Reviewerly/exHarmony huggingface dataset:
Real Time Gesture Detection Using Convolutional Neural Network

Gestures make touch less connectivity and systems easier to use. In many applications, such as Augmented Reality (AR) and Virtual Reality (VR), computer vision is crucial. Systems created for people with physical disabilities and sign language detection rely heavily on gesture detection. In this study, Convolutional Neural Networks (CNN) are used to demonstrate a real-time system that enables efficient feature extraction for gesture detection. A CNN is trained for certain gestures that are essential in Human Machine Interaction (HMI), primarily for persons who struggle to communicate vocally. The performance of thresholding and depth perception, in addition to gesture detection, enables reliable results to be produced even in noisy backgrounds. Effective background elimination and contours aid in properly mapping hand gestures in a diverse range of backgrounds.
An Efficient Multi-view 3D Object Recognition Mechanism for Distributed Edge Devices

This paper proposes an efficient multi-view 3D object recognition mechanism for distributed resource-constrained edge devices. Multi-view based vision processing has various applications on resource-constrained edge devices, like collaborative drone swarms, distributed smart cameras etc. However, huge data transmission from the different edge devices leads to this solution suffering from large bandwidth consumption, long latency, and energy consumption. The efficient multi-view 3D object recognition mechanism includes not only a feature map enhanced scheme, which exploits semantic information to bolster the useful attributes and suppress those less important feature maps, but also the feature maps selection schemes to decrease the amount of data transmission. The proposed fuses and transmits the enhanced feature map, achieves the almost same recognition accuracy with 9-12 times data reduction that can save power consumption and transmission bandwidth compared to the traditional multi-view 3D object recognition mechanism.
A Novel Algorithm for Bayesian Network Parameter Learning with Informative Range Constraints and Sorting Model

Learning the parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are small data available. Maximum A Posteriori (MAP) estimation is alternate way to compute the parameter. But the hyper parameters in MAP are difficult to determine when the samples are small. A conventional way to address this challenge is to introduce domain knowledge/expert judgments that are encoded as qualitative parameter constraints. This paper proposed a Sorting Constraint Auxiliary Model (SCAM) algorithm for learning BN parameter. We firstly translate informative range constraints into mathematics inequality formulation with regards to the relationships among BN parameters by sorting. Then we recruit conjugate distribution fitting using equivalence uniform distribution to generate virtual samplings in parameter space. The sampling counts, combined with small data statistics, help to reconstruct the BN parameter in MAP estimation. The experimental results show that the SCAM algorithm can obtain more accurate parameters than the existing parameter learning algorithms. Moreover, we demonstrate successful application to real threat assessment case studies in case of scarce data.
Aggregation Transformer for Human Pose Estimation

Transformers, which are famous for their attention mechanisms, have a strong ability to extract global information. The advantage of CNNs is parameter sharing, which can extract local information well. Both global information and local information are important for pose recognition. However, the existing human pose estimation methods based on Transformers or their variants can not extract the local information of images very well in mid-sized datasets (See [1] for more details). Therefore, we propose a novel Transformer framework to solve the problem of human pose estimation, termed ATPose (Aggregation Transformer for Human Pose Estimation): (1) We embed the convolution operations into the decoder of the Transformer to extract the local information. The attention module of Transformer first extracts global information from feature maps, and then the convolution layers perform convolution operations on feature maps to focus on local information. Thus, the Transformer can extract global information and local information at the same time. (2) We introduce the sparse attention mechanism and multi-scale attention mechanism into the Transformer. The sparse attention mechanism allows the feature pixels to only interact with sampled pixels, rather than all pixels, which reduces the computation cost. Multi-scale attention mechanism can calculate attention on feature maps with different resolutions to better extract small target features. (3) The Keypoint Head module is added to the decoder. The Keypoint Head can refine the prediction results of the model, making the predicted coordinates more accurate, and can also guide the training to prevent the model from deviating. The experimental results show ATPose has achieved state-of-the-art performance and become a new baseline in regression-based methods.
EK-BERT: An Enhanced K-BERT Model for Chinese Sentiment Analysis

