Reviewerly/exHarmony huggingface dataset:
A hybrid active contour model based on pre-fitting energy and adaptive functions for fast image segmentation

• An adaptive edge indicator function is proposed to make evolution process more stable and robust to initial contours. • An adaptive sign function is proposed to enable the evolution curve to adjust evolution direction and speed. • New pre-fitting functions are computed before the iteration without online update to reduce computation cost. • A single-well potential function is provided to prevent segmentation failure and increase segmentation speed. In this study, a hybrid active contour model driven by pre-fitting energy with an adaptive edge indicator function and an adaptive sign function is proposed. The key idea of employing the pre-fitting energy is to define two pre-fitting functions to calculate mean intensities of two sub-regions separated from the selected local region based on pre-calculated median intensity of the selected local region before the curve evolves, which saves a huge amount of computation cost. In addition, a brand-new single-well potential function and its associated evolution speed function are put forward to enable evolution process to converge faster as well as more robust. Experimental outcomes indicate that this model is competent to obtain motion boundaries of different targets effectively and efficiently. Compared with traditional models and recently developed models, this model not only reduces the CPU running time and iteration number significantly as well as improves segmentation accuracies (Dice similarity coefficient (DSC) and Jaccard similarity coefficient (JSC)), but also exhibits insensitivity to initialization.
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams

Data streams are potentially unbounded sequences of instances arriving over time to a classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving information is one of the most dynamically developing areas of machine learning. Such learners must be able to deal with a phenomenon known as concept drift, where the data stream may be subject to various changes in its characteristics over time. Furthermore, distributions of classes may evolve over time, leading to a highly difficult non-stationary class imbalance. In this work we introduce Robust Online Self-Adjusting Ensemble (ROSE), a novel online ensemble classifier capable of dealing with all of the mentioned challenges. The main features of ROSE are: (1) online training of base classifiers on variable size random subsets of features; (2) online detection of concept drift and creation of a background ensemble for faster adaptation to changes; (3) sliding window per class to create skew-insensitive classifiers regardless of the current imbalance ratio; and (4) self-adjusting bagging to enhance the exposure of difficult instances from minority classes. The interplay among these features leads to an improved performance in various data stream mining benchmarks. An extensive experimental study comparing with 30 ensemble classifiers shows that ROSE is a robust and well-rounded classifier for drifting imbalanced data streams, especially under the presence of noise and class imbalance drift, while maintaining competitive time complexity and memory consumption. Results are supported by a thorough non-parametric statistical analysis.
The impact of service robots in retail: Exploring the effect of novelty priming on consumer behavior

This study aims to bridge a gap in the extant research by examining consumer behavior that is unrelated to, but elicited by, service robots. The results of six studies showed that participants primed with robots (vs. humans) were more likely to engage in exploratory consumption behaviors. This effect was mediated through the elicitation of a sense of novelty, affected by the degree of service robots' intelligence and moderated by consumers' subjective knowledge. The study also examines different types of exploratory buying behaviors that have implications for marketers' and retailers’ use of service robots to promote exploratory consumption.
Rational use of cognitive resources in human planning

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near optimal under some circumstances but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive edges or in the low-contrast regions. To address the issues, we advocate to firstly constrain the consistency of pixels with and without strong perturbations to apply a sufficient smoothness constraint and further encourage the class-level separation to exploit the low-entropy regularization for the model training. Particularly, in this paper, we propose the SS-Net for semi-supervised medical image segmentation tasks, via exploring the pixel-level Smoothness and inter-class Separation at the same time. The pixel-level smoothness forces the model to generate invariant results under adversarial perturbations. Meanwhile, the inter-class separation encourages individual class features should approach their corresponding high-quality prototypes, in order to make each class distribution compact and separate different classes. We evaluated our SS-Net against five recent methods on the public LA and ACDC datasets. Extensive experimental results under two semi-supervised settings demonstrate the superiority of our proposed SS-Net model, achieving new state-of-the-art (SOTA) performance on both datasets. The code is available at https://github.com/ycwu1997/SS-Net .
A Blockchain-Based Decentralized, Fair and Authenticated Information Sharing Scheme in Zero Trust Internet-of-Things

