Reviewerly/exHarmony huggingface dataset:
Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.

In this paper, we present the first Asynchronous Distributed Key Generation (ADKG) algorithm which is also the first distributed key generation algorithm that can generate cryptographic keys with a dual (f,2f+1)-threshold (where f is the number of faulty parties). As a result, using our ADKG we remove the trusted setup assumption that the most scalable consensus algorithms make. In order to create a DKG with a dual (f,2f+1)- threshold we first answer in the affirmative the open question posed by Cachin et al. [7] on how to create an Asynchronous Verifiable Secret Sharing (AVSS) protocol with a reconstruction threshold of f+1<k łe 2f+1, which is of independent interest. Our High-threshold-AVSS (HAVSS) uses an asymmetric bivariate polynomial to encode the secret. This enables the reconstruction of the secret only if a set of k nodes contribute while allowing an honest node that did not participate in the sharing phase to recover his share with the help of f+1 honest parties. Once we have HAVSS we can use it to bootstrap scalable partially synchronous consensus protocols, but the question on how to get a DKG in asynchrony remains as we need a way to produce common randomness. The solution comes from a novelEventually Perfect Common Coin (EPCC) abstraction that enables the generation of a common coin from n concurrent HAVSS invocations. EPCC's key property is that it is eventually reliable, as it might fail to agree at most f times (even if invoked a polynomial number of times). UsingEPCC we implement anEventually Efficient Asynchronous Binary Agreement (EEABA) which is optimal when the EPCC agrees and protects safety when EPCC fails. Finally, using EEABA we construct the first ADKG which has the same overhead and expected runtime as the best partially-synchronous DKG (O(n4) words, O(f) rounds). As a corollary of our ADKG, we can also create the first Validated Asynchronous Byzantine Agreement (VABA) that does not need a trusted dealer to setup threshold signatures of degree n-f. Our VABA has an overhead of expected O(n2) words and O(1) time per instance, after an initial O(n4) words and O(f) time bootstrap via ADKG.
Mobile Robot Path Planning Based on Improved Localized Particle Swarm Optimization

Mobile robot path planning is a key technology and challenge in robot automation field. For a long time, particle swarm optimization has been used in path planning, while the well-known shortcomings such as local minimum, premature and low efficiency have prevented its extensive application. In this article, an improved localized particle swarm optimization algorithm is proposed to address these problems. Firstly, the improvements in inertia weights, acceleration factors, and localization prevent the algorithm falling into a local minimum value and increase convergence speed of the algorithm. Then, fitness variance is used to measure the diversity of particles, and increasing of diversity can help to overcome the shortcoming of premature. Particle's diversity is increased by the defined extended Gaussian distribution. Finally, the smoothing principle is applied in path planning. In the process of simulation experiments and real-world validations, our proposed method is compared with the basic particle swarm optimization and A-star method in four simulation scenarios and four real-world maps, and the comparative study show that the proposed algorithm outperforms as well as particle swarm optimization and A-star algorithms in terms of path length, running time, path optimal degree, and stability. Finally, the related results demonstrate that the proposed method is more effective, robust and feasible for mobile robot path planning.
Learning to Combine: Knowledge Aggregation for Multi-source Domain Adaptation

Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermore, the increase of modalities brings more difficulty in aligning feature distributions among multiple domains. To mitigate these problems, we propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework via exploring interactions among domains. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. On such basis, a graph model is learned to predict query samples under the guidance of correlated prototypes. In addition, we design a Relation Alignment Loss (RAL) to facilitate the consistency of categories’ relational interdependency and the compactness of features, which boosts features’ intra-class invariance and inter-class separability. Comprehensive results on public benchmark datasets demonstrate that our approach outperforms existing methods with a remarkable margin. Our code is available at https://github.com/ChrisAllenMing/LtC-MSDA .
AdvPulse: Universal, Synchronization-free, and Targeted Audio Adversarial Attacks via Subsecond Perturbations

Existing efforts in audio adversarial attacks only focus on the scenarios where an adversary has prior knowledge of the entire speech input so as to generate an adversarial example by aligning and mixing the audio input with corresponding adversarial perturbation. In this work we consider a more practical and challenging attack scenario where the intelligent audio system takes streaming audio inputs (e.g., live human speech) and the adversary can deceive the system by playing adversarial perturbations simultaneously. This change in attack behavior brings great challenges, preventing existing adversarial perturbation generation methods from being applied directly. In practice, (1) the adversary cannot anticipate what the victim will say: the adversary cannot rely on their prior knowledge of the speech signal to guide how to generate adversarial perturbations; and (2) the adversary cannot control when the victim will speak: the synchronization between the adversarial perturbation and the speech cannot be guaranteed. To address these challenges, in this paper we propose AdvPulse, a systematic approach to generate subsecond audio adversarial perturbations, that achieves the capability to alter the recognition results of streaming audio inputs in a targeted and synchronization-free manner. To circumvent the constraints on speech content and time, we exploit penalty-based universal adversarial perturbation generation algorithm and incorporate the varying time delay into the optimization process. We further tailor the adversarial perturbation according to environmental sounds to make it inconspicuous to humans. Additionally, by considering the sources of distortions occurred during the physical playback, we are able to generate more robust audio adversarial perturbations that can remain effective even under over-the-air propagation. Extensive experiments on two representative types of intelligent audio systems (i.e., speaker recognition and speech command recognition) are conducted in various realistic environments. The results show that our attack can achieve an average attack success rate of over 89.6% in indoor environments and 76.0% in inside-vehicle scenarios even with loud engine and road noises.
Towards Streaming Perception

Embodied perception refers to the ability of an autonomous agent to perceive its environment so that it can (re)act. The responsiveness of the agent is largely governed by latency of its processing pipeline. While past work has studied the algorithmic trade-off between latency and accuracy, there has not been a clear metric to compare different methods along the Pareto optimal latency-accuracy curve. We point out a discrepancy between standard offline evaluation and real-time applications: by the time an algorithm finishes processing a particular image frame, the surrounding world has changed. To these ends, we present an approach that coherently integrates latency and accuracy into a single metric for real-time online perception, which we refer to as “streaming accuracy”. The key insight behind this metric is to jointly evaluate the output of the entire perception stack at every time instant, forcing the stack to consider the amount of streaming data that should be ignored while computation is occurring. More broadly, building upon this metric, we introduce a meta-benchmark that systematically converts any image understanding task into a streaming perception task. We focus on the illustrative tasks of object detection and instance segmentation in urban video streams, and contribute a novel dataset with high-quality and temporally-dense annotations. Our proposed solutions and their empirical analysis demonstrate a number of surprising conclusions: (1) there exists an optimal “sweet spot” that maximizes streaming accuracy along the Pareto optimal latency-accuracy curve, (2) asynchronous tracking and future forecasting naturally emerge as internal representations that enable streaming image understanding, and (3) dynamic scheduling can be used to overcome temporal aliasing, yielding the paradoxical result that latency is sometimes minimized by sitting idle and “doing nothing”.
Funnel Activation for Visual Recognition

We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition. The forms of ReLU and PReLU are $$y=max(x,0)$$ and $$y=max(x,px)$$ , respectively, while FReLU is in the form of $$y=max(x, \mathbb {T}(x))$$ , where $$\mathbb {T}(\cdot )$$ is the 2D spatial condition. Moreover, the spatial condition achieves a pixel-wise modeling capacity in a simple way, capturing complicated visual layouts with regular convolutions. We conduct experiments on ImageNet, COCO detection, and semantic segmentation tasks, showing great improvements and robustness of FReLU in the visual recognition tasks. Code is available at https://github.com/megvii-model/FunnelAct .
Challenge-Aware RGBT Tracking

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking. In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking. In particular, we design several parameter-shared branches in each layer to model the target appearance under the modality-shared challenges, and several parameter-independent branches under the modality-specific ones. Based on the observation that the modality-specific cues of different modalities usually contains the complementary advantages, we propose a guidance module to transfer discriminative features from one modality to another one, which could enhance the discriminative ability of some weak modality. Moreover, all branches are aggregated together in an adaptive manner and parallel embedded in the backbone network to efficiently form more discriminative target representations. These challenge-aware branches are able to model the target appearance under certain challenges so that the target representations can be learnt by a few parameters even in the situation of insufficient training data. From the experimental results we will show that our method operates at a real-time speed while performing well against the state-of-the-art methods on three benchmark datasets.
Artificial bee colony algorithm based on adaptive neighborhood search and Gaussian perturbation

Artificial bee colony (ABC) is a type of popular swarm intelligence optimization algorithm. It is widely concerned because of its easy implementation, few parameters and strong global search ability. However, there are some limitations for ABC, such as weak exploitation ability and slow convergence. In this paper, a novel ABC with adaptive neighborhood search and Gaussian perturbation (called ABCNG) is proposed to overcome these shortcomings. Firstly, an adaptive method is used to dynamically adjust the neighborhood size. Then, a modified global best solution guided search strategy is constructed based on the neighborhood structure. Finally, a new Gaussian perturbation with evolutionary rate is designed to evolve the unchanged solutions at each iteration. Performance of ABCNG is tested on two benchmark sets and compared with some excellent ABC variants. Results show ABCNG is more competitive than six other ABCs.
Multiple Kinect based system to monitor and analyze key performance indicators of physical training

Abstract Using a single Kinect device for human skeleton tracking and motion tracking lacks of reliability required in sports medicine and rehabilitation domains. Human joints reconstructed from non-standard poses such as squatting, sitting and lying are asymmetric and have unnatural lengths while their recognition error exceeds the error of recognizing standard poses. In order to achieve higher accuracy and usability for practical smart health applications we propose a practical solution for human skeleton tracking and analysis that performs the fusion of skeletal data from three Kinect devices to provide a complete 3D spatial coverage of a subject. The paper describes a novel data fusion algorithm using algebraic operations in vector space, the deployment of the system using three Kinect units, provides analysis of dynamic characteristics (position of joints, speed of movement, functional working envelope, body asymmetry and the rate of fatigue) of human motion during physical exercising, and evaluates intra-session reliability of the system using test–retest reliability metrics (intra-class correlation, coefficient of variation and coefficient of determination). Comparison of multi-Kinect system vs single-Kinect system shows an improvement in accuracy of 15.7%, while intra-session reliability is rated as excellent.
Smartwatch-based Human Activity Recognition Using Hybrid LSTM Network

As a result of the rapid development of wearable sensor technology, the use of smartwatch sensors for human activity recognition (HAR) has recently become a popular area of research. Currently, a large number of mobile applications, such as healthcare monitoring, sport performance tracking, etc., are applying the results of major HAR research studies. In this paper, an HAR framework that employs spatial-temporal features that are automatically extracted from data obtained from smartwatch sensors is proposed. The hybrid deep learning approach is used in the framework through the employment of Long Short-Term Memory Networks and the Convolutional Neural Network, eliminating the need for the manual extraction of features. The advantage of tuning the hyperparameters of each of the considered networks by Bayesian optimization is also utilized. It was indicated by the results that the baseline models are outperformed by the proposed hybrid deep learning model, which has an average accuracy of 96.2% and an F-measure of 96.3%.
Language models are an effective representation learning technique for electronic health record data

Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. However, this process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5% mean improvement in AUROC on five prediction tasks compared to standard baselines, with the average improvement rising to 19% when only a small number of patient records are available for training the clinical prediction model.
ReMOT: A model-agnostic refinement for multiple object tracking

Although refinement is commonly used in visual tasks to improve pre-obtained results, it has not been studied for Multiple Object Tracking (MOT) tasks. This could be attributed to two reasons: i) it has not been explored what kinds of errors should — and could — be reduced in MOT refinement; ii) the refinement target, namely, the tracklets, are intertwined and interactive in a 3D spatio-temporal space, and therefore changing one tracklet may affect the others. To tackle these issues, i) we define two types of errors in imperfect tracklets, as Mix-up Error and Cut-off Error, to clarify the refinement goal; ii) we propose a Refining MOT Framework (ReMOT), which first splits imperfect tracklets and then merges the split tracklets with appearance features improved by self-supervised learning. Experiments demonstrate that ReMOT can make significant improvements to state-of-the-art MOT results as powerful post-processing. As a new application, we demonstrate that ReMOT has the potential of being used to assist semi-automatic MOT data annotation and partially release humans from the tedious work.
Fairness-Based Packing of Industrial IoT Data in Permissioned Blockchains

In recent years, blockchain has been broadly applied to industrial Internet of Things (IIoT) due to its features of decentralization, transparency, and immutability. In existing permissioned blockchain based IIoT solutions, transactions submitted by IIoT devices are arbitrarily packed into blocks without considering their waiting times. Hence, there will be a high deviation of the transaction response times, which is known as the lack of fairness. Unfair permissioned blockchain decreases the quality of experience from the perspective of the IIoT devices. Moreover, some transactions can get timeouts if not responded for a long time. In this article, we propose Fair-Pack, the first fairness-based transaction packing algorithm for permissioned blockchain empowered IIoT systems. First, we gain the insight that fairness is positively related to the sum of waiting times of the selected transactions through theoretical analysis. Based on this insight, we transform the fairness problem into the subset sum problem, which is to find a valid subset from a given set with subset sum as large as possible. However, it is time consuming to solve the problem using a brute-force approach because there is an exponential number of subsets for a given set. To this end, we propose a heuristic and a min-heap-based optimal algorithm for different parameter settings. Finally, we analyze the time complexity of Fair-Pack and conduct extensive experiments. The results reveal that Fair-Pack is time-efficient and outperforms the existing algorithms significantly in terms of both fairness and average transaction response time.
Multi-strategy serial cuckoo search algorithm for global optimization

Cuckoo search algorithm (CS) is a simple and effective nature-inspired optimization algorithm, which has been widely applied to solve the complex engineering optimization problem in the real world. However, all cuckoos have similar search behavior because of the unitary search strategy is used in CS, which easily makes the algorithm get trapped in the local optimum and enter the state of premature convergence. Inspired by the three serial behaviors of the cuckoo's growth process, a multi-strategy serial CS (MSSCS) is proposed to overcome the above problems. In MSSCS, three new learning strategies are proposed to enhance the performance of the algorithm, which observed from the cuckoo's behavior of seeking host nest, eviction and begging. Based on these three serial behaviors, we propose a multi-strategy serial framework to reduce the complexity of the multi-strategy algorithm and maximize the performance of each learning strategy. In addition, the adaptive parameter is designed to dynamically regulate the serial framework. To verify the effectiveness of MSSCS, extensive experiments are carried out on the CEC2013 test suite. The experimental results illustrate that the proposed algorithm has better performance when considering the quality of the obtained solutions.
FogFL: Fog-Assisted Federated Learning for Resource-Constrained IoT Devices

In this article, we propose a fog-enabled federated learning framework-FogFL-to facilitate distributed learning for delay-sensitive applications in resource-constrained IoT environments. While federated learning (FL) is a popular distributed learning approach, it suffers from communication overheads and high computational requirements. Moreover, global aggregation in FL relies on a centralized server, prone to malicious attacks, resulting in inefficient training models. We address these issues by introducing geospatially placed fog nodes into the FL framework as local aggregators. These fog nodes are responsible for defined demographics, which help share location-based information for applications with similar environments. Furthermore, we formulate a greedy heuristic approach for selecting an optimal fog node for assuming a global aggregator's role at each round of communication between the edge and cloud, thereby reducing the dependence on the execution at the centralized server. Fog nodes in the FogFL framework reduce communication latency and energy consumption of resource-constrained edge devices without affecting the global model's convergence rate, thereby increasing the system's reliability. Extensive deployment and experimental results corroborate that, in addition to a decrease in global aggregation rounds, FogFL reduces energy consumption and communication latency by 92% and 85%, respectively, as compared to state of the art.
SynergyChain: Blockchain-Assisted Adaptive Cyber-Physical P2P Energy Trading

Industrial investments into distributed energy resource technologies are increasing and playing a pivotal role in the global transactive energy, as part of a wider drive to provide a clean and stable source of energy. The management of prosumers, which consume and as well as generate energy, with heterogeneous energy sources is critical for sustainable and efficient energy trading procedures. This article proposes a blockchain-assisted adaptive model, namely SynergyChain, for improving the scalability and decentralization of the prosumer grouping mechanism in the context of peer-to-peer energy trading. Smart contracts are used for storing the transaction information and for the creation of the prosumer groups. SynergyChain integrates a reinforcement learning module to further improve the overall system performance and profitability by creating a self-adaptive grouping technique. The proposed SynergyChain is developed using Python and Solidity and has been tested using Ethereum test nets. The comprehensive analysis using the hourly energy consumption dataset shows a 39.7% improvement in the performance and scalability of the system as compared to the centralized systems. The evaluation results confirm that SynergyChain can reduce the request completion time along with an 18.3% improvement in the overall profitability of the system as compared to its counterparts.