Reviewerly/exHarmony huggingface dataset:
Blockchain Based Secure Data Aggregation and Distributed Power Dispatching for Microgrids

Power generation systems tend to be distributed and decentralized, and therefore the concept of microgrid has been proposed, which needs to implement decentralized data storage and power dispatching. The traditional power system architecture is no longer suitable in the decentralized microgrid system because there are no trusted third parties such as control centers. Therefore, it is challenging to securely implement data aggregation and power dispatching in microgrids without any trusted third party. In this paper, by leveraging blockchain, we propose secure data aggregation based on homomorphic encryption and the PBFT (Practical Byzantine Fault Tolerance) consensus, and meanwhile we propose automatic power dispatching by utilizing the PSO (Particle Swarm Optimization) algorithm and smart contracts. The security and performance analysis shows the effectiveness and efficiency of our proposed solutions.
Multi‐swarm and chaotic whale‐particle swarm optimization algorithm with a selection method based on roulette wheel

Abstract The particle swarm optimization (PSO) and the whale optimization algorithm (WOA) are two admired optimization methods that have drawn various researchers' attention. The PSO implements some particles' intelligent movements in a search space, and the WOA is originated based on the hunting mechanism of humpback whales. The PSO and WOA have different strategies for moving towards the optimum solution. Nevertheless, both algorithms' performances encounter several problems, such as premature convergence and falling in local optimums. Several approaches have been proposed to enhance meta‐heuristic algorithms' performance, such as applying the chaotic maps, adding mathematical or stochastic operators or local searches, and hybridizing the algorithms. In this article, a new hybrid algorithm denoted as chaotic‐based hybrid whale and PSO has been presented by improving the WOA, combining it with PSO, and using the chaotic maps. The hybrid algorithm has significantly more diverse movements than both of the mentioned algorithms. Therefore, it explores different regions of a problem's search space more precisely and avoids local optima. The roulette wheel selection operator has also been applied based on their fitness value to select the proposed algorithm's search agents and exploit promising regions of the search space. In the hybrid algorithm, the chaotic maps have been applied to initialize the whales' population, particles of the particle swarm, and adjust motion parameters to increase population diversity. The multi‐swarm version of the proposed algorithm with higher performance than the single‐swarm version and other methods has been introduced in this article too. The proposed algorithms have been evaluated using 23 mathematical benchmark functions, including unimodal, multimodal, and composite functions and four engineering optimization problems. The obtained results and statistical tests prove that the proposed algorithms provide competitive solutions for most of the experiments, compared to the state‐of‐the‐art and well‐known optimization meta‐heuristic methods in terms of convergence towards the global optimum, local optima avoidance, exploration, and exploitation.
Spike frequency adaptation supports network computations on temporally dispersed information

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well-known property of a substantial fraction of neurons in the neocortex – especially in higher areas of the human neocortex – moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.
MLPD: Multi-Label Pedestrian Detector in Multispectral Domain

Multispectral pedestrian detection has been actively studied as a promising multi-modality solution to handle illumination and weather changes. Most multi-modality approaches carry the assumption that all inputs are fully-overlapped. However, these kinds of data pairs are not common in practical applications due to the complexity of the existing sensor configuration. In this letter, we tackle multispectral pedestrian detection, where all input data are not paired. To this end, we propose a novel single-stage detection framework that leverages multi-label learning to learn input state-aware features by assigning a separate label according to the given state of the input image pair. We also present a novel augmentation strategy by applying geometric transformations to synthesize the unpaired multispectral images. In extensive experiments, we demonstrate the efficacy of the proposed method under various real-world conditions, such as fully-overlapped images and partially-overlapped images, in stereo-vision. Code and a demonstration video are available at https://github.com/sejong-rcv/MLPD-Multi-Label-Pedestrian-Detection.
COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy

Federated learning can collaboratively train a global model without gathering clients’ private data. Many works focus on reducing communication cost by designing kinds of client selection method or averaging algorithm. But they all consider whether the client will participant or not, and the training time could not be reduced as data size of update for each client is not changed. We proposed COFEL, a novel federated learning system which can both reduce the communication time by layer-based parameter selection and enhance the privacy protection by applying local differential privacy mechanism on the selected parameters. We present COFEL-AVG algorithm for global aggregation and designed layer-based parameter selection method which can select the valuable parameters for global aggregation to optimize the communication and training process. And it can reduce the update data size as only selected part will be transferred. We compared with traditional federated learning system and CMFL which also applies a parameter selection method but model-based and performed experiments on MNIST, Fashion-MNIST and CIFAR-10 to verify the effectiveness of COFEL. The results denoted that it can improve at most 22.8% accuracy compared with CMFL on CIFAR-10 and reduce around 20% and 48% training time to reach an accuracy of 0.85 compared with traditional FL and CMFL on Fashion-MNIST dataset.
Adaptive Asynchronous Federated Learning in Resource-Constrained Edge Computing

Federated learning (FL) has been widely adopted to train machine learning models over massive data in edge computing. However, machine learning faces critical challenges, e.g., data imbalance, edge dynamics, and resource constraints, in edge computing. The existing FL solutions cannot well cope with data imbalance or edge dynamics, and may cause high resource cost. In this paper, we propose an adaptive asynchronous federated learning (AAFL) mechanism. To deal with edge dynamics, a certain fraction <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> of all local updates will be aggregated by their arrival order at the parameter server in each epoch. Moreover, the system can intelligently vary the number of local updated models for global model aggregation in different epochs with network situations. We then propose experience-driven algorithms based on deep reinforcement learning (DRL) to adaptively determine the optimal value of <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> in each epoch for two cases of AAFL, single learning task and multiple learning tasks, so as to achieve less completion time of training under resource constraints. Extensive experiments on the classical models and datasets show high effectiveness of the proposed algorithms. Specifically, AAFL can reduce the completion time by about 70 percent and improve the learning accuracy by about 28 percent under resource constraints, compared with the state-of-the-art solutions.
Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization

We tackle the problem of localizing temporal intervals of actions with only a single frame label for each action instance for training. Owing to label sparsity, existing work fails to learn action completeness, resulting in fragmentary action predictions. In this paper, we propose a novel framework, where dense pseudo-labels are generated to provide completeness guidance for the model. Concretely, we first select pseudo background points to supplement point-level action labels. Then, by taking the points as seeds, we search for the optimal sequence that is likely to contain complete action instances while agreeing with the seeds. To learn completeness from the obtained sequence, we introduce two novel losses that contrast action instances with background ones in terms of action score and feature similarity, respectively. Experimental results demonstrate that our completeness guidance indeed helps the model to locate complete action instances, leading to large performance gains especially under high IoU thresholds. Moreover, we demonstrate the superiority of our method over existing state-of-the-art methods on four benchmarks: THUMOS’14, GTEA, BEOID, and ActivityNet. Notably, our method even performs comparably to recent fully-supervised methods, at the 6× cheaper annotation cost. Our code is available at https://github.com/Pilhyeon.
Energy-Efficient Online Path Planning of Multiple Drones Using Reinforcement Learning

Drones, typically unmanned aerial vehicles (UAVs), have many purposes. However, simultaneous operation of multiple drones is challenging, considering the real-time interactions and the environment; the drone must avoid collision with the other drones or obstacles. The proposed Advanced TD3 model performs energy-efficient path planning at the edge-level drone. We modify the twin-delayed deep deterministic policy gradient (TD3), which is the state-of-the-art policy gradient reinforcement learning. The frame stacking technique considers the continuous action space of the drone to the TD3 model. During the training, we gradually increase the observation range of agents for fast and stable convergence. We train the modified TD3 model through Offline RL to reduce the overhead for the RL model training. Drones mount the converged RL model on their onboard computer. The Advanced TD3 model in the drones selects an energy-efficient path without the overhead of the training process of the RL model in real-time, considering external factors such as wind or another drone. The total energy consumption of drones in flight along with online path planning is approximately 106% of the total energy consumption of drones that follow offline path planning, even though the trained TD3 model does not require complex computations for real-time execution.
A Unified Target-Oriented Sequence-to-Sequence Model for Emotion-Cause Pair Extraction

