Reviewerly/exHarmony huggingface dataset:
Application of Computer Network Video Monitoring Technology in Substation Management

The safety problem of electric power project has been widely concerned. How to find and solve the problems and hidden dangers in electric power project in time is the research focus of electric power enterprises. In China’s substation management, various cutting-edge technologies are widely and deeply applied, playing an important role in substation management. The development and promotion of computer and intelligent technology have far-reaching significance for China’s substation management. This paper explores the application of computer network video monitoring technology in substation management, analyzes the principle of network video monitoring technology, puts forward the process of ground substation fixed video monitoring technology in production practice, analyzes the application prospect of this technology, and puts forward the development direction of intelligent monitoring system. The analysis results show that computer network video monitoring technology can effectively solve the problems and hidden dangers in power engineering projects, reduce operational risks, improve construction quality and safety management level, and is an effective means to strengthen substation management. In practice, this technology can be used in unattended management of substations, as well as other unattended sites.
Object-dependent Document-level Sentiment Analysis Based on Sentence Features

In the field of natural language processing, document-level sentiment analysis is one of the tasks that are widely studied. Traditional deep-learning method is facing the challenge of long-distance dependence of document-level text. At the same time, traditional document-level sentiment analysis assumes that the article contains only the view of one object, which has some limitations. By selecting four features including object feature, sentiment feature, weight feature and discourse relation feature, we propose a document-level sentiment analysis model based on sentence features(DABS) and an object-dependent document-level sentiment analysis model based on sentence features(ODABS) to improve document-level sentiment classification. The experimental results show that the features selected in this paper are representative, and the model has better effect than the baseline model.
Error-state Kalman Filter-Based Heave Motion Fusion Method of Unmanned Surface Vessel

This paper presents a novel heave motion fusion method based on the Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS). Firstly, velocity measurement is obtained via Time-differenced Carrier Phase (TDCP) technique. Then, by assigning the weight factors through the carrier-to-noise density ratio to control the velocity error at the millimeter level. Finally, this paper proposes an Error-state Kalman Filter (ESKF) method to integrate the accuracy velocity of GNSS into the heave motion estimation framework of IMU. Compared with the heave estimation based only on IMU, the proposed method omits the high-pass filtering of the heave velocity and only needs to high-pass filter the heave displacement. Results of the simulation are given to verify the availability and advantages of the proposed fusion method in Unmanned Surface Vessel (USV).
Indoor Video Layout Estimation Based on Plane Features and Motion Information

Estimating the 3D layout of an indoor scene is a critical task in computer vision. Its objective is to reconstruct the 3D structure of an enclosed space from an RGB images. At present, image-based layout estimation has been greatly developed. Unfortunately, the study of layout estimation based on video scenes that better match the real life have received little attention. In this paper, we focus on the task of indoor layout estimation in video scenes. In an indoor video, the room layout has no distortion and the only movement is from the camera, hence it is feasible to propagate the room layout information between the neighbouring video frames. Building on this understanding, we create a video-based dataset for 3D layout estimation, where the ground truth layout information is obtained by plane annotation and fitting as well as inter-frame layout transfer based on the camera pose. We also propose a method for video-based 3D layout estimation with a combination of intra-frame layout estimation and inter-frame camera pose estimation. The experimental results show that our method can efficiently estimate the indoor 3D layout in the videos. Compared to the existing image-based layout estimation methods, our method achieves significant performance gains.
Efficient Exploitation for Off-Policy Reinforcement Learning via a Novel Multi-scale Tabular Buffer

This paper is concerned with sample efficiency in off-policy reinforcement learning. For efficient exploitation of samples, we propose a novel method for updating buffer that allows important samples to remain in the buffer longer. Specifically, we first propose a new metric to determine the importance of a sample, which takes into account the value of the state and the period confidence level. Secondly, based on this metric, the multi-scale tabular buffer method is proposed, which sets multiple tables in the original buffer, and the size of the tables changes with time. Experiments show that the proposed method outperforms some state-of-the-art methods on a range of benchmark tasks. In particular, on some tasks, our method only requires half of samples to achieve the state-of-the-art performance.
AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target labeled images are available for training in addition to sufficient source labeled images. Critically, in FSDAOD, the data scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves to augment the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is available at https://github.com/Hlings/AsyFPD.
Enhanced Multi-scale Transformer Network for Temporal Action Localization

