Reviewerly/exHarmony huggingface dataset:
A conflict opinion recognition method based on graph neural network in Aspect-based Sentiment Analysis

Aspect-based sentiment analysis is a research direction of fine-grained sentiment analysis, and is usually used in comment texts. We found that most studies ignored the conflict sentiment during analysis. However, comments with conflicting emotions are usually longer, contain more information, and can reflect the changes of users' opinions. If we can effectively recognize the conflict sentiment in online review texts, we can better help merchants find the shortcomings of products and make improvements. Therefore, based on the research of others, we propose a new D-MA-EGCN model to promote the accuracy of conflict sentiment recognition. The model uses pre-trained BERT model to encode the sentences, and uses edge-convolutional neural network to extract the relationship between aspect word and sentiment word, so as to avoid the misclassification problem caused by the long distance between aspect words and emotion words or multiple sentiments at the same time. The experiment on SemEval dataset shows that our model can dramatically improve the recognition accuracy of conflicting sentiments.
Efficient Augmentation of Human Action Recognition Datasets with Warped Windows

In this paper, an approach to augment action recognition time series datasets, devoted to improving the accuracy of deep learning classifiers, is proposed. In the introduced method, two operators are sequentially introduced that perform linear and nonlinear modifications in the time scale of the input time series. The resulting data samples contribute to the variability within classes and allow a deep learning-based classifier to better capture their boundaries, leading to a significant improvement in the classification accuracy. The extensive experiments performed on eight publicly available action recognition datasets using the popular Bidirectional Long Short-Term Memory (BiLSTM) classifier reveal the superiority of the proposed algorithm over related approaches.
Language-Guided Multi-Granularity Context Aggregation for Temporal Sentence Grounding

Temporal sentence grounding in videos is a crucial task in vision-language learning. Its goal is retrieving a video segment from an untrimmed video that semantically corresponds to a natural language query. A video usually contains multiple semantic events, which are rarely isolated. They tend to be temporally ordered and semantically correlated ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , some event is often the precursor of another event). To precisely localize a semantic moment from a video, it is critical to effectively extract and aggregate multi-granularity contextual information, including the fine-grained local context around the moment-related video segment (in short snippet-level) and coarse-grained semantic correlation (in segment-level). Additionally, a second main insight in this work is that the above context aggregation should be favorably guided by the queries, rather than fully query-agnostic. Putting above ideas together, we here present a new network that does language-guided multi-granularity context aggregation. It is comprised of two major modules. The core of the first module is a novel language-guided temporal adaptive convolution (LTAC) devised to extract fine-grained information over video snippets around the ground-truth video segment. It decomposes a convolution into two channel-oriented / temporal-oriented ones. In particular, the convolutional channels are supposed to be more susceptible to queries, thus we learn to generate a dynamic channel-oriented kernel with respect to the querying sentence. As a second module, we propose a language-guided global relation block (LGRB) that extracts video-level context. It augments the contextual feature by using a multi-scale temporal attention that tackles the scale variation of ground-truth video segments, and a multi-modal semantic attention that relies on syntactic of the query. For the validation purpose, we have conducted comprehensive experiments on two popularly-adopted video benchmarks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , ActivityNet Captions and Charades-STA). All experimental results and ablation studies have clearly corroborated the effectiveness of our model designs, outstripping prior state-of-the-art methods in terms of major performance metrics for the task.
An Online Dirichlet Model based on Sentence Embedding and DBSCAN for Noisy Short Text Stream Clustering

Short text stream clustering has received widespread attention due to the rise of various social medias. However, short text streams present the following characteristics such as infinite length, text sparsity and ambiguity, topic evolution and containing noisy data. Existing short text clustering methods do not make full use of the semantic information of short texts to solve the sparsity and ambiguity of short texts and few methods take the noise into account in short text stream. Therefore, in this paper, we propose an Online Dirichlet model based on Sentence Embedding and DBSCAN for noisy short text stream clustering, called ODSE. Firstly, to handle the text sparsity and ambiguity, we use Sentence-Bert to represent each short text for achieving the globally semantic information of each short text. Secondly, to handle the noisy data contained in short texts, we introduce the buffer mechanism and refine the Dirichlet process multinomial mixture model using the DBSCAN algorithm except the above sentence embedding input. This model can handel the short texts one by one. Besides, to adapt to the infinite length and topic evolution, we introduce the forgetting mechanism to update the clusters. Finally, extensive experiments demonstrate that as compared to several state-of-art algorithms, our proposed approach can achieve better performances on four benchmark short text datasets.
Prototype-Based Clustered Federated Learning for Semantic Segmentation of Aerial Images

