Reviewerly/exHarmony huggingface dataset:
Users' experiences with chatbots: findings from a questionnaire study

Abstract For chatbots to be broadly adopted by users, it is critical that they are experienced as useful and pleasurable. While there is an emerging body of research concerning user uptake and use of chatbots, there is a lack of theoretically grounded studies detailing what constitutes good or poor chatbot user experiences. In this paper, we present findings from a questionnaire study involving more than 200 chatbot users who reported on episodes of chatbot use that they found particularly satisfactory or frustrating. The user reports were analysed with basis in theory on user experience, with particular concern for pragmatic and hedonic attributes. We found that pragmatic attributes such as efficient assistance (positive) and problems with interpretation (negative) were important elements in user reports of satisfactory and frustrating episodes. Hedonic attributes such as entertainment value (positive) and strange and rude responses (negative) were also frequently mentioned. Older participants tended to report on pragmatic attributes more often, whereas younger participants tended to report on hedonic attributes more often. Drawing on the findings, we propose four high-level lessons learnt that may benefit chatbot service providers, and we suggest relevant future research.
Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis

Harris hawks optimizer (HHO) is a recently developed, efficient meta-heuristic optimization approach, which is inspired by the chasing style and collaborative behavior of Harris hawks in nature. However, for some optimization cases, the algorithm suffers from an immature balance between exploitation and exploration. Therefore, in the present study, four effective strategies are introduced into conventional HHO, such as proposing a non-linear energy parameter for the nergy of prey, differor rapid dives, a greedy selection mechanism, and opposition-based learning. These strategies enhance the search-efficiency of HHO and help to alleviate the issues of stagnation at the sub-optimal solution and premature convergence. A well-known collection of 33 benchmark problems is taken to examine the effectiveness of the proposed m-HHO, and the comparison is performed with conventional HHO and other state-of-the-art algorithms. Accordingly, the proposed m-HHO can serve as an effective and efficient optimization tool for global optimization problems.
A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection

Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation

Inductive transfer learning has had a big impact on computer vision and NLP domains but has not been used in the area of recommender systems. Even though there has been a large body of research on generating recommendations based on modeling user-item interaction sequences, few of them attempt to represent and transfer these models for serving downstream tasks where only limited data exists.
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks

We explore the problem of selectively forgetting a particular subset of the data used for training a deep neural network. While the effects of the data to be forgotten can be hidden from the output of the network, insights may still be gleaned by probing deep into its weights. We propose a method for "scrubbing" the weights clean of information about a particular set of training data. The method does not require retraining from scratch, nor access to the data originally used for training. Instead, the weights are modified so that any probing function of the weights is indistinguishable from the same function applied to the weights of a network trained without the data to be forgotten. This condition is a generalized and weaker form of Differential Privacy. Exploiting ideas related to the stability of stochastic gradient descent, we introduce an upper-bound on the amount of information remaining in the weights, which can be estimated efficiently even for deep neural networks.
Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran

Earthquakes are natural phenomena, which induce natural hazard that seriously threatens urban areas, despite significant advances in retrofitting urban buildings and enhancing the knowledge and ability of experts in natural disaster control. Iran is one of the most seismically active countries in the world. The purpose of this study was to evaluate and analyze the extent of earthquake vulnerability in relation to demographic, environmental, and physical criteria. An earthquake risk assessment (ERA) map was created by using a Fuzzy-Analytic Hierarchy Process coupled with an Artificial Neural Networks (FAHP-ANN) model generating five vulnerability classes. Combining the application of a FAHP-ANN with a geographic information system (GIS) enabled to assign weights to the layers of the earthquake vulnerability criteria. The model was applied to Sanandaj City in Iran, located in the seismically active Sanandaj-Sirjan zone which is frequently affected by devastating earthquakes. The Multilayer Perceptron (MLP) model was implemented in the IDRISI software and 250 points were validated for grades 0 and 1. The validation process revealed that the proposed model can produce an earthquake probability map with an accuracy of 95%. A comparison of the results attained by using a FAHP, AHP and MLP model shows that the hybrid FAHP-ANN model proved flexible and reliable when generating the ERA map. The FAHP-ANN model accurately identified the highest earthquake vulnerability in densely populated areas with dilapidated building infrastructure. The findings of this study are useful for decision makers with a scientific basis to develop earthquake risk management strategies.
Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

