Reviewerly/exHarmony huggingface dataset:
Exploiting Frame Similarity for Efficient Inference on Edge Devices

Deep neural networks (DNNs) are being widely used in various computer vision tasks as they can achieve very high accuracy. However, the large number of parameters employed in DNNs can result in long inference times for vision tasks, thus making it even more challenging to deploy them in the compute- and memory-constrained mobile/edge devices. To boost the inference of DNNs, some existing works employ compression (model pruning or quantization) or enhanced hardware. How-ever, most prior works focus on improving model structure and implementing custom accelerators. As opposed to the prior work, in this paper, we target the video data that are processed by edge devices, and study the similarity between frames. Based on that, we propose two runtime approaches to boost the performance of the inference process, while achieving high accuracy.Specifically, considering the similarities between successive video frames, we propose a frame-level compute reuse algorithm based on the motion vectors of each frame. With frame-level reuse, we are able to skip 53% of frames in inference with negligible overhead and remain within less than 1% mAP (accuracy) drop for the object detection task. Additionally, we implement a partial inference scheme to enable region/tile-level reuse. Our experiments on a representative mobile device (Pixel 3 Phone) show that the proposed partial inference scheme achieves 2 × speedup over the baseline approach that performs full inference on every frame. We integrate these two data reuse algorithms to accelerate the neural network inference and improve its energy efficiency. More specifically, for each frame in the video, we can dynamically select between (i) performing a full inference, (ii) performing a partial inference, or (iii) skipping the inference altogether. Our experimental evaluations using six different videos reveal that the proposed schemes are up to 80% (56% on average) energy efficient and 2.2× performance efficient compared to the conventional scheme, which performs full inference, while losing less than 2% accuracy. Additionally, the experimental analysis indicates that our approach outperforms the state-of-the-art work with respect to accuracy and/or performance/energy savings.
Rethinking CAM in Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) generally utilizes the Class Activation Map (CAM) to synthesize pseudo-labels. However, the current methods of obtaining CAM focus on salient features of a specific layer, resulting in highlighting the most discriminative regions and further leading to rough segmentation results for WSSS. In this paper, we rethink the potential of the ordinary classifier and find that if features of all the layers are applied, the classifier will obtain CAM with complete discriminative regions. Inspired by this, we propose Fully-CAM for WSSS, which can fully exploit the potential of the ordinary classifier and yield more accurate segmentation results. Precisely, Fully-CAM firstly weights feature with their corresponding gradients to yield CAMs of each layer, then fusing these layers’ CAMs could generate an ultimate CAM with complete discriminative regions. Furthermore, Fully-CAM is encapsulated into a plug-in, which can be mounted on any trained ordinary classifier with convolution layer, and it exceeds its previous performance without extra training.
Explaining Differential Evolution Performance Through Problem Landscape Characteristics

Providing comprehensive details on how and why a stochastic optimization algorithm behaves in a particular way, on a single problem instance or a set of problem instances is a challenging task. For this purpose, we propose a methodology based on problem landscape features and explainable machine learning models, for automated algorithm performance prediction. Performing this for ten different configurations of the Differential evolution (DE) algorithm on the 24 COCO benchmark problems, it can be estimated which set of landscape features contributes the most to the correct performance prediction of each DE configuration. From the results we concluded that different landscape features are important when predicting the performance of the different DE configurations. However, for DE configurations with similar performance on the benchmark problems, the regression models identify similar feature portfolios as important for the performance prediction task. We provided explanations for the behaviour of the algorithms in this scenario, by identifying the set of most important features and further using this information to compare the different algorithms and find algorithms with similar exploration and exploitation capabilities.
Detection of Moving Object with Dynamic Mode Decomposition and Yolov5

Object detection is essential to timely restore the video, especially over the complex underwater environment. We propose a novel moving object detection algorithm with dynamic mode decomposition (DMD) and Yolov5. First, DMD is exploited to compress and retrieve the dynamic foreground and the static background by decomposing the snapshot sequence matrix into a low-rank and a sparse matrices, respectively. Then, the foreground video is retrieved from the low-rank matrix and the reconstructed matrix is recovered together by the two matrices. Finally, the moving object buried in the dynamic foreground and reconstructed images or videos are recognized by Yolov5. Experiments reveal that both the foreground and the reconstructed videos have higher detection accuracy than the Yolov5. Also, compared with the original video, the proposed algorithm bears the advantages of improved detection accuracy, lower compressing rate and decreased computing cost.
Self-Supervised Learning Hyperspectral Image Denoiser with Separated Spectral-Spatial Feature Extraction