Despite its impressive performance on semantic segmentation of remote sensing imagery, deep learning requires a large amount of labeled data for model training, which is both laborious and timeconsuming for an individual institution. To cope with this obstacle, federated Learning (FL) has been proposed to enable multiple institutions to train a global model collaboratively without violating privacy rules. However, the performance of FL is poor in the presence of heterogeneous training data, i.e. the data is not independently and identically distributed (non-i.i.d) among participating clients, especially for remote sensing images with high spatial and spectral heterogeneity. In this paper, we propose an FL algorithm combined with prototype-based hierarchical clustering (Fed-PHC). Instead of updating a single global model to capture the shared knowledge of all clients, we utilize a mixture of multiple global models to handle the heterogeneity between various clients using hierarchical clustering (HC) based on the prototypical representations of clients' datasets. As a result, FedPHC can reduce the domain discrepancy within each group and obtain more representative models for heteroge-neous datasets. Extensive experiments on the Inria Aerial Image Dataset confirm the effectiveness of FedPHC.
Does Video Compression Impact Tracking Accuracy?

Everyone “knows” that compressing a video will degrade the accuracy of object tracking. Yet, a literature search on this topic reveals that there is very little documented evidence for this presumed fact. Part of the reason is that, until recently, there were no object tracking datasets for uncompressed video, which made studying the effects of compression on tracking accuracy difficult. In this paper, using a recently published dataset that contains tracking annotations for uncompressed videos, we examined the degradation of tracking accuracy due to video compression using rigorous statistical methods. Specifically, we examined the impact of quantization parameter (QP) and motion search range (MSR) on Multiple Object Tracking Accuracy (MOTA). The results show that QP impacts MOTA at the 95% confidence level, while there is insufficient evidence to claim that MSR impacts MOTA. Moreover, regression analysis allows us to derive a quantitative relationship between MOTA and QP for the specific tracker used in the experiments.
Learning to Reduce Scale Differences for Large-Scale Invariant Image Matching

Most image matching methods perform poorly when encountering large scale changes in images. To solve this problem, we propose a Scale-Difference-Aware Image Matching method (SDAIM) that reduces image scale differences before local feature extraction, via resizing both images of an image pair according to an estimated scale ratio. In order to accurately estimate the scale ratio for the proposed SDAIM, we propose a Covisibility-Attention-Reinforced Matching module (CVARM) and then design a novel neural network, termed as Scale-Net, based on CVARM. The proposed CVARM can lay more stress on covisible areas within the image pair and suppress the distraction from those areas visible in only one image. Quantitative and qualitative experiments confirm that the proposed Scale-Net has higher scale ratio estimation accuracy and much better generalization ability compared with all the existing scale ratio estimation methods. Further experiments on image matching and relative pose estimation tasks demonstrate that our SDAIM and Scale-Net are able to greatly boost the performance of representative local features and state-of-the-art local feature matching methods.
GGViT:Multistream Vision Transformer Network in Face2Face Facial Reenactment Detection

Detecting manipulated facial images and videos on social networks has been an urgent problem to be solved. The compression of videos on social media has destroyed some pixel details that could be used to detect forgeries. Hence, it is crucial to detect manipulated faces in videos of different quality. We propose a new multi-stream network architecture named GGViT, which utilizes global information to improve the generalization of the model. The embedding of the whole face extracted by ViT will guide each stream network. Through a large number of experiments, we have proved that our proposed model achieves state-of-the-art classification accuracy on FF++ dataset, and has been greatly improved on scenarios of different compression rates. The accuracy of Raw/C23, Raw/C40 and C23/C40 was increased by 24.34%, 15.08% and 10.14% respectively.
Hierarchy SeparateEMD for Few-Shot Learning

