Reviewerly/exHarmony huggingface dataset:
A novel topic clustering algorithm based on graph neural network for question topic diversity

In community question answering, many questions have no topic labeling or the topic labeling is very diverse, which has become the biggest obstacle to building the bridge between users and posts. Topic clustering methods could alleviate this issue. However, existing research employed words as topic representation units and could not express topic semantic relevance. In this paper, we propose a novel Topic Clustering framework based on the Graph Neural Network (called TCGNN) to alleviate topic diversity in Community Question Answering. Firstly, we separately consider the relationship representation of existing topics and unlabeled topics. For manually labeled topics, we count the frequency of topics in community questions and construct a topic co-occurrence matrix to represent the topic relation. For unmarked topics, we extract the core phrases from community questions and employ them to indicate the topics of questions. Then, we transform the topic co-occurrence matrix into a topic relation graph, optimizing the topic relevance and improving presentation efficiency. Next, we employ a graph neural network for embedding the topic connection graph and get the vector representation of each topic. Finally, an improved K-mean method is proposed for topic clustering based on the distance of topic vectors. Additionally, we briefly discuss the extended effect of topic clustering methods in other domains (bibliographic information and reviews). In the literature we have, it is a primary work that conders topic clustering in multiple situations and offers innovative cogitation to apply graph neural networks in topic clustering. Our experiment compared prevalent clustering methods and some combination methods of text representation and graph embedding. The outcome of experiments on four extensive and varied datasets (Stack Overflow, DBLP, Yelp, and Zhihu) illustrate that TCGNN leads the prevalent baseline in Entropy and Purity.
An n-Sigmoid Activation Function to Improve the Squeeze-and-Excitation for 2D and 3D Deep Networks

The Squeeze-and-Excitation (SE) structure has been designed to enhance the neural network performance by allowing it to execute positive channel-wise feature recalibration and suppress less useful features. SE structures are generally adopted in a plethora of tasks directly in existing models and have shown actual performance enhancements. However, the various sigmoid functions used in artificial neural networks are intrinsically restricted by vanishing gradients. The purpose of this paper is to further improve the network by introducing a new SE block with a custom activation function resulting from the integration of a piecewise shifted sigmoid function. The proposed activation function aims to improve the learning and generalization capacity of 2D and 3D neural networks for classification and segmentation, by reducing the vanishing gradient problem. Comparisons were made between the networks with the original design, the addition of the SE block, and the proposed n-sigmoid SE block. To evaluate the performance of this new method, commonly used datasets, CIFAR-10 and Carvana for 2D data and Sandstone Dataset for 3D data, were considered. Experiments conducted using SE showed that the new n-sigmoid function results in performance improvements in the training accuracy score for UNet (up 0.25% to 99.67%), ResNet (up 0.9% to 95.1%), and DenseNet (up 1.1% to 98.87%) for the 2D cases, and the 3D UNet (up 0.2% to 99.67%) for the 3D cases. The n-sigmoid SE block not only reduces the vanishing gradient problem but also develops valuable features by combining channel-wise and spatial information.
Consumer-brand Relationships with AI Anthropomorphic Assistant: Role of Product Usage Barrier, Psychological Distance and Trust

Human likeness in technology (i.e. AI anthropomorphic assistants) continues to transform and build relationships with consumers today. This study aims to examine the role of product usage barrier, psychological distance, and trust between physical AI anthropomorphic assistants and consumers to build brand relationships in the retail sector. Data was collected through a questionnaire from Indian consumers, and was analyzed, using the AMOS and SPSS PROCESS macro. The findings reveal that both product usage barriers and psychological distance do mediate the relationship between AI anthropomorphic assistants and consumer-brand relationships, while trust plays the role of a moderator. These findings are unique, and could assist retail managers in leveraging AI anthropomorphization of retail brands as part of building effective relationships with their consumers. We believe that the brands stand to benefit when retail managers make AI anthropomorphization an integral part of the retail experience.
Comparing cybersickness in virtual reality and mixed reality head-mounted displays

