Reviewerly/exHarmony huggingface dataset:
Characterizing the Sense of Embodiment: the Development of a Sensorimotor Robotic Platform

Embodiment is the experience of owning and controlling our bodies. It is a product of cognitive sensorimotor integration processes and by manipulating what we see and feel, we can extend the perceived boundaries of our bodies to include non-biological objects. Consequently, it is of interest to many human-robotic control applications that aim to promote the seamless interaction of the human user and machine. The sense of embodiment has three constituent components that robotic systems may engage with: the Sense of Ownership (SoO), the Sense of Agency (SoA), and our peripersonal space (PPS). Despite the recognitionthat each is linked to the other, how these components interact to form a cohesive sense of embodiment remains poorly understood. To address this issue, in this work, we designed an embodiment research platform that allows us to explore the relationships between the SoO, SoA, and PPS. We developed a sensorimotor interface, experimental setup, and protocol in which a robotic hand mirrored the actions of the participants’ real hand. Participants performed a series of grasping tasks in which we manipulated the cutaneous sensations they experienced and the position of the robot relative to their body. We then employed multiple independent measures of SoO, SoA, and PPS. Our results indicated that (1) cutaneous feedback improved all three components of embodiment, and (2) there appeared to be multiple complex interrelationships among the three components and their measures.
QWriter System for Robot-Assisted Alphabet Acquisition

The present study applies a novel Reinforcement Learning-based (RL) alphabet learning system named QWriter for the acquisition of the Kazakh Latin alphabet. We conducted a between-subject design experiment with 108 Kazakh children aged 6-8 years old in a public school and compared their learning rates across the two conditions: an RL-based QWriter robot and a human tutor (HT) as a baseline. The results show that children learned significantly more letters with the HT compared to the QWriter robot, showing that the RL-based robot is not effective for learning in the short term. Yet, we observe some interesting results by children’s age and gender. The results need further investigation comparing the QWriter with other robot baselines with different roles and across various learning tasks.
Pattern Matching

Pattern matching is a fundamental concept in computer science and data analysis, serving as a powerful tool for recognizing and extracting meaningful information from complex data sets. This abstract provides an overview of pattern matching, its significance, and its applications across various domains.Pattern matching involves the identification of specific patterns or structures within a given dataset or sequence of data. These patterns can manifest in diverse forms, ranging from simple regular expressions to more complex algorithms and machine learning techniques. The primary objective of pattern matching is to locate instances of a particular pattern within the data and derive valuable insights or perform subsequent actions based on these findings.Pattern matching finds wide-ranging applications across several domains. In text processing and natural language processing, it facilitates tasks such as keyword extraction, sentiment analysis, and named entity recognition. In bioinformatics, pattern matching enables DNA sequence alignment, protein motif identification, and genetic variant detection. Additionally, in image and signal processing, it aids in object recognition, image classification, and speech recognition.Various algorithms and techniques have been developed to effectively perform pattern matching. These include exact matching algorithms like the Boyer-Moore and Knuth-Morris-Pratt algorithms, approximate matching algorithms like the Smith-Waterman algorithm, and more advanced methods like regular expressions and finite automata. Additionally, with the advent of machine learning, pattern matching can be achieved through supervised and unsupervised learning approaches, enabling the discovery of complex patterns in large datasets.Efficient pattern matching algorithms and techniques have been extensively studied and optimized to handle the challenges posed by large-scale data processing. Performance considerations, such as time complexity and space efficiency, are crucial to ensure effective pattern matching on massive datasets
Make Segment Anything Model Perfect on Shadow Detection

Compared to models pre-trained on ImageNet, the segment anything model (SAM) has been trained on a massive segmentation corpus, excelling in both generalization ability and boundary localization. However, these strengths are still insufficient to enhance shadow detection without additional training, and it raises the question: do we still need precise manual annotations to fine-tune SAM for high detection accuracy? This paper proposes an annotation-free framework for deep unsupervised shadow detection (USD) by leveraging SAM’s capabilities. The key lies in how to exploit the abilities acquired from a large-scale corpus and utilize them to improve downstream tasks. Instead of directly fine-tuning SAM, we propose a prompt-like tuning method to inject task-specific cues into SAM in a light-weight manner, namely ShadowSAM. This adaptation manner can ensure a good fitting when training data is limited. Moreover, considering that the pseudo labels used in our framework are generated by traditional USD approaches and may contain severe label noises, we propose an illumination and texture-guided updating strategy to selectively boost the quality of pseudo masks. To further improve the model’s robustness, we design a mask diversity index to establish easy-to-hard subsets for incremental curriculum learning. Extensive experiments on benchmark datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., SBU, UCF, ISTD, and CUHK-Shadow) demonstrate that our unsupervised solution can achieve comparable performance to state-of-the-art (SOTA) fully supervised methods. Our code is available at this repository.
DeSeal: Semantic-Aware Seal2Clear Attention for Document Seal Removal

