Reviewerly/exHarmony huggingface dataset:
In Pursuit of Beauty: Aesthetic-Aware and Context-Adaptive Photo Selection in Crowdsensing

The pervasive view of the mobile crowd bridges various real-world scenes and people's perceptions with the gathering of distributed crowdsensing photos. To elaborate informative visuals for viewers, existing techniques introduce photo selection as an essential step in crowdsensing. Yet, the aesthetic preference of viewers, at the very heart of their experiences under various crowdsensing contexts (e.g., travel planning), is seldom considered and hardly guaranteed. We propose CrowdPicker, a novel photo selection framework with adaptive aesthetic awareness for crowdsensing. With the observations on aesthetic uncertainty and bias in different crowdsensing contexts, we exploit a joint effort of mobile crowdsourcing and domain adaptation to actively learn contextual knowledge for dynamically tailoring the aesthetic predictor. Concretely, an aesthetic utility measure is invented based on the probabilistic balance formalization to quantify the benefit of photos in improving the adaptation performance. We prove the NP-hardness of sampling the best-utility photos for crowdsourcing annotation and present a (1-1/e) approximate solution. Furthermore, a two-stage distillation-based adaptation architecture is designed based on fusing contextual and common aesthetic preferences. Extensive experiments on three datasets and four raw models demonstrate the performance superiority of CrowdPicker over four photo selection baselines and four typical sampling strategies. Cross-dataset evaluation illustrates the impacts of aesthetic bias on selection.
Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this article, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.
Dynamic Obstacle Avoidance for Cable-Driven Parallel Robots With Mobile Bases via Sim-to-Real Reinforcement Learning

A Cable-Driven Parallel Robot (CDPR) with Mobile Bases (MBs) can modify its geometric architecture and is suitable for manipulation tasks in constrained environments. In manipulation tasks, a CDPR with MBs inevitably encounters obstacles, including dynamic obstacles. However, the high dimensional state space and a considerable number of constraints caused by multiple cables and MBs make the real-time dynamic obstacle avoidance of a CDPR with MBs challenging. This letter proposes a Reinforcement Learning (RL)-based dynamic obstacle avoidance method for a CDPR with MBs to deal with dynamic obstacles in real time. To explain the RL-based dynamic obstacle avoidance method, this letter focuses on a CDPR with four fixed-length cables connected to four MBs. An RL-based Obstacle Avoidance Controller (OAC) is developed and integrated into a trajectory tracking controller to address the dynamic obstacle avoidance problem of a CDPR with MBs tracking a target trajectory. To explain and evaluate the RL-based dynamic obstacle avoidance method further, an RL-based OAC is trained in a Mujoco simulator and transferred to a CDPR with four fixed-length cables connected to four MBs in the real world.
Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model

Obesity and laziness are some of the common issues in the majority of the youth today. This has led to the development of a proposed exergaming solution where users can play first-person physical games. This research study not only proposes a solution for physical fitness in the form of a game using wearable sensors but also proposes a multi-purpose system that provides different applications when trained for the domain-specific dataset. Critical tasks of gesture recognition and depiction in virtual reality can be applied to many applications in the domains of crime detection, fitness, healthcare, online learning, and sports. In particular, the proposed system enables a user to perform, detect, and depict different gestures in the virtual reality game. First, the system pre-processes input data by applying a median filter to overcome the anomalies. Then, features are extracted through a convolutional neural network, power spectral density, skewness, and kurtosis methods. Further, the system optimizes different features by using the grey wolf optimization. Lastly, the feature set which is optimized is fed to a recurrent neural network for classification. When Compared to the traditional methods, the suggested system gives better results while being easier to use. The IMSporting behaviors (IMSB) dataset includes badminton and other physical activities, the WISDM dataset includes common locomotor motions, and the ERICA dataset which includes a variety of exercises, were used in the experimentation. According to experimental findings, the suggested approach outperformed current methods, which showed detection accuracies of 85.01%, 88.46%, and 93.18% over the IMSB, WISDM, and ERICA datasets, respectively.
Brama: a new freeware Python software for reduction and imaging of LA-ICP-MS data from U–Pb scans

