Reviewerly/exHarmony huggingface dataset:
OAC-HAS: outsourced access control with hidden access structures in fog-enhanced IoT systems

Fog computing is recently a novel distributed computing paradigm that performs a significant achievement in the latency-sensitive smart Internet of Things (IoT) applications. However, the security and privacy issues, such as data leakage, still challenge the wide deployment of fog computing infrastructure. To guarantee data confidentiality and meanwhile achieving fine-grained access control, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) promises to provide a flexible access policy for securely sharing data among users, fog nodes, and cloud center. However, due to the complicated cryptographic operations, CP-ABE has met a significant drawback that requires heavy computation resources on the user-side. In this paper, we propose an outsourced access control scheme with hidden access structures, named OAC-HAS, in fog-enhanced IoT systems. The contributions of our OAC-HAS scheme are three-folds. Firstly, we introduce a fog-cloud computing (FCC) environment which has the outsourcing capability. Then, we design an outsource verification mechanism to guarantee the correctness of executing cryptographic operations on the fog nodes. Finally, we also provide a privacy guarantee that prevents information leakage from the access structures. Security analysis and experimental results show that the proposed OAC-HAS scheme achieves flexible access policy, privacy-preserving, and high efficiency in fog-enhanced IoT systems.
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction

Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document.Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE.However, traditional GCN simply takes word nodes and adjacency matrix to represent graphs, which is difficult to establish direct connections between distant entity pairs.In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document.Two hierarchical blocks, Context-aware Attention Guided Graph Convolution (CAGGC) for partially connected graphs and Multi-head Attention Guided Graph Convolution (MAGGC) for fully connected graphs, could take progressively more global context into account.Meantime, we leverage a large-scale distantly supervised dataset to pre-train a GCGCN model with curriculum learning, which is then fine-tuned on the human-annotated dataset for further improving document-level RE performance.The experimental results on DocRED show that our model could effectively capture rich global context information in the document, leading to a state-of-the-art result.
The recognition of emotions beyond facial expressions: Comparing emoticons specifically designed to convey basic emotions with other modes of expression

The development of information and communication technologies has provided a new non-verbal channel to convey emotions using emoticons. Although a great diversity of emoticons is widely used today in text-based communications, little is known about the way emotions are recognized when using emoticons compared to other modes of expression. In a pretest and three studies (N = 1203), ‘new’ emoticons specifically designed to represent the six basic emotions were proposed to participants who had to recognized the emotions conveyed by each. The quality of recognition was compared to other modes of emotional expression, including facial expressions. In using a between-subject design, the first two studies revealed that the emotions conveyed by ‘new’ emoticons were recognized more effectively than other modes of expression, including facial expressions. Using a within-subject design, a third study confirmed the more successful recognition of ‘new’ emoticons than other modes of expression, and with a greater intensity. For all the studies, this effect was mainly due to the negative emotions of disgust (Study 1, 2, and 3) and sadness (Study 2 and 3). These findings suggest the need to use specific emoticons to convey easily recognized basic emotions for communication technologies, and implement them in social media.
Limitation of capsule networks

A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the output of capsules from lower-level layers to upper-level layers. In this paper, we prove that state-of-the-art routing procedures decrease the expressivity of capsule networks. More precisely, it is shown that EM-routing and routing-by-agreement prevent capsule networks from distinguishing inputs and their negative counterpart. Therefore, only symmetric functions can be expressed by capsule networks, and it can be concluded that they are not universal approximators. We also theoretically motivate and empirically show that this limitation affects the training of deep capsule networks negatively. Therefore, we present an incremental improvement for state-of-the-art routing algorithms that solves the aforementioned limitation and stabilizes the training of capsule networks.
Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.
A Systematic Review and Empirical Analysis of Blockchain Simulators

In recent years, distributed ledger technologies, and especially blockchain, have gained tremendous interest from academia, government, and industry. Although various blockchain-based solutions were created, the lack of tools to evaluate these complex distributed systems may hinder the development of the field. Many advantages of blockchain systems can be demonstrated only at large scales, e.g., using thousands of nodes. An investigation of different implementations and design choices is complicated and hardly feasible on real systems. Meanwhile, blockchain simulators give the possibility to repeat the complex real-world processes at a low cost. This work provides the first and an up-to-date systematic review and empirical analysis of blockchain simulators. Simulators are easily extensible and can test the performance of distributed ledgers using different settings and parameters on a single computer. The features and limitations of selected simulators are summarized and experimentally validated. Finally, recommendations for potential future research directions in the field are provided.
A Privacy-Preserving Online Ride-Hailing System Without Involving a Third Trusted Server

