Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models

Abstract Worldwide websites publish enormous amounts of text, audio, and video content every day. This valuable information allows for the assessment of regional trends and general public opinion. Based on consumers’ online behavioural habits, businesses are showing them their chosen ads. It is difficult to carefully analyse these raw data to find valuable trends, especially for a language with limited resources like Urdu. There have not been many studies or efforts to create language resources for the Urdu language and analyse people’s sentiment, even though there are more than 169 million Urdu speakers in the world and a sizable amount of Urdu data is generated on various social media platforms every day. However, there has been relatively little research on sentiment analysis in Urdu. Researchers have primarily performed studies in English and Chinese. In response to this gap, we suggest an emotion analyser for Urdu, the primary language of Asia, in this research study. In this paper, we propose to assess sentiment in Urdu review texts by integrating a bidirectional long short-term memory (BiLSTM) model with a convolutional neural network (CNN). We contrast the CNN, LSTM, BiLSTM, and CNN-LSTM models with the CNN-BiLSTM model. With an accuracy rate of 0.99 %, the CNN-BiLSTM model performed better than the other models in an initial investigation.
Uncertainty-Guided Cross-Modality Semi-Supervised Learning for MRI Segmentation

Semi-supervised learning emerges as a viable strategy for addressing the challenge posed by the limited availability of annotated data in medical image segmentation tasks. Nonetheless, current semi-supervised methods for medical image segmentation mainly concentrate on single-modality data. Leveraging multi-modality data effectively to enhance the effectiveness of semi-supervised segmentation methods is a valuable research topic because such data provides additional complementary information. Nevertheless, the majority of current multi-modality semi-supervised segmentation methods are reliant on highly coupled fusion networks, requiring the presence of multi-modality data during both the training and inference phases. It imposes constraints on the deployment of these solutions in clinical practice. To tackle this difficulty, we present UGCM-Semi, a novel multi-modality semi-supervised segmentation framework with an uncertainty-guided distribution calibration (UGDC) student network and a cross-model feature alignment loss. Specifically, we propose the UGDC student network which utilizes the uncertainty modeling technique to quantify and reduce the distribution shift due to labeled and unlabeled data with different modalities. Meanwhile, we add the cross-model feature alignment loss to mitigate the incorrect supervision from the teacher network to the student network caused by the aforementioned distribution shift. Extensive experiments on two public multi-modality MRI datasets demonstrate that UGCN-Semi provides superiority over the state-of-the-art methods.
Learning Stable Task-Level Manifold for Few-Shot Learning

Few-shot learning (FSL) aims to learn to new concepts based on very limited data. One of the main challenges in FSL is the use of pretrained embeddings whose dimension is too high for the small sample size. While the large dimensionality helps capture diverse features in the training example, not all the features are relevant to the task at hand. Such task-irrelevant features from pre-trained embeddings can negatively impact the performance of the FSL model. To address this challenge, we propose a simple yet effective way to learn task-level manifolds that explicitly eliminate the negative effect of nuisance features. One key novelty of our approach is to address class imbalance in the training data by injecting supervised information through key virtual samples, which we named Manifold Support Points. Empirically, we demonstrate that the stability of the learnt task-level manifolds and their effectiveness in challenging tasks in FSL. Our approach shows competitive performance on several benchmark datasets, including tieredImageNet and CUB.
Aircraft Route Planning Based on Improved Particle Swarm Optimization Algorithm

The particle swarm optimization is widely used in optimization questions for its advantage of simply realized, high precision, small adjusted parameter and quick convergence velocity. The article analyses their influences of inertia and acceleration gene to optimization capability of particle swarm optimization and brings forward the improved means of dynamicly modulating the inertia gene which will improve the global and individual search capability. The simulation indicates that the impoved arithmetic is obviously enhanced in search velocity and precision.
Vector Representation and Machine Learning for Short-Term Photovoltaic Power Prediction

Short-term photovoltaic (PV) energy production forecasting is critical for managing grid-connected systems and energy trading. Machine learning models are widely used for accurate prediction, and this study proposes using Time2Vec as an embedding for a transformer-based neural network architecture. Experiments on two PV power plants in India showed significant improvements comparing our proposed architecture to MLP, LSTM, and the persis-tence model, which is a standard baseline prediction in this type of forecasting, with over 20 % improvements in some horizons. These findings demonstrate the effectiveness of the proposed approach for short-term PV forecasting using machine learning models.
Uncovering Variability in Human Driving Behavior Through Automatic Extraction of Similar Traffic Scenes from Large Naturalistic Datasets