A new software, Brama, was used for processing of large amounts of individual low-count data from LA-ICP-MS U–Pb scans. The data processed by the software can be analyzed with Bayesian regression to calculate age and common Pb.
Hyperspectral Image Denoising: Reconciling Sparse and Low-Tensor-Ring-Rank Priors in the Transformed Domain

Recently, the transform-based tensor nuclear norm (TNN) framework has yielded promising results for hyperspectral image (HSI) denoising as compared with previous original-domain tensor-based models. However, the TNN framework only exploits the low-rankness of each band of HSIs (tensors) under a single spectral transform. The correlation between all bands under the transform (i.e., the global low-rankness of the transformed tensor) and the sparsity of the transformed HSI, which are beneficial for HSI denoising, is usually neglected in the TNN framework. In this article, we propose to reconcile sparse and low-tensor-ring (TR)-rank priors in the learned transformed domain (called T-RSTR model) for HSI denoising. In T-RSTR, the transform-based low-TR-rank and sparse regularizers are designed to characterize the global low-rankness and sparsity of the transformed tensors, respectively, and then the transform-based low-TR-rank and sparse regularizers are organically integrated and benefit from each other for substantially boosting denoising performance. To tackle the T-RSTR model, we elaborately design a proximal alternating minimization-based algorithm with the theoretical convergence. Extensive numerical results demonstrate that T-RSTR is superior to the competing methods.
Efficient Accelerator/Network Co-Search With Circular Greedy Reinforcement Learning

Recently, accelerator/network co-search has shown great promise in reducing the complexity of co-design and achieving higher model accuracy. Unlike the manual design methodology, it automatically learns the optimal network architecture and corresponding accelerator under the given constraints. However, prior works do not consider the direct feedback of searched accelerators on the network search in each step, which leads to low converge speed and sub-optimal solutions. To address this issue, we propose DAN, a reinforcement learning-based framework for fast and accurate accelerator/network co-search. The fundamental idea is to model the co-search as interleaved network-aware accelerator search (AS) and accelerator-aware network search (NS) using separate RL agents, which improves the performance of AS and NS, and encourages a tight interaction between AS and NS. Experimental results show that our proposed method consistently outperforms single-agent based method in terms of converge speed and performance.
SVT-SDE: Spatiotemporal Vision Transformers-Based Self-Supervised Depth Estimation in Stereoscopic Surgical Videos

Dense depth estimation plays a crucial role in developing context-aware computer-assisted intervention systems. However, it is a challenging task due to low image quality and highly dynamic surgical environment. The task is further complicated by the difficulty in acquiring per-pixel ground truth depth data in a surgical setting. Recent works on self-supervised depth estimation use image reconstruction (i.e., the warped images) as supervisory signal, which helps to eliminate the requirement of ground truth depth annotations but also causes over-smoothed depth predictions. Additionally, most existing depth estimation methods are built upon static laparoscopic images, ignoring rich temporal information. To address these challenges, we propose a novel spatiotemporal vision transformers-based self-supervised depth estimation method, referred as SVT-SDE. Unlike previous works, SVT-SDE features a novel spatiotemporal vision transformers (SVT) architecture, which can learn complementary visual and temporal information from the input stereoscopic video clips. We further introduce high-frequency-based supervisory signal, which helps to preserve fine-grained details of depth estimation. Results from experiments conducted on two publicly available datasets demonstrate the superior performance of SVT-SDE over the state-of-the-art self-supervised depth estimation methods.
Noise-Tolerant Identification and Tuning Approach Using Deep Neural Networks for Visual Servoing Applications

