Reviewerly/exHarmony huggingface dataset:
Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. Then, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better.
Connect With Me. Exploring Influencing Factors in a Human-Technology Relationship Based on Regular Chatbot Use

Companion technologies, such as social robots and conversational chatbots, take increasing responsibility for daily tasks and support our physical and mental health. Especially in the domain of healthcare, where technologies are often applied for long-term use, our experience with and relationship to such technologies become ever more relevant. Based on a 2-week interaction period with a conversational chatbot, our study ( N = 58) explores the relationship between humans and technology. In particular, our study focuses on felt social connectedness of participants to the technology, possibly related characteristics of technology and users (e.g., individual tendency to anthropomorphize, individual need to belong), as well as possibly affected outcome variables (e.g., desire to socialize with other humans). The participants filled in short daily and 3 weekly questionnaires. Results showed that interaction duration and intensity positively predicted social connectedness to the chatbot. Thereby, perceiving the chatbot as anthropomorphic mediated the interrelation of interaction intensity and social connectedness to the chatbot. Also, the perceived social presence of the chatbot mediated the relationship between interaction duration as well as interaction intensity and social connectedness to the chatbot. Characteristics of the user did not affect the interrelations of chatbot interaction duration or intensity and perceived anthropomorphism or social presence. Furthermore, we did not find a negative correlation between felt social connectedness of users to the technology and their desire to socialize with other humans. In sum, our findings provide both theoretical and practical contributions. Our study suggests that regular interaction with a technology can foster feelings of social connectedness, implying transferability of dynamics known from interpersonal interaction. Moreover, social connectedness could be supported by technology design that facilitates perceptions of anthropomorphism and social presence. While such means could help to establish an intense relationship between users and technology and long-term engagement, the contexts in which anthropomorphic design is, actually, the means of choice should be carefully reflected. Future research should examine individual and societal consequences to foster responsible technology development in healthcare and beyond.
Cross-Cross Resonance Gate

The implementation of high-fidelity swapping operations is of vital importance for executing quantum algorithms on a quantum processor with limited connectivity. We present an efficient pulse-control technique, the cross-cross resonance (CCR) gate, to implement iswap and swap gates with dispersively coupled fixed-frequency transmon qubits. The key to the operation of the CCR gate is the simultaneous driving of both of the coupled qubits at the frequency of another qubit, whereby a fast two-qubit interaction roughly equivalent to ZX+XZ entangling gates is realized without strongly driving the qubits. We develop a calibration technique for the CCR gate and evaluate the performance of the iswap and swap gates. The CCR gate shows remarkable improvements in the average gate error from the conventional decomposition based on the cross-resonance gate.3 MoreReceived 9 June 2021Accepted 19 October 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040336Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum computationQuantum controlQuantum gatesQuantum Information
Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization

Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize residual/dense networks but reduce search space by eliminating fine-grained network setting choices. To address the aforementioned weaknesses, we propose a novel particle swarm optimization (PSO)-based deep architecture generation algorithm, to devise deep networks with residual connections, whilst performing a thorough search which optimizes important design choices. A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal architectures. Specifically, the proposed encoding scheme is able to describe convolutional neural network architecture configurations with residual connections. Evaluated using benchmark datasets, the proposed model outperforms existing state-of-the-art methods for architecture generation. Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment of residual architectures with great diversity. Our devised networks show better capabilities in tackling vanishing gradients with up to 4.34% improvement of mean accuracy in comparison with those of existing studies.
IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification

Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Therefore, there is a need for a correct classification of white blood cells. The approach developed in this article is based on an optimized and small IoT-friendly neural network architecture. The application of learning transfer in hybrid artificial intelligence systems is offered. The hybrid system consisted of a MobileNet v2 encoder pre-trained on the ImageNet dataset and machine learning algorithms performing the role of the head. These were the XGBoost, Random Forest, and Decision Tree algorithms. In this work, the average accuracy was over 90%, reaching 97.4%. This work proves that using hybrid artificial intelligence systems for tasks with a low computational complexity of the processing units demonstrates a high classification accuracy. The methods used in this study, confirmed by the promising results, can be an effective tool in diagnosing other blood diseases, facilitating the work of a network of medical institutions to carry out the correct treatment schedule.
Blockchain State Channels: A State of the Art