Seal removal aims to eliminate the seal portion from documents to facilitate better OCR and document reconstruction. However, existing seal removal methods often lack publicly available code and pre-trained models, and suffer from a lack of publicly available seal datasets. To address these issues, we propose DeSeal for seal removal and introduce a SealBank dataset containing 100K paired seal images. In DeSeal, we introduce the Semantic-Aware Seal2Clear Attention and Color-Adapter Module, where the former identifies seal regions in the entire image and focuses on removing seals from these areas, while the latter significantly improves the model's generalization performance, enabling it to perform well on both real and synthetic data. Experimental results on the SealBank dataset demonstrate the effectiveness of our proposed DeSeal.
Privacy-Preserving Machine Learning [Cryptography]

Privacy challenges in machine learning can stem from leakage by the model or from distributed data sources. Differential privacy addresses model leakage and computation over encrypted data the other. During training cryptographic approaches need to be augmented with techniques such as federated learning.
Blind Assistance System using Digital Image Processing

Research provides an advanced blind assistance system that seamlessly integrates the YOLOv3 algorithm and OpenCV's DNN (Deep Neural Network) module for real-time object detection and voice alerts. The primary goal is to enhance independence and safety for visually impaired individuals by providing rapid object recognition and auditory feedback. The system uses a webcam to capture live video input and employs the YOLOv3 algorithm, optimized for real-time inference, to accurately detect objects. Subsequently, it generates voice alerts using GTTS (Google Text-to Speech) technology, conveying essential information about the detected objects. The research's remarkable adaptability allows it to produce voice outputs in the user's language of choice, making it more user-friendly and accessible. Furthermore, its ability to handle diverse object classes with precision highlights its versatility, rendering it an invaluable tool for significantly improving the lives of visually impaired individuals.
Classification of Activities of Daily Living for Older Adults Using Machine Learning and Fixed Time Windowing Technique

The classification of activities of daily living (ADLs) in the home of older adults makes it possible to identify risk situations and changes in behavior that may be associated with some type of problem. This information allows caregivers and health professionals to take action when these types of situations are detected. Although many machine learning classification techniques have been proposed, the effectiveness of the solution in a real-world context remains unclear in most cases due to the large number of sensors required, the type of sensors used which may pose privacy issues, and the assumption of considering only segmented sensor events for each activity before training the models. This article presents an evaluation of different machine learning techniques using fixed time windows to extract spatiotemporal features and classify ten human activities in a real smart home with unobtrusive sensors using the Aruba CASAS dataset. The three classification techniques that achieved better performance were random forest, XGBoost, and support vector machine (SVM), achieving an accuracy of 97% with our best model, outperforming other approaches from the literature that were using the same dataset under similar conditions. The proposed classification techniques were also evaluated under a more realistic scenario by reducing the amount of hardware required and using an additional class labeled “Other” to consider all raw sensor events, including those that do not belong to any specific activity, achieving an accuracy of 89%, outperforming other approaches from the literature using the same dataset under similar conditions.
Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release

Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans. Maintaining common ground between the remotely-located partners is a challenge, one that can be facilitated by multi-modal communication. In this paper, we explore how participants utilized multiple modalities to investigate a remote location with the help of a robotic partner. Participants issued spoken natural language instructions and received from the robot: text-based feedback, continuous 2D LIDAR mapping, and upon-request static photographs. We noticed that different strategies were adopted in terms of use of the modalities, and hypothesize that these differences may be correlated with success at several exploration sub-tasks. We found that requesting photos may have improved the identification and counting of some key entities (doorways in particular) and that this strategy did not hinder the amount of overall area exploration. Future work with larger samples may reveal the effects of more nuanced photo and dialogue strategies, which can inform the training of robotic agents. Additionally, we announce the release of our unique multi-modal corpus of human-robot communication in an exploration context: SCOUT, the Situated Corpus on Understanding Transactions.
Edge-Based Local and Global Energy Active Contour Model Driven by Signed Pressure Force for Image Segmentation

