Reviewerly/exHarmony huggingface dataset:
MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features; (2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction; (2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task.
Message from The President: Trials and Tribulations of Transitions and Transformations in Publishing: What It Means—For <scp>ASLO</scp> and for You

Limnology and Oceanography BulletinVolume 32, Issue 3 p. 110-112 ASLO News Message from The President: Trials and Tribulations of Transitions and Transformations in Publishing: What It Means—For ASLO and for You Patricia M. Glibert, Corresponding Author Patricia M. Glibert [email protected] orcid.org/0000-0001-5690-1674 Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD, USASearch for more papers by this author Patricia M. Glibert, Corresponding Author Patricia M. Glibert [email protected] orcid.org/0000-0001-5690-1674 Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD, USASearch for more papers by this author First published: 27 July 2023 https://doi.org/10.1002/lob.10591Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References Curto, T. 2023. Message from the executive director: transformational agreements: the bridge to the future of gold open access publishing—interview with Fiona Sarne, Publisher, Wiley food, Earth & Environmental Sciences. Limnol. Oceanogr.: Bull. 32: 62–65. doi:10.1002/lob.10571. 10.1002/lob.10571 Google Scholar Hotaling, S., B. R. Deemer, K. Poulson-Ellestad, and L. J. Falkenberg. 2023. Taking steps to address inequities in open-access publishing through an early career publication honor. Limnol. Oceanogr.: Lett. 8: 385–387. doi:10.1002/lol2.10283. 10.1002/lol2.10283 Web of Science®Google Scholar Pace, M. 2018. Message from the president: OA world. Limnol. Oceanogr.: Bull. 28: 31–32. doi:10.1002/lob.10286. 10.1002/lob.10286 Google Scholar Volume32, Issue3August 2023Pages 110-112 ReferencesRelatedInformation
A Smart Traffic Control System Based on Pixel-Labeling and SORT Tracker

Autonomous vehicle detection and tracking are crucial for intelligent transportation management and control systems. Although many techniques are used to develop smart traffic systems, this article discusses vehicle detection and tracking using pixel-labeling and real-time tracking. We propose a novel smart traffic control system that segments the image using an Extreme Gradient Boost (XGBoost) classifier to extract the foreground objects. The proposed model is divided into the following steps: 1) at first, all the images are preprocessed to remove noise; 2) pixel-labeling is performed by using the XGBoost classifier to separate the background from the foreground; 3) all the pixels classified as a vehicle was extracted and converted into a binary image, then blob extraction technique is used to localize each vehicle; 4) to verify the detected vehicles Intersection over Union (IoU) score using the ground truth is calculated; 5) all verified vehicles were subjected to Visual Geometry Group (VGG) feature extraction and based on which a unique identifier was assigned to each of them to enable multi-object tracking across the image frames; 6) vehicles are counted and categorized into stationary and moving cars by detecting motion in each of them using Farneback optical flow algorithm and 6) finally, the Simple Online and Real-time Tracker (SORT) is used for tracking. The proposed model outperforms existing state-of-the-art traffic monitoring techniques in terms of precision, achieving 0.86 for detection and 0.92 for tracking with the Karlsruher Institut for Technology Aerial Image Sequences (KIT-AIS) dataset, 0.83 for detection, and 0.87 for tracking with the Vision Meets Drone Single Object-Tracking (VisDrone) dataset. The proposed system can be used for several purposes, such as vehicle identification in traffic, traffic density detection at intersections, traffic flow conditions on the road, and providing a pedestrian way.
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models

