Reviewerly/exHarmony huggingface dataset:
Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is an active research area with recent works undertaken to embed accurate models of pedestrians social interactions and their contextual compliance into dynamic spatial graphs. However, existing works rely on spatial assumptions about the scene and dynamics, which entails a significant challenge to adapt the graph structure in unknown environments for an online system. In addition, there is a lack of assessment approach for the relational modeling impact on prediction performance. To fill this gap, we propose Social Trajectory Recommender-Gated Graph Recurrent Neighborhood Network (STR-GGRNN), which uses data-driven adaptive online neighborhood recommendation based on the contextual scene features and pedestrian visual cues. The neighborhood recommendation is achieved by online Nonnegative Matrix Factorization (NMF) to construct the graph adjacency matrices for predicting the pedestrians’ trajectories. Experiments based on widely-used datasets show that our method outperforms the state-of-the-art. Our best performing model achieves 12 cm ADE and $\sim 15$ cm FDE on ETH-UCY dataset.
Fedrec: Federated Learning of Universal Receivers Over Fading Channels

Wireless communications is often subject to channel fading. Various statistical models have been proposed to capture the inherent randomness in fading, and conventional model-based receiver designs rely on accurate knowledge of this underlying distribution, which, in practice, may be complex and intractable. In this work, we propose a neural network-based symbol detection technique for down-link fading channels, which is based on the maximum a-posteriori probability (MAP) detector. To enable training on a diverse ensemble of fading realizations, we propose a federated training scheme, in which multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics, while inducing a substantially reduced communication overhead in its training procedure compared to centralized training.
Exercise in virtual reality with a muscular avatar influences performance on a weightlifting exercise

Virtual Reality (VR) technology can be used to influence performance on endurance exercises. In this study, we focused on manipulating perception of own-body strength by exercising in VR as a muscular avatar. In this repeated-measure study, twenty-nine participants performed biceps curl exercise in a maximum repetitions protocol, up to exhaustion. The exercise was done either in VR as a muscular avatar, or without VR, in front of the mirror. Dependent variables were the number of exercise repetitions and self-reported exertion. We also controlled blood glucose level, perceived weight of the barbell and level of embodiment. Participants performed significantly more biceps curl repetitions in the VR condition (Z = -2.05, p &lt; .05) with an effect size of d = 0.36. The self-reported effort did not differ significantly between conditions. The results of this study may have an applied significance since number of exercise repetitions is an ecologically valid measure, directly relevant to real training protocols.
RGB-Infrared Person Re-Identification Via Multi-Modality Relation Aggregation and Graph Convolution Network

RGB-Infrared person Re-identification (RGB-IR Re-ID) task aims at using RGB (infrared) query image to matching person in infrared (RGB) gallery image, the large modality gap between two modalities makes the task very challenging. Different modality image’s feature lacked modality-shared information essentially, leading to large cross-modality discrepancy, existing method are not using modality relations to handle the discrepancy. In this paper, we proposed a novel Graph-based Modality-aware Relation Network (GMRN) to solve this problem, our method contains two parts: 1) fine-granularity multi-modality feature aggregation module to incorporate cross-modality information, 2) modality-aware graph convolution network utilize intra-modality relations to further learn discriminative features under two different modalities. Extensive experiments have made on two public RGB-IR Re-ID dataset SYSU-MMOI and RegDB, experiment results show that our method outperforms current state-of-the-art methods by a large margin. Our code is available at https://github.com/clsrsun/GMRN-ReID
Low-Rank And Sparse Tensor Representation For Multi-View Subspace Clustering

Learning an effective affinity matrix as the input of spectral clustering to achieve promising multi-view clustering is a key issue of subspace clustering. In this paper, we propose a low-rank and sparse tensor representation (LRSTR) method that learns the affinity matrix through a self-representation tensor and retains the similarity information of the view dimensions for multi-view subspace clustering. Specifically, the proposed LRSTR method imposes the tensor nuclear norm and tensor sparse constraints on self-representation tensor to characterize the relationship between views. The optimization model is solved under the framework of alternating direction method of multiplier. Experimental results on four datasets show that the proposed LRSTR method is better than several state-of-the-art methods.
BBNP: A Blockchain-Based Novel Paradigm for Fair and Secure Smart Grid Communications

