Reviewerly/exHarmony huggingface dataset:
Incorporating Trust into Context-Aware Services

Enabling technologies concerning hardware, networking, and sensing have inspired the development of context-aware IT services. These adapt to the situation of the user, such that service provisioning is specific to his/her corresponding needs. We have seen successful applications of context-aware services in healthcare, well-being, and smart homes. It is, however, always a question what level of trust the users can place in the fulfillment of their needs by a certain IT-service. Trust has two major variants: policy-based, where a reputed institution provides guarantees about the service, and reputation-based, where other users of the service provide insight into the level of fulfillment of user needs. Services that are accessible to a small and known set of users typically use policy-based trust only. Services that have a wide community of users can use reputation-based trust, policy-based trust, or a combination. For both types of trust, however, context awareness poses a problem. Policy-based trust works within certain boundaries, outside of which no guarantees can be given about satisfying the user needs, and context awareness can push a service out of these boundaries. For reputation-based trust, the fact that users in a certain context were adequately served, does not mean that the same would happen when the service adapts to another user’s needs. In this paper we consider the incorporation of trust into context-aware services, by proposing an ontological conceptualization for user-system trust. Analyzing service usage data for context parameters combined with the ability to fulfill user needs can help in eliciting components for the ontology.
Remote sensing image segmentation using feature based fusion on FCM clustering algorithm

Abstract Image segmentation of heterogeneous comparable objects lying beneath the earth’s surface is a fundamental but challenging research area in remote sensing. Learning approaches are used in remote sensing image segmentation to improve segmentation accuracy at the expense of time and a large amount of data, but their performance need to be finely classified due to information diversity constraints. In this work, we proposed an novel feature based fuzzy C -means-extreme learning machine (FBFCM-ELM) algorithm for remote sensing image segmentation in which the classification based on entropy, intensity, and edge features is performed in such a way that it updates the intensity value to preserve the most local characteristics in the image while still being able to clearly distinguish the image’s boundaries by assigning the pixel values of each cluster to the peak value of the cluster’s sub-histogram. Using FBFCM, features are extracted and used as reliable samples for ELM training. Undetermined segmented pixels are obtained using the trained ELM classifier. Experiments performed over number of images that confirmed the proposed method yields a better segmented RGB image, as evidenced by observable details, edges, and improved appearance that resembles the ground truth image and outperforms state-of-the-art algorithms.
A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm

The reasonable scheduling of agricultural machinery can avoid their purposeless flow during the operational service and reduce the scheduling cost of agricultural machinery service centers. In this research, a multiregional agricultural machinery scheduling model with a time window was established considering the timeliness of agricultural machinery operation. This model was divided into two stages: In the first stage, regions were divided through the Voronoi diagram, and farmlands were distributed to intraregional service centers. In the second stage, the model was solved using the hybrid particle swarm optimization (HPSO). The algorithm improves the performance of the algorithm by introducing a crossover, mutation, and particle elimination mechanism, and by using a linear differential to reduce the inertia weight and trigonometric function learning factor. Next, the accuracy and effectiveness of the algorithm are verified by different experimental samples. The results show that the algorithm can effectively reduce the scheduling cost, and has the advantages of strong global optimization ability, high stability, and fast convergence speed. Subsequent algorithm comparison proves that HPSO has better performance in different situations, can effectively solve the scheduling problem, and provides a reasonable scheduling scheme for multiarea and multifarmland operations.
Quantum computing for power systems: Tutorial, review, challenges, and prospects

As a large number of renewable energy resources are connected to power systems, the operation, planning, and optimization of power systems have been becoming more and more complex. Power flow calculation, unit commitment, economic dispatch, energy pricing, and power system planning are essentially computation problems. A lot of computing resources are required for these problems, which are non-trivial, especially for large-scale power systems with the high penetration of renewable energy. Traditionally, the calculation and optimization of power systems are completed by classical computers based on the classical computing theory and the von Neumann architecture. However, with Moore's law getting closer and closer to the limit, the importance of quantum computing has become increasingly prominent. Quantum computing has been applied to some fields to a certain extent, yet the applications of quantum computing in power systems are rare. As the power industry is the foundation of the national economy, introducing quantum computing into the power system has far-reaching and crucial significance, such as improving the penetration of renewable energy, enhancing the computing efficiency, and helping in achieving the goal of net zero and climate neutrality by 2050. This paper first introduces the core concepts, essential ideas and theories of quantum computing, and then reviews the existing literature on the applications of quantum computing in power systems, and puts forward our critical thinking about the applications of quantum computing in power systems. In brief, this paper is dedicated to a tutorial on quantum computing targeting power system professionals and a review of its applications in power systems. The main contributions of this paper are: (1) introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor; (2) based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams; (3) elaborate and derive some critical quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems; (4) critically summarize and comment on the existing literature on the applications of quantum computing in power systems; (5) the future applications and challenges of quantum computing in power systems are prospected and remarked.
CFormerFaceNet: Efficient Lightweight Network Merging a CNN and Transformer for Face Recognition