Internet-of-Things (IoT) are increasingly operating in the zero-trust environments where any devices and systems may be compromised and hence untrusted. In addition, data collected by and sent from IoT devices may be shared with and processed by edge computing systems, in order to reduce the reliance on centralized (cloud) servers, leading to further security and privacy issues. To cope with these challenges, this paper proposes an innovative blockchain-enabled information sharing solution in zero-trust context to guarantee anonymity yet entity authentication, data privacy yet data trustworthiness, and participant stimulation yet fairness. This new solution is able to support filtering of fabricated information through smart contracts, effective voting, and consensus mechanisms, which can prevent unauthenticated participants from sharing garbage information. We also prove that the proposed solution is secure in the universal composability framework, and further evaluate its performance over an Ethereum-based blockchain platform to demonstrate its utility.
LNIFT: Locally Normalized Image for Rotation Invariant Multimodal Feature Matching

Severe nonlinear radiation distortion (NRD) is the bottleneck problem of multimodal image matching. Although many efforts have been made in the past few years, such as the radiation-variation insensitive feature transform (RIFT) and the histogram of orientated phase congruency (HOPC), almost all these methods are based on frequency-domain information that suffers from high computational overhead and memory footprint. In this article, we propose a simple but very effective multimodal feature matching algorithm in the spatial domain, called locally normalized image feature transform (LNIFT). We first propose a local normalization filter to convert original images into normalized images for feature detection and description, which largely reduces the NRD between multimodal images. We demonstrate that normalized matching pairs have a much larger correlation coefficient than the original ones. We then detect oriented FAST and rotated brief (ORB) keypoints on the normalized images and use an adaptive nonmaximal suppression (ANMS) strategy to improve the distribution of keypoints. We also describe keypoints on the normalized images based on a histogram of oriented gradient (HOG), such as a descriptor. Our LNIFT achieves rotation invariance the same as ORB without any additional computational overhead. Thus, LNIFT can be performed in near real-time on images with 1024 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 1024$ </tex-math></inline-formula> pixels (only costs 0.32 s with 2500 keypoints). Four multimodal image datasets with a total of 4000 matching pairs are used for comprehensive evaluations, including synthetic aperture radar (SAR)–optical, infrared–optical, and depth–optical datasets. Experimental results show that LNIFT is far superior to RIFT in terms of efficiency (0.49 s versus 47.8 s on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024 \times 1024$ </tex-math></inline-formula> image), success rate (99.9% versus 79.85%), and number of correct matches (309 versus 119). The source code and datasets will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ljy-rs.github.io/web</uri> .
ARL: An adaptive reinforcement learning framework for complex question answering over knowledge base

Recently, reinforcement learning (RL)-based methods have achieved remarkable progress in both effectiveness and interpretability for complex question answering over knowledge base (KBQA). However, existing RL-based methods share a common limitation: the agent is usually misled by aimless exploration, as well as sparse and delayed rewards, leading to a large number of spurious relation paths. To address this issue, a new adaptive reinforcement learning (ARL) framework is proposed to learn a better and interpretable model for complex KBQA. First, instead of using a random walk agent, an adaptive path generator is developed with three atomic operations to sequentially generate the relation paths until the agent reaches the target entity. Second, a semantic policy network is presented with both character-level and sentence-level information to better guide the agent. Finally, a new reward function is introduced by considering both the relation paths and the target entity to alleviate sparse and delayed rewards. The empirical results on five benchmark datasets show that our model is more effective than state-of-the-art approaches. Compared with the strong baseline model SRN, the proposed model achieves performance improvements of 23.7% on MetaQA-3 using the metric [email protected]
Driver Anomaly Quantification for Intelligent Vehicles: A Contrastive Learning Approach With Representation Clustering

Driver anomaly quantification is a fundamental capability to support human-centric driving systems of intelligent vehicles. Existing studies usually treat it as a classification task and obtain discrete levels for abnormalities. Meanwhile, the existing data-driven approaches depend on the quality of dataset and provide limited recognition capability for unknown activities. To overcome these challenges, this paper proposes a contrastive learning approach with the aim of building a model that can quantify driver anomalies with a continuous variable. In addition, a novel clustering supervised contrastive loss is proposed to optimize the distribution of the extracted representation vectors to improve the model performance. Compared with the typical contrastive loss, the proposed loss can better cluster normal representations while separating abnormal ones. The abnormality of driver activity can be quantified by calculating the distance to a set of representations of normal activities rather than being produced as the direct output of the model. The experiment results with datasets under different modes demonstrate that the proposed approach is more accurate and robust than existing ones in terms of recognition and quantification of unknown abnormal activities.
Anthropomorphism and OTA chatbot adoption: a mixed methods study