Recently, multiple naturalistic traffic datasets of human-driven trajectories have been published (e.g., highD, NGSim, and pNEUMA). These datasets have been used in studies that investigate variability in human driving behavior, for example for scenario-based validation of autonomous vehicle (AV) behavior, modeling driver behavior, or validating driver models. Thus far, these studies focused on the variability on an operational level (e.g., velocity profiles during a lane change), not on a tactical level (i.e., to change lanes or not). Investigating the variability on both levels is necessary to develop driver models and AV s that include multiple tactical behaviors. To expose multi-level variability, the human responses to the same traffic scene could be investigated. However, no method exists to automatically extract similar scenes from datasets. Here, we present a four-step extraction method that uses the Hausdorff distance, a mathematical distance metric for sets. We performed a case study on the highD dataset that showed that the method is practically applicable. The human responses to the selected scenes exposed the variability on both the tactical and operational levels. With this new method, the variability in operational and tactical human behavior can be investigated, without the need for costly and time-consuming driving-simulator experiments.
Attention to Hazardous Regions: Pseudo Point Cloud Generation for Real-time 3D Object Detection

3D object detection plays a vital role in the perception system of self-driving cars for it provides accurate structural information and classification of objects in the scene. Recent works leverage pseudo point clouds to compensate for the sparsity of raw point clouds. However, the coarse pseudo point cloud generation brings huge computational costs to the inference process, resulting in inferior inference speed. In this work, we aim to solve two critical yet not well-addressed issues in pseudo point cloud generation, including the loss of 3D detection inference speed and the additional memory cost due to the massive generation of the pseudo point cloud. We propose attention-guided pseudo point cloud generation to direct the focus of pseudo point cloud generation to hazardous regions. In our experiments, our method improves the inference speed by 21.72% and reduces the memory usage of generated data by 90.03%.
Effect of latency on social presence in traditional video conference and VR conference: a comparative study

Virtual reality (VR) conference, as a typical social VR application, has gained popularity in recent years. It offers users located at different locations a fully immersive experience and a sense of togetherness. However, the remote communication also introduces inevitable latencies, which may adversely affect the so-called social presence. There is still a lack of research on the effect of latency on social presence. To fill the gap, this paper aims to examine the impact of latency on social presence of VR conference and contrast it with that of traditional video conference. Here, the social presence is measured using the Networked Minds Social Presence Inventory (NMSPI). We design and conduct two conversation-based subjective tests for both types of conference and compare the impact of the latency based on the test results. The conclusions of these studies can be used as guidelines for VR service providers to optimize their conference systems.
Estimator-Analyzer of Power Quality Based on Space Vector and Instantaneous Power Theory

The paper presents some mathematical methods for evaluating the power quality indices based on the theory of instantaneous power and space vector. The main idea was to overcome the short comings of current power quality analyzers, namely sensitivity to fast fluctuations of input signal parameters. Based on the applied methods and algorithms, the device hardware was developed and the experimental results are presented in this article. The device has been tested with signals of known characteristics and also in the power limited electrical system (electric power system of a ship).
An Analysis of Evolutionary Migration Models for Multi-Objective, Multi-Fidelity Automl

Methods have been proposed to maximize the efficiency of Automated Machine Learning (AutoML) systems while simplifying the search for solutions. Multi-fidelity approaches have been shown as an alternative to achieve this since they are straightforward to implement and reduce the computational cost when modeling large datasets. However, they are not suited to shifting data distributions, and they remove configurations too fast. Therefore, they must be combined with other techniques. Combining multi-fidelity methods with evolutionary algorithms and island models helps to adapt better and can contribute to maintaining diversity. This paper presents a comparative analysis of 10 network topologies distributed over a generalized island model for AutoML. A dynamic migration model with multi-objective and multi-fidelity evaluation is proposed to reduce the complexity of the tasks. This proposal is compared against state-of-the-art AutoML frameworks. It was found that Hypercube, Grid 2-dim, and Grid 3-dim topologies have the best performance as they maintain a balance in the number of connections. Furthermore, these topologies were shown to be competitive against other frameworks in the state of the art.
Seam-Aware Rendering Quality Enhancement Network For Compressed 3D Scene

