Reviewerly/exHarmony huggingface dataset:
Knowledge from the original network: restore a better pruned network with knowledge distillation

Abstract To deploy deep neural networks to edge devices with limited computation and storage costs, model compression is necessary for the application of deep learning. Pruning, as a traditional way of model compression, seeks to reduce the parameters of model weights. However, when a deep neural network is pruned, the accuracy of the network will significantly decrease. The traditional way to decrease the accuracy loss is fine-tuning. When over many parameters are pruned, the pruned network’s capacity is reduced heavily and cannot recover to high accuracy. In this paper, we apply the knowledge distillation strategy to abate the accuracy loss of pruned models. The original network of the pruned network was used as the teacher network, aiming to transfer the dark knowledge from the original network to the pruned sub-network. We have applied three mainstream knowledge distillation methods: response-based knowledge, feature-based knowledge, and relation-based knowledge (Gou et al. in Knowledge distillation: a survey. arXiv:200605525 , 2020), and compare the result to the traditional fine-tuning method with grand-truth labels. Experiments have been done on the CIFAR100 dataset with several deep convolution neural network. Results show that the pruned network recovered by knowledge distillation with its original network performs better accuracy than it recovered by fine-tuning with sample labels. It has also been validated in this paper that the original network as the teacher performs better than differently structured networks with same accuracy as the teacher.
Adaptive convolution kernel for artificial neural networks

Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 × 3) kernels. This paper describes a method for learning the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagation-trainable Gaussian envelope which can grow or shrink in a base grid. Our experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network, a deeper residual network, and a U-Net architecture. The results in the popular image classification datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and "Faces in the Wild" showed that the adaptive kernels can provide statistically significant improvements on ordinary convolution kernels. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing ordinary convolution layers in a U-shaped network with 7 × 7 adaptive layers can improve its learning performance and ability to generalize.
A Competitive Generalized Gamma Mixture Model for Medical Image Diagnosis

Parametric family of statistical distributions are of great importance for several applications. In particular, we propose to investigate the generalized Gamma mixture model ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{g}\Gamma $ </tex-math></inline-formula> MM) for modeling and classifying medical imaging (Chest x-ray and CT-scans). The main advantage of this mixture over some existing Gaussian models is that it allows additional flexibility in shape modeling, which is crucial for classification systems. In order to capture accurately the intrinsic nature of medical images, we propose to derive some efficient measures based on Fisher, Kullback-Leibler and Bhattacharyya distances for the mixtures of generalized Gamma distributions. Indeed, the main idea is to investigate these distances effectively via the statistical model parameters in order to make our proposed scheme particularly appropriate for image classification problem. The proposed approach involves the extraction of robust texture descriptors, the learning of mixture model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{g}\Gamma $ </tex-math></inline-formula> MM via the expectation-maximization (EM) and Newton-Raphson algorithms, and the classification of images using the derived mixtures-based distances. We evaluate our model against the challenging problem of early diagnosis of pneumonia diseases. Experimental results on different datasets show the merits of our developed framework compared with the other methods.
Noise dependent training for deep parallel ensemble denoising in magnetic resonance images

In this paper, a deep learning technique is proposed for the removal of Gaussian-impulse noise from Magnetic Resonance images (MRI). The proposed technique is inspired from the Bayesian maximum a posteriori (MAP) derivation of the Gaussian-impulse likelihood. A discriminative learning strategy under fully convolutional neural network (CNN) is used which focuses on the importance of loss layer during training. Residual learning is combined with 3D convolution for multi-dimensional extraction of image features from noisy data, on a wide range of noise levels. The problem of vanishing gradient in a very deep network is handled through the usage of a wide network, which is built by incorporating two parallel models (thereby resulting in decreased depth of the network). The approach is called ensemble because features are obtained along the two parallel paths working independently, using normal and dilated convolutions. Results on model convergence support advantages observed by these considerations. Experiments are conducted on synthetically corrupted MRI data and real spin-echo MRI sequence. Better visual and metric results as well as fast testing performance support the argument of boosted denoising capability against a majority of the benchmarks for MRI noise removal.
Research Problems in Recommender systems

