Reviewerly/exHarmony huggingface dataset:
Lotse: A Practical Framework for Guidance in Visual Analytics

Co-adaptive guidance aims to enable efficient human-machine collaboration in visual analytics, as proposed by multiple theoretical frameworks. This paper bridges the gap between such conceptual frameworks and practical implementation by introducing an accessible model of guidance and an accompanying guidance library, mapping theory into practice. We contribute a model of system-provided guidance based on design templates and derived strategies. We instantiate the model in a library called Lotse that allows specifying guidance strategies in definition files and generates running code from them. Lotse is the first guidance library using such an approach. It supports the creation of reusable guidance strategies to retrofit existing applications with guidance and fosters the creation of general guidance strategy patterns. We demonstrate its effectiveness through first-use case studies with VA researchers of varying guidance design expertise and find that they are able to effectively and quickly implement guidance with Lotse. Further, we analyze our framework's cognitive dimensions to evaluate its expressiveness and outline a summary of open research questions for aligning guidance practice with its intricate theory.
SCAM! Transferring Humans Between Images with Semantic Cross Attention Modulation

A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not only transferring the pose but also the appearance and background. In this work, we introduce SCAM (Semantic Cross Attention Modulation), a system that encodes rich and diverse information in each semantic region of the image (including foreground and background), thus achieving precise generation with emphasis on fine details. This is enabled by the Semantic Attention Transformer Encoder that extracts multiple latent vectors for each semantic region, and the corresponding generator that exploits these multiple latents by using semantic cross attention modulation. It is trained only using a reconstruction setup, while subject transfer is performed at test time. Our analysis shows that our proposed architecture is successful at encoding the diversity of appearance in each semantic region. Extensive experiments on the iDesigner, CelebAMask-HD and ADE20K datasets show that SCAM outperforms competing approaches; moreover, it sets the new state of the art on subject transfer.
Dynamic connectedness among regional FinTech indices in times of turbulences

This study examines the connectedness among several regional FinTech indices and analyses the impact of Russia’s invasion of Ukraine on the dynamic spillovers. We employ the time-varying parameter vector autoregression and time-frequency connectedness network approach using data spanning from 2 September 2021 until 4 April 2022. We find that the returns and volatility spillovers among FinTech indices burst with the outbreak of the invasion. During this invasion, Asia-Pacific and Japan FinTech appeared to be the largest recipients of the spillovers. In contrast, Europe and North America FinTech appeared to be the largest transmitters of spillovers. The short-term effect among regional FinTech indices is stronger than the long-term relationship.
Entanglement Verification of Hyperentangled Photon Pairs

We experimentally investigate the properties of hyperentangled states displaying simultaneous entanglement in multiple degrees of freedom and find that Bell tests beyond the standard Clauser-Horne-Shimony-Holt inequality can reveal a higher-dimensional nature in a device-independent way. Specifically, we show that hyperentangled states possess more than just simultaneous entanglement in separate degrees of freedom but also entanglement in a higher-dimensional Hilbert space. We also verify the steerability of hyperentangled quantum states by steering different photonic degrees of freedom.
Phase Covariant Channel: Quantum Speed Limit of Evolution

Abstract The quantum speed of evolution for the phase covariant map is investigated. This involves absorption, emission, and dephasing processes. The maps under various combinations of the above processes are considered to investigate the effect of phase covariant maps on quantum speed limit time. For absorption‐free phase covariant maps, combinations of dissipative and CP‐(in)divisible (non)‐Markovian dephasing noises are considered. The role of coherence‐mixedness balance on the speed limit time is checked in the presence of both vacuum and finite temperature effects. The rate at which Holevo's information changes and the action quantum speed of evolution for specific cases of the phase covariant map are also investigated.
Risk classification of contaminated sites - Comparison of the Swedish and the German method

To classify contaminated sites into different risk classes, many different methods exist in Europe and worldwide. However, no systematic comparison of European risk classification methods has been carried out so far to carve out the advantages and disadvantages of the methods and to homogenize them. To address this research gap, this study aims at comparing the Swedish Method for Inventories of Contaminated Sites (MIFO) with the German Individual Assessment of Contaminated Sites Method (EB) from the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG) regarding the risk class categorization of 51 contaminated sites. The results revealed that with the MIFO 39% fewer contaminated sites are assigned to risk classes 1 and 2 and thus, subject to remediation compared to the EB. Moreover, in comparison to the EB, the MIFO showed a lower comparability, traceability, and a larger room for interpretation, which could be related to the lack of a quantitative approach such as a point or ranking system in the MIFO. Hence, we recommend providing the MIFO and other methods that lack a quantitative approach with a point and/or ranking system, similar to the EB, to increase their objectivity for the risk class categorization of contaminated sites.
Efficient Video Deblurring Guided by Motion Magnitude

Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur. An intuitive approach for video deblurring includes two steps: a) detecting the blurry region in the current frame; b) utilizing the information from clear regions in adjacent frames for current frame deblurring. To realize this process, our idea is to detect the pixel-wise blur level of each frame and combine it with video deblurring. To this end, we propose a novel framework that utilizes the motion magnitude prior (MMP) as guidance for efficient deep video deblurring. Specifically, as the pixel movement along its trajectory during the exposure time is positively correlated to the level of motion blur, we first use the average magnitude of optical flow from the high-frequency sharp frames to generate the synthetic blurry frames and their corresponding pixel-wise motion magnitude maps. We then build a dataset including the blurry frame and MMP pairs. The MMP is then learned by a compact CNN by regression. The MMP consists of both spatial and temporal blur level information, which can be further integrated into an efficient recurrent neural network (RNN) for video deblurring. We conduct intensive experiments to validate the effectiveness of the proposed methods on the public datasets. Our codes are available at https://github.com/sollynoay/MMP-RNN .
Dual-Semantic Consistency Learning for Visible-Infrared Person Re-Identification

Visible-Infrared person Re-Identification (VI-ReID) conducts comprehensive identity analysis on non-overlapping visible and infrared camera sets for intelligent surveillance systems, which face huge instance variations derived from modality discrepancy. Existing methods employ different kinds of network structure to extract modality-invariant features. Differently, we propose a novel framework, named Dual-Semantic Consistency Learning Network (DSCNet), which attributes modality discrepancy to channel-level semantic inconsistency. DSCNet optimizes channel consistency from two aspects, fine-grained inter-channel semantics, and comprehensive inter-modality semantics. Furthermore, we propose Joint Semantics Metric Learning to simultaneously optimize the distribution of the channel-and-modality feature embeddings. It jointly exploits the correlation between channel-specific and modality-specific semantics in a fine-grained manner. We conduct a series of experiments on the SYSU-MM01 and RegDB datasets, which validates that DSCNet delivers superiority compared with current state-of-the-art methods. On the more challenging SYSU-MM01 dataset, our network can achieve 73.89% Rank-1 accuracy and 69.47% mAP value. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/bitreidgroup/DSCNet</uri> .
A Decoupled Head and Coordinate Attention Detection Method for Ship Targets in SAR Images

Currently, deep learning-based synthetic aperture radar (SAR) image ship target detection methods have been widely used in the field of SAR image ship detection. However, these methods suffer from high model complexity and poor performance when detecting small dense targets. To address this problem, this paper proposes a ship target detection algorithm based on the improved YOLO (You Only Look Once) algorithm. In addition, considering the real-time requirements and computational constraints in mobile applications, the YOLOv4 network is modified to make it more lightweight. Moreover, decoupled head and coordinate attention are introduced to preserve YOLOv4’s superb detection performance as much as possible after lightweighting it. First, as the detection head of the YOLOv4 degrades the performance, this study decouples the classification and regression tasks. Second, since the channel attention mechanism ignores the spatial position information, coordinate attention is used to obtain long-range dependencies and accurate position information in the spatial domain. Moreover, the effects of the coordinate attention mechanism in different hierarchical YOLOv4 structures are analyzed. Furthermore, on the basis of the YOLOv4 backbone, another lightweight backbone is added to the model structure to improve model detection performance. Experimental results on the SAR ship detection dataset (SSDD) and the high-resolution SAR images dataset (HRSID) demonstrate that the proposed method can achieve high detection accuracy in complex scenes. The proposed lightweight model has fewer parameters compared to the original YOLOv4 structure. Furthermore, two massive SAR images are used to confirm the proposed model’s migration application performance. The experimental results demonstrate that the proposed model has a strong migration ability and can be used in maritime monitoring.
Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system.
A TCN-Based Hybrid Forecasting Framework for Hours-Ahead Utility-Scale PV Forecasting

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting.The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven cloud-event forecasting (CF) model.Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the CF model to achieve more accurate forecast of intrahour PV power drops, and iii) reconciling TF and CF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved.To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the CF model on capturing large power fluctuations caused by cloud movements.The framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina.Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by approximately 20% compared with state-of-the-art methods.
A new approach to software defect prediction based on convolutional neural network and bidirectional long short-term memory

Software defect prediction (SDP) plays an important role in improving software quality and reliability while reducing software maintenance cost. The problem in the field of SDP is how to determine the defective source code with high accuracy. To build more accurate predictor models, a lot of features are presented, e.g., static code features, social network features, and process features, etc. Several machine learning (ML) and deep learning (DL) algorithms have been developed and adopted to identify and remove defects from the source code, where previous studies have proved that DL algorithms are promising techniques for predicting software defects. The aim of this study is to investigate the prediction performance of two DL algorithms namely, Convolutional Neural Network (CNN) and Bidirectional Long short-term memory (BI-LSTM) in the domain of SDP. To establish the effectiveness of the proposed approach, the experiments were conducted on the available benchmark datasets which obtained from open-source java projects GitHub repository and the models were evaluated by applying seven evaluation metrics which are accuracy, precision, recall, f-measure, matthews correlation coefficient (MCC), area under the ROC curve (AUC), mean square error (MSE). We found out that the best accuracy obtained on training dataset is 81% by using CNN model, while the best accuracy obtained on validation dataset is 80% by using BI-LSTM model. The best AUC obtained on training dataset is 88% by using CNN model, while the best AUC obtained on validation dataset is 83% by using the both models. It is nearly impossible to rule which model is better than the other so every model can be analyzed separately and the best model according to the problem at hand can be used, therefore, based on the problem of this study, The evaluation results show the effectiveness of our proposed models based on standard performance evaluation criteria.
Live360: Viewport-Aware Transmission Optimization in Live 360-Degree Video Streaming

