Reviewerly/exHarmony huggingface dataset:
Chewing gum reduces visually induced motion sickness

Visually induced motion sickness (VIMS) is a common side-effect of exposure to virtual reality (VR). Its unpleasant symptoms may limit the acceptance of VR technologies for training or clinical purposes. Mechanical stimulation of the mastoid and diverting attention to pleasant stimuli-like odors or music have been found to ameliorate VIMS. Chewing gum combines both in an easy-to-administer fashion and should thus be an effective countermeasure against VIMS. Our study investigated whether gustatory-motor stimulation by chewing gum leads to a reduction of VIMS symptoms. 77 subjects were assigned to three experimental groups (control, peppermint gum, and ginger gum) and completed a 15-min virtual helicopter flight, using a VR head-mounted display. Before and after VR exposure, we assessed VIMS with the Simulator Sickness Questionnaire (SSQ), and during the virtual flight once every minute with the Fast Motion Sickness Scale (FMS). Chewing gum (peppermint gum: M = 2.44, SD = 2.67; ginger gum: M = 2.57, SD = 3.30) reduced the peak FMS scores by 2.05 (SE = 0.76) points as compared with the control group (M = 4.56, SD = 3.52), p < 0.01, d = 0.65. Additionally, taste ratings correlated slightly negatively with both the SSQ and the peak FMS scores, suggesting that pleasant taste of the chewing gum is associated with less VIMS. Thus, chewing gum may be useful as an affordable, accepted, and easy-to-access way to mitigate VIMS in numerous applications like education or training. Possible mechanisms behind the effect are discussed.
A blockchain security scheme to support fog-based internet of things

This paper proposes a fog-based secure data sharing framework for IoT devices that uses blockchain mechanism for this purpose. Fog computation is a popular paradigm that facilitates computation/storage facilities closer to the users and placed in between cloud environment and the users. Here, the IoT devices share data among each other through a secure mechanism. We propose two software agents: Software agent for Network formation and Monitoring (SAN) and Software agent for Blockchain formation and monitoring (SAB) placed in the fog node where SAN forms/monitors the network of IoT devices and SAB implements the security framework for blockchain implementation. Here, three different versions of AES (128/192/256) are used for group key sharing. Group key is used for encryption/decryption of the data present in a block of a blockchain. An AES 128 based Proof of Work (PoW) is used for block addition by an authorized IoT device. SHA 256 is used for implementing hashing in blockchain. Three different systems namely System 1, 2, and 3 are considered for the experiment under different settings of design parameters. A comparison between different settings is depicted in the result section. The execution time (in seconds) is used as an evaluation parameter. The performance is found to be best in case of System 3 configuration. The time requirement is found to be a monotonically increasing function of block size and AES key length.
Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection

Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals. The three proposed models include an attention autoencoder that maps input data to a lower-dimensional latent representation with maximum feature retention, and a reconstruction decoder with minimum remodeling loss. The autoencoder has an embedded attention module at the bottleneck to learn the salient activations of the encoded distribution. Additionally, a variational autoencoder (VAE) and a long short-term memory (LSTM) network is designed to learn the Gaussian distribution of the generative reconstruction and time-series sequential data analysis. The three proposed models displayed outstanding ability to detect anomalies on the evaluated five thousand electrocardiogram (ECG5000) signals with 99% accuracy and 99.3% precision score in detecting healthy heartbeats from patients with severe congestive heart failure.
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective

In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse real-world applications. To improve the model capacity, besides designing aggregation operations, GNN topology design is also very important. In general, there are two mainstream GNN topology design manners. The first one is to stack aggregation operations to obtain the higher-level features but easily got performance drop as the network goes deeper. Secondly, the multiple aggregation operations are utilized in each layer which provides adequate and independent feature extraction stage on local neighbors while are costly to obtain the higher-level information. To enjoy the benefits while alleviating the corresponding deficiencies of these two manners, we learn to design the topology of GNNs in a novel feature fusion perspective which is dubbed F$^2$GNN. To be specific, we provide a feature fusion perspective in designing GNN topology and propose a novel framework to unify the existing topology designs with feature selection and fusion strategies. Then we develop a neural architecture search method on top of the unified framework which contains a set of selection and fusion operations in the search space and an improved differentiable search algorithm. The performance gains on eight real-world datasets demonstrate the effectiveness of F$^2$GNN. We further conduct experiments to show that F$^2$GNN can improve the model capacity while alleviating the deficiencies of existing GNN topology design manners, especially alleviating the over-smoothing problem, by utilizing different levels of features adaptively.
A Novel Variational Model for Detail-Preserving Low-Illumination Image Enhancement