Pre-trained language models (PLMs), such as BERT, have achieved good results on many natural language processing (NLP) tasks. Recently, some studies have attempted to integrate factual knowledge into PLMs for adapting to various downstream tasks. For sentiment analysis task, sentiment knowledge largely helps determine the sentiment tendencies of texts, such as sentiment words. For Chinese sentiment analysis, historical stories and fables give richer connotations and more complex emotions to words, which makes sentiment knowledge injection more necessary. But clearly, this knowledge has not been fully considered. In this paper, we propose EK-BERT, an Enhanced K-BERT model for Chinese sentiment analysis, which is based on the K-BERT model and utilizes sentiment knowledge graph to achieve better results on sentiment analysis task. In order to construct a high-quality sentiment knowledge graph, we collect a large number of emotional words by combining several existing emotional dictionaries. Moreover, in order to understand texts better, we enhance local attention through syntactic analysis to make EK-BERT pay more attention to syntactically relevant words. EK-BERT is compatible with BERT and existing structural knowledge. Experimental results show that our proposed EK-BERT achieves better performance on Chinese sentiment analysis task.
Ingenuity and Curiosity to Supercharge Your Pipeline Safety Management System

Abstract Everyone working in the pipeline industry knows that they “Don’t Know What They Don’t Know”. Our behaviours and actions to address this simple phrase have led to significant successes. Pipeline Safety Management Systems and Integrity Management Programs have evolved over the last 40 years. This evolution has been a result of adopting quality management systems, learnings from failures and incidents, and regulatory involvement. These advances and areas of improvement are investigated through examples and observations made by the authors. The examples and observations in this presentation will demonstrate how organizations of various sizes have successfully addressed safety and integrity challenges over the past forty years. Examples will be discussed from small, medium, and large sized organizations. The management systems have continually improved; these advancements have enabled some of these systems to provide significant value to the pipeline system operators. The following will be included in the presentation: a) important elements required to meet policy requirements associated with a pipeline safety management system, b) the advantages and disadvantages of changing versus constant management system requirements defined within a regulatory framework, c) compliance versus beyond-compliance approaches, d) the importance of integrated management systems, e) the importance of planning and performance verification to meet goals and targets, and f) examples to demonstrate continual improvement and alignment with corporate goals. Continual improvement in the ISO 9000 series of standards for quality management systems have preceded changes in pipeline safety management systems. This presentation will show correlations and relationships between Quality Management Systems and Pipeline Safety Management Systems. Forecasts about the future of Pipeline Safety Management Systems will also be presented. The examples and observations in this presentation are intended to promote ingenuity and curiosity as methods to supercharge pipeline safety management systems.
Evidential Clustering by Competitive Agglomeration

A new clustering method, named Evidential clustering by Competitive Agglomeration (ECA), is introduced by applying the framework of belief functions to a competitive strategy. It has two-fold advantages: Firstly, with the help of the credal partition, it has a good ability to deal with noise objects since it can mine the ambiguity and uncertainty of the data structure; secondly, through a competitive strategy, it can automatically gain the number of clusters under the rule of intra-class compactness and inter-class dispersion. Results demonstrate the effectiveness of the proposed method on synthetic and real-world datasets.
Class Incremental Learning based on Local Structure Constraints in Feature Space

When the training data is provided in the streamed or staged manner, the traditional deep learning methods have severe performance degradation on previous tasks or classes due to the updating of the parameters that have been well learned on old data. Incremental learning aims to avoid the catastrophic forgetting problem which the traditional deep learning framework have suffered from, and enable the model to learn in a continuous way. However, how to make a good trade-off between plasticity and stability, so as to perform well both for the new and the old classes is still an open problem for incremental learning. In this paper, we proposed a new incremental learning method called local structure constrained network(LSC-Net), which employs local relation constraints rather than strong global similarity constraints when updating the model. With the attentively designed inter-class and intra-class restriction, the proposed LSC-Net can make samples of the same class be more compact in the feature space, while expand the distance between samples of different classes. And LSC-Net adopts HM-SoftMax loss function rather than cross-entropy loss function in the classifier, so it can alleviate the effect of data imbalance. Experimental results on CIFAR-100 and ImageNet-100 show that LSC-Net can effectively overcame catastrophic forgetting and obtain competitive results that surpass state-of-the-art methods.
Detection of the Optimal Reflection Symmetry Axis with the Jaccard Index by Projection-based Method

