Reviewerly/exHarmony huggingface dataset:
Scarcity in the Metaverse: Space, Location and the Attention Economy

Critics argue that virtual land lacks inherent value as it is not truly scarce. In this paper we propose a conceptual framework that distinguishes between space and location and may serve as an explanation why people still pay significant price premiums for certain virtual land parcels. While space is indeed plentiful, location possesses distinct attributes that can serve as a valuable asset within the attention economy of the metaverse. We introduce several terms, including «static and dynamic links», or «address sets», and provide managers with the necessary tools to evaluate whether their application can benefit from acquiring virtual land.
On the adversarial robustness of full integer quantized TinyML models at the edge

The recent surge in deploying machine learning (ML) models at the edge has revolutionized various industries by enabling real-time decision-making on resource-constrained devices, such as TinyML models on microcontrollers. However, this trend brings forth a critical concern - the vulnerability of these models to adversarial examples. ML at the edge offers tremendous potential but demands heightened vigilance in the realm of cybersecurity.
Container-based low-delay microservice data offloading and processing for distribution substation area gateway in smart grid

A large number of source-load-storage devices are connected to the power system in the new power system, which leads to the surge of power service and puts forward new requirements for the operation efficiency of the distribution substation area. Container technology provides computing capacity for microservice of different devices by integrating computing resources of distribution substation area gateway. However, the differentiation of microservice priorities and continuous processing requirements bring new challenges to the optimization of microservice data offloading and processing. In this paper, we propose a container-based low-delay microservice data offloading and processing method for distribution substation area gateway. Firstly, we construct a container-based low-delay microservice data offloading and processing architecture for distribution substation area gateway. Then, an empirical matching-based joint gateway and container selection algorithm for low-delay microservice data offloading and processing is proposed. By introducing empirical performance and microservice priority, realizing the gateway and container selection of microservice data, reducing the microservice data offloading and processing delay. Simulation results show that the proposed algorithm is superior to the existing algorithm in total delay of microservice data offloading and processing.
The Role of Human-Centered Design in Healthcare Innovation: a Digital Health Equity Case Study

Healthcare delivery has become more complicated, particularly with the addition of digital tools and advanced technologies that can further exacerbate existing disparities. New approaches to solve complex, multi-faceted problems are needed. Human-centered design (HCD), also known as design thinking, is an innovative set of methods to develop solutions to these types of issues using collaborative, team-based, and empathetic approaches focused on end user experiences. Originally advanced in technology sectors, HCD has garnered growing attention in quality improvement, healthcare redesign, and public health and medical education. During the COVID-19 pandemic, our healthcare organization recognized notable differences in utilization of virtual (video-based) services among specific patient populations. In response, we mobilized, and using HCD, we collectively brainstormed ideas, rapidly developed prototypes, and iteratively adapted solutions to work toward addressing this digital divide and clinic and systems-level struggles with improving and maintaining digital health access. HCD approaches create a cohesive team-based structure that permits the dismantling of organizational hierarchies and departmental silos. Here we share lessons learned on implementing HCD into clinical care settings and how HCD can result in the development of site-specific, patient-centered innovations to address access disparities and to improve digital health equity.
Bayesian Spline-Based Hidden Markov Models with Applications to Actimetry Data and Sleep Analysis

B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modeling approach to data than conventional parametric HMMs.We introduce a Bayesian framework for inference, enabling the simultaneous estimation of all unknown model parameters including the number of states.A parsimonious knot configuration of the B-splines is identified by the use of a transdimensional Markov chain sampling algorithm, while model selection regarding the number of states can be performed based on the marginal likelihood within a parallel sampling framework.Using extensive simulation studies, we demonstrate the superiority of our methodology over alternative approaches as well as its robustness and scalability.We illustrate the explorative use of our methods for data on activity in animals, that is whitetip-sharks.The flexibility of our Bayesian approach also facilitates the incorporation of more realistic assumptions and we demonstrate this by developing a novel hierarchical conditional HMM to analyse human activity for circadian and sleep modeling.
CSA: Channel-Wise Similarity Attention for Vehicle State Classification