Temporal action localization aims to identify the positions of actions of interest that appear in continuous video streams, and it has important applications in many real-world scenarios, such as video search, security monitoring, and home care. Human daily actions usually have different durations, which has always been a difficult problem in temporal action localization. This paper proposes a temporal action localization algorithm based on an enhanced Multi-scale Transformer network to address the difficulty of the temporal scale. Specifically, the algorithm first introduces temporal convolutional attention module into the multi-scale Transformer to adaptively adjust long-term and short-term temporal information by combining traditional ID temporal convolution and self-attention mechanism. Second, temporal deformable attention is introduced in the multi-scale Transformer to adjust the flexibility of the model in extracting temporal information to enhance the model’s sensitivity to action boundaries. The experiments show that the improved method proposed in this paper improves the accuracy of localization on datasets, proving the effectiveness of the improved schemes proposed in this paper.
FPGA-based Real-time Video Quality Enhancement for Unmanned Aerial Vehicle

With the rapid improvement of UAV imaging equipment in terms of image resolution and video frame rate, relying solely on software to enhance images with extremely high data volume is no longer sufficient to meet real-time requirements. Therefore, a new method of processing image signals utilizing field programmable gate array (FPGA) hardware is proposed. This method involves analyzing traditional image enhancement algorithms to solve problems such as incorrect frame mapping, low contrast image enhancement, and incomplete comparison image enhancement mechanism resulting in noise during the engineering process. A limited contrast enhancement mechanism is also proposed to suppress the appearance of enhancement noise. Using the FPGA system, a hardware optimization design method is employed, including parallel processing, pipeline operation, and equivalent conversion, to achieve hardware image enhancement with extremely low resource consumption. This allows for the realization of video image enhancement with a resolution of 1920*1080 and a frame rate of 100Hz. The system has improved the overall brightness of images, target contrast, and image information restoration, and has achieved good results. This algorithm can be widely used in engineering design.
Face Micro-expression Recognition Based on Online Sequence Limit Learning Machine

Face micro-expression recognition is a popular research area combining machine vision and image recognition, and is an important branch of face recognition technology, the core of which is feature extraction and classification recognition. In this paper, the feature extraction and dimensionality reduction of micro-expressions are performed by combining Gabor wavelet transform and principal component analysis methods, and then an online sequence limit learning machine classifier is first trained and then tested to complete the classification and recognition of micro-expressions. This paper further validates the feasibility of the method by using the publicly available JAFFE expression dataset for micro-expression recognition experiments in Matlab to obtain the recognition accuracy.
Pseudo-label Assisted Optimization of Multi-branch Network for Cross-domain Person Re-identification

Person re-identification plays an important role in the field of robot intelligence perception and public safety. Its main task is to identify person targets under cross-camera. However, the domain diversity between different datasets poses a clear challenge for adapting a model trained on one dataset to another. Currently, person re-identification methods based on domain adaptive learning and pseudo-label have made good progress on this problem. Unfortunately, inferior pseudo-labels and source domain noise affect the performance. In order to improve the quality of generated pseudo-labels and enhance the feature representation capability of the model, we propose a pseudo-label assisted optimization of a multi-branch person re-identification method. The multi-branch network is able to extract and represent more effective global and local features, and the generated pseudo-labels are optimized by using cosine similarity and DBSCAN clustering on the feature vectors, thus improving the consistency of the supervised information to enhance the cross-domain recognition performance. We also use a loss function combining cross-entropy loss and triplet loss to make the best feature learning. Experiments show that our method performs well in the Market-to-Duke and Duke-to-Market cross-domain recognition tasks.
Improving Selective Visual Question Answering by Learning from Your Peers

Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains under-explored. Recent work has shown that VQA models, out-of-the-box, can have difficulties abstaining from answering when they are wrong. The option to abstain, also called Selective Prediction, is highly relevant when deploying systems to users who must trust the system's output (e.g., VQA assistants for users with visual impairments). For such scenarios, abstention can be especially important as users may provide out-of-distribution (OOD) or adversarial inputs that make incorrect answers more likely. In this work, we explore Selective VQA in both in-distribution (ID) and OOD scenarios, where models are presented with mixtures of ID and OOD data. The goal is to maximize the number of questions answered while minimizing the risk of error on those questions. We propose a simple yet effective Learning from Your Peers (LYP) approach for training multimodal selection functions for making abstention decisions. Our approach uses predictions from models trained on distinct subsets of the training data as targets for optimizing a Selective VQA model. It does not require additional manual labels or held-out data and provides a signal for identifying examples that are easy/difficult to generalize to. In our extensive evaluations, we show this benefits a number of models across different architectures and scales. Overall, for ID, we reach 32.92% in the selective prediction metric coverage at 1 % risk of error <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathcal{C} {@} 1\%)$</tex> which doubles the previous best coverage of 15.79% on this task. For mixed ID/OOD, using models' softmax confidences for abstention decisions performs very poorly, answering <5% of questions at 1 % risk of error even when faced with only 10% OOD examples, but a learned selection function with LYP can increase that to 25.38% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{C}$</tex> @1%.
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation

Most existing online knowledge distillation (OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability. In this paper, we strive to fully utilize multi-model settings instead of well-designed modules to achieve a distillation effect with excellent generalization performance. Generally, model generalization can be reflected in the flatness of the loss landscape. Since averaging parameters of multiple models can find flatter minima, we are inspired to extend the process to the sampled convex combinations of multi-student models in OKD. Specifically, by linearly weighting students' parameters in each training batch, we construct a Hybrid-Weight Model (HWM) to represent the parameters surrounding involved students. The supervision loss of HWM can estimate the landscape's curvature of the whole region around students to measure the generalization explicitly. Hence we integrate HWM's loss into students' training and propose a novel OKD framework via parameter hybridization (OKDPH) to promote flatter minima and obtain robust solutions. Considering the redundancy of parameters could lead to the collapse of HWM, we further introduce a fusion operation to keep the high similarity of students. Compared to the state-of-the-art (SOTA) OKD methods and SOTA methods of seeking flat minima, our OKDPH achieves higher performance with fewer parameters, benefiting OKD with lightweight and robust characteristics. Our code is publicly available at https://github.com/tianlizhang/OKDPH.
Modular Memorability: Tiered Representations for Video Memorability Prediction

The question of how to best estimate the memorability of visual content is currently a source of debate in the memorability community. In this paper, we propose to explore how different key properties of images and videos affect their consolidation into memory. We analyze the impact of several features and develop a model that emulates the most important parts of a proposed “pathway to memory”: a simple but effective way of representing the different hurdles that new visual content needs to surpass to stay in memory. This framework leads to the construction of our M3-S model, a novel memorability network that processes input videos in a modular fashion. Each module of the network emulates one of the four key steps of the pathway to memory: raw encoding, scene understanding, event understanding and memory consolidation. We find that the different representations learned by our modules are non-trivial and substantially different from each other. Additionally, we observe that certain representations tend to perform better at the task of memorability prediction than others, and we introduce an in-depth ablation study to support our results. Our proposed approach surpasses the state of the art on the two largest video memorability datasets and opens the door to new applications in the field. Our code is available at https://github.com/tekal-ai/modular-memorability.
The Capacity of Oblivious Transfer with Replicated Databases and Binary Erasure Multiple Access Channel

Both the oblivious transfer (OT) problem and the symmetric private information retrieval (SPIR) problem studies the scenario where a client retrieves information privately and securely from databases, i.e., the privacy of the client is protected from the databases, and the undesired information is protected from the client. The OT problem studies the case of one database plus additional noisy resources between the database and the client. The SPIR problem studies the case of multiple replicated and non-colluding databases. In this paper, we combine the two models and propose a new problem of oblivious transfer (OT) with two replicated databases and a binary erasure multiple access channel connecting the databases and the client. We first provide an upper bound on the OT capacity. We then propose a protocol which achieves the upper bound. Therefore, we obtain the capacity of OT for this model. In our achievability and converse proofs, we utilized the techniques from both traditional OT and PIR. Compared to schemes that utilizes only techniques from OT, we see a 100% increase in the achieved OT rate.
Fault-Tolerant Coding for Entanglement-Assisted Communication