Introduction: Defence Research and Development Canada is developing guidance on the use of Mixed Reality head-mounted displays for naval operations in the Royal Canadian Navy. Virtual reality head-mounted displays display graphics to the user in 3D and completely occlude the user’s view of the real world. Mixed Reality head-mounted displays overlay and integrate graphics onto the real world allowing the user to perceive the real world and rich 3D graphic elements simultaneously. Nausea and other debilitating symptoms caused by the use of head-mounted displays, known as ‘cybersickness’, is well documented during Virtual reality head-mounted display exposure and can be quite severe. However, it is not yet clear from the literature on Mixed Reality head-mounted displays whether CS differs in Virtual reality vs. Mixed Reality head-mounted displays. The objective of this study was to determine the impact of MR HMDs on CS. Method: This was done by modulating the quantity of graphics in two Mixed Reality conditions and one Virtual reality condition. Only foreground objects were graphically rendered in the first Mixed Reality condition (called ‘Mixed Reality’ condition), while the entire scene was graphically rendered in the second Mixed Reality condition (called ‘Mixed Reality +’ condition). The Virtual reality condition simulated the Mixed Reality + condition but was displayed in a Virtual reality head-mounted display. Participants observed the virtually rendered scene in one of the three conditions and reported their CS with the simulator sickness questionnaire six times throughout the 30-min experiment. We hypothesized that CS severity would increase as quantity of graphics in the display increased. Results and Discussion: Findings indicated that CS was significantly greater in the ‘Mixed Reality +’ condition compared to the ‘Mixed Reality’ and ‘Virtual reality’ conditions, providing partial evidence for our main hypothesis. Moreover, CS increased significantly and meaningfully after 25 min in the ‘Mixed Reality +’ condition. These findings indicate safe use of Mixed Reality head-mounted displays by the RCN for shore-based applications provided quantity of graphics is limited.
Type-3 Fuzzy Control of Robotic Manipulators

In this paper, the control of robotic manipulators (RMs) is studied. The RMs are widely used in industry. The RMs are multi-input-multi-output systems, and their dynamics are highly nonlinear. To improve the accuracy in practice, it is impossible to ignore the influence of nonlinear dynamics and the interaction of inputs–outputs. Non-structural uncertainties such as friction, disturbance, and unmodeled dynamics are other challenges of these systems. Recently, type-3 (T3) fuzzy logic systems (FLSs) have been suggested that result in better accuracy in a noisy environment. In this paper, a new control idea on the basis of T3-FLSs is suggested. T3-FLSs are used to estimate the dynamics of RMs and the symmetrical perturbations. The T3-FLSs are learned using online laws to enhance the stability. To eliminate the effect of the interconnection of inputs and estimation errors, a compensator is developed. By several simulations, the superiority of the suggested controller is demonstrated.
Cross-silo heterogeneous model federated multitask learning

Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants utilizing cross-silo FL (CS-FL) settings are independent organizations with different task needs, and they are concerned not only with data privacy but also with independently training their unique models due to intellectual property considerations. Most existing FL methods are incapable of satisfying the above scenarios. In this study, we present a novel FL method CoFED based on unlabeled data pseudolabeling via a process known as cotraining, which meets the needs of heterogeneous models, tasks and training processes in CS-FL. Experimental results suggest that the proposed method outperforms competing methods. This is especially true for non-independent and identically distributed (non-IID) settings and heterogeneous models, where the proposed method achieves a 35% performance improvement.
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection

Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-trained language models or/and unstructured text, but the knowledge in these resources are often incomplete and noisy. Some other methods prefer to use knowledge graphs (KGs) which often have intensive structured knowledge, but the research is still quite preliminary. In this paper, we propose LaKo, a knowledge-driven VQA method via Late Knowledge-to-text Injection. To effectively incorporate an external KG, we transfer triples into textual format and propose a late injection mechanism for knowledge fusion. Finally we address VQA as a text generation task with an effective encoder-decoder paradigm, which achieves state-of-the-art results on OKVQA datasets.
Towards Advancing Body Maps as Research Tool for Interaction Design

Body maps are a popular tool in body-centric design, facilitating a sensitization and expression of felt sensations and emotions. Yet, they also bring forth assumptions about the body and our somatic experience. Based on an open and exploratory design ideation inquiry, we have started to explore how body maps could be advanced so as to cater to a plurality of bodies and aspects that shape somatic experiences. We present an annotated portfolio featuring six design themes (temporality, sociality, representativeness, granularity, context, focus). These themes help us examine implicit assumptions of current body maps, and offer possible alternatives for what future body maps could become. We contribute our themes, inspirational design ideas and practical design techniques to help craft novel body maps. Our contributions can serve as inspiration to others, towards advancing body maps as a research tool for body-centric interaction design.
Wisp: Drones as Companions for Breathing

The spectrum of applications for social drones is broadening as they become an increasingly accessible technology. In order to expand on the immensely rich but poorly researched field of Human-Drone Interaction (HDI), we present a minimal, explorative, and anti-solutionist design. We describe the first steps of a Research through Design (RtD) project focused on the concept-driven exploration of an unlikely pairing: drones and breathing. We present Wisp, a micro-drone probe controlled by a user’s breath. Informed by experts on breathing, drawing inspiration from soma design, Wisp is described as platform for the development of defamiliarising views towards intimate somatic interactions between humans and drones. In this paper we describe the initial studies in a RtD development process, including expert interviews, prototyping, and informal evaluations. We contribute to the field of HDI with a design composite framework combining soma design and slow technology for exploratory somatic slow interactions between humans and drones.
What does method validation look like for forensic voice comparison by a human expert?