The images captured under low-light conditions often show low sharpness and low contrast, which inevitably influences the quality of the images and degrades the performance of many computer vision systems designed for pattern recognition. Although many low-light image enhancement methods have been put forward to deal with the problem, the existing methods may bring about contrast distortion and insufficient or excessive enhancement of the source images. To tackle this challenge, a novel variational enhancement model is presented. This method focuses on enhancing the visibility of low-illumination images while preserving the details and the texture information. To begin with, we introduce a new variational model, which includes novel regularization terms for the illumination, the reflectance and the noise component. Specifically, we use the l1 norm to implement the piece-wise continuousness operation on the reflectance map, present a novel structure-preserving regularizer to assure the scale-aware structure smoothness of the illumination map, and take noise map estimation into account to suppress the noise of the given image. Then, an alternating direction method of multipliers (ADMM) is utilized to solve the specified problem accurately. The extensive experimental results have shown that the proposed approach yields comparative and even better performance in comparison with some state-of-the-art techniques in both qualitative and quantitative evaluations.
A co-evolutionary approach to interpretable reinforcement learning in environments with continuous action spaces

Machine learning (ML) has lately achieved impressive breakthroughs in several fields, enabling a plethora of exciting applications. However, mainstream ML techniques often have an undesirable property: they are not directly understandable by humans, thus humans cannot trust them in high-stakes or life-critical scenarios. A subfield of AI called interpretable AI (IAI) addresses this problem: generating models that are easy to understand for humans and, consequently, trustworthy. While several approaches apply IAI techniques to reinforcement learning problems, addressing the case in which an agent has to act in a continuous action space is still an open question. In this work, we propose a cooperative co-evolutionary approach based on grammatical evolution, Q-learning, and the univariate marginal distribution algorithm, specifically designed to train IAI agents (in the form of binary decision trees) capable of acting in environments with continuous action spaces. The experimental results show that our method is able to solve two well-known OpenAI Gym test cases reaching state-of-the-art performance. Moreover, a quantitative post hoc analysis reveals that the obtained solutions are more interpretable than those reported in the literature.
Efficient Instance Segmentation Paradigm for Interpreting SAR and Optical Images

Instance segmentation in remote sensing images is challenging due to the object-level discrimination and pixel-level segmentation for the objects. In remote sensing applications, instance segmentation adopts the instance-aware mask, rather than horizontal bounding box and oriented bounding box in object detection, or category-aware mask in semantic segmentation, to interpret the objects with the boundaries. Despite these distinct advantages, versatile instance segmentation methods are still to be discovered for remote sensing images. In this paper, an efficient instance segmentation paradigm (EISP) for interpreting the synthetic aperture radar (SAR) and optical images is proposed. EISP mainly consists of the Swin Transformer to construct the hierarchical features of SAR and optical images, the context information flow (CIF) for interweaving the semantic features from the bounding box branch to mask branch, and the confluent loss function for refining the predicted masks. Experimental conclusions can be drawn on the PSeg-SSDD (Polygon Segmentation—SAR Ship Detection Dataset) and NWPU VHR-10 instance segmentation dataset (optical dataset): (1) Swin-L, CIF, and confluent loss function in EISP acts on the whole instance segmentation utility; (2) EISP* exceeds vanilla mask R-CNN 4.2% AP value on PSeg-SSDD and 11.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The poorly segmented masks, false alarms, missing segmentations, and aliasing masks can be avoided to a great extent for EISP* in segmenting the SAR and optical images; (4) EISP* achieves the highest instance segmentation AP value compared to the state-of-the-art instance segmentation methods.
Flexible neural network for fast and accurate road scene perception

Abstract Accurate object detection on the road is the most important requirement of autonomous vehicles. Extensive work has been accomplished for car, pedestrian, and cyclist detection; however, comparatively, very few efforts have been put into 2D object detection. In this article, a dynamic approach is investigated to design a perfect unified neural network that could achieve the best results based on our available hardware. The proposed architecture is based on CSPNet for feature extraction in an end-to-end way. The net extracts visual features by using backbone subnet, visual object detection is based on a feature pyramid network (FPN). In order to increase the net flexibility, an auto-anchor generating method is applied to the detection layer that makes the net suitable for any datasets. For fine-tuning the net, activation, optimization, and loss functions are considered along with multiple check points. The proposed net is trained and tested based on the benchmark KITTI dataset. Our extensive experiments show that the proposed model for visual object detection is superior to others, where other nets output very low accuracy for pedestrian and cyclist detection, our proposed model achieves 99.3% recall rate based on our dataset.
Learning Scene Dynamics from Point Cloud Sequences

