Reviewerly/exHarmony huggingface dataset:
RED-MAM: A residual encoder-decoder network based on multi-attention fusion for ultrasound image denoising

The dependence of speckle noise in ultrasound images on image data makes the research of ultrasound image denoising tasks a great challenge. Several deep learning-based ultrasound image denoising methods have been proposed, but almost all of them suffer from the inability to focus on the importance of feature information on multiple domains at the same time. Therefore, this paper proposes a residual encoder-decoder based on multi-attention fusion attention module (RED-MAM) for ultrasound image denoising, which consists of five convolution layers, five deconvolution layers and two multi-attention fusion attention blocks. In order to make the denoising model better adapted to the properties of speckle noise, a multi-attention fusion attention block is constructed by several different attention modules. The multi-attention fusion attention block is introduced into the residual encoder-decoder denoising network to make the model focus on the ultrasound image texture structure information on multiple domains, thus enhancing the denoising effect of the model on ultrasound images. The performance of our proposed model is evaluated on three ultrasound image datasets. In terms of quantitative metrics, RED-MAM shows substantial improvement in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Root Mean Square Error (RMSE). The qualitative results show that RED-MAM has significantly improved denoising performance and has good results in noise suppression as well as structure retention. Our method achieves state-of-the-art performance on all three ultrasound datasets.
Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Energy Mater. 33/2022)

Lithium Batteries In article number 2200889, Kai Yang, Shichun Yang and co-workers demonstrate the potential of cyber hierarchy and interactional network framework (CHAIN) to bridge multi-scale characterization and modeling technologies to evaluate lithium battery overall lifecycle. CHAIN combing digital twin, cyber twin and artificial intelligence can enable the digitalization of cyber-physical space, further encouraging battery full-lifespan management from new material discovery, intelligent manufacturing and cloud management system design.
Higher-Order Masked Saber

Side-channel attacks are formidable threats to the cryptosystems deployed in the real world. An effective and provably secure countermeasure against side-channel attacks is masking. In this work, we present a detailed study of higher-order masking techniques for the key-encapsulation mechanism Saber. Saber is one of the lattice-based finalist candidates in the National Institute of Standards of Technology’s post-quantum standardization procedure. We provide a detailed analysis of different masking algorithms proposed for Saber in the recent past and propose an optimized implementation of higher-order masked Saber. Our proposed techniques for first-, second-, and third-order masked Saber have performance overheads of 2.7x, 5x, and 7.7x respectively compared to the unmasked Saber. We show that compared to Kyber which is another lattice-based finalist scheme, Saber’s performance degrades less with an increase in the order of masking. We also show that higher-order masked Saber needs fewer random bytes than higher-order masked Kyber. Additionally, we adapt our masked implementation to uSaber, a variant of Saber that was specifically designed to allow an efficient masked implementation. We present the first masked implementation of uSaber, showing that it indeed outperforms masked Saber by at least 12% for any order. We provide optimized implementations of all our proposed masking schemes on ARM Cortex-M4 microcontrollers.
An Improved Helmet Detection Algorithm Based on YOLO V4

The existing helmet detection algorithms have disadvantages such as difficulty in detecting occluded targets, small targets, etc. To address those problems, a YOLO V4-based helmet detection improvement algorithm has been proposed. Firstly, the model’s backbone structure is improved, and the backbone’s multi-scale feature extraction capability is enhanced by using MCM modules with different sized convolutional kernels, the FSM channel attention module is used to guide the model to dynamically focus on the channel features of extracted small targets and obscured target information. Secondly, in order to optimize the model training, the latest loss function Eiou is used to replace Ciou for anchor frame regression prediction to improve the convergence speed and regression accuracy of the model. Finally, a helmet dataset is constructed from this paper, and a K-means clustering algorithm is used to cluster the helmet dataset and select the appropriate a priori candidate frames. The experimental results show that the improved algorithm has a significant improvement in detection accuracy compared with the original YOLO V4 algorithm, and can have a positive detection effect on small targets and obscured targets.
Human abnormal behavior detection using CNNs in crowded and uncrowded surveillance – A survey