Few-shot learning methods are studied for the problem of insufficient samples in neural network tasks. Taking advantage of the excellent feature extraction capabilities of neural networks, meta-learning was proposed and became the mainstream for few-shot learning. Among the few shot learning methods, the metric-based method has the characteristics of simple mode and no need for iterative training in the inference process, which is more in line with the original intention of few-shot learning task and has been widely studied. The metric-based method with Euclidean distance has established a relatively complete training and inference system, but the accuracy has gradually stagnated. The Earth Mover’s Distance has gradually emerged in the field of few-shot learning, and greatly surpassed Euclidean distance methods in terms of accuracy. Aiming at the problem that the current EMD method can only be trained with 1-shot learning and cannot be trained with multi-shot, we proposed a SeparateEMD that can train in multi-shot mode. In order to make up for the lack of global information of the SeparateEMD, Hierarchy Attention Module for proto is constructed to increase the intra-category correlation and intra-category global information of the support samples, and also improve the sample discrimination. We build the Half Pyramid Merge Module for query to increase the correlation between local information and global information of a single query sample. The Global Sampling is constructed on the basis of the Random Sampling of the EMD, which can not only increase the randomness of the samples, but also ensure the stability of the information. Experiments show that our proposed method can achieve 5-shot training of EMD-like method and outperform the current state-of-the-art few-shot learning methods on popular benchmark datasets.
Hierarchical Collaborative Hyper-Parameter Tuning

Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model. The proposed method employs a distributedly formed hierarchical agent-based architecture for the cooperative searching procedure of tuning hyper-parameter values. The presented generic model is used to develop a guided randomized agent-based tuning technique, and its behavior is investigated in both machine learning and global function optimization applications. According the empirical results, the proposed model outperformed both of its underlying randomized tuning strategies in terms of classification error and function evaluations, notably in higher number of dimensions.
Multi-level Feature Reweighting and Fusion for Instance Segmentation

Accurate instance segmentation requires high-resolution features for performing a dense pixel-wise prediction task. However, using high-resolution feature maps results in highly expensive model complexity and ineffective receptive fields. To overcome the problems of high-resolution features, conventional methods explore multi-level feature fusion that exchanges the information between low-level features at earlier layers and high-level features at top layers. Both low and high information is extracted by the hierarchical backbone network where high-level features contain more semantic cues and low-level features encompass more specific patterns. Thus, adopting these features to the training segmentation model is necessary, and designing a more efficient multi-level feature fusion is crucial. Existing methods balance such information by using top-down and bottom-up pathway connections with more inefficient convolution layers to produce richer multi-scale features. In this work, we contribute two folds: (1) a simple but effective multilevel feature reweighting layer is proposed to strengthen deep high-level features based on channel reweighting generated from multiple features of the backbone, and (2) an efficient fusion block is proposed to process low-resolution features in a depth-to-spatial manner and combine enhanced multi-level features together. These designs enable the segmentation models to predict instance kernels for mask generation on high-level feature maps. To verify the effectiveness of the proposed method, we conduct experiments on the challenging benchmark dataset MS-COCO. Surprisingly, our simple network outperforms the baseline in both accuracy and inference speed. More specifically, we achieve 35.4% AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mask</sup> at 19.5 FPS on a GPU device, becoming a state-of-the-art instance segmentation method.
Smart NGO Tracking System Using Blockchain Technology

Blockchain is an optimistic technology that is dominating for providing solution to many problems related to safety that comes with respect to the command of both personal and nonpublic enterprises. It is acquiring a wide acceptance inside the charity domain. Due to absence in clarity transactions concerned in the donations that the donor(s) they were helpless in getting to verify that their contributions are getting credited without fail, that made public to drop the belief in Charities. This work proposes a blockchain based organ system for tracking and tracking the donations. This is built on SHA algorithm where blockchain will give the complete see-through, liability and contribute to the particular receivers are direct. The existing donation system is a way for donors to donate directly to several donor organizations. It is the spotlight because it can secure transparency in transaction. Propose application, to solve these problem, we are planning to introduce the blockchain technology into this donation system. Similarly also proposes a blockchain based Organ Donation tracking system to keep track of organ donated by donor’s properly adopted to respective recipient’s. Further, to identify and evaluate the method, we will spontaneously send polls for registered users to create awareness and discuss on issues about organ donation. Finally, this method goal is to create a web application that provides a secured and transparent access.
Consistent Video Inpainting Using Axial Attention-Based Style Transformer