Deep learning-based methods have achieved remarkable results in the field of hyperspectral image (HSI) denoising, and these methods are typically trained on pairs of noisy input and clean target images. How to deal with the noise in real-world HSIs when clean targets are unavailable is still a challenging problem. In this paper, we propose a self-supervised HSI denoiser in which only a single noisy HSI is utilized. We exploit the blind-spot network and extend the method in the spatial-spectral space to accomplish self-supervised learning. In order to better extract spatial-spectral features with limited training samples, we use separable feature extraction modules to extract spectral-spatial joint information of HSI separately and finally fuse these features. Experimental results on both simulated and real hyperspectral datasets show that our proposed method outperforms some state-of-the-art denoising approaches.
High Efficiency Dataset Generation for Semantic Video Segmentation on Road Intersection

This work proposes a highly efficient dataset generation method for semantic video segmentation at road intersections. Semantic segmentation is an excellent method to determine the class for each pixel in an image. However, semantic segmentation requires the human cost of generating a dataset with annotations for each pixel. For this reason, the dataset must be generated in an ingenious way. The dataset is dedicated to road intersections that have complex situations and high accident rates. The simulation results show that the proposed method can achieve the fast generation of the dataset. Additionally, the proposed segmentation performance can be achieved with the easy generation method of the dataset.
PFMixer: Point Cloud Frequency Mixing

Convolutional Neural Networks and Transformer are popu-lar for various computer vision tasks. Recently, a modern multi-layer perceptrons (MLPs) architecture, MLP-Mixer, has achieved remarkable performance in visual recognition tasks. MLP-Mixer includes two types of MLPs: channel-mixing and token-mixing MLPs. Inspired by its simplicity and success, we introduce the application of MLP-Mixer for point cloud processing. We design token-mixing operations to allow interaction between high-frequency (edge) regions and low-frequency (smooth) regions. Furthermore, channel-mixing operations are proposed to allow interaction between different channels of local structures and global shapes. Ex-tensive experiments demonstrate the superiority of PFMixer: POINT CLOUD FREQUENCY MIXING
An Analysis of SMS Spam Detection using Machine Learning Model

social-media is a very common medium for spammers to unethically overwhelm normal users with unsolicited or false content via social-networking. Now a days most of the people are aware of social media. In this paper, we have used SMS Spam Collection dataset that is taken from Kaggle. The Bow with TF and TF-IDF weighing schemes features are used for feature selection. And we used chi-square matrix for features selection. We have done the comparison of state of the art and proposed model. The results show that our proposed model Multinomial Naïve Bayes gave highest accuracy. We offer the variable status of each feature so that it is easy to abolish the inappropriate features. Our results illustrated that our proposed model accomplished effectively high detection rates in terms of high accuracy, compared with other considered researches
Machine Learning in Healthcare: Two Case Studies

Machine learning (ML) is making significant inroads into the field of medicine. It can be used as a preventative measure by predicting a patient’s diagnosis and introducing early treatment to prevent adverse outcomes or lessen their impact. However, despite many demonstrated advantages of machine learning tools in health-care, their performance assessment remains partial at best. In particular, human interactions with machine learning tools in clinical settings remain poorly researched. This review examined machine learning tools in two important areas, sepsis diagnosis and suicide prediction. However, our exploration into the use of machine learning in sepsis and suicide prediction turned up no thorough human factors analyses of provider interactions with their machine learning tools, suggesting a critical research gap waiting to be filled.
Nonparametric Temporal Downscaling of GHI Clear-sky Indices using Gaussian Copula

Small-scale variabilities of solar irradiance are important for many applications. Downscaling approaches need to be developed where only the averaged state of solar irradiance is known. In this study, we investigate the use of copula for temporally downscaling GHI clear-sky indices. With the correlation structure and distribution information derived from measurements at 10 stations across the United States, the copula approach is capable of downscaling clear-sky indices from hourly averages to any arbitrary fine scale whilst preserving its original power spectra.
Face Recognition in Different Scenarios Using Siamese Network

Face detection and face recognition are the most sought applications in image processing and computer vision domains. Face recognition has a wide range of applications, and some of its performance must be improved in unconstrained and surveillance applications. In this paper, face recognition has been performed for a person with different backgrounds and different people around him and in various costumes, which is helpful for surveillance applications and HRI. This work proposes an algorithm to recognize a person by matching similarities between the two faces using Siamese Architecture alongside VGG-Face and MTCNN algorithms. The system gives the similarity score for both images with 90% accuracy.
MLAN: Multi-Level Attention Network

