Reviewerly/exHarmony huggingface dataset:
GRALA: modeling social information for microblog sentiment analysis from the view of balancing sparsity and smoothness of social contexts

As online social networks provide people with a popular and convenient platform to express opinions, microblog sentiment analysis has become a hot research area. Currently, social relations between microblogs are extracted and integrated into content-based microblog sentiment analysis models. However, these methods take all links between microblogs into consideration, ignoring that some links are noisy and some neighbor microblogs are ambiguous semantically, resulting in the insufficiency of sentiment analysis. To deal with this problem, we propose a method that introduces sparse modeling of social contexts and tries to balance the sparsity and smoothness of social contexts to integrate social information prior knowledge into the content-based models in this paper. Specifically, a microblog graph is built based on sentiment consistency and emotional contagion, then we not only formalize the smoothness assumption of this graph but also introduce a graph-structured sparse regularization term to automatically select microblogs with high quality among all neighbors to facilitate sentiment analysis. Extensive experiments on two real-world public datasets show that our method achieves better performance than other baseline methods, indicating the effectiveness of sparse modeling of graph structure for microblog sentiment analysis.
Progressive Multi-scale Self-supervised Learning for Speech Recognition

Self-supervised learning has shown great potential in improving automatic speech recognition (ASR) systems. However, further improvements in recognition performance could be achieved if models focus on audio content information learning. In this paper, we propose a progressive multi-scale self-supervised learning method that reinforces the learning process from easy to difficult. Our progressive strategy utilizes fine-grained target sets to compute self-supervised learning loss at the top layer while using coarse-grained target sets at intermediate layers. Additionally, to match the difficulty of the learning process, we introduce a multi-scale structure into the multi-head self-attention module. We evaluate our method on the Librispeech dataset and demonstrate its effectiveness. Our proposed method achieves a relative word error rate (WER) reduction of 13.7% and 12.7% on the test_other evaluation subsets, respectively, when fine-tuned on 10-hour and 100-hour subsets, outperforming HuBERT.
Is Averaging Always the Best? Improving Aggregation Method for Federated Knowledge Graph Embedding

In recent years, along with the rapid development of big data and AI technologies, knowledge graphs have also experienced significant growth. The vectorized representation of entities and relations in knowledge graphs has proven beneficial for various knowledge graph-related applications. However, traditional knowledge graph embedding methods are designed for centralized graphs, which cannot effectively represent the distributed knowledge graphs in real-world while ensuring data security. In this paper, we improve the aggregation method for federated knowledge graph embedding and propose a Federated knowledge graph embedding model with CompGCN, which is called FedComp for short. FedComp is an innovative server-client framework which implement CompGCN for federated KGE, along with the design of three novel aggregation methods. We conduct link prediction experiments on two datasets to demonstrate the performance of our model.
A new formulation for the multi-period vessel-drone routing problem

To address the limitations of conventional offshore oil-and-gas field inspections, such as high cost, low frequency, and high risk, a new method called vessel-drone collaborative inspection is proposed. This approach involves a drone-carrying vessel departing from the port and sailing to a designated distance from the oil-and-gas field. The drones are then released to conduct field inspections. Once the drones complete their tasks, the vessel retrieves them at a specific location. This paper focuses on addressing the multi-period "vessel-drone" collaborative inspection route planning problem. The objective is to minimize the fixed cost of the vessel over the planning horizon while considering the overall path cost of both the vessel and the drone. The planning model takes into account various factors, including the coordination of space and time between the vessel and drone, drone endurance limitations, and the inspection cycle limitations for each period. To solve the problem, a mixed-integer second-order cone optimization formulation is proposed, along with several valid inequalities. Through empirical research and sensitivity analyses, the effectiveness of the proposed method is demonstrated. It is shown that the method can provide a cost-effective plan for oilfield inspections, offering potential value in guiding offshore oil-and-gas field inspection operations.
Learning Meta Soft Prompt for Few-Shot Language Models

Prompt-based learning is powerful to utilize the large-scaled pre-trained language model (PLM) for language understanding where the input sentences are augmented by either adding the hard prompt using word tokens or the soft prompt in a form of trainable tokens. However, the learned soft prompt in training domain may not really help a frozen PLM to handle domain shift in test domain. This paper presents an approach to incorporate meta learning into domain adaptation to train new soft prompt which sufficiently generalizes the frozen PLM to a number of domains. The meta soft prompt is then developed for few-shot unsupervised domain adaptation where a frozen PLM can be quickly adapted to a target domain. This soft prompt is optimized according to meta learning where the domain adaptation loss and the prompt-based classification loss are jointly minimized. The experiments on multi-domain natural language understanding show the benefits of the proposed meta soft prompt in pre-trained language model by using BERT under the few-shot setting.
Dual Feature Reconstruction Network For Few-shot Image Classification