Live 360-degree video streaming is emerging as the next disruptive innovation in the coming era. However, compared to conventional live video streaming, it is much more challenging to provide high-quality live 360-degree video streaming due to high bandwidth demand, stringent latency requirement, and diverse user viewports. In this paper, we propose a viewport-aware live 360-degree video streaming framework called Live360 to optimize end-to-end video stream transmission. To make the problem tractable, we decompose the transmission optimization problem into two sub-problems, and optimize upstream and downstream transmission in an asynchronous way. We prioritize 360-degree cameras according to users’ real-time viewing interests and upload attractive contents with a higher bitrate. We also re-define the metric of QoE (Quality-of-Experience) for live 360-degree video viewers, and solve the optimization problems efficiently using dynamic programming. Extensive experiments show that Live360 is lightweight and significantly outperforms other state-of-the-art methods in terms of various QoE metrics. The average QoE of Live360 is twice that of other baseline methods.
Pythagorean-Hodograph curves-based trajectory planning for pick-and-place operation of Delta robot with prescribed pick and place heights

Abstract In this paper, a Pythagorean-Hodograph (PH) curve-based pick-and-place operation trajectory planning method for Delta parallel robots is proposed, which realizes the flexible control of pick-and-place operations to meet the requirements of various practical scenarios. First, according to the geometric relationship of pick-and-place operation path, different pick-and-place operations are classified. Then trajectory planning is carried out for different situations, respectively, and in each case, the different polynomial motion laws adopted by the linear motion segment and the curved motion segment are solved. Trajectory optimization is performed with the motion period as optimization objective. The proposed method is easier to implement, and at the same time satisfies the safety, optimization, mobility, and stability of the robot; that is, the proposed method realizes obstacle avoidance, optimal time, flexible control of the robot trajectory, and stable motion. Simulations and experiments verify the effectiveness of the method proposed in this paper. The proposed method can not only realize the fast, accurate, and safe operation in intelligent manufacturing fields such as rapid classification, palletizing, grasping, warehousing, etc., but its research route can also provide a reference for trajectory planning of intelligent vehicles in logistics system.
Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches

The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.
Unified Architecture Adaptation for Compressed Domain Semantic Inference

Advances in both lossy image compression and semantic content understanding have been greatly fueled by deep learning techniques, yet these two tasks have been developed separately for the past decades. In this work, we address the problem of directly executing semantic inference from quantized latent features in the deep compressed domain without pixel reconstruction. Although different methods have been proposed for this problem setting, they either are restrictive to a specific architecture, or are sub-optimal in terms of compressed domain task accuracy. In contrast, we propose a lightweight, plug-and-play solution which is generally compliant with popular learned image coders and deep vision models, making it attractive to vast applications. Our method adapts prevalent pixel domain neural models that are deployed for various vision tasks to directly accept quantized latent features (other than pixels). We further suggest training the compressed domain model by transferring knowledge from its corresponding pixel domain counterpart. Experiments show that our method is compliant with popular learned image coders and vision task models. Under fair comparison, our approach outperforms a baseline method by a) more than 3% top-1 accuracy for compressed domain classification, and b) more than 7% mIoU for compressed domain semantic segmentation, at various data rates.
Fuzzy logic for situation awareness: a systematic review

Abstract Situation awareness is the cognitive capability of human and artificial agents to perceive, understand and predict the status of the situation in an environment. Situation awareness systems aim at supporting the situation awareness of human and artificial agents using computational techniques, models, and approaches for supporting the assessment, tracking, and prediction of critical situations. Fuzzy logic formalisms have been extensively used in situation awareness systems thanks to their capability of dealing with uncertainties while providing agents with easily understandable models of situations and decisions. This paper proposes a systematic, unbiased, and updated review of the literature on fuzzy logic for situation awareness from 2010 to 2021, conducted using the PRISMA methodology, analyzing 139 articles. An in-depth discussion of the main open challenges and future research directions is provided.
LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors

We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up conveniently and worn lightly. Specifically, to fully utilize the global geometry information captured by LiDAR and local dynamic motions captured by IMUs, we design a two-stage pose estimator in a coarse-to-fine manner, where point clouds provide the coarse body shape and IMU measurements optimize the local actions. Furthermore, considering the translation deviation caused by the view-dependent partial point cloud, we propose a pose-guided translation corrector. It predicts the offset between captured points and the real root locations, which makes the consecutive movements and trajectories more precise and natural. Moreover, we collect a LiDAR-IMU multi-modal mocap dataset, LIPD, with diverse human actions in long-range scenarios. Extensive quantitative and qualitative experiments on LIPD and other open datasets all demonstrate the capability of our approach for compelling motion capture in large-scale scenarios, which outperforms other methods by an obvious margin. We will release our code and captured dataset to stimulate future research.