Most face recognition methods rely on deep convolutional neural networks (CNNs) that construct multiple layers of processing units in a cascaded form and employ convolution operations to fuse local features. However, these methods are not conducive to modeling the global semantic information of the face and lack attention to important facial feature regions and their spatial relationships. In this work, a Group Depth-Wise Transpose Attention (GDTA) block is designed to effectively capture both local and global representations, mitigate the issue of limited receptive fields in CNNs, and establish long-range dependencies among different feature regions. Based on GDTA and CNNs, a novel, efficient, and lightweight face recognition model called CFormerFaceNet, which combines a CNN and Transformer, is proposed. The model significantly reduces the parameters and computational cost without compromising performance, greatly improving the computational efficiency of deep neural networks in face recognition tasks. The model achieves competitive accuracy on multiple challenging benchmark face datasets, including LFW, CPLFW, CALFW, SLLFW, CFP_FF, CFP_FP, and AgeDB-30, while maintaining the minimum computational cost compared to all other advanced face recognition models. The experimental results using computers and embedded devices also demonstrate that it can meet real-time requirements in practical applications.
Formal Analysis of Reentrancy Vulnerabilities in Smart Contract Based on CPN

A smart contract is a special form of computer program that runs on a blockchain and provides a new way to implement financial and business transactions in a conflict-free and transparent environment. In blockchain systems such as Ethereum, smart contracts can handle and autonomously transfer assets of considerable value to other parties. Hence, it is particularly important to ensure that smart contracts function as intended since bugs or vulnerabilities may lead, and indeed have led, to substantial economic losses and erosion of trust for blockchain. While a number of approaches and tools have been developed to find vulnerabilities, formal methods present the highest level of confidence in the security of smart contracts. In this paper, we propose a formal solution to model a smart contract based on colored Petri nets (CPNs). Herein, we focus on the most common type of security bugs in smart contract, i.e., reentrancy bugs, which led to a serious financial loss of around USD 34 million for the Cream Finance project in 2021. We present a hierarchical CPN modelling method to analyze potential security vulnerabilities at the contract’s source code level. Then, modeling analysis methods such as correlation matrix, state space report and state space graph generated via CPN Tools simulation are exploited for formal analysis of smart contracts. The example shows the full state space and wrong path in accordance with our expected results. Finally, the conclusion was verified on the Ethereum network based on the Remix platform.
Impact of the introduction of service robots on consumer satisfaction: Empirical evidence from hotels

Service robots have become integral to the service industry in the context of intelligent upgrading and transformation, both of which affect consumer evaluations of their overall service experience. The different service experiences of customers helped by service robots and customers helped by humans merit scholarly attention. This study explored the relations among service robots, consumer service experience perceptions, and consumer satisfaction. Based on social perception theory, the study developed a multi-period difference-in-differences model to empirically test the formulated hypotheses using operational data from 3790 hotels and 8,127,772 online reviews. The results showed that the introduction of service robots in hotels had a positive effect on consumer satisfaction, with perceived warmth and competence mediating this effect. In addition, perceived competence was moderated by hotel class and size, whereas consumer satisfaction and perceived warmth were moderated by membership tiers. Finally, consumer satisfaction showed heterogeneity across different city classes and customer segments. This study offered theoretical and practical implications for the use of service robots in hotels.
Servant by default? How humans perceive their relationship with conversational AI

