Reviewerly/exHarmony huggingface dataset:
Chirp Signal Denoising Based on Convolution Neural Network

Many classic chirp signal processing algorithms may show significant performance degradation when the signal-to-noise ratio (SNR) is low. To address this problem, this paper proposes a pre-filtering method in time-domain based on deep learning. Different from traditional signal filtering methods, the proposed denoising convolutional neural network (DCNN) is trained to recover the pure signal from the noisy signal as much as possible. Following denoising, we use two classic chirp signal parameter estimation algorithms to estimate the parameters of the DCNN output. The simulation results show that, compared with no DCNN processing, the parameter estimation accuracy is significantly improved. This is mainly due to the powerful pure signal extraction ability of DCNN, which can significantly improve the SNR and the accuracy of signal parameter estimation.
The Application Analysis of Artificial Intelligence in Computer Network Technology

In the information age, computer network technology has covered different areas of social life and involved various fields, and artificial intelligence, as an emerging technology with a very rapid development momentum in recent years, is important in promoting the development of computer network systems. This article explains the concept of artificial intelligence technology, describes the problems faced by computer networks, further analyses the advantages of artificial intelligence and the inevitability of application in network technology, and then studies the application of artificial intelligence in computer network technology.
Detective: An Attentive Recurrent Model for Sparse Object Detection

In this work, we present Detective - an attentive object detector that identifies objects in images in a sequential manner. Our network is based on an encoder-decoder architecture, where the encoder is a convolutional neural network, and the decoder is a convolutional recurrent neural network coupled with an attention mechanism. At each iteration, our decoder focuses on the relevant parts of the image using an attention mechanism, and then estimates the object's class and the bounding box coordinates. Current object detection models generate dense predictions and rely on post-processing to remove duplicate predictions. Detective is a sparse object detector that generates a single bounding box per object instance. However, training a sparse object detector is challenging, as it requires the model to reason at the instance level and not just at the class and spatial levels. We propose a training mechanism based on the Hungarian algorithm and a loss that balances the localization and classification tasks. This allows Detective to achieve promising results on the PASCAL VOC dataset for object detection. Our experiments demonstrate that sparse object detection is possible and has great potential for future developments in applications where the order of the desired objects is of interest.
Few-Shot Image Classification with Multi-Facet Prototypes

The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples. A central challenge is that the available training examples are normally insufficient to determine which visual features are most characteristic of the considered categories. To address this challenge, we organise these visual features into facets, which intuitively group features of the same kind (e.g. features that are relevant to shape, color, or texture). This is motivated from the assumption that (i) the importance of each facet differs from category to category and (ii) it is possible to predict facet importance from a pre-trained embedding of the category names. In particular, we propose an adaptive similarity measure, relying on predicted facet importance weights for a given set of categories. This measure can be used in combination with a wide array of existing metric-based methods. Experiments on miniImageNet and CUB show that our approach improves the state-of-the-art in metric-based FSL.
Modelling vicious networks with P-graph causality maps

P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.
Md-knn: An Instance-based Approach for Multi-Dimensional Classification

Multi-dimensional classification (MDC) deals with the problem where each instance is associated with multiple class variables, each of which corresponds to a specific class space. One of the mainstream solutions for MDC is to adapt traditional machine learning techniques to deal with MDC data. In this paper, a first attempt towards adapting instance-based techniques for MDC is investigated, and a new approach named Md-knn is proposed. Specifically, Md-knn identifies unseen instance's k nearest neighbors and obtains its corresponding kNN counting statistics for each class space, based on which maximum a posteriori (MAP) inference is made for each pair of class spaces. After that, the class label w.r.t. each class space is determined by synergizing predictions from the learned classifiers via consulting empirical kNN accuracy. Comparative studies over ten benchmark data sets clearly validate Md-knn's effectiveness.
Adversarial Knowledge Distillation for a Compact Generator

In this paper, we propose memory-efficient Generative Adversarial Nets (GANs) in line with knowledge distillation. Most existing GANs have a shortcoming in terms of the number of model parameters and low processing speed. Here, to tackle the problem, we propose Adversarial Knowledge Distillation for Generative models (AKDG) for highly efficient GANs, in terms of unconditional generation. Using AKDG, model size and processing speed are substantively reduced. Through an adversarial training exercise with a distillation discriminator, a student generator successfully mimics a teacher generator in fewer model layers and fewer parameters and at a higher processing speed. Moreover, our AKDG is network architecture-agnostic. A Comparison of AKDG-applied models to vanilla models suggests that it achieves closer scores to a teacher generator and more efficient performance than a baseline method with respect to Inception Score (IS) and Frechet Inception Distance (FID). In CIFAR-10 experiments, improving IS/FID 1.17pt/55.19pt and in LSUN bedroom experiments, improving FID 71.1pt in comparison to the conventional distillation method for GANs. Our project page is https://maguro27.github.io/AKDG/.
Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