Understanding 3D scenes is a critical prerequisite for autonomous agents. Recently, LiDAR and other sensors have made large amounts of data available in the form of temporal sequences of point cloud frames. In this work, we propose a novel problem—sequential scene flow estimation (SSFE)—that aims to predict 3D scene flow for all pairs of point clouds in a given sequence. This is unlike the previously studied problem of scene flow estimation which focuses on two frames. We introduce the SPCM-Net architecture, which solves this problem by computing multi-scale spatiotemporal correlations between neighboring point clouds and then aggregating the correlation across time with an order-invariant recurrent unit. Our experimental evaluation confirms that recurrent processing of point cloud sequences results in significantly better SSFE compared to using only two frames. Additionally, we demonstrate that this approach can be effectively modified for sequential point cloud forecasting (SPF), a related problem that demands forecasting future point cloud frames. Our experimental results are evaluated using a new benchmark for both SSFE and SPF consisting of synthetic and real datasets. Previously, datasets for scene flow estimation have been limited to two frames. We provide non-trivial extensions to these datasets for multi-frame estimation and prediction. Due to the difficulty of obtaining ground truth motion for real-world datasets, we use self-supervised training and evaluation metrics. We believe that this benchmark will be pivotal to future research in this area. All code for benchmark and models will be made accessible at ( https://github.com/BestSonny/SPCM ).
BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks

We introduce an area/energy-efficient precision-scalable neural network accelerator architecture. Previous precision-scalable hardware accelerators have limitations such as the under-utilization of multipliers for low bit-width operations and the large area overhead to support various bit precisions. To mitigate the problems, we first propose a bitwise summation, which reduces the area overhead for the bit-width scaling. In addition, we present a channel-wise aligning scheme (CAS) to efficiently fetch inputs and weights from on-chip SRAM buffers and a channel-first and pixel-last tiling (CFPL) scheme to maximize the utilization of multipliers on various kernel sizes. A test chip was implemented in 28-nm CMOS technology, and the experimental results show that the throughput and energy efficiency of our chip are up to 7.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and 1.64 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> higher than those of the state-of-the-art designs, respectively. Moreover, additional 1.5–3.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> throughput gains can be achieved using the CFPL method compared to the CAS.
Sequential inter-hop graph convolution neural network (SIhGCN) for skeleton-based human action recognition

Skeleton-based human action recognition has attracted a lot of attention due to its capability and potential to provide more information than just using the sequence of RGB images. The use of Graph Convolutional Neural Network (GCN) becomes more popular since it can model the human skeleton very well. However, the existing GCN architectures ignore the different levels of importance on each hop during the feature aggregation and use the final hop information for further calculation, resulting in considerable information loss. Besides, they use the standard Laplacian or adjacency matrix to encode the property of a graph into a set of vectors which has a limitation in terms of graph invariants. In this work, we propose a Sequential Inter-hop Graph Convolution Neural Network (SIhGCN) which can capture salient graph information from every single hop rather than the final hop only and our work utilizes the normalized Laplacian matrix which provides better representation since it relates well to graph invariants. The proposed method is validated on two large datasets, NTU-RBG + D and Kinetics, to demonstrate the superiority of our proposed method.
Enhanced Edge Detection Using SR-Guided Threshold Maneuvering and Window Mapping: Handling Broken Edges and Noisy Structures in Canny Edges

Preserving edges in a noisy environment is a challenging task as even some of the latest endto-end deep learning (DL) algorithms continue to struggle in achieving high pixel-level accuracy.As the Canny Edge Detector (CED) continues to be one of the most popular edge detection operators, this paper presents an enhanced CED using Stochastic Resonance (SR) guided threshold maneuvering and window mapping, which takes the same input parameter set as that of the conventional Canny but produces the edge map with better-connected edges and reduced noise.The SR-based analysis informs the steps that should be followed to enhance the performance of the classical CED.We also propose a new measure for efficient edge detection; a unique, efficient way of edge content extraction and its combination for various channels; and a framework to handle repercussions of the randomness of the noise.Since the proposed solution comes in the form of a modular patch-based framework, it can be easily incorporated into other algorithm developments.Qualitative and quantitative results are presented along with the BSDS500 & BIPED benchmarking to showcase the proposed algorithm's effectiveness.On BIPED benchmarking, our algorithm gives the human-level performance (F1 score .79),which is appreciable considering that it is a non-DL-based algorithm.
Adaptive Reversible Data Hiding With Contrast Enhancement Based on Multi-Histogram Modification