Developed for specific missions, CNNs have gradually improved the performance of object classification networks by using various architectures. The weight of the convolutional layer is a crucial factor in feature extraction. However, as the number of layers increases, performance degradation can occur due to problems such as the vanishing gradient. To overcome this problem, networks have evolved to continuously incorporate information from previous feature maps using various attention mechanisms. In this study, a Channel-wise Similarity Attention (CSA) method is proposed to measure the similarity of feature maps between channels and enhance positive information by highlighting it. Additionally, a deformable convolutional kernel is embedded to apply a flexible receptive field around the object area in the image, replacing the fixed receptive field of the conventional CNN layer. The network is trained end-to-end to classify the condition of vehicles on the road using collected drone flight images. The proposed model achieves an accuracy of 86.13% and 302 frames per second with a number of parameters of 1,273,504.
Artificial Intelligence Analysis for Small Object Detection in Urine Sediment Images

Over recent years, artificial intelligence (AI) methods have reformed the zone of medical imaging. Many AI-based models have been created and improved to related medical image analysis and interpretation. Object detection in medical images is a fundamental task in computer vision. With the continuous development of deep learning technology in recent years, we have been able to achieve good detection performance for conventional objects, but it has still been challenging for small objects due to their small size. In this paper, we propose an artificial intelligence analysis method for object detection in urine sediment images for auxiliary diagnosis. Firstly, we introduce a New Path Aggregation Network (NPANet), which improves the multi-scale fusion section. It can be more beneficial to help detect relatively small cellular objects, thus reducing the rate of missed detection. Secondly, we design an Adaptive-IoU ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AIoU$</tex> ) loss that can be more easily adopted to achieve better detection accuracy for the model. Finally, the experiments are conducted on our Dataset (named UriSed2K) and the public Urine Microscopic Image Dataset, and achieve the state-of-the-art results. Especially, our detector reaches 61.8% on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP_{small}$</tex> on the UriSed2K and our work also contributes to the development of medical image processing.
A Distributed Privacy-Preserving Algorithm using Row Stochastic Weight Matrix with Locally Balanced Noise

Privacy preservation for distributed optimization algorithms in multi-agent systems has warranted widespread concern in recent years due to the urgent security requirements. In this paper, a novel distributed subgradient privacy-preserving algorithm is proposed, where the weight matrix is row stochastic. The proposed algorithm holds an advanced noise-adding mechanism, where locally balanced noise is adopted to mask the real data that agents transmit to neighbors. It is shown that all the agents converge to an optimal solution of the optimization problem, while the subgradients and cost functions are preserved simultaneously.
Motion Reproduction of Sky and Water Surface From an Omnidirectional Still Image

Applications such as Google Street View allow users to experience the atmosphere of a place without physically being there. However, one issue with the applications is that the displayed images are still images, which lack a sense of presence when users see them in VR. In this study, we propose a method to enhance the sense of presence by focusing on sky and water areas in an omnidirectional still image and reproducing their motion for video generation.
GCA-Net: A Global Context Aggregation Network for Effective Optical Flow

Optical flow seeks to estimate the per-pixel 2D motion between two frames by identifying corresponding pixels. Current flow estimators typically involve per-pixel feature extraction, multi-scale 4D correlation volume construction, and iterative flow field updates through a Conv-GRU module. However, the locality of convolutional features in these methods renders the calculated correlations vulnerable to different noises. In addition, the Conv-GRU module of these methods is only executed with a convolution layer, which is incapable of exploiting context clues from larger window sizes even inside the image being queried itself, thus raising it more difficult for the model to process images with challenging regions, e.g., textureless areas. To this end, we introduce GCA-Net, a global context aggregation network for credible yet effective optical flow estimation. More specifically, we propose a highly efficient multi-scale transformer (MSFormer) layer which enables the per-pixel feature to aggregate long-range information from other features, hence building more accurate 4D correlation volumes. Besides, we develop an attention-enhanced Conv-GRU block (AttGRU) that can incorporate information alongside a larger context window even within itself empowering our network to estimate optical flow in even the most challenging regions. Experimentally, we demonstrate that GCA-Net outperforms previous state-of-the-art methods by large margins on Sintel (Final) and KITTI 2015 (background and foreground) benchmarks.
Motion Dynamics RRT Algorithm for Optimal TF /TA Routing Planning

The existing route planning algorithms are directly applied to the remote <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{TF}/\text{TA}$</tex> route planning of large and medium-sized fixed wing aircraft, and cannot simultaneously meet the requirements of efficiency, route consistency, and optimality. To deal with the problem, a motion dynamics RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is proposed. Firstly, the characteristic of ellipses is utilized to effectively reduce the planning space in order to improve search efficiency; Next, in order to reduce the randomness of the RRT algorithm, a motion dynamics model of the aircraft is established and the state transition mode of nodes based on this model is designed; Finally, a cost function was designed to penalize the detour distance, climb altitude, and unsafe flight path. The experimental results on real DEM data show that the proposed algorithm can simultaneously meet the requirements of high efficiency, route consistency, and optimality for long-distance TF/TA route planning of large and medium-sized fixed wing aircraft.
A Resource Efficient Model Training Based on Uncertainty Comparison to Detect Performance Anomalies in 5G Networks

