Reviewerly/exHarmony huggingface dataset:
Fine Segmentation on Faces With Masks Based on a Multistep Iterative Segmentation Algorithm

Innovation in facial recognition technology is urgent in the current epidemic situation.In order to extract more face features to perform facial recognition on images of faces with masks, we propose a multi-step iterative face-mask segmentation algorithm (MISA).The improved Mask R-CNN algorithm is used to detect the target in the face region to realize coarse segmentation, and the generalization ability and accuracy of the segmentation are improved.Then, the proposed approach uses an R-Pairwise Differential Siamese Network (R-PDSN) to train a mask occlusion classifier to subdivide the edge blocks of coarse segmentation results.The segmentation accuracy is further improved by optimizing the edge information of the masks step by step.The self-built dataset of faces with masks was used for training and testing.The experimental results showed that the mean pixel accuracy of the proposed method was improved by 2.69% compared with the original Mask R-CNN segmentation algorithm, and the target detection accuracy was more than 98%.These results indicate that the proposed method can achieve good segmentation performance on face images with complex backgrounds, self-occlusion and different types of masks.These results demonstrate that our method can improve the accuracy of segmentation methods for imaged of faces wearing masks.
A Fast Threshold Neural Network for Ship Detection in Large-Scene SAR Images

Multiscale ship detection in large-scene offshore synthetic aperture radar (SAR) images is of great significance in civil and military fields, such as maritime management and wartime reconnaissance. Methods based on deep learning apply a deep neural network to extract multiscale information from SAR images, which improves detection performance. However, deep neural networks are computationally complex, and even with GPU acceleration, the timeliness of ship detection in large-scene SAR images is still constrained. Methods based on threshold segmentation, in contrast, are efficient and straightforward, but they are less robust and need to be adjusted with complex and changing scenes. This article combines two methods and proposes a lightweight framework based on a threshold neural network (TNN) to achieve fast detection. Specifically, the TNN is carefully designed to extract the grayscale features of the SAR image, which predicts the optimal detection threshold within the sliding window and separates the targets adaptively. In addition, a false alarm rejection network is used to discriminate candidate targets and improve detection accuracy. Experiments are carried out on the public SSDD offshore dataset and the FUSAR-Ship-Detection dataset. The results show that the proposed framework performs 14.43% better than the Multi-CFAR for the SSDD offshore dataset and 7.36% better for the FUSAR-Ship-Detection dataset when using F1 as the metric. Furthermore, the floating point operations of the proposed framework are only 1/240 of those of YOLO-v4 with comparable performance.
A Motion Matching-based Framework for Controllable Gesture Synthesis from Speech

Recent deep learning-based approaches have shown promising results for synthesizing plausible 3D human gestures from speech input. However, these approaches typically offer limited freedom to incorporate user control. Furthermore, training such models in a supervised manner often does not capture the multi-modal nature of the data, particularly because the same audio input can produce different gesture outputs. To address these problems, we present an approach for generating controllable 3D gestures that combines the advantage of database matching and deep generative modeling. Our method predicts 3D body motion by sequentially searching for the most plausible audio-gesture clips from a database using a k-Nearest Neighbors (k-NN) algorithm that considers the similarity to both the input audio and the previous body pose information. To further improve the synthesis quality, we propose a conditional Generative Adversarial Network (cGAN) model to provide a data-driven refinement to the k-NN result by comparing its plausibility against the ground truth audio-gesture pairs. Our novel approach enables direct and more varied control manipulation that is not possible with prior learning-based counterparts. Our experiments show that our proposed approach outperforms recent models on control-based synthesis tasks using high-level signals such as motion statistics while enabling flexible and effective user control for lower-level signals. 1
Multi-criteria Selection of Rehearsal Samples for Continual Learning

Retaining a small subset to replay is a direct and effective way to prevent catastrophic forgetting in continual learning. However, due to data complexity and restricted memory, picking a proper subset for rehearsal is challenging and still being explored. In this work, we present a Multi-criteria Subset Selection approach that can stabilize and advance replay-based continual learning. The method picks rehearsal samples by integrating multiple criteria, including distance to prototype, intra-class cluster variation, and classifier loss. By doing so, it maximizes the comprehensive representation power of the sampled subset by ensuring its representativeness, diversity, and discriminability. We empirically find that singular criteria are likely to fail in particular tasks, while multi-criteria minimizes this risk and stabilizes task training throughout the continual learning process. Moreover, our method improves replay-based methods consistently and achieves state-of-the-art performance on both CIFAR100 and Tiny-Imagenet datasets.
Privacy protection federated learning system based on blockchain and edge computing in mobile crowdsourcing

