as well as subjective data (ground truth) is limited, making the evaluation process difficult. This survey aims to guide the image enhancement research community to fill the above-mentioned gap and develop better and robust enhancement techniques. This survey 1) summarizes previous surveys of image enhancement techniques and existing classifications; 2) classifies the amount of techniques and analyzes their components, including how the performance has been evaluated; 3) surveys and recommends image quality metrics and databases that were designed for enhanced images; 4) discusses the challenges for image enhancement and its evaluation issues; and 5) proposes perspectives for the development of future image enhancement algorithms. This survey mainly focuses on image enhancement techniques that improve the perceived quality of natural images.
Federated Graph Neural Network for Cross-graph Node Classification

In this paper, we propose a novel distributed scalable federated graph neural network (FGNN) to solve the cross-graph node classification problem. In the existing cross-graph node classification methods, the source graph and target graph need to share their graph data and label, for the nodes in the source graph and target graph are in the same semantic space. However, source graphs cannot share graph data and label without encryption due to regulations and interests. In order to satisfy the privacy of all parties, the universal classification rules of cross-graph nodes are learned. We add PATE mechanism into the domain adversarial neural network (DANN) to construct a cross-network node classification model, and extract effective information from node features of source and target graphs for encryption and spatial alignment. Moreover, we use a one-to-one approach to construct cross-graph node classification models for multiple source graphs and the target graph. Federated learning is used to train the model jointly through multi-party cooperation to complete the target graph node classification task. Finally, we carry out extensive experiments on five datasets to demonstrate the effectiveness of the proposed method.
Sliding-Mode Control for Interval Type-2 Fuzzy Systems: Event-Triggering WTOD Scheme

This article investigates the sliding-mode control issue for interval type-2 (IT2) T-S fuzzy systems under limited communication resources. An event-triggering weight try-once-discard (ET-WTOD) protocol is formulated via two thresholds to determine the transmission of the state signal. The proposed ET-WTOD protocol can dynamically adjust the transmitted nodes and permits only partial components with larger error to be sent at each triggering instant, which is just the key distinction from the existing protocols. Under the imperfect premise matching framework, the controller's membership functions are reconstructed via the received state and the known upper and lower bounds, and then, a new scheduling signal set is established to design the scheduling-signal-dependent fuzzy sliding-mode controller. With the aid of the membership-function-dependent approach, the mismatching premise variables of the fuzzy model and the controller are effectively handled by introducing some slack matrices, while the relaxed stability conditions are derived to ensure the stability of the closed-loop system and the reachability of the specified sliding surface. Moreover, an optimized sliding domain is further obtained via the genetic algorithm (GA). Finally, the proposed control strategy is verified via the mass-spring-damper system.
A High-Performance Domain-Specific Processor With Matrix Extension of RISC-V for Module-LWE Applications

The 5G edge computing infrastructure should be empowered with quantum attack resistance by implementing post-quantum cryptography (PQC). Among various PQC schemes, lattice-based cryptography (LBC) based on learning with error (LWE) has attracted much attention because of its performance efficiency and security guarantee. In LWE-based LBCs, the Module-LWE-based schemes gain advantage over the others benefiting from the unique polynomial matrix and vector structure. To provide a high-performance implementation of Module-LWE applications for the edge computing paradigm, we propose a domain-specific processor based on a matrix extension of RISC-V architecture. This custom extension encapsulates the matrix-based ring operations with a high-level functional abstraction. A 2-D systolic array with configurable functionality is proposed to perform matrix-based number theoretic transform (NTT) and other arithmetic operations, achieving high data-level parallelism with support for the variable-sized polynomial matrix and vector structure. As this structure of Module-LWE involves no data dependency between different inner elements, an out-of-order mechanism is further developed to exploit the instruction-level parallelism. We implement the proposed architecture under TSMC 28nm technology. The evaluation results show that our implementation can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3\times $ </tex-math></inline-formula> improvement in cycle count respectively in Kyber and Dilithium, compared to the state-of-the-art crypto-processor counterparts.
The Impact of High-Immersion Virtual Reality on Foreign Language Anxiety When Speaking in Public

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