Vision-based control of unmanned aerial vehicles (UAVs) has been adopted in a wide range of applications due to the availability of low-cost onboard sensors and computers. Tuning such systems to work properly requires extensive domain-specific experience, which limits the growth of emerging applications. Moreover, obtaining performance limits for UAVs performing visual servoing is difficult due to the complexity of the models used. In this article, we propose a novel noise-tolerant approach for real-time identification and tuning of visual servoing systems. This is based on the deep neural networks (DNNs) classification of the system response generated by the modified relay feedback test (MRFT). The proposed method, called DNN with noise-protected MRFT (DNN-NP-MRFT), can be used with a multitude of vision sensors and estimation algorithms despite high levels of sensor noise. The response of DNN-NP-MRFT to noise perturbations is investigated and its effect on identification and tuning performance is analyzed. The proposed DNN-NP-MRFT is able to detect performance changes induced by the use of high latency vision sensors or by integrating an inertial measurement unit sensor into the UAV states estimation pipeline. Experimental identification closely matches simulation results, which can be used to explain the system behavior and predict the closed-loop performance limits for a given hardware and software setup. We also demonstrate the ability of DNN-NP-MRFT-tuned UAVs to reject external disturbances like wind or human push and pull. Finally, we discuss the advantages of the proposed DNN-NP-MRFT visual servoing design approach compared with other approaches in the literature.
High-Quality Temporal Link Prediction for Weighted Dynamic Graphs via Inductive Embedding Aggregation

Temporal link prediction (TLP) is an inference task on dynamic graphs that predicts future topology using historical graph snapshots. Existing TLP methods are usually designed for unweighted graphs with fixed node sets. Some of them cannot be generalized to the prediction of weighted graphs with non-fixed node sets. Although several methods can still be used to predict weighted graphs, they can only derive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low-quality</i> prediction snapshots sensitive to large edge weights but fail to distinguish small and zero weights in adjacency matrices. In this study, we consider the challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-quality</i> TLP on weighted dynamic graphs and propose a novel inductive dynamic embedding aggregation (IDEA) method, inspired by the high-resolution video prediction. IDEA combines conventional error minimization objectives with a scale difference minimization objective, which can generate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-quality</i> weighted prediction snapshots, distinguishing differences among large, small, and zero weights in adjacency matrices. Since IDEA adopts an inductive dynamic embedding scheme with an attentive node aligning unit and adaptive embedding aggregation module, it can also tackle the TLP on weighted graphs even with non-fixed node sets. Experiments on datasets of various scenarios validate that IDEA can derive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-quality</i> prediction results for weighted dynamic graphs and tackle the variation of node sets.
Managing the supply chain

In recent years, technology has profoundly impacted strategies as well as their implementation. Digital technology, by which everything is going to be connected, is forcing companies across all industries to rethink their operations. The development of the internet and mobile technologies has led to a fundamental impact of new technologies on economics and business. Every technology has distinctive elements that account for its employment in specific domains. The present chapter presents the principal innovative solutions for managing and increasing the efficiency of supply chains. Cloud computing, Big Data, Internet of Things (IoT), Blockchain, Robotics, Additive Manufacturing (AM), Autonomous Vehicles (AV), Artificial Intelligence (AI), Co-creation, and Digital Value Chain (DVC) are some of the many innovations that digital technology has made possible. According to Kumar et al., the 'new-age technologies' - in particular, the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning and Blockchain - are widely considered the way of the future.
The influence of time on dynamic signature: An exploratory data analysis

Dynamic signatures are a digitalized form of handwritten signatures. Their use has seen a steep increase for important transactions, such as life insurance and telecommunication contracts, sales and banking operations. A dynamic signature can be disputed and a forensic handwriting examiner may be hired to help determine whether it is genuine or not. Such a conflict may emerge years after the questioned signature has been affixed. Since reference material contemporary to the relevant period may not be made available to the expert, it can be questioned whether time influences dynamic signature data, which could affect the expert's results. This study was designed to explore this possible influence. Dynamic signatures of three participants were collected over a duration of 18 months, during 44 acquisition sessions. Based on this sample, the goals of describing variation of dynamic features over short and longer time periods, defining adequate sample collection strategies and sampling time frames, as well as laying down the foundation for using the time information for comparative analysis of dynamic signatures, were pursued. Both the relative stability and the slow 'drift' of signatures over time were illustrated by our results. The findings of this study lead to recommendations for sampling in casework, validate statements previously made by forensic scientist through an empirical investigation on dynamic signatures and strengthen the statistical basis for forensic signature comparison.
Category Knowledge-Guided Parameter Calibration for Few-Shot Object Detection

