Reviewerly/exHarmony huggingface dataset:
Blockchain-assisted handover authentication for intelligent telehealth in multi-server edge computing environment

Intelligent telehealth system (ITS) provides patients and medical institutions with a lot of convenience, medical institutions can achieve medical services for patients in time through monitored health data. However, as the scope of people's daily activities extends, the traditional single-server architecture is no longer applicable. To deal with this problem, a multi-server architecture has been proposed recently while there remains security and privacy challenges, including handover authentication. In this paper, we investigate a blockchain-assisted handover authentication and key agreement scheme for ITS in a multi-server edge computing environment. Specifically, we first propose a novel handover authentication model of ITS with multi-server edge computing architecture. Second, the proposed handover authentication scheme allows the authenticated server to assist users subsequently authenticate with other server, thereby achieving interactions with the server anytime and anywhere with low overhead. Finally, blockchain technology and strong anonymity mechanism are introduced to protect users’ privacy strictly. To our best knowledge, the proposed scheme is the first in the literature to provide efficient authentication, strict anonymity and computational load transfer simultaneously. The security analysis and performance evaluation show that our scheme can not only satisfy the security requirements but also achieve higher efficiency in computation and communication cost.
Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks

Tremendous academic information causes serious information overload problems while supporting scientific research. Scientific paper and citation recommendation systems have been developed to relieve this problem and work as a filter to furnish only relevant papers to researchers. Although previous studies have made comparative progress, this problem is still challenging because current paper recommendation systems rely on heterogeneous and multi-sourced features, thereby requiring a unified learning representation to cover different types and modalities of information. Additionally, the implicit influence of scholars’ previous preferences of writing and citing on his/her new manuscript has not been well considered in the previous studies. Facing the issue from these two aspects, in this paper, a heterogeneous knowledge embedding-based attentive RNN model is proposed to recommend scientific paper citations. First, the preparation of features consists of two parts: (1) building a unified learning representation of structural entities and relations for recommending paper citations; and (2) defining and constructing a bibliographic network comprising five types of entities and five relations. The bibliographic network enables learning a unified representation so that all graphical entities and relations can be vectorized using TransD. To establish textual representations, the PV-DM model is utilized to generate numeric features for the title of each paper. Second, by combining structural and textual representations focusing on the “author-text query” scenario, an attentive bidirectional RNN is constructed to recommend paper and citation based on an user’s identity with a length-limited inquiry to capture the scholars’ previous writing and citing preferences, thereby reducing recommendation error. Through the DBLP dataset, our experiment results show the feasibility and effectiveness of our method, both in terms of the number as well as the quality of the first few recommended items. In specific, compared with existing models, our model has improved MRR and NDCG by approximately 4.8% and 2.4%, respectively.
Color image segmentation using Kapur, Otsu and Minimum Cross Entropy functions based on Exchange Market Algorithm

Abstract Color image segmentation is the primary step to elicit detailed information of the image with RGB color space. The key to acquire the region of interest is the straightforward, instinctual technique called thresholding. Bi-level thresholding can be assessed concretely for simple images, whereas revealing various classes from a complex image is achieved only through multiple threshold values. Hence, multilevel thresholding with the most propitious maximizing objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE) to extract the precise threshold are employed in this paper. As the segmentation level increases, execution of non-parametric objective functions leads to exponential increase in computational time. Several researches to enhance the speed of objective functions along with various metaheuristics such as Krill Herd Algorithm (KHA), Teaching-Learning Based Optimization (TLBO) and Cuckoo Search Algorithm (CSA) are performed. The need to unlock the computational complexity drifted our view towards the most powerful, robust, recently introduced metaheuristic Exchange Market Algorithm (EMA). EMA involves exchange of shares among investors to gain profit. The strategic approach of share members in stable and unstable modes of the market results in precise exploration and exploitation. Furthermore, low fitness shareholders gaining experience from their high and moderate counterparts, stands out as an exceptional step to reduce the time consumption. This concept is utilized in our paper for the first time to obtain the exact details from the image without any delay. Empirical outcome of the results indicates that exceptional image segmentation is achieved by EMA in less time compared to extensive search techniques such as KHA, TLBO and CSA. Quantitative and qualitative validation furnished by metrics such as Structural Similarity Index (SSIM), computational time, Peak Signal to Noise Ratio (PSNR) and statistical Wilcoxon test affirm that Otsu, Kapur and MCE based EMA outperforms the existing techniques to analyze the real-world images.
SCDetector