This paper introduces an efficient model training method for anomaly detection of radio units (RUs) in 5G cellular networks. The proposed method uses Bayesian neural network (BNN)-based models that are selectively trained to reduce computing resources required for the model updating. In particular, an interquartile range (IQR)-based outlier detection of the uncertainty data from the BNN model is used for the selective learning. Our simulation results show that the resource required for model retraining is reduced by about 40%, and the reliability of the model improved by 1.5~7.9%. We expect that the proposed method can contribute to a more cost-effective and reliable anomaly monitoring in 5G Network.
Token Merging with Class Importance Score

Vision Transformers have achieved high performance in computer vision tasks, but their high computational cost and low throughput are weaknesses. Therefore, much research has been done to reduce the size of Vision Transformers. Among them, studies on pruning unnecessary tokens are being actively conducted to reduce the number of tokens used for self-attention computation inside the Vision Transformer. Recently, token merging has been proposed as a new alternative approach. These studies aim to increase throughput with a small accuracy drop by merging similar tokens instead of pruning them. A previous study finds similar tokens using cosine similarity and merges them with a weighted average. However, merging a large number of tokens at once may lead to an accuracy drop because of the underestimating of important information. In this paper, we propose ToMeCIS, a method that merges similar tokens through a weighted average using the class importance score of tokens to reduce the accuracy drop. When ToMeCIS is applied to a pretrained DeiT-S and evaluated on the ImageNet-1k dataset, the throughput is increased by about 50% with an accuracy drop of less than 1% without additional training. In addition, importance scores were evaluated with different metrics to find the best accuracy versus throughput trade-off.
Path Planning of Unmanned Surface Vessel Based on Improved RRT*

With the continuous development of artificial intelligence, the application of unmanned surface vessels (USVs) in the field of intelligent maritime vessels is becoming increasingly mature. Significant achievements have been made in several areas such as ocean environmental monitoring, maritime rescue, and maritime security. To further enhance the autonomous exploration capability, navigation safety, and operational efficiency of USV in route planning, this paper proposes an improved rapidly-exploring random tree* (RRT*) algorithm for USV path planning in known environments. Through simulation experiments, it has been verified that the proposed RRT* can achieve superior results in USV path planning. The proposed RRT* algorithm imposes restrictions on the sampling point range, building upon the original RRT* algorithm, resulting in more precise path planning. By comparing with the classical RRT algorithm and other traditional RRT algorithms, our approach significantly reduces the required distance for path planning while ensuring the safety of ship navigation. However, this improvement also comes with an increase in the time cost as it enhances the quality of the planned path.
Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML

Among renewable energies, photovoltaic power generation, which can be introduced relatively easily in buildings and houses, is being used, and its further introduction is desired. Therefore, there is a need for technology to accurately predict the amount of electricity generated at potential sites for photovoltaic power generation facilities. In this study, we tried various machine learning methods for predicting the amount of electricity generated by photovoltaic power generation without using the information of the solar radiation meters, and examined the effect of the training period of machine learning on the accuracy of the prediction.
Video Salient Object Detection Using Multi-Scale Self-Attention

How to effectively model both spatial information and temporal dynamics is crucial to Video Salient Object Detection (VSOD). Recently, there are some works using self-attention mechanism to capture the spatiotemporal information due to its ability of modeling long-range dependencies of patch tokens. However, these models designate similar receptive fields of the spatiotemporal feature maps, which limits the ability of the models in handling the frames with multiple salient objects of different scales. To address this issue, we propose a Multi-Scale Self-Attention (MSSA) operation to better model the spatiotemporal features of salient objects with different scales. The experimental results demonstrate that our method achieves better performance on challenge datasets by using MSSA operation.
An Efficient Approach for Training Time Minimization in Distributed Split Neural Network

In this paper, we present a novel approach for minimizing training time in distributed split neural network. This approach allocates parts of the neural network across different servers, taking into account two critical factors: the latency among the training servers and the execution sequence of the neural network. This does not only achieve faster training in distributed neural networks but also provides a significant boost to the research and development of large-scale neural networks.
3D Ball Trajectory Reconstruction of a Ballistic Shot from a Monocular Basketball Video