Conversational AI, like Amazon’s Alexa, are often marketed as tools assisting owners, but humans anthropomorphize computers, suggesting that they bond with their devices beyond an owner-tool relationship. Little empirical research has studied human-AI relationships besides relational proxies such as trust. We explored the relationships people form with conversational AI based on the Relational Models Theory (RMT, Fiske, 1992). Results of the factor analyses among frequent users (Ntotal = 729) suggest that they perceive the relationship more as a master-assistant relationship (i.e., authority ranking) and an exchange relationship (i.e., market pricing) than as a companion-like relationship (i.e., peer bonding). The correlational analysis showed that authority ranking barely correlates with system perception or user characteristics, whereas market pricing and peer bonding do. The relationship perception proved to be independent of demographic factors and label of the digital device. Our research enriches the traditional dichotomous approach. The extent to which users see their conversational AI as exchange partners or peer-like has a stronger predictive value regarding human-like system perception of conversational AI than the perception of it as servants.
Artificial intelligence and frozen section histopathology: A systematic review

Frozen sections are a useful pathologic tool, but variable image quality may impede the use of artificial intelligence and machine learning in their interpretation. We aimed to identify the current research on machine learning models trained or tested on frozen section images. We searched PubMed and Web of Science for articles presenting new machine learning models published in any year. Eighteen papers met all inclusion criteria. All papers presented at least one novel model trained or tested on frozen section images. Overall, convolutional neural networks tended to have the best performance. When physicians were able to view the output of the model, they tended to perform better than either the model or physicians alone at the tested task. Models trained on frozen sections performed well when tested on other slide preparations, but models trained on only formalin-fixed tissue performed significantly worse across other modalities. This suggests not only that machine learning can be applied to frozen section image processing, but also use of frozen section images may increase model generalizability. Additionally, expert physicians working in concert with artificial intelligence may be the future of frozen section histopathology.
Small object intelligent detection method based on adaptive recursive feature pyramid

As we all know, YOLOv4 can achieve excellent detection performance in object detection and has been effectively applied in many fields. However, the inconsistency of scale features affects the prediction accuracy of the path aggregation network (PANet) in YOLOv4 for small objects, resulting in low detection accuracy. This paper presents YOLOv4, which uses an adaptive recursive path aggregation network (AR-PANet) to improve the detection accuracy of small objects. First, the output characteristics of the PANet are fed back into the backbone network by using a recursive structure to enrich the characteristic information of the object. Second, an adaptive approach is developed to eliminate conflicting information in multi-scale feature space, thereby enhancing scale invariance and promoting feature extraction accuracy for small objects. Finally, the CBAM is used to map the multi-scale features obtained from the AR-PANet to independent channels and spatial dimensions to achieve feature refinement, thus improving the detection accuracy of small objects. Experimental results show that our proposed method can effectively improve the accuracy of small object detection in multiple datasets, addressing this challenging problem with impressive results. Thus, our proposed approach has great potential and valuable applications in the fields of remote sensing and intelligent transportation.
The harms of unattainable pedagogical exemplars on social media

This paper scrutinizes the nature and scope of deleterious consequences arising from the pursuit of unattainable pedagogical exemplars on social media. We cash out this phenomenon using exemplarist theory to emphasize the fact that social media (e.g., Facebook, Instagram, TikTok) are platforms in which the vast majority of users present idealized and curated versions of themselves. We focus specifically on educational practitioners and show that attempting to emulate unattainable pedagogical exemplars has negative impacts on agents' emotional well-being: It can cause burnout and self-conflict, decrease motivation, and also inflict detrimental outcomes on agents' self-esteem. We conclude that attainable, relevant, relatable, and authentic exemplars are key to a successful exemplarist approach and that safeguarding against unattainable pedagogical exemplars is of paramount importance for the wellbeing of pedagogues, and by extension, the success of educational systems.
Interval type-2 fuzzy set induced fuzzy rank-level fusion for face recognition

Type-2 fuzzy set is extensively studied in the past due to its superiority over type-1 fuzzy set, especially while dealing with data with higher uncertainty and higher association amongst them. This paper proposes a framework by introducing interval type-2 fuzzy set induced fuzzy rank-level fusion for face recognition utilizing multi-feature vectors. It utilizes the outputs of a classifier as confidence factors. We address the wide intra-class variability issue by introducing interval type-2 fuzzy sets by using these confidence factors to generate secondary membership values from the intra-class face images, which are then reduced to a primary membership value. It also mitigates the influence of inter-class similarity by excluding the inter-class face images in the computation. Furthermore, multi-feature vectors for a face image are used to utilize the underlying different discriminant features, which also in turn address the issues of inter-class similarity. For each feature vector, we generate interval type-2 fuzzy set based fuzzy ranks corresponding to all classes. To reduce the complexity, top k fuzzy ranks are fused with corresponding membership values to obtain the fuzzy ranks. Likewise, the interval type-2 fuzzy set based multiple fuzzy ranks are obtained using multiple feature vectors. Finally, these fuzzy ranks are fused with the corresponding complemented confidence factors to get the final fuzzy ranks, based on which a face image is classified and recognized. The method is evaluated on several face databases and found to be superior to the many state-of-the-art methods.
Building digital twins of cities using the Inter Model Broker framework