Code clone detection is to find out code fragments with similar functionalities, which has been more and more important in software engineering. Many approaches have been proposed to detect code clones, in which token-based methods are the most scalable but cannot handle semantic clones because of the lack of consideration of program semantics. To address the issue, researchers conduct program analysis to distill the program semantics into a graph representation and detect clones by matching the graphs. However, such approaches suffer from low scalability since graph matching is typically time-consuming.
SoK: Hate, Harassment, and the Changing Landscape of Online Abuse

We argue that existing security, privacy, and antiabuse protections fail to address the growing threat of online hate and harassment. In order for our community to understand and address this gap, we propose a taxonomy for reasoning about online hate and harassment. Our taxonomy draws on over 150 interdisciplinary research papers that cover disparate threats ranging from intimate partner violence to coordinated mobs. In the process, we identify seven classes of attacks—such as toxic content and surveillance—that each stem from different attacker capabilities and intents. We also provide longitudinal evidence from a three-year survey that hate and harassment is a pervasive, growing experience for online users, particularly for at-risk communities like young adults and people who identify as LGBTQ+. Responding to each class of hate and harassment requires a unique strategy and we highlight five such potential research directions that ultimately empower individuals, communities, and platforms to do so.
The convenience of shopping via voice AI: Introducing AIDM

The purpose of this paper is to propose an updated view of consumer choice based on AI and inherent convenience addiction to smart speakers. Following the MacInnis framework for developing conceptual contributions of summarization, integration, and delineation, we review the current consumer decision-making literature and theory to demonstrate consumers' increasing tendency to outsource decisions to AI. Today's customers value convenience: the less time and effort they spend on a purchase, the better they perceive the transaction. AI is taking convenience to higher levels for consumers as they outsource their decisions to bots and inherent algorithms. This is particularly accurate for low-involvement everyday purchases. Our study's contribution is fourfold. First, we introduce a new model of AI-influenced decision-making (AIDM) processes. Second, our conceptual model suggests that managers need to change their interpretation of their customers' decision-making-processes in the new, AI-influenced marketplace. The shift in consumers' behavior toward reliance on home voice bots for purchase has significant implications for the retail sector. Third, our model differentiates between high and low involvement AI-influenced decision-making processes. Fourth, our study highlights how branding as we know it is challenged in an AI-dominated environment.
Manipulation Attacks in Local Differential Privacy

Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems. We initiate a systematic study of a fundamental limitation of locally differentially private protocols: they are highly vulnerable to adversarial manipulation. While any algorithm can be manipulated by adversaries who lie about their inputs, we show that any noninteractive locally differentially private protocol can be manipulated to a much greater extent—when the privacy level is high, or the domain size is large, a small fraction of users in the protocol can completely obscure the distribution of the honest users’ input. We also construct protocols that are optimally robust to manipulation for a variety of common tasks in local differential privacy. Finally, we give simple experiments validating our theoretical results, and demonstrating that proto-cols that are optimal without manipulation can have dramatically different levels of robustness to manipulation. Our results suggest caution when deploying local differential privacy and reinforce the importance of efficient cryptographic techniques for the distributed emulation of centrally differentially private mechanisms.
Multimodal Cross-Layer Bilinear Pooling for RGBT Tracking