In this paper, we proposed a “Multi-Level Attention Network” (MLAN), which defines a multi-level structure, including layer, block, and group levels to get hierarchical attention and combines corresponding residual information for better feature extraction.We also constructed a shared mask attention module (SMA) which can significantly reduce the number of parameters compared with conventional attention methods. Based on the MLAN and SMA, we further investigated a variety of information fusion modules for better feature fusion at different levels. We conducted classification task experiments based on the ResNet backbone with different depths, and the experimental results show that our method has a significant performance improvement over the backbone on CIFAR10 and CIFAR100 datasets. Meanwhile, compared with the mainstream attention methods, our MLAN performs better with higher accuracy as well as less parameters and computation complexity. We also visualized some intermediate feature maps and explained why our MLAN performs well.
An Approach to Identify Indic Languages using Text Classification and Natural Language Processing

India is one of the most culturally and linguistically diverse nations in the world. India stands second in the world for the most languages spoken by its diverse population, who speak their own regional languages for communication. English is offered as a second additional official language in India. However, there is a communication gap in India because of how little English is used there. It's nearly impossible for humans to bridge this breach by translating from one language into another. However, it is possible to translate languages by taking the help of a machine. As per the literature survey, it was observed that Neural Machine Translation (NMT) is a cutting-edge strategy that significantly outperformed more conventional machine translation methods for translating one language into another.The main objective of this proposed work is to achieve accurate identification of Indic language texts and scripts and provide relevant names of the language after the detection process. The entire work is carried out in stages which includes, collection of the dataset from different sources, preprocessing with the help of data mining techniques, identifying the language of input and in future, approaches like rule based, statistical and neural networks will be used followed by post-processing and efficient tasks like Machine Translations, Named Entity recognition, etc. will be carried out.
DJMix: Unsupervised Task-agnostic Image Augmentation for Improving Robustness of Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are vulnerable to unseen test-time noise on input images, such as defocus blur or JPEG-compression artifacts. Improving the robustness to such noise is important for real-world applications. In this paper, we propose DJMix, a training method for CNNs to obtain identical representations to a given image and its discretized one. As a result, CNNs trained with DJMix can ignore unnecessary details of inputs and become robust to input noise. We verify the effectiveness of our method on several datasets of various tasks, namely, classification, semantic segmentation, and object detection using clean and noisy test images.
Multi-scale Subgraph Contrastive Learning for Link Prediction

Existing works about link prediction rely mainly on pooling operations which cause loss of edge information or similarity assumptions, so that they are limited in specific networks, and mainly supervised learning methods. We propose a Multi-scale Subgraph Contrastive Learning (MSCL) method. To adapt to networks of different sizes and make direct use of edge information, MSCL converts a sampled subgraph centered on the target link into a line graph as a node-scale to represent links, and mines deep representations by combining two scales information, subgraph-scale and line graph node-scale. After learning the information of the two subgraphs separately by encoders, we use contrastive learning to balance the information of two scales to alleviate the over-reliance of the model on labels and enhance the model's robustness. MSCL outperforms a set of state-of-the-art graph representation learning solutions on link prediction task in a variety of graphs including biology networks and social networks.
GENHOP: An Image Generation Method Based on Successive Subspace Learning

Being different from deep-learning-based (DL-based) image generation methods, a new image generative model built upon successive subspace learning principle is proposed and named GenHop (an acronym of Generative PixelHop) in this work. GenHop consists of three modules: 1) high-to-low dimension reduction, 2) seed image generation, and 3) low-to-high dimension expansion. In the first module, it builds a sequence of high-to-low dimensional subspaces through a sequence of whitening processes, each of which contains samples of joint-spatial-spectral representation. In the second module, it generates samples in the lowest dimensional subspace. In the third module, it finds a proper high-dimensional sample for a seed image by adding details back via locally linear embedding (LLE) and a sequence of coloring processes. Experiments show that GenHop can generate visually pleasant images whose FID scores are comparable or even better than those of DL-based generative models for MNIST, Fashion-MNIST and CelebA datasets.
AIM

