Reviewerly/exHarmony huggingface dataset:
Learning-based elephant herding optimization algorithm for solving numerical optimization problems

The elephant herding optimization (EHO) is a recent swarm intelligence algorithm. This algorithm simulates the clan updating and separation behavior of elephants. The EHO method has been successfully deployed in various fields. However, a more reliable implementation of the standard EHO algorithm still requires improving the control and selection of the parameters, convergence speed, and efficiency of the optimal solutions. To cope with these issues, this study presents an improved EHO algorithm terms as IMEHO. The proposed IMEHO method uses a global velocity strategy and a novel learning strategy to update the velocity and position of the individuals. Furthermore, a new separation method is presented to keep the diversity of the population. An elitism strategy is also adopted to ensure that the fittest individuals are retained at the next generation. The influence of the parameters and strategies on the IMEHO algorithm is fully studied. The proposed method is tested on 30 benchmark functions from IEEE CEC 2014. The obtained results are compared with other eight metaheuristic algorithms and evaluated according to Friedman rank test. The results imply the superiority of the IMEHO algorithm to the standard EHO and other existing metaheuristic algorithms.
Recognizing Long-Form Speech Using Streaming End-to-End Models

All-neural end-to-end (E2E) automatic speech recognition (ASR) systems that use a single neural network to transduce audio to word sequences have been shown to achieve state-of-the-art results on several tasks. In this work, we examine the ability of E2E models to generalize to unseen domains, where we find that models trained on short utterances fail to generalize to long-form speech. We propose two complementary solutions to address this: training on diverse acoustic data, and LSTM state manipulation to simulate long-form audio when training using short utterances. On a synthesized long-form test set, adding data diversity improves word error rate (WER) by 90% relative, while simulating long-form training improves it by 67% relative, though the combination doesn't improve over data diversity alone. On a real long-form call-center test set, adding data diversity improves WER by 40% relative. Simulating long-form training on top of data diversity improves performance by an additional 27% relative.
Hand sign language recognition using multi-view hand skeleton

Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition.
Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model

In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term correlation in the series on top of the features inferred from the VAE module. As a result, our detection algorithm is capable of identifying anomalies that span over multiple time scales. We demonstrate the effectiveness of our detection algorithm on five real world problems and find our method outperforms three other commonly used detection methods.
Improved Artificial Potential Field Method Applied for AUV Path Planning

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.
Evaluating the feasibility of blockchain in logistics operations: A decision framework

The main purpose of this study is to investigate the feasibility of blockchain technology in logistics industry using a quantitative approach. To this end, a decision framework is proposed based on a multi-criteria decision structure that incorporates AHP into VIKOR under Intuitionistic Fuzzy Theory. This integration presents different solutions and rankings based on different decision-making strategies and also captures uncertainty in the evaluation process. While Intuitionistic Fuzzy AHP calculates the importance weights of the proposed criteria indicated as scalability, privacy, interoperability, audit, latency, visibility, trust, and security, Fuzzy VIKOR ranks the logistics operations demonstrated as materials handling, warehousing, order processing, transportation, packaging, fleet management, labeling, vehicle routing and product returns management. The proposed decision framework was applied in a large-scale logistics company located in Turkey. The findings of this study suggest that while the most important criteria are security, visibility and audit, the most feasible logistics operations proved to be transportation, materials handling, warehousing, order processing and fleet management in a possible blockchain implementation. The decision framework in this study may enable decision makers to evaluate the feasibility of blockchain in logistics operations, which is one of the main research gaps in the current blockchain research. Furthermore, this is the first study that integrates AHP and VIKOR methods under Intuitionistic Fuzzy Theory in the context of blockchain.
Optimization of constrained mathematical and engineering design problems using chaos game optimization

In the past few decades, many different metaheuristic algorithms have been developed for optimization purposes each of which have specific advantages and disadvantages due to multiple applications in different optimization fields. The Chaos Game Optimization (CGO) is proposed in this paper as a new metaheuristic algorithm for optimization of constrained mathematical and engineering design problems. The proposed CGO method is formulated based on some principles of chaos theory in which the fractals configuration by chaos game methodology alongside the fractals self-similarity issues are in perspective. A total number of 34 constrained mathematical problems are collected which have been benchmarked and proposed in the Competitions on Evolutionary Computation (CEC) and 15 constrained engineering design problems are selected in order to evaluate the overall performance of the proposed novel CGO method. In order to validate the results of the novel CGO algorithm, the best results of different standard, improved and hybrid metaheuristic algorithms in dealing with the considered constrained problems are selected from the literature for comparative purposes. In addition, the statistical results of the CGO algorithm including the minimum, mean, maximum and the standard deviation values are all calculated and compared to the results of other metaheuristics. The obtained results proved that the proposed algorithm is capable of providing very competitive results and outperforms the other metaheuristics in most of the cases.
On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset

The Matthews Correlation Coefficient (MCC) is one of the popular measurements for classification accuracy. It has been generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The study of this paper finds that this is not true. MCC deteriorates seriously when the dataset in classification are imbalanced. Experiment results and analysis show that MCC is not suitable for classification accuracy measurement on imbalanced datasets.
TPM: Multiple object tracking with tracklet-plane matching

Multiple object tracking (MOT) aims to model the temporal relationship among detected objects and associate them into trajectories. Thus, one major challenge of MOT lies in the confusion from noisy object detection results. In this paper, we propose Tracklet-Plane Matching (TPM), a new approach which improves the performance of MOT by modeling and reducing the interferences from noisy or confusing object detections. TPM first constructs good temporally-related object detections into short tracklets. Then, a tracklet-plane matching process is introduced to organize related tracklets into planes and associate them into long trajectories. The tracklet-plane matching process assigns visually confusing tracklets into different tracklet planes according to their contextual information, thus properly reducing the confusion among similar tracklets. At the same time, it also allows association among temporally non-neighboring or overlapping tracklets, which provides good flexibility to handle confusion from noisy detections. Under this process, a tracklet-importance evaluation scheme and a representative-based similarity modeling scheme are introduced. These two schemes can properly evaluate the reliability of detection results and identify reliable ones during association so that the impact of noisy or confusing detections can be well-mitigated. Experimental results on benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art MOT methods.
Movie Recommendation System Using Sentiment Analysis From Microblogging Data

Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results.
A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition

Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/quadrature (I/Q) symbols of the modulated data. The proposed framework integrates one-dimensional (1D) convolutional, two-dimensional (2D) convolutional and long short-term memory (LSTM) layers to extract features more effectively from a time and space perspective. Experiments on the benchmark dataset show the proposed framework has efficient convergence speed and achieves improved recognition accuracy, especially for the signals modulated by higher dimensional schemes such as 16 quadrature amplitude modulation (16-QAM) and 64-QAM.
Video Object Segmentation and Tracking

Object segmentation and object tracking are fundamental research areas in the computer vision community. These two topics are difficult to handle some common challenges, such as occlusion, deformation, motion blur, scale variation, and more. The former contains heterogeneous object, interacting object, edge ambiguity, and shape complexity; the latter suffers from difficulties in handling fast motion, out-of-view, and real-time processing. Combining the two problems of Video Object Segmentation and Tracking (VOST) can overcome their respective difficulties and improve their performance. VOST can be widely applied to many practical applications such as video summarization, high definition video compression, human computer interaction, and autonomous vehicles. This survey aims to provide a comprehensive review of the state-of-the-art VOST methods, classify these methods into different categories, and identify new trends. First, we broadly categorize VOST methods into Video Object Segmentation (VOS) and Segmentation-based Object Tracking (SOT). Each category is further classified into various types based on the segmentation and tracking mechanism. Moreover, we present some representative VOS and SOT methods of each time node. Second, we provide a detailed discussion and overview of the technical characteristics of the different methods. Third, we summarize the characteristics of the related video dataset and provide a variety of evaluation metrics. Finally, we point out a set of interesting future works and draw our own conclusions.
The effect of political and economic uncertainty on the cryptocurrency market