Few-shot image classification aims to provide accurate predictions for novelty by learning from a limited number of samples. Classical few-shot image classification methods usually use data augmentation and self-supervision to compensate for the lack of training sample, and introduce migration learning and meta-learning to pre-train the model or accelerate the model optimization, which improves the classification performance of the model. However, with a small amount of labeled sample data, these methods cannot meet the requirements of the model’s ability to characterize sample features, resulting in a model that is highly susceptible to overfitting problems. In this paper, we propose a Dual Feature Reconstruction Network (DFRN) for few-shot image classification. The network constructs the double feature vector by two modules, in which the first-level feature module generates an attention mask based on the image to make the feature vector characterize more of the target region, and the secondary feature module interferes with the feature vector to improve its generalization performance. In addition, the network also enhances the classification performance of the model by considering the contextual information of the support classes through an auxiliary loss function. Through extensive experiments, the network proposed in this paper achieves excellent performance on Flowers, CUB and Cars datasets and outperforms other reference fine-grained image classification methods such as FRN.
Local Contrast Enhancement with Multiscale Filtering

Multiscale processing is fundamental for image processing. Most multiscale processing splits signals into base and detail signals and then manipulates the detail signal. Local Laplacian filtering (LLF), multiscale processing, has an unusual form: manipulation and then separation. This structure brings out the high performance of the filter. However, LLFs are limited to the form of Laplacian pyramids, which requires downsampling to create image pyramids; it causes misalignments around edges. In this study, we break away from the pyramid and extend the interpretation to multiscale filters that locally enhance the contrast. We propose a local difference of Gaussian (DoG) filter (LDF) for the solution. DoG filtering can remove the downsampling in the filter. Moreover, we further improve the computational speed using the precomputing technique introduced in LLF. Furthermore, to accelerate it, we utilize O(1) Gaussian filtering for large-scale filtering. Experimental results show that the proposed method suppresses overshoot and undershoot around sharp edges caused by the misalignment. Furthermore, the proposed method can keep computational speed even when the convolutional radius is large by O(1) Gaussian filtering.
Cross-Frame Foreground Structural Similarity Modeling by Convolutional Sparse Representation

In this paper, we propose a foreground-background separation (FBS) method using a novel foreground model based on convolutional sparse representation (CSR). Existing FBS methods are based on the assumption that the foreground component is sparse or continuous in time, resulting in poor separation performance for videos containing sparse noise/fast moving objects or for low frame rate ones. Therefore, we propose to use CSR to characterize the similar structure of the foreground component scattered in each frame. This CSR-based model can adaptively capture the "shape" of the foreground component and thus can accurately separate it even under the above difficulties. We formulate FBS as an optimization problem incorporating the CSR-based model and develope an algorithm to solve it based on alternating minimization. Experiments using an infrared video and an electron microscope video show that the proposed foreground model is superior to existing models.
Integrating Object Detection and Advanced Analytics for Smart City Crowd Management

In the context of rapidly advancing smart cities, efficient crowd analysis plays a crucial role in ensuring public safety, urban planning, and resource management. This paper presents a novel framework that combines the popular You Only Look Once (YOLO) object detection algorithm with advanced crowd analysis techniques, aiming to improve the understanding and management of urban crowd dynamics. The proposed framework leverages YOLO’s real-time object detection capabilities to detect various objects within video frames, with a particular focus on identifying individuals. To initiate the crowd analysis process, the detected persons are isolated and tracked over time, enabling the collection of valuable data for comprehensive crowd behavior analysis. By leveraging this rich dataset, the framework enables the extraction of key crowd characteristics, such as crowd density, crowd flow patterns, crowd distribution, and crowd congestion levels. Moreover, the framework incorporates techniques to analyze the extracted data, offering valuable insights into crowd dynamics.
Zero-shot multi-speaker accent TTS with limited accent data