As the future energy infrastructure, smart grid aims to overcome the disadvantages of traditional power grid, e.g., low efficiency and unstable service. However, the frequent collection and analysis of the user’s electricity data may bring various security and privacy threats. Besides, the traditional centralized data storage model in the smart grid is prone to the single point of failure. To address these challenges, in this article, for fair and secure smart grid communication, a blockchain-based novel paradigm, named BBNP, is proposed. Specifically, based on the pseudorandom function and auxiliary information generation and sharing technology, a lightweight data aggregation protocol is designed first to protect the user’s data privacy and ensure communication confidentiality. Then, a novel efficient authentication mechanism is proposed to generate and share session keys in a noninteractive way, which is leveraged for MAC authentication to achieve data integrity of the transmitted data. After that, based on the subjective logic reputation model, a blockchain node consensus mechanism is studied to efficiently store smart grid big data and effectively solve the single point failure problem. By constructing the long-term reputation model for consensus nodes (CNs) and integrating batch verification technology, the problems of CN fair selection and scalability of large-scale nodes are solved simultaneously. Finally, the performance evaluation indicates that BBNP outperforms the state-of-the-art similar schemes in computing complexity, communication cost, system availability, and fairness of block generation.
Image Enhanced Rotation Prediction for Self-Supervised Learning

The rotation prediction (Rotation) is a simple pretext-task for self-supervised learning (SSL), where models learn useful representations for target vision tasks by solving pretext-tasks. Although Rotation captures information of object shapes, it hardly captures information of textures. To tackle this problem, we introduce a novel pretext-task called image enhanced rotation prediction (IE-Rot) for SSL. IE-Rot simultaneously solves Rotation and another pretext-task based on image enhancement (e.g., sharpening and solarizing) while maintaining simplicity. Through the simultaneous prediction of rotation and image enhancement, models learn representations to capture the information of not only object shapes but also textures. Our experimental results show that IE-Rot models outperform Rotation on various standard benchmarks including ImageNet classification, PASCAL-VOC detection, and COCO detection/segmentation.
PixStabNet: Fast Multi-Scale Deep Online Video Stabilization with Pixel-Based Warping

Online video stabilizaton is increasingly needed for real-time applications such as live streaming, drone remote control, and video communication. We propose a multi-scale convolutional neural network (PixStabNet) which stabilizes video in real time without using future frames. Instead of calculating a global homography or multiple homographies, we estimate a pixel-based warping map to make the transformation of each pixel to achieve more precise modelling. In addition, we propose well-designed loss functions along with a two-stage training scheme to enhance network robustness. The quantitative result shows that our method outperforms other learning-based online methods in terms of stability with excellent geometric and temporal consistency. Moreover, to the best of our knowledge, the proposed algorithm is the most efficient approach for video stabilization. The models and results are available at: https://yu-ta-chen.github.io/PixStabNet.
Chat-XAI: A New Chatbot to Explain Artificial Intelligence

Explaining artificial intelligence (AI) to people is crucial since the large number of AI-generated results can greatly affect people's decision-making process in our daily life. Chatbots have great potential to serve as an effective tool to explain AI. Chatbots have the advantage of conducting proactive interactions and collecting customer requests with high availability and scalability. We make the first-step exploration of using chatbots to explain AI. We propose a chatbot explanation framework which includes proactive interactions on the explanation of the AI model and the explanation of the confidence level of AI-generated results. In addition, to understand what users would like to know about AI for further improvement on the chatbot design, our framework also collects users' requests about AI-generated results. Our preliminary evaluation shows the effectiveness of our chatbot to explain AI and gives us important design implications for further improvements.
Measuring higher-order photon correlations of faint quantum light: A short review

Normalized correlation functions provide expedient means for determining the photon-number properties of light. These higher-order moments, also called the normalized factorial moments of photon number, can be utilized both in the fast state classification and in-depth state characterization. Further, non-classicality criteria have been derived based on their properties. Luckily, the measurement of the normalized higher-order moments is often loss-independent making their observation with lossy optical setups and imperfect detectors experimentally appealing. The normalized higher-order moments can for example be extracted from the photon-number distribution measured with a true photon-number-resolving detector or accessed directly via manifold coincidence counting in the spirit of the Hanbury Brown and Twiss experiment. Alternatively, they can be inferred via homodyne detection. Here, we provide an overview of different kind of state classification and characterization tasks that take use of normalized higher-order moments and consider different aspects in measuring them with free-traveling light.
You look so different! Haven’t I seen you a long time ago?