Image segmentation is a tedious task that suffers from constraints, such as blurred or weak edges and intensity inhomogeneity. Active contour models, including edge-based and region-based methods, are extensively used for image segmentation. Each of these methods has its pros and cons that affect image-segmentation accuracy and CPU processing time. This study combines local and global region-based fitting energies and uses statistical image information to drag contours toward object boundaries, thus overcoming image inhomogeneity. The bias field, the region affected by image artefacts, is calculated and added with the local fitting energy model to capture inhomogeneous object boundaries. Furthermore, the combined local and global statistical information is appended with the edge-indicator function to rapidly move the contour over objects with strong edges, thereby avoiding boundary leakage. A region-based length term is driven by the signed pressure force function that evolves the curve on either the outer or inner side of the object, depending on its sign. The signed pressure force function contributes to achieving a smoother version of energy minimization over gradient descent flow. The proposed active contour model is applied to multiple synthetically generated, and medical images, together with online available public databases: the PH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> database, the skin-cancer-mnist-ham10000 THUS10000 database and the specific images from PascalVOC2007 database [1]. All the experiments confirm the better segmentation accuracy and improved time potency of the proposed methodology over previous level set-based approaches.
Marine Small Floating Target Detection Method Based on Fusion Weight and Graph Dynamic Attention Mechanism

Sea surface target detection with Graph Neural Networks (GNN) is an emerging method. However, the correlation information of radar returns cannot be efficiently exploited by the conventional Graph Convolutional Network (GCN). Therefore, this paper proposes a small floating target detection method based on graph attention network (GAT) with spatio-temporal correlation of clutter maps, and designs fusion weighting and dynamic attention mechanism for practical problem. First, the dwell radar data is modeled as a graph structure according to its spatio-temporal information. The proposed graph structure allows Doppler spectra of same-type samples to be accumulated separately for sea clutter and target returns. Then, we propose a GAT-based detector and optimize it to create variants: GATv2, GAT-Fused, and GATv2-Fused. These variants aim to reduce sea spike interference and jointly utilize spatio-temporal clutter map information and feature correlation. Both measured and simulated data demonstrate that the proposed attention-based detectors effectively identify marine small floating targets, including out-of-distribution (OOD) detection, outperforming conventional feature-based detectors, the GCN detector, and the pure GAT detector.
Dreaming Up Smart Home Futures: A Story Completion Study

Virtual assistants, vacuum robots, security systems, and other smart home technologies are rapidly advancing, evolving, and gaining popularity. This raises questions of how people envision future interactions with smart home systems and how they imagine the future roles of such technologies in society. We deployed an online study that collected fictional short stories from 60 participants about smart home interactions. We identified themes regarding the roles of smart home technologies, social interactions with AI, and concerns about data privacy in the context of the home. We describe our method, discuss insights from the stories that explicitly reflect possible futures and implicitly reflect the present, and make design recommendations based on our findings.
Knowledge-Aided Momentum Contrastive Learning for Remote-Sensing Image Text Retrieval

Remote sensing image-text retrieval (RSITR) has attracted widespread attention due to its great potential for rapid information mining ability on remote sensing images. Although significant progress has been achieved, existing methods typically overlook the challenge posed by the extremely analogous descriptions, where the subtle differences remain largely unexploited or, in some cases, are entirely disregarded. To address the limitation, we propose a Knowledge Aided Momentum Contrastive Learning (KAMCL) method for RSITR. Specifically, we propose a novel Knowledge Aided Learning framework, including knowledge initialization, construction, filtration, and alignment operations, which aims at providing valuable concepts and learning discriminative representations. On this basis, we integrate Momentum Contrastive Learning to promote the capture of key concepts within the representation via expanding the scale of negative sample pairs. Moreover, we design a hierarchical aggregator module to better capture the multi-level information from remote sensing images. Finally, we introduce an innovative two-step training strategy designed to effectively harness the synergy among concepts and leverage their respective functionalities. Extensive experiments conducted on the three public datasets showcase the remarkable performance of our approach in terms of retrieval accuracy and computational efficiency. For instance, compared with the existing state-of-the-art method, our method exhibits notable performance improvements of 2.65% on the RSICD dataset, simultaneously achieving improvements in inference efficiency by 48%. Our source code will be released at https://github.com/mcx-mcx/KAMCL.
An Image Steganoganalyzer With Comprehensive Detection Performance

