Reviewerly/exHarmony huggingface dataset:
Introduction to Metareasoning

Robots have limited computational resources, often due to size, weight, and power (SWaP) constraints, and these resources must perform the reasoning and computation tasks for sensor management, image processing, mapping, planning, control, collaboration, and communications. In this dynamic environment, as the robot executes different reasoning tasks that compete for resources, the constraints on processors, memory, communication bandwidth, and power can interact in unpredictable ways to degrade the robot’s performance. To avoid this problem, the robot needs a way to monitor and control its reasoning processes. An intelligent robot can use metareasoning (reasoning about reasoning) to improve its reasoning and decision-making processes by adapting them in response to changes in the environment or the system. This makes it safer and more resilient as well. This chapter introduces some key concepts related to robots and autonomous systems. It then discusses why metareasoning is needed and the benefits of metareasoning, which is a branch of artificial intelligence (AI). It presents a list of key sources that one should read for more information about metareasoning. Finally, it discusses the systems engineering approach that informs the structure and contents of this book.
Fine-Grained Sentiment Analysis of Online-Offline Danmaku Based on CNN and Attention

Sending danmaku is a popular way of social communication and It provides a direct way for users to express their opinions and sentiments. However, the classification of the sentiment intents of danmaku is not a trivial task. In this study, we propose a new approach to depict the relationship between the users’ sentiment and contents of online videos and offline events by analyzing the sentiment of Danmaku. Firstly, we specify the classification of the danmaku intents by considering the characteristics of danmaku and the association between the intents and the sentiments. Secondly, we get the sentiment intensity of danmaku through the sentiment classifier constructed based on the long short-term memory network (LSTM). Thirdly, the sentiment embedding of danmaku encoded by the sentiment classier is used to improve the performance of the intent classifier constructed based on convolution neural network (CNN). The sentiment embedding is injected into the intent classifier through the self-attention mechanism. The experiment results show that the accuracy of this model is higher than four recent baseline models. Finally, we compute the overall sentiment curves of danmaku and specific intents to achieve the fine-grained sentiment analysis for online videos and offline events.
A Feature Reduction-Induced Subspace Multiple Kernel Fuzzy Clustering Algorithm

High-dimensional data poses a great challenge to clustering, and subspace clustering algorithms have unique advantages when working with high-dimensional data. However, there are still some difficulties in adapting the nonlinear feature dimension, the selection and collaborative settings of kernel functions. Therefore, in this study, we come up with a feature reduction-induced subspace multiple kernel fuzzy clustering. Firstly, in order to solve the information loss caused by the nonlinear characteristics of the data, the multiple kernel clustering method is introduced, and some nonlinear features of the data are collaboratively mapped to the high-dimensional linear space, so as to better understand the characteristic information of the data. Secondly, due to the complexity of subspace and multiple kernel learning, we introduce the idea of feature reduction, and reduce the data dimension according to the importance of information, which can reduce the complexity of the algorithm on the one hand, and improve the role of important feature attributes in clustering on the other hand. Finally, the proposed RS-MKFC algorithm and the related 6 algorithms are compared on 6 ordinary datasets and 4 high-dimensional datasets, and it is found that the proposed algorithms are superior over the other 6 algorithms. At the same time, we discover the ability of RS-MKFC algorithm to screen important features as well as the function of feature reduction, and good results are achieved.
Collaborative Analysis on Code Structure and Semantics

In this paper, we propose the collaborative method that analyzes both code structure and function semantics for code comparison. First, we create the function call graph of code and use it to obtain the structure semantics with the graph auto-encoder. Then the function semantics are obtained with the names and definition of the used library functions and built-in classes in code. Finally, we integrate the structure and function semantics to collaboratively analyze the similarity of codes. We adopt several real code datasets to validate our method and the experimental results show that it outperforms other baselines. The ablation experiments show that the function call structure contributes the most to the performance. We also visualize the semantics of function structures to illustrate that the proposed method can extract the correlations and differences between codes.
A Knowledge Tracing Model Based on Graph Attention Mechanism and Incorporating External Features

In recent years, research has focused heavily on Knowledge Tracing (KT), a crucial technique for learner state modeling in intelligent education. There are several KT models based on graph convolutional networks (GCN-KTs), but none of them can distinguish the importance of exercises or knowledge concepts. Existing GCN-KTs treat all neighboring nodes “equally” when performing graph convolution operations for exercise and concept embeddings, resulting in insufficient robustness of the generated node representations. Based on GCN-KTs, we offer a Knowledge Tracing model that is based on the Graph Attention Mechanism (GAFKT) and has an encoder-decoder structure. The encoder applies the self-attention layer to the topology and node features of the exercises and concepts, respectively, and the decoder uses the inner product to reconstruct the graph structure. In GAFKT, the semantic model of student knowledge and exercises is further enriched by mapping the external feature embeddings in the original data to the embeddings of the exercises and concepts in the same space. This work was experimented with several the most advanced models on two open source datasets for comparison, and the results demonstrated the effectiveness of GAFKT.
GSM Filter: A Learning Model of Measurement Noise Distributions for Bayesian Filtering