Method validation has gained traction within forensic speech science. The community recognises the need to demonstrate that the analysis methods used are valid, but finding a way to do so has been more straightforward for some analysis methods than for others. This article addresses the issue of method validation for the Auditory Phonetic and Acoustic (AuPhA) approach to forensic voice comparison. Although it is possible to take inspiration from general regulatory guidance on method validation, it is clear that these cannot be transposed on to all forensic analysis methods with the same degree of success. Particularly with respect to an analysis method like AuPhA, and in a field of the size and characteristics of forensic speech science, a bespoke approach to method validation is required. In this article we address the discussions that have been taking place around method validation, and illustrate one possible solution to demonstrating the validity of voice comparison by a human expert using the AuPhA method. In doing so we consider the constraints placed on sole practitioners, which generally go unacknowledged.
Prediction of PGA in earthquake early warning using a long short-term memory neural network

SUMMARY Peak ground acceleration (PGA) is a key parameter used in earthquake early warning systems to measure the ground motion strength and initiate emergency protocols at major projects. The traditional P-wave peak displacement-dependent PGA prediction model (Pd-PGA model) tends to underestimate the PGA for large earthquakes because it cannot make full use of the fault continuity rupture information hidden in the time-varying process of ground motion. In this paper, a continuous PGA prediction long short-term memory (LSTM) neural network model is proposed. The model takes eight sequential features of stations that are proxies of the energy and other physical parameters as input and provides the recorded PGA at the station as the target output. A total of 5961 records from 119 earthquakes recorded by the Japanese Strong-Motion Earthquake Network (K-NET) in Japan are used to train the neural network and 3433 records from 73 earthquakes are used as the test set to verify the model’s generalization ability. The results show that within the same data set, the residuals of the predicted PGA for the proposed model are smaller than those of the Pd-PGA model and that the problem of PGA underestimation is resolved. The prediction accuracy also improves with increasing sequence length, which indicates that the LSTM neural network learns the rules hidden in the time series. To further verify the model’s generalization ability, the model performance is analyzed for an M 7.3 earthquake that was not included in the training or test data sets. The results show that the residuals of the predicted PGA for the event are consistent with those for the test data set, indicating that the model has good generalization ability.
Wolverine: A Scalable and Transaction-Consistent Redactable Permissionless Blockchain

The immutability of blockchains is critical for cryptocurrencies, but an imperative need arises for the redaction of on-chain data due to privacy-protecting laws like GPDR. Recently, Ateniese et al. (EuroS&P 2017) proposed an elegant solution to this problem based on chameleon hash functions, followed by many subsequent works. While these works offered a solution to the permissioned blockchain, the approaches were not efficient enough for the permissionless setting, in terms of either security (which may cause inconsistent historical transactions) or performance (only up to a few hundred nodes). In this paper, we investigate this problem and present Wolverine, a redactable permissionless blockchain. First, we present a formal redactable blockchain model, carefully considering transaction consistency. Next, towards a practical scheme, we introduce the novel concept of non-interactive chameleon hash (NITCH). NITCHs dynamically distribute a trapdoor key among a group and each party in the group can compute its partial share without communicating with others. Anyone who possesses enough shares can then find a valid hash collision. To prevent the static group from being compromised after a sufficiently long time, we provide a generic transform from NITCHs to decentralized random beacons (DRBs) and design a committee evolution protocol based on DRBs that refresh the group after every fixed interval of time. Based on NITCH and the committee evolution protocol, we construct Wolverine which offers important features such as scalability, transaction consistency, and public accountability. Finally, we demonstrate the practicality of Wolverine by giving a proof-of-concept implementation based on Bitcoin in Golang.
TCD: Task-Collaborated Detector for Oriented Objects in Remote Sensing Images