The rapid increase in data volume and features dimensionality have a negative influence on machine learning and many other fields, such as decreasing classification accuracy and increasing computational cost.Feature selection technique has a critical role as a preprocessing step in reducing these issues.It works by eliminating the features that may negatively influence the classifiers' performance, such as irrelevant, redundant and less informative features.This paper aims to introduce an improved Harris hawks optimization (IHHO) by utilizing elite opposite-based learning and proposing a new search mechanism.Harris hawks optimization (HHO) is a novel metaheuristic general-purpose algorithm recently introduced to solve continuous search problems.Compared to conventional HHO, the proposed IHHO can avoid trapping in local optima and has an enhanced search mechanism, relying on mutation, mutation neighborhood search, and rollback strategies to raise the search capabilities.Moreover, it improves population diversity, computational accuracy, and accelerates convergence rate.To evaluate the performance of IHHO, we conducted a series of experiments on twenty benchmark datasets collected from the UCI repository and the scikitfeature project.The datasets represent different levels of feature dimensionality, such as low, moderate, and high.Further, four criteria were adopted to determine the superiority of IHHO: classification accuracy, fitness value, number of selected features, and statistical tests.Furthermore, a comparison between IHHO and other well-known algorithms such as Generic algorithm (GA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Slime Mould Algorithm (SMA) was performed.The experimental results have confirmed the dominance of IHHO over the other optimization algorithms in different aspects, such as accuracy, fitness value, and feature selection.
Impacts of service robots on service quality

With rapid advances in technologies, especially in artificial intelligence, smart sensors, big data analytics, and robotics, the service industry began introducing robots to perform a variety of functions. While the main purpose of deploying robots has been productivity improvement, the current COVID-19 pandemic has brought more urgent purpose, providing contactless service for social distancing. This study explores the service quality provided by robots based on real data in a hotel setting. A sample of 201 guests provided their expected service quality by robots and the actual performance experience after the service. We analyzed this relationship using importance performance analysis (IPA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The results revealed that customers’ top priorities for robots’ service quality are assurance and reliability, while tangible and empathy were not as important. Customers were not satisfied with robots’ responsiveness, but this construct was found to be a low priority.
Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

Extensive studies have been conducted on the Dijkstra algorithm owing to its bright prospect. However, few of them have studied the surface path planning of mobile robots. Currently, some application fields (e.g., wild ground, planet ground, and game scene) need to solve the optimal surface path. This paper proposes an extended Dijkstra algorithm. We utilize the Delaunay triangulation to model the surface environment. Based on keeping the triangle side length unchanged, the triangle mesh on the surface is equivalently converted into a triangle on the two-dimensional plane. Through this transformation, we set up the two-dimensional developable passable channel of the surface and solve the optimal route on this channel. Traversing all the two-dimensional developable and passable paths of the surface, we can get the shortest route among all the optimal paths. Then the inverse transformation from the two-dimensional plane coordinates to the corresponding surface coordinates obtains the surface optimal path. The simulation results show that, compared with the traditional Dijkstra algorithm, this method improves the accuracy of the surface optimization path in single-robot single-target and multi-robot multi-target path planning tasks.
Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance for Improved Shrinkage and Patch Matching

Collaborative filters perform denoising through transform-domain shrinkage of a group of similar patches extracted from an image. Existing collaborative filters of stationary correlated noise have all used simple approximations of the transform noise power spectrum adopted from methods which do not employ patch grouping and instead operate on a single patch. We note the inaccuracies of these approximations and introduce a method for the exact computation of the noise power spectrum. Unlike earlier methods, the calculated noise variances are exact even when noise in one patch is correlated with noise in any of the other patches. We discuss the adoption of the exact noise power spectrum within shrinkage, in similarity testing (patch matching), and in aggregation. We also introduce effective approximations of the spectrum for faster computation. Extensive experiments support the proposed method over earlier crude approximations used by image denoising filters such as Block-Matching and 3D-filtering (BM3D), demonstrating dramatic improvement in many challenging conditions.
Image Restoration via Simultaneous Nonlocal Self-Similarity Priors

Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar patches to construct patch groups, recent studies have revealed that structural sparse representation (SSR) models can achieve promising performance in various image restoration tasks. However, most existing SSR methods only exploit the NSS prior from the input degraded (internal) image, and few methods utilize the NSS prior from external clean image corpus; how to jointly exploit the NSS priors of internal image and external clean image corpus is still an open problem. In this paper, we propose a novel approach for image restoration by simultaneously considering internal and external nonlocal self-similarity (SNSS) priors that offer mutually complementary information. Specifically, we first group nonlocal similar patches from images of a training corpus. Then a group-based Gaussian mixture model (GMM) learning algorithm is applied to learn an external NSS prior. We exploit the SSR model by integrating the NSS priors of both internal and external image data. An alternating minimization with an adaptive parameter adjusting strategy is developed to solve the proposed SNSS-based image restoration problems, which makes the entire algorithm more stable and practical. Experimental results on three image restoration applications, namely image denoising, deblocking and deblurring, demonstrate that the proposed SNSS produces superior results compared to many popular or state-of-the-art methods in both objective and perceptual quality measurements.
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning

Automatic cost learning for steganography based on deep neural networks is receiving increasing attention. Steganographic methods under such a framework have been shown to achieve better security performance than methods adopting hand-crafted costs. However, they still exhibit some limitations that prevent a full exploitation of their potentiality, including using a function-approximated neural-network-based embedding simulator and a coarse-grained optimization objective without explicitly using pixel-wise information. In this article, we propose a new embedding cost learning framework called SPAR-RL (Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning) that overcomes the above limitations. In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment. A sampling process is utilized to emulate the message embedding of an optimal embedding simulator. Through the iterative interactions between the agent and the environment, the policy network learns a secure embedding policy which can be converted into pixel-wise embedding costs for practical message embedding. Experimental results demonstrate that the proposed framework achieves state-of-the-art security performance against various modern steganalyzers, and outperforms existing cost learning frameworks with regard to learning stability and efficiency.
KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations

Due to its excellent performance, U-Net is the most widely used backbone architecture for biomedical image segmentation in the recent years. However, in our studies, we observe that there is a considerable performance drop in the case of detecting smaller anatomical structures with blurred noisy boundaries. We analyze this issue in detail, and address it by proposing an over-complete architecture (Ki-Net) which involves projecting the data onto higher dimensions (in the spatial sense). This network, when augmented with U-Net, results in significant improvements in the case of segmenting small anatomical landmarks and blurred noisy boundaries while obtaining better overall performance. Furthermore, the proposed network has additional benefits like faster convergence and fewer number of parameters. We evaluate the proposed method on the task of brain anatomy segmentation from 2D Ultrasound (US) of preterm neonates, and achieve an improvement of around \(4\%\) in terms of the DICE accuracy and Jaccard index as compared to the standard-U-Net, while outperforming the recent best methods by \(2\%\). Code: https://github.com/jeya-maria-jose/KiU-Net-pytorch
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization

We consider the problem of cross-view geo-localization. The primary challenge is to learn the robust feature against large viewpoint changes. Existing benchmarks can help, but are limited in the number of viewpoints. Image pairs, containing two viewpoints, e.g., satellite and ground, are usually provided, which may compromise the feature learning. Besides phone cameras and satellites, in this paper, we argue that drones could serve as the third platform to deal with the geo-localization problem. In contrast to traditional ground-view images, drone-view images meet fewer obstacles, e.g., trees, and provide a comprehensive view when flying around the target place. To verify the effectiveness of the drone platform, we introduce a new multi-view multi-source benchmark for drone-based geo-localization, named University-1652. University-1652 contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. As the name implies, drone-view target localization intends to predict the location of the target place via drone-view images. On the other hand, given a satellite-view query image, drone navigation is to drive the drone to the area of interest in the query. We use this dataset to analyze a variety of off-the-shelf CNN features and propose a strong CNN baseline on this challenging dataset. The experiments show that University-1652 helps the model to learn viewpoint-invariant features and also has good generalization ability in real-world scenarios.
Arbitrary Style Transfer via Multi-Adaptation Network

Arbitrary style transfer is a significant topic with research value and application prospect. A desired style transfer, given a content image and referenced style painting, would render the content image with the color tone and vivid stroke patterns of the style painting while synchronously maintaining the detailed content structure information. Style transfer approaches would initially learn content and style representations of the content and style references and then generate the stylized images guided by these representations. In this paper, we propose the multi-adaptation network which involves two self-adaptation (SA) modules and one co-adaptation (CA) module:the SA modules adaptively disentangle the content and style representations, i.e., content SA module uses position-wise self-attention to enhance content representation and style SA module uses channel-wise self-attention to enhance style representation; the CA module rearranges the distribution of style representation based on content representation distribution by calculating the local similarity between the disentangled content and style features in a non-local fashion. Moreover, a new disentanglement loss function enables our network to extract main style patterns and exact content structures to adapt to various input images, respectively. Various qualitative and quantitative experiments demonstrate that the proposed multi-adaptation network leads to better results than the state-of-the-art style transfer methods.
VFL: A Verifiable Federated Learning With Privacy-Preserving for Big Data in Industrial IoT

Due to the strong analytical ability of big data, deep learning has been widely applied to model on the collected data in industrial Internet of Things (IoT). However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets, such as face recognition and medical systems. Recently, federated learning has received widespread attention, since it trains a model by only sharing gradients without accessing training sets. But existing research works reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this article, we propose the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the privacy gradients. If no more than <inline-formula><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula>-2 of <inline-formula><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula> participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL with high accuracy and efficiency.
A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter

The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00).