We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally generates synthetic data from the noisy measurements. It uses a set of innovative features to iteratively select the most useful measurements, reflecting both their relevance to the workload and their value in approximating the input data. We also provide analytic expressions to bound per-query error with high probability which can be used to construct confidence intervals and inform users about the accuracy of generated data. We show empirically that AIM consistently outperforms a wide variety of existing mechanisms across a variety of experimental settings.
NeuRes: Highly Activated Neurons Responses Transfer via Distilling Sparse Activation Maps

In recent years, Knowledge Distillation has obtained a significant interest in deep learningbased applications on mobile and IoT devices due to its ability to transfer knowledge from the large and complex teacher to the lightweight student network.Intuitively, Knowledge Distillation refers to forcing the student to mimic the teacher's neuron responses to improve the generalization of the student by deploying the distillation losses as the regularization terms.However, the non-linearity of the hidden layers and the high dimensionality of the feature maps make the knowledge transfer a rigorous task.Though numerous methods have been proposed to transfer the teacher's neuron responses in the form of diverse feature characteristics such as attention, contrastive representation, and so on, to the best of our knowledge, no prior works considered feature-level non-linearity during distillation.In this work, we ask, does feature-level non-linearity-based approaches can improve student performance?To investigate this concern, we propose a novel knowledge distillation technique called the NeuRes (Neuron's Responses) via distilling the Sparse Activation Maps (SAMs) to transfer the highly activated Neurons Responses to the student to enhance the representation capability.Proposed NeuRes selects the highly activated neuron responses that produce Sparse Activation Maps (SAMs) while transferring the knowledge based on activation normalization.Our proposed NeuRes also transfers the translation invariant features using auxiliary classifiers and augmented data to improve students' generalization.The details ablation studies and extensive experiments on model compression, transferability, adversarial robustness, and few-shot learning verify that NeuRes performs better compared to the existing state-of-the-art distillation techniques on the standard benchmark datasets.
Self-feature Distillation with Uncertainty Modeling for Degraded Image Recognition

Despite the remarkable performance on high-quality (HQ) data, the accuracy of deep image recognition models degrades rapidly in the presence of low-quality (LQ) images. Both feature de-drifting and quality agnostic models have been developed to make the features extracted from degraded images closer to those of HQ images. In these methods, the $$l_2$$ -norm is usually used as a constraint. It treats each pixel in the feature equally and may result in relatively poor reconstruction performance in some difficult regions. To address this issue, we propose a novel self-feature distillation method with uncertainty modeling for better producing HQ-like features from low-quality observations in this paper. Specifically, in a standard recognition model, we use the HQ features to distill the corresponding degraded ones and conduct uncertainty modeling according to the diversity of degradation sources to adaptively increase the weights of feature regions that are difficult to recover in the distillation loss. Experiments demonstrate that our method can extract HQ-like features better even when the inputs are degraded images, which makes the model more robust than other approaches.
Path planning with static obstacles for USVs via the Hybrid A* algorithm and the artificial potential field method

This paper considers the problem of excessive iterations and unnecessary steering actions for unmanned surface vehicles (USVs) by a path planning method combining the Hybrid A* algorithm and the APF method. First, the proposed path planning method is used to generate the collsion-free global path as the reference line in the Frenet coordinate system. Through decoupling the motions into the longitudinal and horizontal motion, the optional trajectory sets of USVs are selected based on the relative position to the reference line. According to the cost function within the lateral and longitudinal orientations, the minimum-cost path can be calculated. In addition, the jerk optimization is adopted to smooth the path. Finally, three numerical examples, including two kinds of extra penalties, are given to illustrate the effectiveness of path planning for USVs on a grid map with static obstacles.
An End-to-End Conformer-Based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation

In civil aviation radiotelephony communications, misunderstandings between air traffic controllers and flight crews can result in serious aviation accidents. Automatic semantic verification is a promising assistant solution to decrease miscommunication, thanks to advancements in speech and language processing. Unfortunately, existing general speech recognition models are ineffective when it comes to capturing contextual long-distance dependent local similarity features in radiotelephony communications. To address these problems, this paper proposes an end-to-end Conformer-based multi-task learning speech recognition model for Mandarin radiotelephony communications in civil aviation. The Conformer model improves local information capture while retaining the global information modeling capabilities of contextual long-distance dependencies, owing to the introduction of the convolution module to the Transformer model. Meanwhile, multi-task learning is used to further improve performance by combining connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. The experimental results show that the proposed model can perform global and local acoustic modeling effectively, making it particularly suitable for extracting acoustic features of Mandarin civil aviation radiotelephony communications.