In this paper, we present a multi-speaker accent speech synthesis framework. It can generate accented speech of unseen speakers using only a limited amount of accent training data. Without relying on the accent lexicon, the proposed network is able to learn the accent phoneme embedding via a simple model adaptation. In specific, a standard multi-speaker speech synthesis is first trained with native speech. Then, an additional neural network module is appended for adaptation to map the native speech to the accented speech. In the experiments, we have synthesized English speech with Singapore and Hindi accents. Both objective and subjective evaluation results successfully confirm that our proposed technique with phoneme mapping is effective to generate high-quality accent speech for unseen speakers.
Accurate and Practical Query-by-Example Using Multiple Deep Learning Models and Frame Compression Methods

Recently, studies of spoken term detection (STD) and spoken query STD (SQ-STD), also known as query-by-example (QbE), have been actively pursued. A representative method of QbE is posteriorgram matching using outputs of deep neural networks. However, that method requires much retrieval time and memory size. To address this difficulty, we proposed a maximum likelihood state sequence method (MLSS) for retrieval time reduction. This paper presents a proposal of two methods named "blank-cut (b-cut)" and "frame de-duplication (FDD)" to compress posteriorgram frames, by which we reduce retrieval times and memory sizes. Multiple matching scores are obtained using multiple deep learning models and architectures in the proposed methods. Then they are integrated. We achieved state-of-the-art retrieval accuracy as shown by evaluation experiments using two open test sets of about 30 hr of speech data. Furthermore, the proposed method achieved a retrieval time of less than 1 s and a memory requirement of about 1 GB. These results demonstrated the effectiveness of the proposed method.
Distributed on-line anomaly detection using kernel methods

In this paper we develop an online distributed learning algorithm for unlabeled data. Here a goal is to detect anomalies from streaming data. The assumption is that data is drawn from a probability distribution, but that at some unknown time data could be drawn from a different distribution. We assume the data comes from distributed sources and develop an online Federated Learning (FL) algorithm based on online kernel methods using Random Fourier Features (RFF). This work combines previous work on online unsupervised kernel algorithms with online supervised kernel algorithms using FL and RFF. There are many applications of this work including detecting bad power grid data.
Solving the Many to Many Grouped Task Allocation Problem via E-CARGO

The task allocation between team members of each organization's departments is a crucial project management activity that needs adequate attention to improve organizational efficiency. If a related decision is not made properly, it may immediately cause problems with coordination and communication, task delays, and increased costs. Tasks are usually grouped and then these groups are assigned to the agents. Moreover, it is important to consider the difficulty level of the tasks and the member’s ability. The task allocation process becomes increasingly complex when administrators strive to maintain the balance between the workload of allocated task groups and the agents' capabilities. The E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model, created for the purpose of Role-Based Collaboration (RBC), has been effectively utilized to address the given problem. In this paper, a novel approach is presented to formalize the Grouped Task Allocation Problem (GTAP) employing an extended Group Multirole Assignment with Conflicting Roles problem (GMRACR), which is a sub-model of E-CARGO. In this paper, a role negotiation method is introduced, which is based on GMRACR. It employs a partitioning clustering algorithm along with the Analytic Hierarchy Process (AHP) to evaluate the difficulty level of tasks and the abilities of agents, respectively. This formalization of GTAP facilitates the identification of a solution using the IBM ILOG CPLEX optimization package (CPLEX). The proposed method helps decision-makers with a framework to successfully assign balanced grouped tasks. Through simulation experiments on a real-world problem, the efficacy of the suggested approach is substantiated. Experimental results reveal the practicality of the solutions recommended in this paper.
A Highly Accurate Data Synchronization and Full-text Search Algorithm for Canal and Elasticsearch

Currently, there are numerous thorny issues in structured data and semi-structured full-text search scheme with large-scale text nature, like long data synchronization delay, inconvenient personalized business processing and low efficiency. To address these issues, this paper proposes an efficient algorithm based on Canal data synchronization framework and Elasticsearch full-text search engine. Firstly, we rewrite the Canal adapter component to obtain the flexible configuration of business data processing, thereby enhancing the secondary data processing ability of the framework and achieving the purpose of improving the efficiency of data synchronization. Secondly, by recording the synchronization time of nearby data in Canal framework, the weight of time series data is gradually decreased by combining with the exponential weighted average function to highlight the influence of recent data and present the novelty of data, which can achieve effective control the synchronization interval and duration by dynamically and flexibly setting the synchronization trigger period. Lastly, the Elasticsearch word tokenizer is modified, and then the configuration of custom expansion words and stop words dictionary are proposed to filter the query data effectively, thereby enhancing the query hit rate and accuracy. Extensive experiments on the data of traditional Chinese medicine demonstrate that the designed algorithm obtains high data synchronization efficiency, full text search speed and accuracy. Hence, the proposed algorithm is a milestone in smart healthcare.
Advancing human-robot interaction in a manufacturing environment by incorporating hand movements for remote control