Blockchain technology has been quite popular during recent years and it finally seems to present a significant rise with respect to its use for real-world applications. This advancement has brought up a critical challenge that public blockchain systems face, which is scalability. Most of the currently deployed systems fail to cope with increasing usage. In order to provide the promised security guarantees, large delays and high usage fees are imposed for submitted transactions and thus widespread adoption of the technology is hindered. A number of different approaches have been proposed to increase the capacity of blockchain systems with respect to processing transactions. The present survey focuses on one of the most popular ones, that of state channels, and to the extent of our knowledge constitutes the first collective survey of research in this field. An extensive analysis of relevant publications is conducted and a general view on the domain is provided. We have identified the limitations discussed through all relevant research efforts along with the various features that differentiate proposed designs. A comparison between retrieved papers is carried out on the basis of those limitations and features. Finally, future research directions are analysed while the role of state channels in the general public blockchain ecosystem is also discussed.
Crossbar based Processing in Memory Accelerator Architecture for Graph Convolutional Networks

Graph data structures are central to many applications such as social networks, citation networks, molecular interactions, and navigation systems. Graph Convolutional Networks (GCNs) are used to process and learn insights from the graph data for tasks such as link prediction, node classification, and learning node embeddings. The compute and memory access characteristics of GCNs differ, both from conventional graph analytics algorithms and from convolutional neural networks, rendering the existing accelerators for graph analytics as well as deep learning, inefficient. In this work, we propose PIM-GCN, a crossbar-based processing-in-memory (PIM) accelerator architecture for GCNs. PIM-GCN incorporates a node-stationary dataflow with support for both Compressed Sparse Row (CSR) and Compressed Sparse Column (CSC) graph data representations. We propose techniques for graph traversal in the compressed sparse domain, feature aggregation, and feature transformation operations in GCNs mapped to in-situ analog compute functions of crossbar memory, and present the trade-offs in performance, energy, and scalability aspects of the PIM-GCN architecture for CSR, and CSC graph data representations. PIM-GCN shows an average speedup of over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3-16\times$</tex> and an average energy reduction of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4-12\times$</tex> compared to the existing accelerator architectures.
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization

Due to the complex and irregular shapes of handwritten text, it is challenging to spot and recognize the handwritten words. In low-resource scripts, retrieval of words is a difficult and laborious task. The need for increasing the number of samples and introducing variations in the extended training datasets occur with the use of deep learning and neural network models. All possible variations and occurrences cannot be covered in an efficient manner with the use of the existing preprocessing strategies and theories. A scalable and elastic methodology for wrapping the extracted features is presented with the introduction of an adversarial feature deformation and regularization module in this paper. In the original deep learning framework, this module is introduced between the intermediate layers while training in an alternative manner. When compared to the conventional models, highly informative features are learnt in an efficient manner with the help of this setup. Extensive word datasets are used for testing the proposed model, which is built on popular frameworks available for word recognition and spotting, while enhancing them with the proposed module. While varying the training data size, the results are recorded and compared with the conventional models. Improvement in the mAP scores, word-error rate and low data regime is observed from the results of comparison.
Impact of Different Role Types and Gender on Presence and Cybersickness in Immersive Virtual Reality Setups

Several factors have been identified to contribute to the sense of presence and cybersickness, including the preponderance users have in the virtual environments (VE) and their gender. This work focuses on studying the Role Type and gender in a VE and their impact on the sense of presence and cybersickness when immersive Virtual Reality (VR) setups are used. For this, a set of psychophysical experiments were conducted to evaluate a VR scenario with three Role Types: Viewer, Explorer, and Searcher. Results revealed statistically significant differences in Spatial Presence, Cybersickness, Nausea, Oculomotor Discomfort, and Disorientation for Role Type. In the evaluated scenario, it was observed that a more dominant Role Type on the VE leads to a higher reported spatial presence (sense of physically being present in the VE) and higher cybersickness scores. We conclude that a higher relevance of the Role Type makes the users more sensitive to the stimuli present in the VE regarding the coherency of the interaction/simulation and, consequently, more prone to develop cybersickness symptoms. No differences were found between the genders.
Using game theory to thwart multistage privacy intrusions when sharing data