Few-shot object detection (FSOD) aims to adapt generic detectors to the novel categories with only a few annotations, which is an important and realistic task. Although the generic object detection has been widely studied over the past years, the FSOD is under explored. In this paper, we propose a novel Category Knowledge-guided Parameter Calibration (CKPC) framework to solve the FSOD task. We first propagate the category relation information to explore the representative category knowledge. Then, we explore the RoI-RoI and RoI-Category relations to capture the local-global context information to enhance the RoI (Region of Interest) features. Next, we project the knowledge representations of foreground categories into a parameter space by a linear transformation to generate the parameters of the category-level classifier. For the background, we learn a proxy category by concluding the global characteristics of all foreground categories to help ensure the discrepancy between the foreground and background, which is then projected into the parameter space by the same linear transformation. Finally, we leverage the parameters of the category-level classifier to explicitly calibrate the instance-level classifier learned on the enhanced RoI features for both the foreground and background categories to improve the detection performance. We conduct extensive experiments on two popular FSOD benchmarks (i.e., Pascal VOC and MS COCO), and the experimental results show that the proposed framework can outperform state-of-the-art methods.
Corner Detection via Scale-Space Behavior-Guided Trajectory Tracing

Existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">curvature scale-space</i> (CSS) methods detect corners by tracing the CSS trajectories from a determined high scale toward the lowest one. For those images with sophisticated details, such approach could often yield unsatisfactory corner detection results; i.e., miss-detected <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">true</i> corners (false negatives) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">round</i> corners (false positives). In this letter, these two fundamental problems are investigated. To tackle them, a novel CSS-based corner detector is proposed by incorporating our mathematically derived scale-space properties of the planar curves and corner points into the developed trajectory tracing algorithm, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scale-space behavior-guided trajectory tracing</i> (SBTT). In view of lacking a benchmark dataset with the ground truth, another contribution from our work is on the establishment of an augmented test image dataset, containing 147 test images with manually-labelled ground truth and their augmented images up to 62,328 images in total. Based on the ground truth, four commonly-used metrics are exploited to conduct corner detection performance evaluation. The obtained simulation results show that our proposed corner detector yields the highest F-score, when compared with that of nine state-of-the-art methods.
Noise-Against Skeleton Extraction Framework and Application on Hand Gesture Recognition

Extracting stable skeletons from noisy images is a challenging problem since the skeletonization method is prone to be affected by inner and border noise. Although many methods have been proposed in the past for increasing the antinoise ability of skeletonization methods, most of them either only overcome border noise or, at the cost of lost topology, degrade the effects of two noises. In this paper, we propose a skeleton extraction framework to enhance the robustness of the existing skeletonization method against both inner and border noise. In our approach, we first use the different scales of Gaussian filters to smooth the input image and obtain multiple representations. Then, binarization and skeletonization were performed to produce a series of binary images and a series of skeletal images. Next, we use our measure on these binary and skeletal images to find the most suitable skeleton. Since our measure considers both the skeleton image changes and binary image changes caused by using a filter, the selected skeleton is sufficiently robust and has all the necessary skeletal branches. The inner noise experiment and border noise experiment are conducted for comparison. From the perspective of the measure of the rate of variation in the skeleton, the proposed framework can reduce the inner noise by approximately 92% and the border noise by approximately 40%. In addition, the experiment on static hand gesture recognition has demonstrated that the introduction of our framework can increase up to 11% mean recognition accuracy.
An Efficient Multi-Secret Image Sharing System Based on Chinese Remainder Theorem and Its FPGA Realization

Multi-Secret Image Sharing (MSIS) is important in information security when multiple images are shared in an unintelligible form to different participants, where the images can only be recovered using the shares from participants. This paper proposes a simple and efficient ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n,n$ </tex-math></inline-formula> )-MSIS system for colored images based on XOR and Chinese Remainder Theorem (CRT), where all the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> share are required in the recovery. The system improves the security by adding dependency on the input images to be robust against differential attacks, and by using several delay units. It works with even and odd number of inputs, and has a long sensitive system key design for the CRT. Security analysis and a comparison with related literature are introduced with good results including statistical tests, differential attack measures, and key sensitivity tests as well as performance analysis tests such as time and space complexity. In addition, Field Programmable Gate Array (FPGA) realization of the proposed system is presented with throughput 530 Mbits/sec. Finally, the proposed MSIS system is validated through software and hardware with all statistical analyses and proper hardware resources with low power consumption, high throughput and high level of security.