In this paper, a new universal Bayesian filter with a Gaussian scale mixture (GSM) model, called the Gaussian scale mixture (GSM) filter, for adaptively learning any measurement noise distribution of a state space system is presented. It is demonstrated that the discrete measurement noise distribution of the system takes on a Gaussian sum form when described by a GSM model. It implies that, as the Gaussian sum model does, the GSM model can also be used to approximate any measurement distribution as closely as desired, whatever the probability density function (pdf) of the measurement noise is. If its covariance is known as a priori or can be estimated, it is found that the scale parameter and matrix of the GSM model at each time step can be achieved adaptively to make the following Bayesian inference much simpler. Further analyses show that the GSM model's auxiliary random variable and the system's state posterior at each time step can be inferred by a variational Bayesian (VB) approach adaptively. At each time step, the posterior distribution of system states shown is a Gaussian whatever the measurement noise pdf of the system is. As a result, the Bayesian filtering for nonlinear systems with any measurement noise pdf can be done by our GSM filter, almost as simple as the general Gaussian filters for filtering a nonlinear Gaussian system. Both simulations and experiments are conducted to verify the effectiveness and correctness of the analytical results. The results on the performances of our algorithms fully outperforming those of the existing state of the art Bayesian filters are given.
Blockchain Mining With Multiple Selfish Miners

This paper studies a fundamental problem regarding the security of blockchain PoW consensus on how the existence of multiple misbehaving miners influences the profitability of selfish mining. Each selfish miner maintains a private chain and makes it public opportunistically for acquiring more rewards incommensurate to his Hash power. We first establish a general Markov chain model to characterize the state transition of public and private chains for Basic Selfish Mining ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BSM</i> ), and derive the stationary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">profitable threshold</i> of Hash power in closed form. It reduces from 25% for a single attacker to below 21.48% for two symmetric attackers theoretically, and further reduces to around 10% with eight symmetric attackers experimentally. We next explore the profitable threshold when one of the attackers performs strategic mining based on Partially Observable Markov Decision Process (POMDP) that only half of the attributes pertinent to a mining state are observable to him. An online algorithm is presented to compute the nearly optimal policy efficiently despite the large state space and high dimensional belief space. The profitable threshold is much lower for the strategic attacker. Last, we formulate a simple model of absolute mining revenue that yields an interesting observation: selfish mining is never profitable at the first difficulty adjustment period, but relies on the reimbursement of stationary selfish mining gains in future periods. The delay till being profitable of an attacker increases with the decrease of his Hash power, making blockchain miners more cautious about performing selfish mining.
Multi-Task Probabilistic Regression With Overlap Maximization for Visual Tracking

Recent researches made a breakthrough in visual tracking accuracy. Many trackers benefit from the object state representations and network loss functions, which mine the output space and improve the power of supervision, respectively. Probabilistic regression method models the noises and uncertainties in the annotations. However, advanced trackers with probabilistic regression are not studied sufficiently in the aspect of supervision and the aspect of robustness of evaluation maximization. In this paper, an overlap maximization network in the manner of probabilistic regression is proposed to improve the learning ability of the network and the discriminative ability in the evaluation maximization. Firstly, the probabilistic regression is extended with the intersection over union (IoU) evaluation, which is normalized as a probability density in the regression space. Secondly, the classification probability is added as a branch of the iterative evaluation module to improve the ability of distinguishing objects in the evaluation maximization. Moreover, the two branches are constructed into a joint probabilistic regression task of IoU evaluation, which makes the network learn from two types of ground truth and provide a consistent result with multi-branch outputs. For feature interpretation, the strip pooling network and the space-time memory network are introduced to encode long-range context and provide dynamic features, respectively. Compared to the state-of-the-art probabilistic regression trackers and other advanced trackers, the experiments show that the proposed tracker achieves outstanding performance across the six datasets, including GOT-10k, LaSOT, TrackingNet, UAV123, OTB-100 and VOT2018.
Enhancing DNN Training Efficiency Via Dynamic Asymmetric Architecture