Object detection in remote sensing images is a challenging task due to objects in the bird-view perspective appearing with arbitrary orientations. Though considerable progress has been made, there still exist challenges with the interference from complex backgrounds, dense arrangement, and large-scale variations. In this paper, we propose an oriented detector named Cascade Saliency Attention Network (CSAN), designed for comprehensively suppressing interference in remote sensing images. Specifically, we first combine context and pixel attention on feature maps to enhance saliency of objects for suppressing interference from backgrounds. Then, in cascade network, we apply instance segmentation on ROI to increase saliency of the central object, thus preventing object features from mutual interference in dense arrangement. Additionally, to alleviate large-scale variations, we devise a multi-scale merge module during FPN merging process to learn richer scale representations. Experimental results on DOTA and HRSC2016 datasets outperform other state-of-the-art object detection methods and verify the effectiveness of our method.
DASH: Design Automation for Synthesis and Hardware Generation for CNN

Deployment of complex convolutional neural network (CNN) algorithms on Field Programmable Gate Arrays (FPGAs) is a non-trivial task and it becomes even more challenging when the implementation has to be done on resource-constrained devices like smaller FPGAs with less computational resources. To make hardware implementation as easy as software based implementation, a high-level framework with well-optimized hardware relevant libraries, is needed to quickly map a CNN model to such devices without much effort on the part of CNN designer or hardware designer. Only a few such frameworks are available targeting FPGAs, but without the consideration for resource constraints. In this work, we present a Python-based open source design automation tool called “DASH” for hardware generation for convolutional neural networks. Based on user given CNN parameters and hardware resource constraints, the tool trains the network model and generates the hardware code to map the CNN model to FPGAs under the specified hardware resource constraints with the help of a library of optimized design templates. The evaluation results of the generated hardware design show better performance compared to other works on the hardware implementation of CNNs in terms of resources and throughput.
Software Engineering: A Profession in Waiting

Ongoing efforts to make software development an engineering discipline will fail until we have legislation requiring that creators of certain types of software be licensed, establishing a licensing authority, and detailing the capabilities that a licensed developer must possess.
Extending Single Beam Lidar To Full Resolution By Fusing with Single Image Depth Estimation

Depth estimation is playing an important role in indoor and outdoor scene understanding, autonomous driving, augmented reality and many other tasks. Vehicles and robotics are able to use active illumination sensors such as LIDAR to receive high precision depth estimation. However, high-resolution LIDARs are usually too expensive, which limits its massive production on various applications. Though single beam LIDAR enjoys the benefits of low cost, one beam depth sensing is not usually sufficient to perceive the surrounding environment in many scenarios. In this paper, we propose a deep learning based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR. After the accurate calibration with a visible camera, the single beam LIDAR can adjust the scale uncertainty of the depth map estimated by the visible camera. The adjusted depth map enjoys the benefits of high resolution and sensing accuracy as high beam LIDAR and maintains low-cost as single beam LIDAR. Thus we can achieve similar sensing effect of high beam LIDAR with more than a 30-100 times cheaper price (e.g., $80000 Velodyne HDL-64E LIDAR v.s. $2000 SICK TIM-781 2D LIDAR and normal camera). The proposed approach is verified on our collected dataset and public dataset with superior depth-sensing performance.
Plagiarism Detection in Students' Theses Using The Cosine Similarity Method

The main requirement for graduation from students is to make a final scientific paper. One of the factors determining the quality of a student's scientific work is the uniqueness and innovation of the work. This research aims to apply data mining methods to detect similarities in titles, abstracts, or topics of students' final scientific papers so that plagiarism does not occur. In this research, the cosine similarity method is combined with the preprocessing method and TF-IDF to calculate the level of similarity between the title and the abstract of a student's final scientific paper, then the results will be displayed and compared with the existing final project repository based on the threshold value to make a decision whether scientific work can be accepted or rejected. Based on the test data and training data that has been applied to the TF-IDF method, it shows that the percentage level of similarity between the training data document and the test data document is 8%. This shows that the student thesis is still classified as unique and does not contain plagiarism content. The findings of this study can help the university in managing the administration of student theses so that plagiarism does not occur. Furthermore, it is necessary to study further adding methods to increase the accuracy of system performance so that when the process is run the system will work faster and optimally.
DAIL: Dataset-Aware and Invariant Learning for Face Recognition