Person re-identification (re-id) aims to match a query identity (ID) to an element in a gallery set, composed of elements collected from multiple cameras. Most of the existing re-id methods assume the short-term setting, where the query/gallery samples share the clothing style. In this setting, the optimal feature representations are based in the visual appearance of clothes, which considerably drops the identification performance for long-term settings. Having this problem in mind, we propose a model that learns long-term representations of persons by ignoring any features previously learned by a short-term re-id model, which naturally makes it invariant to clothing styles. We start by synthesizing a data set in which we distort the most relevant biometric information (based in face, body shape, height, and weight cues), keeping the short-term cues (color and texture of clothes) unchanged. This way, while the original data contains both ID-related and other varying features, the synthesized representations are composed mostly of short-term attributes. Then, the key to obtaining stable long-term representations is to learn embeddings of the original data that maximize the dissimilarity with the previously inferred short-term embeddings. In practice, we use the synthetic data to learn a model that embeds the ID-unrelated features and then learn a second model from the original data, where long-term embeddings are obtained, keeping their independence with respect to the previously obtained ID-unrelated features. Our experiments were performed on three challenging cloth-changing sets (LTCC, PRCC, and NKUP) and the results support the effectiveness of the proposed method, for both short and long-term re-id settings. The source code is available at https://github.com/Ehsan-Yaghoubi/You-Look-So-Different-Haven-t-I-Seen-You-a-Long-Time-Ago?
Automatic Recharging Path Planning for Cleaning Robots

To solve the problem of automatic recharging path planning for cleaning robots in complex industrial environments, this paper proposes two environmental path planning types based on designated charging location and multiple charging locations. First, we use the improved Maklink graph to plan the complex environment; then, we use the Dijkstra algorithm to plan the global path to reduce the complex two-dimensional path planning to one dimension; finally, we use the improved fruit fly optimization algorithm (IFOA) to adjust the path nodes for shorting the path length. Simulation experiments show that the effectiveness of using this path planning method in a complex industrial environment enables the cleaning robot to select a designated location or the nearest charging location to recharge when the power is limited. The proposed improved algorithm has the characteristics of a small amount of calculation, high precision, and fast convergence speed.
AAN-Face: Attention Augmented Networks for Face Recognition

Convolutional neural networks are capable of extracting powerful representations for face recognition. However, they tend to suffer from poor generalization due to imbalanced data distributions where a small number of classes are over-represented (e.g. frontal or non-occluded faces) and some of the remaining rarely appear (e.g. profile or heavily occluded faces). This is the reason why the performance is dramatically degraded in minority classes. For example, this issue is serious for recognizing masked faces in the scenario of ongoing pandemic of the COVID-19. In this work, we propose an Attention Augmented Network, called AAN-Face, to handle this issue. First, an attention erasing (AE) scheme is proposed to randomly erase units in attention maps. This well prepares models towards occlusions or pose variations. Second, an attention center loss (ACL) is proposed to learn a center for each attention map, so that the same attention map focuses on the same facial part. Consequently, discriminative facial regions are emphasized, while useless or noisy ones are suppressed. Third, the AE and the ACL are incorporated to form the AAN-Face. Since the discriminative parts are randomly removed by the AE, the ACL is encouraged to learn different attention centers, leading to the localization of diverse and complementary facial parts. Comprehensive experiments on various test datasets, especially on masked faces, demonstrate that our AAN-Face models outperform the state-of-the-art methods, showing the importance and effectiveness.
Univariate model for hour ahead multi-step solar irradiance forecasting

The present work considers the task of forecasting the GHI for 1-11h ahead forecast horizon from the previous GHI time-series data. Dealing with the non-stationary and non-linear time series is a challenging and vital part of time-series forecasting. This paper aims to forecast the GHI, comparing the performance of two time-series decomposition techniques, i.e., EMD and EEMD. These techniques divide the original time series into a set of orthogonal series, which are termed as Intrinsic Mode Functions (IMFs). Further, the problem is converted into a supervised learning problem (input-output problem). A feature selection algorithm selects the highly correlated lag variables to reduce the complexity of the model. Finally, the forecasting is performed by utilizing machine learning algorithms i.e., Support Vector Machine (SVM) and ensemble learning-based Random Forest (RF). The parameters of ML algorithms are optimized for each IMF, and the final result is obtained by summing all the forecasted IMFs. This work also discusses the annual and seasonal variation of the forecasting model’s performance.
Tracking of Multiple Maneuvering Random Hypersurface Extended Objects Using High Resolution Sensors