3D ball location in sports can be advantageous and has many applications such as automatic scoring system, ball trajectory highlight and offside detection system for football game. Currently, this task can be solved through the use of costly multi-view approaches or the legacy physics-based methods that require complicated setups and human intervention. In this paper, we propose a method to reconstruct the 3D ball trajectory of the basketball projectile shot by using only a single view. The method relies on accurate camera calibration and 2D ball detection of sequence images. Extra information in the 3D world also plays an important role in improving the 3D reconstruction. The experimental result illustrates the accurately reconstructed 3D trajectory when enough information is given.
Fast 3D Convolution with Winograd Algorithm

As 3D data has many applications in different fields, including autonomous car, robotics, medical science, etc. With the expansion of these applications, computing ability has gradually become an important evaluation indicator. In this work, this paper uses the 3D Winograd algorithm to realize the fast calculation method of the 3D convolution layer in the neural network. The technology of reusing data and parallel processing units are used to achieve the acceleration effect. Finally, the design can process 1748 mega pixels of operations per second and realize the chip according to the digital integrated circuit design process of the standard component library.
Score prediction using multiple object tracking for analyzing movements in 2-vs-2 Handball

Automatic evaluation of players’ movement is an important issue in sports. In handball, researchers have realized it using sensors of local positioning systems and machine learning. However, we cannot always use such sensors, especially in nonprofessional teams. Here, we propose a system to evaluate players’ movement using multiple object tracking from videos and logistic regression models trained with score labels. Using handball 2-vs-2 videos, we showed that the proposed system predicted a score with an average accuracy of over 60%. We also analyzed the factors contributing to the score prediction.
Generation Management of White-Box DNN Model Watermarking

Techniques have been proposed to claim and protect copyrights by embedding watermarks on DNN (Deep Neural Network) models. However, there was insufficient verification of the generation management of watermarks, such as copyright indications for derived models. In this paper, we assume a white-box setting in which all parameters in the model are observable, verify the interference between watermarks when multiple watermarks are embedded, and propose an embedding method to reduce the interference. The experimental results confirm that watermarks can be embedded for two generations, Gen1 and Gen2. Furthermore, it was confirmed that the proposed method reduces the interference between watermarks. It was also found that embedding the watermark requires a number of parameters corresponding to the number of bits in the watermark. As a future issue, there was a difference in the magnitude of the embedding error of the watermark depending on the embedding layer.
Security Evaluation of Compressible and Learnable Image Encryption against Jigsaw Puzzle Solver Attacks

Several learnable image encryption schemes have been developed for privacy-preserving image classification. This paper focuses on the security of block-based image encryption methods that are learnable and JPEG-friendly. Permuting divided blocks in an image is known to enhance robustness against ciphertext-only attacks (COAs), but recently jigsaw puzzle solver attacks have been demonstrated to be able to restore visual information on the encrypted images. In contrast, it has never been confirmed whether encrypted images including noise caused by JPEG-compression are robust. Accordingly, the aim of this paper is to evaluate the security of compressible and learnable encrypted images against jigsaw puzzle solver attacks. In experiments, the security evaluation was carried out on the CIFAR-10 and STL-10 datasets under JPEG-compression.
Cross-Lingual Speaker Identification for Japanese-English Bilinguals

While most people in the world speak more than one language, it has been reported that the performance of existing monolingual speaker identification deteriorates when there is a language mismatch. In this study, we examine whether speakers are correctly identified when the languages of the training set and test set are changed for 13 Japanese-English bilinguals of both genders. The experiment was conducted using an x-vector calculated from 20-dimensional log MFBs as features. The results showed that the accuracy was higher when the training set was English and test set was Japanese than the opposite condition, regardless of the lengths of the input segment, although it was inferior to monolingual speaker identification. When we conducted similar experiments by removing some of the dimensionality of the log MFBs, we found that there were differences in accuracy depending on the language in which they were trained, indicating that the frequency bands with the most important amount of information differ among the languages.
Proposal on Secure Password Reminder System Using Bloom Filter

Since password autofill function or biometric authentications improve usability, users tend to forget part of the password at a fallback authentication. To overcome this problem, several systems providing appropriate hint when an incorrect password is entered are shown. Previous study proposed the method of showing precise hint by using partial hash matching algorithm. However, it takes much time to calculate the hint. In addition, the security of the system deteriorates compared to the conventional password authentication system. In this paper, we propose a password reminder hint providing system that utilizes Bloom filter. Hence, we show that the proposed system requires less time than the previous system and has almost same security as conventional password system.