Reviewerly/exHarmony huggingface dataset:
UnShadowNet: Illumination Critic Guided Contrastive Learning for Shadow Removal

Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of aligned shadowed and non-shadowed images which are difficult to obtain. We introduce a novel weakly supervised shadow removal framework <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UnShadowNet</i> trained using contrastive learning. It is composed of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeShadower</i> network responsible for the removal of the extracted shadow under the guidance of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Illumination</i> network which is trained adversarially by the illumination critic and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Refinement</i> network to further remove artefacts. We show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UnShadowNet</i> can be easily extended to a fully-supervised set-up to exploit the ground-truth when available. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UnShadowNet</i> outperforms existing state-of-the-art approaches on three publicly available shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully supervised setups.
Display Visibility Improvement Through Content and Ambient Light-Adaptive Image Enhancement

An image in a display device under strong illuminance can be perceived as darker than the original due to the nature of the human visual system (HVS). In order to alleviate this degradation in terms of software, existing schemes employ global luminance compensation or tone mapping. However, since such approaches focus on restoring luminance only, it has a fundamental drawback that chrominance cannot be sufficiently restored. Also, the previous approaches seldom provide acceptable visibility because it does not consider the content of an input image. Furthermore, because they focus mainly on global image quality, they may show unsatisfactory image quality for certain local areas. This paper introduces VisibilityNet, a neural network model designed to restore both chrominance and luminance. By leveraging VisibilityNet, we generate an optimally enhanced dataset tailored to the ambient light conditions. Furthermore, employing the generated dataset and a convolutional neural network (CNN), we estimate weighted piece-wise linear enhancement curves (WPLECs) that take into account both ambient light and image content. These WPLECs effectively enhance global contrast by addressing both luminance and chrominance aspects. Ultimately, through the utilization of a salient object detection algorithm that emulates the HVS, visibility enhancement is achieved not only for the overall region but also for visually salient areas. We verified the performance of the proposed method by comparing it with five existing approaches in terms of two quantitative metrics for a dataset we built ourselves. Experimental findings substantiate that the proposed method surpasses alternative approaches by significantly improving visibility.
Oppositional Jellyfish Search Optimizer With Deep Transfer Learning Enabled Secure Content-Based Biomedical Image Retrieval

Recently, a drastic increase in medical imaging such as X-rays, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) offers essential anatomical and functional details related to different body parts for monitoring, treatment planning, detection, and classification. The use of Content-Based Medical Image Retrieval (CBMIR) technologies helps to handle massive amounts of medical images, and also encryption can be considered an effective solution to attain security in the CBMIR process. In this regard, this research develops an optimal deep transfer learning enabled secure CBMIR technique, called ODTL-SCBMIR model. The proposed ODTL-SCBMIR prototype aim is to provide image encryption techniques with retrieval procedures. To accomplish this, the presented ODTL-SCBMIR model initially employs multikey homomorphic encryption (MKHE) with an oppositional jellyfish search optimizer (OJSO) algorithm for security. Next, the image retrieval process includes a series of processes namely capsule network (CapsNet) based feature extraction, chaos game optimization (CGO) based hyperparameter optimizer, and Manhattan distance based similar measurement. The performance validation of the ODTL-SCBMIR prototype is experimented with by employing a set of medical images. The investigational results implied the enhanced performance of the ODTL-SCBMIR prototype over current approaches.
Complete Rotated Localization Loss Based on Super-Gaussian Distribution for Remote Sensing Images