Emotion-cause pair extraction is a recently proposed task that aims at extracting all potential clause-level pairs of emotion and cause in text. To solve this task, researchers first proposed a two-step pipeline method. This method extracts the emotions and causes individually in the first step, then pairs the extracted emotions and causes and filters the invalid emotion-cause pairs in the second step. Due to that the two-step method has the error accumulation problem and is hard to be optimized jointly, several one-step end-to-end models have been proposed. These models share a similar underlying idea, that is, reframing the emotion-cause pair extraction task as a classification problem of candidate clause pairs. Unlike these models, in this paper, we reframe the emotion-cause pair extraction task as a unified sequence labeling problem, which allows to extract emotion-cause pairs through one pass of sequence labeling. This is realized by designing a special set of unified labels. In the unified label, we design a content part for emotion/cause identification and a pairing part for clause pairing. Then the emotion-cause pairs can be implicitly derived from the unified labels. To address this unified sequence labeling problem, we propose a unified target-oriented sequence-to-sequence model, which comprehensively utilizes the information of target clause, global context, and former decoded label, to perform end-to-end unified sequence labeling. The experimental results demonstrate the effectiveness of both our proposed unified sequence labeling scheme and unified target-oriented sequence-to-sequence model. All the code and data of this work can be obtained at https://github.com/zifengcheng/UTOS.
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters

Weakly-supervised object detection (WSOD) has emerged as an inspiring recent topic to avoid expensive instance-level object annotations. However, the bounding boxes of most existing WSOD methods are mainly determined by precomputed proposals, thereby being limited in precise object localization. In this paper, we defend the problem setting for improving localization performance by leveraging the bounding box regression knowledge from a well-annotated auxiliary dataset. First, we use the well-annotated auxiliary dataset to explore a series of learnable bounding box adjusters (LBBAs) in a multi-stage training manner, which is class-agnostic. Then, only LBBAs and a weakly-annotated dataset with non-overlapped classes are used for training LBBA-boosted WSOD. As such, our LBBAs are practically more convenient and economical to implement while avoiding the leakage of the auxiliary well-annotated dataset. In particular, we formulate learning bounding box adjusters as a bi-level optimization problem and suggest an EM-like multi-stage training algorithm. Then, a multi-stage scheme is further presented for LBBA-boosted WSOD. Additionally, a masking strategy is adopted to improve proposal classification. Experimental results verify the effectiveness of our method. Our method performs favorably against state-of-the-art WSOD methods and knowledge transfer model with similar problem setting. Code is publicly available at https://github.com/DongSky/lbba_boosted_wsod.
ToStaGAN: An end-to-end two-stage generative adversarial network for brain tumor segmentation

Brain tumor segmentation using MRI data remains challenging for some reasons. Hence, how to accurately segment the brain tumor is kept as a significant topic in the area of medical image segmentation. To follow the idea of “coarse-to-fine” in practice, this paper proposes an end-to-end two-stage generative adversarial neural network (ToStaGAN) to improve the braintumor is kept as a significant topic in the area of medical image segmentation. To follow the idea of “coarse-to-fine” in practice, this paper proposes an end-to-end two-stage generative adversarial neural network (ToStaGAN) to improve the brain tumor segmentation performance by making full use of semantic information in high-level.In the proposed ToStaGAN, the UNET network is adopted as the “coarse” generation network in the first stage, while a U-Shaped contextual autoencoder (ConEnDer), which consists of the proposed fine-grained extraction module (FEM) and dense skip connection, is proposed as the “fine” generation network in the second stage. To be more specific, the FEM is mainly used to effectively extract “fine-grained” features that plays a crucial role in semantic segmentation with only increasing a few parameters. Furthermore, it has the ability to obtain more diverse and abstract features. And by further utilizing the features extracted from the FEM and the coarse prediction map from the first stage, the ConEnDer holds the great potential to generate the optimized segmentation results. We then evaluate the performance of ToStaGAN on the BARATS 2015. Findings from the evaluations suggest that the proposed two-stage generation network achieves a better performance than the one-stage network; thus, also implying the effectiveness of the proposed ConEnder for “fine” segmentation. A comparative summary also shows that the ToStaGAN achieves a better segmentation performance in comparison to other competing approaches.
Lifting path planning of mobile cranes based on an improved RRT algorithm