Recently, there has been a notable shift in manual assembly tasks being replaced by robots. These robots are controlled by operators using keyboards. To enhance this practice, we have introduced a remote human-robot collaboration system. This system allows operators to remotely control robots using hand gestures, utilizing an online hand gesture recognition system. A model-driven display system has been implemented to complement the remote robot control system.For data acquisition, we have carefully selected Leap Motion technology. The designed approach for remote hand gesture recognition has been implemented and tested for accuracy and response time using the well-known SHREC’21 Gesture Benchmark. The final analysis demonstrates the significant potential of the developed system in improving assembly processes and environments. It optimizes various key performance indicators, leading to enhanced efficiency and effectiveness.
Accurate continuous action and gesture recognition method based on skeleton and sliding windows techniques

Vision-based action recognition with the main aim to determine the action class from image sequence has emerged recently thanks to its potential applications. Numerous methods have been proposed for action recognition. However, majority of them mainly focus on isolated action recognition with the assumption that videos are segmented in different clips or shots, each of them contains an image sequence of one sole action. Different from isolated action recognition, continuous action recognition involves recognizing human actions from a continuous stream of input data without requiring pre-segmentation into discrete action instances. In this paper, we propose a method for continuous action recognition. The proposed method incorporates sliding window technique and a light weight action classification model named DDNet. We evaluate the proposed approach on several benchmark datasets. Experimental results show that the proposed method the accuracy of 0.95, 0.97 and 0.71 for IPN, UOW and InHARD datasets respectively on isolated action recognition task. On continuous recognition task, it has achieved a performance of 0.57, 0.76 and 0.33 on IPN, UOW and InHARD datasets.
Incorporating Event Information for High-Quality Video Interpolation

Optical flow-based video interpolation is a commonly used method to enrich video details by generating new intermediate frames from existing ones. However, it cannot accurately reproduce the trajectories of irregular and fast-moving objects. To achieve the accurate reconstruction of high-speed scenes, incorporating event information during the interpolation process is an effective method. However, existing methods are not suitable for scenarios where events are sparse. To implement high-quality video interpolation for these scenarios, this study incorporates event information into the interpolation process with three-fold ideas: a) treating the moving target as the foreground and using events to delineate the target area, b) matching foreground to background based on the event information to generate temporal frame between keyframes, and c) generating intermediate frames between keyframe and temporal frame with optical flow interpolation. Empirical studies indicate that owing to its efficient incorporation of event information, the proposed framework outperforms state-of-the-art methods in generating high-quality frames for video interpolation.
Efficient Reversible Data Hiding for 3D Mesh Models Based on Multi-LSB Substitution and Ring-prediction

Reversible data hiding (RDH) technology can hide secret data in multimedia to realize the purpose of secret communication, copyright and content protection. However, the current RDH methods for 3D models are challenging to balance embedding capacity and carrier distortion. This paper proposes an RDH method for 3D mesh models with high embedding capacity and low model distortion based on multiple least significant bit (multi-LSB) substitution and ring-prediction. The coordinates of the vertices are converted into specific format by preprocessing. The secret data are embedded in multi-LSB of converted coordinates by multi-LSB substitution. The cover 3D mesh model is reconstructed by correlation of 1-ring of neighborhood vertices. Experimental results show that the proposed method achieves higher embedding capacity and lower distortion than the state-of-the-art methods.
A Reliable Feature-Based Framework for Vehicle Tracking in Advanced Driver Assistance Systems

Vehicle tracking has always been a vital aspect of modern transportation systems. This phenomenon has gained even more interest with the introduction of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles. Most state-of-the-art (SOTA) vehicle trackers, and their enhanced versions, commonly rely on mathematical motion models (e.g., Kalman Filter) as the core information. However, these models may produce unreliable outputs, especially when objects exhibit complex motion patterns. Hence, we propose a reliable feature-based tracking framework that fully exploits distinct vehicle appearance and conduct a comparative analysis with classic motion-based trackers. Additionally, we revisit previously proposed track handling strategies to incorporate a specially designed track management system for feature-based tracking. The proposed method achieves the highest score on all selected multi-object-tracking (MOT) evaluation metrics compared to the current SOTA methods on the KITTI dataset. Notably, our approach experienced significantly low False Positive (FP) errors, ensuring its performance in minimizing unreliable information.