Localization regression in oriented object detection tasks has long faced boundary discontinuity and angular discontinuity problems induced by periodic angles. These problems were successfully resolved by using a 2d Gaussian distribution to modelling the oriented bounding box (OBB). However, the angular information of square-like objects will be lost when they are converted to 2d Gaussian distribution, forming a systematic problem. Its fundamental reason is that when the aspect ratio of the object tends to 1, the equiprobability curve of 2d Gaussian distribution degenerates from an ellipse to a circle, thus losing the orientation information of the rotated object. This results in the bounding boxes of such square-like objects not being learned effectively. To resolve this problem, we used the Lamé curve (or superellipse) to modify the existing 2d Gaussian function and designed a super-Gaussian distribution. This distribution can maintain anisotropy at arbitrary aspect ratios, thus preserving the angular information of the oriented object. We used the Kullback-Leibler (KL) divergence to measure the distance between two super-Gaussian distributions and convert it into a localization loss (SGKLD) by a function. SGKLD is an improved version of KLD loss. By modifying the form of the probability distribution, we elegantly fix the angle missing problem of the traditional Gaussian distribution. We validated the effectiveness of the proposed algorithm on several datasets and obtained the performance of SOTA. Our algorithm achieves a mean average precision (mAP) of 80.07, 76.59, 62.27, and 90.55/98.13 on the DOTA-v1.0, DOTA-v1.5, DOTA-v2.0, and HRSC2016 datasets, respectively.
OCro: Open-Set Cross-Domain Human Activity Recognition Based on Radio Frequency

With the help of machine learning, models are trained to recognize human activity based on radio frequency signals, which are widely used in human-computer interaction, healthcare, etc. Cross-domain human activity recognition (HAR) aims to adapt a model trained in a specific source domain (including environment and user) for another target domain. Most existing cross-domain recognition methods are proposed under an ideal closed-set assumption, which means the training set and the testing set contain the same categories of human activities. However, when a model is applied in practice for HAR, it often encounters new categories of activities which are not contained in the training set. Under such open-set condition, the traditional closed-set cross-domain recognition model usually incorrectly identifies the new activity as a known activity, which decline the recognition accuracy. In this paper, a model is proposed for open-set cross-domain HAR. The model is established based on generative adversarial network, and a unique generation module is designed to generate confusing samples whose features are similar to known classes. Thanks to such design, the proposed model can autonomously select appropriate unlabeled samples under the open-set condition to improve the open-set recognition ability of the model in the target domain. Extensive experiments are conducted based on four real datasets, one is collected by ourselves and the other three are public. The results show that the proposed model outperforms other state-of-the-art methods in the open-set and the cross-domain contexts.
A comparative study between the performance of relational and graph databases applied to hierarchical queries

The use of hierarchical queries makes it possible to search for information in databases that are organized in different levels of hierarchy. Both in relational databases and in graph databases, it is possible to use specific languages to make this type of query and efficiently obtain the desired results. However, depending on the case study, these queries may be better suited to one database paradigm than another. This article aimed to carry out a comparative analysis of the performance of hierarchical queries in the DBMS Oracle (relational) and Neo4j (graphs). To this end, a comparative study was carried out, with real data from the National Council of the Public Ministry (CNMP), with a focus on evaluating the execution time of such consultations. As a result, it was identified that, for the specific case studied, the DBMS Oracle presented better results than Neo4j.
Latent Topic-Aware Multioutput Learning

Multioutput learning targeting predicting multiple outputs for each input has attracted increasing attention due to its ability to handle diverse data types in outputs. Most feature extraction-based methods for tackling multioutput problems neglect the local correlation between different outputs, while most sample extraction-based methods overlook the gap between inputs and outputs. To overcome these two major limitations, in this article, we propose a topic-aware method where we assume inputs and outputs can be jointly embedded in a topic space. Both feature and sample extraction are performed in a latent topic space that encodes output correlation at a topic level and aligns inputs and outputs in an explainable manner. Different from current topic models, we extract independent and interdependent topics based on informative topics. These two components are verified as useful in performance improvement in an ablation study. The proposed method can also be easily extended and applied in multiview learning. The experimental results of the proposed topic-aware model on multiple benchmarks for different multioutput tasks in single-/multi-views with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -values less than 0.05 indicate that the improvement achieved by the proposed method over compared baselines is statistically significant.
Human Motion Prediction by Combining Spatial and Temporal Information With Independent Global Orientation