With the rapid popularization and development of the Internet of Things (IoT) and 5G networks, mobile crowdsourcing (MCS) has become an indispensable part in today’s society. However, when task participants submit tasks, they are likely to expose their data privacy and location privacy. These privacy will be maliciously attacked and exploited by attackers (external attackers and untrusted third party). With the rapid increase of MCS data throughput , traditional cloud platforms can no longer meet the huge data processing needs. To solve these problems, this paper proposes an MCS federated learning system based on Blockchain and edge computing. This paper uses federated learning as the framework of the MCS system. The system protects data privacy and location privacy by using the Double local disturbance Localized Differential Privacy (DLD-LDP) proposed in this paper. Because the sensed data exists in multiple modalities (text, video, audio, etc.), this paper uses the Multi-modal Transformer (MulT) method to merge the multi-modal data before subsequent operations. To solve the problem that the third party is untrusted, we utilize Blockchain to distribute tasks and collect models in a distributed way. A reputation calculation method (Sig-RCU) is proposed to calculate the real-time reputation of task participants. Through conducting experiments on real data sets , the effectiveness and adaptation of the proposed DLD-LDP algorithm and Sig-RCU algorithm are verified.
Wearable technology for early detection of COVID-19: A systematic scoping review

Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.
Security and Privacy Protection Obstacles with 3D Reconstructed Models of People in Applications and the Metaverse: A Survey

In this scientific research, a survey of different security and privacy protection issues and flaws in the context of reconstruction of 3D models of real people is made. The intent of this analysis, is to consider the threats that a realistic digital clone, a.k.a avatar of a person can have in the wrong hands. This paper will also offer different approaches that can be used in overcoming this privacy problem.
Improved CNN Based on Batch Normalization and Adam Optimizer

After evaluating the difficulty of CNNs in extracting convolution features, this paper suggested an improved convolutional neural network (CNN) method (ICNN-BNDOA), which is based on Batch Normalization (BN), Dropout (DO), and Adaptive Moment Estimation (Adam) optimizer. To circumvent the gradient challenge and quicken convergence, the ICNN-BNDOA uses a sequential CNN structure with the Leaky rectified linear unit (LeakyReLU) as the activation function (AF). The approach employs an Adam optimizer to handle the overfitting problem, which is done by introducing BN and DO layers to the entire connected CNN layers and the output layers, respectively, to decrease cross-entropy. Through a small regularization impact, BN was utilized to substantially speed up the training process of a neural network, as well as to increase the model's performance. The performance of the proposed system with conventional CNN (CCNN) was studied using the CIFAR-10 datasets as the benchmark data, and it was discovered that the suggested method demonstrated high recognition performance with the addition of BN and DO layers. CCNN and ICNN-BNDOA performance were compared. The statistical results showed that the proposed ICNN-BNDOA outperformed the CCNN with a training and testing accuracy of 0.6904 and 0.6861 respectively. It also outperformed with training and testing loss of 0.8910 and 0.9136 respectively.
Blockchain Technology - Emerging Research Themes Opportunities in Higher Education

In the industrial era 4.0, technological development is quite rapid, which can be seen from the development that is all with the system. One of which is the Blockchain system. Blockchain is a digital system that stores all transaction records in a ledger. Blockchain technology systems opportunities in research are relevant because they observe the perceived impact and potential disruptions that arise but are slow to generate significant momentum in the information systems literature. This study uses a literature review method that develops the main themes of blockchain technology research with a comprehensive review. This literature analysis highlights that although some commercial-grade blockchain applications currently exist, the technology also provides significant potential for benefit in several cases in higher education use. This study highlights the points by articulating the themes to develop a detailed account of the many blockchain applications and addresses the barriers to blockchain adoption. This study aims to contribute to several UN Sustainability Development Goals in the world of higher education.
Infrared Image Target Detection of Substation Electrical Equipment Using an Improved Faster R-CNN