Hierarchical deep features can provide multilevel abstractions of target objects, which play an important role in target localization and classification. Determining how to effectively aggregate abstract information from different levels in RGB and thermal modalities is the key to exploiting their complementary advantages for robust RGBT tracking. However, existing RGBT tracking algorithms either focus on the semantic information of the last layer or aggregate hierarchical deep features from each modal using simple operations (e.g., summation and concatenation), which limit the capability of the multimodal tracker. To address these issues, in this paper, we propose a novel multimodal cross-layer bilinear pooling network for RGBT tracking. In our network, firstly, to boost the performance of the tracker, we use a channel attention mechanism to implement the adaptive calibration of feature channels for all convolutional layer features before realizing hierarchical feature fusion. Then, a bilinear pooling operation is performed on any two layers through the cross product, which is a second-order computation that effectively aggregates the deep semantic and shallow texture information of the target. Finally, a quality-aware fusion module is designed to aggregate the bilinear pooling features of different layer interactions between different modalities in an adaptive manner. The results of a large number of experiments on two public benchmark datasets demonstrate the effectiveness of our tracker compared with other state-of-the-art tracking methods.
Cross Fusion Net: A Fast Semantic Segmentation Network for Small-Scale Semantic Information Capturing in Aerial Scenes

Capturing accurate multiscale semantic information from the images is of great importance for high-quality semantic segmentation. Over the past years, a large number of methods attempt to improve the multiscale information capturing ability of the networks via various means. However, these methods always suffer unsatisfactory efficiency (e.g., speed or accuracy) on the images that include a large number of small-scale objects, for example, aerial images. In this article, we propose a new network named cross fusion net (CF-Net) for fast and effective extraction of the multiscale semantic information, especially for small-scale semantic information. In particular, the proposed CF-Net can capture more accurate small-scale semantic information from two aspects. On the one hand, we develop a channel attention refinement block to select the informative features. On the other hand, we propose a cross fusion block to enlarge the receptive field of the low-level feature maps. As a result, the network can encode more accurate semantic information from the small-scale objects, and the segmentation accuracy of the small-scale objects is improved accordingly. We have compared the proposed CF-Net with several state-of-the-art semantic segmentation methods on two popular aerial image segmentation data sets. Experimental results reveal that the average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score gain brought by our CF-Net is about 0.43% and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score gain of the small-scale objects ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , cars) is about 2.61%. In addition, our CF-Net has the fastest inference speed, which proves its superiority in the aerial scenes. Our code will be released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pcl111/CF-Net</uri> .
Suitability and Comparison of Questionnaires Assessing Virtual Reality-Induced Symptoms and Effects and User Experience in Virtual Environments

Although virtual reality (VR) has already achieved technological maturity, there are still some significant drawbacks for technology acceptance and broader user adoption, presenting research challenges. Thus, there is a need for standard, reliable, and quick assessment tools for Virtual Reality-Induced Symptoms and Effects (VRISE) and user experience in VR Assessing VRISE and user experience could be time consuming, especially when using objective physiological measures. In this study, we have reviewed, compared, and performed a suitability assessment of existing standard measures for evaluating VRISE and user experience in VR We have developed a first-person VR game with different scenes and different conditions. For assessing VRISE symptoms, we have used the Simulator Sickness Questionnaire (SSQ) and Fast Motion Sickness Score (FMS). For assessing user experience, we have used the short version of the User Experience Questionnaire (UEQ-S). We have also used a novel Virtual Reality Neuroscience Questionnaire (VRNQ) for assessing VRISE and user experience aspects. The result has shown that FMS and VRNQ (VRISE section) are suitable for quick assessment of VRISE and that VRNQ (User experience section) is suitable for assessing user experience. The advantage of FMS and VRNQ questionnaires is that they are shorter to fulfill and easier to understand. FMS also enables to record the VRISE levels during the virtual experience and thus capturing its trend over time. Another advantage of the VRNQ is that it also provides the minimum and parsimonious cut-offs to appraise the suitability of VR software, which we have confirmed in our study to be adequate.
Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from non-medical style sources such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology. Our code is available at https://github.com/rikiyay/style-transfer-for-digital-pathology.
The Invisible Museum: A User-Centric Platform for Creating Virtual 3D Exhibitions with VR Support