The demand for surveillance networks is increasing universally on account of decreasing the faith in people. This leads to monitor the people during working, roaming, traveling, and shopping, etc. Thus, a surveillance system is needed to monitor human behaviors as a third eye in crowded and uncrowded indoor and outdoor areas. This system records the incidents that contain the patterns of various human behaviors. The video is checked to identify an incident manually is time-consuming. Hence, an automation system is needed for processing lengthy videos. The growth of graphics processors and Convolutional Neural Networks (CNNs) addresses video processing challenges to identify the incidents. This paper examines human abnormal behaviors using various CNNs to recognize the abnormal behaviors in the video. This study observed that 3D Convolutional Neural Network is performing better than machine learning algorithms. The comparison showed the various CNN model's performance to identify the various human abnormal behaviors with diverse datasets.
Argumentation: A calculus for Human-Centric AI

This paper aims to expose and analyze the potential foundational role of Argumentation for Human-Centric AI, and to present the main challenges for this foundational role to be realized in a way that will fit well with the wider requirements and challenges of Human-Centric AI. The central idea set forward is that by endowing machines with the ability to argue with forms of machine argumentation that are cognitively compatible with those of human argumentation, we will be able to support a naturally effective, enhancing and ethical human-machine cooperation and "social" integration.
Criminogenic and harm-enabling features of social media platforms: The case of sharenting practices

Sharenting – that is, the sharing of identifying and sensitive information of minors, who are often overexposed online by parents or guardians – has, at times, criminogenic potential, as the information shared can enable both heinous crimes and other types of harmful conduct. Whilst most research on sharenting has focused on the sharenters and their agency, there is a gap in addressing whether and to what extent social media platforms display criminogenic or other harm-enabling features that can render sharenting risky for affected minors. By relying on an adapted crime proofing of legislation approach, our contribution analyses the self-regulations (in the form of corporate documents and forms of self-organisation) of five major social media platforms and identifies several risks and vulnerabilities to harmful sharenting practices embedded in the platforms’ policies. In doing so, the study demonstrates how criminological imagination can effectively contribute to the multidisciplinary debates on digital ecosystems and their regulation, paving the way for a reduction of criminogenic and harmful opportunities online.
Customer acceptance of service robots under different service settings

Purpose This paper investigates the reasons for the differences in customers' acceptance of service robots (CASR) in actual experience and credence service settings for the following two aspects: (1) different antecedents affecting CASR and (2) different customer perceptions of their own characteristics (role clarity and ability) and service robot characteristics (anthropomorphism and ability). Design/methodology/approach The data were collected using online surveys in an experience service setting (Hotel, N = 426) and a credence service setting (Hospital, N = 406). Differences in experience and credence service settings were examined using two statistical methods, namely, PLS-SEM to test the differences in antecedents affecting CASR and independent-samples t -tests to test the differences in customer perceptions of their own characteristics and service robot characteristics. Findings The results indicate that customers in an experience (vs credence) service setting have stronger positive attitudes toward and a greater intention to use service robots. Further, this paper finds there are two key reasons for the differences in CASR. The first is different antecedents. Perceived usefulness is positively influenced by the anthropomorphism of a service robot and customer ability in the experience service setting, but is influenced not in the credence service setting. Conversely, service robot autonomy positively relates to perceived ease of use in the credence service setting, but does not in the experience service setting. The second reason for CASR differences is different customer perceptions. Customers' ability and perceived ease of use are higher, while their perception of anthropomorphism of the service robot is lower in the experience (vs credence) service setting. Originality/value This study helps explain why there are differences in the CASR in different settings and presents two perspectives: (1) antecedents' affecting CASR and (2) customer perceptions of their own as well as service robot characteristics.
Integration of Computer Virtual Reality Technology to College Physical Education