Typically, UV maps generated from real-world reconstructed meshes tend to have extensive fragmentation, i.e., numerous UV charts. This fragmentation of the UV map results in severe discontinuities in the texture map, which degrades texture map compression performance and causes texture bleeding artifacts near the seams of the rendered images of the compressed texture map. To solve this problem, this paper introduces a convolutional neural network (CNN)-based post-processing method for enhancing the rendering result of compressed texture map, especially focusing on reducing bleed artifacts. The CNN architecture of the proposed method is designed to perform both rendering quality enhancement and texture bleed artifact removal by being aware of seams in the rendered image. Through the proposed post-processing method, rendered images have a lower distortion in terms of rendering quality, consequently, this enhancement leads to better compression performance in terms of bitrate and rendering distortion. Our experiments show average bitrate savings of 5.69% and 14.1% in PSNR and MS-SSIM with proposed method compared to where it is not performed, and it also notably reduce texture bleed artifacts.
Unifying Domain Adaptation and Energy-Based Techniques for Person Search

Person search is a challenging task that involves detecting persons and identifying their identities in images. Previous studies are conducted to address the need for large-scale datasets with bounding box and person identity labels via domain adaptation. Recent domain adaptive person search studies rely on softmax-based methods, facing overfitting issues in detection training to source domain. This paper proposes the use of energy-based out-of-distribution detection instead of softmax-based classifier. Our approach separates the distributions of person and background clutter without overfitting issues. The integration of energy-based techniques into the Domain Adaptive Person Search framework improves detection performance, with an average precision increase of 2.11% and 4.25% on CUHKSYSU and PRW datasets. These results highlight the potential of energy-based approaches for domain adaptive person search and pave the way for accurate person search applications in real-world scenarios.
Enhancing Video Encoding for Cloud Virtual Reality Gaming Based on User Types

Cloud Virtual Reality (VR) gaming is a novel technology that allows users to enjoy complex games on their thin clients by offloading the graphics rendering to cloud servers. The thin clients only need to perform basic decoding functions, which reduces the hardware requirements and costs. However, cloud VR gaming also faces the challenge of high bandwidth consumption when transmitting high-resolution game video streams. This paper presents a cloud VR gaming system that can transmit users’ gaze point data to the server in real time to identify users’ regions of interest. With this system, we verify the difference in spatial visual sensitivity caused by the different types of users. Then, a user-type-based video encoding method is proposed. Through conducting the subjective test experiment, the proposed video encoding method can reduce the bitrate for players and viewers by at least 71% and 69%, respectively, without compromising the perceptual quality.
MorphVAD: Efficient Video Anomaly Detection Using Morphological Transformation

Video anomaly detection aims to identify unusual events within video sequences. Approaches that rely on reconstruction and prediction have shown impressive success in this area and continue to be actively explored. However, recent techniques often rely on pre-trained external components such as optical flow or segmentation maps to handle the challenge of removing background that constitutes a substantial portion of video content. These additional modules, due to their computational complexity, introduce a noteworthy trade-off, particularly in real-time video anomaly detection, where timely monitoring is paramount. So, this paper introduces an efficient video anomaly detection method (MorphVAD) that is based on a novel morphology-based masking module (Morph-Mask). The Morph-Mask harnesses the straightforward concept of morphological transformations to create masks that highlight foreground elements. So, our MorphVAD strategically incorporates these masks during the training phase, focusing on retaining only foreground-related information in memory. Through illustrative experiments and various evaluations, we demonstrate the efficiency and effectiveness of these masks detection, showing the significant enhancements in video anomaly detection performance.
Human Action Recognition Using Multi-Stream Fusion and Hybrid Deep Neural Networks

Action Recognition in videos is a topic of interest in the area of computer vision, due to potential applications such as multimedia indexing and surveillance in public areas. In this research, we first propose spatial and temporal Convolutional Neural Network (CNNs), based on transfer learning using ResNetl0l, GoogleNet and VGG16, for undertaking human action recognition. Besides that, hybrid networks such as CNN-Recurrent Neural Network (RNN) models are also exploited as encoder-decoder architectures for video action classification. In particular, different types of RNNs such as Long Short-Term Memory (LSTM), Bidirectional-LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional-GRU (BiGRU), are exploited as the decoders for action recognition. To further enhance performance, diverse aggregation networks of CNN and CNN-RNN models are implemented. Specifically, an Average Fusion method is used to integrate spatial and temporal CNN s trained on images, as well as CNN - RNN trained on videos, where the final classification is formed by combining Softmax scores of these models via a late fusion. A total of 22 models (1 motion CNN, 3 spatial CNNs, 12 CNN-RNNs and 6 fusion networks) are implemented which are evaluated using UCF11, UCFSO, and UCF10l datasets for performance comparison. The empirical results indicate the significant efficiency of Average Fusion of multiple Spatial-CNNs with one Motion-CNN, and ResNet101-BiGRU, among all the networks for undertaking realistic video action recognition.
Path Planning in Dynamic Environments through Trajectory Prediction and Reinforcement Learning