With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.
Autoencoder-Based Unequal Error Protection Codes

We present a novel autoencoder-based approach for designing codes that provide unequal error protection (UEP) capabilities.The proposed approach, based on a generalization of an autoencoder loss function, provides a versatile framework for the design of message-wise and bit-wise UEP codes.Using an associated weight vector, the generalized loss function can be used to trade off error probabilities corresponding to different importance classes and to explore the region of achievable error probabilities.For message-wise UEP, we compare the proposed autoencoder-based UEP codes with a union of random coset codes.For bit-wise UEP, the proposed codes are compared with UEP rateless spinal codes and the superposition of random Gaussian codes.In all cases, the autoencoder-based codes show superior performance while providing design simplicity and flexibility in trading off error protection among different importance classes.
Neural Symbolic Representation Learning for Image Captioning

Traditional image captioning models mainly rely on one encoder-decoder architecture to generate one natural sentence for a given image. Such an architecture mostly uses deep neural networks to extract the neural representations of the image while ignoring the information of abstractive concepts as well as their intertwined relationships conveyed in the image. To this end, to comprehensively characterize the image content and bridge the gap between neural representations and high-level abstractive concepts, we make the first attempt to investigate the ability of neural symbolic representation of the image for the image captioning task. We first parse and convert a given image to neural symbolic representation in the form of an attributed relational graph, with the nodes denoting the abstractive concepts and the branches indicating the relationships between connected nodes, respectively. By performing computations over the attributed relational graph, the neural symbolic representation evolves step by step, with the node and branch representations as well as their corresponding importance weights transiting step by step. Empirically, extensive experiments validate the effectiveness of the proposed method. It enables a more comprehensive understanding of the given image by integrating the neural representation and neural symbolic representation, with the state-of-the-art results being achieved on both the MSCOCO and Flickr30k datasets. Besides, the proposed neural symbolic representation is demonstrated to better generalize to other domains with significant performance improvements compared with existing methods on the cross domain image captioning task.
Mobile Cloud Computing Framework for Securing Data

Mobile cloud computing provides on-demand resources. The architecture of mobile cloud computing is composed of a cluster of mobile devices. It is gaining popularity because of its cost-effectiveness and availability. There are numerous security issues like data breaches due to many data being stored with all of its benefits. According to recent searches, about 70% of the operations are now performed on the cloud. Data loss from mobile devices, unsecured exchange of information through rogue access points are the security threats of mobile cloud computing. Data breaches, account hijacking, denial of services, loss of encryption key are additional security and privacy threats. Examples of mobile cloud applications are Google maps, GMAIL, and Cisco’s WebEx on iPad. The security issues mentioned before in mobile cloud computing are now applying more complicated authentication schemes. We can secure the architecture by integrating a multi-agent system. The simulations used for the analysis are OPNET and SPSS, where OPNET is used to evaluate and develop a network and information security model for cloud computing security, and SPSS be used to build a statistical analysis of how much this is affecting and how much it occurs. In this paper, the protocols to implement different kinds of multi- factor authentication are discussed.
Data synthesis method preserving correlation of features

Abundant data are essential for improving the performance of machine learning algorithms. Thus, if only limited data are available, data synthesis can be used to enlarge datasets. Data synthesis methods based on the covariance matrix are useful because of their fast data synthesis capabilities. However, artificial datasets generated via classical techniques show statistical discrepancies when compared to original datasets. To address this problem, we developed a new data synthesis method that preserves the correlation (between features) observed in the original dataset. This preservation was realized by considering not only the correlation but also the random noises used in data synthesis process. This method was applied to various biosignals (i.e., electrocortiography, electromyogram, and electrocardiogram), wherein data points are insufficient. Several classifiers (i.e., convolutional neural network, support vector machine, and k-nearest neighbor) were used to verify that the classification accuracy can be improved by the proposed data synthesis method.