Abstract With continuous growth of web applications around the globe, it is a challenge to find the suitable information needed for the user in a limited time.Number of handheld mobile devices is increasing and most of the business revolves around the correct search of the data. Without a proper recommender system it is very difficult to get required information from the web applications. Web applications use recommender systems to provide suitable data to users based on their choices and interests. For different kinds of needs different types of recommender systems have been proposed. Two most basic types of recommender systems are collaborative filtering recommender system and content based recommender system. Sometimes these two recommender systems are combined to increase the efficiency of a recommender system The generated new recommender system is known as hybrid recommender system. The purpose of this paper is to help readers understand the basics of recommender systems. This paper identifies key areas of research openly available for new researchers. After reading this paper new researchers can understand basic problems of recommender systems which need improvement and hence they can make those problems their area of research.
Attentive U-recurrent encoder-decoder network for image dehazing

Haze removal is an important pre-processing step in many computer vision tasks. Convolutional neural networks, especially the U-shaped networks, have shown to be effective in image dehazing. Nevertheless, these networks have three main limitations. First, the relevant haze information, e.g. concentration of haze, is totally ignored. Second, spatial inconsistency and information dilution usually occur when the networks refine the dehazed results with a coarse-to-fine strategy. Third, the receptive field of the network is not large enough to capture structural information. Motivated by these problems, a new attentive U-recurrent encoder-decoder dehazing network is presented, which consists of an attentive recurrent network and a U-recurrent encoder-decoder network. By assuming that haze layers with different depths can be detected by multiple stages, we use an attentive recurrent network to generate the haze attention map for guiding the U-recurrent encoder-decoder network with the concentration of haze to better estimate the clear image. Meanwhile, the features for dehazing are further enhanced and the dehazing results are refined in the U-recurrent encoder-decoder network. This design not only enables spatial consistency but also reduces information dilution with short recurrent pathways. Furthermore, a novel residual pyramid pooling module is also proposed and used in the U-recurrent encoder-decoder network, which provides the network with structural information and with an enlarged receptive field. The experimental results demonstrate that our method outperforms state-of-the-art dehazing algorithms on both synthetic and real hazy images.
Adversarial Reinforcement Learning for Unsupervised Domain Adaptation

Transferring knowledge from an existing labeled domain to a new domain often suffers from domain shift in which performance degrades because of differences between the domains. Domain adaptation has been a prominent method to mitigate such a problem. There have been many pre-trained neural networks for feature extraction. However, little work discusses how to select the best feature instances across different pre-trained models for both the source and target domain. We propose a novel approach to select features by employing reinforcement learning, which learns to select the most relevant features across two domains. Specifically, in this framework, we employ Q-learning to learn policies for an agent to make feature selection decisions by approximating the action-value function. After selecting the best features, we propose an adversarial distribution alignment learning to improve the prediction results. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods.
Mobile Robot Simulation and Navigation in ROS and Gazebo

Mobile robots are entering our daily lives as well as in the industry. Their task is usually associated with carrying out transportation. This leads to the need to perform autonomous movement of mobile robots. On the other hand, modern practice is that the planning of most processes is done through simulations. Thus, various future production problems can be anticipated and remedied or improved. The article describes the creation of a mobile robot model in the Gazebo simulation environment. Specific settings and features for running a mobile robot in autonomous navigation mode under the robot operating system are presented. The steps for creating a map, localization and navigation are presented. Experiments have been conducted to optimize and tune the parameters of both the robot model itself and the simulation control parameters.
Session-Based Cyberbullying Detection: Problems and Challenges