Lifting operations of mobile cranes are one of the commonly-seen and most important activities for prefabrication housing production (PHP) on sites. However, relevant operations are normally based on the experience of operators or project managers, this often leads to low efficiency as well as high accident rate due to dynamic and complex construction sites. Thus, it is important and necessary to develop an appropriate approach to the lifting planning of mobile cranes so as to guide on-site operations. This paper proposes an improved Rapidly-exploring Random Tree (RRT) algorithm for lifting path planning of mobile cranes. Considering the critical role of Nearest Neighbor Search (NNS) in the implementation of RRT algorithm, a novel strategy for searching the nearest neighbor is developed, i.e., Generalized Distance Method and Cell Method. Both methods are tested in simulation-based experiments. The results show that 1) the Generalized distance method not only reduces the search time, but also unifies the unit of distance measurement and clarifies the physical meaning of distance; 2) the Cell method dramatically reduces the traversal range as well as the search time; and 3) both methods improve the quality of lifting path planning of mobile cranes. This improved RRT algorithm enables rapid path planning of mobile cranes in a dynamic and complex construction environment. The outcomes of this research not only contribute to the body of knowledge in spatial path planning of crane lifting operations, but also have the potential of significantly improving efficiency and safety in crane lifting practices.
Towards Face Encryption by Generating Adversarial Identity Masks

As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information from face photos, with the aid of, e.g., image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against face recognition systems. Our goal in this paper is to develop a technique that can encrypt the personal photos such that they can protect users from unauthorized face recognition systems but remain visually identical to the original version for human beings. To achieve this, we propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks which can be overlaid on facial images, such that the original identities can be concealed without sacrificing the visual quality. Extensive experiments demonstrate that TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models under practical test scenarios. Besides, we also show the practical and effective applicability of our method on a commercial API service.
Evaluating Effects of Background Stories on Graph Perception

A graph is an abstract model that represents relations among entities, for example, the interactions between characters in a novel. A background story endows entities and relations with real-world meanings and describes the semantics and context of the abstract model, for example, the actual story that the novel presents. Considering practical experience and prior research, human viewers who are familiar with the background story of a graph and those who do not know the background story may perceive the same graph differently. However, no previous research has adequately addressed this problem. This research article thus presents an evaluation that investigated the effects of background stories on graph perception. Three hypotheses that focused on the role of visual focus areas, graph structure identification, and mental model formation on graph perception were formulated and guided three controlled experiments that evaluated the hypotheses using real-world graphs with background stories. An analysis of the resulting experimental data, which compared the performance of participants who read and did not read the background stories, obtained a set of instructive findings. First, having knowledge about a graph's background story influences participants' focus areas during interactive graph explorations. Second, such knowledge significantly affects one's ability to identify community structures but not high degree and bridge structures. Third, this knowledge influences graph recognition under blurred visual conditions. These findings can bring new considerations to the design of storytelling visualizations and interactive graph explorations.
Learning Open-World Object Proposals Without Learning to Classify

Object proposals have become an integral pre-processing step of many vision pipelines including object detection, weakly supervised detection, object discovery, tracking, etc. Compared to the learning-free methods, learning-based proposals have become popular recently due to the growing interest in object detection. The common paradigm is to learn object proposals from data labeled with a set of object regions and their corresponding categories. However, this approach often struggles with novel objects in the open world that are absent in the training set. In this letter, we identify that the problem is that the binary classifiers in existing proposal methods tend to overfit to the training categories. Therefore, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">classification-free</b> Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlap with any ground-truth object (e.g., centerness and IoU). This strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization on COCO. We further explore more challenging cross-dataset generalization onto RoboNet and EpicKitchens dataset, and long-tail detection on LVIS dataset. We demonstrate clear improvement over the state-of-the-art object detectors and object proposers. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mcahny/object_localization_network</uri> .