Reviewerly/exHarmony huggingface dataset:
Fault text classification of on-board equipment in high-speed railway based on labeled-Doc2vec and BiGRU

Fault text classification is a prerequisite task for railway engineers based historical train operation data to diagnose vehicle on-board equipment (VOBE) faults and formulate maintenance strategies. Aiming at the low efficiency and accuracy of manual fault text classification, based on Bidirectional Gated Recurrent Unit (BiGRU) and improved attention mechanism (IAtt), an intelligent VOBE fault text classification method is proposed in this paper. Combining the characteristics of the VOBE faults text, also called application event log (AElog) files, the Labeled-Doc2vec is used to generate sentence embedding to realize the vectorized representation of the fault texts, then input sentence embedding into BiGRU to extract the fault text features as the improved attention mechanism layer. Finally, the high-dimensional fault text features outputted by hidden are input into Softmax to complete the fault text classification. The experimental results show that the proposed method can analyze the semantics of fault text according to the train running state before and after the fault time, that is, it can realize text classification by combining context. Compared with other methods, the method in this paper obtains the optimal accuracy, precision, recall and F1-score, which shows that the proposed method can be applied to fault text classification of VOBE, effectively reduces the labor cost of fault text classification in practice, and improves the efficiency of fault text classification of VOBE.
AI Based Autonomous Room Cleaning Bot

Daily, technology is growing at a rapid pace. We use machines even for simple jobs like monitoring, cleaning, etc. It is almost as if we depend on them, rely on them, and have an unprecedented urge to always have one on hand. For example, one of the most commonly used machines we have become so attached to the mobile phone. Everywhere we look, we can see people wandering around with their eyes glued to their screens or holding their devices next to their ears and having a conversation with whoever is on the other side of the line. We have almost completely substituted all the earlier forms of communication, transportation, and other aspects of our society with the creation of newer, more industrial methods and apparatuses. This has brought in modern methods and devices for various tasks in various fields. Most of these jobs are done with the help of machines to save time and labor costs. Our idea is one such project which mainly focuses on saving time and money, and also doing its work to maximum efficiency. This robot is fully automated which makes use of the concept of object detection and room mapping to move around the room and clean the floor.
Autonomous Driving Mobile Robot using Q-learning

This study presents a Reinforcement Learning, more specifically Q-Learning for the obstacle avoidance autonomous mobile robot. For the past few years, Reinforcement Learning in Robotics has been a difficult problem to tackle. Numerous in-depth research efforts have been prompted by the opportunity to provide a robot with a tool capable of enabling the development of an ideal behavior via multiple hit and trial interactions with the robotic environment. In this paper, Implementation of Q-learning algorithm and feedback control for the mobile robot has been implemented in ROS. This algorithm is developed and tested with the turtlebot3_burger. The control script and the simulated robot communicate via ROS.
E-Voting via Upgradable Smart Contracts on Blockchain

The proposed work is a blockchain-based E-Voting system which is designed to incorporate privacy, authenticity, and verifiability in the electoral process. Solidity programming language is used to implement smart contracts stored on the Ethereum blockchain which are executed automatically when predefined conditions are met. An Ethereum-based E-Voting decentralised application is deployed which authenticates users with the help of biometric factors and verifies it with the existing E-Aadhaar database.In this model, the paper presents the novel idea of deploying an upgradable smart contract where the election manager can decide whether the voting application would require either a version change, virtual upgrades or feature modifications in the future. This is achieved by using the OpenZeppelin plugin deployed on truffle which uses the concept of ‘‘proxy pattern.’’ This methodology helps us to improve and enhance the logic of smart contracts that are normally non-editable once deployed. The parameters used for authentication include the user’s EmailID, Registration ID(Aadhar), and facial scan for biometric verification along with other details related to the voter’s biodata.
A New Macromodeling Method Based on Deep Gated Recurrent Unit Regularized With Gaussian Dropout for Nonlinear Circuits