The increasing popularity of Online Ride-hailing (ORH) services has greatly facilitated our daily travel. It enables a rider to easily request the nearest driver through mobile devices in a short time. However, existing ORH systems require the collection of users' location information and thus raise critical privacy concerns. While several privacy-preserving solutions for ORH service have been proposed, most of existing schemes rely on an additional trusted party to compute the distance between a rider and a driver. Such a security assumption cannot fully address the privacy concerns for practical deployment. In this paper, we present a new ride-matching scheme for ORH systems, which allows privacy-preserving and effective distance calculation without involving a third-party server. Our proposed scheme enables ORH systems to securely compute the user distance while protecting the location privacy of both riders and drivers. Specifically, we resort to state-of-the-art distance calculation techniques based on Road Network Embedding (RNE), and show how to uniquely bridge cryptographic primitives like Property-preserving Hash (PPH) with RNE in depth to support privacy-preserving ride-matching services. Moreover, we also propose an optimized design to improve the matching efficiency. We formally analyze the security strengths and implement the system prototype. Evaluation results demonstrate that our design is secure and efficient for ORH systems.
Blockchain-Enabled Applications in Next-Generation Wireless Systems: Challenges and Opportunities

Many emerging wireless applications are based on unique requirements such as edge data processing, distributed trust, and automation, which can be provided and enabled by blockchain technology through decentralization, transaction accountability, and smart contract-based automation. This article discusses opportunities and challenges of such blockchain-enabled wireless applications. First, blockchain technology and 5G system architecture are briefly reviewed. Then, an example wireless application use case is presented to illustrate the benefits that blockchain technology can bring to wireless applications. A set of pending research issues addressing some of the challenges faced by blockchain-enabled wireless applications is introduced. As a potential solution, a wireless blockchain middleware architecture (BlockWare) is proposed, based on a blockchain middleware layer connecting wireless applications and underlying blockchain infrastructure. The blockchain middleware layer interacts with various types of blockchain systems on behalf of different wireless applications. Thus, a wireless application does not need to implement complex blockchain functions, rather relying on the blockchain middleware layer and its services to leverage various underlying blockchain systems. This article also points out several research directions for future study, such as inter-blockchain interactions and interoperability, efficient blockchain transaction management, and blockchain-based messaging in future wireless systems such as 5G, beyond 5G, and 6G.
Radar Emitter Identification Based on Novel Time-Frequency Spectrum and Convolutional Neural Network

Radar emitter identification (REI) is significant in both military and civilian application domains. A critical step for REI is signal feature extraction. Most radar emitter signals are non-stationary, and many studies apply time-frequency spectrum features for non-stationary signal analysis in recent years. This letter proposes a novel spectrum calculation method for signal feature analysis using short-time Fourier transform (STFT) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means algorithm. We first compute the time-frequency spectrograms of emitter signals by the proposed method. And we apply the convolutional neural network (CNN) for automatic identification based on the time-frequency images. In the experiment, we simulate different emitter signals for performance evaluation and compare our method with the spectrum analysis methods adopted in the literature. The results prove that our method can achieve excellent performance and has strong robustness in the condition of low signal-to-noise ratio (SNR), and our time-frequency analysis method works well in real-time.
DLnet With Training Task Conversion Stream for Precise Semantic Segmentation in Actual Traffic Scene

Many successful semantic segmentation models trained on certain datasets experience a performance gap when they are applied to the actual scene images, expressing weak robustness of these models in the actual scene. The training task conversion (TTC) and domain adaption field have been originally proposed to solve the performance gap problem. Unfortunately, many existing models for TTC and domain adaptation have defects, and even if the TTC is completed, the performance is far from the original task model. Thus, how to maintain excellent performance while completing TTC is the main challenge. In order to address this challenge, a deep learning model named DLnet is proposed for TTC from the existing image dataset-based training task to the actual scene image-based training task. The proposed network, named the DLnet, contains three main innovations. The proposed network is verified by experiments. The experimental results show that the proposed DLnet not only can achieve state-of-the-art quantitative performance on four popular datasets but also can obtain outstanding qualitative performance in four actual urban scenes, which demonstrates the robustness and performance of the proposed DLnet. In addition, although the proposed DLnet cannot achieve outstanding performance in real time, it can still achieve a moderate performance in real time, which is within an acceptable range.
Library for universal virtual reality experiments (luVRe): A standardized immersive 3D/360° picture and video database for VR based research

Abstract Virtual reality is a promising tool for experimental psychology, enhancing the ecological validity of psychological science. The advantage of VR is that it enables researchers to study emotional and cognitive processes under realistic conditions while maintaining strict experimental control. To make it easier for scientists to get into the world of VR research and to improve the comparability of scientific results, we have created and validated a standardized set of 3D/360° videos and photos. Study 1 investigated the electrophysiological differences between motivational and emotional reactions exhibited under immersive VR and conventional 2D conditions. The obtained frontal alpha asymmetries show diverge patterns between the two conditions giving rise to further speculations that associated psychological processes exhibit more natural functional properties under immersive conditions. The feeling of being at the center of a realistic VR environment creates a sense of self-relevance. In VR, motivational tendencies and emotional reactions are related to objects or persons within the vicinity of the participant and not to the stimuli presented on a screen. Study 2, investigating the memory performance for VR videos as opposed to a conventional 2D screen presentation, provides evidence that memory formed under immersive conditions created more profound memory traces. This so-called memory superiority effect for the VR conditions might again result from the feeling of being in a scene, thus facilitating the formation of autobiographical memory. The implementation of VR experiments using the database is straightforward as it does neither require much technical equipment nor a high level of VR expertise.
Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams

Piping and instrumentation diagrams (P&IDs) are commonly used in the process industry as a transfer medium for the fundamental design of a plant and for detailed design, purchasing, procurement, construction, and commissioning decisions. The present study proposes a method for symbol and text recognition for P&ID images using deep-learning technology. Our proposed method consists of P&ID image pre-processing, symbol and text recognition, and the storage of the recognition results. We consider the recognition of symbols of different sizes and shape complexities in high-density P&ID images in a manner that is applicable to the process industry. We also standardize the training dataset structure and symbol taxonomy to optimize the developed deep neural network. A training dataset is created based on diagrams provided by a local Korean company. After training the model with this dataset, a recognition test produced relatively good results, with a precision and recall of 0.9718 and 0.9827 for symbols and 0.9386 and 0.9175 for text, respectively.
A Scoping Review of Augmented/Virtual Reality Health and Wellbeing Interventions for Older Adults: Redefining Immersive Virtual Reality

Augmented and virtual reality (AR/VR) technologies are regularly used in psychology research to complement psychological interventions and to enable an individual to feel as if they are in an environment other than that of their immediate surroundings. A scoping review was performed to identify how AR/VR was being used with older adult populations to impact their physical and mental health. The review also sought to determine whether the terminology used in AR/VR research was consistent. The results show that 65 studies have been published in the last 20 years that meet the inclusion criteria (virtual/augmented reality) technology to impact older adults’ physical/mental health and wellbeing. Participants included healthy, physically, and cognitively impaired, and emotionally vulnerable older adults. We argue that over 70% of the studies included in this review were mislabeled as VR and only six papers included fully immersive VR/AR. The remaining studies use less immersive variants of virtual reality with their populations, and only one study made use of AR, which prompted the suggestion of a new definition for virtual reality. This paper also calls for an updated taxonomy of augmented and virtual reality definitions to address the lack of consistency found in studies that identify themselves as AR/VR when they are using less immersive technical set-ups, including displaying non-interactive videos on 2D screens.
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk

Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to ap-proximate scene flow is an effective approach. Previous methods often obtain correspondences by applying point-wise matching that only takes the distance on 3D point co-ordinates into account, introducing two critical issues: (1) it overlooks other discriminative measures, such as color and surface normal, which often bring fruitful clues for ac-curate matching; and (2) it often generates sub-par performance, as the matching is operated in an unconstrained situation, where multiple points can be ended up with the same corresponding point. To address the issues, we formulate this matching task as an optimal transport problem. The output optimal assignment matrix can be utilized to guide the generation of pseudo ground truth. In this optimal transport, we design the transport cost by considering multiple descriptors and encourage one-to-one matching by mass equality constraints. Also, constructing a graph on the points, a random walk module is introduced to encourage the local consistency of the pseudo labels. Comprehensive experiments on FlyingThings3D and KITTI show that our method achieves state-of-the-art performance among self-supervised learning methods. Our self-supervised method even performs on par with some supervised learning approaches, although we do not need any ground truth flow for training.
StarGANv2-VC: A Diverse, Unsupervised, Non-Parallel Framework for Natural-Sounding Voice Conversion

We present an unsupervised non-parallel many-to-many voice conversion (VC) method using a generative adversarial network (GAN) called StarGAN v2. Using a combination of adversarial source classifier loss and perceptual loss, our model significantly outperforms previous VC models. Although our model is trained only with 20 English speakers, it generalizes to a variety of voice conversion tasks, such as any-to-many, cross-lingual, and singing conversion. Using a style encoder, our framework can also convert plain reading speech into stylistic speech, such as emotional and falsetto speech. Subjective and objective evaluation experiments on a non-parallel many-to-many voice conversion task revealed that our model produces natural sounding voices, close to the sound quality of state-of-the-art text-to-speech (TTS) based voice conversion methods without the need for text labels. Moreover, our model is completely convolutional and with a faster-than-real-time vocoder such as Parallel WaveGAN can perform real-time voice conversion.
Belief eXtropy: Measure uncertainty from negation

As the duality and complement of the Shannon entropy, the eXtropy is proposed in this years. The eXtropy also can be explained from the perspective of negation, which can be seen as an entropy with one-step negation operation of elements. The Dempster–Shafer theory (DST), as the generalization of probability theory (PT), it can express the more uncertain information than the Bayesian distribution. It contains the uncertainty of the event probability expressed in the Bayesian distribution and the uncertainty of the Bayesian distribution. Based on above, this article proposes a new belief entropy in DST called SU entropy, which is the first belief entropy to use the elements rather than mass functions. And then, a new negation of non-specificity part in DST is proposed, which is the first time to research the negation of non-specificity alone. Finally, a new belief eXtropy was proposed. It is different from the traditional eXtropy thinking, but from the perspective of elements’ negation. After verification, it has a satisfying performance in measurement uncertainty in DST. At the end of the paper, we discuss how to understand eXtropy from the perspective of negation, and the future research direction for the new belief eXtropy.