The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning model. Attribution maps are generally used to highlight the parts of the input image that influence the model to make a specific decision. On the other hand, the robustness of machine learning models to natural noise and adversarial attacks is also being actively explored. This paper focuses on evaluating methods of attribution mapping to find whether robust neural networks are more explainable. We explore this problem within the application of classification for medical imaging. Explainability research is at an impasse. There are many methods of attribution mapping, but no current consensus on how to evaluate them and determine the ones that are the best. Our experiments on multiple datasets (natural and medical imaging) and various attribution methods reveal that two popular evaluation metrics, Deletion and Insertion, have inherent limitations and yield contradictory results. We propose a new explainability faithfulness metric (called EvalAttAI) that addresses the limitations of prior metrics. Using our novel evaluation, we found that Bayesian deep neural networks using the Variational Density Propagation technique were consistently more explainable when used with the best performing attribution method, the Vanilla Gradient. However, in general, various types of robust neural networks may not be more explainable, despite these models producing more visually plausible attribution maps.
Community-Engaged Participatory Methods to Address Lesbian, Gay, Bisexual, Transgender, Queer, and Questioning Young People’s Health Information Needs With a Resource Website: Participatory Design and Development Study

Lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ+) young people (aged 15 to 25 years) face unique health challenges and often lack resources to adequately address their health information needs related to gender and sexuality. Beyond information access issues, LGBTQ+ young people may need information resources to be designed and organized differently compared with their cisgender and heterosexual peers and, because of identity exploration, may have different information needs related to gender and sexuality than older people.The objective of our study was to work with a community partner to develop an inclusive and comprehensive new website to address LGBTQ+ young people's health information needs. To design this resource website using a community-engaged approach, our objective required working with and incorporating content and design recommendations from young LGBTQ+ participants.We conducted interviews (n=17) and participatory design sessions (n=11; total individual participants: n=25) with LGBTQ+ young people to understand their health information needs and elicit design recommendations for the new website. We involved our community partner in all aspects of the research and design process.We present participants' desired resources, health topics, and technical website features that can facilitate information seeking for LGBTQ+ young people exploring their sexuality and gender and looking for health resources. We describe how filters can allow people to find information related to intersecting marginalized identities and how dark mode can be a privacy measure to avoid unwanted identity disclosure. We reflect on our design process and situate the website development in previous critical reflections on participatory research with marginalized communities. We suggest recommendations for future LGBTQ+ health websites based on our research and design experiences and final website design, which can enable LGBTQ+ young people to access information, find the right information, and navigate identity disclosure concerns. These design recommendations include filters, a reduced number of links, conscientious choice of graphics, dark mode, and resources tailored to intersecting identities.Meaningful collaboration with community partners throughout the design process is vital for developing technological resources that meet community needs. We argue for community partner leadership rather than just involvement in community-based research endeavors at the intersection of human-computer interaction and health.
Nonvolatile photonic switch with magnetic materials on a silicon photonic platform [Invited]

This study investigates the performance of a nonvolatile photonic switch driven by the magneto-optical (MO) effect. Thin-film magnets made of ferromagnetic metals have remanence and maintain the magnetization of the MO garnet. Considering integration on silicon photonic platforms, a thin-film magnet is placed beside the waveguide, and the MO garnet is bonded on the waveguide compatible with the back-end-of-line process. The results obtained demonstrate successfully the nonvolatile MO phase shift and high extinction switching.
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection

Monocular 3D object detection aims to locate objects in different scenes with just a single image. Due to the absence of depth information, several monocular 3D detection techniques have emerged that rely on auxiliary depth maps from the depth estimation task. There are multiple approaches to understanding the representation of depth maps, including treating them as pseudo-LiDAR point clouds, leveraging implicit end-to-end learning of depth information, or considering them as an image input. However, these methods have certain drawbacks, such as their reliance on the accuracy of estimated depth maps and suboptimal utilization of depth maps due to their image-based nature. While LiDAR-based methods and convolutional neural networks (CNNs) can be utilized for pseudo point clouds and depth maps, respectively, it is always an alternative. In this paper, we propose a framework named the Adaptive Distance Interval Separation Network (ADISN) that adopts a novel perspective on understanding depth maps, as a form that lies between LiDAR and images. We utilize an adaptive separation approach that partitions the depth map into various subgraphs based on distance and treats each of these subgraphs as an individual image for feature extraction. After adaptive separations, each subgraph solely contains pixels within a learned interval range. If there is a truncated object within this range, an evident curved edge will appear, which we can leverage for texture extraction using CNNs to obtain rich depth information in pixels. Meanwhile, to mitigate the inaccuracy of depth estimation, we designed an uncertainty module. To take advantage of both images and depth maps, we use different branches to learn localization detection tasks and appearance tasks separately. Our approach significantly enhances the baseline and outperforms depth-assisted techniques, as shown by our extensive experiments on the KITTI monocular 3D object detection benchmark.
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis

Breast cancer is the second most common cancer among women worldwide, and the diagnosis by pathologists is a time-consuming procedure and subjective. Computer-aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from the activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to higher accuracy in the classification task and dimension reduction plays an important role. We have proposed reduced DeCAF (R-DeCAF) for this purpose, and different dimension reduction methods are applied to achieve an effective combination of features by capturing the essence of DeCAF features. This framework uses pre-trained CNNs such as AlexNet, VGG-16, and VGG-19 as feature extractors in transfer learning mode. The DeCAF features are extracted from the first fully connected layer of the mentioned CNNs, and a support vector machine is used for classification. Among linear and nonlinear dimensionality reduction algorithms, linear approaches such as principal component analysis (PCA) represent a better combination among deep features and lead to higher accuracy in the classification task using a small number of features considering a specific amount of cumulative explained variance (CEV) of features. The proposed method is validated using experimental BreakHis and ICIAR datasets. Comprehensive results show improvement in the classification accuracy up to 4.3% with a feature vector size (FVS) of 23 and CEV equal to 0.15.
MSKD: Structured knowledge distillation for efficient medical image segmentation

In recent years, deep learning has revolutionized the field of medical image segmentation by enabling the development of powerful deep neural networks. However, these models tend to be complex and computationally demanding, posing challenges for practical implementation in clinical settings. To address this issue, we propose an efficient structured knowledge distillation framework that leverages a powerful teacher network to assist in training a lightweight student network. Specifically, we propose the Feature Filtering Distillation method, which focuses on transferring region-level semantic information while minimizing redundant information transmission from the teacher to the student network. This approach effectively mitigates the problem of inaccurate segmentation caused by similar internal organ characteristics. Additionally, we propose the Region Graph Distillation method, which exploits the higher-order representational capabilities of graphs to enable the student network to better imitate structured semantic information from the teacher. To validate the effectiveness of our proposed methods, we conducted experiments on the Synapse multi-organ segmentation and KiTS kidney tumor segmentation datasets using various network models. The results demonstrate that our method significantly improves the segmentation performance of lightweight neural networks, with improvements of up to 18.56% in Dice coefficient. Importantly, our approach achieves these improvements without introducing additional model parameters. Overall, our proposed knowledge distillation methods offer a promising solution for efficient medical image segmentation, empowering medical experts to make more accurate diagnoses and improve patient treatment.
Beyond Black Box AI generated Plagiarism Detection: From Sentence to Document Level

The increasing reliance on large language models (LLMs) in academic writing has led to a rise in plagiarism.Existing AI-generated text classifiers have limited accuracy and often produce false positives.We propose a novel approach using natural language processing (NLP) techniques, offering quantifiable metrics at both sentence and document levels for easier interpretation by human evaluators.Our method employs a multi-faceted approach, generating multiple paraphrased versions of a given question and inputting them into the LLM to generate answers.By using a contrastive loss function based on cosine similarity, we match generated sentences with those from the student's response.Our approach achieves up to 94% accuracy in classifying human and AI text, providing a robust and adaptable solution for plagiarism detection in academic settings.This method improves with LLM advancements, reducing the need for new model training or reconfiguration, and offers a more transparent way of evaluating and detecting AI-generated text.
Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph

In target-oriented dialogue, the representation and achievement of targets are two interrelated essential issues.In current approaches, the target is typically assumed to be a single object represented as a word, which makes it relatively easy to achieve through dialogue with the help of a knowledge graph (KG).However, when the target has complex semantics, the existing KG is often incomplete in tracking semantic relations.This paper studies target-oriented dialog where the target is a topic sentence.We combine the methods of knowledge retrieval and relationship prediction to construct a contextrelated dynamic KG, in which we can track the implicit semantic paths in the speaker's mind that may not exist in the existing KGs.In addition, we also designed a novel metric to evaluate the tracked path automatically.The experimental results show that our method can control the agent more logically and smoothly toward the complex target.
IoT based Smart Door System for Monitoring Human Body Temperature and Face Mask Detection