The progress of the times has brought about a leap forward in people’s thinking. Under the rapid economic development environment, the physical field of young people has been unable to withstand the current teaching system, and many problems of poor physical fitness have emerged. In order to solve similar problems, to improve the physical quality of young people, it is bound to find a new teaching method different from the traditional physical education teaching in a special environment, The research on the integration of virtual reality technology and teaching has been rolling forward and never stopped in recent years. With the continuous upgrading of virtual reality technology, virtual reality devices that can be used have already joined the ranks of families. Let these virtual reality devices connect to the Internet through the WEB application settings, and then design according to different situations has become a reality. This research is an application development based on Web virtual reality technology, including network virtual reality technology, application modeling design and the use of Internet connection. With reference to this application, physical education teaching and classroom practice data, this research mainly introduces the current research situation of virtual reality technology and teaching, and establishes the model based on the integration of Web application design and virtual reality technology, Extract, including but not limited to the number of projects, activities and experiences, use the Internet of Things data upload and data processing technology to complete the data screening, obtain valuable data, and then use these data for the practical application value of virtual reality technology in physical education teaching. Complete the improvement. It is in line with the integration of modern virtual reality technology and physical education teaching to obtain data that can reflect the real situation, then conduct practical teaching, obtain reliable practical data, and evaluate teaching. It can be clearly seen from the research results that the combination of virtual reality technology and physical education teaching is of great significance to improve students’ interest in learning and enthusiasm for sports, but there are also some shortcomings, such as different teaching steps and goals will affect students’ enthusiasm for learning. Therefore, further improvement is needed to find a stable way to improve students’ enthusiasm for learning. The integration of virtual reality technology and physical education teaching is proposed.
DICE: Leveraging Sparsification for Out-of-Distribution Detection

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking the role of sparsification. In this paper, we reveal important insights that reliance on unimportant weights and units can directly attribute to the brittleness of OOD detection. To mitigate the issue, we propose a sparsification-based OOD detection framework termed DICE. Our key idea is to rank weights based on a measure of contribution, and selectively use the most salient weights to derive the output for OOD detection. We provide both empirical and theoretical insights, characterizing and explaining the mechanism by which DICE improves OOD detection. By pruning away noisy signals, DICE provably reduces the output variance for OOD data, resulting in a sharper output distribution and stronger separability from ID data. We demonstrate the effectiveness of sparsification-based OOD detection on several benchmarks and establish competitive performance. Code is available at: https://github.com/deeplearning-wisc/dice.git .
Measuring the Monetary Value of Online Volunteer Work

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor’s value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours are worth 3.4 million USD based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.
Reactive Task Allocation and Planning for Quadrupedal and Wheeled Robot Teaming

This paper takes the first step towards a reactive, hierarchical multi-robot task allocation and planning framework given a global Linear Temporal Logic specification. The capabilities of both quadrupedal and wheeled robots are leveraged via a heterogeneous team to accomplish a variety of navigation and delivery tasks. However, when deployed in the real world, all robots can be susceptible to different types of disturbances, including but not limited to locomotion failures, human interventions, and obstructions from the environment. To address these disturbances, we propose task-level local and global reallocation strategies to efficiently generate updated action-state sequences online while guaranteeing the completion of the original task. These task reallocation approaches eliminate reconstructing the entire plan or resynthesizing a new task. To integrate the task planner with low-level inputs, a Behavior Tree execution layer monitors different types of disturbances and employs the reallocation methods to make corresponding recovery strategies. To evaluate this planning framework, dynamic simulations are conducted in a realistic hospital environment with a heterogeneous robot team consisting of quadrupeds and wheeled robots for delivery tasks.
CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature

This paper introduces a machine learning based copy-move forgery (CMF) localization method. The basic convolutional neural network cannot be applied to CMF detection because CMF frequently involves rotation transformation. Therefore, we propose a rotation-invariant feature based on the root-mean squared energy using high-frequency wavelet coefficients. Instead of using three color image channels, two-scale energy features and low-frequency subband image are fed into the conventional VGG16 network. A correlation module is used by employing small feature patches generated by the VGG16 network to obtain the possible copied and moved patch pairs. The all-to-all similarity score is computed using the correlation module. To generate the final binary localization map, a simplified mask decoder module is introduced, which is composed of two simple bilinear upsampling and two batch-normalized-inception-based mask deconvolution followed by bilinear upsampling. We perform experiments on four test datasets and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection

Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary $$2$$ D translations. However, these vanilla blocks are not equivariant to arbitrary $$3$$ D translations in the projective manifold. Even then, all monocular $$3$$ D detectors use vanilla blocks to obtain the $$3$$ D coordinates, a task for which the vanilla blocks are not designed for. This paper takes the first step towards convolutions equivariant to arbitrary $$3$$ D translations in the projective manifold. Since the depth is the hardest to estimate for monocular detection, this paper proposes Depth EquiVarIAnt NeTwork (DEVIANT) built with existing scale equivariant steerable blocks. As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not. The additional depth equivariance forces the DEVIANT to learn consistent depth estimates, and therefore, DEVIANT achieves state-of-the-art monocular $$3$$ D detection results on KITTI and Waymo datasets in the image-only category and performs competitively to methods using extra information. Moreover, DEVIANT works better than vanilla networks in cross-dataset evaluation.
Word-Level Sign Language Recognition with Multi-Stream Neural Networks Focusing on Local Regions and Skeletal Information

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Handcrafted Histological Transformer (H2T): Unsupervised representation of whole slide images

Diagnostic, prognostic and therapeutic decision-making of cancer in pathology clinics can now be carried out based on analysis of multi-gigapixel tissue images, also known as whole-slide images (WSIs). Recently, deep convolutional neural networks (CNNs) have been proposed to derive unsupervised WSI representations; these are attractive as they rely less on expert annotation which is cumbersome. However, a major trade-off is that higher predictive power generally comes at the cost of interpretability, posing a challenge to their clinical use where transparency in decision-making is generally expected. To address this challenge, we present a handcrafted framework based on deep CNN for constructing holistic WSI-level representations. Building on recent findings about the internal working of the Transformer in the domain of natural language processing, we break down its processes and handcraft them into a more transparent framework that we term as the Handcrafted Histological Transformer or H2T. Based on our experiments involving various datasets consisting of a total of 10,042 WSIs, the results demonstrate that H2T based holistic WSI-level representations offer competitive performance compared to recent state-of-the-art methods and can be readily utilized for various downstream analysis tasks. Finally, our results demonstrate that the H2T framework can be up to 14 times faster than the Transformer models.
AMP-Net: Appearance-Motion Prototype Network Assisted Automatic Video Anomaly Detection System

As essential tools for industry safety protection, automatic video anomaly detection systems (AVADS) are designed to detect anomalous events of concern in surveillance videos. Existing VAD methods lack effective exploration of the prototypical appearance and motion features leading to poor performance in realistic scenarios. Specifically, they either misreport regular events as anomalies due to insufficient representation power, or lead to missed detections with over-power generalization. In this regard, we propose an appearance-motion prototype network (AMP-net) that uses external memories to record prototype features and augments the appearance-motion prototype with a spatial-temporal fusion. In addition, AMP-net sequentially fuses appearance features from deep to shallow to utilize multiscale spatial context. Additionally, we introduce temporal attention to capture important dynamics and enhance AMP-net for representing regular motion. The proposed method achieves a delicate balance of effective representation of normal events and limited generalization to anomalies. Experiments on three benchmark datasets demonstrate that our method can accurately detect anomalous events, achieving performance comparable to state-of-the-art methods with frame-level AUCs of 98.7%, 92.4%, and 78.8% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets. Moreover, we conducted a case study on the self-collected industrial dataset, and the results indicate that our AMP-net can cope with complex industrial scenarios and outperform existing methods.
Accelerated Singular Value Decomposition (ASVD) using momentum based Gradient Descent Optimization

The limitations of neighborhood-based Collaborative Filtering (CF) techniques over scalable and sparse data present obstacle for efficient recommendation systems. These techniques show poor accuracy and dismal speed in generating recommendations. Model-based matrix factorization is an alternative approach use to overcome aforementioned limitations of CF. Singular value decomposition (SVD) is widely used technique to get low-rank factors of rating matrix and use Gradient Descent (GD) or Alternative Least Square (ALS) for optimization of its error objective function. Most researchers have focused on the accuracy of predictions but they did not accumulate the convergence rate of learning approach. In this paper, we propose a new filtering technique that implements SVD using Stochastic Gradient Descent (SGD) optimization and provides an accelerated version of SVD for fast convergence of learning parameters with improved classification accuracy. Our proposed method accelerates SVD in the right direction and dampens oscillation by adding a momentum value in parameters updates. To support our claim, we have tested our proposed model against the famed real world datasets (MovieLens100k, FilmTrust and YahooMovie). The proposed Accelerated Singular Value Decomposition (ASVD) outperformed the existing models and achieved higher convergence rate and better classification accuracy.