Maintaining spatial and temporal consistency in the inpainted video area of the video is a challenging problem. Recent research focuses on flow information for synthesizing temporally smooth pixels while neglecting semantic structural coherence across the video frames. Thus, it suffers from over-smoothing and shadowy outlines that significantly degrade the inpainted video quality. We propose an end-to-end consistent video inpainting model that will substantially improve the inpainted video region to overcome this problem. The model employs a deep encoder (DE), axial attention block (AAB), style transformer, and decoder to enhance video inpainting with a realistic structure. A deep encoder (DE) encodes features effectively while the axial attention block (AAB) recreates all retrieved attributes by merging recoverable multi-scale characteristics with local spatial structures. Then, a novel-style transformer with the style manipulation block (SMB) fills the missing area with rich visual elements and temporal coherence. We use two publicly available benchmark datasets to assess the model's performance. Experimental results demonstrate that our method performs better than the state-of-the-art methods by a large margin. Besides, an extensive ablation study validates the model's performance.
Self-Aware Microsystems

Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.
NHK Meta Studio: A Compact Volumetric TV Studio for 3-D Reconstruction

Many studies have been conducted on the 3D reconstruction of subjects using multiple fixed cameras. Accepting the trade-off between the number of cameras and reconstruction quality, our studio is designed to capture high-quality models of one or two subjects for TV program use. Several cameras are mounted on a hemispherical dome with the stage in the center and a cloth cover on the frame for chroma-keying. The optimal camera numbers and placements for reconstruction were determined by simulation, and the 3D reconstruction was performed as a point cloud by a combination of visual hull and stereo matching. The quality was still not high enough, however, so we also added a surface light field to the point cloud to obtain the weighted average of rays from camera images close to the viewpoint. In the final stage, the images were then combined to the video, and errors generated during the reconstruction were compensated for by use of a deep neural network (DNN) for video translation. An offline processing studio has been built as a preliminary step towards real-time processing, and the reconstructed 3D images have been evaluated subjectively for a number of subjects. These studies confirm the effectiveness of this studio design.
Escra: Event-driven, Sub-second Container Resource Allocation

This paper pushes the limits of automated resource allocation in container environments. Recent works set container CPU and memory limits by automatically scaling containers based on past resource usage. However, these systems are heavy- weight and run on coarse-grained time scales, resulting in poor performance when predictions are incorrect. We propose Escra, a container orchestrator that enables fine-grained, event- based resource allocation for a single container and distributed resource allocation to manage a collection of containers. Escra performs resource allocation on sub-second intervals within and across hosts, allowing operators to cost-effectively scale resources without performance penalty. We evaluate Escra on two types of containerized applications: microservices and serverless functions. In microservice environments, fine-grained and event- based resource allocation can reduce application latency by up to 96.9% and increase throughput by up to 3.2x when compared against the current state-of-the-art. Escra can increase performance while simultaneously reducing 50th and 99th%ile CPU waste by over 10x and 3.2x, respectively. In serverless environments, Escra can reduce CPU reservations by over 2.1x and memory reservations by more than 2x while maintaining similar end-to-end performance.
Performance Ranking of Recommender Systems on Simulated Data

Recent studies have shown that modelling and simulation of interactions between a recommender system (RS) and its users have a great potential for accelerating the research and industrial deployment of RSs. Frameworks providing such simulations are called simulators and are widely used for RSs of different types. Nevertheless, there exist the problem of simulation validation and of the inconsistency of RS performance ranking on real-world and the corresponding synthetic data. In this paper, using and extending the recently developed SynEvaRec simulator we propose a validation procedure for simulators of this type and study the consistency of RS performance ranking on response matrices of different sparsity. It is observed in our experiments that (i) the procedure is an effective tool to see what one may expect from the simulation on real-world data, (ii) the consistency of RS performance ranking depend on the data considered and even the sample size used for RS training.
Comment on “Federated Learning With Differential Privacy: Algorithms and Performance Analysis”

The research paper [1] proposes a differential privacy algorithm in the context of Federated Learning and provides its performance analysis, mainly focusing on proving a convergence bound for the loss function. In this paper, we show that some of the mathematical derivations given in [1] are not valid. Thus the bounds they prove in the paper do not hold for all loss functions. In this work, we give the correct derivation of the best possible local sensitivity bound, which is valid for all loss functions. We also state the modifications in the bounds for global sensitivity and the standard deviation of the Gaussian noise added, both before and after aggregation.