Cyberbullying has become one of the most pressing online risks for young people, due in part to the rapid increase in social media use, and has raised serious concerns in society. Existing studies have examined various approaches to cyberbullying detection focusing on a single piece of text, whereas relatively little is known about cyberbullying detection within a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social media session</i> . A social media session typically consists of an initial post, images/videos, a sequence of comments that involves user interactions, user information, spatial location, and other social content. By investigating cyberbullying at the level of social media sessions, researchers can draw on data that are more complex, diverse, and crucial for understanding two defining characteristics of cyberbullying, in particular: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">repetitive acts</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">power imbalance</i> . This article thus highlights the importance of studying session-based cyberbullying detection, identifies core challenges, and serves as a resource to help direct future research efforts.
A Behavior-Based Mobile Robot Navigation Method with Deep Reinforcement Learning

Deep reinforcement learning-based mobile robot navigation has attracted some recent interest. In the single-agent case, a robot can learn to navigate autonomously without a map of the environment. In the multi-agent case, robots can learn to avoid collisions with each other. In this work, we propose a behavior-based mobile robot navigation method which directly maps the raw sensor data and goal information to the control command. The learned navigation policy can be applied in both single-agent and multi-agent scenarios. Two basic navigation behaviors are considered in our method, which are goal reaching and collision avoidance. The two behaviors are fused based on the risk-level estimation of the current state. The navigation task is decomposed using the behavior-based framework, which is capable of reducing the complexity of the learning process. The simulations and real-world experiments demonstrate that the proposed method can enable the collision-free autonomous navigation of multiple mobile robots in unknown environments.
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertainties in the classification and regression tasks, and leverage uncertainties to train the fusion network via a sampling mechanism. We validate our method on three datasets with challenging real-world driving scenarios. Experimental results show that the predicted uncertainties reflect complex environmental uncertainty like difficulties of a human expert to label objects. The results also show that our method consistently improves the Average Precision by up to 7% compared to the baseline method. When sensors are temporally misaligned, the sampling method improves the Average Precision by up to 20%, showing its high robustness against noisy sensor inputs.
Hierarchical Phoneme Classification for Improved Speech Recognition

Speech recognition consists of converting input sound into a sequence of phonemes, then finding text for the input using language models. Therefore, phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. However, correctly distinguishing phonemes with similar characteristics is still a challenging problem even for state-of-the-art classification methods, and the classification errors are hard to be recovered in the subsequent language processing steps. This paper proposes a hierarchical phoneme clustering method to exploit more suitable recognition models to different phonemes. The phonemes of the TIMIT database are carefully analyzed using a confusion matrix from a baseline speech recognition model. Using automatic phoneme clustering results, a set of phoneme classification models optimized for the generated phoneme groups is constructed and integrated into a hierarchical phoneme classification method. According to the results of a number of phoneme classification experiments, the proposed hierarchical phoneme group models improved performance over the baseline by 3%, 2.1%, 6.0%, and 2.2% for fricative, affricate, stop, and nasal sounds, respectively. The average accuracy was 69.5% and 71.7% for the baseline and proposed hierarchical models, showing a 2.2% overall improvement.
Functional data clustering using principal curve methods

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An Automatic Glioma Segmentation System Using a Multilevel Attention Pyramid Scene Parsing Network

Due to the significant variances in their shape and size, it is a challenging task to automatically segment gliomas. To improve the performance of glioma segmentation tasks, this paper proposed a multilevel attention pyramid scene parsing network (MLAPSPNet) that aggregates the multiscale context and multilevel features.First, T1 pre-contrast, T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast sequences of each slice are combined to form the input. Afterwards, image normalization and augmentation techniques are applied to accelerate the training process and avoid overfitting, respectively. Furthermore, the proposed MLAPSPNet that introduces multilevel pyramid pooling modules (PPMs) and attention gates is constructed. Eventually, the proposed network is compared with some existing networks.The dice similarity coefficient (DSC), sensitivity and Jaccard score of the proposed system can reach 0.885, 0.933 and 0.8, respectively. The introduction of multilevel pyramid pooling modules and attention gates can improve the DSC by 0.029 and 0.022, respectively. Moreover, compared with Res-UNet, Dense-UNet, residual channel attention UNet (RCA-UNet), DeepLab V3+ and UNet++, the DSC is improved by 0.032, 0.026, 0.014, 0.041 and 0.011, respectively.The proposed multilevel attention pyramid scene parsing network can achieve stateof- the-art performance, and the introduction of multilevel pyramid pooling modules and attention gates can improve the performance of glioma segmentation tasks.
Seeing in Context