This study determines the optimal reflection symmetry axis of an object in a binary image using the Jaccard index. We propose to find the global optimum on the grid by estimating the upper bounds of the Jaccard index with the Radon transform. Several approaches to improving the Jaccard index computation for a given line are considered. Some strategies for enumerating possible symmetry axes and selecting the initial approximation are proposed. The experiments show that the Jaccard index for the optimal axis found by the proposed method is not inferior to the exhaustive bruteforce of the axes passing through the points of the object contour considering the rasterization error. In terms of speed, the proposed method significantly exceeds the previously developed methods for limiting the exhaustive search.
ConIsI: A Contrastive Framework with Inter-sentence Interaction for Self-supervised Sentence Representation

Learning sentence representation is a fundamental task in natural language processing and has been studied extensively. Recently, many works have obtained high-quality sentence representation based on contrastive learning from pre-trained models. However, these works suffer the inconsistency of input forms between the pre-training and fine-tuning stages. Also, they typically encode a sentence independently and lack feature interaction between sentences. To conquer these issues, we propose a novel Contrastive framework with Inter-sentence Interaction (ConIsI), which introduces a sentence-level objective to improve sentence representation based on contrastive learning by fine-grained interaction between sentences. The sentence-level objective guides the model to focus on fine-grained semantic information by feature interaction between sentences, and we design three different sentence construction strategies to explore its effect. We conduct experiments on seven Semantic Textual Similarity (STS) tasks. The experimental results show that our ConIsI models based on $$\mathrm {BERT_{base}}$$ and $$\mathrm {RoBERTa_{base}}$$ achieve state-of-the-art performance, substantially outperforming previous best models SimCSE- $$\mathrm {BERT_{base}}$$ and SimCSE- $$\mathrm {RoBERTa_{base}}$$ by 2.05% and 0.77% respectively.
Adversarial Discriminative Feature Separation for Generalization in Reinforcement Learning

Imporving the generalization ability of an agent is an important and challenging task in deep reinforcement learning (RL). Procedually generated environment is an important benchmark for testing generalization in deep RL. In this benchmark, each game consists of multiple levels, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Existing methods (e.g., regularization, data augmentation) for improving the generalization of RL agent do not learn well the invariant representation among multiple levels. Besides, existing methods for learning invariant representations in RL using adversarial training can only learn invariant information across two levels. To solve this problem, we propose Adversarial Discriminative Feature Separate (ADFS). First, ADFS design a new discriminator for distinguishing whether two observations belong to the same level. Thus, the policy encoder is encouraged to learn invariant information between multiple levels. Second, it separates the representation of observation into level-invariant features and level-discriminative features, so that correction of the optimization direction of the discriminator. The discriminative features are learned by reducing the similarity of specific features intra-levels and increasing that of inter-levels, respectively. Experimental results demonstrate that our method is quite competitive with existing state-of-the-art methods on Procgen Benchmark.
Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration Framework

Due to the restricted on-chip computing capability for deep neural network (DNN) processing, high-definition video recognition (VOR) task is not easily achievable as a real-time task in a consumer SoC. Despite the fact that many accelerators have been proposed for fast VOR, they remain isolated from a video decoder’s inherent video compression knowledge. Therefore, in this article, we propose a video decoder-assisted neural network acceleration framework for real-time video recognition. First, given the fact that the nonkey frames can be dynamically reconstructed by the key frames with high fidelity during video compression, we propose the VR-DANN algorithm that reconstructs the VOR results of nonkey frames in a similar way so as to save a large amount of NN computing power. In VR-DANN, we leverage motion vectors, the tempo-spatial information already available in the video decoding process to facilitate the recognition process, and propose a lightweight NN-based refinement scheme to suppress the nonpixel recognition noise. Moreover, we consider that there is numerous redundant information in the video frames because the objects of interest usually take a small portion in a video frame. We, therefore, propose the object-based acceleration algorithm (Jigsaw-VOR) to avoid unnecessary computation by dropping out the redundant information in the frames before going through the computing-intensive DNN process. Concretely, we adopt the motion vectors to track the rough position for the objects of interest and then merge them into a consolidated frame for DNN processing like a jigsaw game. The acceleration comes from the processing of much fewer consolidated frames compared to the raw frames in a video stream. The VR-DANN and Jigsaw-VOR can be integrated for further speedup. From the hardware side, we propose the VR-DANN and Jigsaw-VOR architectures to, respectively, accelerate the VR-DANN and Jigsaw-VOR algorithms. These two architectures can be combined to gain higher performance improvement. Our experimental results show that the VR-DANN architecture achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula> performance improvement with less than 1% accuracy loss compared with the state-of-the-art “FAVOS” scheme. In addition, the experimental results show that applying Jigsaw-VOR to all frames can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.4\times $ </tex-math></inline-formula> performance improvement with comparable accuracy compared to FAVOS. By combining VR-DANN and Jigsaw-VOR schemes, the performance improvement can reach up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.6\times $ </tex-math></inline-formula> .
Introduction