Deep neural networks (DNNs) require abundant multiply-and-accumulate (MAC) operations. Thanks to DNNs' ability to accommodate noise, some of the computational burden is commonly mitigated by quantization – that is, by using lower precision floating-point operations. Layer granularity is the preferred method, as it is easily mapped to commodity hardware. In this paper, we propose Dynamic Asymmetric Architecture (DAA), in which the micro-architecture decides what the precision of each MAC operation should be during runtime. We demonstrate a DAA with two data streams and a value-based controller that decides which data stream deserves the higher precision resource. We evaluate this mechanism in terms of accuracy on a number of convolutional neural networks (CNNs) and demonstrate its feasibility on top of a systolic array. Our experimental analysis shows that DAA potentially achieves 2x throughput improvement for ResNet-18 while saving 35% of the energy with less than 0.5% degradation in accuracy.
Sparse U-PDP: A Unified Multi-Task Framework for Panoptic Driving Perception

This paper proposes a multi-task unified decoding framework for the panoptic driving perception, Sparse U-PDP. This framework’s primary objective is to combine vehicle object detection, lane detection and drivable area segmentation tasks. This paper mainly builds a multi-task unified decoder and further explores whether the potential connection between multi-tasks can improve the robustness of the model. Experiments demonstrate that the proposed Sparse U-PDP outperforms the present state-of-the-art multi-task model in terms of accuracy. The primary contributions of this work are as follows: First, we present the approach to unified multi-task representation. We abstract the multi-task into the “dynamic convolution kernels” representation form to build a highly unified multi-task decoder. Second, we use the proposed dynamic interaction module to establish different feature sampling pipelines for various task features. Lastly, our model is verified on the BDD100K dataset, where we achieve an AP50 of 84.1 in the “vehicle” category, 32.0 IoU in the lane detection, and 93.0 mIoU in the drivable area segmentation with the helper of CSP-Darknet. That is to say; Sparse U-PDP verifies that a more unified task representation form can implicitly increase the mutual help between different task branches.
G-PReDICT: Generalizable Person Re-ID using Domain Invariant Contrastive Techniques✱

Learning identity-aware, domain-invariant representations is crucial in solving domain generalizable person ReID (DG-ReID). Existing methods commonly use augmentation techniques either in feature space by mixing instance and batch normalization layers or in pixel space by adversarially generating pseudo domains. However, neither of these techniques guarantee identity preservation. Apart from increasing training data diversity, the augmented positive pairs also encode rich semantic relations which have not been fully explored. To address the above issues, we propose a novel framework for Generalizable Person Re-identification using Domain Invariant Contrastive Techniques (G-PReDICT). Specifically, we use simple yet effective perturbation strategies to hallucinate positive samples across domains by realistically modelling domain variations while preserving the target identities. We harness rich sample-sample relations between the hallucinated positive-negative pairs to learn domain-invariant representations using supervised contrastive learning. We also use a domain independent auxiliary task, i.e. attribute prediction to learn robust representations and introduce attribute annotations for two large scale public benchmarks i.e. CUHK-03 and MSMT17. Extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method.
A Novel Statistical High Density Salt-and-Pepper Noise Removal Algorithm for Brain Magnetic Resonance Images

Brain Magnetic Resonance Imaging (MRI) is a non-invasive technique that produces high quality images of the brain and is most suitable for analysis and diagnosis. However, these images can be soiled with noise during image acquisition or transmission. The paper is targeted at removing high density salt and pepper noise from such medical images using a denoising technique based on centroidal mean formulation. The presented method is tested on various noisy brain MRI images and the obtained results are promising even for images with high density corruptions, which is suggestive of the resiliency of the algorithm.
Automatic Annotation of Handwritten Document Images at Word Level

Recent development in deep learning-based recognizers needs a large annotated corpus for creating the model. Manually annotating a large corpus is time-consuming, costly, and tedious. In this work, we propose a framework for automatic annotation at the word level for given handwritten data and corresponding text sequences (or corpora). The proposed framework consists of five modules (i) pre-processing, (ii) word detection, (iii) word recognition, (iv) alignment, and (v) manual correction and verification. The pre-processing module cleans the image and crops the text region from an image. Word detection and recognition modules localize and recognize words. It is necessary to align words in the sequence with the word images during detection and recognition because of errors in writing. The alignment module aligns words in text sequence to the word images. The human annotator will correct the errors in the automatic annotation process and verify the document. Finally, we created an annotated dataset containing word images and their corresponding ground truth transcriptions. In this work, we demonstrate the proposed tool for annotating 14 sets corresponding to 13 Indic languages and English. Each set contains 15000 handwritten document images. On an extensive collection of handwritten document images in 14 languages, 80% of words are correctly annotated by the automatic annotation tool, while the remaining 20% are corrected manually.
Learning from Multiple Datasets for Recognizing Human Actions