Anthropomorphizing chatbots can facilitate effective customer interaction.Based on a mixed method, this study explores perceived chatbot anthropomorphism cues and their effects on customers' chatbot usage intentions in the online travel agency (OTA) context.Findings suggest that (1) social presence cues and emotional message cues are major anthropomorphic cues of interest for customers and enterprises; (2) social presence cues by simply using a human avatar or mentioning the customer's name might not be sufficient; (3) anthropomorphic emotional message cues are essential in shaping customers' usage intentions; and (4) perceived trustworthiness, intelligence, and enjoyment mediate the above effect.
CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model

In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.
Security and Privacy in FinTech

Financial technology (FinTech) has dramatically changed the way of banking and financial services. Computer programs and other technology which used to provide and enable financial services is named as FinTech. However, these services face several security and privacy issues while providing financial services to the users. These services and applications must be secured to enhance the acceptance and usability of these services among the users. The main aim of this research is to provide a policy framework to ensure the security and privacy of user information in financial technology, since FinTech applications and services carry quite sensitive data of its users. This policy framework provides a comprehensive set of policies to secure FinTech services. These policies must be implemented in each organization providing FinTech services.
Use of Mobile Phone Apps for Contact Tracing to Control the COVID-19 Pandemic: A Literature Review

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Reconfigurable Smart Contracts for Renewable Energy Exchange with Re-Use of Verification Rules

Smart contracts constitute the foundation for blockchain distributed applications. These constructs enable transactions in trustless environments using consensus algorithms and software-controlled verification rules. In the current state of the art, there is a shortage of works on the adaptability of smart contracts, and the re-use of their source code is limited mainly to cloning. The paper discusses the pattern of smart contract design and implementation with the overt declaration of verification rules. The author introduces two advantages of the pattern: Firstly, run-time reconfigurability of the list of smart contract verification rules to adjust for various transaction types. Secondly, the re-use of verification rules between different configurations of the smart contract, and among diverse smart contracts. The paper uses blockchain platform-independent stereotypes from a dedicated Unified Modeling Language (UML) profile for designing smart contracts and verification rules. The implementation of the pattern is developed in object-oriented Java language. The pattern exploits polymorphism and controls inheritance by using sealed classes with permission for specialization only for selected final ones. Thus, the pattern ensures two recently highly desired properties in smart contract design and development: re-use and security. Moreover, the declared verification rules list facilitates test automation and reduces test preparation effort due to the re-use of test classes among smart contract configurations. The pattern usage is illustrated in the example of renewable energy exchange within the prosumers community and amid various communities.
R2U++: a multiscale recurrent residual U-Net with dense skip connections for medical image segmentation

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy (EM), X-rays, fundus, and computed tomography (CT). The average gain achieved in IoU score is 1.5+-0.37% and in dice score is 0.9+-0.33% over UNET++, whereas, 4.21+-2.72 in IoU and 3.47+-1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.
Content Removal as a Moderation Strategy

Moderators of online communities often employ comment deletion as a tool. We ask here whether, beyond the positive effects of shielding a community from undesirable content, does comment removal actually cause the behavior of the comment's author to improve? We examine this question in a particularly well-moderated community, the ChangeMyView subreddit. The standard analytic approach of interrupted time-series analysis unfortunately cannot answer this question of causality because it fails to distinguish the effect of having made a non-compliant comment from the effect of being subjected to moderator removal of that comment. We therefore leverage a "delayed feedback" approach based on the observation that some users may remain active between the time when they posted the non-compliant comment and the time when that comment is deleted. Applying this approach to such users, we reveal the causal role of comment deletion in reducing immediate noncompliance rates, although we do not find evidence of it having a causal role in inducing other behavior improvements. Our work thus empirically demonstrates both the promise and some potential limits of content removal as a positive moderation strategy, and points to future directions for identifying causal effects from observational data.
A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets

Abstract The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected.