Reviewerly/exHarmony huggingface dataset:
Is Bitcoin really more than a diversifier? A pre- and post-COVID-19 analysis

We examine whether Bitcoin can act as a safe haven against adverse movements of stock and bond assets in five major economies during the COVID-19 bear market. The empirical analyses are conducted using a Bayesian panel VAR method that captures potential interaction and heterogeneity across country/region-segmented markets. We find that Bitcoin in each target economy contributes to diversification benefits and/or risk mitigation both within and across borders, while its sheltering role against traditional assets varies among different economies. Further, we show that the COVID-19 outbreak has altered the role of Bitcoin in various segmented markets except for the US.
Weakly-supervised Temporal Action Localization by Uncertainty Modeling

Weakly-supervised temporal action localization aims to learn detecting temporal intervals of action classes with only video-level labels. To this end, it is crucial to separate frames of action classes from the background frames (i.e., frames not belonging to any action classes). In this paper, we present a new perspective on background frames where they are modeled as out-of-distribution samples regarding their inconsistency. Then, background frames can be detected by estimating the probability of each frame being out-of-distribution, known as uncertainty, but it is infeasible to directly learn uncertainty without frame-level labels. To realize the uncertainty learning in the weakly-supervised setting, we leverage the multiple instance learning formulation. Moreover, we further introduce a background entropy loss to better discriminate background frames by encouraging their in-distribution (action) probabilities to be uniformly distributed over all action classes. Experimental results show that our uncertainty modeling is effective at alleviating the interference of background frames and brings a large performance gain without bells and whistles. We demonstrate that our model significantly outperforms state-of-the-art methods on the benchmarks, THUMOS'14 and ActivityNet (1.2 &amp; 1.3). Our code is available at https://github.com/Pilhyeon/WTAL-Uncertainty-Modeling.
Boundary Proposal Network for Two-stage Natural Language Video Localization

We aim to address the problem of Natural Language Video Localization (NLVL) — localizing the video segment corresponding to a natural language description in a long and untrimmed video. State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e.g., by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame as a boundary or intermediate frame inside the positive segment. However, both kinds of one-stage approaches have inherent drawbacks: the anchor-based approach is susceptible to the heuristic rules, further limiting the capability of handling videos with variant length. While the anchor-free approach fails to exploit the segment-level interaction thus achieving inferior results. In this paper, we propose a novel Boundary Proposal Network (BPNet), a universal two-stage framework that gets rid of the issues mentioned above. Specifically, in the first stage, BPNet utilizes an anchor-free model to generate a group of high-quality candidate video segments with their boundaries. In the second stage, a visual-language fusion layer is proposed to jointly model the multi-modal interaction between the candidate and the language query, followed by a matching score rating layer that outputs the alignment score for each candidate. We evaluate our BPNet on three challenging NLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive experiments and ablative studies on these datasets demonstrate that the BPNet outperforms the state-of-the-art methods.
Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.
Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark (Web)

Traffic Classification (TC) is an important tool for several tasks, applied in different fields (security, management, traffic engineering, R&D). This process is impaired or prevented by privacy-preserving protocols and tools, that encrypt the communication content, and (in case of anonymity tools) additionally hide the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide classification results with the aim of investigating to which degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of other considered anonymity tools, using five machine learning classifiers. Initially, flow-based TC is considered, and the effects of feature importance and temporal-related features to the network are investigated. Additionally, the role of finer-grained features, such as the (joint) histogram of packet lengths (and inter-arrival times), is determined. Successively, “early” TC of anonymous networks is analyzed. Results show that the considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.87% and 99.80%, in case of flow-based and early-TC, respectively), telling even the specific application generating the traffic (with an accuracy of 73.99% and 66.76%, in case of flow-based and early-TC, respectively).
Historical and Heuristic-Based Adaptive Differential Evolution

As the mutation strategy and algorithmic parameters in differential evolution (DE) are sensitive to the problems being solved, a hot research topic is to adaptively control the strategy and parameters according to the requirements of the problem. In the literature, most adaptive DE use either historical experiences of the population or heuristic information of the individuals to promote adaptation. In this paper, we develop a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation. In this novel historical and heuristic DE (HHDE), each individual dynamically adjusts its mutation strategy and associated parameters not only by learning from previous successful experience of the whole population, but also according to heuristic information related with its own current state. These help the algorithm select a more suitable mutation strategy and determinate better parameters for each individual in different evolutionary stages. The performance of the proposed HHDE is extensively evaluated on 30 benchmark functions with different dimensions. Experimental results confirm the competitiveness of the proposed algorithm to a number of DE variants.
Mutually Guided Image Filtering

Filtering images is required by numerous multimedia, computer vision and graphics tasks. Despite diverse goals of different tasks, making effective rules is key to the filtering performance. Linear translation-invariant filters with manually designed kernels have been widely used. However, their performance suffers from content-blindness. To mitigate the content-blindness, a family of filters, called joint/guided filters, have attracted a great amount of attention from the community. The main drawback of most joint/guided filters comes from the ignorance of structural inconsistency between the reference and target signals like color, infrared, and depth images captured under different conditions. Simply adopting such guidelines very likely leads to unsatisfactory results. To address the above issues, this paper designs a simple yet effective filter, named mutually guided image filter (muGIF), which jointly preserves mutual structures, avoids misleading from inconsistent structures and smooths flat regions. The proposed muGIF is very flexible, which can work in various modes including dynamic only (self-guided), static/dynamic (reference-guided) and dynamic/dynamic (mutually guided) modes. Although the objective of muGIF is in nature non-convex, by subtly decomposing the objective, we can solve it effectively and efficiently. The advantages of muGIF in effectiveness and flexibility are demonstrated over other state-of-the-art alternatives on a variety of applications. Our code is publicly available at https://sites.google.com/view/xjguo/mugif.
Multi-camera multi-player tracking with deep player identification in sports video

Identity switches caused by inter-object interactions remain a critical problem for multi-player tracking in real-world sports video analysis. Existing approaches utilizing the appearance model is difficult to associate detections and preserve identities due to the similar appearance of players in the same team. Instead of the appearance model, we propose a distinguishable deep representation for player identity in this paper. A robust multi-player tracker incorporating with deep player identification is further developed to produce identity-coherent trajectories. The framework consists of three parts: (1) the core component, a Deep Player Identification (DeepPlayer) model that provides an adequate discriminative feature through the coarse-to-fine jersey number recognition and the pose-guided partial feature embedding; (2) an Individual Probability Occupancy Map (IPOM) model for players 3D localization with ID; and (3) a K-Shortest Path with ID (KSP-ID) model that links nodes in the flow graph by a proposed player ID correlation coefficient. With the distinguishable identity, the performance of tracking is improved. Experiment results illustrate that our framework handles the identity switches effectively, and outperforms state-of-the-art trackers on the sports video benchmarks.
Annual Streamflow Time Series Prediction Using Extreme Learning Machine Based on Gravitational Search Algorithm and Variational Mode Decomposition

Accurate annual runoff prediction plays an important role in modern water resources planning and management. Here, a hybrid model using evolutionary extreme learning machine and variational mode decomposition (VMD) is developed to forecast annual runoff time series. In the proposed method, the VMD method is first used to decompose the original streamflow into a series of disjoint subcomponents; second, each subcomponent is forecasted by constructing an appropriate extreme learning machine model while the gravitational search algorithm is adopted to tune the model parameters; finally, the aggregated output generated by the forecasting results of all the models is treated as the final simulated output. The annual runoff data series of three huge hydropower reservoirs in China are chosen to testify the performance of the proposed forecasting model. The results show that the developed model can outperform several traditional methods with respect to the employed statistical indexes. Thus, the decomposition-ensemble idea is helpful to yield accurate and stable forecasting results, while the proposed forecasting method can provide strong technical support for operators in water resources and power systems.
Probabilistic solar power forecasting based on weather scenario generation

Abstract Probabilistic solar power forecasting plays an important role in solar power grid integration and power system operations. One of the most popular probabilistic solar forecasting methods is to feed simulated explanatory weather scenarios into a deterministic forecasting model. However, the correlation among different explanatory weather variables are seldom considered during the scenario generation process. This paper presents an improved probabilistic solar power forecasting framework based on correlated weather scenario generation. Copula is used to model a multivariate joint distribution between predicted weather variables and observed weather variables. Massive weather scenarios are obtained by deriving a conditional probability density function given a current weather prediction by using the Bayesian theory. The generated weather scenarios are used as input variables to a machine learning-based multi-model solar power forecasting model, where probabilistic solar power forecasts are obtained. The effectiveness of the proposed probabilistic solar power forecasting framework is validated by using seven solar farms from the 2000-bus synthetic grid system in Texas. Numerical results of case studies at the seven sites show that the developed probabilistic solar power forecasting methodology has improved the pinball loss metric score by up to 140% compared to benchmark models.
Vision-and-Dialog Navigation

Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs situated in simulated, photorealistic home environments. The Navigator asks questions to their partner, the Oracle, who has privileged access to the best next steps the Navigator should take according to a shortest path planner. To train agents that search an environment for a goal location, we define the Navigation from Dialog History task. An agent, given a target object and a dialog history between humans cooperating to find that object, must infer navigation actions towards the goal in unexplored environments. We establish an initial, multi-modal sequence-to-sequence model and demonstrate that looking farther back in the dialog history improves performance. Sourcecode and a live interface demo can be found at https://cvdn.dev/
Human Activity Recognition and Embedded Application Based on Convolutional Neural Network

With the improvement of people's living standards, the demand for health monitoring and exercise detection is increasing. It is of great significance to study human activity recognition methods that are different from traditional feature extraction methods. This article uses convolutional neural network algorithms in deep learning to automatically extract features of activities related to human life. It uses a stochastic gradient descent algorithm to optimize the parameters of the convolutional neural network. The trained network model is compressed on STM32CubeMX-AI. Finally, this article introduces the use of neural networks on embedded devices to recognize six human activities of daily life, such as sitting, standing, walking, jogging, upstairs and downstairs. The acceleration sensor related to human activity information is used to obtain the relevant characteristics of the activity, thereby solving the human activity recognition (HAR) problem. The network structure of the constructed CNN model is shown in Figure 1, including an input layer, two convolutional layers and two pooling layers. After comparing the average accuracy of each set of experiments and the test set of the best model obtained from it, the best model is then selected.
A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies

A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.
Quantum Security Analysis of AES

In this paper we analyze for the first time the post-quantum security of AES. AES is the most popular and widely used block cipher, established as the encryption standard by the NIST in 2001. We consider the secret key setting and, in particular, AES-256, the recommended primitive and one of the few existing ones that aims at providing a post-quantum security of 128 bits. In order to determine the new security margin, i.e., the lowest number of non-attacked rounds in time less than 2128 encryptions, we first provide generalized and quantized versions of the best known cryptanalysis on reduced-round AES, as well as a discussion on attacks that don’t seem to benefit from a significant quantum speed-up. We propose a new framework for structured search that encompasses both the classical and quantum attacks we present, and allows to efficiently compute their complexity. We believe this framework will be useful for future analysis.Our best attack is a quantum Demirci-Selçuk meet-in-the-middle attack. Unexpectedly, using the ideas underlying its design principle also enables us to obtain new, counter-intuitive classical TMD trade-offs. In particular, we can reduce the memory in some attacks against AES-256 and AES-128.One of the building blocks of our attacks is solving efficiently the AES S-Box differential equation, with respect to the quantum cost of a reversible S-Box. We believe that this generic quantum tool will be useful for future quantum differential attacks. Judging by the results obtained so far, AES seems a resistant primitive in the post-quantum world as well as in the classical one, with a bigger security margin with respect to quantum generic attacks.
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination

Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable. To address this problem, we propose a Siamese GAN (SiGAN) to reconstruct HR faces that visually resemble their corresponding identities. On top of a Siamese network, the proposed SiGAN consists of a pair of two identical generators and one discriminator. We incorporate reconstruction error and identity label information in the loss function of SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the generator pair and discriminator of SiGAN, we cannot only achieve photo-realistic face reconstruction, but also ensures the reconstructed information is useful for identity recognition. Experimental results demonstrate that SiGAN significantly outperforms existing face hallucination GANs in objective face verification performance, while achieving photo-realistic reconstruction. Moreover, for input LR faces from unknown identities who are not included in training, SiGAN can still do a good job.
Searching with Consistent Prioritization for Multi-Agent Path Finding

We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time.
An Edge Intelligence Empowered Recommender System Enabling Cultural Heritage Applications

Recommender systems are increasingly playing an important role in our life, enabling users to find “what they need” within large data collections and supporting a variety of applications, from e-commerce to e-tourism. In this paper, we present a Big Data architecture supporting typical cultural heritage applications. On the top of querying, browsing, and analyzing cultural contents coming from distributed and heterogeneous repositories, we propose a novel user-centered recommendation strategy for cultural items suggestion. Despite centralizing the processing operations within the cloud, the vision of edge intelligence has been exploited by having a mobile app (Smart Search Museum) to perform semantic searches and machine-learning-based inference so as to be capable of suggesting museums, together with other items of interest, to users when they are visiting a city, exploiting jointly recommendation techniques and edge artificial intelligence facilities. Experimental results on accuracy and user satisfaction show the goodness of the proposed application.
Neural Network-Based Information Transfer for Dynamic Optimization

In dynamic optimization problems (DOPs), as the environment changes through time, the optima also dynamically change. How to adapt to the dynamic environment and quickly find the optima in all environments is a challenging issue in solving DOPs. Usually, a new environment is strongly relevant to its previous environment. If we know how it changes from the previous environment to the new one, then we can transfer the information of the previous environment, e.g., past solutions, to get new promising information of the new environment, e.g., new high-quality solutions. Thus, in this paper, we propose a neural network (NN)-based information transfer method, named NNIT, to learn the transfer model of environment changes by NN and then use the learned model to reuse the past solutions. When the environment changes, NNIT first collects the solutions from both the previous environment and the new environment and then uses an NN to learn the transfer model from these solutions. After that, the NN is used to transfer the past solutions to new promising solutions for assisting the optimization in the new environment. The proposed NNIT can be incorporated into population-based evolutionary algorithms (EAs) to solve DOPs. Several typical state-of-the-art EAs for DOPs are selected for comprehensive study and evaluated using the widely used moving peaks benchmark. The experimental results show that the proposed NNIT is promising and can accelerate algorithm convergence.