Action recognition has evolved as an important research problem in the computer vision community. Majority of the human action recognition methods focus mainly on training from a single dataset. Scarcity of labelled data in a single dataset often leads to over-fitting for recognizing each and every action. So, use of multiple datasets is expected to alleviate this situation. However, in the real world, labelled data is frequently obtained from a variety of origins which can exhibit substantial variations. In this paper, we demonstrate how human actions can be accurately recognized by using multiple datasets based on the concept of unsupervised domain adaptation. We propose a novel approach termed as Multi-Dataset Human Action Recognition (MDHAR) for recognizing human actions using two or more publicly available benchmark datasets. Common and heterogeneous features for a dataset pair are aligned. Distributions across a dataset pair are also balanced to reduce class imbalance. We further show that our unsupervised domain adaptation approach can be extended to more than two datasets. Extensive experimentation on three publicly available benchmark datasets clearly reveal the efficacy of the proposed solution for cross dataset human action recognition from multiple datasets.
A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single Image Dehazing✱

Dehazing is a difficult process because of the damage caused by the non-uniform fog and haze distribution in images. To address these issues, a Multi-Scale Residual dense Dehazing Network (MSRDNet) is proposed in this paper. A Contextual feature extraction module (CFM) for extracting multi-scale features and an Adaptive Residual Dense Module (ARDN) are used as sub-modules of MSRDNet. Moreover, all the hierarchical features extracted by each ARDN are fused, which helps to detect hazy maps of varying lengths with multi-scale features. This framework outperforms the state-of-the-art dehazing methods in removing haze while maintaining and restoring image detail in real-world and synthetic images captured under various scenarios.
One shot learning in StyleALAE: Preserving facial identity during semantic modification

Semantic face editing of real-world facial images is an important application of generative models. Recently, several works have explored possible techniques to generate such modifications by utilizing the latent structure of pre-trained GAN models. However, such approaches frequently necessitate the training of an encoder network, which is a time-consuming and resource-intensive procedure. StyleALAE [17], a latent-space based autoencoder that can generate photorealistic images of high quality, could be a viable alternative to a GAN-based architecture. Unfortunately, the reconstructed image in StyleALAE does not preserve the identity of the input facial image. This limits the application of StyleALAE for semantic face editing of images. In our work, we propose a one-shot learning approach to address this problem in StyleALAE. Our work ensures that the identity of the reconstructed image in StyleALAE is the same as the given input image. We also present ways to efficiently use the latent space of the pre-trained model to create semantic modification over the reconstructed image within the proposed one-shot learning approach. Results shows that our approach can generate semantic modifications on any real-world facial image while preserving identity.
A Dataset and Model for Crossing Indian Roads

Roads in medium-sized Indian towns often have lots of traffic but no (or disregarded) traffic stops. This makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety classifiers thus have the potential to help the blind to cross Indian roads. Yet, we currently lack datasets collected on Indian roads from the pedestrian point-of-view, labelled with road crossing safety information. Existing classifiers from other countries are often intended for crossroads, and hence rely on the detection and presence of traffic lights, which is not applicable in Indian conditions. We introduce INDRA (INdian Dataset for RoAd crossing), the first dataset capturing videos of Indian roads from the pedestrian point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and vehicle bounding boxes. We train various classifiers to predict road crossing safety on this data, ranging from SVMs to convolutional neural networks (CNNs). The best performing model DilatedRoadCrossNet is a novel single-image architecture tailored for deployment on the Nvidia Jetson Nano. It achieves 79% recall at 90% precision on unseen images. Lastly, we present a wearable road crossing assistant running DilatedRoadCrossNet, which can help the blind cross Indian roads in real-time. The project webpage is https://roadcross-assistant.github.io/Website/index.html
Research on the Resilience Enhancement Path of Technology-Based SMEs Using Dynamic Fuzzy Cognitive Maps—Organizational Context and the Employee Loyalty Perspective

Technology-based small and medium enterprises (SMEs) represent an important organizational aspect of technological innovation and economic growth. The complex and turbulent operating environment resulting from COVID-19 is a fatal threat to the survival and growth of technology-based SMEs. Based on the Wuli-Shili-Renli (WSR) methodology, this research uses dynamic fuzzy cognitive maps to analyze the mechanisms underlying the interactions among organizational context, employee loyalty and organizational resilience in the context of technology-based SMEs. The study also includes a sensitivity analysis, and the results of these analyses are mutually corroborated. The study finds that organizational context, employee loyalty and organizational resilience are correlated to varying degrees but also that organizational learning and sustained loyalty are weak, while normative organizational loyalty is relatively weak. This study enriches the research concerning organizational resilience and has practical value with regard to improving the organizational resilience of technology-based SMEs.