The requirement for automated entry devices which checks verifies a human’s temperature and assess people with mask is increasing rapidly in public enclosed places like Marriage Halls, Malls, Restaurants, Offices, and Theatres. These procedures are now carried out by hand, allowing staff to communicate with one another while checking the temperature. To address the issue, our primary goal is to create an IoT-enabled smart door system that opens and closes in accordance with government safety standards, monitors body temperature, detects face masks, and counts the number of people who enter the door. This system will be used to prevent unsafe entry into a designated area, thereby improving public safety.
A blind color image watermarking algorithm based on Hadamard transform and TLBO algorithm

For the sake of better protect the copyright of color images, an efficient color image watermarking algorithm based on Hadamard transform and teaching-learning-based optimization (TLBO) algorithm is proposed in this paper. In the stage of embedding watermark, different watermark embedding strategies are employed in 4 × 4 blocks based on the impact level of pixel caused by the high and low bit of watermark and taking into account the energy concentration characteristics of the Hadamard matrix. To enhance the robustness of the watermarking algorithm, the voting system is designed for the high-four bits of watermark information. Simultaneously, the permutation ordered binary (POB) number system is used to compress the low-four bits of watermark information for improving the invisibility of the watermarked image and the embedding capacity. When the watermark needs to be extracted, the inverse process of POB number system transform is optimized, and the time of watermark extraction is shortened. Finally, the TLBO algorithm is used to optimize the watermarking algorithm parameters for saving the experimental time while considering both invisibility and robustness of the algorithm. In comparison to the other algorithms examined in this research, the experimental results demonstrate that the proposed watermarking algorithm exhibits several performance enhancements, these include aspects such as robustness, running time, watermark embedding capacity, security and invisibility.
Metal surface defect detection based on improved YOLOv5

This paper proposes an improved YOLOv5 algorithm for metal surface defect detection to address the issues of slow detection speed and low accuracy in traditional YOLO algorithms. The proposed algorithm introduces a Convolutional Block Attention Module (CBAM) consisting of two sub-modules: Spatial Attention Module (SAM) and Channel Attention Module (CAM), which collect attention information in both spatial and channel dimensions and combine them to obtain more comprehensive and reliable attention information. Additionally, the paper introduces the SIOU loss function, which accurately measures the accuracy of object localization by considering the vector angle in boundary box regression. The proposed method achieves significant improvements in detection accuracy by minimizing the difference in vector angles, which allows for better adjustment of the position and orientation of the boundary boxes. Experimental results demonstrate that the improved algorithm increases detection accuracy by 5.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> compared to the traditional algorithm.
Learning dynamic spatial-temporal regularized correlation filter tracking with response deviation suppression via multi-feature fusion

Visual object tracking (VOT) for intelligent video surveillance has attracted great attention in the current research community, thanks to advances in computer vision and camera technology. Meanwhile, discriminative correlation filter (DCF) trackers garnered significant interest owing to their high accuracy and low computing cost. Many researchers have introduced spatial and temporal regularization into the DCF framework to achieve a more robust appearance model and further improve tracking performance. However, these algorithms typically set fixed spatial and temporal regularization parameters, which limit flexibility and adaptability under cluttered and challenging scenarios. To overcome these problems, in this work, we propose a new dynamic spatial–temporal regularization for the DCF tracking model that emphasizes the filter to concentrate on more reliable regions during the training stage. Furthermore, we present a response deviation-suppressed regularization term for responses to encourage temporal consistency and avoid model degradation by suppressing relative response changes between two consecutive frames. Moreover, we introduce a multi-memory tracking framework to exploit various features and each memory contributes to tracking the target across all frames. Significant experiments on the OTB-2013, OTB-2015, TC-128, UAV-123, UAVDT, and DTB-70 datasets have revealed that the performance thereof outperformed many state-of-the-art trackers based on DCF and deep-based frameworks in terms of tracking accuracy and tracking success rate.