In this paper, for the first time, the deep gated recurrent unit (Deep GRU) is used as a new macromodeling approach for nonlinear circuits. Similar to Long Short-Term Memory (LSTM), the GRU has gating units that control the information flow and makes the network less prone to the vanishing gradient problem. Having a smaller number of gates causes GRU to have fewer parameters compared to LSTM leading to better model accuracy. Using the gates leads gradient formulations to have additive nature which helps them to be more resistant to vanishing and consequently learn long sequences of data. The proposed macromodeling method is capable of modeling nonlinear circuits more accurately and using fewer parameters compared to the conventional LSTM macromodeling method. To further improve the GRU performance, a regularization technique called Gaussian dropout is applied in this paper on deep GRU (GDGRU) to reduce the overfitting problem resulting in better test error. Additionally, the models obtained from the proposed techniques are remarkably faster than the original transistor-level models. To verify the superiority of the proposed method, time-domain modeling of three nonlinear circuits is provided. For these circuits, the comparisons of the accuracy and speed between the conventional recurrent neural network (RNN), the LSTM, and the proposed macromodeling methods are provided.
A Traffic Sign Recognition Method Under Complex Illumination Conditions

Environment awareness technology is one of the most critical technologies for autonomous vehicles, and traffic sign recognition is an important branch of environment awareness system, which has important research significance for ensuring traffic safety.To solve the problems of easy omission and inaccurate positioning for traffic sign detection under complex illumination conditions, there are two main innovations in this paper: firstly, an adaptive image enhancement algorithm was proposed to improve the image quality under complex illumination conditions; Secondly, a novel and lightweight attention block named Feature difference (FD) model was proposed to detect and recognize traffic signs.Unlike state-of-the-art attention models, which utilizes the difference between two feature maps to generate the attention mask.In this work, the single-stage target detection algorithm SSD was selected as the basic network, the backbone network was set as ResNet and VGG respectively and FD module was added.A large number of experiments were carried out to evaluate the optimization effect of FD module.The following conclusions can be drawn: image enhancement algorithms can provide better image samples; The FD model can be integrated into most of CNNs just by adding some shortcut connections, which does not introduce any additional parameters and layers; The average detection accuracy with FD module is 1.80% higher and the average recall rate was 1.51% higher than that without FD module, which has no great influence on the running speed.The FD module has better enhancement effect for dark image detection; The detection accuracy of the FD module is slightly better than other attention modules including BAM, CBAM and SE; The FD model can be used to evaluate the effect of the convolutional operation on features, which could help to find convolutional layers that have little effect on the accuracy of recognition for the network pruning.
Robustness Meets Low-Rankness: Unified Entropy and Tensor Learning for Multi-View Subspace Clustering

In this paper, we develop the weighted error entropy-regularized tensor learning method for multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal and subspace structure discovery into one unified framework. Unlike most existing methods which focus only on the affinity matrix learning for the subspace discovery by different optimization models and simply assume that the noise is independent and identically distributed (i.i.d.), our WETMSC method adopts the weighted error entropy to characterize the underlying noise by assuming that noise is independent and piecewise identically distributed (i.p.i.d.). Meanwhile, WETMSC constructs the self-representation tensor by storing all self-representation matrices from the view dimension, preserving high-order correlation of views based on the tensor nuclear norm. To solve the proposed nonconvex optimization method, we design a half-quadratic (HQ) additive optimization technology and iteratively solve all subproblems under the alternating direction method of multipliers framework. Extensive comparison studies with state-of-the-art clustering methods on real-world datasets and synthetic noisy datasets demonstrate the ascendancy of the proposed WETMSC method.
CACTO: Continuous Actor-Critic With Trajectory Optimization—Towards Global Optimality

This paper presents a novel algorithm for the continuous control of dynamical systems that combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single framework. The motivations behind this algorithm are the two main limitations of TO and RL when applied to continuous nonlinear systems to minimize a non-convex cost function. Specifically, TO can get stuck in poor local minima when the search is not initialized close to a "good" minimum. On the other hand, when dealing with continuous state and control spaces, the RL training process may be excessively long and strongly dependent on the exploration strategy. Thus, our algorithm learns a "good" control policy via TO-guided RL policy search that, when used as initial guess provider for TO, makes the trajectory optimization process less prone to converge to poor local optima. Our method is validated on several reaching problems featuring non-convex obstacle avoidance with different dynamical systems, including a car model with 6D state, and a 3-joint planar manipulator. Our results show the great capabilities of CACTO in escaping local minima, while being more computationally efficient than the Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) RL algorithms.
A Survey on Consensus Algorithms in Blockchain-Based Applications: Architecture, Taxonomy, and Operational Issues