To achieve good performance in face recognition, a large scale training dataset is usually required. A simple yet effective way to improve the recognition performance is to use a dataset as large as possible by combining multiple datasets in the training. However, it is problematic and troublesome to naively combine different datasets due to two major issues. First, the same person can possibly appear in different datasets, leading to an identity overlapping issue between different datasets. Naively treating the same person as different classes in different datasets during training will affect back-propagation and generate nonrepresentative embeddings. On the other hand, manually cleaning labels may take formidable human efforts, especially when there are millions of images and thousands of identities. Second, different datasets are collected in different situations and thus will lead to different domain distributions. Naively combining datasets will make it difficult to learn domain invariant embeddings across different datasets. In this paper, we propose DAIL: Dataset-Aware and Invariant Learning to resolve the above-mentioned issues. To solve the first issue of identity overlapping, we propose a dataset-aware loss for multi-dataset training by reducing the penalty when the same person appears in multiple datasets. This can be readily achieved with a modified softmax loss with a dataset-aware term. To solve the second issue, domain adaptation with gradient reversal layers is employed for dataset invariant learning. The proposed approach not only achieves the state-of-the-art results on several commonly used face recognition validation sets, including LFW, CFP-FP, and AgeDB-30, but also shows great benefit for practical use.
Edge-based Fashion Detection by Transfer Learning

Fashion detection is a technology of detection, classification, and localization that is applied to fashion attributes. This technology is usually implemented by object detection which is becoming especially popular recently. It can be applied in real life, such as extracting fashion attribute of fashion in a certain picture, which is the first step to achieve fashion retrieval, or used as a detection module of the dress-changing application, which helps consumer transform their garment to allow them to know whether this costume suits them or not and further boosts e-commerce platform's sales volume. Therefore, fashion detection has become a new emerging promising research field that has captured many researchers' attention. Transfer-learning is a recently developed new technology and extensively applied in multiple fields. This paper applies transfer-learning-based object detection on a fashion dataset, which is fashion detection. Specifically, first the operation is packing training and testing data into a specific file format which is “.tfrecord”, then uploading them and its' corresponding bounding box labels on Google Cloud Storage. After configuring the pre-trained model and training parameter, the training process follows with Tensorflow Object Detection which is Google's deep learning open-source API. Finally, under particular conditions, this model achieves good prediction performance on Color-Fashion Dataset. Besides, this work includes employing a light-weight model on the Android platform with Android Studio, which allows visualization of this work.
Poisson Shot Noise Removal by an Oracular Non-Local Algorithm

In this paper, we address the problem of denoising images obtained under low-light conditions for the Poisson shot noise model. Under such conditions, the variance stabilization transform (VST) is no longer applicable, so that the state-of-the-art algorithms which are proficient for the additive white Gaussian noise cannot be applied. We first introduce an oracular non-local algorithm and prove its convergence with the optimal rate of convergence under a Hölder regularity assumption for the underlying image, when the search window size is suitably chosen. We also prove that the convergence remains valid when the oracle function is estimated within a prescribed error range. We then define a realizable filter by a statistical estimation of the similarity function which determines the oracle weight. The convergence of the realizable filter is justified by proving that the estimator of the similarity function lies in the prescribed error range with high probability. The experiments show that under low-light conditions the proposed filter is competitive compared with the recent state-of-the-art algorithms.
Stratigraphic variability of Mississippian Meramec chemofacies and petrophysical properties using machine learning and geostatistical modeling, STACK trend, Anadarko Basin, Oklahoma