Reversible data hiding with contrast enhancement (RDH-CE) is proposed to aim at improving the contrast of images while embedding data. After deeply analyzing and studying the RDH-CE method proposed by Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , it is found that there are three main problems in their method. Firstly, their method ignores the fact that the left-bottom neighbors of a pixel contribute to increasing the accuracy of the local-complexity evaluation. Secondly, Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s method employs K-means clustering in combination with one single feature to split pixels into five classes, leading to a weak clustering performance. Finally, Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s method uniformly embedded 1 bit into each pixel irrespective of the local complexity, and thus, the embedding capacity is limited. To this end, an improved RDH-CE method is proposed in this paper. Considering that the complexity evaluation plays a vital role in both contrast enhancement and payload increase, we improve embedding performance by including left-bottom neighbors of a pixel into complexity evaluation. Compared with one single feature in Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s method, we extract multiple features to assist K-means clustering such that a better cluster performance is obtained. In addition, our method provides an adaptive pixel modification strategy based on the local complexity, in which we can adaptively embed 1 or 2 bits into a pixel according to the corresponding complexity. By these three improvements, our method is capable of achieving high capacity while enhancing contrast. The experimental results also show that our method achieves higher accuracy of the complexity evaluation, larger payload, and better local contrast enhancement than those existing RDH-CE related methods.
Augmented reality enhanced experiences in restaurants: Scale development and validation

This study aims to develop a measurement scale to assess consumer's AR-enhanced experiences in a restaurant setting. This study employed multiple waves of data collection. In the first stage, 84 respondents were approached to inquire about AR-based experiences in a restaurant setting. Based on the qualitative data and subsequent checks, 31 items were included in the pool, sent to 7 subject experts for assessment, resulting in 25 items. Based on the data collected from 346 respondents, these 25 items were then subjected to an Exploratory Factor Analysis, resulting in 19 items under three dimensions, including (1) Utilitarian, (2) Hedonics, and (3) Social. This factorial structure was then subjected to a Confirmatory Factor Analysis (CFA) for validation using the second wave of data collected from 664 respondents. Results showed a good model fit for a three-dimensional structure with no higher-order construct. Thus, this is the first study to develop and validate a measurement scale to assess consumer's AR-enhanced experiences to explore one of the most recent technologies that are just finding its way into the hospitality industry.
Attention-based CNNs for Image Classification: A Survey

Abstract Deep learning techniques as well as CNNs can learn power context information, they have been widely applied in image recognition. However, deep CNNs may reply on large width and large depth, which may increase computational costs. Attention mechanism fused into CNNs can address this problem. In this paper, we summary an attention mechanism acts a CNN for image classification. Firstly, the survey shows the development of CNNs for image classification. Then, we illustrate basis of CNNs and attention mechanisms for image classification. Next, we give the main architecture of CNNs with attentions, public and our collected datasets, experimental results in image classification. Finally, we point out potential research points, challenges attention-based for image classification and summary the whole paper.
Hypergraph Convolutional Network for Group Recommendation

Group activities have become an essential part of people’s daily life, which stimulates the requirement for intensive research on the group recommendation task, i.e., recommending items to a group of users. Most existing works focus on aggregating users’ interests within the group to learn group preference. These methods are faced with two problems. First, these methods only model the user preference inside a single group while ignoring the collaborative relations among users and items across different groups. Second, they assume that group preference is an aggregation of user interests, and factually a group may pursue some targets not derived from users’ interests. Thus they are insufficient to model the general group preferences which are independent of existing user interests. To address the above issues, we propose a novel dual channel Hypergraph Convolutional network for group Recommendation (HCR), which consists of member-level preference network and group-level preference network. In the member-level preference network, in order to capture cross-group collaborative connections among users and items, we devise a member-level hypergraph convolutional network to learn group members’ personal preferences. In the group-level preference network, the group’s general preference is captured by a group-level graph convolutional network based on group similarity. We evaluate our model on two real-world datasets and the experimental results show that the proposed model significantly and consistently outperforms state-of-the-art group recommendation techniques.