Recently, Blockchain-based applications have become immensely popular because of limited reliance on a single entity, unlike a centralized system.However, reaching a consensus among blockchain networks is a challenging and vital aspect of blockchain-based applications.There are various types of blockchain networks for different kinds of application scenarios.Among all of them, the consensus algorithm is the most crucial part of reaching an agreement in the complex blockchain network.Over the years, researchers have focused on dealing with the challenges like distributed computing, storage, transaction speed, security, validity, interoperability, and many more.However, only some of them are appropriate for all domains.Therefore, this paper presents an extensive study of different types of consensus protocols used in existing blockchain solutions with the strength and limitations of each algorithm.We also provide an inherent comparison among different algorithms to understand consensus protocol selection better.Moreover, we investigate operational and interoperability issues in existing blockchain-based applications to understand challenges and provide recommendations for future developers.
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection

Anomaly detection of multivariate time series is meaningful for system behavior monitoring. This paper proposes an anomaly detection method based on unsupervised Short- and Long-term Mask Representation learning(SLMR). The main idea is to extract short-term local dependency patterns and long-term global trend patterns by using multi-scale residual dilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore, our approach can comprehend temporal contexts and feature correlations by combining spatial-temporal masked self-supervised representation learning and sequence split. It considers the importance of features is different, and we introduce the attention mechanism to adjust the contribution of each feature. Finally, a forecasting-based model and a reconstruction-based model are integrated to focus on single timestamp prediction and latent representation of time series. Experiments show that the performance of our method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method is good at anomaly localization.
Intention-Aware User Modeling for Personalized News Recommendation

Although tremendous efforts have been made in the field of personalized news recommendations, how to accurately model users' reading preferences to recommend satisfied news remains a critical challenge. In fact, users' reading preferences are often driven by his/her high-level goal-oriented intentions. For example, in order to satisfy the intention of traveling, a user may prefer to read news about national parks or hiking activities. However, existing methods for news recommendations often focus on capturing users' low-level preferences towards specific news only, neglecting to model their intrinsic reading intentions, leading to insufficient modeling of users and thus suboptimal recommendation performance. To address this problem, in this paper, we propose a novel intention-aware personalized news recommendation model (IPNR), to accurately model both a user's reading intentions and his/her preference for personalized next-news recommendations. In addition to modeling users' reading preferences, our proposed model IPNR can also capture users' reading intentions and the transitions over intentions for better predicting the next piece of news which may interest the user. Extensive experimental results on real-world datasets demonstrate that IPNR outperforms the state-of-the-art news recommendation methods in terms of recommendation accuracy (The source code is available at: https://github.com/whonor/IPNR ).
Adversarial Learning-Based Stance Classifier for COVID-19-Related Health Policies

The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of the virus and further alleviate the crisis, various health policies (e.g., stay-at-home orders) have been issued which spark heated discussions as users turn to share their attitudes on social media. In this paper, we consider a more realistic scenario on stance detection (i.e., cross-target and zero-shot settings) for the pandemic and propose an adversarial learning-based stance classifier to automatically identify the public's attitudes toward COVID-19-related health policies. Specifically, we adopt adversarial learning that allows the model to train on a large amount of labeled data and capture transferable knowledge from source topics, so as to enable generalize to the emerging health policies with sparse labeled data. To further enhance the model's deeper understanding, we incorporate policy descriptions as external knowledge into the model. Meanwhile, a GeoEncoder is designed which encourages the model to capture unobserved background factors specified by each region and then represent them as non-text information. We evaluate the performance of a broad range of baselines on the stance detection task for COVID-19-related health policies, and experimental results show that our proposed method achieves state-of-the-art performance in both cross-target and zero-shot settings.
Multi-granularity Item-Based Contrastive Recommendation

Contrastive learning (CL) has shown its power in recommendation. However, most CL-based models build their CL tasks merely focusing on the user's aspects, ignoring systematically modeling the rich and diverse information in items. In this work, we propose a novel Multi-granularity item-based contrastive learning (MicRec) for the matching stage in recommendation, which systematically introduces multi-aspect item-related correlations to representation learning via CL. Specifically, we build three item-based CL tasks as a set of plug-and-play auxiliary objectives to capture item correlations in feature, semantic and session levels. In experiments, we conduct both offline and online evaluations on real-world datasets, verifying the effectiveness and universality of three proposed CL tasks. Currently, MicRec has been deployed on a real-world recommender system of WeChat Top Stories, affecting millions of users.
AdaptChain: Adaptive Scaling Blockchain With Transaction Deduplication