This article explores the effect of uncertainty on the cryptocurrency market. Previous work has been limited to analyzing the effect of uncertainty on Bitcoin. We depart from the previous literature by focusing on top 25 cryptocurrencies and find that the cryptocurrency market can serve as a strong hedge against geopolitical risks in most cases, but it could be considered a weak hedge and safe haven against economic policy uncertainty during a bull market. Notably, the cryptocurrency market reacts to uncertainty differently, depending on the type of uncertainty. Overall, our findings suggest that uncertainty is an essential determinant of cryptocurrency returns.
A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and affinity computation. Meanwhile, the model size of the sophisticated affinity network is usually non-trivial. In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure. In particular, UMA integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. Such design brings advantages of improved computation efficiency, low memory requirement and simplified training procedure. In addition, we equip our model with a task-specific attention module, which is used to boost task-aware feature learning. The proposed UMA can be easily trained end-to-end, and is elegant - requiring only one training stage. Experimental results show that it achieves promising performance on several MOT Challenge benchmarks.
Graph Embedded Pose Clustering for Anomaly Detection

We propose a new method for anomaly detection of human actions. Our method works directly on human pose graphs that can be computed from an input video sequence. This makes the analysis independent of nuisance parameters such as viewpoint or illumination. We map these graphs to a latent space and cluster them. Each action is then represented by its soft-assignment to each of the clusters. This gives a kind of ”bag of words” representation to the data, where every action is represented by its similarity to a group of base action-words. Then, we use a Dirichlet process based mixture, that is useful for handling proportional data such as our soft-assignment vectors, to determine if an action is normal or not. We evaluate our method on two types of data sets. The first is a fine-grained anomaly detection data set (e.g. ShanghaiTech) where we wish to detect unusual variations of some action. The second is a coarse-grained anomaly detection data set (e.g., a Kinetics-based data set) where few actions are considered normal, and every other action should be considered abnormal. Extensive experiments on the benchmarks show that our method performs considerably better than other state of the art methods.
Conditional Channel Gated Networks for Task-Aware Continual Learning

Convolutional Neural Networks experience catastrophic forgetting when optimized on a sequence of learning problems: as they meet the objective of the current training examples, their performance on previous tasks drops drastically. In this work, we introduce a novel framework to tackle this problem with conditional computation. We equip each convolutional layer with task-specific gating modules, selecting which filters to apply on the given input. This way, we achieve two appealing properties. Firstly, the execution patterns of the gates allow to identify and protect important filters, ensuring no loss in the performance of the model for previously learned tasks. Secondly, by using a sparsity objective, we can promote the selection of a limited set of kernels, allowing to retain sufficient model capacity to digest new tasks. Existing solutions require, at test time, awareness of the task to which each example belongs to. This knowledge, however, may not be available in many practical scenarios. Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available. We validate our proposal on four continual learning datasets. Results show that our model consistently outperforms existing methods both in the presence and the absence of a task oracle. Notably, on Split SVHN and Imagenet-50 datasets, our model yields up to 23.98% and 17.42% improvement in accuracy w.r.t. competing methods.
Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization

Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension. LF spatial super-resolution (SR) thus becomes an indispensable part of the LF camera processing pipeline. The high-dimensionality characteristic and complex geometrical structure of LF images makes the problem more challenging than traditional single-image SR. The performance of existing methods are still limited as they fail to thoroughly explore the coherence among LF views and are insufficient in accurately preserving the parallax structure of the scene. In this paper, we propose a novel learning-based LF spatial SR framework, in which each view of an LF image is first individually super-resolved by exploring the complementary information among views with combinatorial geometry embedding. For accurate preservation of the parallax structure among the reconstructed views, a regularization network trained over a structure-aware loss function is subsequently appended to enforce correct parallax relationships over the intermediate estimation. Our proposed approach is evaluated over datasets with a large number of testing images including both synthetic and real-world scenes. Experimental results demonstrate the advantage of our approach over state-of-the-art methods, i.e., our method not only improves the average PSNR by more than 1.0 dB but also preserves more accurate parallax details, at a lower computation cost.
sbi: A toolkit for simulation-based inference

Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena.In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters.Despite these advantages, tuning a simulator's parameters so that its outputs match data is challenging.Simulation-based inference (SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations.Importantly, SBI does not seek to recover a single 'best' data-compatible parameter set, but rather to identify all high probability regions of parameter space that explain observed data, and thereby to quantify parameter uncertainty.In Bayesian terminology, SBI aims to retrieve the posterior distribution over the parameters of interest.In contrast to conventional Bayesian inference, SBI is also applicable when one can run model simulations, but no formula or algorithm exists for evaluating the probability of data given parameters, i.e. the likelihood.We present sbi, a PyTorch-based package that implements SBI algorithms based on neural networks.sbi facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.