Urban areas are becoming increasingly crowded, and as a result it is challenging to ensure that these areas remain liveable and accessible. Municipalities need to make strategic long-term decisions regarding urban planning, which is often a difficult and time-consuming task. Digital twins of cities can assist policy makers during this process, providing an integral view on e.g. traffic intensities, air quality and noise. Furthermore, a digital twin enables policy makers to simulate 'what if scenarios' in a scenario-based analysis. Digital twins connect data, analytics and visualizations, which requires a system which integrates all these aspects in a single framework. This paper presents the Inter Model Broker framework, which is a scalable framework based on which large-scale digital twins can be built. A key property of our framework is that all connected system modules hold the relevant data locally in memory, which means that no central database is required. The effectiveness of our framework is demonstrated in a case study based on the city of Amsterdam in The Netherlands.
A Comparison Of Deep Learning MOS Predictors For Speech Synthesis Quality

Speech synthesis quality prediction has made remarkable progress with the development of supervised and self-supervised learning (SSL) MOS predictors but some aspects related to the data are still unclear and require further study. In this paper, we evaluate several MOS predictors based on wav2vec 2.0 and the NISQA speech quality prediction model to explore the role of the training data, the influence of the system type, and the role of cross-domain features in SSL models. Our evaluation is based on the VoiceMOS challenge dataset. Results show that SSL-based models show the highest correlation and lowest mean squared error compared to supervised models. The key point of this study is that benchmarking the statistical performance of MOS predictors alone is not sufficient to rank models since potential issues hidden in the data could bias the evaluated performances.
Human Activity Recognition Using Machine Learning Algorithms Based on IMU Data

Enhancing Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors has garnered significant attention in healthcare, particularly wearable devices. HAR plays a crucial role in prosthetic and wearable robotics by accurately identifying and responding to user movements, enabling personalized, adaptive, and intuitive control of these devices. IMU sensors capture both acceleration and angular velocity, delivering valuable data for HAR. These sensors find widespread use in prosthetic and wearable robotics, allowing for the detection and monitoring of user limb or body activities. Various Machine Learning algorithms can be leveraged further to advance HAR systems. These techniques hold promise in enhancing the accuracy and robustness of HAR systems, especially when dealing with complex and diverse movements. In this study, we implement four Machine Learning models, including Artificial Neural Networks (ANN), Decision Tree Classifiers (DTC), Random Forest Classifiers (RFC), and K-Nearest Neighbors (KNN). Our objective is to accurately recognize human activities such as walking upstairs, walking downstairs, walking normally, walking on the toe, walking on the heel, and performing sit-up tasks. Among these models, the RFC model exhibits superior performance compared to the others, achieving an impressive accuracy score of 97.67% across all evaluation metrics for activity recognition. This work is a foundational step in augmenting wearable robotics’ functionality, usability, and personalization, ultimately improving the quality of life for individuals with disabilities or injuries.
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops

As neural networks become more integrated into the systems that we depend on for transportation, medicine, and security, it becomes increasingly important that we develop methods to analyze their behavior to ensure that they are safe to use within these contexts. The methods used in this paper seek to certify safety for closed-loop systems with neural network controllers, i.e., neural feedback loops, using backward reachability analysis. Namely, we calculate backprojection (BP) set over-approximations (BPOAs), i.e., sets of states that lead to a given target set that bounds dangerous regions of the state space. The system’s safety can then be certified by checking its current state against the BPOAs. While over-approximating BPs is significantly faster than calculating exact BP sets, solving the relaxed problem leads to conservativeness. To combat conservativeness, partitioning strategies can be used to split the problem into a set of sub-problems, each less conservative than the unpartitioned problem. We introduce a hybrid partitioning method that uses both target set partitioning (TSP) and back- reachable set partitioning (BRSP) to overcome a lower bound on estimation error that is present when using BRSP. Numerical results demonstrate a near order-of-magnitude reduction in estimation error compared to BRSP or TSP given the same computation time.