There is a risk that modern practices of information communication and visualization in human-computer interaction can sideline communities due to their prioritization of scientific rationality. Such ideological hegemony can complicate interactions with data and computers, especially for low-literate communities in the global south. Through a six-month long ethnographic study with Nakshi-Katha makers, Hindu Idol makers, and witchcraft practitioners, we investigated how rural practitioners use their own forms of representation and narrative in record keeping, social and religious storytelling, and information mediated decision making. We find that traditionally developed approaches towards presenting and communicating information often make use of concrete units to represent entities and connect to designers' cultural practices and the physical location. Further, we identify how medium has significant influence in meaning-making. Often these strategies and conventions are passed down through generations within the community. In this paper, we discuss how this rural tradition differs from the modern information communication practices, discussing how an understanding of traditional practices for representing information can be useful in developing more accessible, and culturally appropriate modern tools and technologies for the people of rural Bangladesh and similar communities.
Task-Based Visual Interactive Modeling: Decision Trees and Rule-Based Classifiers

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.
Neural Contrast Enhancement of CT Image

Contrast materials are often injected into body to contrast specific tissues in Computed Tomography (CT) images. Contrast Enhanced CT (CECT) images obtained in this way are more useful than Non-Enhanced CT (NECT) images for medical diagnosis, but not available for everyone due to side effects of the contrast materials. Motivated by this, we develop a neural network that takes NECT images and generates their CECT counterparts. Learning such a network is extremely challenging since NECT and CECT images for training are not aligned even at the same location of the same patient due to movements of internal organs. We propose a two-stage framework to address this issue. The first stage trains an auxiliary network that removes the effect of contrast enhancement in CECT images to synthesize their NECT counterparts well-aligned with them. In the second stage, the target model is trained to predict the real CECT images given a synthetic NECT image as input. Experimental results and analysis by physicians on abdomen CT images suggest that our method outperforms existing models for neural image synthesis.
Tightened Convex Relaxations for Neural Network Robustness Certification

In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data. Such certification is imperative for the application of neural networks in safety-critical decision-making and control systems. Certification techniques using convex optimization have been proposed, but they often suffer from relaxation errors that void the certificate. Our work exploits the structure of ReLU networks to improve relaxation errors through a novel partition-based certification procedure. The proposed method is proven to tighten existing linear programming relaxations, and asymptotically achieves zero relaxation error as the partition is made finer. We develop a finite partition that attains zero relaxation error and use the result to derive a tractable partitioning scheme that minimizes the worst-case relaxation error. Experiments using real data show that the partitioning procedure is able to issue robustness certificates in cases where prior methods fail. Consequently, partition-based certification procedures are found to provide an intuitive, effective, and theoretically justified method for tightening existing convex relaxation techniques.
Temporal Shift GAN for Large Scale Video Generation

Video generation models have become increasingly popular in the last few years, however the standard 2D architectures used today lack natural spatio-temporal modelling capabilities. In this paper, we present a network architecture for video generation that models spatio-temporal consistency without resorting to costly 3D architectures. The architecture facilitates information exchange between neighboring time points, which improves the temporal consistency of both the high level structure as well as the low-level details of the generated frames. The approach achieves state-of-the- art quantitative performance, as measured by the inception score on the UCF-101 dataset as well as better qualitative results. We also introduce a new quantitative measure (S3) that uses downstream tasks for evaluation. Moreover, we present a new multi-label dataset MaisToy, which enables us to evaluate the generalization of the model.