Oriented object detection (OOD) in remote sensing image interpretation is challenging due to the difficulty of locating objects with arbitrary orientations. Existing methods have made considerable progress based on oriented heads or anchors. However, most of them follow the classical detection paradigm, such as assigning samples based on Intersection-over-Unions (IoU) and predicting through two independent tasks. These fixed strategies impair the consistency between classification and localization predictions, resulting in the prediction with optimal localization accuracy being suppressed by the nonoptimal ones during nonmaximum suppression (NMS). To address this problem, a task-collaborated detector (TCD) is proposed. Compared with current single-stage methods, its improvements include two aspects: task-collaborated assignment (TCA) and task-collaborated head (TCH). Specifically, to better pull closer the best anchors for two tasks, TCA introduces classification and localization confidence into sample assignment and tends to select the anchors with accurate and consistent predictions as positive during training. TCH provides a better balance for learning interactive and discriminative features. It can flexibly adjust the spatial feature distribution of classification and localization tasks by learning the joint features from the aggregation layer. Extensive experiments are conducted on HRSC2016, DOTA, and DIOR-R, and the proposed TCD achieves the state-of-the-art performance [90.60, 80.89, and 65.04 mean average precision (mAP), respectively]. Consistency analysis also demonstrates that TCD can significantly improve prediction consistency.
Automatic Modulation Classification in Impulsive Noise: Hyperbolic-Tangent Cyclic Spectrum and Multibranch Attention Shuffle Network

Automatic modulation classification plays an essential role in cognitive communication systems. Traditional automatic modulation classification approaches are primarily developed under Gaussian noise assumptions. Nevertheless, recent empirical studies show that impulsive noise has emerged in numerous wireless communication systems. The bursty nature of impulsive noise fundamentally challenges the applicability of the traditional automatic modulation classification approaches. To accurately identify the modulation schemes in impulsive noise environment, in this article, we propose a novel modulation classification approach through using hyperbolic-tangent cyclic spectrum and multibranch attention shuffle neural networks. First, based on the designed hyperbolic-tangent autocorrelation function, hyperbolic-tangent cyclic spectrum is proposed to effectively suppress the impulsive noise and extract the discriminating features. Then, based on the hyperbolic-tangent cyclic spectrum, a novel deep shuffle neural network is proposed as a classifier to perform the modulation classification through the multibranch attention mechanism to reweight all the features. Both the numerical and real-data experimental results demonstrate that the proposed algorithm can correctly classify modulation schemes with high accuracy and robustness in impulsive noise.
SWSEL: Sliding Window-based Selective Ensemble Learning for class-imbalance problems

For class-imbalance problems, traditional supervised learning algorithms tend to favor majority instances (also called negative instances). Therefore, it is difficult for them to accurately identify the minority instances (also called positive instances). Ensemble learning is a common method to solve the class-imbalance problem. They build multiple classifier systems on the training dataset to improve the recognition accuracy of minority instances. Sliding window is a commonly used method for processing data stream. Few researchers have used sliding windows to select majority instances and construct ensemble learning models. Traditional ensemble learning methods use some or all of the majority instances for modeling by oversampling or undersampling. However, they also inherit the drawbacks of the preprocessing methods. Therefore, in this paper, we try to use similarity mapping to construct pseudo-sequences of majority instances. Then, according to the sliding window idea, we fully use all existing majority instances, and a novel sliding window-based selective ensemble learning method (SWSEL) is proposed to deal with the class-imbalance problem. This method uses the idea of distance alignment in multi-view alignment to align the centers of the minority instances with the majority instances, and slide to select the majority instances on the sequence of pseudo-majority instances. In addition, to prevent too many classifiers from leading to long running times, we use distance metric to select a certain number of base classifiers to build the final ensemble learning model. Extensive experimental results on various real-world datasets show that using SVM, MLP and RF as the base classifier, SWSEL achieves a statistically significant performance improvement on two evaluation metrics, AUC and G-mean, compared to state-of-the-art methods.
Short-term Forecasting Approach Based on bidirectional long short-term memory and convolutional neural network for Regional Photovoltaic Power Plants

Accurate photovoltaic (PV) generation output prediction is one of the effective ways to ensure the safe operation of power grid, develop reasonable dispatching plan and improve the efficiency of clean energy. With the large-scale operation of PV power plants in recent years, forecasting regional PV output becomes more significant. We proposed a short-term forecasting approach based on bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) for regional PV power plants. First, the k-means algorithm is used to divide power plants with similar generation characteristics into the same output subregion. Second, a representative power plant in each subregion is selected based on three correlation coefficients. Then, we develop a regional prediction model based on BiLSTM-CNN method. This model takes historical operation and meteorological data of the representative power plant as input, and takes the total subregional power generation as output. Finally, this short-term forecasting approach is tested using real data from PV power plants in Chuxiong and Dali region, Yunnan province, China. The comparison of numerical results shows this proposed method can effectively improve the short-term prediction accuracy of regional PV generation output.