With the ever-advancing availability of digitized museum artifacts, the question of how to make the vast collection of exhibits accessible and explorable beyond what museums traditionally offer via their websites and exposed databases has recently gained increased attention. This research work introduces the Invisible Museum: a user-centric platform that allows users to create interactive and immersive virtual 3D/VR exhibitions using a unified collaborative authoring environment. The platform itself was designed following a Human-Centered Design approach, with the active participation of museum curators and end-users. Content representation adheres to domain standards such as International Committee for Documentation of the International Council of Museums (CIDOC-CRM) and the Europeana Data Model and exploits state-of-the-art deep learning technologies to assist the curators by generating ontology bindings for textual data. The platform enables the formulation and semantic representation of narratives that guide storytelling experiences and bind the presented artifacts with their socio-historic context. Main contributions are pertinent to the fields of (a) user-designed dynamic virtual exhibitions, (b) personalized suggestions and exhibition tours, (c) visualization in web-based 3D/VR technologies, and (d) immersive navigation and interaction. The Invisible Museum has been evaluated using a combination of different methodologies, ensuring the delivery of a high-quality user experience, leading to valuable lessons learned, which are discussed in the article.
Blockchain Education Smart Courses of Massive Online Open Course Using Business Model Canvas

With the development of internet technology, online education, a new model of education, has been very popular. However, this mode of education still has many problems in terms of credibility, credit certification and certificates, student privacy, and various courses. The accepted question addressed was whether universities would discuss blockchain technology differently from traditional research and education. Through a research approach, literature summary and case analysis as well as Business Model Canvas which provides three theoretical contributions and guidance on what Blockchain is by taking sponsored studies. This study shows the results that integrating blockchain technology with the MOOC-based Smart Program Education platform is a trend that is promised on the Internet of online education development. The conclusion can provide a critique of the technical features and basic applications of Blockchain and provide solutions to online education problems based on Blockchain technology. Assistance, discussion, discussion on blockchain and discussion on how educational institutions work must be approved by Blockchain technology as digital technology, so that more can be expected in prototypes and will be developed to provide a better replacement.
Collaborative deep learning framework for fault diagnosis in distributed complex systems

In distributed complex systems, condition monitoring and fault diagnosis have received considerable attention, especially for recent developments of data-driven methods with deep learning structures, which greatly enhance the performance as of superior representation capacity over big data. To apply these methods, massive data needs to be collected from distributed systems, requiring high costs for data transmission and causing more and more concerns on privacy issues. For the naturally distributed data in such scenario, this work presents a novel collaborative deep learning framework with the idea that the features, as representations of data, can be transmitted through latent parameters of deep learning structure while the raw data won’t be shared in the distributed network. Based on the collaborative learning setup, the proposed framework adopts a secure communicating strategy with no need of transmitting raw data, and obtains a consensus for distributed deep learning models that can be geographically located. To validate the proposed scheme, four case studies are carried out and the results show that it is able to improve the diagnosis accuracy compared with local learning models. Also, it is robust and adaptive for diagnosis problems with data that is imbalanced or from different distributions.
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications. This paper proposes a decentralized partially observable multi-agent path planning with evolutionary reinforcement learning (MAPPER) method to learn an effective local planning policy in mixed dynamic environments. Reinforcement learning-based methods usually suffer performance degradation on long-horizon tasks with goal-conditioned sparse rewards, so we decompose the long-range navigation task into many easier sub-tasks under the guidance of a global planner, which increases agents' performance in large environments. Moreover, most existing multi-agent planning approaches assume either perfect information of the surrounding environment or homogeneity of nearby dynamic agents, which may not hold in practice. Our approach models dynamic obstacles' behavior with an image-based representation and trains a policy in mixed dynamic environments without homogeneity assumption. To ensure multi-agent training stability and performance, we propose an evolutionary training approach that can be easily scaled to large and complex environments. Experiments show that MAPPER is able to achieve higher success rates and more stable performance when exposed to a large number of non-cooperative dynamic obstacles compared with traditional reaction-based planner LRA* and the state-of-the-art learning-based method.