Infrared camera can be used to monitor the condition of substation electrical equipment. Fast and accurate target detection algorithm is the key for infrared intelligent on-line routing inspection. However, the performance of traditional detection algorithm is poor due to the complex background of substation. To solve this problem, this paper proposes a target detection model based on the improved faster region-based convolutional neural networks (Faster R-CNN), which can be used for automatic detection of five kinds of electrical equipment in substations. The feature extraction network of this model is improved based on VGG16 by abandoning some high-level convolutions to accelerate the training and testing speed. Meanwhile, the 1:3 and 3:1 aspect ratio of anchor are added to improve the detection accuracy of elongated equipment. Experiments are performed on an infrared image dataset of substation to detect five types of equipment. The robustness tests of our model are carried out, too. The results show that our model performs well in detection accuracy and speed, achieving a mean detected accuracy of 95.32% and running at 11 frame/second. In addition, our model is robust to noise and lightness, which is suitable to other substations. Comparing to other models, our model has the highest mAP@0.5 of 92.78%.
DRR-Net: A Dense-Connected Residual Recurrent Convolutional Network for Surgical Instrument Segmentation From Endoscopic Images

The precise segmentation of surgical instruments is the key link for the stable and reasonable operation of surgical robots. However, accurate surgical instrument segmentation is still a challenging task due to the complex surgical environment in endoscopic images, low contrast between surgical instruments and tissues, and the diversity of surgical instruments and their morphological variability. In recent years, deep learning has been widely applied into medical image segmentation and achieved a certain achievements, especially U-Net and its variants. However, existing surgical instrument segmentation networks still suffer from some shortcomings, such as insufficient processing of local feature maps, lack of temporal modeling information, etc. To address the above issues, in order to effectively improve the segmentation accuracy of surgical instruments, based on an encoder-decoder network structure, a novel dense residual recurrent convolutional network, called DRR-Net, is proposed in this paper for automatic and accurate surgical instrument segmentation from endoscopic images. Faced with lack of temporal modeling information, inspired by the recurrent neural networks (RNNs), an attention dense-connected recurrent convolutional block (ADRCB) is proposed to optimize the backbone network to obtain temporal information and learn the correspond pixel relationship between frames. To address the insufficient processing issue of local feature maps, to replace the simple skip connections, a residual path is proposed to enhance the context feature representation. Meanwhile, it could also reduce the effect of semantic gap issue. Further, to improve the segmentation accuracy on segmented objects with different sizes, a context fusion block (CFB) is proposed to embed into the bottleneck layer to extract multi-scale attention context features. Multiple public data sets on surgical instrument segmentation are adopted for model evaluation and comparison, including kvasir-instrument set and UW-Sinus-Surgery-C/L set. Experimental results demonstrate that proposed DRR-Net network could achieve an excellent performance compared with other advanced segmentation models.
Exposing the grey wolf, moth‐flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by <i>bestial</i> metaphors

Abstract We present a rigorous, component‐based analysis of six widespread metaphor‐based algorithms for tackling continuous optimization problems. In addition to deconstructing the six algorithms into their components and relating them with equivalent components proposed in well‐established techniques, such as particle swarm optimization and evolutionary algorithms , we analyze the use of the metaphors that inspired these algorithms to understand whether their usage has brought any novel and useful concepts to the field of metaheuristics. Our result is that the ideas proposed in the six studied algorithms have been in the literature of metaheuristics for years and that the only novelty in these self‐proclaimed novel algorithms is six different terminologies derived from the use of new metaphors. We discuss the reasons why the metaphors that inspired these algorithms are misleading and ultimately useless as a source of inspiration to design effective optimization tools. Finally, we discuss the rationale often presented by the authors of metaphor‐based algorithms as their motivation to propose more algorithms of this type, which is based on a wrong understanding of the no‐free‐lunch theorems for optimization.
Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling

Due to the inherent unorderliness and irregularity of point cloud, points emerge inconsistently across different frames in a point cloud video. To capture the dynamics in point cloud videos, tracking points and limiting temporal modeling range are usually employed to preserve spatio-temporal structure. However, as points may flow in and out across frames, computing accurate point trajectories is extremely difficult, especially for long videos. Moreover, when points move fast, even in a small temporal window, points may still escape from a region. Besides, using the same temporal range for different motions may not accurately capture the temporal structure. In this paper, we propose a Point Spatio-Temporal Transformer (PST-Transformer). To preserve the spatio-temporal structure, PST-Transformer adaptively searches related or similar points across the entire video by performing self-attention on point features. Moreover, our PST-Transformer is equipped with an ability to encode spatio-temporal structure. Because point coordinates are irregular and unordered but point timestamps exhibit regularities and order, the spatio-temporal encoding is decoupled to reduce the impact of the spatial irregularity on the temporal modeling. By properly preserving and encoding spatio-temporal structure, our PST-Transformer effectively models point cloud videos and shows superior performance on 3D action recognition and 4D semantic segmentation.
Practical Asynchronous Distributed Key Generation

Distributed Key Generation (DKG) is a technique to bootstrap threshold cryptosystems without a trusted third party and is a building block to decentralized protocols such as randomness beacons, threshold signatures, and general multiparty computation. Until recently, DKG protocols have assumed the synchronous model and thus are vulnerable when their underlying network assumptions do not hold. The recent advancements in asynchronous DKG protocols are insufficient as they either have poor efficiency or limited functionality, resulting in a lack of concrete implementations. In this paper, we present a simple and concretely efficient asynchronous DKG (ADKG) protocol. In a network of n nodes, our ADKG protocol can tolerate up to $t\lt n/3$ malicious nodes and have an expected $O(\kappa n^{3})$ communication cost, where $\kappa$ is the security parameter. Our ADKG protocol produces a field element as the secret and is thus compatible with off-the-shelf threshold cryptosystems. We implement our ADKG protocol and evaluate it using a network of up to 128 nodes in geographically distributed AWS instances. Our evaluation shows that our protocol takes as low as 3 and 9.5 seconds to terminate for 32 and 64 nodes, respectively. Also, each node sends only 0.7 Megabytes and 2.9 Megabytes of data during the two experiments, respectively.
Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System

An increase in renewable energy injected into the power system will directly cause a fluctuation in the overall voltage and frequency of the power system. Thus, renewable energy prediction accuracy becomes vital to maintaining good power dispatch efficiency and power grid operation security. This article compares the one-day-ahead PV power forecasting results of three models paired with three groups of weather data. Since the number, loss, and matching problem of weather data will all influence the training results of the model, a pre-processing data framework is proposed to solve the problem in this study. The models used are a deep learning algorithm-based artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU). The weather data groups are Central Weather Bureau (CWB), local weather station (LWS), and hybrid data (the combination of CWB and LWS data). Compared to the other two groups, hybrid data showed a 5–8% improvement in measurements. In addition, when it comes to different weather conditions, the advantages of the LSTM model were highlighted. After further analysis, the LSTM model combined with hybrid data showed the most accurate measurements, which was proved through forecasting results for one month. Finally, the results indicate that when the amount of data is limited, using hybrid data and the five weather features is helpful for training the model. Accordingly, the proposed model shows better one-day-ahead PV forecasting.
A dynamic ensemble algorithm for anomaly detection in IoT imbalanced data streams

With the rapid development of ambient intelligence (AmI) in the Internet of Things (IoT), many data streams are generated from sensing devices in intelligent scenarios. Due to the deployment issues of IoT devices and the system’s complexity, abnormal behavior is inevitable, resulting in imbalanced data categories. Moreover, data streams generated in IoT systems are dynamic, continuous, and as the environment changes, further increasing the difficulty of anomaly detection . Therefore, we model the monitored historical and current data from the perspective of dynamic imbalanced data streams classification to discover abnormal behaviors in IoT systems. In this paper we propose a dynamic ensemble algorithm for anomaly detection in IoT environments. First the abnormal data samples are synthesized by the borderline-synthetic minority over-sampling technique (Borderline-SMOTE) to relieve the sample imbalance problem. Then considering the dynamics and continuity of data streams we adopt a chunk-based strategy to train a LightGBM classifier for each chunk of data to adapt to the current data distribution. To improve the ensemble model’s processing efficiency and anomaly detection accuracy we adopt a dynamic weighting strategy for base classifiers and remove the classifier whose accuracy performance is lower than the threshold. Finally we evaluate our proposed algorithm by conducting comparative experiments on real-world data streams. Experimental results show that our proposed algorithm outperforms the comparative anomaly detection methods in IoT scenarios.