In this study, we address the challenge of 3D human motion prediction from motion capture data, which has become critical in various applications such as autonomous vehicles and human-robot interaction. Previous deep learning-based methods have improved prediction accuracy, but require significant network parameters and do not effectively consider independent joint movements. To overcome the limitations, we propose two lightweight network structures for human motion prediction: LG-Net and LGT-Net, which focus on the individual movements of distinct human limbs and their inter-dependencies. The LG-Net comprises local and global networks, while the LGT-Net combines the proposed LG-Net structure with Long and Short Term Memory (LSTM) cells to exploit temporal information. Our networks, designed to be extremely lightweight with only 0.08M and 0.5M parameters, achieve higher prediction performance compared to state-of-the-art methods. In addition, this study is the first to consider the root joint to improve motion prediction performance. The proposed approach demonstrates the potential for efficient and accurate human motion prediction in various applications.
Contrastive Learning for Action Assessment Using Graph Convolutional Networks With Augmented Virtual Joints

A fine-grained detection of posture problems for action assessment has a wide range of applications for health care, sports, and rehabilitation.However, there exist many design challenges, e.g., the difficulty of detecting subtle deviations in actions from standard ones, lack of annotated datasets, and even multiple posture problems that may be present in a single action.In this paper, we propose a contrastive learning framework leveraging graph convolutional networks to address these challenges.We introduce Augmented Virtual Joint which is a learned position in space where its associated graphs provide a holistic view of spatio-temporal dynamics of body joints, offering a flexible and generalized representation of actions.Next, we propose Degraded Negative Contrasting, which judiciously contrasts incorrect action samples for effective discrimination of incorrect actions from correct ones.We also propose Frame-Selective Pooling which provides a simple yet effective selection of important frames from action clips.Experiments show that, as compared with the state-of-the-art architectures, the proposed model consistently achieves the best performance under a lack of training data and in the presence of multiple posture problems, which demonstrates its efficacy for fine-grained evaluation of actions.
A BERT-Based Joint Channel–Temporal Modeling for Action Recognition

Action recognition provides an application for human action classification utilizing datasets captured by various sensor cameras. However, how to capture the key semantic features and subtle differences for fine-grained action recognition from redundant motion sequence is still a challenging task. To address this issue, we propose a novel bidirectional encoder representations from transformers (BERT)-based joint channel–temporal module to explore channel interaction correlation through channel–temporal embedded module and self-attention mechanism. The channel view branch is developed to capture the key channel semantic features through the interactive correlation between the subchannel feature sequences across frames. Our studies reveal that the channel interaction is crucial to explore these discriminative features among fine-grained action recognition categories. Furthermore, the channel view branch can work collaboratively with temporal view branch to take full advantage of channel interaction and channel–temporal dependencies through the joint learning via weight-sharing strategy. The proposed BERT-based joint channel–temporal module works in a plug-and-play way and can be integrated with 2-D backbones, such as temporal shift module (TSM), multiview fusion network (MVFNet), MotionSqueeze network (MSNet), and temporal difference network (TDN). Extensive experiments are carried out on the HMDB51, the MiniKinetics, the fine-grained Something-Something V1 & V2, and the multimodal N-UCLA datasets, and the results demonstrate the effectiveness of our joint channel–temporal module. Our method achieves 83.8%, 83.6%, 57.1%, and 68.2% top-1 accuracy on these single-modal datasets, respectively. The multimodal experiments on the N-UCLA dataset achieve 98.7% and 98.9% accuracy in RGB + skeleton and RGB + depth fusions.
Automatically Prioritizing Tasks in Software Development

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context</i> : Within the domain of managing software development teams, effective task prioritization is a critical responsibility that should not be underestimated, particularly for larger organizations with significant backlogs. Current approaches primarily rely on predicting task priority without considering information about other tasks, potentially resulting in inaccurate priority predictions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : This paper presents the benefits of considering the entire backlog when prioritizing tasks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method</i> : We employ an iterative approach using particle swarm optimization to optimize a linear model with various preprocessing methods to determine the optimal model for task prioritization within a backlog. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : The findings of our study demonstrate the usefulness of constructing a task prioritization model based on complete information from the backlog. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion</i> : The method proposed in our study can serve as a valuable resource for future researchers and can also facilitate the development of new tools to aid IT management teams.
Research on an Underwater Target-Tracking Method Based on Zernike Moment Feature Matching