Effectively enhancing the weak stego signal while striking the balance among three evaluation metrics, i.e., detection accuracy, time cost, as well as the number of parameters (NP) is indeed a huge challenge for existing deep learning-based steganalysis detectors. In this letter, a novel steganalysis detector called GFS-Net is proposed, aiming at enhancing the stego signal as much as possible while balancing the three metrics to obtain comprehensive detection performance. In preprocessing, combining highly lightweight gated channel transformation with a pointwise convolution layer used for enlarging the number of channels enriches the expression of the stego signal by promoting cooperation among enlarged channels, thereby significantly improving the signal-to-noise ratio while avoiding occupying a large NP. Moreover, two FasterNet blocks equipped with partial convolution having a small NP, rather than residual blocks, are applied to the last two layers of feature extraction to efficiently extract the stego signal by reducing the calculation of similar features, so that the computational cost and NP are saved. Finally, compared to using the global average pooling (GAP) alone, the stylepooling jointly utilizing the global standard deviation pooling and GAP helps the subsequent fully connected better identify weak stego signal, and thus improves detection accuracy. By means of the above three perspectives, GFS-Net with only 0.13 M parameters is obtained. Experimental results also demonstrate that GFS-Net achieves higher detection accuracy and lower computational cost than state-of-the-art steganalysis detectors.
A Design Framework of Exploring Moderating Factors in Perceived Security Risk for Blockchain Technology Applications in Cloud Storage: Enhancing Secure Healthcare Systems

The secure and decentralized storage of cloud medical data, including patient records, medical images, and other sensitive information, is a critical challenge in healthcare. Centralized storage solutions have been vulnerable to cyber-attacks and data breaches, and traditional encryption methods have proven to be inadequate in protecting sensitive information. Hospitals have placed a high priority on safeguarding electronic health records, given their significant volume. However, ensuring data security in the cloud is challenging due to its parallel computing and distributed nature. To address this concern, hospitals have turned to the utilization of blockchain (BC) technology within the cloud. By implementing blockchain, medical data can be preserved and secured effectively, mitigating potential security breaches, forgery, manipulation, and privacy leaks. To gain a comprehensive understanding of how blockchain technology in cloud storage enhances healthcare system security, refer to the contents of this paper. In this text, we will examine the advantages and disadvantages of utilizing a basic cloud storage system. Subsequently, we will provide a concise introduction to blockchain cloud storage technology. Numerous studies have focused on incorporating blockchain technology into healthcare systems as a potential solution to address security concerns in the healthcare industry. This has resulted in the implementation of more stringent and advanced security measures. These research efforts aim to explore the potential of blockchain-based solutions for safeguarding cloud outsourced healthcare data. By evaluating and comparing simulation tests conducted in various blockchain-focused studies, we can assess the integrity verification of medical data and cloud storage, data interchange with reduced computational complexity, as well as security and privacy protection.
Clean Architecture: Impact on Performance and Maintainability of Native Android Projects

In software development, following an architecture is extremely essential for any project. Clean Architecture, since 2017, has become popular among the Native Android development community. It helps to improve the efficiency of the development process by establishing a clear separation of concerns, achieving modular, scalable, and maintainable code. Its advantages and how it can improve the efficiency of development projects in the Android ecosystem are shown. It examined the challenges faced by Android developers and how Clean Architecture moving from its original version to a shorter version that revolves around native Android development can solve them. The main objective is to introduce the adaptation of the original Clean Architecture model to the current state of native Android development, all oriented towards an application called “InstaFlix”, by creating a shorter and more coupled format. The developer will follow best practices and promote the use of presentation patterns such as Model-View-ViewModel (MVVM) or Model View Presenter (MVP), as well as encourage dependency injection. In short, it makes it easier for many developers to work simultaneously on different parts of the system. This separation also generated a noticeable improvement in the code base, as changes can be made to specific components without affecting the rest of the system; focusing on modularity and maintainability, making it clear that it is valuable for any Android developer who wants to create high-quality software.
Recent Advances in Machine Learning for Differential Cryptanalysis

Differential cryptanalysis has proven to be a powerful tool to identify weaknesses in symmetric-key cryptographic systems such as block ciphers. Recent advances have shown that machine learning methods are able to produce very strong distinguishers for certain cryptographic systems. This has generated a large interest in the topic of machine learning for differential cryptanalysis as evidenced by a growing body of work in the last few years. In this paper we aim to provide a guide to the current state of the art in this topic in the hope that a unified view can better highlight the challenges and opportunities for researchers joining the field.