Channel capacities quantify the optimal rates of sending information reliably over noisy channels. Usually, the study of capacities assumes that the circuits which sender and receiver use for encoding and decoding consist of perfectly noiseless gates. In the case of communication over quantum channels, however, this assumption is widely believed to be unrealistic, even in the long-term, due to the fragility of quantum information, which is affected by the process of decoherence. Christandl and Müller-Hermes have therefore initiated the study of fault-tolerant channel coding for quantum channels, i.e. coding schemes where encoder and decoder circuits are affected by noise, and have used techniques from fault-tolerant quantum computing to establish coding theorems for sending classical and quantum information in this scenario. Here, we extend these methods to the case of entanglement-assisted communication, in particular proving that the fault-tolerant capacity approaches the usual capacity when the gate error approaches zero. A main tool, which might be of independent interest, is the introduction of fault-tolerant entanglement distillation. We furthermore focus on the modularity of the techniques used, so that they can be easily adopted in other fault-tolerant communication scenarios.
Few-Shot Geometry-Aware Keypoint Localization

Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labels is time-consuming and costly, and is often error-prone due to inconsistent labeling. Thus, we desire an approach that can learn keypoint localization with fewer yet consistently annotated images. To this end, we present a novel formulation that learns to localize semantically consistent keypoint definitions, even for occluded regions, for varying object categories. We use a few user-labeled 2D images as input examples, which are extended via self-supervision using a larger unlabeled dataset. Unlike unsupervised methods, the few-shot images act as semantic shape constraints for object localization. Furthermore, we introduce 3D geometry-aware constraints to uplift keypoints, achieving more accurate 2D localization. Our general-purpose formulation paves the way for semantically conditioned generative modeling and attains competitive or state-of-the-art accuracy on several datasets, including human faces, eyes, animals, cars, and never-before-seen mouth interior (teeth) localization tasks, not attempted by the previous few-shot methods. Project page: https://xingzhehe.github.io/FewShot3DKP/
Text Classification Accuracy Enhancement Using Deep Neural Networks

Machine-learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), using object and learning approaches, performs well in statistical tasks, such as text classification or sentiment mining. Currently, in the field of natural language processing (NLP), various models, methods, and architectures have been proposed and a wide range of NLP tasks have been developed. Most natural language processing (NLP) problems can be formulated as classification problems (decide on the class of this object depending on the object and its context). In this field, machine learning methods and especially using neural network models are the most used. In this research, we propose a deep learning architecture for text classification that can influence model performance. The basic steps of this proposed method include text pre-processing (including sentence segmentation, punctuation, rooting, speech tagging, and vector conversion), text feature extraction, and LSTM algorithm-based classification model construction. The dataset used in this study is 20NewsGroup and IMDB. The evaluation results are provided in the form of MSE, Precision, Recall, Accuracy, and F-Measure evaluation criteria.
Transformer with Task Selection for Continual Learning

The goal of continual learning is to let the models continuously learn the new incoming knowledge without catastrophic forgetting. To address this issue, we propose a transformer-based framework with the task selection module. The task selection module will select corresponding task tokens to assist the learning of incoming samples of new tasks. For previous samples, the selected task tokens can retain the previous knowledge to assist the prediction of samples of learned classes. Compared with the state-of-the-art methods, our method achieves good performance on the CIFAR-100 dataset especially for the testing of the last task to show that our method can better prevent catastrophic forgetting.
A New Family of Perfect Polyphase Sequences with Low Cross-Correlation

Spread spectrum multiple access systems demand minimum possible cross-correlation between the sequences within a set of sequences having good auto-correlation properties. Through a connection between generalised Frank sequences and Florentine arrays, we present a family of perfect sequences with low cross-correlation having a larger family size, compared with previous works. In particular, the family size can be equal to the square root of the period when the period of the perfect sequences is even. In contrast, the number of the perfect sequences of even period with low cross-correlation is equal to one in all previous works.