Mississippian Meramec deposits and reservoirs in the Sooner Trend in the Anadarko (Basin) in Canadian and Kingfisher counties (STACK) play of Oklahoma are comprised of silty limestones, calcareous sandstones, argillaceous-calcareous siltstones, argillaceous siltstones, and mudstones. We used core-derived X-ray fluorescence (XRF) data and established environmental proxies to evaluate the occurrence of specific elements (Al, K, Ti, Zr, Sr, Ca, and Si) and to illustrate their stratigraphic variability. For the Mississippian Meramec, six indicator elements or element ratios serve as proxies for clay (Al and K), detrital sediment (Ti and Zr), carbonate deposits (Sr and Ca), calcite cement (Sr/Ca), and quartz (Si/Ti and Si/Al). We used an unsupervised K-means classification to cluster elemental data from which we interpret three chemofacies: (1) calcareous sandstone, (2) argillaceous-calcareous siltstone, and (3) detrital mudstone. We used random forest to relate core-derived chemofacies to well logs and classify chemofacies in noncored wells with an accuracy of up to 83% based on blind tests. We integrated core-derived XRF, conventional well logs, and chemofacies logs to produce a cross-sectional chemofacies model that trends from the northwest to the southeast across the STACK trend. Chemofacies distribution indicates an upward increase of detrital mudstone from parasequences 1 to 3. Parasequence 3 is capped by a maximum flooding surface. From parasequences 4 to 5, an increase in argillaceous-calcareous siltstone and calcareous sandstone reflects the progradational stacking. Porosity is low in calcareous sandstones primarily due to calcite cement. Water saturation is high in argillaceous-calcareous siltstone, moderate in calcareous sandstone, and low in detrital mudstone. Interpreted biogenic quartz from element profiles corresponds to the calcareous sandstone chemofacies, which can be estimated from well logs and mapped. Effective porosity and water saturation models reflect the stratigraphic variability of chemofacies and rock types and can be predicted within the defined chemostratigraphic framework.
Performance Study of AV1 Encoders and Presets for 360-degree Video in Virtual Reality

Recently, due to the pandemic of coronavirus (known as COVID-19), travel options for people are very limited. However, technology Virtual Reality (VR), one of the emerging fields in the multimedia services, can help to overcome these barriers. Providing of a VR-based video content in a form of online streaming has very high requirements on the network capacity. Hence, advanced video compression algorithms with high efficiency for delivery of a video in the format of VR are vital. In this paper, we present an objective-based performance study of different implementations of the AOMedia Video 1 (AV1) video codec for 360-degree videos with a resolution of UHD. Different quality presets that set the balance between the visual quality and encoding speed for the same bitrate are also considered. The Video Multimethod Assessment Fusion (VMAF) objective metric, developed by Netflix, is used to measure the quality of the video sequences. Results show that all the tested implementations of the AV1 encoder have a problem with the real-time encoding of the 360-degree videos.
Roosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning

Naming conventions are an important concern in large verification projects using proof assistants, such as Coq. In particular, lemma names are used by proof engineers to effectively understand and modify Coq code. However, providing accurate and informative lemma names is a complex task, which is currently often carried out manually. Even when lemma naming is automated using rule-based tools, generated names may fail to adhere to important conventions not specified explicitly. We demonstrate a toolchain, dubbed Roosterize, which automatically suggests lemma names in Coq projects. Roosterize leverages a neural network model trained on existing Coq code, thus avoiding manual specification of naming conventions. To allow proof engineers to conveniently access suggestions from Roosterize during Coq project development, we integrated the toolchain into the popular Visual Studio Code editor. Our evaluation shows that Roosterize substantially outperforms strong baselines for suggesting lemma names and is useful in practice. The demo video for Roosterize can be viewed at: https://youtu.be/HZ5ac7Q14rc.
Signal Enhancement of Low Magnetic Field Magnetic Resonance Image Using a Conventional- and Cyclic-Generative Adversarial Network Models With Unpaired Image Sets

In this study, the signal enhancement ratio of low-field magnetic resonance (MR) images was investigated using a deep learning-based algorithm. Unpaired image sets (0.06 Tesla and 1.5 Tesla MR images for different patients) were used in this study following three steps workflow . In the first step, the deformable registration of a 1.5 Tesla MR image into a 0.06 Tesla MR image was performed to ensure that the shapes of the unpaired set matched. In the second step, a cyclic-generative adversarial network (GAN) was used to generate a synthetic MR image of the original 0.06 Tesla MR image based on the deformed or original 1.5 Tesla MR image. Finally, an enhanced 0.06 Tesla MR image could be generated using the conventional-GAN with the deformed or synthetic MR image. The results from the optimized flow and enhanced MR images showed significant signal enhancement of the anatomical view, especially in the nasal septum, inferior nasal choncha, nasopharyngeal fossa, and eye lens. The signal enhancement ratio, signal-to-noise ratio (SNR) and correlation factor between the original and enhanced MR images were analyzed for the evaluation of the image quality . A combined method using conventional- and cyclic-GANs is a promising approach for generating enhanced MR images from low-magnetic-field MR.