Sonar images have the characteristics of lower resolution and blurrier edges compared to optical images, which make the feature-matching method in underwater target tracking less robust. To solve this problem, we propose a particle filter (PF)-based underwater target-tracking method utilizing Zernike moment feature matching. Zernike moments are used to construct the feature-description vector for feature matching and contribute to the update of particle weights. In addition, the particle state transition method is optimized by using a first-order autoregressive model. In this paper, we compare Hu moments and Zernike moments, and we also compare whether to optimize the particle state transition on the tracking results or not based on the effects of each option. The experimental results based on the AUV (autonomous underwater vehicle) prove that the robustness and accuracy of this innovative method is better than the other combined methods mentioned in this paper.
A Fuzzy Dempster–Shafer Evidence Theory Method with Belief Divergence for Unmanned Surface Vehicle Multi-Sensor Data Fusion

The safe navigation of unmanned surface vehicles in the marine environment requires multi-sensor collaborative perception, and multi-sensor data fusion technology is a prerequisite for realizing the collaborative perception of different sensors. To address the problem of poor fusion accuracy for existing multi-sensor fusion methods without prior knowledge, a fuzzy evidence theory multi-sensor data fusion method with belief divergence is proposed in this paper. First of all, an adjustable distance for measuring discrepancies between measurements is devised to evaluate the degree of measurement closeness to the true value, which improves the adaptability of the method to different classes of sensor data. Furthermore, an adaptive multi-sensor measurement fusion strategy is designed for the case where the sensor accuracy is known in advance. Secondly, the affiliation function of the fuzzy theory is introduced into the evidence theory approach to assign initial evidence of measurements in terms of defining the degree of fuzzy support between measurements, which improves the fusion accuracy of the method. Finally, the belief Jensen–Shannon divergence and the Rényi divergence are combined for measuring the conflict between the evidence pieces to obtain the credibility degree as the reliability of the evidence, which solves the problem of high conflict between evidence pieces. Three examples of multi-sensor data fusion in different domains are employed to validate the adaptability of the proposed method to different kinds of multi-sensors. The maximum relative error of the proposed method for multiple sensor experiments is greater than or equal to 0.18%, and its error accuracy is much higher than the best result of 0.46% among other comparative methods. The experimental results verify that the proposed data fusion method is more accurate than other existing methods.
InterviewBot: Real-Time End-to-End Dialogue System for Interviewing Students for College Admission

We present the InterviewBot, which dynamically integrates conversation history and customized topics into a coherent embedding space to conduct 10 min hybrid-domain (open and closed) conversations with foreign students applying to U.S. colleges to assess their academic and cultural readiness. To build a neural-based end-to-end dialogue model, 7361 audio recordings of human-to-human interviews are automatically transcribed, where 440 are manually corrected for finetuning and evaluation. To overcome the input/output size limit of a transformer-based encoder–decoder model, two new methods are proposed, context attention and topic storing, allowing the model to make relevant and consistent interactions. Our final model is tested both statistically by comparing its responses to the interview data and dynamically by inviting professional interviewers and various students to interact with it in real-time, finding it highly satisfactory in fluency and context awareness.
A Survey of Side-Channel Leakage Assessment

As more threatening side-channel attacks (SCAs) are being proposed, the security of cryptographic products is seriously challenged. This has prompted both academia and industry to evaluate the security of these products. The security assessment is divided into two styles: attacking-style assessment and leakage detection-style assessment. In this paper, we will focus specifically on the leakage detection-style assessment. Firstly, we divide the assessment methods into Test Vector Leakage Assessment (TVLA) and its optimizations and summarize the shortcomings of TVLA. Secondly, we categorize the various optimization schemes for overcoming these shortcomings into three groups: statistical tool optimizations, detection process optimizations, and decision strategy optimizations. We provide concise explanations of the motivations and processes behind each scheme, as well as compare their detection efficiency. Through our work, we conclude that there is no single optimal assessment scheme that can address all shortcomings of TVLA. Finally, we summarize the purposes and conditions of all leakage detection methods and provide a detection strategy for actual leakage detection. Additionally, we discuss the current development trends in leakage detection.