Abstract The subject of random or stochastic processes analysis is a very important part of scientific inquire. The terms stochastic and random process are used interchangeably. Random processes are used as mathematical models for a large number of phenomena in physics, chemistry, biology, computer science, information theory, economics, environmental science and others. Each subject is described and problems are implemented in the R code, with real data collected in experiments performed by the authors or taken from the literature. With this intent, the reader can promptly apply the analysis to her or his own data, making the subject operational. Consistent with modern trends in university instruction, the book make readers active learners, with hands-on computer experiments directing readers through applications of random process analysis (RPA).
The Effect of Convolutional Neural Network Layers on Payload-Based Traffic Classification

Different applications in modern networks produce various types of traffic with diverse service requirements. In the network traffic classification, "unknown applications" are regarded as a difficult problem that remains unsolved, especially in the healthcare sector. Traffic classification helps in classifying and aggregate traffic flows into categories with the same traffic patterns. Identification and classification of traffic are critical for network management efficiency, which includes Quality of Service (QoS), detection of intrusions, and lawful interception. Only the network traffic classification technology based on payloads is fitting because most of the applications are IP based, whether is attached to a specific port number or is dynamic or is temporary. Payload-based classifiers consist of finding the features in the payload of data packets to differentiate between the application protocols. In this work, we propose a model using machine learning (ML) for an accurate and efficient traffic classification. ML allows for an automatic response to various applications by classifying traffic without a network operator interference. Experimental results demonstrate that ML-based traffic classification methods are effective and obtained high accuracy and a low data loss rate in front of other available models.
SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data

Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherical images, superpixels). Such data are usually processed using networks and algorithms specialized for each type. In this work, we show that it may not always be necessary to use specialized neural networks to operate on such spaces. Instead, we introduce a new structured graph convolution operator that can copy 2D convolution weights, transferring the capabilities of already trained traditional CNNs to our new graph network. This network can then operate on any data that can be represented as a positional graph. By converting non-rectilinear data to a graph, we can apply these convolutions on these irregular image domains without requiring training on large domain-specific datasets. Results of transferring pre-trained image networks for segmentation, stylization, and depth prediction are demonstrated for a variety of such data forms.
Towards a HOI application for digital home assistants

Human-object interaction detection (HOI) aims to visually deduce how people interact with surrounding objects. Although there is significant progress in advancing classification models for HOI, data remains a key problem limiting the application of HOI for real-world problems, which in turn results in insufficient applied research in HOI. In this position paper, we hence present a research agenda to aid the creation of this system - a data-centric HOI-based digital home assistant framework. The eventual goal of this digital home assistant is to understand how to apply HOI to enhance daily living experiences for the elderly. The research will embark in two phases - (1) create varied relevant HOI datasets through a novel synthetic data generator, and (2) iteratively develop the digital assistant software artifact using a system-based, user-focused design approach.
Auto-augmentation with Differentiable Renderer for High-frequency Shape Recovery

We propose a technique to estimate a high-resolution depth image from a sparse depth image captured by depth camera and a high-resolution shading image obtained by a RGB camera using deep neural network (DNN). In our technique, the network model is pretrained by synthetic images which are generated by rendering high-frequency shapes created by arithmetic model, such as sinusoidal wave of wide variation of parameters. Although the preparation of an appropriate synthetic dataset is critical for such tasks, it is not trivial to find a compact and optimal distribution of shape parameters. In this paper, we propose an auto augmentation technique to optimize hyperparameters for shapes achieving minimum number for training DNN. The proposed augmentation network directly optimizes the hyperparameters of a 3D scene including parameters of procedural shapes and their positions by gradient descent algorithm via a differentiable rendering technique. Unlike previous data augmentation techniques which only have basic image processing methods, such as affine and color transformations, the proposed method can generate optimal training dataset by changing the 3D shape and its position by using a differentiable renderer. In our experiments, we confirmed that our method improved the accuracy of high-resolution depth estimation as well as efficiency of training the network.