Due to the presence of dynamic obstacles, like pedestrians and other mobile robots, on-line path planning for mobile robots in dynamic environments becomes challenging. Many traditional methods are based on off-line path planning, and replan a feasible path when dynamic obstacles appear. However, these methods often fail to plan a path in many cases due to robot deadlocks and oscillating movements. Many deep neural network-based approaches try to solve these problems by introducing learning-based techniques for dynamic environments. However, such models often cause detours because of the lack of the motion characteristics of dynamic obstacles. To address this issue, we propose a two-stage learning-based model. In the first stage, our model extracts surrounding environment features and dynamic characteristics of obstacles, and outputs predicted trajectories of obstacles. In the second stage, our model plans a feasible and efficient path for mobile robots by reinforcement learning and the predicted trajectories. We evaluate our method across different maps. The result shows that our model successfully completes planning in various dynamic environments, and shows outstanding predictability and processing capacity for dynamic obstacles.
HR-Chain: A Blockchain-Based Solution for Managing and Securing Heterogeneous Robots

In modern factories and daily life, the application of heterogeneous robot groups is becoming more and more widespread. However, there are still many areas for improvement in the management and communication of heterogeneous robot swarms, including the need for different connection interfaces for heterogeneous robots and the use of heterogeneous robot action logs to identify possible bottlenecks in the production line or record unplanned behaviors, whether malicious or not. In order to better manage heterogeneous robot swarms, this paper introduces the Heterogeneous Robots Chain (HR-Chain), a blockchain-based solution that can manage different types of robots and prevent unnecessary changes in robot operation logs to help improve production efficiency or other management requirements. HR-Chain is a Tezos-based blockchain project that securely stores robot logs in the blockchain using smart contracts. Finally, this paper conducts experimental research on HR-Chain, the real experimental results show that the robot system based on HR-Chain has better response speed and execution force, showing its potential application prospects in the industrial and consumer fields.
Cascade Cost Volume Multi-View Stereo Network with Transformer and Pseudo 3D

Learning-based Multi-view Stereo (MVS) and stereo matching methods typically construct 3D cost volumes based on the camera frustum of the reference view. Regularization and regression of the cost volume are performed to obtain a depth map. However, the resolution of the output depth map is limited by the computational cost, and when performing feature extraction, the characteristics of convolution local perception make it impossible to capture global context information. In this paper, we propose CTPMVSNet by using the Global Feature Aware Transformer (GFT) to aggregate global context information within and across images. In order to make better use of GFT, we use Deformable Convolution Module (DCM) to ensure a smooth transition of the extracted feature range. In addition, in the cost volume regularization stage, to improve efficiency and generation accuracy, we design a lightweight regularization network with integrated pseudo-three-dimensional convolution, and our experiments on multiple dataset have achieved promising results.
Vectorized and Optimized Dependent Quantization for Practical VVC Encoding

Versatile Video Coding (VVC) introduces dependent quantization (DQ) as a key coding tool, but it also has a high computational complexity. Therefore, optimizing DQ is crucial for practical VVC encoder implementations. In this paper, we propose optimization techniques for reducing the complexity of the DQ process, including efficient structure initialization, vectorized state update, rate distortion (RD) cost calculation, and last nonzero (LNZ) coefficient identification. The proposed optimizations are incorporated into the uvg266 practical VVC encoder. According to our evaluations, these optimization techniques accelerate the DQ process by over 1.7× and the entire VVC intra encoder by 1.4×, without any coding efficiency degradation. The proposed optimizations achieve more than twice the speedup over the existing techniques.
A Visual SLAM Algorithm Based on Instance Segmentation and Background Inpainting in Dynamic Scenes

Traditional SLAM algorithms are difficult to efficiently match feature points in dynamic scenes, which therefore directly affects trajectory accuracy. Therefore, a visual SLAM algorithm based on instance segmentation and background inpainting in dynamic scenes(IDV-SLAM) is proposed. This algorithm firstly designs a LaS-FPN module to increase the feature learning ratio of large objects, thereby accurately marking potential dynamic objects. We also apply multi-view geometry method to remove dynamic points to reduce the impact of dynamic objects on the positioning accuracy of the SLAM system. According to the optimal grid mapping method, the static background of the culling area is completed, and the ORB features are extracted for feature matching. We use the public TUM dataset to evaluate our algorithm, and the experimental results show that the our algorithm in this paper improves the trajectory accuracy and has better composition ability.