Although existing schemes improve blockchain throughput by allowing concurrent blocks to be appended to the blockchain, little attention has been devoted to adjusting blockchain throughput dynamically and deduplicating transactions between concurrent blocks. In this article, we propose AdaptChain, an adaptive scaling blockchain with transaction deduplication. When the transaction demand of users in the network is high, the blockchain expands to meet the demand; when the transaction demand is low, the blockchain shrinks to save communication and storage costs. Our transaction deduplication mechanism ensures that no duplicate transactions are added to the blockchain, thereby improving bandwidth utilization and achieving higher effective throughput. Besides, we randomly split the mining power of the system to achieve mining power load balancing and resist attacks. We formally analyze the blockchain security and implement the proposed prototype on Amazon EC2. Experimental results show that AdaptChain achieves dynamic and higher effective blockchain throughput.
DANet: Dual-Branch Activation Network for Small Object Instance Segmentation of Ship Images

In maritime scenes, instance segmentation of small object ships is of vital importance. Small ship objects in images have the characteristics of smaller size, lower image cover rate and fewer appearance features. However, existing instance segmentation methods fail to recognize and segment them and can cause missed ship segmentation. To this end, we propose a dual-branch activation network (DANet) for small object instance segmentation of ship images. DANet consists of a Feature Encoding, a Dual Mask Branch, and a Dual Activation Branch. The Feature Encoding adopts feature refinement and a pyramid structure to obtain more fine-grained features. The proposed Dual Mask Branch extracts dual-path mask features for encoding small object information. We propose a Dual Activation Branch to activate more small object regions and generate instance features. Furthermore, we build the Small ShipInsSeg dataset from a total of 5,256 images and 11,612 instances. The experiments show that DANet outperforms the SparseNet baseline and achieves state-of-the-art performance. Additionally, our method achieves a good trade-off between accuracy and speed.
Actor-Aware Self-Supervised Learning for Semi-Supervised Video Representation Learning

Self-supervised contrastive learning has shown a significant improvement in performance for action recognition tasks by discovering useful signals from unlabeled videos. Nevertheless, the unique features of existing video benchmark datasets have led the learned video representations to be contextually biased toward dominant backgrounds and scene correlations. Thus, ultimately leading to poor generalizations on scene-invariant action recognition. Therefore, we propose Actor-aware Self-supervised Learning for Semi-supervised Video Representation Learning (ActorSL). We aligned localized actors and their corresponding scene information to encourage the model to learn discriminative regions and mitigate the model’s dependency on the video background during contrastive training. Furthermore, we present an inter-video Background Mixing (iBM) augmentation strategy to introduce scene consistency into the model. We patch inter-video crops of four randomly selected frames for iBM to create a unique frame for each video. The patched frame is blended with the target video frames to generate a spatially augmented sample. Then, the actor-scene aligned features and features of iBM-augmented videos are utilized to optimize contrastive loss and consistency regularization jointly in a semi-supervised way. Moreover, iBM combines the one-hot-encoded labels of patches with the label of the target video as a label smoothing regularizer to soften the decision boundaries of the semi-supervised model. Our experimental results reveal that, ActorSL notably improved current state-of-the-art semi-supervised methods on the Kinetics-400, UCF101, and HMDB51 datasets under a low-label regime. Code released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Endarzboy/ActorSL</uri> .
A Novel Meta-Heuristic Algorithm for Numerical and Engineering Optimization Problems: Piranha Foraging Optimization Algorithm (PFOA)

This paper provides a novel meta-heuristic optimization algorithm for solving continuous optimization problems efficiently in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The algorithm is inspired by the flexible and mobile foraging behaviour of piranha swarm and divides their foraging behavior into three patterns: localized group attack, bloodthirsty cluster attack and scavenging foraging, simulates the above behaviors to construct two dynamic search processes for exploration and exploitation. PFOA uses three strategies of non-linear parameter control, population survival and reverse evasion search to enable populations to have better population diversity at different stages of the search and to help find better solutions. To gain insight into the performance of PFOA, visualization methods were used to assess the efficiency of PFOA optimization and to analyse the impact of the characteristics of the three foraging modes, the sensitivity of the parameters and the size of the piranha population on the algorithm. The algorithm performance was further tested with 27 CEC benchmark functions and four real engineering design optimization problems, and the results were compared with 13 well-known meta-heuristics. Test results based on statistical methods such as box-line plots, Wilcoxon rank sum test and Friedman test in multiple dimensions (30, 50, 100 and fixed dimensions) show significant differences compared to other algorithms and that the performance of the algorithm is stable and in significant improvement. The unique advantages of PFOA in terms of the equilibrium of convergence speed and exploration can avoid getting trapped in local optimum regions and effectively solve optimization problems in complex search spaces.