Game theory indicates that minimal edits are needed to protect personal data against attacks on anonymity.
Nearshore Ship Detection on SAR Image Based on Yolov5

Nearshore ship detection faces big challenge due to the missing alarms and false alarms caused by onshore ship-like objects and close arrangement of ships. This paper proposes a method to detect nearshore ships, which is based on You Only Look Once Version 5 (Yolov5). To improve the precision, attention model and Circle Smooth Label (CSL) are unified into the detection network. The main research content and experimental work of this paper are as follows. First, Yolov5 network, attention model and CSL algorithm are analyzed. After that, the detection experiment is carried out based on Yolov5. Next, the attention model is introduced to improve the network. Then, combined with the CSL algorithm, the Yolov5 rotation detection network is reconstructed. Finally, by adjusting the training parameters and improving the attention, the test result of the detection network for inshore targets reached mAP above 80%, and the feasibility of the CSL+Yolov5 algorithm to achieve rotation detection is confirmed.
Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics for event graphs. This gap leaves biological relations unlinked and prevents the application of machine learning techniques to promote discoveries. Taking advantage of recent deep graph kernel solutions and pre-trained language models, we propose Deep Divergence Event Graph Kernels (DDEGK), an unsupervised inductive method to map events into low-dimensional vectors, preserving their structural and semantic similarities. Unlike most other systems, DDEGK operates at a graph level and does not require task-specific labels, feature engineering, or known correspondences between nodes. To this end, our solution compares events against a small set of anchor ones, trains cross-graph attention networks for drawing pairwise alignments (bolstering interpretability), and employs transformer-based models to encode continuous attributes. Extensive experiments have been done on nine biomedical datasets. We show that our learned event representations can be effectively employed in tasks such as graph classification, clustering, and visualization, also facilitating downstream semantic textual similarity. Empirical results demonstrate that DDEGK significantly outperforms other state-of-the-art methods.
Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning

Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.
Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis

Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists' assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95% CI = 0.801-0.905) and the cut-off value for risk score was 34.5% with a sensitivity of 72.8% and a specificity of 88.3% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3% for radiologists, 72.7% for AI, and 83.6% for radiologist and AI team-up. AI detected 72.7% of all cancers on its own, of which 77.5% were screen-detected, 15% were interval cancers, and 7.5% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.
Creating and Augmenting Keyboards for Extended Reality with the <u>K</u> eyboard <u>A</u> ugmentation <u>T</u> oolkit

This article discusses the Keyboard Augmentation Toolkit (KAT), which supports the creation of virtual keyboards that can be used both for standalone input (e.g., for mid-air text entry) and to augment physically tracked keyboards/surfaces in mixed reality. In a user study, we firstly examine the impact and pitfalls of visualising shortcuts on a tracked physical keyboard, exploring the utility of virtual per-keycap displays. Supported by this and other recent developments in XR keyboard research, we then describe the design, development, and evaluation-by-demonstration of KAT. KAT simplifies the creation of virtual keyboards (optionally bound to a tracked physical keyboard) that support enhanced display —2D/3D per-key content that conforms to the virtual key bounds; enhanced interactivity —supporting extensible per-key states such as tap, dwell, touch, swipe; flexible keyboard mappings that can encapsulate groups of interaction and display elements, e.g., enabling application-dependent interactions; and flexible layouts —allowing the virtual keyboard to merge with and augment a physical keyboard, or switch to an alternate layout (e.g., mid-air) based on need. Through these features, KAT will assist researchers in the prototyping, creation and replication of XR keyboard experiences, fundamentally altering the keyboard’s form and function.
An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

Abstract Still image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study, we aim to automate the process of optimal configuration identification. Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still images. It incorporates Gaussian fitness surface prediction and exponential search coefficients to overcome stagnation. It optimises the learning rate, batch size, and number of re-trained layers of a pre-trained convolutional neural network (CNN). To overcome bias of single optimised networks, an ensemble model with three optimised CNN streams is introduced. The first and second streams employ raw images and segmentation masks yielded by mask R-CNN as inputs, while the third stream fuses a pair of networks with raw image and saliency maps as inputs, respectively. The final prediction results are obtained by computing the average of class predictions from all three streams. By leveraging differences between learned representations within optimised streams, our ensemble model outperforms counterparts devised by PSO and other state-of-the-art methods for HAR. In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.